Creating core properties and classes...
Creating primitive properties...
Creating primitive classes...
Done
Loading RDF file /local/ferre/data/ontologies/mondial/link_prediction_no_type/train.nt
Performing PageRank iterations
Loading RDF file /local/ferre/data/ontologies/mondial/link_prediction_no_type/valid.nt
Performing PageRank iterations
EVAL	Entity	Relation	Value	Algo	Hits@1	Hits@3	Hits@10	MRR	$nb_concepts	$nb_concepts_used	$nb_predicted_values	$max_measure	Relation (long)
Max priority: 3
Max branching: 
Inverse triples: true

#1044-ANG
PRED entity: ANG

PRED relation: neighbor
PRED expected values: ZRE Z
=> 31 concepts (29 used for prediction)
PRED predicted values (max 10 best out of 195): Z (0.91 #1768, 0.90 #3216, 0.90 #2733), ZRE (0.91 #1768, 0.90 #3216, 0.90 #2571), SSD (0.40 #203, 0.28 #324, 0.26 #1129), CAM (0.40 #252, 0.26 #1608, 0.11 #4022), RSA (0.38 #370, 0.33 #46, 0.28 #324), EAT (0.38 #453, 0.28 #324, 0.26 #1129), ANG (0.28 #324, 0.26 #1129, 0.26 #1608), RCA (0.28 #324, 0.26 #1129, 0.26 #1608), G (0.28 #324, 0.26 #1129, 0.26 #1608), MOC (0.28 #324, 0.26 #1129, 0.25 #3861)
>> best conf = 0.91
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #1768 for best value:
>> intensional similarity = 5
>> extensional distance = 85
>> proper extension:  BIH; ET; R; THA; MNE; TN; RL; D; HR; SK; ...
>> query: (?x934, ?x138) <- ?x934[ has ethnicGroup ?x197; is locatedIn of ?x182[ is flowsInto of ?x137; is locatedInWater of ?x112;]; is neighbor of ?x138;]
ranks of expected_values: 1, 2
EVAL	ANG	neighbor	Z	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	31.000	29.000	195.000	0.908	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	ANG	neighbor	ZRE	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	31.000	29.000	195.000	0.908	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: ZRE Z
=> 91 concepts (91 used for prediction)
PRED predicted values (max 10 best out of 220): ZRE (0.91 #5198, 0.90 #6996, 0.90 #12877), Z (0.91 #5198, 0.90 #6996, 0.90 #12877), ANG (0.62 #1788, 0.50 #1116, 0.49 #11892), GQ (0.62 #1788, 0.50 #965, 0.14 #1139), RB (0.62 #1788, 0.49 #11892, 0.47 #1465), RSA (0.62 #1788, 0.47 #1465, 0.45 #6667), RA (0.62 #1788, 0.33 #231, 0.25 #11074), GUY (0.62 #1788, 0.33 #388, 0.25 #11074), FGU (0.62 #1788, 0.33 #455, 0.14 #11073), G (0.62 #1788, 0.30 #2595, 0.29 #4872)
>> best conf = 0.91
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #5198 for best value:
>> intensional similarity = 13
>> extensional distance = 15
>> proper extension:  EAT;
>> query: (?x934, ?x138) <- ?x934[ has ethnicGroup ?x197; has government ?x1721; has religion ?x95; is locatedIn of ?x436[ a River; has hasEstuary ?x2275;]; is locatedIn of ?x509[ a River; has flowsInto ?x113;]; is locatedIn of ?x927[ has locatedIn ?x348<ZRE>;]; is neighbor of ?x138;]
ranks of expected_values: 1, 2
EVAL	ANG	neighbor	Z	CNN-1.+1._MA	1.000	1.000	1.000	1.000	91.000	91.000	220.000	0.907	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	ANG	neighbor	ZRE	CNN-1.+1._MA	1.000	1.000	1.000	1.000	91.000	91.000	220.000	0.907	http://www.semwebtech.org/mondial/10/meta#neighbor

#1043-S
PRED entity: S

PRED relation: encompassed
PRED expected values: Europe
=> 43 concepts (43 used for prediction)
PRED predicted values (max 10 best out of 5): Europe (0.56 #27, 0.52 #77, 0.52 #37), America (0.36 #105, 0.35 #130, 0.35 #135), Africa (0.30 #64, 0.29 #144, 0.28 #99), Asia (0.25 #161, 0.24 #181, 0.23 #186), Australia-Oceania (0.22 #153, 0.20 #178, 0.14 #33)
>> best conf = 0.56
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #27 for best value:
>> intensional similarity = 7
>> extensional distance = 25
>> proper extension:  GB;
>> query: (?x402, Europe) <- ?x402[ has language ?x566; has neighbor ?x170; is locatedIn of ?x191; is locatedIn of ?x855[ a Lake;]; is locatedIn of ?x881[ a Mountain;];]
ranks of expected_values: 1
EVAL	S	encompassed	Europe	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	43.000	43.000	5.000	0.556	http://www.semwebtech.org/mondial/10/meta#encompassed

PRED relation: encompassed
PRED expected values: Europe
=> 128 concepts (128 used for prediction)
PRED predicted values (max 10 best out of 5): Europe (0.87 #427, 0.80 #612, 0.79 #600), America (0.64 #208, 0.60 #310, 0.59 #401), Asia (0.50 #62, 0.38 #144, 0.33 #31), Africa (0.33 #593, 0.30 #563, 0.28 #528), Australia-Oceania (0.26 #460, 0.25 #475, 0.25 #496)
>> best conf = 0.87
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #427 for best value:
>> intensional similarity = 11
>> extensional distance = 43
>> proper extension:  BZ; AND;
>> query: (?x402, ?x195) <- ?x402[ has ethnicGroup ?x1473[ a EthnicGroup;]; has language ?x566; has religion ?x352<RomanCatholic>; is neighbor of ?x170[ has language ?x1260; is locatedIn of ?x121;]; is neighbor of ?x565[ has encompassed ?x195; is locatedIn of ?x631;];]
ranks of expected_values: 1
EVAL	S	encompassed	Europe	CNN-1.+1._MA	1.000	1.000	1.000	1.000	128.000	128.000	5.000	0.872	http://www.semwebtech.org/mondial/10/meta#encompassed

#1042-Islay
PRED entity: Islay

PRED relation: belongsToIslands
PRED expected values: InnerHebrides
=> 48 concepts (48 used for prediction)
PRED predicted values (max 10 best out of 38): InnerHebrides (0.34 #886, 0.34 #885, 0.31 #64), OuterHebrides (0.34 #886, 0.34 #885, 0.23 #31), BritishIsles (0.34 #886, 0.34 #885, 0.16 #87), OrkneyIslands (0.34 #886, 0.34 #885, 0.16 #85), ScillyIslands (0.34 #886, 0.34 #885, 0.10 #2452), ShetlandIslands (0.34 #886, 0.34 #885, 0.10 #2452), Canares (0.29 #159, 0.26 #295, 0.20 #431), LesserAntilles (0.27 #831, 0.25 #1037, 0.24 #1105), Azores (0.20 #412, 0.12 #684, 0.11 #752), WestfriesischeInseln (0.14 #489, 0.12 #557, 0.08 #967)
>> best conf = 0.34
=> the first rule below is the first best rule for 6 predicted values
>> Best rule #886 for best value:
>> intensional similarity = 5
>> extensional distance = 54
>> proper extension:  SaintVincent; Barbuda; Montserrat; Antigua;
>> query: (?x1432, ?x503) <- ?x1432[ has locatedIn ?x81[ is locatedIn of ?x502[ has belongsToIslands ?x503;];]; has locatedInWater ?x182<AtlanticOcean>;]
>> Best rule #885 for best value:
>> intensional similarity = 5
>> extensional distance = 54
>> proper extension:  SaintVincent; Barbuda; Montserrat; Antigua;
>> query: (?x1432, ?x2364) <- ?x1432[ has locatedIn ?x81[ is locatedIn of ?x2413[ has belongsToIslands ?x2364;];]; has locatedInWater ?x182<AtlanticOcean>;]
ranks of expected_values: 1
EVAL	Islay	belongsToIslands	InnerHebrides	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	48.000	48.000	38.000	0.338	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

PRED relation: belongsToIslands
PRED expected values: InnerHebrides
=> 128 concepts (128 used for prediction)
PRED predicted values (max 10 best out of 51): InnerHebrides (0.34 #3347, 0.34 #3346, 0.34 #2323), OuterHebrides (0.34 #3347, 0.34 #3346, 0.34 #2323), BritishIsles (0.34 #3347, 0.34 #3346, 0.34 #2323), OrkneyIslands (0.34 #3347, 0.34 #3346, 0.34 #2323), ScillyIslands (0.34 #3347, 0.34 #3346, 0.34 #2323), ShetlandIslands (0.34 #3347, 0.34 #3346, 0.34 #2323), Canares (0.30 #432, 0.20 #637, 0.19 #841), LesserAntilles (0.27 #2268, 0.25 #2680, 0.24 #2816), Azores (0.20 #618, 0.14 #2051, 0.13 #1913), WestfriesischeInseln (0.15 #900, 0.12 #1513, 0.12 #1718)
>> best conf = 0.34
=> the first rule below is the first best rule for 6 predicted values
>> Best rule #3347 for best value:
>> intensional similarity = 8
>> extensional distance = 106
>> proper extension:  Saipan;
>> query: (?x1432, ?x945) <- ?x1432[ a Island; has locatedIn ?x81[ has ethnicGroup ?x1196; has ethnicGroup ?x1617[ a EthnicGroup;]; has language ?x247; is locatedIn of ?x153[ has belongsToIslands ?x945;];];]
>> Best rule #3346 for best value:
>> intensional similarity = 8
>> extensional distance = 106
>> proper extension:  Saipan;
>> query: (?x1432, ?x2364) <- ?x1432[ a Island; has locatedIn ?x81[ has ethnicGroup ?x1196; has ethnicGroup ?x1617[ a EthnicGroup;]; has language ?x247; is locatedIn of ?x2301[ has belongsToIslands ?x2364;];];]
ranks of expected_values: 1
EVAL	Islay	belongsToIslands	InnerHebrides	CNN-1.+1._MA	1.000	1.000	1.000	1.000	128.000	128.000	51.000	0.340	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

#1041-Matterhorn
PRED entity: Matterhorn

PRED relation: locatedIn
PRED expected values: CH
=> 30 concepts (24 used for prediction)
PRED predicted values (max 10 best out of 99): CH (0.42 #2351, 0.36 #761, 0.29 #56), F (0.42 #2351, 0.27 #1652, 0.18 #2593), A (0.42 #2351, 0.14 #803, 0.05 #1978), D (0.42 #2351, 0.11 #4262, 0.11 #4734), SLO (0.42 #2351, 0.09 #3769, 0.09 #3294), E (0.24 #1672, 0.16 #2613, 0.16 #2849), USA (0.24 #4077, 0.18 #4313, 0.17 #4549), R (0.20 #4011, 0.15 #4247, 0.14 #4483), TR (0.14 #1686, 0.09 #2627, 0.09 #2863), CDN (0.10 #4304, 0.09 #4540, 0.09 #4776)
>> best conf = 0.42
=> the first rule below is the first best rule for 5 predicted values
>> Best rule #2351 for best value:
>> intensional similarity = 4
>> extensional distance = 153
>> proper extension:  Manaslu; MaunaLoa; MontGreboun; Huascaran; GranitePeak; Dychtau;
>> query: (?x2191, ?x207) <- ?x2191[ a Mountain; has inMountains ?x261[ is inMountains of ?x1123[ has locatedIn ?x207;];];]
ranks of expected_values: 1
EVAL	Matterhorn	locatedIn	CH	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	30.000	24.000	99.000	0.416	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: CH
=> 134 concepts (132 used for prediction)
PRED predicted values (max 10 best out of 217): CH (0.56 #3320, 0.50 #528, 0.45 #3556), F (0.56 #3320, 0.45 #3556, 0.45 #8781), A (0.56 #3320, 0.45 #3556, 0.45 #8781), D (0.56 #3320, 0.45 #3556, 0.45 #8781), SLO (0.56 #3320, 0.45 #3556, 0.45 #8781), USA (0.54 #15026, 0.43 #4572, 0.42 #16443), CDN (0.38 #13829, 0.28 #15254, 0.24 #15725), E (0.36 #2153, 0.33 #2391, 0.28 #9996), Yugoslavia (0.35 #1652, 0.08 #23738, 0.08 #29211), R (0.35 #16615, 0.34 #16851, 0.33 #17089)
>> best conf = 0.56
=> the first rule below is the first best rule for 5 predicted values
>> Best rule #3320 for best value:
>> intensional similarity = 8
>> extensional distance = 44
>> proper extension:  NangaParbat; TirichMir;
>> query: (?x2191, ?x234) <- ?x2191[ a Mountain; has inMountains ?x261[ is inMountains of ?x2038[ has locatedIn ?x234;];]; has locatedIn ?x207[ a Country; has neighbor ?x78; is wasDependentOf of ?x1184;];]
ranks of expected_values: 1
EVAL	Matterhorn	locatedIn	CH	CNN-1.+1._MA	1.000	1.000	1.000	1.000	134.000	132.000	217.000	0.559	http://www.semwebtech.org/mondial/10/meta#locatedIn

#1040-BalticSea
PRED entity: BalticSea

PRED relation: flowsInto!
PRED expected values: Narva Dalaelv
=> 35 concepts (25 used for prediction)
PRED predicted values (max 10 best out of 461): Goetaaelv (0.33 #150, 0.04 #1321, 0.04 #1613), Petschora (0.11 #761, 0.04 #1347, 0.04 #1639), NorthernDwina (0.11 #670, 0.04 #1256, 0.04 #1548), Paatsjoki (0.11 #669, 0.04 #1255, 0.04 #1547), Vuoksi (0.11 #790, 0.04 #1960, 0.03 #1171), Swir (0.11 #607, 0.04 #1777, 0.03 #1171), Ounasjoki (0.11 #708, 0.03 #1171, 0.03 #4100), Paeijaenne (0.11 #808, 0.03 #1171, 0.03 #4100), Oulujaervi (0.11 #793, 0.03 #1171, 0.03 #4100), Kallavesi (0.11 #789, 0.03 #1171, 0.03 #4100)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #150 for best value:
>> intensional similarity = 6
>> extensional distance = 1
>> proper extension:  Kattegat;
>> query: (?x146, Goetaaelv) <- ?x146[ has locatedIn ?x962[ has neighbor ?x73;]; is locatedInWater of ?x1662<Seeland>; is locatedInWater of ?x1958[ a Island;];]

*> Best rule #1171 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 10
*> proper extension:  Alz;
*> query: (?x146, ?x631) <- ?x146[ has locatedIn ?x120<D>; is flowsInto of ?x660[ has locatedIn ?x565[ is locatedIn of ?x631;];];]
*> conf = 0.03
ranks of expected_values: 274, 302
EVAL	BalticSea	flowsInto!	Dalaelv	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.004	35.000	25.000	461.000	0.333	http://www.semwebtech.org/mondial/10/meta#flowsInto
EVAL	BalticSea	flowsInto!	Narva	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.003	35.000	25.000	461.000	0.333	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto!
PRED expected values: Narva Dalaelv
=> 98 concepts (98 used for prediction)
PRED predicted values (max 10 best out of 469): Elbe (0.25 #819, 0.25 #233, 0.09 #6452), Thames (0.25 #785, 0.25 #199, 0.09 #6452), Maas (0.25 #694, 0.25 #108, 0.09 #6452), Goetaaelv (0.25 #736, 0.09 #6452, 0.08 #1617), Glomma (0.25 #210, 0.08 #1677, 0.04 #4609), Narew (0.25 #487, 0.07 #586, 0.05 #2055), WesternBug (0.25 #307, 0.07 #586, 0.05 #2055), Mosel (0.10 #1270, 0.07 #586, 0.06 #1860), Main (0.10 #1254, 0.07 #586, 0.06 #1844), Bodensee (0.10 #1403, 0.07 #586, 0.06 #1993)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #819 for best value:
>> intensional similarity = 10
>> extensional distance = 2
>> proper extension:  NorthSea; Kattegat;
>> query: (?x146, Elbe) <- ?x146[ has locatedIn ?x793<DK>; is flowsInto of ?x660[ a River; is flowsInto of ?x905;]; is flowsInto of ?x1094[ has hasEstuary ?x737; has locatedIn ?x471;]; is locatedInWater of ?x145[ a Island;];]
>> Best rule #233 for best value:
>> intensional similarity = 11
>> extensional distance = 2
>> proper extension:  Skagerrak;
>> query: (?x146, Elbe) <- ?x146[ has locatedIn ?x120[ has encompassed ?x195; has ethnicGroup ?x237; has neighbor ?x424[ has language ?x511;]; has religion ?x95; is locatedIn of ?x1100[ a Island;];]; has locatedIn ?x793<DK>; is flowsInto of ?x590;]

*> Best rule #2057 for first EXPECTED value:
*> intensional similarity = 9
*> extensional distance = 15
*> proper extension:  Bahrel-Djebel-Albert-Nil;
*> query: (?x146, ?x191) <- ?x146[ has locatedIn ?x793[ has government ?x92;]; is flowsInto of ?x660[ is flowsInto of ?x905;]; is flowsInto of ?x2331[ a Lake; has locatedIn ?x402[ a Country; is locatedIn of ?x191;];];]
*> conf = 0.04
ranks of expected_values: 218, 278
EVAL	BalticSea	flowsInto!	Dalaelv	CNN-1.+1._MA	0.000	0.000	0.000	0.005	98.000	98.000	469.000	0.250	http://www.semwebtech.org/mondial/10/meta#flowsInto
EVAL	BalticSea	flowsInto!	Narva	CNN-1.+1._MA	0.000	0.000	0.000	0.004	98.000	98.000	469.000	0.250	http://www.semwebtech.org/mondial/10/meta#flowsInto

#1039-Iceland
PRED entity: Iceland

PRED relation: locatedInWater
PRED expected values: AtlanticOcean
=> 60 concepts (54 used for prediction)
PRED predicted values (max 10 best out of 46): AtlanticOcean (0.93 #919, 0.92 #960, 0.61 #704), PacificOcean (0.39 #846, 0.37 #554, 0.35 #1096), IndianOcean (0.27 #333, 0.27 #292, 0.24 #374), ArcticOcean (0.27 #124, 0.20 #139, 0.20 #96), JavaSea (0.23 #340, 0.23 #299, 0.21 #381), CaribbeanSea (0.23 #931, 0.22 #972, 0.17 #515), MediterraneanSea (0.21 #553, 0.19 #1347, 0.19 #1263), NorthSea (0.20 #128, 0.20 #85, 0.18 #1752), BarentsSea (0.20 #137, 0.20 #94, 0.18 #1752), LabradorSea (0.18 #1752, 0.18 #1751, 0.14 #178)
>> best conf = 0.93
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #919 for best value:
>> intensional similarity = 6
>> extensional distance = 73
>> proper extension:  SaintPierre; Ireland; Arran; Tobago; BishopRock; Benbecula; Barra; Anguilla; EastFalkland; Greenland; ...
>> query: (?x807, AtlanticOcean) <- ?x807[ a Island; has locatedInWater ?x373[ a Sea; has locatedIn ?x81; is locatedInWater of ?x2103<Streymoy>;];]
ranks of expected_values: 1
EVAL	Iceland	locatedInWater	AtlanticOcean	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	60.000	54.000	46.000	0.933	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater
PRED expected values: AtlanticOcean
=> 131 concepts (131 used for prediction)
PRED predicted values (max 10 best out of 50): AtlanticOcean (0.86 #218, 0.65 #1984, 0.65 #1949), ArcticOcean (0.50 #141, 0.33 #14, 0.19 #638), LabradorSea (0.50 #138, 0.33 #11, 0.19 #2734), PacificOcean (0.44 #863, 0.32 #1255, 0.26 #1038), IndianOcean (0.36 #596, 0.29 #723, 0.27 #682), NorthSea (0.33 #45, 0.24 #510, 0.20 #2080), CaribbeanSea (0.32 #613, 0.29 #230, 0.25 #2096), JavaSea (0.29 #647, 0.27 #603, 0.23 #772), MediterraneanSea (0.28 #2093, 0.28 #2137, 0.24 #2438), HudsonBay (0.25 #137, 0.18 #3080, 0.09 #3705)
>> best conf = 0.86
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #218 for best value:
>> intensional similarity = 9
>> extensional distance = 12
>> proper extension:  GreatBritain; Cuba; Hispaniola;
>> query: (?x807, AtlanticOcean) <- ?x807[ a Island; has locatedInWater ?x373[ a Sea; has locatedIn ?x973<SVAX>; is mergesWith of ?x121;]; is locatedOnIsland of ?x1340[ a Mountain; has locatedIn ?x455;];]
ranks of expected_values: 1
EVAL	Iceland	locatedInWater	AtlanticOcean	CNN-1.+1._MA	1.000	1.000	1.000	1.000	131.000	131.000	50.000	0.857	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#1038-Reuss
PRED entity: Reuss

PRED relation: inMountains
PRED expected values: Alps
=> 28 concepts (28 used for prediction)
PRED predicted values (max 10 best out of 24): Alps (0.50 #91, 0.38 #4, 0.27 #265), Vogesen (0.18 #222, 0.04 #483, 0.02 #570), Apennin (0.14 #90, 0.01 #612, 0.01 #699), EastAfricanRift (0.11 #376, 0.06 #550, 0.04 #811), Andes (0.07 #707, 0.07 #620, 0.06 #794), SnowyMountains (0.07 #369, 0.04 #543, 0.02 #630), Pyrenees (0.06 #236, 0.01 #497), Jura (0.06 #206, 0.01 #467), Balkan (0.04 #629, 0.03 #716, 0.03 #890), Karpaten (0.03 #661, 0.03 #748, 0.03 #835)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #91 for best value:
>> intensional similarity = 7
>> extensional distance = 12
>> proper extension:  Tiber; Po; Etsch; Drau; Arno; Adda;
>> query: (?x493, Alps) <- ?x493[ a Source; has locatedIn ?x234[ has language ?x355<Italian>; has language ?x635<German>;]; is hasSource of ?x1178;]
ranks of expected_values: 1
EVAL	Reuss	inMountains	Alps	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	28.000	28.000	24.000	0.500	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains
PRED expected values: Alps
=> 114 concepts (114 used for prediction)
PRED predicted values (max 10 best out of 39): Alps (0.53 #613, 0.45 #697, 0.41 #963), SudetyMountains (0.21 #580, 0.14 #1017, 0.05 #1802), CordilleraIberica (0.15 #839, 0.04 #2321, 0.04 #1886), Apennin (0.13 #612, 0.10 #787, 0.04 #2269), Andes (0.12 #1842, 0.11 #2103, 0.10 #2190), BlackForest (0.11 #698, 0.10 #872, 0.03 #1482), Balkan (0.09 #1589, 0.07 #1501, 0.07 #2112), Beskides (0.09 #989, 0.07 #552, 0.02 #1948), SnowyMountains (0.07 #1067, 0.02 #3072, 0.02 #3944), EastAfricanRift (0.07 #2033, 0.06 #2556, 0.06 #1685)
>> best conf = 0.53
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #613 for best value:
>> intensional similarity = 11
>> extensional distance = 13
>> proper extension:  Mincio;
>> query: (?x493, Alps) <- ?x493[ a Source; has locatedIn ?x234[ has language ?x51; has religion ?x56; is locatedIn of ?x1123<PizBernina>; is locatedIn of ?x1201<Ticino>; is locatedIn of ?x2038[ has inMountains ?x261;]; is neighbor of ?x78;];]
ranks of expected_values: 1
EVAL	Reuss	inMountains	Alps	CNN-1.+1._MA	1.000	1.000	1.000	1.000	114.000	114.000	39.000	0.533	http://www.semwebtech.org/mondial/10/meta#inMountains

#1037-VanuaLevu
PRED entity: VanuaLevu

PRED relation: locatedIn
PRED expected values: FJI
=> 36 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 120): FJI (0.85 #2599, 0.83 #709, 0.82 #1890), USA (0.43 #308, 0.39 #544, 0.35 #781), J (0.29 #255, 0.22 #491, 0.20 #728), P (0.17 #2796, 0.12 #3748, 0.12 #4460), RI (0.14 #4315, 0.09 #5747, 0.09 #2651), NZ (0.11 #582, 0.10 #819, 0.05 #8078), RP (0.11 #3420, 0.07 #3181, 0.06 #5329), E (0.11 #2626, 0.09 #4290, 0.08 #3578), CDN (0.11 #4087, 0.10 #4562, 0.10 #4805), I (0.09 #3599, 0.09 #5507, 0.08 #5031)
>> best conf = 0.85
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2599 for best value:
>> intensional similarity = 8
>> extensional distance = 41
>> proper extension:  Tongatapu; Halmahera; SantaRosaIsland; SantaCruzIsland; Paramuschir; Efate; Ponape; GrandeTerre; SanClementeIsland; SantaCatalinaIsland; ...
>> query: (?x1778, ?x158) <- ?x1778[ a Island; has belongsToIslands ?x2001[ a Islands; is belongsToIslands of ?x532[ a Island; has locatedIn ?x158;];]; has locatedInWater ?x282<PacificOcean>;]
ranks of expected_values: 1
EVAL	VanuaLevu	locatedIn	FJI	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	36.000	36.000	120.000	0.851	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: FJI
=> 49 concepts (49 used for prediction)
PRED predicted values (max 10 best out of 139): FJI (0.85 #2839, 0.83 #947, 0.82 #2130), USA (0.47 #545, 0.43 #308, 0.39 #782), J (0.29 #255, 0.27 #492, 0.22 #729), P (0.18 #3036, 0.12 #4738, 0.12 #5217), RI (0.16 #4348, 0.14 #5072, 0.09 #7979), GR (0.12 #6308, 0.08 #7275, 0.06 #8262), NZ (0.11 #820, 0.10 #1057, 0.06 #9637), E (0.11 #2866, 0.09 #5047, 0.08 #4568), RP (0.11 #4160, 0.07 #5366, 0.06 #819), CDN (0.11 #4841, 0.08 #7671, 0.08 #7497)
>> best conf = 0.85
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2839 for best value:
>> intensional similarity = 8
>> extensional distance = 41
>> proper extension:  Tongatapu; Halmahera; SantaRosaIsland; SantaCruzIsland; Paramuschir; Efate; Ponape; GrandeTerre; SanClementeIsland; SantaCatalinaIsland; ...
>> query: (?x1778, ?x158) <- ?x1778[ a Island; has belongsToIslands ?x2001[ a Islands; is belongsToIslands of ?x532[ a Island; has locatedIn ?x158;];]; has locatedInWater ?x282<PacificOcean>;]
ranks of expected_values: 1
EVAL	VanuaLevu	locatedIn	FJI	CNN-1.+1._MA	1.000	1.000	1.000	1.000	49.000	49.000	139.000	0.851	http://www.semwebtech.org/mondial/10/meta#locatedIn

#1036-GAZA
PRED entity: GAZA

PRED relation: religion
PRED expected values: Jewish
=> 38 concepts (38 used for prediction)
PRED predicted values (max 10 best out of 32): RomanCatholic (0.68 #406, 0.64 #726, 0.64 #887), Protestant (0.53 #522, 0.53 #722, 0.51 #562), CopticChristian (0.51 #1283, 0.51 #1202, 0.08 #149), Jewish (0.51 #1202, 0.33 #3, 0.29 #243), Druze (0.51 #1202, 0.27 #801, 0.12 #73), ChristianOrthodox (0.40 #161, 0.38 #81, 0.32 #561), Buddhist (0.38 #210, 0.27 #801, 0.14 #450), Anglican (0.27 #801, 0.22 #296, 0.12 #56), Bahai (0.27 #801, 0.12 #70, 0.03 #310), Hindu (0.23 #208, 0.16 #328, 0.13 #688)
>> best conf = 0.68
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #406 for best value:
>> intensional similarity = 5
>> extensional distance = 54
>> proper extension:  PK;
>> query: (?x1495, RomanCatholic) <- ?x1495[ a Country; has language ?x1398; is locatedIn of ?x275[ a Sea;]; is neighbor of ?x63;]

*> Best rule #1202 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 139
*> proper extension:  SSD;
*> query: (?x1495, ?x109) <- ?x1495[ a Country; is locatedIn of ?x275; is neighbor of ?x239[ has religion ?x109; has wasDependentOf ?x485; is neighbor of ?x115;];]
*> conf = 0.51
ranks of expected_values: 4
EVAL	GAZA	religion	Jewish	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.250	38.000	38.000	32.000	0.679	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion
PRED expected values: Jewish
=> 67 concepts (67 used for prediction)
PRED predicted values (max 10 best out of 36): Jewish (0.70 #735, 0.68 #1146, 0.68 #1145), CopticChristian (0.70 #735, 0.68 #1146, 0.68 #1145), Druze (0.70 #735, 0.68 #1146, 0.68 #1145), RomanCatholic (0.66 #2254, 0.65 #1720, 0.65 #1559), Protestant (0.63 #1797, 0.53 #2250, 0.52 #1923), Buddhist (0.50 #172, 0.44 #500, 0.40 #531), Hindu (0.50 #170, 0.40 #531, 0.37 #2410), ChristianOrthodox (0.40 #531, 0.38 #1390, 0.38 #1270), Kimbanguist (0.40 #2045, 0.11 #2208, 0.10 #2617), Anglican (0.37 #736, 0.35 #955, 0.25 #1244)
>> best conf = 0.70
=> the first rule below is the first best rule for 3 predicted values
>> Best rule #735 for best value:
>> intensional similarity = 18
>> extensional distance = 8
>> proper extension:  MNG;
>> query: (?x1495, ?x109) <- ?x1495[ a Country; has ethnicGroup ?x852[ a EthnicGroup;]; has language ?x1848[ is language of ?x565[ has religion ?x56; is locatedIn of ?x631;];]; has religion ?x116<Christian>; has religion ?x187<Muslim>; is neighbor of ?x239[ a Country; has ethnicGroup ?x244; has neighbor ?x568[ has encompassed ?x175;]; has religion ?x109; is locatedIn of ?x238;];]
ranks of expected_values: 1
EVAL	GAZA	religion	Jewish	CNN-1.+1._MA	1.000	1.000	1.000	1.000	67.000	67.000	36.000	0.704	http://www.semwebtech.org/mondial/10/meta#religion

#1035-TAD
PRED entity: TAD

PRED relation: wasDependentOf
PRED expected values: SovietUnion
=> 28 concepts (28 used for prediction)
PRED predicted values (max 10 best out of 26): SovietUnion (0.43 #115, 0.38 #243, 0.25 #146), GB (0.29 #258, 0.25 #4, 0.22 #377), F (0.22 #346, 0.15 #376, 0.13 #593), E (0.17 #320, 0.15 #410, 0.14 #440), OttomanEmpire (0.08 #280, 0.08 #339, 0.07 #120), UnitedNations (0.08 #479, 0.07 #730, 0.07 #109), Yugoslavia (0.08 #307, 0.04 #488, 0.04 #520), PK (0.05 #195, 0.05 #189, 0.05 #162), P (0.05 #366, 0.04 #396, 0.03 #550), Czechoslovakia (0.04 #308, 0.04 #338, 0.02 #428)
>> best conf = 0.43
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #115 for best value:
>> intensional similarity = 5
>> extensional distance = 12
>> proper extension:  ARM;
>> query: (?x129, SovietUnion) <- ?x129[ has ethnicGroup ?x1193; has neighbor ?x277[ is locatedIn of ?x289; is neighbor of ?x290<TM>;];]
ranks of expected_values: 1
EVAL	TAD	wasDependentOf	SovietUnion	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	28.000	28.000	26.000	0.429	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

PRED relation: wasDependentOf
PRED expected values: SovietUnion
=> 96 concepts (96 used for prediction)
PRED predicted values (max 10 best out of 55): SovietUnion (0.62 #537, 0.45 #382, 0.40 #209), GB (0.44 #351, 0.36 #779, 0.35 #1947), E (0.28 #754, 0.25 #619, 0.24 #586), F (0.24 #2118, 0.23 #450, 0.17 #1082), J (0.14 #290, 0.03 #753, 0.03 #787), P (0.12 #338, 0.07 #1102, 0.06 #804), NL (0.12 #333, 0.05 #566, 0.04 #2378), OttomanEmpire (0.11 #740, 0.10 #1040, 0.10 #1072), UnitedNations (0.11 #1364, 0.10 #1831, 0.10 #831), PK (0.09 #416, 0.09 #388, 0.08 #449)
>> best conf = 0.62
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #537 for best value:
>> intensional similarity = 12
>> extensional distance = 14
>> proper extension:  ARM;
>> query: (?x129, SovietUnion) <- ?x129[ a Country; has ethnicGroup ?x1193<Russian>; is neighbor of ?x277[ a Country; has encompassed ?x175; has ethnicGroup ?x1326; has language ?x278; has religion ?x187<Muslim>; is locatedIn of ?x289; is neighbor of ?x290;];]
ranks of expected_values: 1
EVAL	TAD	wasDependentOf	SovietUnion	CNN-1.+1._MA	1.000	1.000	1.000	1.000	96.000	96.000	55.000	0.625	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

#1034-Sanaga
PRED entity: Sanaga

PRED relation: hasEstuary!
PRED expected values: Sanaga
=> 32 concepts (26 used for prediction)
PRED predicted values (max 10 best out of 69): Schari (0.33 #70, 0.25 #453, 0.25 #296), Bomu (0.25 #317, 0.05 #771, 0.02 #999), Benue (0.12 #1816, 0.09 #454, 0.07 #2272), Sanga (0.09 #454, 0.07 #2272, 0.03 #1817), Benue (0.09 #454, 0.03 #1817, 0.02 #2954), ChadLake (0.09 #454, 0.03 #1817, 0.02 #2954), Sanaga (0.09 #454, 0.03 #1817, 0.02 #2954), Fako (0.09 #454, 0.03 #1817, 0.02 #2954), Schari (0.09 #454, 0.03 #1817, 0.02 #2954), Sanaga (0.09 #454, 0.03 #1817, 0.02 #2954)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #70 for best value:
>> intensional similarity = 3
>> extensional distance = 1
>> proper extension:  Schari;
>> query: (?x1376, Schari) <- ?x1376[ a Estuary; has locatedIn ?x536<CAM>;]

No rule for expected values
ranks of expected_values: 
EVAL	Sanaga	hasEstuary!	Sanaga	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	32.000	26.000	69.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasEstuary

PRED relation: hasEstuary!
PRED expected values: Sanaga
=> 111 concepts (111 used for prediction)
PRED predicted values (max 10 best out of 163): Schari (0.33 #70, 0.25 #525, 0.25 #296), Luapula (0.25 #675, 0.10 #903, 0.08 #1131), Bomu (0.25 #317, 0.05 #2372, 0.03 #4198), Benue (0.14 #1823, 0.10 #7536, 0.09 #682), Sanga (0.14 #1823, 0.10 #7536, 0.09 #682), BarragedeMbakaou (0.14 #1823, 0.09 #682, 0.09 #455), Niger (0.12 #1822, 0.10 #739, 0.06 #1651), ChadLake (0.12 #1822, 0.09 #682, 0.09 #455), Sanaga (0.12 #1822, 0.08 #453, 0.06 #3424), Zaire (0.12 #1822, 0.08 #453, 0.04 #7535)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #70 for best value:
>> intensional similarity = 3
>> extensional distance = 1
>> proper extension:  Schari;
>> query: (?x1376, Schari) <- ?x1376[ a Estuary; has locatedIn ?x536<CAM>;]

*> Best rule #1822 for first EXPECTED value:
*> intensional similarity = 11
*> extensional distance = 14
*> proper extension:  Gambia; Volta; Benue; Niger;
*> query: (?x1376, ?x929) <- ?x1376[ a Estuary; has locatedIn ?x536[ has ethnicGroup ?x122[ a EthnicGroup;]; has ethnicGroup ?x162<African>; has neighbor ?x1408[ is locatedIn of ?x771;]; has religion ?x116; is locatedIn of ?x2087[ has flowsInto ?x929;];];]
*> conf = 0.12
ranks of expected_values: 9
EVAL	Sanaga	hasEstuary!	Sanaga	CNN-1.+1._MA	0.000	0.000	1.000	0.111	111.000	111.000	163.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasEstuary

#1033-Lagen
PRED entity: Lagen

PRED relation: locatedIn
PRED expected values: N
=> 44 concepts (44 used for prediction)
PRED predicted values (max 10 best out of 64): N (0.77 #1898, 0.75 #8300, 0.68 #6640), R (0.64 #716, 0.15 #953, 0.11 #5223), S (0.33 #92, 0.09 #803, 0.08 #2227), USA (0.31 #1020, 0.14 #1496, 0.14 #3628), ZRE (0.21 #3161, 0.18 #1740, 0.17 #1977), D (0.20 #494, 0.18 #7134, 0.17 #6186), I (0.20 #522, 0.14 #1709, 0.10 #2420), F (0.15 #955, 0.05 #8307, 0.05 #1431), CDN (0.14 #1724, 0.08 #1961, 0.08 #3619), PE (0.13 #2439, 0.13 #2676, 0.11 #4335)
>> best conf = 0.77
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1898 for best value:
>> intensional similarity = 9
>> extensional distance = 20
>> proper extension:  NiagaraRiver;
>> query: (?x2543, ?x170) <- ?x2543[ a Estuary; is hasEstuary of ?x548[ a River; has flowsInto ?x1446[ has flowsInto ?x1664; has locatedIn ?x170[ is locatedIn of ?x612[ a Lake;];];]; is flowsInto of ?x612;];]
ranks of expected_values: 1
EVAL	Lagen	locatedIn	N	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	44.000	44.000	64.000	0.769	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: N
=> 128 concepts (128 used for prediction)
PRED predicted values (max 10 best out of 79): N (0.88 #18132, 0.84 #21957, 0.81 #22194), D (0.71 #6939, 0.71 #21500, 0.60 #21738), USA (0.65 #20118, 0.62 #15342, 0.60 #17488), R (0.62 #21246, 0.45 #13602, 0.44 #10737), S (0.60 #3430, 0.57 #7487, 0.50 #805), ZRE (0.46 #22036, 0.44 #17974, 0.27 #13200), F (0.46 #15754, 0.33 #5256, 0.25 #481), SF (0.44 #10387, 0.33 #5142, 0.19 #3099), CDN (0.40 #12707, 0.37 #19871, 0.25 #9128), I (0.33 #11259, 0.33 #4581, 0.28 #19138)
>> best conf = 0.88
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #18132 for best value:
>> intensional similarity = 18
>> extensional distance = 14
>> proper extension:  Busira; Lualaba; Ruki;
>> query: (?x2543, ?x170) <- ?x2543[ a Estuary; is hasEstuary of ?x548[ a River; has flowsInto ?x1446[ a River; has locatedIn ?x170[ a Country; has encompassed ?x195; has ethnicGroup ?x979; has religion ?x95<Protestant>; has religion ?x187<Muslim>; is neighbor of ?x73;];]; has hasSource ?x2137[ a Source;]; has locatedIn ?x170; is flowsInto of ?x612;];]
ranks of expected_values: 1
EVAL	Lagen	locatedIn	N	CNN-1.+1._MA	1.000	1.000	1.000	1.000	128.000	128.000	79.000	0.875	http://www.semwebtech.org/mondial/10/meta#locatedIn

#1032-Araguaia
PRED entity: Araguaia

PRED relation: locatedIn
PRED expected values: BR
=> 47 concepts (47 used for prediction)
PRED predicted values (max 10 best out of 219): BR (0.93 #3799, 0.92 #9735, 0.92 #10212), PE (0.53 #2677, 0.22 #3560, 0.21 #4036), SK (0.50 #32, 0.20 #506, 0.17 #743), ZRE (0.38 #2452, 0.37 #2928, 0.27 #4830), R (0.33 #5231, 0.21 #3328, 0.21 #3566), RI (0.31 #3613, 0.27 #3851, 0.26 #4089), BOL (0.30 #2051, 0.22 #3560, 0.21 #4036), CO (0.29 #1474, 0.22 #3560, 0.21 #4036), PY (0.25 #331, 0.22 #3560, 0.21 #4036), RA (0.25 #324, 0.22 #3560, 0.21 #4036)
>> best conf = 0.93
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3799 for best value:
>> intensional similarity = 5
>> extensional distance = 27
>> proper extension:  SeaofJapan;
>> query: (?x47, ?x542) <- ?x47[ is locatedInWater of ?x1304[ a Island; has locatedIn ?x542[ has encompassed ?x521; is wasDependentOf of ?x363;];];]
ranks of expected_values: 1
EVAL	Araguaia	locatedIn	BR	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	47.000	47.000	219.000	0.929	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: BR
=> 128 concepts (128 used for prediction)
PRED predicted values (max 10 best out of 221): BR (0.94 #17640, 0.93 #12161, 0.93 #22399), PE (0.67 #952, 0.54 #6736, 0.53 #2855), YV (0.67 #952, 0.53 #2855, 0.53 #2854), BOL (0.67 #952, 0.53 #2855, 0.53 #2854), CO (0.67 #952, 0.53 #2855, 0.53 #2854), PY (0.67 #952, 0.53 #2855, 0.53 #2854), ROU (0.67 #952, 0.53 #2854, 0.42 #4764), SME (0.67 #952, 0.53 #2854, 0.42 #4764), ZRE (0.62 #12240, 0.57 #11922, 0.57 #11762), C (0.60 #1932, 0.50 #741, 0.33 #2882)
>> best conf = 0.94
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #17640 for best value:
>> intensional similarity = 12
>> extensional distance = 27
>> proper extension:  MackenzieRiver;
>> query: (?x47, ?x542) <- ?x47[ a River; has hasEstuary ?x1883[ a Estuary;]; has hasSource ?x48[ a Source; has locatedIn ?x542[ a Country; has ethnicGroup ?x197<European>; has religion ?x95; is locatedIn of ?x182<AtlanticOcean>;];];]
ranks of expected_values: 1
EVAL	Araguaia	locatedIn	BR	CNN-1.+1._MA	1.000	1.000	1.000	1.000	128.000	128.000	221.000	0.935	http://www.semwebtech.org/mondial/10/meta#locatedIn

#1031-Naryn
PRED entity: Naryn

PRED relation: locatedIn
PRED expected values: KGZ
=> 34 concepts (34 used for prediction)
PRED predicted values (max 10 best out of 67): CN (0.67 #949, 0.67 #768, 0.58 #950), KGZ (0.58 #950, 0.57 #4997, 0.57 #1188), KAZ (0.58 #950, 0.57 #1188, 0.49 #1427), UZB (0.57 #4997, 0.56 #5711, 0.56 #4281), TAD (0.57 #7140, 0.54 #4996, 0.52 #5949), R (0.29 #955, 0.20 #479, 0.13 #4048), NEP (0.23 #1206, 0.03 #7157, 0.02 #7395), PK (0.17 #722, 0.08 #1199, 0.01 #7150), ZRE (0.15 #2931, 0.10 #4122, 0.08 #5077), I (0.11 #3853, 0.11 #4330, 0.09 #5284)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #949 for best value:
>> intensional similarity = 7
>> extensional distance = 4
>> proper extension:  BroadPeak;
>> query: (?x1975, ?x232) <- ?x1975[ has inMountains ?x1217[ a Mountains; is inMountains of ?x874[ a Source; has locatedIn ?x232<CN>; is hasSource of ?x319;];];]
>> Best rule #768 for best value:
>> intensional similarity = 7
>> extensional distance = 4
>> proper extension:  BroadPeak;
>> query: (?x1975, CN) <- ?x1975[ has inMountains ?x1217[ a Mountains; is inMountains of ?x874[ a Source; has locatedIn ?x232<CN>; is hasSource of ?x319;];];]

*> Best rule #950 for first EXPECTED value:
*> intensional similarity = 9
*> extensional distance = 4
*> proper extension:  BroadPeak;
*> query: (?x1975, ?x130) <- ?x1975[ has inMountains ?x1217[ a Mountains; is inMountains of ?x874[ a Source; has locatedIn ?x232<CN>; is hasSource of ?x319;]; is inMountains of ?x1143[ has locatedIn ?x130;];];]
*> conf = 0.58
ranks of expected_values: 2
EVAL	Naryn	locatedIn	KGZ	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	34.000	34.000	67.000	0.667	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: KGZ
=> 129 concepts (129 used for prediction)
PRED predicted values (max 10 best out of 70): KGZ (0.81 #6728, 0.78 #6488, 0.77 #24786), R (0.81 #6494, 0.78 #6254, 0.71 #10825), KAZ (0.70 #21416, 0.69 #21899, 0.67 #17079), UZB (0.69 #11061, 0.69 #11059, 0.68 #6729), CN (0.67 #5288, 0.67 #3124, 0.67 #2943), TAD (0.65 #6486, 0.58 #17077, 0.57 #30040), AFG (0.40 #2011, 0.33 #2733, 0.25 #1294), UA (0.40 #3917, 0.27 #4639, 0.25 #4880), SF (0.32 #6861, 0.28 #6140, 0.18 #10711), CH (0.31 #5586, 0.22 #7748, 0.19 #9913)
>> best conf = 0.81
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #6728 for best value:
>> intensional similarity = 20
>> extensional distance = 19
>> proper extension:  Volga; Angara;
>> query: (?x1975, ?x130) <- ?x1975[ a Source; is hasSource of ?x2336[ a River; has flowsInto ?x1019; has hasEstuary ?x486[ a Estuary;]; has locatedIn ?x130[ a Country; has encompassed ?x175; has ethnicGroup ?x58<Ukrainian>; has ethnicGroup ?x1193<Russian>; has language ?x555<Russian>; has religion ?x56; has wasDependentOf ?x903<SovietUnion>; is neighbor of ?x232<CN>;];];]
ranks of expected_values: 1
EVAL	Naryn	locatedIn	KGZ	CNN-1.+1._MA	1.000	1.000	1.000	1.000	129.000	129.000	70.000	0.810	http://www.semwebtech.org/mondial/10/meta#locatedIn

#1030-MK
PRED entity: MK

PRED relation: neighbor
PRED expected values: SRB
=> 34 concepts (34 used for prediction)
PRED predicted values (max 10 best out of 205): SRB (0.90 #2723, 0.89 #2245, 0.89 #2722), MNE (0.43 #966, 0.33 #9, 0.32 #478), MK (0.40 #748, 0.33 #908, 0.33 #113), HR (0.33 #180, 0.24 #957, 0.20 #656), IR (0.33 #369, 0.20 #529, 0.18 #1278), IRQ (0.33 #367, 0.20 #527, 0.18 #1278), AZ (0.33 #373, 0.20 #533, 0.18 #1278), GE (0.33 #378, 0.20 #538, 0.18 #1278), SYR (0.33 #397, 0.20 #557, 0.18 #1278), ARM (0.33 #372, 0.20 #532, 0.18 #1278)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2723 for best value:
>> intensional similarity = 6
>> extensional distance = 98
>> proper extension:  KWT;
>> query: (?x701, ?x177) <- ?x701[ has religion ?x56; is locatedIn of ?x656; is neighbor of ?x177[ has ethnicGroup ?x164; has language ?x511; is locatedIn of ?x98;];]
ranks of expected_values: 1
EVAL	MK	neighbor	SRB	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	34.000	34.000	205.000	0.905	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: SRB
=> 99 concepts (99 used for prediction)
PRED predicted values (max 10 best out of 224): SRB (0.93 #4518, 0.93 #7762, 0.93 #2405), MK (0.57 #3381, 0.57 #3542, 0.55 #5648), MNE (0.43 #1607, 0.42 #1757, 0.41 #5006), RO (0.42 #1757, 0.41 #5006, 0.41 #5007), TR (0.42 #1757, 0.41 #5006, 0.41 #5007), SK (0.42 #1757, 0.36 #158, 0.33 #344), HR (0.42 #1757, 0.36 #158, 0.32 #5005), IR (0.42 #1757, 0.36 #158, 0.32 #5005), A (0.42 #1757, 0.36 #158, 0.31 #2321), CY (0.42 #1757, 0.36 #158, 0.28 #3383)
>> best conf = 0.93
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4518 for best value:
>> intensional similarity = 15
>> extensional distance = 24
>> proper extension:  HR; H; GE; RA; PY; B; NL; L;
>> query: (?x701, ?x904) <- ?x701[ has ethnicGroup ?x354; has language ?x511; has neighbor ?x204[ a Country; is locatedIn of ?x104;]; has neighbor ?x692[ a Country; has ethnicGroup ?x223; has government ?x435;]; has religion ?x352<RomanCatholic>; has wasDependentOf ?x1197; is locatedIn of ?x1489[ has flowsInto ?x132;]; is neighbor of ?x904;]
ranks of expected_values: 1
EVAL	MK	neighbor	SRB	CNN-1.+1._MA	1.000	1.000	1.000	1.000	99.000	99.000	224.000	0.934	http://www.semwebtech.org/mondial/10/meta#neighbor

#1029-TianShan
PRED entity: TianShan

PRED relation: inMountains!
PRED expected values: PikPobeda
=> 25 concepts (20 used for prediction)
PRED predicted values (max 10 best out of 313): Irtysch (0.33 #146, 0.17 #404, 0.14 #661), Bjelucha (0.33 #5, 0.17 #263, 0.14 #520), Katun (0.33 #90, 0.17 #348, 0.14 #605), PikLenina (0.17 #339, 0.14 #853, 0.14 #596), Kongur (0.17 #317, 0.14 #831, 0.14 #574), PikKarl-Marx (0.17 #488, 0.14 #1002, 0.14 #745), Murgab (0.17 #357, 0.14 #871, 0.14 #614), PikMoskva (0.17 #351, 0.14 #865, 0.14 #608), PikRevoluzija (0.17 #350, 0.14 #864, 0.14 #607), Pjandsh (0.17 #337, 0.14 #851, 0.14 #594)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #146 for best value:
>> intensional similarity = 11
>> extensional distance = 1
>> proper extension:  Altai;
>> query: (?x1217, Irtysch) <- ?x1217[ is inMountains of ?x1143[ has locatedIn ?x232[ has neighbor ?x73;]; has locatedIn ?x403<KAZ>;]; is inMountains of ?x1975[ a Source; is hasSource of ?x2336[ has flowsInto ?x1019; has hasEstuary ?x486; has locatedIn ?x277;];];]

*> Best rule #2057 for first EXPECTED value:
*> intensional similarity = 10
*> extensional distance = 16
*> proper extension:  Tibesti;
*> query: (?x1217, ?x662) <- ?x1217[ a Mountains; is inMountains of ?x1143[ a Mountain; has locatedIn ?x130[ a Country; has encompassed ?x175; has government ?x435<"republic">; has neighbor ?x129; is locatedIn of ?x662;];];]
*> conf = 0.13
ranks of expected_values: 39
EVAL	TianShan	inMountains!	PikPobeda	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.026	25.000	20.000	313.000	0.333	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains!
PRED expected values: PikPobeda
=> 60 concepts (60 used for prediction)
PRED predicted values (max 10 best out of 318): BroadPeak (0.33 #1423, 0.26 #8005, 0.26 #8004), Tarim-Yarkend (0.33 #1495, 0.26 #8005, 0.26 #8004), MurrayRiver (0.33 #1223, 0.25 #3032, 0.25 #2773), SnowyRiver (0.33 #1218, 0.25 #3027, 0.25 #2768), MurrumbidgeeRiver (0.33 #1200, 0.25 #3009, 0.25 #2750), EucumbeneRiver (0.33 #1175, 0.25 #2984, 0.25 #2725), Mt.Kosciuszko (0.33 #1133, 0.25 #2942, 0.25 #2683), Mt.Bogong (0.33 #1104, 0.25 #2913, 0.25 #2654), PicoTurquino (0.33 #975, 0.01 #14917, 0.01 #15173), ChangbaiShan (0.33 #740, 0.01 #14941, 0.01 #15197)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1423 for best value:
>> intensional similarity = 13
>> extensional distance = 1
>> proper extension:  Karakorum;
>> query: (?x1217, BroadPeak) <- ?x1217[ a Mountains; is inMountains of ?x874[ a Source; has locatedIn ?x232<CN>; is hasSource of ?x319[ a River; has flowsInto ?x320; has hasEstuary ?x1344; has locatedIn ?x232<CN>;];]; is inMountains of ?x1143[ a Mountain; has locatedIn ?x232<CN>;];]

*> Best rule #6200 for first EXPECTED value:
*> intensional similarity = 26
*> extensional distance = 3
*> proper extension:  Hindukusch;
*> query: (?x1217, ?x127) <- ?x1217[ a Mountains; is inMountains of ?x1143[ has locatedIn ?x130[ a Country; has government ?x435; has language ?x555; has neighbor ?x129;]; has locatedIn ?x232[ a Country; has government ?x831; has neighbor ?x334; is locatedIn of ?x1040<K2>; is locatedIn of ?x1375<BroadPeak>; is neighbor of ?x381<AFG>; is neighbor of ?x924<IND>; is wasDependentOf of ?x1010;]; has locatedIn ?x403[ a Country; has government ?x2502; has language ?x1245; is locatedIn of ?x127; is neighbor of ?x73;];];]
*> conf = 0.27
ranks of expected_values: 14
EVAL	TianShan	inMountains!	PikPobeda	CNN-1.+1._MA	0.000	0.000	0.000	0.071	60.000	60.000	318.000	0.333	http://www.semwebtech.org/mondial/10/meta#inMountains

#1028-Volta
PRED entity: Volta

PRED relation: locatedIn
PRED expected values: BF
=> 24 concepts (24 used for prediction)
PRED predicted values (max 10 best out of 112): GH (0.71 #1668, 0.66 #2620, 0.63 #1669), BF (0.63 #1669, 0.54 #5247, 0.54 #5246), USA (0.62 #1501, 0.19 #785, 0.17 #1023), CDN (0.32 #2683, 0.21 #1492, 0.12 #300), CAM (0.25 #123, 0.07 #597, 0.06 #4532), ANG (0.17 #1378, 0.10 #951, 0.10 #902), Z (0.14 #595, 0.08 #2264, 0.06 #2741), CO (0.14 #525, 0.02 #3384, 0.02 #3624), R (0.13 #4057, 0.12 #242, 0.12 #2388), PE (0.13 #1018, 0.08 #3400, 0.07 #541)
>> best conf = 0.71
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1668 for best value:
>> intensional similarity = 10
>> extensional distance = 22
>> proper extension:  OhioRiver; HudsonRiver; Tennessee; MerrimackRiver; AlleghenyRiver; Arkansas; ConnecticutRiver; StraitsofMackinac; TruckeeRiver; Missouri;
>> query: (?x1886, ?x483) <- ?x1886[ a Source; is hasSource of ?x610[ a River; has locatedIn ?x483[ has neighbor ?x1206; has religion ?x116; has wasDependentOf ?x81<GB>; is locatedIn of ?x182<AtlanticOcean>;];];]

*> Best rule #1669 for first EXPECTED value:
*> intensional similarity = 11
*> extensional distance = 22
*> proper extension:  OhioRiver; HudsonRiver; Tennessee; MerrimackRiver; AlleghenyRiver; Arkansas; ConnecticutRiver; StraitsofMackinac; TruckeeRiver; Missouri;
*> query: (?x1886, ?x811) <- ?x1886[ a Source; is hasSource of ?x610[ a River; has locatedIn ?x483[ has neighbor ?x1206; has religion ?x116; has wasDependentOf ?x81<GB>; is locatedIn of ?x182<AtlanticOcean>;]; has locatedIn ?x811;];]
*> conf = 0.63
ranks of expected_values: 2
EVAL	Volta	locatedIn	BF	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	24.000	24.000	112.000	0.708	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: BF
=> 72 concepts (72 used for prediction)
PRED predicted values (max 10 best out of 114): GH (0.79 #7936, 0.71 #7933, 0.68 #11535), USA (0.79 #7767, 0.43 #9926, 0.29 #4399), BF (0.71 #7933, 0.59 #5771, 0.56 #7452), CDN (0.45 #5596, 0.36 #11360, 0.35 #7277), RG (0.40 #2070, 0.33 #869, 0.29 #4714), Z (0.40 #2287, 0.20 #1804, 0.17 #3250), SUD (0.33 #42, 0.25 #1005, 0.20 #2449), AUS (0.33 #3414, 0.18 #5578, 0.12 #7259), ZRE (0.33 #560, 0.15 #10894, 0.14 #9454), CH (0.29 #7031, 0.25 #8232, 0.25 #4867)
>> best conf = 0.79
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #7936 for best value:
>> intensional similarity = 14
>> extensional distance = 17
>> proper extension:  HudsonRiver; AlleghenyRiver; Arkansas; ConnecticutRiver;
>> query: (?x1886, ?x483) <- ?x1886[ a Source; is hasSource of ?x610[ a River; has locatedIn ?x483[ a Country; has encompassed ?x213; has ethnicGroup ?x197<European>; has neighbor ?x1307; has religion ?x116; has wasDependentOf ?x81<GB>; is locatedIn of ?x182<AtlanticOcean>;];];]

*> Best rule #7933 for first EXPECTED value:
*> intensional similarity = 16
*> extensional distance = 17
*> proper extension:  HudsonRiver; AlleghenyRiver; Arkansas; ConnecticutRiver;
*> query: (?x1886, ?x811) <- ?x1886[ a Source; is hasSource of ?x610[ a River; has locatedIn ?x483[ a Country; has encompassed ?x213; has ethnicGroup ?x197<European>; has neighbor ?x1307; has religion ?x116; has wasDependentOf ?x81<GB>; is locatedIn of ?x182<AtlanticOcean>;]; has locatedIn ?x811[ has neighbor ?x426;];];]
*> conf = 0.71
ranks of expected_values: 3
EVAL	Volta	locatedIn	BF	CNN-1.+1._MA	0.000	1.000	1.000	0.333	72.000	72.000	114.000	0.789	http://www.semwebtech.org/mondial/10/meta#locatedIn

#1027-ES
PRED entity: ES

PRED relation: government
PRED expected values: "republic"
=> 38 concepts (38 used for prediction)
PRED predicted values (max 10 best out of 56): "republic" (0.40 #799, 0.38 #438, 0.37 #1303), "parliamentary democracy and a Commonwealth realm" (0.17 #324, 0.15 #252, 0.11 #612), "parliamentary democracy" (0.14 #870, 0.14 #942, 0.12 #1518), "British Overseas Territories" (0.13 #367, 0.12 #1160, 0.08 #583), "federal republic" (0.12 #147, 0.09 #1012, 0.09 #507), "constitutional democracy" (0.12 #436, 0.08 #148, 0.08 #4), "democratic republic" (0.08 #154, 0.08 #226, 0.08 #10), "constitutional monarchy" (0.08 #1083, 0.07 #867, 0.07 #1011), "constitutional republic" (0.08 #225, 0.07 #81, 0.07 #369), "constitutional democratic republic" (0.08 #11, 0.07 #83, 0.04 #793)
>> best conf = 0.40
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #799 for best value:
>> intensional similarity = 5
>> extensional distance = 40
>> proper extension:  RM;
>> query: (?x654, "republic") <- ?x654[ a Country; has religion ?x95; has wasDependentOf ?x149[ is locatedIn of ?x275<MediterraneanSea>;];]
ranks of expected_values: 1
EVAL	ES	government	"republic"	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	38.000	38.000	56.000	0.405	http://www.semwebtech.org/mondial/10/meta#government

PRED relation: government
PRED expected values: "republic"
=> 99 concepts (99 used for prediction)
PRED predicted values (max 10 best out of 68): "republic" (0.43 #369, 0.41 #1091, 0.39 #2390), "federal republic" (0.25 #76, 0.20 #220, 0.18 #439), "democratic constitutional republic" (0.25 #98, 0.20 #242, 0.17 #4555), "constitutional democratic republic" (0.25 #156, 0.17 #4555, 0.15 #4047), "parliamentary democracy and a Commonwealth realm" (0.20 #253, 0.17 #1482, 0.15 #4047), "parliamentary democracy" (0.19 #3835, 0.18 #4052, 0.18 #4124), "democratic republic" (0.17 #299, 0.15 #4047, 0.14 #373), "parliamentary monarchy" (0.15 #4047, 0.14 #5713, 0.13 #4336), "constitutional democracy" (0.15 #4047, 0.13 #4336, 0.12 #1233), "constitutional republic" (0.15 #4047, 0.13 #4336, 0.11 #5712)
>> best conf = 0.43
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #369 for best value:
>> intensional similarity = 12
>> extensional distance = 5
>> proper extension:  GUY;
>> query: (?x654, "republic") <- ?x654[ has encompassed ?x521<America>; has ethnicGroup ?x79; has religion ?x1151<JehovasWitnesses>; is locatedIn of ?x282[ has locatedIn ?x1154[ has government ?x2344;]; has mergesWith ?x60; is flowsInto of ?x602; is locatedInWater of ?x205;]; is neighbor of ?x181;]
ranks of expected_values: 1
EVAL	ES	government	"republic"	CNN-1.+1._MA	1.000	1.000	1.000	1.000	99.000	99.000	68.000	0.429	http://www.semwebtech.org/mondial/10/meta#government

#1026-ETH
PRED entity: ETH

PRED relation: neighbor
PRED expected values: SP ER
=> 33 concepts (33 used for prediction)
PRED predicted values (max 10 best out of 198): ER (0.90 #3810, 0.89 #3172, 0.89 #3330), ETH (0.33 #81, 0.25 #3811, 0.25 #2854), SP (0.33 #38, 0.25 #3811, 0.25 #2854), EAT (0.25 #3811, 0.25 #2854, 0.25 #3014), EAU (0.25 #3811, 0.25 #2854, 0.25 #3014), ZRE (0.25 #3811, 0.25 #2854, 0.25 #3972), RCA (0.25 #3811, 0.25 #2854, 0.25 #3972), LAR (0.25 #2854, 0.16 #3971, 0.13 #939), ET (0.25 #2854, 0.16 #3971, 0.06 #792), Z (0.16 #3971, 0.09 #880, 0.09 #1039)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3810 for best value:
>> intensional similarity = 6
>> extensional distance = 138
>> proper extension:  F; PK; NEP; NAM; TCH; G; I; SSD; NOK; YV; ...
>> query: (?x476, ?x229) <- ?x476[ a Country; has government ?x140; has neighbor ?x186; is neighbor of ?x229[ is locatedIn of ?x53; is neighbor of ?x348;];]
ranks of expected_values: 1, 3
EVAL	ETH	neighbor	ER	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	33.000	33.000	198.000	0.900	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	ETH	neighbor	SP	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	33.000	33.000	198.000	0.900	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: SP ER
=> 88 concepts (88 used for prediction)
PRED predicted values (max 10 best out of 214): ER (0.92 #10596, 0.90 #10595, 0.90 #10111), EAT (0.60 #1399, 0.50 #2841, 0.36 #2874), TCH (0.50 #977, 0.33 #4965, 0.29 #7049), ETH (0.46 #6567, 0.46 #4324, 0.40 #792), SP (0.46 #6567, 0.46 #4324, 0.40 #792), IL (0.46 #3238, 0.33 #3563, 0.23 #5170), ZRE (0.42 #2771, 0.33 #1488, 0.33 #691), RCA (0.36 #2874, 0.33 #4965, 0.33 #750), EAU (0.36 #2874, 0.33 #4965, 0.33 #743), LAR (0.36 #2874, 0.33 #4965, 0.33 #304)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #10596 for best value:
>> intensional similarity = 9
>> extensional distance = 110
>> proper extension:  ARM;
>> query: (?x476, ?x94) <- ?x476[ has ethnicGroup ?x1179; has government ?x140; has neighbor ?x186[ has religion ?x116;]; has religion ?x56; is neighbor of ?x94[ has government ?x435; is locatedIn of ?x415; is neighbor of ?x220;];]
ranks of expected_values: 1, 5
EVAL	ETH	neighbor	ER	CNN-1.+1._MA	1.000	1.000	1.000	1.000	88.000	88.000	214.000	0.916	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	ETH	neighbor	SP	CNN-1.+1._MA	0.000	0.000	1.000	0.250	88.000	88.000	214.000	0.916	http://www.semwebtech.org/mondial/10/meta#neighbor

#1025-CN
PRED entity: CN

PRED relation: locatedIn!
PRED expected values: ChangbaiShan Hwangho
=> 31 concepts (30 used for prediction)
PRED predicted values (max 10 best out of 1328): PacificOcean (0.75 #17862, 0.33 #4186, 0.32 #20596), SeaofJapan (0.50 #6917, 0.33 #4181, 0.33 #79), NorthSea (0.45 #9595, 0.22 #12330, 0.19 #13697), AtlanticOcean (0.36 #9615, 0.36 #38330, 0.36 #19187), ChangbaiShan (0.33 #1166, 0.25 #8004, 0.09 #34185), AndamanSea (0.33 #2849, 0.15 #11054, 0.09 #16525), GulfofBengal (0.33 #2807, 0.15 #11012, 0.09 #16483), CaspianSea (0.33 #4803, 0.15 #11641, 0.09 #34185), Ryn (0.33 #5454, 0.15 #12292, 0.09 #34185), Tobol (0.33 #5263, 0.15 #12101, 0.09 #34185)
>> best conf = 0.75
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #17862 for best value:
>> intensional similarity = 5
>> extensional distance = 42
>> proper extension:  GUAM;
>> query: (?x232, PacificOcean) <- ?x232[ a Country; has government ?x831; is locatedIn of ?x620[ has locatedIn ?x1568<RC>;];]

*> Best rule #1166 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 1
*> proper extension:  NOK;
*> query: (?x232, ChangbaiShan) <- ?x232[ has encompassed ?x175; is locatedIn of ?x270<YellowSea>; is neighbor of ?x403[ has ethnicGroup ?x58; is locatedIn of ?x127;];]
*> conf = 0.33
ranks of expected_values: 5
EVAL	CN	locatedIn!	Hwangho	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	31.000	30.000	1328.000	0.750	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	CN	locatedIn!	ChangbaiShan	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.200	31.000	30.000	1328.000	0.750	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: ChangbaiShan Hwangho
=> 99 concepts (99 used for prediction)
PRED predicted values (max 10 best out of 1347): PacificOcean (0.89 #63084, 0.89 #78155, 0.87 #38425), IndianOcean (0.85 #36976, 0.40 #8219, 0.33 #3), AndamanSea (0.79 #65736, 0.79 #60253, 0.78 #52032), OzeroBalchash (0.79 #65736, 0.79 #60253, 0.78 #52032), ArabianSea (0.79 #65736, 0.79 #60253, 0.78 #52032), Ganges (0.79 #65736, 0.79 #60253, 0.78 #52032), SeaofOkhotsk (0.79 #65736, 0.79 #60253, 0.78 #52032), Ob (0.79 #65736, 0.79 #60253, 0.78 #52032), Ili (0.77 #61627, 0.77 #53407, 0.76 #60255), Irawaddy (0.77 #61627, 0.77 #53407, 0.76 #60255)
>> best conf = 0.89
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #63084 for best value:
>> intensional similarity = 10
>> extensional distance = 25
>> proper extension:  PITC; WS; NIUE; WAFU; TUV; KIR; COOK; NAU; PAL;
>> query: (?x232, PacificOcean) <- ?x232[ has encompassed ?x175[ is encompassed of ?x924[ has ethnicGroup ?x1553; has religion ?x116;];]; has government ?x831; is locatedIn of ?x270[ has mergesWith ?x271<SeaofJapan>;]; is locatedIn of ?x1881[ has type ?x762;];]

*> Best rule #8219 for first EXPECTED value:
*> intensional similarity = 15
*> extensional distance = 1
*> proper extension:  ROK;
*> query: (?x232, ?x127) <- ?x232[ a Country; has neighbor ?x403[ a Country; has government ?x2502; is locatedIn of ?x127;]; has neighbor ?x409[ a Country; has wasDependentOf ?x81;]; has neighbor ?x641[ a Country; has encompassed ?x175;]; is locatedIn of ?x384[ is mergesWith of ?x241;]; is locatedIn of ?x620<EastChinaSea>;]
*> conf = 0.40
ranks of expected_values: 57
EVAL	CN	locatedIn!	Hwangho	CNN-1.+1._MA	0.000	0.000	0.000	0.000	99.000	99.000	1347.000	0.889	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	CN	locatedIn!	ChangbaiShan	CNN-1.+1._MA	0.000	0.000	0.000	0.018	99.000	99.000	1347.000	0.889	http://www.semwebtech.org/mondial/10/meta#locatedIn

#1024-ZW
PRED entity: ZW

PRED relation: neighbor!
PRED expected values: RB
=> 28 concepts (28 used for prediction)
PRED predicted values (max 10 best out of 189): RB (0.91 #1440, 0.91 #2724, 0.90 #1600), ZW (0.36 #159, 0.33 #154, 0.27 #1441), NAM (0.36 #159, 0.33 #18, 0.27 #1441), LS (0.36 #159, 0.33 #7, 0.27 #1441), SD (0.36 #159, 0.33 #33, 0.26 #797), EAT (0.36 #159, 0.27 #1441, 0.26 #797), ZRE (0.36 #159, 0.27 #1441, 0.26 #797), MW (0.36 #159, 0.27 #1441, 0.26 #797), ANG (0.27 #1441, 0.26 #797, 0.26 #1601), CAM (0.14 #727, 0.12 #569, 0.10 #2081)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1440 for best value:
>> intensional similarity = 7
>> extensional distance = 98
>> proper extension:  BG; SSD; IL; IRQ; AZ; S; CAM; B; EAT; MNG;
>> query: (?x1576, ?x1239) <- ?x1576[ has neighbor ?x1239[ has ethnicGroup ?x2322; has neighbor ?x138; is locatedIn of ?x933;]; is locatedIn of ?x1977[ has flowsInto ?x60;]; is neighbor of ?x192;]
ranks of expected_values: 1
EVAL	ZW	neighbor!	RB	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	28.000	28.000	189.000	0.911	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: RB
=> 88 concepts (88 used for prediction)
PRED predicted values (max 10 best out of 227): RB (0.92 #6190, 0.92 #6191, 0.92 #8485), ZW (0.50 #968, 0.43 #1459, 0.35 #1467), EAT (0.46 #2250, 0.44 #1598, 0.44 #2577), NAM (0.43 #1323, 0.35 #1467, 0.34 #2282), MW (0.40 #779, 0.35 #1467, 0.34 #2282), EAU (0.40 #762, 0.33 #274, 0.31 #2232), RWA (0.40 #744, 0.33 #256, 0.23 #2214), BI (0.40 #713, 0.33 #225, 0.23 #2183), SSD (0.38 #2163, 0.33 #205, 0.20 #693), ZRE (0.35 #1467, 0.34 #2282, 0.33 #1037)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #6190 for best value:
>> intensional similarity = 13
>> extensional distance = 47
>> proper extension:  SP; WEST;
>> query: (?x1576, ?x1239) <- ?x1576[ a Country; has encompassed ?x213; has ethnicGroup ?x162; has neighbor ?x243[ has neighbor ?x89[ has government ?x90;]; is locatedIn of ?x182;]; has neighbor ?x1239; has religion ?x187<Muslim>; is locatedIn of ?x242[ has flowsInto ?x60;]; is neighbor of ?x192;]
ranks of expected_values: 1
EVAL	ZW	neighbor!	RB	CNN-1.+1._MA	1.000	1.000	1.000	1.000	88.000	88.000	227.000	0.917	http://www.semwebtech.org/mondial/10/meta#neighbor

#1023-BandaSea
PRED entity: BandaSea

PRED relation: locatedInWater!
PRED expected values: Halmahera
=> 33 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 291): Sumatra (0.50 #1128, 0.40 #594, 0.35 #3478), Palawan (0.50 #172, 0.25 #439, 0.20 #1240), Lombok (0.40 #729, 0.35 #3478, 0.32 #2673), Bali (0.40 #633, 0.35 #3478, 0.32 #2673), Krakatau (0.40 #549, 0.35 #3478, 0.32 #2673), Sumbawa (0.40 #647, 0.35 #3478, 0.32 #2673), Java (0.40 #545, 0.35 #3478, 0.32 #2673), Bangka (0.35 #3478, 0.32 #2673, 0.25 #339), Bintan (0.35 #3478, 0.32 #2673, 0.25 #235), Batam (0.35 #3478, 0.32 #2673, 0.25 #92)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1128 for best value:
>> intensional similarity = 8
>> extensional distance = 8
>> proper extension:  LakeToba;
>> query: (?x770, Sumatra) <- ?x770[ has locatedIn ?x217<RI>; has locatedIn ?x735[ has encompassed ?x175; has religion ?x95;]; is locatedInWater of ?x216; is locatedInWater of ?x1074[ a Island;];]

*> Best rule #3478 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 37
*> proper extension:  Save; LakeNicaragua; Waag;
*> query: (?x770, ?x240) <- ?x770[ has locatedIn ?x217[ has ethnicGroup ?x425; has religion ?x95; is locatedIn of ?x240[ a Island;];]; is locatedInWater of ?x216;]
*> conf = 0.35
ranks of expected_values: 11
EVAL	BandaSea	locatedInWater!	Halmahera	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.091	33.000	31.000	291.000	0.500	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater!
PRED expected values: Halmahera
=> 102 concepts (102 used for prediction)
PRED predicted values (max 10 best out of 678): Sumatra (0.56 #1400, 0.50 #1669, 0.50 #1131), Palawan (0.50 #440, 0.40 #708, 0.25 #1243), Lombok (0.40 #999, 0.25 #1266, 0.22 #1535), Bali (0.40 #903, 0.25 #1170, 0.22 #1439), Krakatau (0.40 #819, 0.25 #1086, 0.22 #1355), Sumbawa (0.40 #917, 0.25 #1184, 0.22 #1453), Java (0.40 #815, 0.25 #1082, 0.22 #1351), Taiwan (0.33 #57, 0.25 #2203, 0.25 #1339), Mindanao (0.33 #114, 0.25 #1185, 0.25 #382), Leyte (0.33 #63, 0.25 #1134, 0.25 #331)
>> best conf = 0.56
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1400 for best value:
>> intensional similarity = 9
>> extensional distance = 7
>> proper extension:  AndamanSea;
>> query: (?x770, Sumatra) <- ?x770[ has locatedIn ?x217<RI>; has mergesWith ?x282[ has locatedIn ?x482[ has ethnicGroup ?x79;]; has mergesWith ?x271; is locatedInWater of ?x205;]; is locatedInWater of ?x1074[ has locatedIn ?x853;];]

*> Best rule #105 for first EXPECTED value:
*> intensional similarity = 11
*> extensional distance = 1
*> proper extension:  PacificOcean;
*> query: (?x770, Halmahera) <- ?x770[ a Sea; has locatedIn ?x735[ has encompassed ?x175; has religion ?x95;]; has mergesWith ?x241[ is locatedInWater of ?x240;]; is locatedInWater of ?x1005[ a Island; has belongsToIslands ?x875;]; is locatedInWater of ?x1074<NewGuinea>;]
*> conf = 0.33
ranks of expected_values: 14
EVAL	BandaSea	locatedInWater!	Halmahera	CNN-1.+1._MA	0.000	0.000	0.000	0.071	102.000	102.000	678.000	0.556	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#1022-UA
PRED entity: UA

PRED relation: ethnicGroup
PRED expected values: Romanian
=> 33 concepts (33 used for prediction)
PRED predicted values (max 10 best out of 206): Serb (0.25 #39, 0.18 #5728, 0.16 #5979), European (0.25 #1750, 0.25 #4489, 0.24 #1501), Roma (0.21 #6, 0.18 #5728, 0.16 #5979), African (0.20 #4736, 0.20 #3989, 0.19 #4487), German (0.18 #5728, 0.17 #9, 0.16 #5979), Slovak (0.18 #5728, 0.16 #5979, 0.16 #5978), Czech (0.18 #5728, 0.16 #5979, 0.16 #5978), Gagauz (0.18 #5728, 0.16 #5979, 0.16 #5978), Moldavian-Romanian (0.18 #5728, 0.16 #5979, 0.16 #5978), Romanian (0.18 #5728, 0.16 #5979, 0.16 #5978)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #39 for best value:
>> intensional similarity = 6
>> extensional distance = 22
>> proper extension:  P;
>> query: (?x303, Serb) <- ?x303[ has encompassed ?x195<Europe>; has government ?x435; has neighbor ?x73; has wasDependentOf ?x903; is locatedIn of ?x97;]

*> Best rule #5728 for first EXPECTED value:
*> intensional similarity = 4
*> extensional distance = 130
*> proper extension:  LB; AND;
*> query: (?x303, ?x164) <- ?x303[ has ethnicGroup ?x58; has religion ?x56; is neighbor of ?x176[ has ethnicGroup ?x164;];]
*> conf = 0.18
ranks of expected_values: 10
EVAL	UA	ethnicGroup	Romanian	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.100	33.000	33.000	206.000	0.250	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: Romanian
=> 92 concepts (92 used for prediction)
PRED predicted values (max 10 best out of 248): European (0.61 #3502, 0.52 #5747, 0.52 #5497), German (0.50 #2255, 0.50 #1506, 0.43 #2504), Croat (0.43 #2499, 0.40 #2000, 0.20 #8732), Slovene (0.43 #2506, 0.40 #2007, 0.20 #8732), Amerindian (0.36 #2996, 0.33 #5491, 0.27 #8483), Roma (0.33 #2252, 0.33 #755, 0.33 #255), Slovak (0.33 #2449, 0.33 #952, 0.28 #9231), Czech (0.33 #2277, 0.33 #780, 0.28 #9231), Ruthenian (0.33 #826, 0.28 #9231, 0.28 #2246), Tatar (0.33 #580, 0.28 #9231, 0.28 #3495)
>> best conf = 0.61
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3502 for best value:
>> intensional similarity = 10
>> extensional distance = 16
>> proper extension:  SLB;
>> query: (?x303, European) <- ?x303[ a Country; has ethnicGroup ?x1322[ a EthnicGroup;]; has religion ?x95<Protestant>; has religion ?x352<RomanCatholic>; has wasDependentOf ?x903; is locatedIn of ?x457[ has type ?x136;];]

*> Best rule #9231 for first EXPECTED value:
*> intensional similarity = 10
*> extensional distance = 40
*> proper extension:  AND;
*> query: (?x303, ?x164) <- ?x303[ has encompassed ?x195; has ethnicGroup ?x58; has ethnicGroup ?x517[ a EthnicGroup;]; has neighbor ?x163[ has ethnicGroup ?x164; is locatedIn of ?x133[ has hasSource ?x1190;];]; has religion ?x352<RomanCatholic>;]
*> conf = 0.28
ranks of expected_values: 19
EVAL	UA	ethnicGroup	Romanian	CNN-1.+1._MA	0.000	0.000	0.000	0.053	92.000	92.000	248.000	0.611	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#1021-BR
PRED entity: BR

PRED relation: neighbor!
PRED expected values: SME ROU FGU
=> 36 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 213): SME (0.92 #2017, 0.90 #4342, 0.90 #4499), ROU (0.92 #2017, 0.90 #4499, 0.90 #3101), BR (0.50 #396, 0.33 #707, 0.33 #242), RCH (0.33 #190, 0.31 #618, 0.29 #3723), EC (0.31 #618, 0.29 #3723, 0.25 #4343), PA (0.31 #618, 0.29 #3723, 0.25 #4343), D (0.31 #788, 0.14 #1408, 0.14 #1097), FGU (0.25 #4343, 0.05 #5435, 0.05 #2946), F (0.23 #777, 0.11 #1397, 0.07 #1866), R (0.19 #2020, 0.14 #2329, 0.13 #2640)
>> best conf = 0.92
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #2017 for best value:
>> intensional similarity = 6
>> extensional distance = 39
>> proper extension:  PK; NOK;
>> query: (?x542, ?x179) <- ?x542[ a Country; has language ?x539; has neighbor ?x179; is locatedIn of ?x2500[ a Mountain;]; is neighbor of ?x215;]
ranks of expected_values: 1, 2, 8
EVAL	BR	neighbor!	FGU	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.167	36.000	36.000	213.000	0.915	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	BR	neighbor!	ROU	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	36.000	36.000	213.000	0.915	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	BR	neighbor!	SME	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	36.000	36.000	213.000	0.915	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: SME ROU FGU
=> 112 concepts (112 used for prediction)
PRED predicted values (max 10 best out of 230): SME (0.93 #12291, 0.93 #10865, 0.92 #8804), ROU (0.92 #10863, 0.92 #6276, 0.92 #12289), RCH (0.50 #662, 0.35 #4857, 0.33 #36), BR (0.43 #3129, 0.35 #4857, 0.33 #560), AND (0.40 #1530, 0.25 #1218, 0.13 #3405), EC (0.35 #4857, 0.33 #446, 0.31 #5962), PA (0.35 #4857, 0.33 #432, 0.31 #5962), FGU (0.31 #3286, 0.31 #9123, 0.30 #8963), R (0.31 #2351, 0.27 #3132, 0.25 #4386), F (0.25 #1104, 0.20 #1416, 0.14 #16742)
>> best conf = 0.93
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #12291 for best value:
>> intensional similarity = 12
>> extensional distance = 63
>> proper extension:  HONX; PA; LT; HCA; MC;
>> query: (?x542, ?x296) <- ?x542[ has government ?x140; has language ?x539; has neighbor ?x296[ has neighbor ?x202; is locatedIn of ?x1332[ is flowsInto of ?x1331;];]; has neighbor ?x345[ has encompassed ?x521; has religion ?x95; is locatedIn of ?x2451[ a Mountain;];]; is locatedIn of ?x48;]
ranks of expected_values: 1, 2, 8
EVAL	BR	neighbor!	FGU	CNN-1.+1._MA	0.000	0.000	1.000	0.167	112.000	112.000	230.000	0.933	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	BR	neighbor!	ROU	CNN-1.+1._MA	1.000	1.000	1.000	1.000	112.000	112.000	230.000	0.933	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	BR	neighbor!	SME	CNN-1.+1._MA	1.000	1.000	1.000	1.000	112.000	112.000	230.000	0.933	http://www.semwebtech.org/mondial/10/meta#neighbor

#1020-BF
PRED entity: BF

PRED relation: government
PRED expected values: "parliamentary republic"
=> 35 concepts (35 used for prediction)
PRED predicted values (max 10 best out of 46): "republic" (0.62 #368, 0.50 #656, 0.50 #295), "republic; multiparty presidential regime established 1960" (0.25 #362, 0.17 #281, 0.16 #1515), "federal republic" (0.20 #147, 0.08 #437, 0.08 #509), "constitutional democracy" (0.16 #1515, 0.16 #1950, 0.16 #1949), "military junta" (0.12 #342, 0.06 #415, 0.05 #2167), "parliamentary democracy" (0.12 #583, 0.10 #1015, 0.10 #2027), "constitutional monarchy" (0.10 #580, 0.06 #1878, 0.06 #1806), "British Overseas Territories" (0.10 #873, 0.05 #2167, 0.04 #2029), "republic; multiparty presidential regime" (0.06 #391, 0.05 #607, 0.05 #2167), "constitutional republic" (0.06 #731, 0.04 #875, 0.03 #1019)
>> best conf = 0.62
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #368 for best value:
>> intensional similarity = 6
>> extensional distance = 14
>> proper extension:  COM;
>> query: (?x811, "republic") <- ?x811[ has encompassed ?x213<Africa>; has religion ?x116; has wasDependentOf ?x78<F>; is locatedIn of ?x610;]

*> Best rule #2167 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 171
*> proper extension:  NMIS; GBJ; GBG; SVAX; WG;
*> query: (?x811, ?x435) <- ?x811[ a Country; has encompassed ?x213[ is encompassed of ?x651[ has government ?x435;];]; has ethnicGroup ?x2156[ a EthnicGroup;]; is locatedIn of ?x610;]
*> conf = 0.05
ranks of expected_values: 12
EVAL	BF	government	"parliamentary republic"	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.083	35.000	35.000	46.000	0.625	http://www.semwebtech.org/mondial/10/meta#government

PRED relation: government
PRED expected values: "parliamentary republic"
=> 76 concepts (76 used for prediction)
PRED predicted values (max 10 best out of 69): "republic" (0.67 #666, 0.64 #1104, 0.64 #958), "constitutional democracy" (0.33 #733, 0.33 #513, 0.33 #77), "republic; multiparty presidential regime established 1960" (0.33 #733, 0.33 #73, 0.30 #952), "federal republic" (0.25 #146, 0.20 #3282, 0.16 #4809), "military junta" (0.25 #146, 0.14 #786, 0.12 #4227), "operates under a transitional government" (0.25 #146, 0.06 #1266, 0.06 #1484), "constitutional monarchy and Commonwealth realm" (0.21 #2628, 0.05 #1714, 0.02 #3170), "republic; multiparty presidential regime" (0.18 #1054, 0.17 #615, 0.12 #4227), "constitutional monarchy" (0.14 #1973, 0.12 #4227, 0.10 #2848), "parliamentary democracy" (0.14 #1831, 0.12 #4232, 0.11 #1393)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #666 for best value:
>> intensional similarity = 20
>> extensional distance = 4
>> proper extension:  SN; DZ;
>> query: (?x811, "republic") <- ?x811[ a Country; has ethnicGroup ?x2156; has neighbor ?x483[ has encompassed ?x213; has government ?x180; has wasDependentOf ?x81; is locatedIn of ?x135;]; has religion ?x116<Christian>; has wasDependentOf ?x78<F>; is locatedIn of ?x610; is neighbor of ?x1206[ has ethnicGroup ?x2201; has religion ?x187; is locatedIn of ?x350; is neighbor of ?x651<RG>;]; is neighbor of ?x1307[ a Country;];]

*> Best rule #4227 for first EXPECTED value:
*> intensional similarity = 9
*> extensional distance = 100
*> proper extension:  YE;
*> query: (?x811, ?x435) <- ?x811[ a Country; has encompassed ?x213; has neighbor ?x426; has religion ?x116; has wasDependentOf ?x78[ is wasDependentOf of ?x651[ has government ?x435; is locatedIn of ?x182;];]; is locatedIn of ?x610;]
*> conf = 0.12
ranks of expected_values: 12
EVAL	BF	government	"parliamentary republic"	CNN-1.+1._MA	0.000	0.000	0.000	0.083	76.000	76.000	69.000	0.667	http://www.semwebtech.org/mondial/10/meta#government

#1019-Schari
PRED entity: Schari

PRED relation: locatedIn
PRED expected values: RCA
=> 19 concepts (19 used for prediction)
PRED predicted values (max 10 best out of 53): CAM (0.66 #1685, 0.61 #966, 0.61 #965), TCH (0.61 #966, 0.61 #965, 0.57 #484), RCA (0.59 #1687, 0.57 #1926, 0.57 #484), ZRE (0.50 #322, 0.22 #725, 0.22 #563), WAN (0.44 #967, 0.17 #238, 0.14 #480), RN (0.44 #967, 0.17 #238, 0.13 #721), SSD (0.33 #539, 0.14 #480, 0.13 #721), USA (0.25 #1519, 0.18 #1039, 0.10 #800), CN (0.23 #1262, 0.10 #784, 0.06 #2465), R (0.20 #1693, 0.16 #2414, 0.11 #3610)
>> best conf = 0.66
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1685 for best value:
>> intensional similarity = 14
>> extensional distance = 57
>> proper extension:  Kwa; Bani; Fimi; SaintMarysRiver; Senegal; SaintLawrenceRiver; Luapula; RioNegro; Tshuapa; OhioRiver; ...
>> query: (?x1263, ?x536) <- ?x1263[ a Source; is hasSource of ?x695[ a River; has locatedIn ?x536[ has ethnicGroup ?x122; has neighbor ?x139; has religion ?x116; is locatedIn of ?x182<AtlanticOcean>;]; has locatedIn ?x736[ a Country; has ethnicGroup ?x992; has neighbor ?x186; has religion ?x352;];];]

*> Best rule #1687 for first EXPECTED value:
*> intensional similarity = 14
*> extensional distance = 57
*> proper extension:  Kwa; Bani; Fimi; SaintMarysRiver; Senegal; SaintLawrenceRiver; Luapula; RioNegro; Tshuapa; OhioRiver; ...
*> query: (?x1263, ?x736) <- ?x1263[ a Source; is hasSource of ?x695[ a River; has locatedIn ?x536[ has ethnicGroup ?x122; has neighbor ?x139; has religion ?x116; is locatedIn of ?x182<AtlanticOcean>;]; has locatedIn ?x736[ a Country; has ethnicGroup ?x992; has neighbor ?x186; has religion ?x352;];];]
*> conf = 0.59
ranks of expected_values: 3
EVAL	Schari	locatedIn	RCA	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.333	19.000	19.000	53.000	0.662	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: RCA
=> 116 concepts (116 used for prediction)
PRED predicted values (max 10 best out of 64): RCA (0.74 #11386, 0.71 #6051, 0.71 #5807), ZRE (0.71 #11225, 0.67 #11709, 0.65 #12436), CAM (0.70 #18650, 0.67 #19379, 0.66 #12596), TCH (0.67 #7988, 0.66 #12596, 0.65 #11388), WAN (0.52 #11387, 0.44 #13081, 0.43 #2657), RN (0.52 #11387, 0.44 #13081, 0.43 #2657), ETH (0.44 #7864, 0.43 #5681, 0.29 #6167), USA (0.44 #8551, 0.33 #18480, 0.30 #19211), ANG (0.43 #5998, 0.15 #12546, 0.12 #11335), RG (0.40 #3531, 0.38 #7412, 0.33 #4501)
>> best conf = 0.74
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #11386 for best value:
>> intensional similarity = 23
>> extensional distance = 15
>> proper extension:  Fimi; Tshuapa; Cuilo; Lomami; Ruzizi; Aruwimi; Lualaba; Lukuga; Cuango; Luvua; ...
>> query: (?x1263, ?x736) <- ?x1263[ a Source; is hasSource of ?x695[ a River; has flowsInto ?x2238[ has locatedIn ?x139;]; has hasEstuary ?x1378[ a Estuary; has locatedIn ?x169;]; has locatedIn ?x736[ has encompassed ?x213<Africa>; has ethnicGroup ?x992; has neighbor ?x528<RCB>; has religion ?x187<Muslim>; has wasDependentOf ?x78; is locatedIn of ?x388<Ubangi>; is locatedIn of ?x549<Ubangi>; is locatedIn of ?x985<Bomu>;];];]
ranks of expected_values: 1
EVAL	Schari	locatedIn	RCA	CNN-1.+1._MA	1.000	1.000	1.000	1.000	116.000	116.000	64.000	0.737	http://www.semwebtech.org/mondial/10/meta#locatedIn

#1018-RN
PRED entity: RN

PRED relation: locatedIn!
PRED expected values: Niger MontGreboun
=> 29 concepts (26 used for prediction)
PRED predicted values (max 10 best out of 1183): AtlanticOcean (0.88 #11408, 0.60 #4305, 0.52 #15627), Tanezrouft (0.52 #15627, 0.33 #2776, 0.25 #4196), ErgChech (0.52 #15627, 0.33 #2482, 0.25 #3902), Senegal (0.52 #15627, 0.33 #1829, 0.25 #8932), Bani (0.52 #15627, 0.33 #1602, 0.20 #4444), Niger (0.52 #15627, 0.33 #1673, 0.17 #5936), Bani (0.52 #15627, 0.33 #1958, 0.17 #6221), Schari (0.52 #15627, 0.33 #319, 0.10 #1421), Schari (0.52 #15627, 0.33 #759, 0.10 #1421), Djourab (0.52 #15627, 0.33 #1260, 0.10 #1421)
>> best conf = 0.88
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #11408 for best value:
>> intensional similarity = 7
>> extensional distance = 23
>> proper extension:  BVIR; KN; HELX; CDN; CV; IS; GUAD; MART; STP; BERM; ...
>> query: (?x426, AtlanticOcean) <- ?x426[ has ethnicGroup ?x1109[ a EthnicGroup;]; is locatedIn of ?x930[ has type ?x578;]; is locatedIn of ?x2238[ has locatedIn ?x139<WAN>;];]

*> Best rule #15627 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 33
*> proper extension:  RM;
*> query: (?x426, ?x168) <- ?x426[ a Country; has government ?x435<"republic">; is locatedIn of ?x930[ has locatedIn ?x169[ is locatedIn of ?x168;]; has type ?x578;];]
*> conf = 0.52
ranks of expected_values: 6
EVAL	RN	locatedIn!	MontGreboun	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	29.000	26.000	1183.000	0.880	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	RN	locatedIn!	Niger	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.167	29.000	26.000	1183.000	0.880	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Niger MontGreboun
=> 70 concepts (70 used for prediction)
PRED predicted values (max 10 best out of 1410): AtlanticOcean (0.96 #54103, 0.95 #59798, 0.95 #56950), MediterraneanSea (0.54 #59753, 0.50 #19904, 0.50 #18565), MontGreboun (0.44 #8528), RedSea (0.40 #9406, 0.36 #20784, 0.33 #13674), LibyanDesert (0.40 #9218, 0.33 #13486, 0.29 #14908), Nile (0.40 #9743, 0.33 #14011, 0.26 #1421), LakeNasser (0.40 #8759, 0.33 #13027, 0.26 #1421), Niger (0.33 #1673, 0.33 #252, 0.26 #1421), Bani (0.33 #11555, 0.33 #181, 0.26 #1421), Schari (0.33 #6003, 0.29 #49791, 0.29 #14537)
>> best conf = 0.96
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #54103 for best value:
>> intensional similarity = 13
>> extensional distance = 46
>> proper extension:  FALK;
>> query: (?x426, AtlanticOcean) <- ?x426[ a Country; has ethnicGroup ?x1109[ a EthnicGroup;]; has government ?x435; is locatedIn of ?x2238[ has locatedIn ?x169[ has neighbor ?x186; has wasDependentOf ?x78;]; has locatedIn ?x536<CAM>; is flowsInto of ?x695[ has hasEstuary ?x1378; has locatedIn ?x736;];];]

*> Best rule #8528 for first EXPECTED value:
*> intensional similarity = 17
*> extensional distance = 2
*> proper extension:  CV;
*> query: (?x426, ?x1133) <- ?x426[ a Country; has encompassed ?x213<Africa>; has ethnicGroup ?x1109[ a EthnicGroup;]; has ethnicGroup ?x2025[ is ethnicGroup of ?x839[ has neighbor ?x416; is locatedIn of ?x456;];]; is locatedIn of ?x535[ a Mountain; a Volcano; has inMountains ?x1501[ is inMountains of ?x1133;];]; is locatedIn of ?x2238[ has locatedIn ?x536<CAM>;];]
*> conf = 0.44
ranks of expected_values: 3, 8
EVAL	RN	locatedIn!	MontGreboun	CNN-1.+1._MA	0.000	1.000	1.000	0.333	70.000	70.000	1410.000	0.958	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	RN	locatedIn!	Niger	CNN-1.+1._MA	0.000	0.000	1.000	0.143	70.000	70.000	1410.000	0.958	http://www.semwebtech.org/mondial/10/meta#locatedIn

#1017-ETH
PRED entity: ETH

PRED relation: neighbor!
PRED expected values: SUD SP
=> 43 concepts (43 used for prediction)
PRED predicted values (max 10 best out of 198): SUD (0.90 #5535, 0.89 #3160, 0.89 #4582), RWA (0.40 #409, 0.10 #1199, 0.09 #1673), EAU (0.33 #110, 0.27 #5536, 0.26 #5697), ZRE (0.33 #59, 0.27 #5536, 0.26 #5697), ETH (0.33 #82, 0.27 #5536, 0.26 #5697), RCA (0.33 #117, 0.27 #5536, 0.26 #5697), EAT (0.27 #5536, 0.26 #5697, 0.26 #5698), SP (0.27 #5536, 0.26 #5697, 0.26 #5698), LAR (0.27 #5536, 0.26 #5697, 0.26 #5698), ET (0.27 #5536, 0.26 #5697, 0.26 #5698)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5535 for best value:
>> intensional similarity = 6
>> extensional distance = 148
>> proper extension:  MEL;
>> query: (?x476, ?x186) <- ?x476[ a Country; has neighbor ?x186[ has neighbor ?x1184[ has ethnicGroup ?x1215;]; is locatedIn of ?x531;]; is locatedIn of ?x228;]
ranks of expected_values: 1, 8
EVAL	ETH	neighbor!	SP	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.143	43.000	43.000	198.000	0.897	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	ETH	neighbor!	SUD	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	43.000	43.000	198.000	0.897	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: SUD SP
=> 84 concepts (84 used for prediction)
PRED predicted values (max 10 best out of 219): SUD (0.91 #8417, 0.90 #5652, 0.90 #5650), R (0.50 #1452, 0.47 #3381, 0.42 #4513), EAT (0.50 #940, 0.34 #7445, 0.33 #293), RWA (0.50 #905, 0.18 #12226, 0.18 #2507), ETH (0.40 #482, 0.34 #7445, 0.33 #406), SP (0.40 #482, 0.34 #7445, 0.33 #205), H (0.36 #2139, 0.33 #3422, 0.26 #4554), EAU (0.34 #7445, 0.33 #434, 0.33 #275), RCA (0.34 #7445, 0.33 #441, 0.33 #7444), ZRE (0.33 #383, 0.29 #5653, 0.28 #12228)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #8417 for best value:
>> intensional similarity = 12
>> extensional distance = 50
>> proper extension:  PK;
>> query: (?x476, ?x94) <- ?x476[ a Country; has neighbor ?x94[ has ethnicGroup ?x1593; has government ?x435; has neighbor ?x629[ has ethnicGroup ?x996;]; has religion ?x116; is locatedIn of ?x415;]; is locatedIn of ?x655[ a Mountain;]; is locatedIn of ?x1875[ has inMountains ?x2477;];]
ranks of expected_values: 1, 6
EVAL	ETH	neighbor!	SP	CNN-1.+1._MA	0.000	0.000	1.000	0.200	84.000	84.000	219.000	0.915	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	ETH	neighbor!	SUD	CNN-1.+1._MA	1.000	1.000	1.000	1.000	84.000	84.000	219.000	0.915	http://www.semwebtech.org/mondial/10/meta#neighbor

#1016-PK
PRED entity: PK

PRED relation: neighbor
PRED expected values: IR
=> 31 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 183): IR (0.91 #3178, 0.91 #2861, 0.90 #3816), R (0.33 #161, 0.29 #2701, 0.29 #2225), PK (0.33 #163, 0.29 #2701, 0.29 #2225), KAZ (0.33 #227, 0.29 #2701, 0.29 #2225), KGZ (0.33 #174, 0.29 #2701, 0.29 #2225), MYA (0.33 #222, 0.29 #2701, 0.29 #2225), HONX (0.33 #278, 0.29 #2701, 0.29 #2225), MACX (0.33 #264, 0.29 #2701, 0.29 #2225), NEP (0.33 #170, 0.29 #2701, 0.29 #2225), UZB (0.33 #523, 0.29 #2701, 0.29 #2225)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3178 for best value:
>> intensional similarity = 6
>> extensional distance = 102
>> proper extension:  KWT;
>> query: (?x83, ?x304) <- ?x83[ a Country; has neighbor ?x232[ is locatedIn of ?x231;]; is locatedIn of ?x82; is neighbor of ?x304[ has language ?x511;];]
ranks of expected_values: 1
EVAL	PK	neighbor	IR	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	31.000	31.000	183.000	0.912	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: IR
=> 89 concepts (89 used for prediction)
PRED predicted values (max 10 best out of 204): IR (0.95 #7038, 0.93 #8669, 0.92 #12265), MYA (0.62 #3006, 0.38 #1634, 0.38 #1631), MOC (0.45 #4287, 0.18 #6584, 0.16 #3106), SA (0.40 #4047, 0.40 #1753, 0.38 #3389), PK (0.40 #1966, 0.39 #5079, 0.38 #3925), UZB (0.40 #1845, 0.38 #1634, 0.38 #1631), TM (0.40 #1518, 0.38 #1634, 0.38 #1631), KAZ (0.40 #1539, 0.38 #1634, 0.38 #1631), IRQ (0.38 #3320, 0.38 #1634, 0.38 #1631), NEP (0.38 #1634, 0.38 #1631, 0.37 #3269)
>> best conf = 0.95
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #7038 for best value:
>> intensional similarity = 15
>> extensional distance = 25
>> proper extension:  USA;
>> query: (?x83, ?x924) <- ?x83[ has language ?x559; has language ?x2093[ a Language;]; is locatedIn of ?x926[ is mergesWith of ?x918;]; is locatedIn of ?x1375[ a Mountain;]; is neighbor of ?x924[ a Country; has encompassed ?x175; has ethnicGroup ?x1553; has language ?x2392; has religion ?x116; is locatedIn of ?x60; is neighbor of ?x111;];]
ranks of expected_values: 1
EVAL	PK	neighbor	IR	CNN-1.+1._MA	1.000	1.000	1.000	1.000	89.000	89.000	204.000	0.949	http://www.semwebtech.org/mondial/10/meta#neighbor

#1015-NZ
PRED entity: NZ

PRED relation: language
PRED expected values: Maori
=> 61 concepts (61 used for prediction)
PRED predicted values (max 10 best out of 93): Spanish (0.64 #763, 0.52 #670, 0.41 #1507), Arabic (0.33 #56, 0.25 #149, 0.14 #335), Vietnamese (0.33 #42, 0.25 #135, 0.14 #321), Russian (0.16 #3170, 0.15 #2705, 0.15 #2984), Dutch (0.14 #286, 0.12 #379, 0.11 #472), Catalan (0.14 #297, 0.12 #390, 0.11 #483), Basque (0.14 #307, 0.12 #400, 0.11 #493), Norwegian (0.14 #314, 0.12 #407, 0.11 #500), Hungarian (0.13 #944, 0.11 #1688, 0.10 #1967), German (0.11 #477, 0.09 #3360, 0.08 #3546)
>> best conf = 0.64
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #763 for best value:
>> intensional similarity = 6
>> extensional distance = 20
>> proper extension:  CR; BR;
>> query: (?x461, Spanish) <- ?x461[ a Country; has ethnicGroup ?x197<European>; has government ?x1947; has language ?x51; is locatedIn of ?x282;]

No rule for expected values
ranks of expected_values: 
EVAL	NZ	language	Maori	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	61.000	61.000	93.000	0.636	http://www.semwebtech.org/mondial/10/meta#language

PRED relation: language
PRED expected values: Maori
=> 96 concepts (96 used for prediction)
PRED predicted values (max 10 best out of 92): Spanish (0.75 #2998, 0.73 #2905, 0.67 #1601), German (0.41 #3270, 0.30 #1967, 0.20 #943), Arabic (0.33 #428, 0.25 #801, 0.25 #614), Vietnamese (0.33 #414, 0.25 #787, 0.25 #600), Chamorro (0.20 #1676, 0.15 #6889, 0.14 #6983), ChineseLanguage (0.20 #1676), OtherPacificIslandLanguage (0.20 #1676), Portuguese (0.20 #1961, 0.06 #5590, 0.06 #3543), Creole (0.20 #1007, 0.06 #3334, 0.03 #3613), Garifuna (0.20 #990, 0.06 #3317, 0.01 #2607)
>> best conf = 0.75
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2998 for best value:
>> intensional similarity = 11
>> extensional distance = 14
>> proper extension:  BOL;
>> query: (?x461, Spanish) <- ?x461[ has ethnicGroup ?x197; has language ?x51; has religion ?x95[ is religion of ?x641<MACX>; is religion of ?x1554<WL>;]; has wasDependentOf ?x81; is locatedIn of ?x282[ has locatedIn ?x296<PE>;];]

No rule for expected values
ranks of expected_values: 
EVAL	NZ	language	Maori	CNN-1.+1._MA	0.000	0.000	0.000	0.000	96.000	96.000	92.000	0.750	http://www.semwebtech.org/mondial/10/meta#language

#1014-CAM
PRED entity: CAM

PRED relation: encompassed
PRED expected values: Africa
=> 40 concepts (40 used for prediction)
PRED predicted values (max 10 best out of 5): America (0.61 #55, 0.54 #50, 0.51 #90), Africa (0.58 #34, 0.55 #59, 0.50 #19), Europe (0.48 #37, 0.41 #176, 0.33 #62), Asia (0.28 #71, 0.26 #81, 0.25 #136), Australia-Oceania (0.12 #93, 0.11 #189, 0.11 #179)
>> best conf = 0.61
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #55 for best value:
>> intensional similarity = 5
>> extensional distance = 26
>> proper extension:  KN; WG;
>> query: (?x536, America) <- ?x536[ a Country; has ethnicGroup ?x162<African>; has government ?x1721; is locatedIn of ?x182;]

*> Best rule #34 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 17
*> proper extension:  NAM; YV; P;
*> query: (?x536, Africa) <- ?x536[ has wasDependentOf ?x485; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x786[ has flowsInto ?x1525;]; is locatedIn of ?x2087[ a River;];]
*> conf = 0.58
ranks of expected_values: 2
EVAL	CAM	encompassed	Africa	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	40.000	40.000	5.000	0.607	http://www.semwebtech.org/mondial/10/meta#encompassed

PRED relation: encompassed
PRED expected values: Africa
=> 87 concepts (87 used for prediction)
PRED predicted values (max 10 best out of 5): Africa (0.84 #270, 0.83 #211, 0.83 #209), America (0.64 #132, 0.52 #205, 0.52 #221), Europe (0.46 #266, 0.39 #441, 0.39 #363), Asia (0.45 #101, 0.40 #133, 0.35 #288), Australia-Oceania (0.21 #322, 0.21 #162, 0.20 #167)
>> best conf = 0.84
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #270 for best value:
>> intensional similarity = 12
>> extensional distance = 46
>> proper extension:  SSD;
>> query: (?x536, ?x213) <- ?x536[ has neighbor ?x139[ a Country; has encompassed ?x213; has ethnicGroup ?x162; has government ?x140; has neighbor ?x426; is locatedIn of ?x2393[ a Estuary;];]; is locatedIn of ?x182[ is flowsInto of ?x137;]; is locatedIn of ?x2448[ a Source;];]
ranks of expected_values: 1
EVAL	CAM	encompassed	Africa	CNN-1.+1._MA	1.000	1.000	1.000	1.000	87.000	87.000	5.000	0.843	http://www.semwebtech.org/mondial/10/meta#encompassed

#1013-SD
PRED entity: SD

PRED relation: neighbor!
PRED expected values: RSA
=> 42 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 203): RSA (0.89 #4062, 0.89 #4551, 0.89 #4061), Z (0.50 #413, 0.44 #741, 0.40 #162), EAT (0.43 #1301, 0.43 #1269, 0.40 #162), MW (0.40 #292, 0.40 #162, 0.33 #130), SD (0.40 #162, 0.33 #33, 0.32 #815), ZW (0.40 #318, 0.33 #808, 0.32 #815), NAM (0.40 #180, 0.32 #815, 0.31 #1300), ZRE (0.40 #222, 0.22 #876, 0.21 #1198), SSD (0.36 #1180, 0.15 #1996, 0.12 #2159), IND (0.33 #955, 0.07 #4389, 0.05 #3900)
>> best conf = 0.89
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4062 for best value:
>> intensional similarity = 6
>> extensional distance = 95
>> proper extension:  TCH; G; NOK; YV; BI; RCB; ROK; OM; YE; TL; ...
>> query: (?x193, ?x243) <- ?x193[ has encompassed ?x213; has government ?x640; has neighbor ?x243[ has government ?x435; is locatedIn of ?x182;]; has wasDependentOf ?x81;]
ranks of expected_values: 1
EVAL	SD	neighbor!	RSA	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	42.000	36.000	203.000	0.894	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: RSA
=> 93 concepts (93 used for prediction)
PRED predicted values (max 10 best out of 233): RSA (0.90 #13528, 0.90 #9989, 0.89 #10656), ZW (0.55 #2982, 0.38 #2309, 0.36 #2986), MW (0.55 #2982, 0.38 #2309, 0.36 #2986), RB (0.55 #2982, 0.38 #2309, 0.36 #2984), Z (0.50 #251, 0.45 #1742, 0.36 #2986), BR (0.43 #910, 0.20 #1581, 0.14 #2076), EAT (0.36 #2986, 0.36 #2984, 0.34 #3148), SD (0.36 #2986, 0.36 #2984, 0.33 #10490), NAM (0.36 #2984, 0.33 #4152, 0.33 #18), ZRE (0.36 #2984, 0.33 #4152, 0.33 #60)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #13528 for best value:
>> intensional similarity = 14
>> extensional distance = 116
>> proper extension:  MC;
>> query: (?x193, ?x243) <- ?x193[ has encompassed ?x213; has government ?x640; has neighbor ?x192[ has government ?x435; has religion ?x116; is locatedIn of ?x60; is neighbor of ?x525[ is locatedIn of ?x284;];]; has neighbor ?x243[ has neighbor ?x138[ a Country; has language ?x247; is locatedIn of ?x137;]; is locatedIn of ?x182;];]
ranks of expected_values: 1
EVAL	SD	neighbor!	RSA	CNN-1.+1._MA	1.000	1.000	1.000	1.000	93.000	93.000	233.000	0.900	http://www.semwebtech.org/mondial/10/meta#neighbor

#1012-Zaire
PRED entity: Zaire

PRED relation: hasEstuary
PRED expected values: Zaire
=> 41 concepts (35 used for prediction)
PRED predicted values (max 10 best out of 211): Ubangi (0.25 #250, 0.17 #703, 0.17 #477), Oranje (0.19 #6797, 0.03 #1483, 0.02 #1709), Volta (0.19 #6797, 0.03 #1503, 0.02 #1729), Senegal (0.19 #6797, 0.03 #1499, 0.02 #1725), Gambia (0.19 #6797, 0.03 #1453, 0.02 #1679), SaintLawrenceRiver (0.19 #6797, 0.02 #1659, 0.02 #1885), Amazonas (0.19 #6797, 0.02 #1806, 0.02 #2032), Parana (0.19 #6797, 0.02 #1660, 0.02 #1886), Tocantins (0.19 #6797, 0.02 #1681, 0.02 #2360), RioSaoFrancisco (0.19 #6797, 0.02 #1753, 0.02 #2659)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #250 for best value:
>> intensional similarity = 6
>> extensional distance = 2
>> proper extension:  Sanga;
>> query: (?x929, Ubangi) <- ?x929[ a River; has locatedIn ?x348[ is locatedIn of ?x549<Ubangi>;]; has locatedIn ?x528<RCB>;]

*> Best rule #7931 for first EXPECTED value:
*> intensional similarity = 4
*> extensional distance = 1084
*> proper extension:  Araguaia; Bahrel-Ghasal; JabalKatrina; Menorca; Breg; Rigestan; Stromboli; Leine; Moraca; Bjelucha; ...
*> query: (?x929, ?x347) <- ?x929[ has locatedIn ?x348[ is locatedIn of ?x347[ a Estuary;]; is neighbor of ?x229;];]
*> conf = 0.02
ranks of expected_values: 94
EVAL	Zaire	hasEstuary	Zaire	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.011	41.000	35.000	211.000	0.250	http://www.semwebtech.org/mondial/10/meta#hasEstuary

PRED relation: hasEstuary
PRED expected values: Zaire
=> 122 concepts (122 used for prediction)
PRED predicted values (max 10 best out of 225): Chire (0.33 #123, 0.03 #10113, 0.02 #10567), Nile (0.25 #558, 0.14 #2371, 0.14 #2144), Fimi (0.25 #602, 0.14 #2415, 0.12 #2868), Lukuga (0.25 #827, 0.12 #2640, 0.12 #5673), Bahrel-Djebel-Albert-Nil (0.25 #858, 0.12 #2671, 0.05 #6535), Oranje (0.20 #1032, 0.17 #1485, 0.11 #12942), Amazonas (0.20 #1129, 0.17 #1582, 0.11 #12942), Parana (0.20 #983, 0.17 #1436, 0.11 #12942), SaintLawrenceRiver (0.17 #1662, 0.17 #1435, 0.14 #2115), Ubangi (0.17 #1837, 0.12 #2970, 0.12 #5673)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #123 for best value:
>> intensional similarity = 10
>> extensional distance = 1
>> proper extension:  Chire;
>> query: (?x929, Chire) <- ?x929[ has flowsInto ?x182[ has locatedIn ?x50; is locatedInWater of ?x112;]; is flowsInto of ?x113[ has locatedIn ?x348[ has religion ?x95; is neighbor of ?x359<BI>;];]; is flowsInto of ?x265[ a Lake;];]

*> Best rule #5673 for first EXPECTED value:
*> intensional similarity = 10
*> extensional distance = 13
*> proper extension:  Orinoco; Senegal; ConnecticutRiver; Gambia; Guadiana; HudsonRiver; Tajo;
*> query: (?x929, ?x358) <- ?x929[ a River; has flowsInto ?x182<AtlanticOcean>; has hasSource ?x2438; has locatedIn ?x348[ has wasDependentOf ?x543; is locatedIn of ?x358[ a Estuary;];]; has locatedIn ?x528[ has neighbor ?x172;];]
*> conf = 0.12
ranks of expected_values: 31
EVAL	Zaire	hasEstuary	Zaire	CNN-1.+1._MA	0.000	0.000	0.000	0.032	122.000	122.000	225.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasEstuary

#1011-SouthChinaSea
PRED entity: SouthChinaSea

PRED relation: locatedInWater!
PRED expected values: Borneo
=> 35 concepts (26 used for prediction)
PRED predicted values (max 10 best out of 349): Kyushu (0.50 #644, 0.33 #1167, 0.33 #121), Okinawa (0.50 #572, 0.33 #1095, 0.33 #49), Tasmania (0.33 #25, 0.25 #548, 0.22 #1333), Mindanao (0.33 #111, 0.25 #634, 0.22 #1419), NewGuinea (0.33 #99, 0.25 #622, 0.22 #1407), Leyte (0.33 #61, 0.25 #584, 0.22 #1369), Hokkaido (0.33 #28, 0.25 #551, 0.17 #1074), Unalaska (0.33 #180, 0.25 #703, 0.17 #1226), Paramuschir (0.33 #147, 0.25 #670, 0.17 #1193), Halmahera (0.33 #102, 0.25 #625, 0.17 #1148)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #644 for best value:
>> intensional similarity = 4
>> extensional distance = 2
>> proper extension:  EastChinaSea;
>> query: (?x384, Kyushu) <- ?x384[ has locatedIn ?x91; is locatedInWater of ?x716<Taiwan>; is mergesWith of ?x241;]

*> Best rule #1329 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 7
*> proper extension:  IndianOcean; JavaSea; AndamanSea; MalakkaStrait; SulawesiSea; BandaSea;
*> query: (?x384, Borneo) <- ?x384[ has locatedIn ?x217<RI>; has locatedIn ?x1568[ has ethnicGroup ?x298;]; is locatedInWater of ?x518; is mergesWith of ?x241;]
*> conf = 0.11
ranks of expected_values: 90
EVAL	SouthChinaSea	locatedInWater!	Borneo	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.011	35.000	26.000	349.000	0.500	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater!
PRED expected values: Borneo
=> 90 concepts (90 used for prediction)
PRED predicted values (max 10 best out of 681): Sulawesi (0.40 #1137, 0.33 #1661, 0.33 #612), Mindanao (0.33 #635, 0.22 #1684, 0.20 #2472), Leyte (0.33 #585, 0.22 #1634, 0.20 #2422), Borneo (0.33 #545, 0.20 #1070, 0.20 #8934), Bohol (0.33 #755, 0.20 #1280, 0.20 #8934), Negros (0.33 #616, 0.20 #1141, 0.20 #8934), Samar (0.33 #599, 0.20 #1124, 0.20 #8934), Panay (0.33 #572, 0.20 #1097, 0.20 #8934), Cebu (0.33 #544, 0.20 #1069, 0.20 #8934), Kyushu (0.27 #2745, 0.25 #3673, 0.25 #3531)
>> best conf = 0.40
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1137 for best value:
>> intensional similarity = 17
>> extensional distance = 3
>> proper extension:  IndianOcean; BandaSea;
>> query: (?x384, Sulawesi) <- ?x384[ a Sea; has locatedIn ?x217<RI>; has locatedIn ?x376[ a Country;]; has locatedIn ?x538[ has wasDependentOf ?x81;]; has locatedIn ?x617[ has ethnicGroup ?x872;]; has locatedIn ?x641[ has encompassed ?x175;]; has locatedIn ?x773[ has religion ?x116;]; has mergesWith ?x282<PacificOcean>; is locatedInWater of ?x518; is mergesWith of ?x620;]

*> Best rule #545 for first EXPECTED value:
*> intensional similarity = 15
*> extensional distance = 1
*> proper extension:  SulawesiSea;
*> query: (?x384, Borneo) <- ?x384[ a Sea; has locatedIn ?x217<RI>; has locatedIn ?x641[ has encompassed ?x175;]; has locatedIn ?x773[ has ethnicGroup ?x298<Chinese>; has religion ?x116;]; has mergesWith ?x282<PacificOcean>; has mergesWith ?x677<SuluSea>; is locatedInWater of ?x518; is mergesWith of ?x620;]
*> conf = 0.33
ranks of expected_values: 4
EVAL	SouthChinaSea	locatedInWater!	Borneo	CNN-1.+1._MA	0.000	0.000	1.000	0.250	90.000	90.000	681.000	0.400	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#1010-MurrumbidgeeRiver
PRED entity: MurrumbidgeeRiver

PRED relation: locatedIn
PRED expected values: AUS
=> 50 concepts (50 used for prediction)
PRED predicted values (max 10 best out of 69): AUS (0.69 #1896, 0.69 #4980, 0.68 #7113), USA (0.37 #4814, 0.20 #1968, 0.17 #1731), D (0.25 #969, 0.21 #6896, 0.20 #5948), I (0.25 #997, 0.20 #1471, 0.12 #3367), UA (0.25 #307, 0.12 #1019, 0.10 #1256), F (0.22 #4037, 0.20 #481, 0.17 #4749), ZRE (0.22 #5533, 0.20 #5770, 0.19 #5296), PE (0.16 #3386, 0.12 #3149, 0.12 #6232), CN (0.15 #4086, 0.03 #10249, 0.02 #11670), R (0.11 #9961, 0.09 #11383, 0.09 #10435)
>> best conf = 0.69
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1896 for best value:
>> intensional similarity = 9
>> extensional distance = 10
>> proper extension:  SnowyRiver;
>> query: (?x1943, ?x196) <- ?x1943[ a Estuary; is hasEstuary of ?x969[ a River; has flowsInto ?x1356; has flowsThrough ?x970[ a Lake; has locatedIn ?x196; has type ?x136;]; has hasSource ?x1679;];]
ranks of expected_values: 1
EVAL	MurrumbidgeeRiver	locatedIn	AUS	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	50.000	50.000	69.000	0.692	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: AUS
=> 134 concepts (134 used for prediction)
PRED predicted values (max 10 best out of 74): AUS (0.83 #19726, 0.83 #26143, 0.82 #22817), ZRE (0.65 #22659, 0.60 #17904, 0.50 #20043), PE (0.60 #17180, 0.58 #19556, 0.57 #11474), R (0.60 #24723, 0.56 #15932, 0.55 #18543), USA (0.60 #21700, 0.50 #2208, 0.44 #15760), I (0.60 #7650, 0.40 #7413, 0.25 #15499), D (0.58 #24501, 0.41 #22838, 0.29 #13092), CDN (0.40 #6714, 0.33 #9330, 0.33 #1013), CH (0.40 #6471, 0.33 #10989, 0.29 #12892), F (0.40 #21397, 0.25 #14030, 0.25 #13792)
>> best conf = 0.83
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #19726 for best value:
>> intensional similarity = 18
>> extensional distance = 10
>> proper extension:  Ene; Huallaga; Urubamba;
>> query: (?x1943, ?x196) <- ?x1943[ a Estuary; is hasEstuary of ?x969[ a River; has flowsInto ?x1356[ a River; has hasEstuary ?x2049; has hasSource ?x1820;]; has locatedIn ?x196[ a Country; has encompassed ?x211; has ethnicGroup ?x197; has government ?x1903; has language ?x247; has religion ?x95<Protestant>; has wasDependentOf ?x81; is locatedIn of ?x282<PacificOcean>;];];]
ranks of expected_values: 1
EVAL	MurrumbidgeeRiver	locatedIn	AUS	CNN-1.+1._MA	1.000	1.000	1.000	1.000	134.000	134.000	74.000	0.833	http://www.semwebtech.org/mondial/10/meta#locatedIn

#1009-Irawaddy
PRED entity: Irawaddy

PRED relation: hasSource
PRED expected values: Irawaddy
=> 47 concepts (39 used for prediction)
PRED predicted values (max 10 best out of 164): Saluen (0.33 #150, 0.25 #378, 0.14 #607), Ganges (0.12 #795, 0.02 #1480, 0.02 #1708), Limpopo (0.12 #850, 0.02 #1535, 0.02 #1763), Zambezi (0.12 #802, 0.01 #1943, 0.01 #2400), MurrayRiver (0.12 #831, 0.01 #2429, 0.01 #2886), Tarim-Yarkend (0.08 #1076, 0.04 #4568, 0.03 #4798), Ili (0.08 #965, 0.04 #4568, 0.03 #4798), Mekong (0.08 #1142, 0.02 #1370, 0.02 #1599), Jangtse (0.08 #998, 0.02 #1226, 0.02 #1455), Amur (0.08 #1096, 0.02 #1324, 0.02 #1553)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #150 for best value:
>> intensional similarity = 6
>> extensional distance = 1
>> proper extension:  Saluen;
>> query: (?x338, Saluen) <- ?x338[ a River; has hasEstuary ?x1481; has locatedIn ?x232<CN>; has locatedIn ?x366<MYA>;]

*> Best rule #6170 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 224
*> proper extension:  Niger; Dalaelv; Okavango;
*> query: (?x338, ?x231) <- ?x338[ a River; has locatedIn ?x232[ is locatedIn of ?x231; is neighbor of ?x73;]; has locatedIn ?x366[ has neighbor ?x91;];]
*> conf = 0.01
ranks of expected_values: 109
EVAL	Irawaddy	hasSource	Irawaddy	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.009	47.000	39.000	164.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasSource

PRED relation: hasSource
PRED expected values: Irawaddy
=> 102 concepts (102 used for prediction)
PRED predicted values (max 10 best out of 232): Saluen (0.40 #1143, 0.33 #379, 0.20 #1064), Mekong (0.33 #228, 0.20 #1142, 0.20 #685), Jangtse (0.20 #541, 0.17 #1227, 0.12 #8265), Amur (0.20 #639, 0.17 #1325, 0.11 #8264), Limpopo (0.20 #850, 0.09 #1994, 0.02 #4063), MurrayRiver (0.20 #831, 0.09 #1975, 0.02 #4044), Tarim-Yarkend (0.12 #8265, 0.11 #8264, 0.10 #1763), Ili (0.12 #8265, 0.11 #8264, 0.10 #1652), Argun (0.12 #8265, 0.11 #8264, 0.10 #1719), Ganges (0.12 #8265, 0.08 #2399, 0.03 #17244)
>> best conf = 0.40
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1143 for best value:
>> intensional similarity = 12
>> extensional distance = 3
>> proper extension:  SouthChinaSea;
>> query: (?x338, ?x1868) <- ?x338[ has locatedIn ?x232<CN>; has locatedIn ?x366[ has ethnicGroup ?x2461[ a EthnicGroup;]; has government ?x2096; has neighbor ?x91<THA>; has wasDependentOf ?x81; is locatedIn of ?x2428[ a River; has hasSource ?x1868;];];]

*> Best rule #17244 for first EXPECTED value:
*> intensional similarity = 9
*> extensional distance = 663
*> proper extension:  Kasai; Kasai; Cuilo; Okavango; Cuango;
*> query: (?x338, ?x1936) <- ?x338[ has locatedIn ?x232[ has encompassed ?x175; has government ?x831; has neighbor ?x130[ has wasDependentOf ?x903; is locatedIn of ?x662;]; has religion ?x116; is locatedIn of ?x1936[ a Source;];];]
*> conf = 0.03
ranks of expected_values: 63
EVAL	Irawaddy	hasSource	Irawaddy	CNN-1.+1._MA	0.000	0.000	0.000	0.016	102.000	102.000	232.000	0.400	http://www.semwebtech.org/mondial/10/meta#hasSource

#1008-Jordan
PRED entity: Jordan

PRED relation: hasSource
PRED expected values: Jordan
=> 49 concepts (48 used for prediction)
PRED predicted values (max 10 best out of 134): Volga (0.05 #708, 0.04 #936, 0.03 #1164), Ammer (0.05 #893, 0.03 #1349, 0.02 #1805), Nile (0.05 #877, 0.03 #1333, 0.02 #1789), Dnepr (0.05 #856, 0.03 #1312, 0.02 #1768), Adda (0.05 #855, 0.03 #1311, 0.02 #1767), Volta (0.05 #840, 0.03 #1296, 0.02 #1752), Rhone (0.05 #816, 0.03 #1272, 0.02 #1728), Aare (0.05 #807, 0.03 #1263, 0.02 #1719), Alz (0.05 #767, 0.03 #1223, 0.02 #1679), Ticino (0.05 #766, 0.03 #1222, 0.02 #1678)
>> best conf = 0.05
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #708 for best value:
>> intensional similarity = 5
>> extensional distance = 20
>> proper extension:  Murgab; Zaire; Reuss; Vuoksi; Zambezi;
>> query: (?x419, Volga) <- ?x419[ has flowsInto ?x567; has flowsThrough ?x1999; has locatedIn ?x115[ has religion ?x187<Muslim>;];]

*> Best rule #5710 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 205
*> proper extension:  StarnbergerSee; LakeVolta; MaleboPool; LakeTanganjika; LakeNicaragua; LakeCabora-Bassa; KakhovkaReservoir; LakeTiticaca; LagodiComo; LakeNasser; ...
*> query: (?x419, ?x953) <- ?x419[ has flowsInto ?x567; has locatedIn ?x803[ has government ?x92; is locatedIn of ?x953; is neighbor of ?x302;];]
*> conf = 0.02
ranks of expected_values: 61
EVAL	Jordan	hasSource	Jordan	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.016	49.000	48.000	134.000	0.045	http://www.semwebtech.org/mondial/10/meta#hasSource

PRED relation: hasSource
PRED expected values: Jordan
=> 123 concepts (123 used for prediction)
PRED predicted values (max 10 best out of 195): Nile (0.25 #1104, 0.20 #2024, 0.20 #1334), Limpopo (0.20 #1536, 0.10 #2456, 0.08 #2916), Tigris (0.20 #1496, 0.06 #4254, 0.04 #5400), Volta (0.20 #1987, 0.04 #5661, 0.04 #6577), Niger (0.20 #1890, 0.04 #5564, 0.04 #6480), BlueNile (0.14 #2254, 0.07 #3406, 0.04 #5699), WhiteNile (0.14 #2239, 0.07 #3391, 0.04 #5684), Chire (0.10 #2475, 0.08 #2935, 0.08 #3165), Senegal (0.10 #2321, 0.08 #2781, 0.08 #3011), Ganges (0.10 #2401, 0.08 #2861, 0.08 #3091)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1104 for best value:
>> intensional similarity = 13
>> extensional distance = 2
>> proper extension:  Euphrat; Nile;
>> query: (?x419, Nile) <- ?x419[ has flowsInto ?x567; has locatedIn ?x115[ has government ?x435;]; has locatedIn ?x239[ has religion ?x109; is locatedIn of ?x275<MediterraneanSea>; is neighbor of ?x63;]; has locatedIn ?x803[ a Country; has ethnicGroup ?x244<Arab>;]; is flowsInto of ?x1999;]

*> Best rule #1141 for first EXPECTED value:
*> intensional similarity = 14
*> extensional distance = 2
*> proper extension:  Euphrat; Nile;
*> query: (?x419, ?x1564) <- ?x419[ has flowsInto ?x567; has locatedIn ?x115[ has government ?x435; is locatedIn of ?x1564;]; has locatedIn ?x239[ has religion ?x109; is locatedIn of ?x275<MediterraneanSea>; is neighbor of ?x63;]; has locatedIn ?x803[ a Country; has ethnicGroup ?x244<Arab>;]; is flowsInto of ?x1999;]
*> conf = 0.03
ranks of expected_values: 106
EVAL	Jordan	hasSource	Jordan	CNN-1.+1._MA	0.000	0.000	0.000	0.009	123.000	123.000	195.000	0.250	http://www.semwebtech.org/mondial/10/meta#hasSource

#1007-IRQ
PRED entity: IRQ

PRED relation: neighbor
PRED expected values: SA KWT
=> 43 concepts (42 used for prediction)
PRED predicted values (max 10 best out of 186): SA (0.92 #2870, 0.91 #2228, 0.90 #3829), KWT (0.90 #3829, 0.90 #2869, 0.90 #4151), IRQ (0.50 #208, 0.33 #50, 0.31 #318), UZB (0.40 #365, 0.09 #681, 0.06 #1636), GE (0.33 #60, 0.31 #318, 0.29 #2871), ARM (0.33 #54, 0.31 #318, 0.29 #2871), GR (0.33 #67, 0.31 #318, 0.29 #2871), BG (0.33 #27, 0.31 #318, 0.29 #2871), AZ (0.33 #55, 0.31 #318, 0.29 #2871), AFG (0.31 #318, 0.29 #2871, 0.27 #1429)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2870 for best value:
>> intensional similarity = 7
>> extensional distance = 94
>> proper extension:  BIH; ET; R; THA; MNE; UAE; RL; D; TAD; KGZ; ...
>> query: (?x302, ?x304) <- ?x302[ has ethnicGroup ?x1259[ a EthnicGroup;]; is locatedIn of ?x255; is neighbor of ?x304[ has language ?x511; is locatedIn of ?x573; is neighbor of ?x290;];]
ranks of expected_values: 1, 2
EVAL	IRQ	neighbor	KWT	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	43.000	42.000	186.000	0.922	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	IRQ	neighbor	SA	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	43.000	42.000	186.000	0.922	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: SA KWT
=> 83 concepts (83 used for prediction)
PRED predicted values (max 10 best out of 195): SA (0.92 #6081, 0.92 #2955, 0.91 #5583), KWT (0.92 #2955, 0.91 #8713, 0.91 #3950), IL (0.60 #3837, 0.41 #2954, 0.40 #1686), R (0.48 #5263, 0.28 #6089, 0.22 #491), AZ (0.44 #2850, 0.39 #1640, 0.39 #1637), WEST (0.41 #2954, 0.40 #1572, 0.39 #1640), IRQ (0.41 #2954, 0.39 #1640, 0.39 #1637), RL (0.40 #1805, 0.40 #1654, 0.39 #1640), GAZA (0.40 #1792, 0.29 #3955, 0.25 #2780), UAE (0.39 #1640, 0.39 #1637, 0.35 #9709)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #6081 for best value:
>> intensional similarity = 19
>> extensional distance = 32
>> proper extension:  F; I; YV;
>> query: (?x302, ?x751) <- ?x302[ is locatedIn of ?x255[ a Estuary;]; is locatedIn of ?x596[ a Source;]; is neighbor of ?x466[ a Country; has ethnicGroup ?x244; has government ?x2550; is locatedIn of ?x275; is neighbor of ?x115[ a Country; has religion ?x187<Muslim>; has wasDependentOf ?x485;];]; is neighbor of ?x751[ a Country; has encompassed ?x175; has language ?x1848; is neighbor of ?x107;];]
ranks of expected_values: 1, 2
EVAL	IRQ	neighbor	KWT	CNN-1.+1._MA	1.000	1.000	1.000	1.000	83.000	83.000	195.000	0.922	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	IRQ	neighbor	SA	CNN-1.+1._MA	1.000	1.000	1.000	1.000	83.000	83.000	195.000	0.922	http://www.semwebtech.org/mondial/10/meta#neighbor

#1006-IRQ
PRED entity: IRQ

PRED relation: encompassed
PRED expected values: Asia
=> 35 concepts (35 used for prediction)
PRED predicted values (max 10 best out of 5): Asia (0.84 #81, 0.81 #160, 0.81 #159), Europe (0.81 #160, 0.81 #159, 0.80 #92), Africa (0.57 #49, 0.33 #54, 0.32 #85), Australia-Oceania (0.38 #38, 0.36 #33, 0.33 #28), America (0.21 #170, 0.21 #122, 0.21 #175)
>> best conf = 0.84
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #81 for best value:
>> intensional similarity = 7
>> extensional distance = 111
>> proper extension:  BIH; ET; R; DJI; MNE; TN; KGZ; NAM; WAN; IRL; ...
>> query: (?x302, ?x175) <- ?x302[ a Country; has wasDependentOf ?x485; is locatedIn of ?x255; is neighbor of ?x466[ has encompassed ?x175; has government ?x2550; has religion ?x187;];]
ranks of expected_values: 1
EVAL	IRQ	encompassed	Asia	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	35.000	35.000	5.000	0.843	http://www.semwebtech.org/mondial/10/meta#encompassed

PRED relation: encompassed
PRED expected values: Asia
=> 97 concepts (97 used for prediction)
PRED predicted values (max 10 best out of 5): Asia (0.84 #133, 0.84 #372, 0.81 #159), Europe (0.84 #133, 0.81 #159, 0.79 #146), Africa (0.53 #192, 0.47 #202, 0.44 #322), America (0.40 #175, 0.33 #231, 0.33 #132), Australia-Oceania (0.33 #96, 0.31 #482, 0.30 #80)
>> best conf = 0.84
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #133 for best value:
>> intensional similarity = 16
>> extensional distance = 22
>> proper extension:  SK; N; RO; CO; H; RSA; PE; UA; USA; RA; ...
>> query: (?x302, ?x175) <- ?x302[ has ethnicGroup ?x557[ a EthnicGroup;]; has neighbor ?x185[ has encompassed ?x175; has language ?x511; has neighbor ?x177; is locatedIn of ?x275[ is locatedInWater of ?x68;]; is locatedIn of ?x468[ has hasEstuary ?x2351;]; is locatedIn of ?x761[ has type ?x762;];]; has religion ?x116; has wasDependentOf ?x485; is locatedIn of ?x255[ a Estuary;];]
ranks of expected_values: 1
EVAL	IRQ	encompassed	Asia	CNN-1.+1._MA	1.000	1.000	1.000	1.000	97.000	97.000	5.000	0.840	http://www.semwebtech.org/mondial/10/meta#encompassed

#1005-OhioRiver
PRED entity: OhioRiver

PRED relation: flowsInto
PRED expected values: Mississippi
=> 37 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 189): AtlanticOcean (0.17 #12, 0.11 #1356, 0.10 #1523), GulfofMexico (0.17 #128, 0.09 #296, 0.08 #799), LakeOntario (0.17 #104, 0.05 #3683, 0.03 #1846), LakeErie (0.17 #163, 0.05 #3683, 0.03 #1846), Donau (0.09 #1015, 0.09 #1352, 0.08 #1519), MediterraneanSea (0.09 #1030, 0.09 #1198, 0.07 #1534), Zaire (0.09 #1098, 0.07 #1435, 0.07 #1602), DetroitRiver (0.09 #183, 0.08 #686, 0.08 #518), NiagaraRiver (0.09 #271, 0.08 #774, 0.08 #606), SaintLawrenceRiver (0.08 #790, 0.08 #958, 0.05 #3683)
>> best conf = 0.17
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #12 for best value:
>> intensional similarity = 6
>> extensional distance = 4
>> proper extension:  NiagaraRiver;
>> query: (?x759, AtlanticOcean) <- ?x759[ a River; is flowsInto of ?x268[ is flowsInto of ?x267;]; is flowsInto of ?x2118[ has locatedIn ?x315<USA>;];]

*> Best rule #3683 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 154
*> proper extension:  DeadSea;
*> query: (?x759, ?x1325) <- ?x759[ has locatedIn ?x315[ has religion ?x95; is locatedIn of ?x1325[ is flowsInto of ?x1288;];]; is flowsInto of ?x268;]
*> conf = 0.05
ranks of expected_values: 18
EVAL	OhioRiver	flowsInto	Mississippi	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.056	37.000	31.000	189.000	0.167	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto
PRED expected values: Mississippi
=> 119 concepts (119 used for prediction)
PRED predicted values (max 10 best out of 217): AtlanticOcean (0.33 #179, 0.28 #5191, 0.28 #5036), GulfofMexico (0.33 #128, 0.25 #799, 0.11 #968), LakeOntario (0.25 #775, 0.09 #10721, 0.06 #2115), LakeErie (0.25 #665, 0.09 #10721, 0.05 #2508), PacificOcean (0.22 #1034, 0.17 #1536, 0.15 #2370), LakeHuron (0.22 #1023, 0.17 #1525, 0.12 #2025), Mississippi (0.17 #1712, 0.11 #1044, 0.09 #2715), OhioRiver (0.17 #1758, 0.11 #1090, 0.09 #2761), Amazonas (0.15 #2358, 0.09 #3528, 0.06 #4534), Donau (0.14 #1850, 0.12 #3523, 0.10 #4696)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #179 for best value:
>> intensional similarity = 10
>> extensional distance = 1
>> proper extension:  SaintLawrenceRiver;
>> query: (?x759, AtlanticOcean) <- ?x759[ a River; has hasEstuary ?x1718[ a Estuary;]; has hasSource ?x760; has locatedIn ?x315<USA>; is flowsInto of ?x268[ has hasSource ?x832;]; is flowsInto of ?x2118[ a River;];]

*> Best rule #1712 for first EXPECTED value:
*> intensional similarity = 9
*> extensional distance = 10
*> proper extension:  ConnecticutRiver; HudsonRiver; RioGrande; Arkansas; AlleghenyRiver;
*> query: (?x759, Mississippi) <- ?x759[ a River; has hasEstuary ?x1718[ a Estuary; has locatedIn ?x315<USA>;]; has hasSource ?x760[ a Source; has locatedIn ?x315<USA>;]; has locatedIn ?x315<USA>;]
*> conf = 0.17
ranks of expected_values: 7
EVAL	OhioRiver	flowsInto	Mississippi	CNN-1.+1._MA	0.000	0.000	1.000	0.143	119.000	119.000	217.000	0.333	http://www.semwebtech.org/mondial/10/meta#flowsInto

#1004-European-CaribbeanAmerindian
PRED entity: European-CaribbeanAmerindian

PRED relation: ethnicGroup!
PRED expected values: ARU
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 96): EAU (0.03 #132), ETH (0.03 #100), UA (0.03 #57), EAK (0.03 #99), CZ (0.02 #98), SN (0.02 #83), MYA (0.02 #70), IR (0.02 #58), SK (0.02 #25), RCA (0.02 #139)
>> best conf = 0.03
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #132 for best value:
>> intensional similarity = 1
>> extensional distance = 279
>> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
>> query: (?x2309, EAU) <- ?x2309[ a EthnicGroup;]

No rule for expected values
ranks of expected_values: 
EVAL	European-CaribbeanAmerindian	ethnicGroup!	ARU	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	2.000	2.000	96.000	0.032	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup!
PRED expected values: ARU
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 96): EAU (0.03 #132), ETH (0.03 #100), UA (0.03 #57), EAK (0.03 #99), CZ (0.02 #98), SN (0.02 #83), MYA (0.02 #70), IR (0.02 #58), SK (0.02 #25), RCA (0.02 #139)
>> best conf = 0.03
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #132 for best value:
>> intensional similarity = 1
>> extensional distance = 279
>> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
>> query: (?x2309, EAU) <- ?x2309[ a EthnicGroup;]

No rule for expected values
ranks of expected_values: 
EVAL	European-CaribbeanAmerindian	ethnicGroup!	ARU	CNN-1.+1._MA	0.000	0.000	0.000	0.000	2.000	2.000	96.000	0.032	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#1003-ROU
PRED entity: ROU

PRED relation: ethnicGroup
PRED expected values: Mestizo
=> 41 concepts (41 used for prediction)
PRED predicted values (max 10 best out of 236): Amerindian (0.43 #1026, 0.38 #2, 0.29 #3074), Mestizo (0.43 #1058, 0.33 #1314, 0.25 #34), Russian (0.32 #1607, 0.27 #839, 0.22 #583), Ukrainian (0.28 #1537, 0.18 #769, 0.14 #4866), Bulgarian (0.27 #906, 0.22 #650, 0.12 #394), Jewish (0.27 #811, 0.13 #9990, 0.13 #6915), Mulatto (0.25 #57, 0.17 #2361, 0.17 #1337), German (0.20 #1544, 0.18 #3592, 0.14 #4873), Polish (0.20 #1740, 0.12 #460, 0.12 #3788), Belorussian (0.20 #1620, 0.12 #340, 0.11 #596)
>> best conf = 0.43
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1026 for best value:
>> intensional similarity = 7
>> extensional distance = 12
>> proper extension:  GCA; RCH; USA; CR; RA; NIC; MEX; BR; ES; PA; ...
>> query: (?x363, Amerindian) <- ?x363[ has ethnicGroup ?x197<European>; has religion ?x95<Protestant>; has religion ?x352<RomanCatholic>; is neighbor of ?x379;]

*> Best rule #1058 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 12
*> proper extension:  GCA; RCH; USA; CR; RA; NIC; MEX; BR; ES; PA; ...
*> query: (?x363, Mestizo) <- ?x363[ has ethnicGroup ?x197<European>; has religion ?x95<Protestant>; has religion ?x352<RomanCatholic>; is neighbor of ?x379;]
*> conf = 0.43
ranks of expected_values: 2
EVAL	ROU	ethnicGroup	Mestizo	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	41.000	41.000	236.000	0.429	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: Mestizo
=> 114 concepts (114 used for prediction)
PRED predicted values (max 10 best out of 255): Mestizo (0.53 #5157, 0.39 #5413, 0.38 #7463), Amerindian (0.43 #7431, 0.41 #5125, 0.35 #10250), Jewish (0.40 #2860, 0.20 #812, 0.20 #555), Russian (0.30 #2888, 0.30 #9037, 0.29 #1864), Bulgarian (0.30 #2955, 0.29 #1931, 0.20 #907), Arab-Berber (0.30 #2844, 0.20 #539, 0.15 #9735), Mulatto (0.29 #4924, 0.27 #3130, 0.25 #3643), German (0.25 #9486, 0.18 #13841, 0.17 #13328), Asian (0.25 #2066, 0.15 #27943, 0.15 #9735), Chinese (0.25 #18193, 0.24 #6916, 0.23 #8467)
>> best conf = 0.53
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5157 for best value:
>> intensional similarity = 16
>> extensional distance = 15
>> proper extension:  BZ;
>> query: (?x363, Mestizo) <- ?x363[ a Country; has encompassed ?x521<America>; has ethnicGroup ?x197[ a EthnicGroup; is ethnicGroup of ?x408[ is locatedIn of ?x282;]; is ethnicGroup of ?x783<PA>; is ethnicGroup of ?x1364<HCA>;]; has government ?x700; has language ?x796; has religion ?x95; is neighbor of ?x542[ has language ?x539;];]
ranks of expected_values: 1
EVAL	ROU	ethnicGroup	Mestizo	CNN-1.+1._MA	1.000	1.000	1.000	1.000	114.000	114.000	255.000	0.529	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#1002-PE
PRED entity: PE

PRED relation: locatedIn!
PRED expected values: Amazonas Huascaran LagoJunin Urubamba
=> 42 concepts (37 used for prediction)
PRED predicted values (max 10 best out of 1361): RioDesaguadero (0.72 #27804, 0.69 #25022, 0.33 #192), Amazonas (0.72 #27804, 0.69 #25022, 0.11 #1441), CaribbeanSea (0.67 #1493, 0.58 #7053, 0.58 #4273), AtlanticOcean (0.51 #20893, 0.44 #8382, 0.39 #50096), LicancaburCraterLake (0.33 #1265, 0.12 #10995, 0.11 #12511), Licancabur (0.33 #903, 0.12 #10633, 0.11 #12511), RioMadeira (0.33 #874, 0.11 #12511, 0.09 #33366), Illimani (0.33 #1276, 0.11 #12511, 0.09 #33366), RioMadeira (0.33 #1208, 0.11 #12511, 0.09 #33366), RioMamore (0.33 #1172, 0.11 #12511, 0.09 #33366)
>> best conf = 0.72
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #27804 for best value:
>> intensional similarity = 6
>> extensional distance = 65
>> proper extension:  AUS; CDN; IS;
>> query: (?x296, ?x214) <- ?x296[ a Country; has ethnicGroup ?x79; is locatedIn of ?x949[ has flowsInto ?x214;]; is locatedIn of ?x1518[ a Estuary;];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 2, 24
EVAL	PE	locatedIn!	Urubamba	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	42.000	37.000	1361.000	0.715	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	PE	locatedIn!	LagoJunin	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.043	42.000	37.000	1361.000	0.715	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	PE	locatedIn!	Huascaran	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	42.000	37.000	1361.000	0.715	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	PE	locatedIn!	Amazonas	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	42.000	37.000	1361.000	0.715	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Amazonas Huascaran LagoJunin Urubamba
=> 107 concepts (106 used for prediction)
PRED predicted values (max 10 best out of 1404): Amazonas (0.92 #19474, 0.89 #33383, 0.89 #13910), RioDesaguadero (0.84 #26429, 0.71 #70958, 0.33 #1583), CaribbeanSea (0.65 #37663, 0.50 #26532, 0.50 #5666), Licancabur (0.64 #16691, 0.35 #50090, 0.33 #2294), RioMamore (0.64 #16691, 0.35 #50090, 0.33 #1976), Llullaillaco (0.64 #16691, 0.35 #50090, 0.29 #12044), OjosdelSalado (0.64 #16691, 0.35 #50090, 0.29 #11630), Cotopaxi (0.64 #16691, 0.35 #50090, 0.25 #3773), Chimborazo (0.64 #16691, 0.35 #50090, 0.25 #3513), NevadodelRuiz (0.64 #16691, 0.35 #50090, 0.20 #5125)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #19474 for best value:
>> intensional similarity = 13
>> extensional distance = 8
>> proper extension:  IR;
>> query: (?x296, ?x214) <- ?x296[ has encompassed ?x521; has ethnicGroup ?x79; has language ?x702[ a Language;]; has neighbor ?x202; has religion ?x95; is locatedIn of ?x705[ a Volcano; has type ?x706;]; is locatedIn of ?x1646[ has inMountains ?x1287;]; is locatedIn of ?x2254[ is hasSource of ?x214;];]
ranks of expected_values: 1, 20, 21, 30
EVAL	PE	locatedIn!	Urubamba	CNN-1.+1._MA	0.000	0.000	0.000	0.053	107.000	106.000	1404.000	0.925	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	PE	locatedIn!	LagoJunin	CNN-1.+1._MA	0.000	0.000	0.000	0.037	107.000	106.000	1404.000	0.925	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	PE	locatedIn!	Huascaran	CNN-1.+1._MA	0.000	0.000	0.000	0.053	107.000	106.000	1404.000	0.925	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	PE	locatedIn!	Amazonas	CNN-1.+1._MA	1.000	1.000	1.000	1.000	107.000	106.000	1404.000	0.925	http://www.semwebtech.org/mondial/10/meta#locatedIn

#1001-SaintKitts
PRED entity: SaintKitts

PRED relation: locatedIn
PRED expected values: KN
=> 31 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 125): WL (0.35 #5685, 0.35 #5923, 0.35 #5922), MART (0.35 #5685, 0.35 #5923, 0.35 #5922), MNTS (0.35 #5685, 0.35 #5923, 0.35 #5922), KN (0.35 #5685, 0.35 #5923, 0.35 #5922), WV (0.35 #5685, 0.35 #5923, 0.35 #5922), WG (0.35 #5685, 0.35 #5923, 0.35 #5922), WD (0.35 #5685, 0.35 #5923, 0.35 #5922), AG (0.35 #5685, 0.35 #5923, 0.35 #5922), TT (0.35 #5685, 0.35 #5923, 0.35 #5922), BDS (0.35 #5685, 0.35 #5923, 0.35 #5922)
>> best conf = 0.35
=> the first rule below is the first best rule for 14 predicted values
>> Best rule #5685 for best value:
>> intensional similarity = 6
>> extensional distance = 190
>> proper extension:  Jersey; Ameland; Texel; Samos; Spiekeroog; NorthUist; WestFalkland;
>> query: (?x1843, ?x124) <- ?x1843[ a Island; has belongsToIslands ?x877[ is belongsToIslands of ?x123[ a Island; has locatedIn ?x124; has locatedInWater ?x182;];];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 4
EVAL	SaintKitts	locatedIn	KN	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.250	31.000	31.000	125.000	0.354	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: KN
=> 57 concepts (57 used for prediction)
PRED predicted values (max 10 best out of 155): MART (0.50 #3579, 0.50 #3097, 0.41 #5515), WV (0.50 #3579, 0.50 #3097, 0.41 #5515), WD (0.50 #3579, 0.50 #3097, 0.41 #5515), MNTS (0.40 #6965, 0.35 #11103, 0.35 #11352), WG (0.40 #6965, 0.35 #11103, 0.35 #11352), WL (0.40 #6965, 0.35 #11103, 0.35 #11352), KN (0.40 #6965, 0.35 #11103, 0.35 #11352), AG (0.40 #6965, 0.35 #11103, 0.35 #11352), BDS (0.40 #6965, 0.35 #11103, 0.35 #11352), TT (0.35 #11103, 0.35 #11352, 0.35 #11351)
>> best conf = 0.50
=> the first rule below is the first best rule for 3 predicted values
>> Best rule #3579 for best value:
>> intensional similarity = 17
>> extensional distance = 44
>> proper extension:  VitiLevu;
>> query: (?x1843, ?x124) <- ?x1843[ a Island; has belongsToIslands ?x877[ a Islands; is belongsToIslands of ?x123[ has locatedIn ?x124; is locatedOnIsland of ?x1806;]; is belongsToIslands of ?x817[ a Island; has locatedInWater ?x182;]; is belongsToIslands of ?x1380[ a Island; has locatedIn ?x50;]; is belongsToIslands of ?x2152[ a Island; is locatedOnIsland of ?x1435;];]; has type ?x150<"volcanic">;]
>> Best rule #3097 for best value:
>> intensional similarity = 12
>> extensional distance = 42
>> proper extension:  Savaii; SaoMiguel;
>> query: (?x1843, ?x124) <- ?x1843[ a Island; has belongsToIslands ?x877[ a Islands; is belongsToIslands of ?x123[ has locatedIn ?x124; is locatedOnIsland of ?x1806;]; is belongsToIslands of ?x1380[ a Island; has locatedIn ?x50; has locatedInWater ?x182;];]; has type ?x150<"volcanic">;]

*> Best rule #6965 for first EXPECTED value:
*> intensional similarity = 11
*> extensional distance = 73
*> proper extension:  Guam;
*> query: (?x1843, ?x1130) <- ?x1843[ a Island; has belongsToIslands ?x877[ a Islands; is belongsToIslands of ?x1046[ a Island; has locatedIn ?x1130; has type ?x704;]; is belongsToIslands of ?x2161[ a Island; has locatedInWater ?x182;];]; has type ?x150;]
*> conf = 0.40
ranks of expected_values: 7
EVAL	SaintKitts	locatedIn	KN	CNN-1.+1._MA	0.000	0.000	1.000	0.143	57.000	57.000	155.000	0.500	http://www.semwebtech.org/mondial/10/meta#locatedIn

#1000-Mohilla
PRED entity: Mohilla

PRED relation: locatedIn
PRED expected values: COM
=> 29 concepts (29 used for prediction)
PRED predicted values (max 10 best out of 105): MAYO (0.71 #948, 0.69 #710, 0.34 #949), COM (0.71 #948, 0.40 #211, 0.36 #1898), RI (0.55 #288, 0.46 #525, 0.43 #763), USA (0.17 #1970, 0.15 #2209, 0.13 #2448), AUS (0.12 #994, 0.12 #1468, 0.12 #1230), P (0.11 #1858, 0.08 #2811, 0.08 #3048), RP (0.09 #2485, 0.08 #2246, 0.04 #3198), E (0.09 #1688, 0.06 #2641, 0.06 #2878), GB (0.08 #3098, 0.08 #3338, 0.08 #3575), COCO (0.08 #659, 0.07 #897, 0.05 #6433)
>> best conf = 0.71
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #948 for best value:
>> intensional similarity = 9
>> extensional distance = 12
>> proper extension:  Mahe;
>> query: (?x226, ?x1248) <- ?x226[ a Island; has belongsToIslands ?x227[ a Islands; is belongsToIslands of ?x1619[ a Island; has locatedIn ?x1248; has locatedInWater ?x60<IndianOcean>;];]; has locatedInWater ?x60<IndianOcean>;]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 2
EVAL	Mohilla	locatedIn	COM	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	29.000	29.000	105.000	0.706	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: COM
=> 55 concepts (55 used for prediction)
PRED predicted values (max 10 best out of 165): MAYO (0.78 #3852, 0.76 #2881, 0.71 #1433), COM (0.73 #473, 0.71 #1433, 0.71 #1432), RI (0.62 #526, 0.55 #288, 0.54 #768), USA (0.26 #2714, 0.22 #4900, 0.20 #3682), RP (0.24 #2991, 0.18 #3476, 0.16 #4207), J (0.15 #2661, 0.10 #3629, 0.10 #3872), CDN (0.14 #4161, 0.06 #6814, 0.04 #12658), AUS (0.12 #7956, 0.12 #1964, 0.12 #2200), TL (0.12 #2395, 0.10 #4580, 0.09 #3122), P (0.12 #5748, 0.12 #5986, 0.11 #6224)
>> best conf = 0.78
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3852 for best value:
>> intensional similarity = 20
>> extensional distance = 39
>> proper extension:  Kiritimati; Tarawa; Majuro; Fongafale;
>> query: (?x226, ?x787) <- ?x226[ a Island; has belongsToIslands ?x227[ a Islands; is belongsToIslands of ?x594[ a Island; has locatedIn ?x787; has type ?x150;];]; has locatedInWater ?x60[ has locatedIn ?x192[ a Country; has ethnicGroup ?x197; has neighbor ?x193;]; has locatedIn ?x196<AUS>; is flowsInto of ?x242; is locatedInWater of ?x333[ a Island; is locatedOnIsland of ?x1571;]; is mergesWith of ?x262[ has locatedIn ?x366;];];]

*> Best rule #473 for first EXPECTED value:
*> intensional similarity = 16
*> extensional distance = 9
*> proper extension:  Java; Krakatau; Sumatra; Bali; Sumbawa; Lombok;
*> query: (?x226, ?x1248) <- ?x226[ a Island; has belongsToIslands ?x227[ a Islands; is belongsToIslands of ?x594[ a Island; has locatedIn ?x787; has locatedInWater ?x60<IndianOcean>; has type ?x150<"volcanic">;]; is belongsToIslands of ?x1666[ a Island; has locatedIn ?x1248; has locatedInWater ?x60<IndianOcean>; is locatedOnIsland of ?x1247;];]; has locatedInWater ?x60<IndianOcean>;]
*> conf = 0.73
ranks of expected_values: 2
EVAL	Mohilla	locatedIn	COM	CNN-1.+1._MA	0.000	1.000	1.000	0.500	55.000	55.000	165.000	0.778	http://www.semwebtech.org/mondial/10/meta#locatedIn

#999-F
PRED entity: F

PRED relation: neighbor
PRED expected values: D MC
=> 34 concepts (34 used for prediction)
PRED predicted values (max 10 best out of 197): D (0.90 #4700, 0.89 #2978, 0.89 #4231), MC (0.89 #2978, 0.89 #4231, 0.89 #4075), F (0.40 #318, 0.33 #4, 0.25 #4701), P (0.33 #139, 0.25 #4701, 0.14 #922), GBZ (0.33 #153, 0.14 #936, 0.10 #4857), NL (0.25 #4701, 0.20 #409, 0.20 #252), PL (0.25 #4701, 0.20 #347, 0.11 #2388), A (0.25 #4701, 0.20 #388, 0.10 #4857), DK (0.25 #4701, 0.20 #433, 0.10 #4857), CZ (0.25 #4701, 0.20 #394, 0.08 #2279)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4700 for best value:
>> intensional similarity = 4
>> extensional distance = 151
>> proper extension:  LB; AND;
>> query: (?x78, ?x120) <- ?x78[ has religion ?x95; is neighbor of ?x120[ is locatedIn of ?x70; is neighbor of ?x194;];]
ranks of expected_values: 1, 2
EVAL	F	neighbor	MC	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	34.000	34.000	197.000	0.896	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	F	neighbor	D	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	34.000	34.000	197.000	0.896	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: D MC
=> 86 concepts (86 used for prediction)
PRED predicted values (max 10 best out of 241): D (0.94 #4479, 0.91 #4641, 0.91 #5125), MC (0.91 #4641, 0.91 #5125, 0.91 #11507), F (0.60 #3034, 0.50 #962, 0.45 #156), RG (0.45 #156, 0.44 #2659, 0.41 #1114), DZ (0.45 #156, 0.41 #1114, 0.39 #1920), TN (0.45 #156, 0.41 #1114, 0.39 #1920), SN (0.45 #156, 0.41 #1114, 0.39 #1920), CI (0.45 #156, 0.41 #1114, 0.39 #1920), RIM (0.45 #156, 0.41 #1114, 0.39 #1920), BEN (0.45 #156, 0.41 #1114, 0.39 #1920)
>> best conf = 0.94
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4479 for best value:
>> intensional similarity = 13
>> extensional distance = 13
>> proper extension:  RH;
>> query: (?x78, ?x120) <- ?x78[ is locatedIn of ?x121; is neighbor of ?x120[ has religion ?x352<RomanCatholic>; is locatedIn of ?x70; is neighbor of ?x424[ is locatedIn of ?x155;]; is neighbor of ?x575[ is wasDependentOf of ?x179;];]; is neighbor of ?x207[ has government ?x435; has language ?x51;]; is wasDependentOf of ?x94;]
ranks of expected_values: 1, 2
EVAL	F	neighbor	MC	CNN-1.+1._MA	1.000	1.000	1.000	1.000	86.000	86.000	241.000	0.939	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	F	neighbor	D	CNN-1.+1._MA	1.000	1.000	1.000	1.000	86.000	86.000	241.000	0.939	http://www.semwebtech.org/mondial/10/meta#neighbor

#998-St.Barthelemy
PRED entity: St.Barthelemy

PRED relation: locatedIn
PRED expected values: SBAR
=> 25 concepts (25 used for prediction)
PRED predicted values (max 10 best out of 137): MART (0.44 #2367, 0.35 #3792, 0.35 #3791), WD (0.44 #2367, 0.35 #3792, 0.35 #3791), WV (0.44 #2367, 0.35 #3792, 0.35 #3791), TT (0.35 #3792, 0.35 #3791, 0.35 #3790), AG (0.35 #3792, 0.35 #3791, 0.35 #3790), WG (0.35 #3792, 0.35 #3791, 0.35 #3790), AXA (0.35 #3792, 0.35 #3791, 0.35 #3790), SMAR (0.35 #3792, 0.35 #3791, 0.35 #3790), MNTS (0.35 #3792, 0.35 #3791, 0.35 #3790), VIRG (0.35 #3792, 0.35 #3791, 0.35 #3790)
>> best conf = 0.44
=> the first rule below is the first best rule for 3 predicted values
>> Best rule #2367 for best value:
>> intensional similarity = 6
>> extensional distance = 87
>> proper extension:  Ireland; Cebu; VictoriaIsland; GreatBritain; Mindoro; TeWaka-a-Maui-SouthIsland-; Panay; Luzon; PrinceofWalesIsland; Samar; ...
>> query: (?x2161, ?x124) <- ?x2161[ a Island; has belongsToIslands ?x877[ is belongsToIslands of ?x123[ has locatedIn ?x124; is locatedOnIsland of ?x1806;];]; has locatedInWater ?x317;]

*> Best rule #5220 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 270
*> proper extension:  Samosir;
*> query: (?x2161, ?x50) <- ?x2161[ has locatedInWater ?x182[ has locatedIn ?x50; has locatedIn ?x416[ has ethnicGroup ?x122;]; is locatedInWater of ?x1037[ a Island;];];]
*> conf = 0.03
ranks of expected_values: 90
EVAL	St.Barthelemy	locatedIn	SBAR	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.011	25.000	25.000	137.000	0.436	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: SBAR
=> 35 concepts (35 used for prediction)
PRED predicted values (max 10 best out of 164): MART (0.37 #1918, 0.35 #5609, 0.35 #5608), WD (0.37 #1918, 0.35 #5609, 0.35 #5608), WV (0.37 #1918, 0.35 #5609, 0.35 #5608), MNTS (0.35 #5609, 0.35 #5608, 0.35 #5607), WG (0.35 #5609, 0.35 #5608, 0.35 #5607), WL (0.35 #5609, 0.35 #5608, 0.35 #5607), KN (0.35 #5609, 0.35 #5608, 0.35 #5607), AG (0.35 #5609, 0.35 #5608, 0.35 #5607), BDS (0.35 #5609, 0.35 #5608, 0.35 #5607), TT (0.35 #5609, 0.35 #5608, 0.35 #5607)
>> best conf = 0.37
=> the first rule below is the first best rule for 3 predicted values
>> Best rule #1918 for best value:
>> intensional similarity = 13
>> extensional distance = 68
>> proper extension:  VitiLevu; Savaii; Shikoku;
>> query: (?x2161, ?x922) <- ?x2161[ a Island; has belongsToIslands ?x877[ a Islands; is belongsToIslands of ?x609[ a Island; has locatedIn ?x922; is locatedOnIsland of ?x1972;]; is belongsToIslands of ?x703[ has locatedInWater ?x182; has type ?x704;]; is belongsToIslands of ?x817[ a Island; has type ?x150;];];]

*> Best rule #7818 for first EXPECTED value:
*> intensional similarity = 14
*> extensional distance = 245
*> proper extension:  Khark; Gheschm; Bahrain;
*> query: (?x2161, ?x138) <- ?x2161[ has locatedInWater ?x182[ has locatedIn ?x78[ has language ?x51; has religion ?x95; is neighbor of ?x120;]; has locatedIn ?x138[ has encompassed ?x213;]; has locatedIn ?x149[ has ethnicGroup ?x2540;]; has locatedIn ?x351[ a Country;]; has mergesWith ?x60; is locatedInWater of ?x112[ a Island;];];]
*> conf = 0.03
ranks of expected_values: 98
EVAL	St.Barthelemy	locatedIn	SBAR	CNN-1.+1._MA	0.000	0.000	0.000	0.010	35.000	35.000	164.000	0.369	http://www.semwebtech.org/mondial/10/meta#locatedIn

#997-M
PRED entity: M

PRED relation: religion
PRED expected values: RomanCatholic
=> 36 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 32): RomanCatholic (0.71 #640, 0.64 #177, 0.64 #51), Muslim (0.68 #258, 0.59 #511, 0.58 #343), Protestant (0.67 #213, 0.64 #129, 0.57 #550), ChristianOrthodox (0.43 #170, 0.32 #254, 0.31 #675), Anglican (0.36 #61, 0.36 #145, 0.33 #440), Jewish (0.33 #3, 0.29 #296, 0.27 #927), Christian (0.33 #4, 0.28 #510, 0.27 #927), Buddhist (0.29 #296, 0.27 #927, 0.25 #1012), Hindu (0.29 #296, 0.27 #927, 0.25 #1012), Sikh (0.29 #296, 0.27 #927, 0.25 #1012)
>> best conf = 0.71
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #640 for best value:
>> intensional similarity = 5
>> extensional distance = 46
>> proper extension:  AND;
>> query: (?x850, RomanCatholic) <- ?x850[ a Country; has encompassed ?x195; has language ?x247[ is language of ?x783<PA>;];]
ranks of expected_values: 1
EVAL	M	religion	RomanCatholic	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	36.000	36.000	32.000	0.708	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion
PRED expected values: RomanCatholic
=> 101 concepts (101 used for prediction)
PRED predicted values (max 10 best out of 40): RomanCatholic (0.73 #1764, 0.71 #1552, 0.67 #3386), Muslim (0.67 #692, 0.67 #387, 0.67 #348), Protestant (0.67 #1758, 0.62 #943, 0.62 #900), ChristianOrthodox (0.62 #604, 0.57 #986, 0.56 #1629), Buddhist (0.56 #170, 0.55 #255, 0.50 #43), Jewish (0.56 #170, 0.55 #255, 0.50 #43), Anglican (0.56 #170, 0.55 #255, 0.50 #43), Hindu (0.56 #170, 0.55 #255, 0.50 #43), Sikh (0.56 #170, 0.55 #255, 0.50 #43), JehovasWitnesses (0.50 #43, 0.43 #1028, 0.41 #1756)
>> best conf = 0.73
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1764 for best value:
>> intensional similarity = 12
>> extensional distance = 28
>> proper extension:  B;
>> query: (?x850, RomanCatholic) <- ?x850[ a Country; has government ?x435; has language ?x247[ is language of ?x161[ a Country; has ethnicGroup ?x162;]; is language of ?x671<BZ>; is language of ?x853[ has religion ?x95;];]; has wasDependentOf ?x81; is locatedIn of ?x275;]
ranks of expected_values: 1
EVAL	M	religion	RomanCatholic	CNN-1.+1._MA	1.000	1.000	1.000	1.000	101.000	101.000	40.000	0.733	http://www.semwebtech.org/mondial/10/meta#religion

#996-WS
PRED entity: WS

PRED relation: locatedIn!
PRED expected values: Silisili
=> 35 concepts (27 used for prediction)
PRED predicted values (max 10 best out of 1164): AtlanticOcean (0.63 #8571, 0.43 #32745, 0.36 #37011), CaribbeanSea (0.38 #5789, 0.31 #11478, 0.21 #32808), SouthChinaSea (0.25 #11513, 0.17 #2982, 0.15 #12934), TheChannel (0.25 #6340, 0.10 #10608, 0.04 #23407), ArcticOcean (0.21 #7181, 0.03 #21403, 0.02 #29933), Nauru (0.20 #2646, 0.20 #1225, 0.17 #4067), IndianOcean (0.20 #12797, 0.20 #14221, 0.19 #15643), Rarotonga (0.20 #992, 0.17 #3834, 0.14 #5255), Mt.Victoria (0.20 #658, 0.17 #3500, 0.14 #4921), VitiLevu (0.20 #231, 0.17 #3073, 0.14 #4494)
>> best conf = 0.63
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #8571 for best value:
>> intensional similarity = 8
>> extensional distance = 17
>> proper extension:  WAN; G; SN; GH; RCB; CAM; RG; BEN; MA; WAL; ...
>> query: (?x453, AtlanticOcean) <- ?x453[ a Country; has religion ?x116<Christian>; is locatedIn of ?x282[ has locatedIn ?x202<RCH>; has mergesWith ?x60; is locatedInWater of ?x205;];]

No rule for expected values
ranks of expected_values: 
EVAL	WS	locatedIn!	Silisili	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	35.000	27.000	1164.000	0.632	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Silisili
=> 99 concepts (93 used for prediction)
PRED predicted values (max 10 best out of 1418): Silisili (0.79 #22756, 0.78 #27022, 0.73 #19909), AtlanticOcean (0.65 #72607, 0.60 #75457, 0.58 #76880), CaribbeanSea (0.50 #66977, 0.44 #15744, 0.43 #58427), SouthChinaSea (0.36 #21473, 0.27 #42804, 0.26 #54190), Tutuila (0.36 #62597, 0.32 #75415, 0.23 #41239), Nauru (0.33 #2648, 0.14 #12598, 0.14 #11177), Jordan (0.33 #12954, 0.10 #20069, 0.10 #17220), Dominica (0.33 #3115, 0.10 #18756, 0.08 #25869), Koror (0.33 #4894, 0.07 #27020, 0.06 #62596), IndianOcean (0.31 #41242, 0.24 #55478, 0.23 #64027)
>> best conf = 0.79
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #22756 for best value:
>> intensional similarity = 11
>> extensional distance = 9
>> proper extension:  HELX;
>> query: (?x453, ?x1229) <- ?x453[ a Country; has encompassed ?x211; has ethnicGroup ?x454[ a EthnicGroup; is ethnicGroup of ?x179<SME>;]; has government ?x254; is locatedIn of ?x282[ is locatedInWater of ?x205;]; is locatedIn of ?x1205[ is locatedOnIsland of ?x1229;];]
ranks of expected_values: 1
EVAL	WS	locatedIn!	Silisili	CNN-1.+1._MA	1.000	1.000	1.000	1.000	99.000	93.000	1418.000	0.786	http://www.semwebtech.org/mondial/10/meta#locatedIn

#995-Murgab
PRED entity: Murgab

PRED relation: hasEstuary!
PRED expected values: Murgab
=> 2 concepts (2 used for prediction)
No prediction
ranks of expected_values: 
EVAL	Murgab	hasEstuary!	Murgab	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	2.000	2.000	0.000	0.000	http://www.semwebtech.org/mondial/10/meta#hasEstuary

PRED relation: hasEstuary!
PRED expected values: Murgab
=> 2 concepts (2 used for prediction)
No prediction
ranks of expected_values: 
EVAL	Murgab	hasEstuary!	Murgab	CNN-1.+1._MA	0.000	0.000	0.000	0.000	2.000	2.000	0.000	0.000	http://www.semwebtech.org/mondial/10/meta#hasEstuary

#994-IRQ
PRED entity: IRQ

PRED relation: ethnicGroup
PRED expected values: Arab
=> 37 concepts (37 used for prediction)
PRED predicted values (max 10 best out of 223): Arab (0.60 #523, 0.50 #1035, 0.50 #267), Russian (0.33 #1609, 0.29 #840, 0.20 #2122), European (0.29 #2058, 0.24 #5646, 0.23 #6414), Armenian (0.29 #860, 0.25 #1116, 0.25 #348), Circassian (0.25 #333, 0.20 #589, 0.18 #7176), Turkish (0.23 #3075, 0.23 #3589, 0.22 #4614), Turkmen (0.23 #3075, 0.23 #3589, 0.22 #4614), Arabic (0.23 #3075, 0.23 #3589, 0.22 #4614), GilakiMazandarani (0.23 #3075, 0.23 #3589, 0.22 #4614), Azerbaijani (0.23 #3075, 0.23 #3589, 0.22 #4614)
>> best conf = 0.60
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #523 for best value:
>> intensional similarity = 5
>> extensional distance = 3
>> proper extension:  SA;
>> query: (?x302, Arab) <- ?x302[ a Country; has neighbor ?x304[ is locatedIn of ?x573;]; is locatedIn of ?x953<SyrianDesert>;]
ranks of expected_values: 1
EVAL	IRQ	ethnicGroup	Arab	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	37.000	37.000	223.000	0.600	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: Arab
=> 86 concepts (86 used for prediction)
PRED predicted values (max 10 best out of 250): Arab (0.50 #1814, 0.45 #4386, 0.45 #3872), Russian (0.43 #5222, 0.40 #3419, 0.27 #7282), Jewish (0.40 #4634, 0.33 #2619, 0.33 #815), Armenian (0.33 #1895, 0.33 #1120, 0.30 #3439), Turkish (0.33 #1542, 0.33 #1470, 0.29 #13127), Arabic (0.33 #1316, 0.29 #13127, 0.25 #14929), Circassian (0.33 #1105, 0.25 #14929, 0.25 #1285), Afro-Asian (0.33 #360, 0.25 #1285, 0.25 #771), European (0.31 #9532, 0.29 #10820, 0.27 #11333), African (0.26 #8501, 0.24 #6958, 0.22 #15193)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1814 for best value:
>> intensional similarity = 16
>> extensional distance = 4
>> proper extension:  RL;
>> query: (?x302, Arab) <- ?x302[ a Country; has religion ?x116; is locatedIn of ?x1422[ a River;]; is neighbor of ?x185[ a Country; has ethnicGroup ?x638; is locatedIn of ?x98;]; is neighbor of ?x466<SYR>; is neighbor of ?x803[ has encompassed ?x175; has ethnicGroup ?x1420[ a EthnicGroup;]; is locatedIn of ?x419; is neighbor of ?x239;];]
ranks of expected_values: 1
EVAL	IRQ	ethnicGroup	Arab	CNN-1.+1._MA	1.000	1.000	1.000	1.000	86.000	86.000	250.000	0.500	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#993-HCA
PRED entity: HCA

PRED relation: ethnicGroup
PRED expected values: Amerindian
=> 44 concepts (44 used for prediction)
PRED predicted values (max 10 best out of 212): Amerindian (0.60 #257, 0.50 #2, 0.47 #1022), Russian (0.20 #4660, 0.19 #4915, 0.13 #6190), Black (0.19 #819, 0.19 #564, 0.08 #4134), Asian (0.18 #1292, 0.15 #2567, 0.14 #1547), Mulatto (0.18 #1076, 0.15 #2606, 0.14 #1586), Chinese (0.17 #3073, 0.12 #778, 0.12 #523), Ukrainian (0.15 #4591, 0.12 #4846, 0.09 #7141), Indian (0.14 #3131, 0.05 #5681, 0.05 #6701), White (0.12 #829, 0.12 #574, 0.06 #4144), EastIndian (0.12 #900, 0.12 #645, 0.06 #4215)
>> best conf = 0.60
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #257 for best value:
>> intensional similarity = 6
>> extensional distance = 3
>> proper extension:  GCA;
>> query: (?x1364, Amerindian) <- ?x1364[ has ethnicGroup ?x162; has wasDependentOf ?x149; is locatedIn of ?x282; is neighbor of ?x181[ has neighbor ?x482<MEX>;];]
ranks of expected_values: 1
EVAL	HCA	ethnicGroup	Amerindian	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	44.000	44.000	212.000	0.600	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: Amerindian
=> 97 concepts (97 used for prediction)
PRED predicted values (max 10 best out of 252): Amerindian (0.60 #1790, 0.60 #257, 0.55 #1789), Mulatto (0.40 #1078, 0.33 #2613, 0.33 #1846), Chinese (0.33 #13, 0.29 #15864, 0.26 #8452), Russian (0.25 #15154, 0.20 #12857, 0.20 #12601), EastIndian (0.24 #9207, 0.14 #21990, 0.13 #21989), Afro-EastIndian (0.24 #9207, 0.14 #21990, 0.13 #21989), Afro-European (0.24 #9207, 0.14 #21990, 0.13 #21989), Afro-Chinese (0.24 #9207, 0.14 #21990, 0.13 #21989), Asian (0.20 #15340, 0.18 #1294, 0.17 #9718), French (0.20 #15340, 0.17 #9718, 0.16 #20706)
>> best conf = 0.60
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1790 for best value:
>> intensional similarity = 12
>> extensional distance = 9
>> proper extension:  BR;
>> query: (?x1364, ?x197) <- ?x1364[ has encompassed ?x521; has ethnicGroup ?x162; has government ?x1535; is neighbor of ?x181[ has ethnicGroup ?x79<Amerindian>; has ethnicGroup ?x197; has religion ?x352<RomanCatholic>; is locatedIn of ?x317<CaribbeanSea>; is neighbor of ?x482;];]
>> Best rule #257 for best value:
>> intensional similarity = 15
>> extensional distance = 1
>> proper extension:  CR;
>> query: (?x1364, ?x197) <- ?x1364[ a Country; has encompassed ?x521; has language ?x796; is neighbor of ?x181[ a Country; has ethnicGroup ?x79<Amerindian>; has ethnicGroup ?x197; has religion ?x95; is locatedIn of ?x282; is neighbor of ?x482[ is locatedIn of ?x288; is neighbor of ?x315;];]; is neighbor of ?x408<NIC>;]
ranks of expected_values: 1
EVAL	HCA	ethnicGroup	Amerindian	CNN-1.+1._MA	1.000	1.000	1.000	1.000	97.000	97.000	252.000	0.600	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#992-NevadodeColima
PRED entity: NevadodeColima

PRED relation: inMountains
PRED expected values: CordilleraVolcanica
=> 58 concepts (58 used for prediction)
PRED predicted values (max 10 best out of 51): CordilleraVolcanica (0.67 #152, 0.60 #65, 0.21 #239), Andes (0.14 #446, 0.12 #794, 0.12 #272), Alps (0.11 #1570, 0.08 #2092, 0.07 #2266), EastAfricanRift (0.09 #463, 0.08 #811, 0.07 #985), RockyMountains (0.08 #1834, 0.07 #2182, 0.06 #2530), CanaryIslands (0.07 #317, 0.06 #404, 0.05 #491), Hawaii (0.05 #590, 0.05 #329, 0.04 #416), Himalaya (0.05 #2442, 0.05 #2355, 0.04 #3051), CascadeRange (0.05 #302, 0.04 #389, 0.04 #476), Kaukasus (0.04 #1846, 0.04 #454, 0.03 #628)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #152 for best value:
>> intensional similarity = 5
>> extensional distance = 4
>> proper extension:  Iztaccihuatl;
>> query: (?x2327, CordilleraVolcanica) <- ?x2327[ a Mountain; a Volcano; has locatedIn ?x482<MEX>; has type ?x706;]
ranks of expected_values: 1
EVAL	NevadodeColima	inMountains	CordilleraVolcanica	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	58.000	58.000	51.000	0.667	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains
PRED expected values: CordilleraVolcanica
=> 114 concepts (114 used for prediction)
PRED predicted values (max 10 best out of 66): CordilleraVolcanica (0.67 #152, 0.60 #65, 0.36 #239), Andes (0.26 #620, 0.21 #1055, 0.19 #707), Hawaii (0.15 #329, 0.11 #416, 0.05 #2417), CascadeRange (0.15 #302, 0.11 #389, 0.05 #1607), RockyMountains (0.13 #2530, 0.08 #268, 0.07 #3574), Kaukasus (0.11 #367, 0.05 #1585, 0.03 #3673), EastAfricanRift (0.10 #1420, 0.08 #1942, 0.08 #985), CanaryIslands (0.09 #839, 0.08 #1013, 0.07 #1448), SierraMadre (0.09 #224, 0.04 #659, 0.03 #746), CordilleradeTalamanca (0.09 #634, 0.06 #721, 0.06 #808)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #152 for best value:
>> intensional similarity = 5
>> extensional distance = 4
>> proper extension:  Iztaccihuatl;
>> query: (?x2327, CordilleraVolcanica) <- ?x2327[ a Mountain; a Volcano; has locatedIn ?x482<MEX>; has type ?x706;]
ranks of expected_values: 1
EVAL	NevadodeColima	inMountains	CordilleraVolcanica	CNN-1.+1._MA	1.000	1.000	1.000	1.000	114.000	114.000	66.000	0.667	http://www.semwebtech.org/mondial/10/meta#inMountains

#991-Bosnian
PRED entity: Bosnian

PRED relation: language!
PRED expected values: MNE
=> 30 concepts (28 used for prediction)
PRED predicted values (max 10 best out of 163): MK (0.56 #699, 0.50 #822, 0.44 #606), A (0.50 #65, 0.43 #428, 0.40 #185), BG (0.43 #385, 0.34 #1460, 0.34 #1094), I (0.42 #1003, 0.20 #760, 0.19 #1369), SK (0.40 #139, 0.29 #382, 0.25 #19), MNE (0.34 #1460, 0.34 #1094, 0.33 #616), RO (0.34 #1460, 0.34 #1094, 0.33 #607), BIH (0.34 #1460, 0.34 #1094, 0.33 #607), KOS (0.34 #1460, 0.34 #1094, 0.33 #607), HR (0.33 #259, 0.25 #17, 0.20 #747)
>> best conf = 0.56
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #699 for best value:
>> intensional similarity = 8
>> extensional distance = 7
>> proper extension:  Montenegrin;
>> query: (?x1769, MK) <- ?x1769[ is language of ?x904[ has ethnicGroup ?x775<Serb>; has neighbor ?x692<KOS>; has religion ?x56; has wasDependentOf ?x1197; is locatedIn of ?x132;];]

*> Best rule #1460 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 25
*> proper extension:  Punjabi; Siraiki; Pashtu; Urdu; Brahui; Balochi; Sindhi; Hindko;
*> query: (?x1769, ?x177) <- ?x1769[ a Language; is language of ?x904[ a Country; has neighbor ?x177[ has language ?x511;]; is locatedIn of ?x132; is wasDependentOf of ?x106;];]
*> conf = 0.34
ranks of expected_values: 6
EVAL	Bosnian	language!	MNE	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.167	30.000	28.000	163.000	0.556	http://www.semwebtech.org/mondial/10/meta#language

PRED relation: language!
PRED expected values: MNE
=> 47 concepts (47 used for prediction)
PRED predicted values (max 10 best out of 167): MK (0.62 #1239, 0.53 #1527, 0.53 #1526), SK (0.53 #1527, 0.53 #1526, 0.50 #1295), A (0.53 #1527, 0.53 #1526, 0.50 #65), RO (0.53 #1527, 0.53 #1526, 0.50 #650), MNE (0.53 #1527, 0.53 #1526, 0.50 #382), HR (0.53 #1527, 0.53 #1526, 0.44 #3051), H (0.53 #1527, 0.53 #1526, 0.44 #3051), KOS (0.53 #1527, 0.53 #1526, 0.44 #3051), MD (0.50 #994, 0.37 #2294, 0.36 #2020), SF (0.47 #2251, 0.39 #2501, 0.32 #2755)
>> best conf = 0.62
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1239 for best value:
>> intensional similarity = 26
>> extensional distance = 6
>> proper extension:  Macedonian;
>> query: (?x1769, MK) <- ?x1769[ a Language; is language of ?x904[ has ethnicGroup ?x164[ a EthnicGroup;]; has wasDependentOf ?x1197; is locatedIn of ?x132[ a River;]; is locatedIn of ?x152[ has hasSource ?x1363;]; is locatedIn of ?x1489<SouthernMorava>; is neighbor of ?x55[ a Country; has ethnicGroup ?x160;]; is neighbor of ?x176[ has encompassed ?x195; has ethnicGroup ?x58; is locatedIn of ?x98; is neighbor of ?x303;]; is neighbor of ?x177<BG>; is neighbor of ?x236[ has government ?x254; has religion ?x95; is locatedIn of ?x155; is neighbor of ?x163;];];]

*> Best rule #1527 for first EXPECTED value:
*> intensional similarity = 20
*> extensional distance = 6
*> proper extension:  Maltese;
*> query: (?x1769, ?x106) <- ?x1769[ a Language; is language of ?x904[ a Country; has encompassed ?x195<Europe>; has government ?x435<"republic">; has language ?x1296[ is language of ?x106;]; has wasDependentOf ?x1197; is locatedIn of ?x133[ is flowsInto of ?x475; is locatedInWater of ?x1264;]; is locatedIn of ?x151[ a Island;]; is locatedIn of ?x152[ has locatedIn ?x55<BIH>; has locatedIn ?x156<HR>;];];]
*> conf = 0.53
ranks of expected_values: 5
EVAL	Bosnian	language!	MNE	CNN-1.+1._MA	0.000	0.000	1.000	0.200	47.000	47.000	167.000	0.625	http://www.semwebtech.org/mondial/10/meta#language

#990-AZ
PRED entity: AZ

PRED relation: ethnicGroup
PRED expected values: Russian
=> 36 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 211): Russian (0.52 #766, 0.52 #582, 0.33 #72), European (0.38 #263, 0.38 #1285, 0.30 #1540), Ukrainian (0.38 #511, 0.24 #3063, 0.15 #3064), Yezidi (0.33 #117, 0.30 #767, 0.18 #7655), Georgian (0.30 #767, 0.24 #3063, 0.18 #7655), Arab (0.24 #3063, 0.18 #7655, 0.18 #7654), Turkmen (0.24 #3063, 0.18 #7655, 0.18 #7654), GilakiMazandarani (0.24 #3063, 0.18 #7655, 0.18 #7654), Azerbaijani (0.24 #3063, 0.18 #7655, 0.18 #7654), Lur (0.24 #3063, 0.18 #7655, 0.18 #7654)
>> best conf = 0.52
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #766 for best value:
>> intensional similarity = 6
>> extensional distance = 27
>> proper extension:  TAD; CN;
>> query: (?x332, ?x1193) <- ?x332[ is locatedIn of ?x468; is neighbor of ?x331[ has ethnicGroup ?x1193<Russian>; has government ?x435; has religion ?x670;];]
>> Best rule #582 for best value:
>> intensional similarity = 6
>> extensional distance = 27
>> proper extension:  TAD; CN;
>> query: (?x332, Russian) <- ?x332[ is locatedIn of ?x468; is neighbor of ?x331[ has ethnicGroup ?x1193<Russian>; has government ?x435; has religion ?x670;];]
ranks of expected_values: 1
EVAL	AZ	ethnicGroup	Russian	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	36.000	36.000	211.000	0.517	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: Russian
=> 107 concepts (107 used for prediction)
PRED predicted values (max 10 best out of 245): Russian (0.60 #2886, 0.60 #2115, 0.58 #5443), Ukrainian (0.50 #2044, 0.40 #3839, 0.40 #2815), Belorussian (0.50 #2127, 0.33 #3922, 0.23 #13307), Polish (0.50 #2246, 0.27 #7112, 0.27 #4041), European (0.37 #12035, 0.35 #8708, 0.34 #9476), Kurdish (0.33 #1100, 0.33 #80, 0.31 #5628), Arab (0.33 #776, 0.31 #14331, 0.31 #9468), Georgian (0.33 #575, 0.31 #5628, 0.31 #5627), Turkish (0.33 #184, 0.31 #5628, 0.31 #5627), Arabic (0.33 #32, 0.31 #5628, 0.31 #5627)
>> best conf = 0.60
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2886 for best value:
>> intensional similarity = 14
>> extensional distance = 8
>> proper extension:  CY;
>> query: (?x332, Russian) <- ?x332[ a Country; has ethnicGroup ?x1420[ is ethnicGroup of ?x115[ has encompassed ?x175; has neighbor ?x239; is locatedIn of ?x275;]; is ethnicGroup of ?x177<BG>;]; has government ?x435<"republic">; has language ?x843; has religion ?x56; has wasDependentOf ?x903; is locatedIn of ?x468;]
>> Best rule #2115 for best value:
>> intensional similarity = 13
>> extensional distance = 8
>> proper extension:  PL;
>> query: (?x332, Russian) <- ?x332[ has government ?x435; has language ?x843; has neighbor ?x73<R>; has neighbor ?x304[ has ethnicGroup ?x244; has language ?x511; is locatedIn of ?x1620[ a River;];]; has religion ?x56[ is religion of ?x962<LT>;]; is locatedIn of ?x468;]
ranks of expected_values: 1
EVAL	AZ	ethnicGroup	Russian	CNN-1.+1._MA	1.000	1.000	1.000	1.000	107.000	107.000	245.000	0.600	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#989-Raab
PRED entity: Raab

PRED relation: flowsInto
PRED expected values: Donau
=> 33 concepts (22 used for prediction)
PRED predicted values (max 10 best out of 85): Donau (0.46 #173, 0.36 #339, 0.24 #671), Drau (0.33 #67, 0.08 #232, 0.06 #2325), MediterraneanSea (0.18 #354, 0.04 #2014, 0.04 #1185), Po (0.14 #406, 0.02 #1237, 0.02 #1071), AtlanticOcean (0.10 #1174, 0.09 #1671, 0.09 #2504), Weser (0.09 #801, 0.02 #1631, 0.01 #2129), Inn (0.08 #245, 0.06 #2325, 0.06 #743), BlackSea (0.08 #168, 0.05 #334, 0.04 #501), Aare (0.07 #1089, 0.01 #2324), Rhein (0.06 #2325, 0.06 #829, 0.06 #682)
>> best conf = 0.46
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #173 for best value:
>> intensional similarity = 6
>> extensional distance = 11
>> proper extension:  Donau; Rhein; Iller; Enns; Drau; Inn; Lech; March; Isar; Salzach;
>> query: (?x1838, Donau) <- ?x1838[ a River; has hasEstuary ?x1265[ a Estuary; has locatedIn ?x236;]; has locatedIn ?x424<A>;]
ranks of expected_values: 1
EVAL	Raab	flowsInto	Donau	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	33.000	22.000	85.000	0.462	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto
PRED expected values: Donau
=> 100 concepts (98 used for prediction)
PRED predicted values (max 10 best out of 150): Donau (0.53 #2001, 0.40 #506, 0.36 #3665), Drau (0.33 #67, 0.25 #498, 0.25 #398), Drina (0.33 #751, 0.17 #1748, 0.05 #3919), MediterraneanSea (0.30 #1517, 0.19 #2517, 0.12 #4023), Po (0.30 #1569, 0.14 #2569, 0.07 #4075), BlackSea (0.25 #1830, 0.19 #2164, 0.14 #833), Morava (0.25 #1335, 0.17 #1834, 0.17 #1668), Elbe (0.25 #308, 0.12 #1305, 0.06 #2304), BalticSea (0.25 #1172, 0.08 #4677, 0.08 #4843), Save (0.17 #675, 0.12 #1339, 0.08 #1838)
>> best conf = 0.53
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2001 for best value:
>> intensional similarity = 10
>> extensional distance = 13
>> proper extension:  Save;
>> query: (?x1838, Donau) <- ?x1838[ a River; has locatedIn ?x424[ has ethnicGroup ?x160; has neighbor ?x471[ has ethnicGroup ?x164; has religion ?x95;]; is locatedIn of ?x155<Mur>; is locatedIn of ?x614<Drau>;];]
ranks of expected_values: 1
EVAL	Raab	flowsInto	Donau	CNN-1.+1._MA	1.000	1.000	1.000	1.000	100.000	98.000	150.000	0.533	http://www.semwebtech.org/mondial/10/meta#flowsInto

#988-IS
PRED entity: IS

PRED relation: locatedIn!
PRED expected values: Katla
=> 32 concepts (25 used for prediction)
PRED predicted values (max 10 best out of 1386): CaribbeanSea (0.34 #17044, 0.27 #18457, 0.26 #5750), PacificOcean (0.33 #1496, 0.27 #19849, 0.25 #28317), Llullaillaco (0.33 #2337, 0.15 #3748, 0.09 #6571), OjosdelSalado (0.33 #1918, 0.15 #3329, 0.09 #6152), BarentsSea (0.33 #66, 0.13 #21176, 0.12 #22588), Svalbard (0.33 #554, 0.04 #7058, 0.04 #7057), MediterraneanSea (0.27 #8551, 0.19 #19846, 0.18 #21258), Donau (0.21 #8495, 0.09 #22614, 0.09 #26847), NorthSea (0.18 #8491, 0.13 #21176, 0.12 #22588), Uruguay (0.18 #4782, 0.17 #1959, 0.13 #6193)
>> best conf = 0.34
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #17044 for best value:
>> intensional similarity = 6
>> extensional distance = 59
>> proper extension:  GCA; CO; CR; JA; NIC; MEX; PA; CAYM; WG; HCA;
>> query: (?x455, CaribbeanSea) <- ?x455[ has ethnicGroup ?x1309; is locatedIn of ?x182[ has locatedIn ?x148<C>; has mergesWith ?x60; is flowsInto of ?x137;];]

No rule for expected values
ranks of expected_values: 
EVAL	IS	locatedIn!	Katla	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	32.000	25.000	1386.000	0.344	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Katla
=> 96 concepts (86 used for prediction)
PRED predicted values (max 10 best out of 1414): NorthSea (0.67 #8499, 0.50 #8475, 0.33 #29729), PacificOcean (0.65 #41105, 0.60 #53833, 0.59 #59493), ArcticOcean (0.50 #8475, 0.50 #7134, 0.36 #4238), MediterraneanSea (0.50 #8475, 0.46 #79278, 0.36 #4238), CaribbeanSea (0.50 #8475, 0.40 #21319, 0.40 #19903), TheChannel (0.50 #8475, 0.36 #4238, 0.33 #3477), IndianOcean (0.50 #8475, 0.36 #4238, 0.33 #9899), GulfofMexico (0.50 #8475, 0.36 #4238, 0.30 #2825), LabradorSea (0.50 #8475, 0.36 #4238, 0.30 #2825), Skagerrak (0.50 #8475, 0.33 #2347, 0.22 #17902)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #8499 for best value:
>> intensional similarity = 13
>> extensional distance = 4
>> proper extension:  B; NL;
>> query: (?x455, NorthSea) <- ?x455[ a Country; has ethnicGroup ?x1309; has language ?x1850; is locatedIn of ?x182[ has locatedIn ?x149[ is wasDependentOf of ?x181;]; is flowsInto of ?x137; is locatedInWater of ?x153[ has belongsToIslands ?x945;];]; is locatedIn of ?x373[ is locatedInWater of ?x634<ShetlandMainland>; is mergesWith of ?x121;];]

*> Best rule #4237 for first EXPECTED value:
*> intensional similarity = 20
*> extensional distance = 1
*> proper extension:  GB;
*> query: (?x455, ?x1236) <- ?x455[ a Country; has encompassed ?x195<Europe>; has ethnicGroup ?x1309; has language ?x1850[ a Language;]; has religion ?x95<Protestant>; has religion ?x352<RomanCatholic>; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x373<NorwegianSea>; is locatedIn of ?x806[ has type ?x150;]; is locatedIn of ?x807[ is locatedOnIsland of ?x1236;]; is locatedIn of ?x1419[ has mergesWith ?x263;];]
*> conf = 0.33
ranks of expected_values: 108
EVAL	IS	locatedIn!	Katla	CNN-1.+1._MA	0.000	0.000	0.000	0.009	96.000	86.000	1414.000	0.667	http://www.semwebtech.org/mondial/10/meta#locatedIn

#987-Rif
PRED entity: Rif

PRED relation: inMountains!
PRED expected values: DjebelAures
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 259): MtRobson (0.01 #259), BarredesEcrins (0.01 #258), Grossglockner (0.01 #257), PicodeAgulhasNegras (0.01 #256), Mantaro (0.01 #255), PicoBolivar (0.01 #254), Drin (0.01 #253), Vignemale (0.01 #252), Dychtau (0.01 #251), GranitePeak (0.01 #250)
>> best conf = 0.01
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #259 for best value:
>> intensional similarity = 1
>> extensional distance = 91
>> proper extension:  BlackForest; Ahaggar; Apennin; Alps; Troodos; Himalaya; RockyMountains; Karakorum; GreatDividingRange; Kunlun; ...
>> query: (?x2546, MtRobson) <- ?x2546[ a Mountains;]

No rule for expected values
ranks of expected_values: 
EVAL	Rif	inMountains!	DjebelAures	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	2.000	2.000	259.000	0.011	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains!
PRED expected values: DjebelAures
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 259): MtRobson (0.01 #259), BarredesEcrins (0.01 #258), Grossglockner (0.01 #257), PicodeAgulhasNegras (0.01 #256), Mantaro (0.01 #255), PicoBolivar (0.01 #254), Drin (0.01 #253), Vignemale (0.01 #252), Dychtau (0.01 #251), GranitePeak (0.01 #250)
>> best conf = 0.01
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #259 for best value:
>> intensional similarity = 1
>> extensional distance = 91
>> proper extension:  BlackForest; Ahaggar; Apennin; Alps; Troodos; Himalaya; RockyMountains; Karakorum; GreatDividingRange; Kunlun; ...
>> query: (?x2546, MtRobson) <- ?x2546[ a Mountains;]

No rule for expected values
ranks of expected_values: 
EVAL	Rif	inMountains!	DjebelAures	CNN-1.+1._MA	0.000	0.000	0.000	0.000	2.000	2.000	259.000	0.011	http://www.semwebtech.org/mondial/10/meta#inMountains

#986-Sanaga
PRED entity: Sanaga

PRED relation: hasEstuary
PRED expected values: Sanaga
=> 38 concepts (24 used for prediction)
PRED predicted values (max 10 best out of 154): Niger (0.20 #436, 0.04 #1792, 0.03 #2245), RioSaoFrancisco (0.11 #622, 0.07 #848, 0.05 #1074), Volta (0.11 #598, 0.07 #824, 0.05 #1050), SaintLawrenceRiver (0.11 #528, 0.05 #980, 0.05 #1206), Amazonas (0.11 #675, 0.05 #1127, 0.05 #1353), Oranje (0.11 #578, 0.05 #1030, 0.05 #1256), Parana (0.11 #529, 0.05 #981, 0.05 #1207), ColumbiaRiver (0.07 #823, 0.04 #1727, 0.04 #1954), Colorado (0.07 #692, 0.04 #1596, 0.04 #1823), Missouri (0.07 #769, 0.04 #1673, 0.04 #1900)
>> best conf = 0.20
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #436 for best value:
>> intensional similarity = 3
>> extensional distance = 3
>> proper extension:  ChadLake;
>> query: (?x1525, Niger) <- ?x1525[ is flowsInto of ?x786[ has locatedIn ?x536<CAM>;];]

*> Best rule #4527 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 103
*> proper extension:  MackenzieRiver;
*> query: (?x1525, ?x182) <- ?x1525[ a River; has hasSource ?x1899[ a Source;]; is flowsInto of ?x786[ has locatedIn ?x536[ is locatedIn of ?x182;];];]
*> conf = 0.01
ranks of expected_values: 116
EVAL	Sanaga	hasEstuary	Sanaga	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.009	38.000	24.000	154.000	0.200	http://www.semwebtech.org/mondial/10/meta#hasEstuary

PRED relation: hasEstuary
PRED expected values: Sanaga
=> 113 concepts (113 used for prediction)
PRED predicted values (max 10 best out of 211): Niger (0.33 #437, 0.20 #1345, 0.17 #1798), Volta (0.25 #600, 0.20 #24083, 0.20 #20222), RioSaoFrancisco (0.25 #624, 0.20 #24083, 0.20 #20222), Amazonas (0.20 #24083, 0.20 #20222, 0.20 #1131), SaintLawrenceRiver (0.20 #24083, 0.20 #20222, 0.20 #757), MerrimackRiver (0.20 #24083, 0.20 #20222, 0.20 #817), Senegal (0.20 #24083, 0.20 #20222, 0.20 #1050), Gambia (0.20 #24083, 0.20 #20222, 0.20 #1004), Tocantins (0.20 #24083, 0.20 #20222, 0.20 #22036), Parana (0.20 #24083, 0.20 #20222, 0.20 #22036)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #437 for best value:
>> intensional similarity = 13
>> extensional distance = 1
>> proper extension:  Niger;
>> query: (?x1525, Niger) <- ?x1525[ a River; has hasSource ?x1899; is flowsInto of ?x786[ a Lake; has locatedIn ?x536[ has government ?x1721; has neighbor ?x139; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x1858<Benue>;]; has type ?x136<"dam">;];]

*> Best rule #454 for first EXPECTED value:
*> intensional similarity = 14
*> extensional distance = 1
*> proper extension:  Niger;
*> query: (?x1525, ?x286) <- ?x1525[ a River; has hasSource ?x1899; is flowsInto of ?x786[ a Lake; has locatedIn ?x536[ has government ?x1721; has neighbor ?x139; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x286; is locatedIn of ?x1858<Benue>;]; has type ?x136<"dam">;];]
*> conf = 0.10
ranks of expected_values: 39
EVAL	Sanaga	hasEstuary	Sanaga	CNN-1.+1._MA	0.000	0.000	0.000	0.026	113.000	113.000	211.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasEstuary

#985-Uzbek
PRED entity: Uzbek

PRED relation: ethnicGroup!
PRED expected values: AFG
=> 27 concepts (19 used for prediction)
PRED predicted values (max 10 best out of 202): R (0.50 #192, 0.43 #571, 0.33 #568), RO (0.43 #594, 0.33 #26, 0.30 #785), CN (0.36 #1138, 0.33 #568, 0.19 #1897), AFG (0.36 #1138, 0.33 #568, 0.19 #1897), LV (0.33 #85, 0.29 #653, 0.25 #274), EW (0.33 #114, 0.29 #682, 0.25 #303), MD (0.33 #151, 0.29 #719, 0.25 #340), BY (0.33 #42, 0.29 #610, 0.25 #231), UA (0.33 #53, 0.29 #621, 0.25 #242), LT (0.33 #158, 0.25 #347, 0.21 #758)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #192 for best value:
>> intensional similarity = 11
>> extensional distance = 2
>> proper extension:  Tatar;
>> query: (?x1948, R) <- ?x1948[ a EthnicGroup; is ethnicGroup of ?x129[ has government ?x435; is locatedIn of ?x300[ has flowsInto ?x301;]; is locatedIn of ?x1019<Syrdarja>;]; is ethnicGroup of ?x277<UZB>; is ethnicGroup of ?x403<KAZ>;]

*> Best rule #1138 for first EXPECTED value:
*> intensional similarity = 9
*> extensional distance = 8
*> proper extension:  Ukrainian; Dungan; Kyrgyz; Uighur;
*> query: (?x1948, ?x381) <- ?x1948[ a EthnicGroup; is ethnicGroup of ?x277[ has language ?x278; has religion ?x56; has wasDependentOf ?x903; is locatedIn of ?x301[ has locatedIn ?x381;]; is locatedIn of ?x2336<Naryn>;];]
*> conf = 0.36
ranks of expected_values: 4
EVAL	Uzbek	ethnicGroup!	AFG	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.250	27.000	19.000	202.000	0.500	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup!
PRED expected values: AFG
=> 85 concepts (85 used for prediction)
PRED predicted values (max 10 best out of 218): R (0.77 #7850, 0.50 #1909, 0.50 #574), CN (0.77 #7850, 0.50 #570, 0.50 #379), UA (0.50 #571, 0.47 #2480, 0.47 #2341), IR (0.50 #379, 0.41 #2150, 0.39 #1142), AFG (0.50 #379, 0.39 #1142, 0.37 #2478), RO (0.50 #571, 0.38 #1361, 0.33 #8041), LV (0.50 #571, 0.33 #8041, 0.33 #1229), BY (0.50 #571, 0.33 #8041, 0.33 #1186), MD (0.50 #571, 0.33 #8041, 0.33 #1295), EW (0.50 #571, 0.33 #8041, 0.33 #1258)
>> best conf = 0.77
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #7850 for best value:
>> intensional similarity = 14
>> extensional distance = 53
>> proper extension:  Celt;
>> query: (?x1948, ?x232) <- ?x1948[ is ethnicGroup of ?x129[ a Country; is locatedIn of ?x652[ a Source;];]; is ethnicGroup of ?x130[ a Country; has language ?x555; has religion ?x56; is locatedIn of ?x1143[ a Mountain; has locatedIn ?x232;];]; is ethnicGroup of ?x290[ has wasDependentOf ?x903; is locatedIn of ?x289;];]

*> Best rule #379 for first EXPECTED value:
*> intensional similarity = 19
*> extensional distance = 2
*> proper extension:  Turkmen;
*> query: (?x1948, ?x232) <- ?x1948[ a EthnicGroup; is ethnicGroup of ?x129[ has encompassed ?x175; is locatedIn of ?x592[ a River;]; is locatedIn of ?x652[ a Source;];]; is ethnicGroup of ?x130[ has ethnicGroup ?x1193<Russian>; has government ?x435; has language ?x555; is locatedIn of ?x662; is neighbor of ?x232;]; is ethnicGroup of ?x290<TM>; is ethnicGroup of ?x403[ a Country; is locatedIn of ?x127;];]
*> conf = 0.50
ranks of expected_values: 5
EVAL	Uzbek	ethnicGroup!	AFG	CNN-1.+1._MA	0.000	0.000	1.000	0.200	85.000	85.000	218.000	0.766	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#984-Estonian
PRED entity: Estonian

PRED relation: ethnicGroup!
PRED expected values: SF
=> 35 concepts (21 used for prediction)
PRED predicted values (max 10 best out of 191): LV (0.62 #387, 0.62 #283, 0.56 #388), R (0.56 #388, 0.45 #391, 0.33 #3), UA (0.50 #250, 0.50 #57, 0.45 #640), BY (0.50 #237, 0.50 #44, 0.36 #432), LT (0.50 #355, 0.36 #550, 0.33 #162), KGZ (0.33 #17, 0.27 #974, 0.25 #210), KAZ (0.33 #78, 0.27 #466, 0.25 #271), MD (0.33 #155, 0.27 #974, 0.25 #348), SF (0.33 #115, 0.27 #974, 0.25 #308), RO (0.33 #28, 0.25 #221, 0.20 #2144)
>> best conf = 0.62
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #387 for best value:
>> intensional similarity = 9
>> extensional distance = 6
>> proper extension:  Lithuanian; Polish;
>> query: (?x2317, ?x448) <- ?x2317[ a EthnicGroup; is ethnicGroup of ?x591[ has government ?x1174; has language ?x555<Russian>; has neighbor ?x448<LV>; has religion ?x56; is locatedIn of ?x145;];]
>> Best rule #283 for best value:
>> intensional similarity = 9
>> extensional distance = 6
>> proper extension:  Lithuanian; Polish;
>> query: (?x2317, LV) <- ?x2317[ a EthnicGroup; is ethnicGroup of ?x591[ has government ?x1174; has language ?x555<Russian>; has neighbor ?x448<LV>; has religion ?x56; is locatedIn of ?x145;];]

*> Best rule #115 for first EXPECTED value:
*> intensional similarity = 3
*> extensional distance = 4
*> proper extension:  Ukrainian; Russian; Belorussian; Finn;
*> query: (?x2317, SF) <- ?x2317[ a EthnicGroup; is ethnicGroup of ?x591<EW>;]
*> conf = 0.33
ranks of expected_values: 9
EVAL	Estonian	ethnicGroup!	SF	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.111	35.000	21.000	191.000	0.625	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup!
PRED expected values: SF
=> 64 concepts (62 used for prediction)
PRED predicted values (max 10 best out of 205): LV (0.67 #1275, 0.62 #783, 0.57 #1381), R (0.62 #783, 0.57 #1381, 0.57 #1380), BY (0.57 #632, 0.50 #44, 0.47 #1777), UA (0.57 #645, 0.50 #57, 0.47 #1777), KGZ (0.47 #1777, 0.47 #1776, 0.47 #1775), LT (0.47 #1777, 0.47 #1776, 0.47 #1775), SF (0.47 #1777, 0.47 #1776, 0.47 #1775), GE (0.47 #1777, 0.47 #1776, 0.47 #1775), MD (0.47 #1777, 0.47 #1776, 0.47 #1775), UZB (0.47 #1777, 0.47 #1776, 0.47 #1775)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1275 for best value:
>> intensional similarity = 18
>> extensional distance = 7
>> proper extension:  Latvian; Lithuanian;
>> query: (?x2317, LV) <- ?x2317[ a EthnicGroup; is ethnicGroup of ?x591[ has ethnicGroup ?x58<Ukrainian>; has ethnicGroup ?x1193<Russian>; has ethnicGroup ?x1322<Belorussian>; has government ?x1174; has language ?x555<Russian>; is locatedIn of ?x802[ a River;]; is neighbor of ?x448[ has encompassed ?x195; has ethnicGroup ?x516; has religion ?x56; is locatedIn of ?x885;];];]

*> Best rule #1777 for first EXPECTED value:
*> intensional similarity = 14
*> extensional distance = 9
*> proper extension:  Croat; Slovene; Bosniak;
*> query: (?x2317, ?x402) <- ?x2317[ a EthnicGroup; is ethnicGroup of ?x591[ a Country; has encompassed ?x195<Europe>; has ethnicGroup ?x1473[ a EthnicGroup; is ethnicGroup of ?x402;]; has government ?x1174<"parliamentary republic">; has language ?x555[ a Language;]; has religion ?x56; is locatedIn of ?x145;];]
*> conf = 0.47
ranks of expected_values: 7
EVAL	Estonian	ethnicGroup!	SF	CNN-1.+1._MA	0.000	0.000	1.000	0.143	64.000	62.000	205.000	0.667	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#983-Gambia
PRED entity: Gambia

PRED relation: locatedIn
PRED expected values: RG
=> 38 concepts (38 used for prediction)
PRED predicted values (max 10 best out of 160): RG (0.95 #3754, 0.95 #4926, 0.90 #4457), RMM (0.51 #469, 0.37 #6340, 0.37 #6339), RIM (0.51 #469, 0.33 #587, 0.33 #118), GNB (0.51 #469, 0.33 #228, 0.25 #462), ZRE (0.37 #6340, 0.37 #6339, 0.33 #79), USA (0.37 #6340, 0.37 #6339, 0.33 #72), BR (0.37 #6340, 0.37 #6339, 0.33 #124), E (0.37 #6340, 0.37 #6339, 0.33 #27), F (0.37 #6340, 0.37 #6339, 0.33 #7), P (0.37 #6340, 0.37 #6339, 0.33 #196)
>> best conf = 0.95
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3754 for best value:
>> intensional similarity = 5
>> extensional distance = 182
>> proper extension:  Neckar; Buna; Rhein; Enns; Niger; OhioRiver; VictoriaNile; Narva; Uruguay; RioNegro; ...
>> query: (?x952, ?x651) <- ?x952[ has hasEstuary ?x1290; has hasSource ?x1131[ a Source; has locatedIn ?x651;]; has locatedIn ?x416;]
ranks of expected_values: 1
EVAL	Gambia	locatedIn	RG	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	38.000	38.000	160.000	0.948	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: RG
=> 100 concepts (95 used for prediction)
PRED predicted values (max 10 best out of 228): RG (0.95 #4974, 0.95 #8989, 0.94 #7102), USA (0.59 #10008, 0.46 #11664, 0.44 #1253), ZRE (0.56 #2210, 0.53 #5525, 0.33 #314), GNB (0.53 #943, 0.46 #706, 0.44 #705), WAL (0.53 #943, 0.44 #705, 0.33 #433), LB (0.53 #943, 0.44 #705, 0.22 #1416), CAM (0.53 #943, 0.33 #357, 0.32 #2490), WAN (0.53 #943, 0.33 #260, 0.25 #494), BR (0.53 #943, 0.33 #359, 0.25 #593), GH (0.53 #943, 0.33 #351, 0.25 #585)
>> best conf = 0.95
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4974 for best value:
>> intensional similarity = 10
>> extensional distance = 113
>> proper extension:  Saone;
>> query: (?x952, ?x651) <- ?x952[ a River; has flowsInto ?x182; has hasSource ?x1131[ a Source; has locatedIn ?x651[ has encompassed ?x213; has language ?x51; has neighbor ?x416; has religion ?x116;];]; has locatedIn ?x1051;]
ranks of expected_values: 1
EVAL	Gambia	locatedIn	RG	CNN-1.+1._MA	1.000	1.000	1.000	1.000	100.000	95.000	228.000	0.948	http://www.semwebtech.org/mondial/10/meta#locatedIn

#982-NorfolkIsland
PRED entity: NorfolkIsland

PRED relation: locatedIn
PRED expected values: NORF
=> 24 concepts (24 used for prediction)
PRED predicted values (max 10 best out of 109): USA (0.28 #72, 0.23 #544, 0.22 #308), J (0.16 #19, 0.12 #255, 0.07 #491), RI (0.15 #1893, 0.15 #1708, 0.12 #1419), P (0.11 #1143, 0.09 #1617, 0.09 #2091), KIR (0.09 #394, 0.05 #630, 0.05 #5235), RP (0.09 #1765, 0.07 #817, 0.06 #1291), I (0.09 #994, 0.07 #1468, 0.06 #1942), WAFU (0.08 #131, 0.06 #367, 0.05 #5235), E (0.07 #973, 0.06 #1447, 0.05 #1921), GB (0.07 #2857, 0.07 #2380, 0.07 #2619)
>> best conf = 0.28
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #72 for best value:
>> intensional similarity = 5
>> extensional distance = 23
>> proper extension:  Hokkaido; Futuna; Niihau; Tutuila; Okinawa; Hawaii; Maui; Kauai; Honshu; Koror; ...
>> query: (?x1921, USA) <- ?x1921[ a Island; has locatedInWater ?x282<PacificOcean>; has type ?x150<"volcanic">;]

*> Best rule #1894 for first EXPECTED value:
*> intensional similarity = 10
*> extensional distance = 99
*> proper extension:  Ambon; Cebu; Borneo; Panay; Samar; Ternate; Sulawesi; Negros; Timor; Ceram; ...
*> query: (?x1921, ?x73) <- ?x1921[ has locatedInWater ?x282[ has locatedIn ?x73; has locatedIn ?x217<RI>; is locatedInWater of ?x716[ is locatedOnIsland of ?x1175;]; is locatedInWater of ?x1313[ a Island; has belongsToIslands ?x1345;]; is mergesWith of ?x60;];]
*> conf = 0.03
ranks of expected_values: 48
EVAL	NorfolkIsland	locatedIn	NORF	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.021	24.000	24.000	109.000	0.280	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: NORF
=> 37 concepts (37 used for prediction)
PRED predicted values (max 10 best out of 137): USA (0.28 #72, 0.23 #544, 0.23 #780), J (0.16 #19, 0.12 #255, 0.09 #2414), RI (0.15 #3141, 0.15 #2951, 0.12 #2415), P (0.11 #2860, 0.09 #3339, 0.09 #3575), RP (0.11 #1797, 0.08 #3008, 0.07 #2039), R (0.10 #4099, 0.08 #3140, 0.05 #8501), GB (0.09 #3623, 0.09 #3869, 0.09 #4114), KIR (0.09 #394, 0.05 #630, 0.05 #866), AUS (0.09 #2414, 0.08 #4838, 0.07 #3138), NZ (0.09 #2414, 0.08 #4838, 0.05 #8501)
>> best conf = 0.28
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #72 for best value:
>> intensional similarity = 5
>> extensional distance = 23
>> proper extension:  Hokkaido; Futuna; Niihau; Tutuila; Okinawa; Hawaii; Maui; Kauai; Honshu; Koror; ...
>> query: (?x1921, USA) <- ?x1921[ a Island; has locatedInWater ?x282<PacificOcean>; has type ?x150<"volcanic">;]

*> Best rule #3142 for first EXPECTED value:
*> intensional similarity = 20
*> extensional distance = 90
*> proper extension:  Samosir;
*> query: (?x1921, ?x73) <- ?x1921[ has locatedInWater ?x282[ has locatedIn ?x73; has locatedIn ?x117[ has encompassed ?x175; is wasDependentOf of ?x334;]; has locatedIn ?x196[ has government ?x1903; has religion ?x187<Muslim>; has wasDependentOf ?x81; is locatedIn of ?x60<IndianOcean>;]; has locatedIn ?x217<RI>; has locatedIn ?x461[ has ethnicGroup ?x197; has religion ?x95;]; is locatedInWater of ?x1224[ a Island; has type ?x150;];];]
*> conf = 0.03
ranks of expected_values: 53
EVAL	NorfolkIsland	locatedIn	NORF	CNN-1.+1._MA	0.000	0.000	0.000	0.019	37.000	37.000	137.000	0.280	http://www.semwebtech.org/mondial/10/meta#locatedIn

#981-ARM
PRED entity: ARM

PRED relation: encompassed
PRED expected values: Asia
=> 45 concepts (45 used for prediction)
PRED predicted values (max 10 best out of 5): Asia (0.81 #192, 0.80 #161, 0.60 #21), Europe (0.81 #192, 0.80 #161, 0.53 #17), Africa (0.41 #64, 0.40 #69, 0.40 #59), America (0.30 #95, 0.29 #75, 0.29 #100), Australia-Oceania (0.12 #38, 0.12 #215, 0.12 #200)
>> best conf = 0.81
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #192 for best value:
>> intensional similarity = 5
>> extensional distance = 158
>> proper extension:  WSA;
>> query: (?x331, ?x175) <- ?x331[ has neighbor ?x304[ a Country; has encompassed ?x175; has neighbor ?x83; is locatedIn of ?x573;];]
ranks of expected_values: 1
EVAL	ARM	encompassed	Asia	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	45.000	45.000	5.000	0.806	http://www.semwebtech.org/mondial/10/meta#encompassed

PRED relation: encompassed
PRED expected values: Asia
=> 92 concepts (92 used for prediction)
PRED predicted values (max 10 best out of 5): Asia (0.82 #358, 0.81 #364, 0.79 #411), Europe (0.79 #411, 0.77 #271, 0.77 #335), Africa (0.43 #193, 0.37 #188, 0.36 #91), America (0.41 #210, 0.36 #215, 0.31 #296), Australia-Oceania (0.13 #437, 0.13 #426, 0.12 #443)
>> best conf = 0.82
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #358 for best value:
>> intensional similarity = 16
>> extensional distance = 91
>> proper extension:  RN;
>> query: (?x331, ?x175) <- ?x331[ has ethnicGroup ?x1193; has neighbor ?x304[ a Country; has government ?x2318; has language ?x511; has language ?x1897[ a Language;]; is locatedIn of ?x1337[ is flowsInto of ?x445;]; is locatedIn of ?x1422[ a River;]; is neighbor of ?x381[ has encompassed ?x175; has government ?x2442; has religion ?x187; is locatedIn of ?x82;];];]
ranks of expected_values: 1
EVAL	ARM	encompassed	Asia	CNN-1.+1._MA	1.000	1.000	1.000	1.000	92.000	92.000	5.000	0.821	http://www.semwebtech.org/mondial/10/meta#encompassed

#980-Lolland
PRED entity: Lolland

PRED relation: locatedInWater
PRED expected values: BalticSea
=> 39 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 115): BalticSea (0.71 #5, 0.42 #48, 0.35 #487), Kattegat (0.35 #487, 0.33 #532, 0.29 #41), NorthSea (0.35 #487, 0.33 #532, 0.28 #218), Skagerrak (0.35 #487, 0.33 #532, 0.04 #353), AtlanticOcean (0.34 #181, 0.31 #315, 0.27 #718), PacificOcean (0.25 #191, 0.19 #816, 0.18 #860), MediterraneanSea (0.18 #458, 0.17 #504, 0.13 #324), JavaSea (0.09 #317, 0.08 #362, 0.08 #407), CaribbeanSea (0.08 #862, 0.08 #730, 0.07 #597), IndianOcean (0.08 #845, 0.07 #801, 0.07 #713)
>> best conf = 0.71
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5 for best value:
>> intensional similarity = 3
>> extensional distance = 5
>> proper extension:  Falster; Fuenen; Seeland; Bornholm; Langeland;
>> query: (?x2172, BalticSea) <- ?x2172[ a Island; has locatedIn ?x793<DK>;]
ranks of expected_values: 1
EVAL	Lolland	locatedInWater	BalticSea	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	39.000	31.000	115.000	0.714	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater
PRED expected values: BalticSea
=> 107 concepts (105 used for prediction)
PRED predicted values (max 10 best out of 124): BalticSea (0.71 #5, 0.51 #1382, 0.45 #88), NorthSea (0.51 #1382, 0.45 #88, 0.45 #1383), Kattegat (0.51 #1382, 0.45 #88, 0.45 #1383), AtlanticOcean (0.46 #371, 0.41 #231, 0.37 #794), Skagerrak (0.45 #1383, 0.41 #2635, 0.40 #2030), PacificOcean (0.29 #427, 0.25 #754, 0.19 #2464), MediterraneanSea (0.22 #1076, 0.22 #1217, 0.20 #1353), JavaSea (0.14 #514, 0.13 #700, 0.13 #655), IndianOcean (0.11 #1157, 0.11 #1248, 0.11 #507), IrishSea (0.10 #266, 0.07 #406, 0.07 #452)
>> best conf = 0.71
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5 for best value:
>> intensional similarity = 3
>> extensional distance = 5
>> proper extension:  Falster; Fuenen; Seeland; Bornholm; Langeland;
>> query: (?x2172, BalticSea) <- ?x2172[ a Island; has locatedIn ?x793<DK>;]
ranks of expected_values: 1
EVAL	Lolland	locatedInWater	BalticSea	CNN-1.+1._MA	1.000	1.000	1.000	1.000	107.000	105.000	124.000	0.714	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#979-USA
PRED entity: USA

PRED relation: neighbor!
PRED expected values: CDN
=> 34 concepts (34 used for prediction)
PRED predicted values (max 10 best out of 227): AND (0.33 #444, 0.25 #605, 0.05 #1407), GCA (0.33 #30, 0.13 #2245, 0.11 #672), USA (0.33 #55, 0.13 #2245, 0.11 #697), BZ (0.33 #112, 0.11 #754, 0.06 #3692), D (0.25 #496, 0.17 #335, 0.15 #1939), R (0.25 #484, 0.17 #323, 0.13 #2245), BOL (0.22 #756, 0.13 #1557, 0.08 #1397), F (0.17 #324, 0.13 #2245, 0.12 #485), E (0.17 #340, 0.13 #2245, 0.12 #501), P (0.17 #465, 0.13 #2245, 0.12 #626)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #444 for best value:
>> intensional similarity = 6
>> extensional distance = 4
>> proper extension:  F; GB; E; CN;
>> query: (?x315, AND) <- ?x315[ has neighbor ?x482; is dependentOf of ?x322; is locatedIn of ?x1077[ a Lake;]; is locatedIn of ?x2128[ has inMountains ?x616;];]

*> Best rule #2245 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 62
*> proper extension:  DJI; KGZ; TCH; UZB; TM; AZ; BI; KAZ; RN; ETH; ...
*> query: (?x315, ?x73) <- ?x315[ has neighbor ?x482; is locatedIn of ?x282[ has locatedIn ?x73;]; is locatedIn of ?x1077[ a Lake;];]
*> conf = 0.13
ranks of expected_values: 67
EVAL	USA	neighbor!	CDN	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.015	34.000	34.000	227.000	0.333	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: CDN
=> 120 concepts (120 used for prediction)
PRED predicted values (max 10 best out of 239): USA (0.51 #10500, 0.49 #15537, 0.48 #16351), CDN (0.51 #10500, 0.49 #15537, 0.48 #16351), TM (0.38 #1656, 0.14 #7472, 0.12 #8446), GCA (0.33 #30, 0.25 #670, 0.16 #2089), BZ (0.33 #112, 0.25 #752, 0.15 #3660), CN (0.30 #2296, 0.25 #1650, 0.20 #1005), THA (0.30 #2260, 0.08 #3069, 0.05 #15546), SSD (0.29 #3913, 0.23 #7627, 0.15 #3429), PE (0.25 #691, 0.23 #3599, 0.20 #8283), AZ (0.25 #1664, 0.21 #3928, 0.20 #1019)
>> best conf = 0.51
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #10500 for best value:
>> intensional similarity = 9
>> extensional distance = 33
>> proper extension:  DJI; CN; EAK; CAM; EAT;
>> query: (?x315, ?x272) <- ?x315[ a Country; has ethnicGroup ?x79; has neighbor ?x482; has religion ?x95; is locatedIn of ?x1077[ a Lake; has locatedIn ?x272;]; is locatedIn of ?x1371[ has mergesWith ?x317;];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 2
EVAL	USA	neighbor!	CDN	CNN-1.+1._MA	0.000	1.000	1.000	0.500	120.000	120.000	239.000	0.506	http://www.semwebtech.org/mondial/10/meta#neighbor

#978-ChurchChrist
PRED entity: ChurchChrist

PRED relation: religion!
PRED expected values: VU
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 217): GB (0.18 #9), IND (0.13 #179), SLB (0.13 #84), BS (0.13 #61), I (0.13 #46), AUS (0.13 #43), BERM (0.11 #186), PNG (0.11 #170), TT (0.11 #140), VN (0.11 #131)
>> best conf = 0.18
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #9 for best value:
>> intensional similarity = 1
>> extensional distance = 43
>> proper extension:  ChristianOrthodox; Protestant; Jewish; Christian; Muslim; Mayan; Methodist; Adventist; RomanCatholic; Presbyterian; ...
>> query: (?x2227, GB) <- ?x2227[ a Religion;]

*> Best rule #98 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 43
*> proper extension:  ChristianOrthodox; Protestant; Jewish; Christian; Muslim; Mayan; Methodist; Adventist; RomanCatholic; Presbyterian; ...
*> query: (?x2227, VU) <- ?x2227[ a Religion;]
*> conf = 0.07
ranks of expected_values: 53
EVAL	ChurchChrist	religion!	VU	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.019	2.000	2.000	217.000	0.178	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion!
PRED expected values: VU
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 217): GB (0.18 #9), IND (0.13 #179), SLB (0.13 #84), BS (0.13 #61), I (0.13 #46), AUS (0.13 #43), BERM (0.11 #186), PNG (0.11 #170), TT (0.11 #140), VN (0.11 #131)
>> best conf = 0.18
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #9 for best value:
>> intensional similarity = 1
>> extensional distance = 43
>> proper extension:  ChristianOrthodox; Protestant; Jewish; Christian; Muslim; Mayan; Methodist; Adventist; RomanCatholic; Presbyterian; ...
>> query: (?x2227, GB) <- ?x2227[ a Religion;]

*> Best rule #98 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 43
*> proper extension:  ChristianOrthodox; Protestant; Jewish; Christian; Muslim; Mayan; Methodist; Adventist; RomanCatholic; Presbyterian; ...
*> query: (?x2227, VU) <- ?x2227[ a Religion;]
*> conf = 0.07
ranks of expected_values: 53
EVAL	ChurchChrist	religion!	VU	CNN-1.+1._MA	0.000	0.000	0.000	0.019	2.000	2.000	217.000	0.178	http://www.semwebtech.org/mondial/10/meta#religion

#977-Kwa
PRED entity: Kwa

PRED relation: hasEstuary
PRED expected values: Kwa
=> 48 concepts (46 used for prediction)
PRED predicted values (max 10 best out of 217): Ruki (0.20 #166, 0.19 #6559, 0.12 #619), Lualaba (0.20 #46, 0.19 #6559, 0.12 #499), Ubangi (0.20 #24, 0.19 #6559, 0.12 #477), Aruwimi (0.19 #6559, 0.12 #615, 0.12 #388), Lomami (0.19 #6559, 0.12 #493, 0.12 #266), Bomu (0.05 #727, 0.05 #953, 0.03 #1405), Lukuga (0.05 #826, 0.05 #1052, 0.03 #1730), Fimi (0.05 #828, 0.05 #1054, 0.02 #1958), Busira (0.05 #698, 0.05 #924, 0.02 #1828), Luvua (0.05 #856, 0.05 #1082, 0.02 #1986)
>> best conf = 0.20
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #166 for best value:
>> intensional similarity = 5
>> extensional distance = 3
>> proper extension:  Ruki; Ubangi; Lualaba;
>> query: (?x113, Ruki) <- ?x113[ a River; has flowsInto ?x929<Zaire>; has hasSource ?x114; is flowsInto of ?x509;]

*> Best rule #9276 for first EXPECTED value:
*> intensional similarity = 4
*> extensional distance = 1182
*> proper extension:  ReneLevasseurIsland; DarlingRiver; GreatSandyDesert; Tasmania; VictoriaIsland; EucumbeneRiver; MtColumbia; JoekulsaaFjoellum; Hvannadalshnukur; Iceland; ...
*> query: (?x113, ?x347) <- ?x113[ has locatedIn ?x348[ has religion ?x95; is locatedIn of ?x347[ a Estuary;];];]
*> conf = 0.02
ranks of expected_values: 100
EVAL	Kwa	hasEstuary	Kwa	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.010	48.000	46.000	217.000	0.200	http://www.semwebtech.org/mondial/10/meta#hasEstuary

PRED relation: hasEstuary
PRED expected values: Kwa
=> 119 concepts (119 used for prediction)
PRED predicted values (max 10 best out of 218): Ruki (0.33 #3174, 0.26 #15219, 0.24 #15446), Lualaba (0.33 #3174, 0.26 #15219, 0.24 #15446), Ubangi (0.33 #3174, 0.26 #15219, 0.24 #15446), Aruwimi (0.33 #3174, 0.26 #15219, 0.24 #15446), Lomami (0.33 #3174, 0.26 #15219, 0.24 #15446), Lukuga (0.14 #374, 0.11 #1280, 0.09 #1734), Bahrel-Djebel-Albert-Nil (0.14 #405, 0.11 #1311, 0.09 #1765), WhiteNile (0.14 #609, 0.08 #1968, 0.04 #4918), Aare (0.14 #463, 0.04 #4545, 0.04 #4772), VictoriaNile (0.14 #314, 0.01 #11216, 0.01 #11898)
>> best conf = 0.33
=> the first rule below is the first best rule for 5 predicted values
>> Best rule #3174 for best value:
>> intensional similarity = 9
>> extensional distance = 16
>> proper extension:  Uelle;
>> query: (?x113, ?x607) <- ?x113[ a River; has flowsInto ?x929[ a River; has flowsInto ?x182; is flowsInto of ?x2366[ a River; has hasEstuary ?x607;];]; has locatedIn ?x348<ZRE>;]

*> Best rule #19536 for first EXPECTED value:
*> intensional similarity = 9
*> extensional distance = 243
*> proper extension:  LakeMaracaibo; Maelaren;
*> query: (?x113, ?x347) <- ?x113[ has flowsInto ?x929[ has locatedIn ?x528;]; has locatedIn ?x348[ has religion ?x95; is locatedIn of ?x347[ a Estuary;]; is locatedIn of ?x732[ a Source;]; is neighbor of ?x229;];]
*> conf = 0.05
ranks of expected_values: 42
EVAL	Kwa	hasEstuary	Kwa	CNN-1.+1._MA	0.000	0.000	0.000	0.024	119.000	119.000	218.000	0.326	http://www.semwebtech.org/mondial/10/meta#hasEstuary

#976-MNE
PRED entity: MNE

PRED relation: language
PRED expected values: Bosnian
=> 40 concepts (40 used for prediction)
PRED predicted values (max 10 best out of 95): Greek (0.33 #48, 0.20 #381, 0.17 #762), Turkish (0.25 #103, 0.09 #674, 0.08 #960), Roma (0.25 #140, 0.07 #997, 0.07 #711), Macedonian (0.25 #175, 0.02 #746, 0.02 #842), Spanish (0.25 #1354, 0.24 #1544, 0.24 #1449), English (0.25 #2476, 0.21 #1906, 0.19 #1241), Croatian (0.20 #381, 0.17 #762, 0.12 #214), Hungarian (0.20 #381, 0.17 #762, 0.09 #683), Serbo-Croatian (0.20 #381, 0.17 #762, 0.06 #225), Bosnian (0.20 #381, 0.17 #762, 0.02 #720)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #48 for best value:
>> intensional similarity = 6
>> extensional distance = 1
>> proper extension:  AL;
>> query: (?x106, Greek) <- ?x106[ a Country; has ethnicGroup ?x775; has language ?x1251; has religion ?x56; is locatedIn of ?x203<Buna>;]

*> Best rule #381 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 16
*> proper extension:  GBZ;
*> query: (?x106, ?x1251) <- ?x106[ has religion ?x56; is locatedIn of ?x275<MediterraneanSea>; is neighbor of ?x692[ has language ?x1251;]; is neighbor of ?x904[ is locatedIn of ?x132;];]
*> conf = 0.20
ranks of expected_values: 10
EVAL	MNE	language	Bosnian	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.100	40.000	40.000	95.000	0.333	http://www.semwebtech.org/mondial/10/meta#language

PRED relation: language
PRED expected values: Bosnian
=> 93 concepts (93 used for prediction)
PRED predicted values (max 10 best out of 95): Spanish (0.71 #1833, 0.54 #2119, 0.50 #1548), Croatian (0.50 #405, 0.40 #286, 0.33 #953), Hungarian (0.40 #286, 0.33 #953, 0.31 #1256), Greek (0.40 #286, 0.33 #953, 0.30 #3527), Bosnian (0.40 #286, 0.33 #953, 0.30 #3527), English (0.34 #3531, 0.28 #2769, 0.27 #6489), Turkish (0.33 #1057, 0.31 #1342, 0.25 #294), Serbo-Croatian (0.33 #35, 0.25 #511, 0.25 #130), Russian (0.30 #2966, 0.29 #3157, 0.25 #3634), Roma (0.25 #521, 0.25 #235, 0.20 #902)
>> best conf = 0.71
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1833 for best value:
>> intensional similarity = 12
>> extensional distance = 19
>> proper extension:  C;
>> query: (?x106, Spanish) <- ?x106[ has religion ?x56; has wasDependentOf ?x904[ has encompassed ?x195; has ethnicGroup ?x164; has language ?x684; has neighbor ?x692; is locatedIn of ?x1489[ a River; has hasSource ?x2099;]; is neighbor of ?x55;]; is locatedIn of ?x104[ is flowsInto of ?x2296;];]

*> Best rule #286 for first EXPECTED value:
*> intensional similarity = 16
*> extensional distance = 2
*> proper extension:  AL; MK;
*> query: (?x106, ?x878) <- ?x106[ a Country; has ethnicGroup ?x1472<Albanian>; has language ?x1251; has neighbor ?x156[ has ethnicGroup ?x160; has government ?x254; has language ?x878; has neighbor ?x236[ has neighbor ?x163; is locatedIn of ?x133;]; has religion ?x56;]; has neighbor ?x692<KOS>; is locatedIn of ?x104; is neighbor of ?x55;]
*> conf = 0.40
ranks of expected_values: 5
EVAL	MNE	language	Bosnian	CNN-1.+1._MA	0.000	0.000	1.000	0.200	93.000	93.000	95.000	0.714	http://www.semwebtech.org/mondial/10/meta#language

#975-Sanga
PRED entity: Sanga

PRED relation: hasSource
PRED expected values: Sanga
=> 41 concepts (35 used for prediction)
PRED predicted values (max 10 best out of 58): Ubangi (0.33 #26, 0.20 #713, 0.20 #484), Schari (0.33 #315, 0.20 #773, 0.11 #1001), Bomu (0.20 #749, 0.11 #977, 0.07 #1205), Zaire (0.20 #678, 0.07 #1363, 0.04 #1592), Ruki (0.04 #1584, 0.04 #1813, 0.03 #2043), Lualaba (0.04 #1547, 0.04 #1776, 0.03 #2006), Aruwimi (0.04 #1535, 0.04 #1764, 0.03 #1994), Lomami (0.04 #1455, 0.04 #1684, 0.03 #1914), Kwa (0.04 #1376, 0.04 #1605, 0.03 #1835), Busira (0.04 #1597, 0.04 #1826, 0.01 #2284)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #26 for best value:
>> intensional similarity = 7
>> extensional distance = 1
>> proper extension:  Ubangi;
>> query: (?x2087, Ubangi) <- ?x2087[ has locatedIn ?x528<RCB>; has locatedIn ?x536[ a Country; has religion ?x116;]; has locatedIn ?x736<RCA>;]

*> Best rule #1372 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 12
*> proper extension:  Ubangi; Sanga;
*> query: (?x2087, ?x549) <- ?x2087[ has locatedIn ?x736[ has government ?x435; has neighbor ?x169; has religion ?x187; is locatedIn of ?x388<Ubangi>; is locatedIn of ?x549;];]
*> conf = 0.02
ranks of expected_values: 40
EVAL	Sanga	hasSource	Sanga	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.025	41.000	35.000	58.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasSource

PRED relation: hasSource
PRED expected values: Sanga
=> 94 concepts (94 used for prediction)
PRED predicted values (max 10 best out of 165): Ubangi (0.33 #26, 0.26 #6207, 0.26 #3215), Schari (0.33 #315, 0.20 #544, 0.17 #1005), Ruki (0.26 #6207, 0.26 #3215, 0.25 #3673), Lualaba (0.26 #6207, 0.26 #3215, 0.25 #3673), Kwa (0.26 #6207, 0.26 #3215, 0.25 #3673), Aruwimi (0.26 #3215, 0.25 #3673, 0.25 #3672), Lomami (0.26 #3215, 0.25 #3673, 0.25 #3672), Bomu (0.20 #520, 0.16 #2295, 0.12 #1439), Zaire (0.20 #909, 0.12 #1827, 0.04 #4124), Benue (0.17 #1142, 0.12 #1600, 0.10 #229)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #26 for best value:
>> intensional similarity = 11
>> extensional distance = 1
>> proper extension:  Ubangi;
>> query: (?x2087, Ubangi) <- ?x2087[ has flowsInto ?x929<Zaire>; has locatedIn ?x536[ has ethnicGroup ?x122; has religion ?x116; is locatedIn of ?x695<Schari>; is neighbor of ?x169<TCH>;]; has locatedIn ?x736<RCA>;]

*> Best rule #2295 for first EXPECTED value:
*> intensional similarity = 11
*> extensional distance = 8
*> proper extension:  Bahrel-Ghasal; Bahrel-Djebel-Albert-Nil;
*> query: (?x2087, ?x549) <- ?x2087[ a River; has flowsInto ?x929; has locatedIn ?x736[ a Country; is locatedIn of ?x388[ a River; has hasEstuary ?x389;]; is locatedIn of ?x549[ a Source;]; is neighbor of ?x186<SUD>;];]
*> conf = 0.16
ranks of expected_values: 11
EVAL	Sanga	hasSource	Sanga	CNN-1.+1._MA	0.000	0.000	0.000	0.091	94.000	94.000	165.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasSource

#974-MNTS
PRED entity: MNTS

PRED relation: dependentOf
PRED expected values: GB
=> 35 concepts (35 used for prediction)
PRED predicted values (max 10 best out of 85): GB (0.75 #12, 0.71 #2, 0.33 #63), F (0.22 #32, 0.22 #22, 0.17 #82), USA (0.11 #28, 0.08 #58, 0.06 #339), NL (0.10 #50, 0.06 #339, 0.01 #155), DK (0.03 #156, 0.02 #123, 0.01 #134), AUS (0.02 #253, 0.02 #107, 0.02 #265), NZ (0.02 #110, 0.01 #268, 0.01 #290), CN (0.02 #242, 0.01 #163, 0.01 #173), N (0.02 #117, 0.01 #128, 0.01 #150), GNB (0.01 #21)
>> best conf = 0.75
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #12 for best value:
>> intensional similarity = 5
>> extensional distance = 6
>> proper extension:  FALK;
>> query: (?x1444, GB) <- ?x1444[ a Country; has government ?x562<"British Overseas Territories">; has language ?x247; is locatedIn of ?x182;]
ranks of expected_values: 1
EVAL	MNTS	dependentOf	GB	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	35.000	35.000	85.000	0.750	http://www.semwebtech.org/mondial/10/meta#dependentOf

PRED relation: dependentOf
PRED expected values: GB
=> 74 concepts (74 used for prediction)
PRED predicted values (max 10 best out of 104): GB (0.71 #184, 0.33 #268, 0.33 #36), F (0.40 #111, 0.25 #221, 0.25 #78), NL (0.20 #109, 0.06 #861, 0.05 #803), USA (0.17 #169, 0.11 #340, 0.11 #252), N (0.06 #861, 0.04 #371, 0.03 #483), DK (0.06 #861, 0.03 #569, 0.03 #583), CO (0.03 #355, 0.03 #381, 0.02 #417), E (0.03 #355, 0.03 #381, 0.02 #417), RH (0.03 #355, 0.03 #381, 0.02 #417), BR (0.03 #355, 0.03 #381, 0.02 #417)
>> best conf = 0.71
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #184 for best value:
>> intensional similarity = 20
>> extensional distance = 5
>> proper extension:  HELX; TUCA; FALK;
>> query: (?x1444, GB) <- ?x1444[ a Country; has government ?x562<"British Overseas Territories">; has language ?x247<English>; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x317[ has locatedIn ?x633<GUAD>; has locatedIn ?x667<TT>; has locatedIn ?x1073[ has encompassed ?x521;]; has locatedIn ?x1554<WL>; is locatedInWater of ?x123<SaintVincent>; is locatedInWater of ?x1380<St.Martin>;];]
ranks of expected_values: 1
EVAL	MNTS	dependentOf	GB	CNN-1.+1._MA	1.000	1.000	1.000	1.000	74.000	74.000	104.000	0.714	http://www.semwebtech.org/mondial/10/meta#dependentOf

#973-STP
PRED entity: STP

PRED relation: language
PRED expected values: Portuguese
=> 41 concepts (41 used for prediction)
PRED predicted values (max 10 best out of 73): Spanish (0.41 #610, 0.32 #708, 0.29 #806), English (0.30 #200, 0.20 #2552, 0.20 #984), French (0.13 #197, 0.12 #1275, 0.11 #785), Russian (0.09 #305, 0.07 #1383, 0.07 #1677), German (0.08 #3725, 0.06 #15, 0.05 #2465), Portuguese (0.08 #3725, 0.06 #597, 0.04 #107), Afrikaans (0.08 #3725, 0.06 #78, 0.04 #176), Dutch (0.08 #3725, 0.03 #206, 0.03 #2460), Catalan (0.08 #3725, 0.03 #217, 0.03 #413), Icelandic (0.08 #3725, 0.03 #256, 0.03 #452)
>> best conf = 0.41
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #610 for best value:
>> intensional similarity = 7
>> extensional distance = 32
>> proper extension:  TN; ROU; Z; PA; ANG; HCA; XMAS; GNB;
>> query: (?x994, Spanish) <- ?x994[ a Country; has ethnicGroup ?x197<European>; has government ?x435; has religion ?x1283[ a Religion;]; is locatedIn of ?x182;]

*> Best rule #3725 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 182
*> proper extension:  PY; WEST; L;
*> query: (?x994, ?x796) <- ?x994[ has religion ?x316; is locatedIn of ?x182[ has locatedIn ?x315[ has language ?x796;]; is flowsInto of ?x137;];]
*> conf = 0.08
ranks of expected_values: 6
EVAL	STP	language	Portuguese	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.167	41.000	41.000	73.000	0.412	http://www.semwebtech.org/mondial/10/meta#language

PRED relation: language
PRED expected values: Portuguese
=> 96 concepts (96 used for prediction)
PRED predicted values (max 10 best out of 86): Spanish (0.47 #5122, 0.44 #4630, 0.43 #4924), English (0.39 #3043, 0.36 #4122, 0.35 #3337), German (0.33 #15, 0.12 #1682, 0.10 #2074), Afrikaans (0.33 #78, 0.12 #1745, 0.08 #9124), Portuguese (0.17 #1186, 0.14 #1480, 0.11 #1970), French (0.15 #3334, 0.15 #5395, 0.14 #4217), Miskito (0.14 #1566, 0.14 #1468, 0.10 #8338), Russian (0.12 #7661, 0.11 #5012, 0.10 #5307), Arabic (0.10 #8338, 0.10 #8339, 0.10 #8043), Quechua (0.10 #8338, 0.10 #8339, 0.10 #8043)
>> best conf = 0.47
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5122 for best value:
>> intensional similarity = 14
>> extensional distance = 38
>> proper extension:  PY;
>> query: (?x994, Spanish) <- ?x994[ a Country; has encompassed ?x213; has ethnicGroup ?x197[ is ethnicGroup of ?x181<GCA>; is ethnicGroup of ?x202[ has neighbor ?x296<PE>; has wasDependentOf ?x149;]; is ethnicGroup of ?x272[ has wasDependentOf ?x81; is locatedIn of ?x182;]; is ethnicGroup of ?x1364<HCA>;];]

*> Best rule #1186 for first EXPECTED value:
*> intensional similarity = 12
*> extensional distance = 4
*> proper extension:  Z; GNB;
*> query: (?x994, Portuguese) <- ?x994[ a Country; has encompassed ?x213<Africa>; has ethnicGroup ?x197<European>; has government ?x435<"republic">; has religion ?x2256[ a Religion; is religion of ?x476<ETH>;]; is locatedIn of ?x182;]
*> conf = 0.17
ranks of expected_values: 5
EVAL	STP	language	Portuguese	CNN-1.+1._MA	0.000	0.000	1.000	0.200	96.000	96.000	86.000	0.475	http://www.semwebtech.org/mondial/10/meta#language

#972-FALK
PRED entity: FALK

PRED relation: encompassed
PRED expected values: America
=> 42 concepts (42 used for prediction)
PRED predicted values (max 10 best out of 5): America (0.65 #75, 0.64 #65, 0.63 #50), Europe (0.37 #142, 0.36 #202, 0.35 #137), Africa (0.36 #202, 0.31 #114, 0.31 #129), Australia-Oceania (0.32 #78, 0.24 #98, 0.19 #58), Asia (0.20 #182, 0.18 #177, 0.18 #146)
>> best conf = 0.65
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #75 for best value:
>> intensional similarity = 8
>> extensional distance = 24
>> proper extension:  WV; BS; P; AG;
>> query: (?x1087, America) <- ?x1087[ a Country; has government ?x562; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x867[ has belongsToIslands ?x2389;]; is locatedIn of ?x2397[ a Island;];]
ranks of expected_values: 1
EVAL	FALK	encompassed	America	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	42.000	42.000	5.000	0.654	http://www.semwebtech.org/mondial/10/meta#encompassed

PRED relation: encompassed
PRED expected values: America
=> 89 concepts (89 used for prediction)
PRED predicted values (max 10 best out of 5): America (0.69 #261, 0.67 #186, 0.65 #246), Australia-Oceania (0.38 #110, 0.31 #98, 0.30 #356), Europe (0.38 #352, 0.37 #84, 0.37 #368), Africa (0.37 #84, 0.37 #368, 0.37 #325), Asia (0.30 #356, 0.28 #267, 0.24 #268)
>> best conf = 0.69
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #261 for best value:
>> intensional similarity = 12
>> extensional distance = 37
>> proper extension:  GCA; CO; CR; NIC; MEX; PA; HCA;
>> query: (?x1087, America) <- ?x1087[ a Country; has language ?x247; is locatedIn of ?x182[ has locatedIn ?x520<DOM>; has locatedIn ?x536[ has neighbor ?x169; has religion ?x116;]; has locatedIn ?x1209<AG>; is locatedInWater of ?x112; is mergesWith of ?x60;];]
ranks of expected_values: 1
EVAL	FALK	encompassed	America	CNN-1.+1._MA	1.000	1.000	1.000	1.000	89.000	89.000	5.000	0.692	http://www.semwebtech.org/mondial/10/meta#encompassed

#971-BohemianMountains
PRED entity: BohemianMountains

PRED relation: inMountains!
PRED expected values: Moldau
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 259): MtRobson (0.01 #259), BarredesEcrins (0.01 #258), Grossglockner (0.01 #257), PicodeAgulhasNegras (0.01 #256), Mantaro (0.01 #255), PicoBolivar (0.01 #254), Drin (0.01 #253), Vignemale (0.01 #252), Dychtau (0.01 #251), GranitePeak (0.01 #250)
>> best conf = 0.01
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #259 for best value:
>> intensional similarity = 1
>> extensional distance = 91
>> proper extension:  BlackForest; Ahaggar; Apennin; Alps; Troodos; Himalaya; RockyMountains; Karakorum; GreatDividingRange; Kunlun; ...
>> query: (?x2501, MtRobson) <- ?x2501[ a Mountains;]

No rule for expected values
ranks of expected_values: 
EVAL	BohemianMountains	inMountains!	Moldau	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	2.000	2.000	259.000	0.011	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains!
PRED expected values: Moldau
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 259): MtRobson (0.01 #259), BarredesEcrins (0.01 #258), Grossglockner (0.01 #257), PicodeAgulhasNegras (0.01 #256), Mantaro (0.01 #255), PicoBolivar (0.01 #254), Drin (0.01 #253), Vignemale (0.01 #252), Dychtau (0.01 #251), GranitePeak (0.01 #250)
>> best conf = 0.01
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #259 for best value:
>> intensional similarity = 1
>> extensional distance = 91
>> proper extension:  BlackForest; Ahaggar; Apennin; Alps; Troodos; Himalaya; RockyMountains; Karakorum; GreatDividingRange; Kunlun; ...
>> query: (?x2501, MtRobson) <- ?x2501[ a Mountains;]

No rule for expected values
ranks of expected_values: 
EVAL	BohemianMountains	inMountains!	Moldau	CNN-1.+1._MA	0.000	0.000	0.000	0.000	2.000	2.000	259.000	0.011	http://www.semwebtech.org/mondial/10/meta#inMountains

#970-Rhein
PRED entity: Rhein

PRED relation: locatedIn
PRED expected values: CH NL
=> 38 concepts (38 used for prediction)
PRED predicted values (max 10 best out of 178): CH (0.90 #5123, 0.89 #3027, 0.77 #2329), L (0.67 #1862, 0.67 #1861, 0.64 #3261), H (0.33 #986, 0.20 #521, 0.19 #4658), HR (0.33 #956, 0.20 #491, 0.10 #4425), SK (0.28 #1162, 0.20 #494, 0.19 #4658), CZ (0.28 #1162, 0.19 #4658, 0.19 #3959), SLO (0.28 #1162, 0.19 #4658, 0.19 #3959), B (0.28 #1162, 0.19 #4658, 0.19 #3959), DK (0.28 #1162, 0.19 #4658, 0.14 #2095), PL (0.28 #1162, 0.19 #4658, 0.14 #2095)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5123 for best value:
>> intensional similarity = 5
>> extensional distance = 193
>> proper extension:  Araguaia; Leine; Lulua; Vaesterdalaelv; Thames;
>> query: (?x256, ?x234) <- ?x256[ a River; has hasEstuary ?x257; has hasSource ?x1695[ a Source; has locatedIn ?x234;];]
ranks of expected_values: 1, 11
EVAL	Rhein	locatedIn	NL	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.100	38.000	38.000	178.000	0.896	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	Rhein	locatedIn	CH	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	38.000	38.000	178.000	0.896	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: CH NL
=> 129 concepts (129 used for prediction)
PRED predicted values (max 10 best out of 236): NL (0.94 #15712, 0.94 #16415, 0.93 #9374), CH (0.92 #16181, 0.91 #11727, 0.90 #8202), L (0.76 #16180, 0.71 #12196, 0.69 #12197), R (0.50 #4220, 0.40 #472, 0.37 #9615), S (0.50 #2900, 0.17 #6653, 0.10 #7593), I (0.43 #17630, 0.40 #2386, 0.39 #4963), CN (0.40 #521, 0.35 #6146, 0.19 #6854), USA (0.40 #10857, 0.40 #10386, 0.38 #6870), BR (0.32 #5743, 0.29 #6449, 0.17 #6684), ZRE (0.31 #14148, 0.28 #14618, 0.17 #12039)
>> best conf = 0.94
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #15712 for best value:
>> intensional similarity = 9
>> extensional distance = 76
>> proper extension:  Donau; Parana; Busira;
>> query: (?x256, ?x575) <- ?x256[ has hasEstuary ?x257[ a Estuary; has locatedIn ?x575[ has ethnicGroup ?x734; has religion ?x95;];]; has hasSource ?x1695[ a Source;]; has locatedIn ?x78; is flowsInto of ?x613;]
ranks of expected_values: 1, 2
EVAL	Rhein	locatedIn	NL	CNN-1.+1._MA	1.000	1.000	1.000	1.000	129.000	129.000	236.000	0.943	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	Rhein	locatedIn	CH	CNN-1.+1._MA	1.000	1.000	1.000	1.000	129.000	129.000	236.000	0.943	http://www.semwebtech.org/mondial/10/meta#locatedIn

#969-BEN
PRED entity: BEN

PRED relation: religion
PRED expected values: Muslim
=> 34 concepts (34 used for prediction)
PRED predicted values (max 10 best out of 26): Muslim (0.72 #291, 0.61 #168, 0.56 #866), RomanCatholic (0.61 #458, 0.60 #335, 0.58 #376), Protestant (0.49 #371, 0.48 #453, 0.48 #330), ChristianOrthodox (0.21 #616, 0.20 #699, 0.19 #575), JehovasWitnesses (0.17 #225, 0.15 #102, 0.14 #143), Anglican (0.17 #345, 0.16 #386, 0.15 #427), Hindu (0.14 #337, 0.14 #378, 0.13 #173), Jewish (0.14 #208, 0.13 #167, 0.13 #290), Buddhist (0.13 #298, 0.10 #832, 0.10 #1162), Mormon (0.06 #230, 0.03 #271, 0.02 #1176)
>> best conf = 0.72
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #291 for best value:
>> intensional similarity = 6
>> extensional distance = 37
>> proper extension:  SD; LB;
>> query: (?x810, Muslim) <- ?x810[ a Country; has ethnicGroup ?x162; has neighbor ?x1307; has religion ?x116<Christian>; is neighbor of ?x139;]
ranks of expected_values: 1
EVAL	BEN	religion	Muslim	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	34.000	34.000	26.000	0.718	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion
PRED expected values: Muslim
=> 79 concepts (79 used for prediction)
PRED predicted values (max 10 best out of 37): Muslim (0.89 #2464, 0.87 #2587, 0.86 #2628), RomanCatholic (0.65 #2174, 0.64 #1921, 0.61 #1338), Protestant (0.50 #2836, 0.50 #2001, 0.49 #2169), Jewish (0.44 #583, 0.38 #542, 0.29 #417), Hindu (0.36 #2834, 0.35 #1999, 0.32 #3001), JehovasWitnesses (0.36 #2834, 0.35 #1999, 0.32 #3001), ChristianOrthodox (0.26 #1957, 0.21 #2709, 0.21 #2419), Anglican (0.24 #2750, 0.23 #1998, 0.21 #1872), Baptist (0.24 #2750, 0.23 #1998, 0.21 #1872), Methodist (0.24 #2750, 0.23 #1998, 0.21 #1872)
>> best conf = 0.89
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2464 for best value:
>> intensional similarity = 24
>> extensional distance = 70
>> proper extension:  Q;
>> query: (?x810, Muslim) <- ?x810[ a Country; has encompassed ?x213; has ethnicGroup ?x162; has neighbor ?x139; has religion ?x116[ is religion of ?x91<THA>; is religion of ?x115<RL>; is religion of ?x169<TCH>; is religion of ?x186<SUD>; is religion of ?x359<BI>; is religion of ?x376<MAL>; is religion of ?x508<CL>; is religion of ?x621<LB>; is religion of ?x1072<WAL>;]; is locatedIn of ?x182;]
ranks of expected_values: 1
EVAL	BEN	religion	Muslim	CNN-1.+1._MA	1.000	1.000	1.000	1.000	79.000	79.000	37.000	0.889	http://www.semwebtech.org/mondial/10/meta#religion

#968-Tajo
PRED entity: Tajo

PRED relation: hasEstuary
PRED expected values: Tajo
=> 41 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 209): Guadiana (0.20 #357, 0.20 #131, 0.05 #583), Douro (0.20 #102, 0.05 #554, 0.03 #781), Ebro (0.20 #118, 0.03 #797, 0.02 #679), Guadalquivir (0.05 #541, 0.03 #768, 0.02 #679), Tocantins (0.05 #550, 0.02 #1004, 0.01 #1230), Amazonas (0.05 #675, 0.01 #1355, 0.01 #1582), Orinoco (0.05 #479, 0.01 #1159, 0.01 #1386), Garonne (0.05 #659, 0.01 #1339, 0.01 #1566), Loire (0.05 #668, 0.01 #1348, 0.01 #1575), RioSaoFrancisco (0.05 #622, 0.01 #1302, 0.01 #1529)
>> best conf = 0.20
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #357 for best value:
>> intensional similarity = 4
>> extensional distance = 3
>> proper extension:  AtlanticOcean; Guadiana;
>> query: (?x1479, Guadiana) <- ?x1479[ has locatedIn ?x149<E>; has locatedIn ?x1027<P>;]
>> Best rule #131 for best value:
>> intensional similarity = 5
>> extensional distance = 3
>> proper extension:  Guadiana; Douro; Ebro;
>> query: (?x1479, Guadiana) <- ?x1479[ a River; has flowsInto ?x182; has hasSource ?x1700; has locatedIn ?x149<E>;]

*> Best rule #679 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 19
*> proper extension:  Guadalquivir;
*> query: (?x1479, ?x199) <- ?x1479[ a River; has flowsInto ?x182<AtlanticOcean>; has locatedIn ?x1027[ is locatedIn of ?x199;];]
*> conf = 0.02
ranks of expected_values: 51
EVAL	Tajo	hasEstuary	Tajo	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.020	41.000	36.000	209.000	0.200	http://www.semwebtech.org/mondial/10/meta#hasEstuary

PRED relation: hasEstuary
PRED expected values: Tajo
=> 119 concepts (119 used for prediction)
PRED predicted values (max 10 best out of 247): Guadiana (0.33 #131, 0.25 #357, 0.20 #1037), Douro (0.25 #328, 0.20 #781, 0.20 #555), Ebro (0.20 #571, 0.12 #10672, 0.11 #2041), Guadalquivir (0.20 #768, 0.12 #10672, 0.11 #2041), Tajo (0.12 #10672, 0.11 #2041, 0.03 #23194), Amazonas (0.10 #1355, 0.06 #1581, 0.05 #2265), Orinoco (0.10 #1159, 0.06 #1385, 0.05 #2069), HudsonRiver (0.10 #1284, 0.06 #1510, 0.05 #2194), Oranje (0.10 #1258, 0.06 #1484, 0.05 #2168), Garonne (0.10 #1339, 0.05 #2021, 0.05 #2249)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #131 for best value:
>> intensional similarity = 10
>> extensional distance = 1
>> proper extension:  Guadiana;
>> query: (?x1479, Guadiana) <- ?x1479[ a River; has flowsInto ?x182<AtlanticOcean>; has hasSource ?x1700[ a Source; has inMountains ?x1701;]; has locatedIn ?x149<E>; has locatedIn ?x1027<P>;]

*> Best rule #10672 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 161
*> proper extension:  Luapula;
*> query: (?x1479, ?x1739) <- ?x1479[ a River; has hasSource ?x1700; has locatedIn ?x1027[ a Country; has encompassed ?x195; has government ?x2551; is locatedIn of ?x1739[ a Estuary;];];]
*> conf = 0.12
ranks of expected_values: 5
EVAL	Tajo	hasEstuary	Tajo	CNN-1.+1._MA	0.000	0.000	1.000	0.200	119.000	119.000	247.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasEstuary

#967-TT
PRED entity: TT

PRED relation: locatedIn!
PRED expected values: LaBreaPitchLake
=> 34 concepts (22 used for prediction)
PRED predicted values (max 10 best out of 1251): PacificOcean (0.37 #11446, 0.30 #12866, 0.26 #14287), IndianOcean (0.22 #2843, 0.14 #17046, 0.11 #28415), TheChannel (0.22 #654, 0.11 #29833, 0.10 #24148), GulfofBengal (0.17 #2912, 0.06 #15623, 0.03 #21377), MediterraneanSea (0.14 #24230, 0.14 #25652, 0.13 #28494), Tajo (0.12 #28412, 0.08 #5088, 0.06 #25570), Guadiana (0.12 #28412, 0.08 #4905, 0.06 #25570), RioSanJuan (0.12 #28412, 0.06 #15623, 0.06 #25570), SaintLawrenceRiver (0.12 #28412, 0.06 #25570, 0.05 #9243), Senegal (0.12 #28412, 0.06 #25570, 0.05 #8926)
>> best conf = 0.37
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #11446 for best value:
>> intensional similarity = 6
>> extensional distance = 52
>> proper extension:  AND;
>> query: (?x667, PacificOcean) <- ?x667[ a Country; has ethnicGroup ?x298[ is ethnicGroup of ?x1731[ has dependentOf ?x196;];]; has religion ?x352<RomanCatholic>;]

No rule for expected values
ranks of expected_values: 
EVAL	TT	locatedIn!	LaBreaPitchLake	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	34.000	22.000	1251.000	0.370	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: LaBreaPitchLake
=> 79 concepts (79 used for prediction)
PRED predicted values (max 10 best out of 1424): PacificOcean (0.66 #42734, 0.54 #19994, 0.44 #14296), MediterraneanSea (0.50 #4348, 0.40 #8612, 0.25 #5769), SouthChinaSea (0.50 #17201, 0.21 #38525, 0.17 #38386), LakePrespa (0.40 #8874, 0.25 #4610, 0.10 #31624), Korab (0.40 #9383, 0.25 #5119, 0.10 #32133), LakeOhrid (0.40 #9284, 0.25 #5020, 0.10 #32034), BlackDrin (0.40 #8823, 0.25 #4559, 0.10 #31573), NorthSea (0.33 #27042, 0.25 #5709, 0.20 #31302), SaintLucia (0.33 #2565, 0.11 #16777, 0.11 #15351), IndianOcean (0.31 #38389, 0.31 #18488, 0.21 #24178)
>> best conf = 0.66
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #42734 for best value:
>> intensional similarity = 14
>> extensional distance = 36
>> proper extension:  TOK;
>> query: (?x667, PacificOcean) <- ?x667[ has encompassed ?x521; has ethnicGroup ?x1757[ a EthnicGroup;]; has religion ?x95[ is religion of ?x575<NL>;]; is locatedIn of ?x317[ has locatedIn ?x783<PA>; has locatedIn ?x1502[ has government ?x1503;]; has mergesWith ?x1371[ is flowsInto of ?x361;]; is locatedInWater of ?x123;];]

No rule for expected values
ranks of expected_values: 
EVAL	TT	locatedIn!	LaBreaPitchLake	CNN-1.+1._MA	0.000	0.000	0.000	0.000	79.000	79.000	1424.000	0.658	http://www.semwebtech.org/mondial/10/meta#locatedIn

#966-BG
PRED entity: BG

PRED relation: encompassed
PRED expected values: Europe
=> 36 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 5): Europe (0.89 #17, 0.81 #36, 0.81 #32), Asia (0.80 #107, 0.34 #67, 0.25 #118), America (0.40 #56, 0.38 #101, 0.36 #127), Africa (0.34 #70, 0.32 #105, 0.30 #65), Australia-Oceania (0.14 #99, 0.13 #165, 0.12 #125)
>> best conf = 0.89
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #17 for best value:
>> intensional similarity = 6
>> extensional distance = 16
>> proper extension:  MNE; D; HR; PL; CH; UA; KAZ; A; SLO; L;
>> query: (?x177, Europe) <- ?x177[ has ethnicGroup ?x164[ is ethnicGroup of ?x236<H>;]; has neighbor ?x185; has religion ?x56; is locatedIn of ?x98;]
ranks of expected_values: 1
EVAL	BG	encompassed	Europe	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	36.000	36.000	5.000	0.889	http://www.semwebtech.org/mondial/10/meta#encompassed

PRED relation: encompassed
PRED expected values: Europe
=> 98 concepts (98 used for prediction)
PRED predicted values (max 10 best out of 5): Europe (0.90 #176, 0.89 #73, 0.88 #63), Asia (0.81 #219, 0.81 #465, 0.80 #287), America (0.43 #97, 0.42 #413, 0.42 #229), Australia-Oceania (0.39 #126, 0.37 #121, 0.20 #337), Africa (0.38 #223, 0.33 #132, 0.31 #427)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #176 for best value:
>> intensional similarity = 12
>> extensional distance = 44
>> proper extension:  NAM; PNG;
>> query: (?x177, ?x195) <- ?x177[ a Country; has language ?x511; has religion ?x56; has wasDependentOf ?x1656; is locatedIn of ?x98; is neighbor of ?x904[ has encompassed ?x195; has ethnicGroup ?x164; has religion ?x95<Protestant>; is locatedIn of ?x133[ is flowsInto of ?x475;];];]
ranks of expected_values: 1
EVAL	BG	encompassed	Europe	CNN-1.+1._MA	1.000	1.000	1.000	1.000	98.000	98.000	5.000	0.896	http://www.semwebtech.org/mondial/10/meta#encompassed

#965-AUS
PRED entity: AUS

PRED relation: locatedIn!
PRED expected values: SnowyRiver MurrayRiver SnowyRiver MurrumbidgeeRiver
=> 46 concepts (41 used for prediction)
PRED predicted values (max 10 best out of 1396): MurrayRiver (0.90 #34815, 0.71 #9746, 0.69 #34813), SnowyRiver (0.90 #34815, 0.71 #9746, 0.69 #34813), SnowyRiver (0.50 #34814), AtlanticOcean (0.44 #25105, 0.39 #52955, 0.38 #55741), NorthSea (0.40 #21, 0.27 #9767, 0.26 #13944), SouthChinaSea (0.35 #15453, 0.20 #138, 0.16 #12669), MediterraneanSea (0.29 #1475, 0.24 #23754, 0.22 #20969), RedSea (0.29 #2251, 0.15 #6427, 0.07 #18958), PersianGulf (0.29 #1843, 0.08 #42226, 0.08 #43619), Jordan (0.29 #1549, 0.08 #32183, 0.06 #30790)
>> best conf = 0.90
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #34815 for best value:
>> intensional similarity = 8
>> extensional distance = 66
>> proper extension:  SSD;
>> query: (?x196, ?x1041) <- ?x196[ has government ?x1903; is locatedIn of ?x413[ a River;]; is locatedIn of ?x1997[ has flowsInto ?x1356;]; is locatedIn of ?x2381[ is hasEstuary of ?x1041[ has hasSource ?x1782;];];]
ranks of expected_values: 1, 2, 3
EVAL	AUS	locatedIn!	MurrumbidgeeRiver	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	46.000	41.000	1396.000	0.902	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	AUS	locatedIn!	SnowyRiver	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	46.000	41.000	1396.000	0.902	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	AUS	locatedIn!	MurrayRiver	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	46.000	41.000	1396.000	0.902	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	AUS	locatedIn!	SnowyRiver	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	46.000	41.000	1396.000	0.902	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: SnowyRiver MurrayRiver SnowyRiver MurrumbidgeeRiver
=> 130 concepts (128 used for prediction)
PRED predicted values (max 10 best out of 1412): MurrayRiver (0.94 #73847, 0.94 #73846, 0.92 #91953), SnowyRiver (0.94 #73847, 0.94 #73846, 0.91 #150488), AtlanticOcean (0.67 #89208, 0.60 #20935, 0.59 #73888), MurrumbidgeeRiver (0.61 #23680), Donau (0.57 #39034, 0.50 #50178, 0.42 #57148), CaribbeanSea (0.57 #33538, 0.36 #53043, 0.33 #100417), CaspianSea (0.50 #28579, 0.38 #43907, 0.29 #38335), MediterraneanSea (0.50 #8439, 0.33 #1475, 0.31 #58598), ArcticOcean (0.50 #15398, 0.33 #4252, 0.25 #43261), BeringSea (0.50 #15706, 0.33 #4560, 0.25 #43569)
>> best conf = 0.94
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #73847 for best value:
>> intensional similarity = 13
>> extensional distance = 15
>> proper extension:  YV;
>> query: (?x196, ?x1041) <- ?x196[ a Country; has encompassed ?x211; is locatedIn of ?x60[ is locatedInWater of ?x226; is mergesWith of ?x182;]; is locatedIn of ?x371[ a Source; has inMountains ?x372;]; is locatedIn of ?x1103[ a Mountain;]; is locatedIn of ?x2381[ a Estuary; is hasEstuary of ?x1041;];]
>> Best rule #73846 for best value:
>> intensional similarity = 13
>> extensional distance = 15
>> proper extension:  YV;
>> query: (?x196, ?x1356) <- ?x196[ a Country; has encompassed ?x211; is locatedIn of ?x60[ is locatedInWater of ?x226; is mergesWith of ?x182;]; is locatedIn of ?x371[ a Source; has inMountains ?x372;]; is locatedIn of ?x1103[ a Mountain;]; is locatedIn of ?x2049[ a Estuary; is hasEstuary of ?x1356;];]
ranks of expected_values: 1, 2, 4
EVAL	AUS	locatedIn!	MurrumbidgeeRiver	CNN-1.+1._MA	0.000	1.000	1.000	0.500	130.000	128.000	1412.000	0.936	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	AUS	locatedIn!	SnowyRiver	CNN-1.+1._MA	0.000	0.000	0.000	0.000	130.000	128.000	1412.000	0.936	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	AUS	locatedIn!	MurrayRiver	CNN-1.+1._MA	1.000	1.000	1.000	1.000	130.000	128.000	1412.000	0.936	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	AUS	locatedIn!	SnowyRiver	CNN-1.+1._MA	1.000	1.000	1.000	1.000	130.000	128.000	1412.000	0.936	http://www.semwebtech.org/mondial/10/meta#locatedIn

#964-Zambezi
PRED entity: Zambezi

PRED relation: hasEstuary!
PRED expected values: Zambezi
=> 33 concepts (27 used for prediction)
PRED predicted values (max 10 best out of 141): Chire (0.25 #212, 0.12 #438, 0.10 #665), Limpopo (0.25 #11, 0.12 #237, 0.10 #464), Asahan (0.12 #276, 0.08 #729, 0.02 #1637), Ganges (0.12 #369, 0.08 #822, 0.01 #2411), Shabelle (0.12 #437, 0.08 #890), Jubba (0.12 #303, 0.08 #756), MurrayRiver (0.08 #836), EucumbeneRiver (0.08 #819), SnowyRiver (0.08 #794), DarlingRiver (0.08 #713)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #212 for best value:
>> intensional similarity = 3
>> extensional distance = 2
>> proper extension:  Limpopo; Chire;
>> query: (?x1908, Chire) <- ?x1908[ a Estuary; has locatedIn ?x192<MOC>;]

No rule for expected values
ranks of expected_values: 
EVAL	Zambezi	hasEstuary!	Zambezi	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	33.000	27.000	141.000	0.250	http://www.semwebtech.org/mondial/10/meta#hasEstuary

PRED relation: hasEstuary!
PRED expected values: Zambezi
=> 113 concepts (113 used for prediction)
PRED predicted values (max 10 best out of 197): Chire (0.25 #212, 0.20 #438, 0.19 #6156), Limpopo (0.25 #11, 0.20 #237, 0.19 #6156), Luapula (0.20 #446, 0.12 #903, 0.04 #4776), LakeMalawi (0.19 #6156, 0.11 #453, 0.11 #910), LakeCabora-Bassa (0.19 #6156, 0.11 #453, 0.11 #910), IndianOcean (0.16 #6155, 0.11 #453, 0.11 #910), Zambezi (0.16 #6155, 0.06 #9574, 0.05 #12545), Oranje (0.14 #459, 0.06 #2052, 0.05 #2281), Vaal (0.14 #571, 0.06 #2164, 0.03 #683), Okavango (0.14 #620, 0.05 #3125, 0.01 #7235)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #212 for best value:
>> intensional similarity = 3
>> extensional distance = 2
>> proper extension:  Limpopo; Chire;
>> query: (?x1908, Chire) <- ?x1908[ a Estuary; has locatedIn ?x192<MOC>;]

*> Best rule #6155 for first EXPECTED value:
*> intensional similarity = 13
*> extensional distance = 31
*> proper extension:  Thjorsa; JoekulsaaFjoellum;
*> query: (?x1908, ?x1977) <- ?x1908[ a Estuary; has locatedIn ?x192[ a Country; has encompassed ?x213; has ethnicGroup ?x197; has government ?x435; is locatedIn of ?x60[ is flowsInto of ?x750; is locatedInWater of ?x226; is mergesWith of ?x182<AtlanticOcean>;]; is locatedIn of ?x387[ has flowsInto ?x1977;];];]
*> conf = 0.16
ranks of expected_values: 7
EVAL	Zambezi	hasEstuary!	Zambezi	CNN-1.+1._MA	0.000	0.000	1.000	0.143	113.000	113.000	197.000	0.250	http://www.semwebtech.org/mondial/10/meta#hasEstuary

#963-NIUE
PRED entity: NIUE

PRED relation: wasDependentOf
PRED expected values: NZ
=> 40 concepts (40 used for prediction)
PRED predicted values (max 10 best out of 37): E (0.35 #181, 0.25 #36, 0.22 #65), GB (0.30 #149, 0.28 #512, 0.26 #542), UnitedNations (0.20 #160, 0.19 #102, 0.12 #131), F (0.12 #418, 0.10 #1094, 0.10 #998), SovietUnion (0.12 #466, 0.08 #284, 0.07 #801), NL (0.11 #76, 0.06 #134, 0.06 #105), Yugoslavia (0.07 #317, 0.06 #347, 0.06 #377), OttomanEmpire (0.05 #230, 0.04 #319, 0.04 #1056), CO (0.04 #183, 0.04 #1056, 0.02 #213), P (0.04 #1056, 0.03 #835, 0.03 #438)
>> best conf = 0.35
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #181 for best value:
>> intensional similarity = 8
>> extensional distance = 21
>> proper extension:  TOK;
>> query: (?x550, E) <- ?x550[ a Country; has ethnicGroup ?x1335; has religion ?x352<RomanCatholic>; has religion ?x551[ a Religion;]; is locatedIn of ?x282<PacificOcean>;]

No rule for expected values
ranks of expected_values: 
EVAL	NIUE	wasDependentOf	NZ	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	40.000	40.000	37.000	0.348	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

PRED relation: wasDependentOf
PRED expected values: NZ
=> 71 concepts (71 used for prediction)
PRED predicted values (max 10 best out of 95): E (0.39 #355, 0.36 #422, 0.35 #387), GB (0.33 #316, 0.33 #4, 0.30 #861), UnitedNations (0.27 #298, 0.25 #79, 0.20 #142), SovietUnion (0.14 #816, 0.13 #807, 0.10 #2000), F (0.14 #816, 0.12 #1316, 0.12 #1254), NL (0.14 #816, 0.10 #2000, 0.10 #2285), CO (0.14 #816, 0.10 #2000, 0.10 #2285), Yugoslavia (0.07 #1009, 0.06 #944, 0.06 #1172), NZ (0.07 #1686, 0.06 #1441, 0.05 #853), AUS (0.07 #1686, 0.06 #1441, 0.01 #1718)
>> best conf = 0.39
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #355 for best value:
>> intensional similarity = 21
>> extensional distance = 16
>> proper extension:  PE;
>> query: (?x550, E) <- ?x550[ has encompassed ?x211; has ethnicGroup ?x1335; has religion ?x352[ is religion of ?x176<RO>; is religion of ?x207<I>; is religion of ?x315<USA>; is religion of ?x461<NZ>; is religion of ?x899<PR>; is religion of ?x904<SRB>; is religion of ?x1276<AMSA>;]; is locatedIn of ?x282<PacificOcean>; is locatedIn of ?x583[ has type ?x1402;];]

*> Best rule #1686 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 130
*> proper extension:  ARU;
*> query: (?x550, ?x81) <- ?x550[ a Country; has encompassed ?x211; has religion ?x1547[ a Religion;]; is locatedIn of ?x282[ has locatedIn ?x428[ has dependentOf ?x81; has religion ?x429;];];]
*> conf = 0.07
ranks of expected_values: 9
EVAL	NIUE	wasDependentOf	NZ	CNN-1.+1._MA	0.000	0.000	1.000	0.111	71.000	71.000	95.000	0.389	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

#962-NLSM
PRED entity: NLSM

PRED relation: encompassed
PRED expected values: America
=> 42 concepts (42 used for prediction)
PRED predicted values (max 10 best out of 5): America (0.94 #80, 0.89 #50, 0.85 #35), Europe (0.43 #117, 0.42 #102, 0.39 #122), Australia-Oceania (0.33 #18, 0.31 #93, 0.27 #63), Asia (0.33 #1, 0.20 #171, 0.20 #161), Africa (0.26 #109, 0.22 #184, 0.22 #199)
>> best conf = 0.94
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #80 for best value:
>> intensional similarity = 6
>> extensional distance = 29
>> proper extension:  WG;
>> query: (?x50, America) <- ?x50[ is locatedIn of ?x182[ has mergesWith ?x60; is flowsInto of ?x137; is locatedInWater of ?x112;]; is locatedIn of ?x317<CaribbeanSea>;]
ranks of expected_values: 1
EVAL	NLSM	encompassed	America	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	42.000	42.000	5.000	0.935	http://www.semwebtech.org/mondial/10/meta#encompassed

PRED relation: encompassed
PRED expected values: America
=> 94 concepts (94 used for prediction)
PRED predicted values (max 10 best out of 5): America (0.93 #263, 0.89 #207, 0.89 #196), Europe (0.66 #284, 0.43 #377, 0.40 #340), Africa (0.37 #395, 0.36 #486, 0.36 #364), Australia-Oceania (0.36 #364, 0.35 #413, 0.35 #414), Asia (0.36 #364, 0.35 #413, 0.35 #414)
>> best conf = 0.93
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #263 for best value:
>> intensional similarity = 21
>> extensional distance = 28
>> proper extension:  WG;
>> query: (?x50, America) <- ?x50[ a Country; has government ?x2058; is locatedIn of ?x182[ has locatedIn ?x407<AXA>; has locatedIn ?x455[ has wasDependentOf ?x793;]; has locatedIn ?x520<DOM>; has locatedIn ?x899<PR>; is flowsInto of ?x137; is locatedInWater of ?x817<Montserrat>; is locatedInWater of ?x1753[ a Island; has type ?x150;]; is locatedInWater of ?x1928<Cuba>;]; is locatedIn of ?x317<CaribbeanSea>;]
ranks of expected_values: 1
EVAL	NLSM	encompassed	America	CNN-1.+1._MA	1.000	1.000	1.000	1.000	94.000	94.000	5.000	0.933	http://www.semwebtech.org/mondial/10/meta#encompassed

#961-ErdiEnnedi
PRED entity: ErdiEnnedi

PRED relation: locatedIn
PRED expected values: TCH
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 49): AUS (0.13 #45, 0.01 #281), USA (0.11 #72, 0.05 #308), DZ (0.11 #135), CN (0.10 #56, 0.02 #292), LAR (0.06 #206), RIM (0.06 #119), KAZ (0.06 #93), RMM (0.05 #176), SA (0.05 #162), MEX (0.05 #116)
>> best conf = 0.13
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #45 for best value:
>> intensional similarity = 1
>> extensional distance = 61
>> proper extension:  Rigestan; Atacama; Negev; Karakum; Ordos; GreatSandyDesert; ErgIgidi; Namib; Darfur; RubAlChali; ...
>> query: (?x2529, AUS) <- ?x2529[ a Desert;]

*> Best rule #33 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 61
*> proper extension:  Rigestan; Atacama; Negev; Karakum; Ordos; GreatSandyDesert; ErgIgidi; Namib; Darfur; RubAlChali; ...
*> query: (?x2529, TCH) <- ?x2529[ a Desert;]
*> conf = 0.03
ranks of expected_values: 17
EVAL	ErdiEnnedi	locatedIn	TCH	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.059	2.000	2.000	49.000	0.127	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: TCH
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 49): AUS (0.13 #45, 0.01 #281), USA (0.11 #72, 0.05 #308), DZ (0.11 #135), CN (0.10 #56, 0.02 #292), LAR (0.06 #206), RIM (0.06 #119), KAZ (0.06 #93), RMM (0.05 #176), SA (0.05 #162), MEX (0.05 #116)
>> best conf = 0.13
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #45 for best value:
>> intensional similarity = 1
>> extensional distance = 61
>> proper extension:  Rigestan; Atacama; Negev; Karakum; Ordos; GreatSandyDesert; ErgIgidi; Namib; Darfur; RubAlChali; ...
>> query: (?x2529, AUS) <- ?x2529[ a Desert;]

*> Best rule #33 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 61
*> proper extension:  Rigestan; Atacama; Negev; Karakum; Ordos; GreatSandyDesert; ErgIgidi; Namib; Darfur; RubAlChali; ...
*> query: (?x2529, TCH) <- ?x2529[ a Desert;]
*> conf = 0.03
ranks of expected_values: 17
EVAL	ErdiEnnedi	locatedIn	TCH	CNN-1.+1._MA	0.000	0.000	0.000	0.059	2.000	2.000	49.000	0.127	http://www.semwebtech.org/mondial/10/meta#locatedIn

#960-Chire
PRED entity: Chire

PRED relation: locatedIn
PRED expected values: MW
=> 41 concepts (41 used for prediction)
PRED predicted values (max 10 best out of 80): MOC (0.60 #6640, 0.57 #8542, 0.57 #1182), MW (0.60 #6640, 0.57 #1182, 0.56 #8305), ZRE (0.50 #552, 0.50 #316, 0.50 #79), EAT (0.50 #5215, 0.36 #9019, 0.33 #9018), EAU (0.36 #9019, 0.35 #7115, 0.33 #9018), RWA (0.36 #9019, 0.35 #7115, 0.33 #9018), EAK (0.36 #9019, 0.33 #9018, 0.31 #4029), PE (0.33 #1249, 0.32 #1012, 0.20 #1958), Z (0.25 #6641, 0.23 #6878, 0.21 #1891), ZW (0.25 #6641, 0.23 #6878, 0.21 #1891)
>> best conf = 0.60
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #6640 for best value:
>> intensional similarity = 7
>> extensional distance = 99
>> proper extension:  Selenge; Prypjat; Murat; Klaraelv; Kama;
>> query: (?x2154, ?x192) <- ?x2154[ a Source; is hasSource of ?x2061[ a River; has flowsInto ?x1977[ has flowsInto ?x60; has locatedIn ?x138;]; has locatedIn ?x192;];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 2
EVAL	Chire	locatedIn	MW	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	41.000	41.000	80.000	0.597	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: MW
=> 108 concepts (108 used for prediction)
PRED predicted values (max 10 best out of 80): MOC (0.87 #8184, 0.72 #9144, 0.67 #4575), MW (0.80 #8182, 0.71 #5533, 0.70 #9142), CDN (0.71 #5358, 0.67 #8007, 0.50 #8967), ANG (0.67 #4524, 0.33 #673, 0.23 #7168), EAT (0.61 #9383, 0.60 #3852, 0.60 #3851), ZRE (0.55 #6012, 0.54 #6257, 0.50 #6822), EAU (0.55 #6012, 0.54 #6257, 0.42 #21887), RWA (0.55 #6012, 0.54 #6257, 0.41 #6259), Z (0.50 #1688, 0.40 #3012, 0.33 #3850), BR (0.50 #2053, 0.33 #6629, 0.29 #5180)
>> best conf = 0.87
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #8184 for best value:
>> intensional similarity = 17
>> extensional distance = 13
>> proper extension:  MackenzieRiver;
>> query: (?x2154, ?x192) <- ?x2154[ a Source; is hasSource of ?x2061[ a River; has locatedIn ?x192[ a Country; has encompassed ?x213; has ethnicGroup ?x197<European>; has government ?x435; has religion ?x187<Muslim>; has wasDependentOf ?x1027;]; has locatedIn ?x819[ a Country; has religion ?x352<RomanCatholic>; has wasDependentOf ?x81;];];]

*> Best rule #8182 for first EXPECTED value:
*> intensional similarity = 17
*> extensional distance = 13
*> proper extension:  MackenzieRiver;
*> query: (?x2154, ?x819) <- ?x2154[ a Source; is hasSource of ?x2061[ a River; has locatedIn ?x192[ a Country; has encompassed ?x213; has ethnicGroup ?x197<European>; has government ?x435; has religion ?x187<Muslim>; has wasDependentOf ?x1027;]; has locatedIn ?x819[ a Country; has religion ?x352<RomanCatholic>; has wasDependentOf ?x81;];];]
*> conf = 0.80
ranks of expected_values: 2
EVAL	Chire	locatedIn	MW	CNN-1.+1._MA	0.000	1.000	1.000	0.500	108.000	108.000	80.000	0.867	http://www.semwebtech.org/mondial/10/meta#locatedIn

#959-NiagaraRiver
PRED entity: NiagaraRiver

PRED relation: hasSource!
PRED expected values: NiagaraRiver
=> 28 concepts (25 used for prediction)
PRED predicted values (max 10 best out of 258): SaintLawrenceRiver (0.20 #150, 0.08 #378, 0.06 #607), SaintMarysRiver (0.20 #51, 0.08 #279, 0.06 #508), DetroitRiver (0.20 #14, 0.08 #242, 0.06 #471), ColumbiaRiver (0.08 #438, 0.04 #4363, 0.02 #4362), YukonRiver (0.08 #305, 0.04 #4363, 0.02 #4362), RiviereRichelieu (0.08 #449, 0.04 #4363, 0.02 #4362), MackenzieRiver (0.08 #443, 0.04 #4363, 0.02 #4364), SaskatchewanRiver (0.08 #419, 0.02 #4362, 0.02 #4364), Manicouagan (0.08 #376, 0.02 #4362, 0.02 #4364), NelsonRiver (0.08 #343, 0.02 #4364, 0.02 #4133)
>> best conf = 0.20
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #150 for best value:
>> intensional similarity = 5
>> extensional distance = 3
>> proper extension:  SaintMarysRiver; SaintLawrenceRiver; DetroitRiver;
>> query: (?x895, SaintLawrenceRiver) <- ?x895[ a Source; has locatedIn ?x272<CDN>; has locatedIn ?x315<USA>;]

*> Best rule #4363 for first EXPECTED value:
*> intensional similarity = 4
*> extensional distance = 229
*> proper extension:  Bahrel-Ghasal; Sobat; Oder; Reuss; Bartang; Inn; RioDesaguadero; Paatsjoki; Pjandsh; Baro; ...
*> query: (?x895, ?x182) <- ?x895[ a Source; has locatedIn ?x315[ is locatedIn of ?x182[ is flowsInto of ?x137;];];]
*> conf = 0.04
ranks of expected_values: 25
EVAL	NiagaraRiver	hasSource!	NiagaraRiver	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.040	28.000	25.000	258.000	0.200	http://www.semwebtech.org/mondial/10/meta#hasSource

PRED relation: hasSource!
PRED expected values: NiagaraRiver
=> 92 concepts (92 used for prediction)
PRED predicted values (max 10 best out of 297): SaintMarysRiver (0.20 #51, 0.07 #7578, 0.07 #7577), SaintLawrenceRiver (0.20 #150, 0.07 #7578, 0.07 #7577), DetroitRiver (0.20 #14, 0.07 #7578, 0.07 #7577), MerrimackRiver (0.07 #7578, 0.07 #7577, 0.07 #5048), RioGrande (0.07 #7578, 0.07 #7577, 0.07 #5048), HudsonRiver (0.07 #7578, 0.07 #7577, 0.07 #5048), Colorado (0.07 #7578, 0.07 #7577, 0.07 #5048), ConnecticutRiver (0.07 #7578, 0.07 #7577, 0.07 #5048), StraitsofMackinac (0.07 #7578, 0.07 #7577, 0.07 #5048), AlleghenyRiver (0.07 #7578, 0.07 #7577, 0.07 #5048)
>> best conf = 0.20
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #51 for best value:
>> intensional similarity = 5
>> extensional distance = 3
>> proper extension:  SaintMarysRiver; SaintLawrenceRiver; DetroitRiver;
>> query: (?x895, SaintMarysRiver) <- ?x895[ a Source; has locatedIn ?x272<CDN>; has locatedIn ?x315<USA>;]

*> Best rule #7578 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 217
*> proper extension:  Bahrel-Ghasal; Sobat; WhiteNile; Pibor;
*> query: (?x895, ?x2118) <- ?x895[ a Source; has locatedIn ?x272[ a Country;]; has locatedIn ?x315[ has neighbor ?x482; is locatedIn of ?x2118[ a River;];];]
*> conf = 0.07
ranks of expected_values: 19
EVAL	NiagaraRiver	hasSource!	NiagaraRiver	CNN-1.+1._MA	0.000	0.000	0.000	0.053	92.000	92.000	297.000	0.200	http://www.semwebtech.org/mondial/10/meta#hasSource

#958-LagodiBracciano
PRED entity: LagodiBracciano

PRED relation: locatedIn
PRED expected values: I
=> 5 concepts (5 used for prediction)
PRED predicted values (max 10 best out of 66): RI (0.25 #52, 0.06 #760, 0.03 #288), I (0.25 #48, 0.04 #756, 0.03 #520), CAM (0.12 #123, 0.05 #359, 0.02 #595), D (0.12 #20, 0.03 #492, 0.03 #964), USA (0.11 #544, 0.10 #308, 0.08 #780), CDN (0.08 #535, 0.03 #1007, 0.02 #299), AUS (0.07 #281, 0.03 #517, 0.03 #753), R (0.06 #477, 0.04 #949, 0.02 #241), EAT (0.05 #411, 0.04 #647, 0.02 #883), CN (0.05 #292, 0.03 #528, 0.02 #1000)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #52 for best value:
>> intensional similarity = 3
>> extensional distance = 6
>> proper extension:  LakeNyos; LagodiBolsena; LagoTrasimeno; LakeToba; SegaraAnak; LaacherMaar;
>> query: (?x2427, RI) <- ?x2427[ a Lake; has type ?x287<"caldera">;]

*> Best rule #48 for first EXPECTED value:
*> intensional similarity = 3
*> extensional distance = 6
*> proper extension:  LakeNyos; LagodiBolsena; LagoTrasimeno; LakeToba; SegaraAnak; LaacherMaar;
*> query: (?x2427, I) <- ?x2427[ a Lake; has type ?x287<"caldera">;]
*> conf = 0.25
ranks of expected_values: 2
EVAL	LagodiBracciano	locatedIn	I	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	5.000	5.000	66.000	0.250	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: I
=> 5 concepts (5 used for prediction)
PRED predicted values (max 10 best out of 66): RI (0.25 #52, 0.06 #760, 0.03 #288), I (0.25 #48, 0.04 #756, 0.03 #520), CAM (0.12 #123, 0.05 #359, 0.02 #595), D (0.12 #20, 0.03 #492, 0.03 #964), USA (0.11 #544, 0.10 #308, 0.08 #780), CDN (0.08 #535, 0.03 #1007, 0.02 #299), AUS (0.07 #281, 0.03 #517, 0.03 #753), R (0.06 #477, 0.04 #949, 0.02 #241), EAT (0.05 #411, 0.04 #647, 0.02 #883), CN (0.05 #292, 0.03 #528, 0.02 #1000)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #52 for best value:
>> intensional similarity = 3
>> extensional distance = 6
>> proper extension:  LakeNyos; LagodiBolsena; LagoTrasimeno; LakeToba; SegaraAnak; LaacherMaar;
>> query: (?x2427, RI) <- ?x2427[ a Lake; has type ?x287<"caldera">;]

*> Best rule #48 for first EXPECTED value:
*> intensional similarity = 3
*> extensional distance = 6
*> proper extension:  LakeNyos; LagodiBolsena; LagoTrasimeno; LakeToba; SegaraAnak; LaacherMaar;
*> query: (?x2427, I) <- ?x2427[ a Lake; has type ?x287<"caldera">;]
*> conf = 0.25
ranks of expected_values: 2
EVAL	LagodiBracciano	locatedIn	I	CNN-1.+1._MA	0.000	1.000	1.000	0.500	5.000	5.000	66.000	0.250	http://www.semwebtech.org/mondial/10/meta#locatedIn

#957-N
PRED entity: N

PRED relation: locatedIn!
PRED expected values: Paatsjoki Lagen
=> 44 concepts (39 used for prediction)
PRED predicted values (max 10 best out of 1365): AtlanticOcean (0.92 #7062, 0.40 #2849, 0.40 #1445), Paatsjoki (0.83 #4213, 0.33 #279, 0.10 #54773), TheChannel (0.40 #3459, 0.40 #2055, 0.12 #44941), Rhein (0.40 #2876, 0.20 #1472, 0.09 #12706), Mosel (0.40 #3151, 0.20 #1747, 0.07 #12981), Saar (0.40 #2909, 0.20 #1505, 0.05 #12739), PacificOcean (0.33 #84, 0.30 #15531, 0.29 #26767), ArcticOcean (0.33 #72, 0.17 #4285, 0.12 #44941), Irtysch (0.33 #984, 0.17 #5197, 0.08 #50560), BeringSea (0.33 #382, 0.17 #4595, 0.08 #50560)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #7062 for best value:
>> intensional similarity = 6
>> extensional distance = 24
>> proper extension:  NLSM; SPMI; BVIR; C; AXA; TUCA; BDS;
>> query: (?x170, AtlanticOcean) <- ?x170[ a Country; has language ?x1260; has religion ?x95; is locatedIn of ?x373[ is locatedInWater of ?x2103<Streymoy>;];]

*> Best rule #4213 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 3
*> proper extension:  D;
*> query: (?x170, ?x631) <- ?x170[ a Country; has religion ?x95; is locatedIn of ?x121<NorthSea>; is locatedIn of ?x612[ a Lake;]; is locatedIn of ?x632[ is hasEstuary of ?x631;];]
*> conf = 0.83
ranks of expected_values: 2
EVAL	N	locatedIn!	Lagen	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	44.000	39.000	1365.000	0.923	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	N	locatedIn!	Paatsjoki	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	44.000	39.000	1365.000	0.923	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Paatsjoki Lagen
=> 93 concepts (93 used for prediction)
PRED predicted values (max 10 best out of 1422): AtlanticOcean (0.90 #59190, 0.87 #25381, 0.60 #11297), PacificOcean (0.88 #39513, 0.60 #8524, 0.50 #4299), MediterraneanSea (0.73 #45143, 0.45 #32466, 0.41 #36693), Vaenern (0.70 #15474, 0.21 #57742, 0.19 #4215), Klaraelv (0.59 #52100, 0.59 #53512, 0.36 #7032), Lagen (0.59 #52100, 0.59 #53512, 0.36 #7032), WesternBug (0.50 #16940, 0.40 #14123, 0.33 #15532), ArcticOcean (0.50 #108437, 0.34 #57744, 0.32 #123928), EastSibirianSea (0.50 #108437, 0.34 #57744, 0.32 #123928), SeaofOkhotsk (0.50 #108437, 0.34 #57744, 0.32 #123928)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #59190 for best value:
>> intensional similarity = 13
>> extensional distance = 40
>> proper extension:  WV; C; IRL; KN; BS; ROU; CV; BDS; AG; WL;
>> query: (?x170, AtlanticOcean) <- ?x170[ has wasDependentOf ?x402[ a Country; is locatedIn of ?x191;]; is locatedIn of ?x121[ is flowsInto of ?x829; is locatedInWater of ?x495<GreatBritain>;]; is locatedIn of ?x251[ has locatedIn ?x73[ is neighbor of ?x194;]; has mergesWith ?x263;]; is locatedIn of ?x373[ a Sea;];]

*> Best rule #52100 for first EXPECTED value:
*> intensional similarity = 12
*> extensional distance = 33
*> proper extension:  NAM; TCH; MOC; USA; ZRE; SYR; ETH; MEX; Z; RCB; ...
*> query: (?x170, ?x2052) <- ?x170[ a Country; has encompassed ?x195; has neighbor ?x73[ is locatedIn of ?x1038[ a Source;]; is locatedIn of ?x1748[ a River; is flowsInto of ?x1761;]; is locatedIn of ?x2478[ a Desert;];]; is locatedIn of ?x2395[ has hasEstuary ?x2052;];]
*> conf = 0.59
ranks of expected_values: 6, 15
EVAL	N	locatedIn!	Lagen	CNN-1.+1._MA	0.000	0.000	1.000	0.167	93.000	93.000	1422.000	0.905	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	N	locatedIn!	Paatsjoki	CNN-1.+1._MA	0.000	0.000	0.000	0.071	93.000	93.000	1422.000	0.905	http://www.semwebtech.org/mondial/10/meta#locatedIn

#956-KWT
PRED entity: KWT

PRED relation: ethnicGroup
PRED expected values: Arab Iranian SouthAsian
=> 41 concepts (41 used for prediction)
PRED predicted values (max 10 best out of 235): Arab (0.43 #782, 0.40 #525, 0.40 #268), Emiri (0.33 #257, 0.20 #771, 0.20 #514), SouthAsian (0.33 #142, 0.20 #656, 0.20 #399), ArabIranian (0.33 #111, 0.20 #625, 0.20 #368), African (0.30 #2062, 0.24 #2576, 0.24 #4375), Chinese (0.30 #1043, 0.25 #1300, 0.21 #1814), European (0.28 #4377, 0.27 #6434, 0.26 #5148), Russian (0.21 #1872, 0.17 #2386, 0.16 #4185), Afro-Asian (0.20 #362, 0.19 #5912, 0.19 #6941), Indian (0.20 #1102, 0.17 #1359, 0.13 #1616)
>> best conf = 0.43
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #782 for best value:
>> intensional similarity = 8
>> extensional distance = 5
>> proper extension:  BRN;
>> query: (?x1963, Arab) <- ?x1963[ a Country; has encompassed ?x175; has ethnicGroup ?x2169[ a EthnicGroup;]; has religion ?x187<Muslim>; is locatedIn of ?x918<PersianGulf>;]
ranks of expected_values: 1, 3, 13
EVAL	KWT	ethnicGroup	SouthAsian	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	41.000	41.000	235.000	0.429	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	KWT	ethnicGroup	Iranian	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.091	41.000	41.000	235.000	0.429	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	KWT	ethnicGroup	Arab	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	41.000	41.000	235.000	0.429	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: Arab Iranian SouthAsian
=> 79 concepts (79 used for prediction)
PRED predicted values (max 10 best out of 250): Arab (0.50 #1817, 0.50 #1043, 0.50 #786), Armenian (0.40 #3446, 0.33 #4222, 0.33 #94), European (0.38 #10088, 0.33 #12157, 0.31 #11125), Chinese (0.36 #6214, 0.24 #9318, 0.19 #8540), African (0.33 #10086, 0.33 #5946, 0.25 #12155), Circassian (0.33 #78, 0.30 #775, 0.29 #2322), Pakistani (0.30 #775, 0.25 #1419, 0.25 #645), Iranian (0.30 #775, 0.25 #1333, 0.25 #559), Afro-Asian (0.30 #775, 0.25 #880, 0.25 #515), Kurdish (0.30 #775, 0.25 #2918, 0.25 #515)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1817 for best value:
>> intensional similarity = 14
>> extensional distance = 4
>> proper extension:  IR;
>> query: (?x1963, Arab) <- ?x1963[ a Country; has encompassed ?x175; has ethnicGroup ?x2169; has neighbor ?x302<IRQ>; has neighbor ?x751[ a Country; has language ?x1848[ is language of ?x304<IR>;]; has religion ?x187; is locatedIn of ?x1552[ a Sea; has mergesWith ?x2407;];];]
>> Best rule #1043 for best value:
>> intensional similarity = 22
>> extensional distance = 2
>> proper extension:  SYR;
>> query: (?x1963, Arab) <- ?x1963[ a Country; has ethnicGroup ?x2169; has neighbor ?x302<IRQ>; has neighbor ?x751[ a Country; has government ?x640; has language ?x1848; has religion ?x187; is locatedIn of ?x1552<RedSea>; is locatedIn of ?x1629[ a Desert;]; is neighbor of ?x174[ a Country; has encompassed ?x175;]; is neighbor of ?x668[ has religion ?x410; has wasDependentOf ?x2153;]; is neighbor of ?x803<JOR>;]; has wasDependentOf ?x81;]
>> Best rule #786 for best value:
>> intensional similarity = 20
>> extensional distance = 2
>> proper extension:  SA;
>> query: (?x1963, Arab) <- ?x1963[ a Country; has encompassed ?x175; has ethnicGroup ?x2169; has neighbor ?x302<IRQ>; has neighbor ?x751[ a Country; has government ?x640; has religion ?x187<Muslim>; is locatedIn of ?x1552[ is mergesWith of ?x2407;]; is neighbor of ?x107<UAE>; is neighbor of ?x174[ a Country; has wasDependentOf ?x81;]; is neighbor of ?x803[ has religion ?x116; is neighbor of ?x466;];];]
>> Best rule #268 for best value:
>> intensional similarity = 17
>> extensional distance = 2
>> proper extension:  Q;
>> query: (?x1963, Arab) <- ?x1963[ a Country; has ethnicGroup ?x2169; has neighbor ?x302[ a Country; has ethnicGroup ?x557; is locatedIn of ?x953<SyrianDesert>; is neighbor of ?x185[ a Country; is locatedIn of ?x98; is neighbor of ?x177;];]; has neighbor ?x751<SA>; has religion ?x187<Muslim>; has wasDependentOf ?x81; is locatedIn of ?x918;]
ranks of expected_values: 1, 8, 12
EVAL	KWT	ethnicGroup	SouthAsian	CNN-1.+1._MA	0.000	0.000	1.000	0.100	79.000	79.000	250.000	0.500	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	KWT	ethnicGroup	Iranian	CNN-1.+1._MA	0.000	0.000	1.000	0.143	79.000	79.000	250.000	0.500	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	KWT	ethnicGroup	Arab	CNN-1.+1._MA	1.000	1.000	1.000	1.000	79.000	79.000	250.000	0.500	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#955-Manaslu
PRED entity: Manaslu

PRED relation: locatedIn
PRED expected values: NEP
=> 10 concepts (10 used for prediction)
PRED predicted values (max 10 best out of 46): NEP (0.60 #17, 0.58 #473, 0.46 #476), CN (0.58 #473, 0.46 #476, 0.46 #475), IND (0.46 #476, 0.46 #475, 0.45 #715), PK (0.46 #476, 0.46 #475, 0.45 #715), TAD (0.20 #258, 0.03 #498, 0.03 #737), USA (0.15 #548, 0.15 #787, 0.15 #1025), KGZ (0.10 #259, 0.01 #499, 0.01 #738), I (0.06 #1237, 0.06 #1476, 0.06 #1949), E (0.06 #503, 0.06 #742, 0.06 #980), PE (0.06 #1256, 0.05 #1495, 0.05 #1968)
>> best conf = 0.60
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #17 for best value:
>> intensional similarity = 3
>> extensional distance = 8
>> proper extension:  Annapurna; Kangchendzonga; ChoOyu; NandaDevi; Dhaulagiri; MountEverest; NangaParbat; Makalu;
>> query: (?x308, NEP) <- ?x308[ a Mountain; has inMountains ?x309<Himalaya>;]
ranks of expected_values: 1
EVAL	Manaslu	locatedIn	NEP	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	10.000	10.000	46.000	0.600	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: NEP
=> 14 concepts (14 used for prediction)
PRED predicted values (max 10 best out of 47): NEP (0.60 #17, 0.58 #1193, 0.55 #473), CN (0.58 #1193, 0.55 #473, 0.55 #954), IND (0.51 #1194, 0.47 #953, 0.46 #475), PK (0.46 #1196, 0.45 #1676, 0.45 #1675), TAD (0.23 #1218, 0.20 #978, 0.03 #1458), USA (0.15 #1508, 0.15 #1748, 0.15 #1988), KGZ (0.10 #979, 0.08 #1219, 0.01 #1459), I (0.06 #2200, 0.06 #2440, 0.06 #2914), E (0.06 #1463, 0.06 #1703, 0.06 #1943), PE (0.06 #2219, 0.05 #2459, 0.05 #2933)
>> best conf = 0.60
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #17 for best value:
>> intensional similarity = 3
>> extensional distance = 8
>> proper extension:  Annapurna; Kangchendzonga; ChoOyu; NandaDevi; Dhaulagiri; MountEverest; NangaParbat; Makalu;
>> query: (?x308, NEP) <- ?x308[ a Mountain; has inMountains ?x309<Himalaya>;]
ranks of expected_values: 1
EVAL	Manaslu	locatedIn	NEP	CNN-1.+1._MA	1.000	1.000	1.000	1.000	14.000	14.000	47.000	0.600	http://www.semwebtech.org/mondial/10/meta#locatedIn

#954-Canares
PRED entity: Canares

PRED relation: belongsToIslands!
PRED expected values: LaPalma
=> 17 concepts (17 used for prediction)
PRED predicted values (max 10 best out of 237): LaPalma (0.34 #388, 0.24 #1356, 0.20 #194), Ibiza (0.34 #388, 0.24 #1356, 0.20 #194), LagunadeGallocanta (0.34 #388, 0.24 #1356, 0.20 #194), Vignemale (0.34 #388, 0.24 #1356, 0.20 #194), Guadalquivir (0.34 #388, 0.24 #1356, 0.20 #194), Douro (0.34 #388, 0.24 #1356, 0.20 #194), Mulhacen (0.34 #388, 0.24 #1356, 0.20 #194), RoquedelosMuchachos (0.34 #388, 0.24 #1356, 0.20 #194), PicodeAlmanzor (0.34 #388, 0.24 #1356, 0.20 #194), Guadiana (0.34 #388, 0.24 #1356, 0.20 #194)
>> best conf = 0.34
=> the first rule below is the first best rule for 28 predicted values
>> Best rule #388 for best value:
>> intensional similarity = 11
>> extensional distance = 6
>> proper extension:  BermudaIslands;
>> query: (?x1068, ?x68) <- ?x1068[ a Islands; is belongsToIslands of ?x1020[ a Island; has locatedIn ?x149[ is locatedIn of ?x68;];]; is belongsToIslands of ?x1067[ has type ?x150;]; is belongsToIslands of ?x1772[ a Island; has locatedInWater ?x182<AtlanticOcean>;];]
ranks of expected_values: 1
EVAL	Canares	belongsToIslands!	LaPalma	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	17.000	17.000	237.000	0.344	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

PRED relation: belongsToIslands!
PRED expected values: LaPalma
=> 50 concepts (50 used for prediction)
PRED predicted values (max 10 best out of 237): Ibiza (0.42 #195, 0.33 #576, 0.31 #3498), LaPalma (0.42 #195, 0.31 #3498, 0.31 #3887), LagunadeGallocanta (0.42 #195, 0.31 #3498, 0.31 #3887), Vignemale (0.42 #195, 0.31 #3498, 0.31 #3887), Guadalquivir (0.42 #195, 0.31 #3498, 0.31 #3887), Douro (0.42 #195, 0.31 #3498, 0.31 #3887), Mulhacen (0.42 #195, 0.31 #3498, 0.31 #3887), RoquedelosMuchachos (0.42 #195, 0.31 #3498, 0.31 #3887), PicodeAlmanzor (0.42 #195, 0.31 #3498, 0.31 #3887), Guadiana (0.42 #195, 0.31 #3498, 0.31 #3887)
>> best conf = 0.42
=> the first rule below is the first best rule for 28 predicted values
>> Best rule #195 for best value:
>> intensional similarity = 17
>> extensional distance = 1
>> proper extension:  Japan;
>> query: (?x1068, ?x68) <- ?x1068[ a Islands; is belongsToIslands of ?x1661[ a Island; has locatedIn ?x149[ has encompassed ?x195; has ethnicGroup ?x2540; has government ?x1657; has language ?x790; has religion ?x352; is locatedIn of ?x68; is wasDependentOf of ?x148;]; has type ?x150; is locatedOnIsland of ?x1166;]; is belongsToIslands of ?x1861[ a Island; has locatedInWater ?x182[ has locatedIn ?x50;];];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 2
EVAL	Canares	belongsToIslands!	LaPalma	CNN-1.+1._MA	0.000	1.000	1.000	0.500	50.000	50.000	237.000	0.417	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

#953-AND
PRED entity: AND

PRED relation: ethnicGroup
PRED expected values: African Andorran
=> 35 concepts (35 used for prediction)
PRED predicted values (max 10 best out of 233): European (0.89 #1284, 0.70 #1794, 0.67 #1539), Mestizo (0.52 #1567, 0.52 #1822, 0.50 #1312), Amerindian (0.44 #1278, 0.43 #1533, 0.39 #1788), African (0.44 #1282, 0.35 #1792, 0.29 #1537), German (0.35 #2052, 0.31 #1031, 0.23 #2817), MediterraneanNordic (0.33 #253, 0.16 #6126, 0.14 #6383), Russian (0.30 #2369, 0.23 #2879, 0.22 #3644), Polish (0.26 #2245, 0.19 #2500, 0.16 #3010), Hungarian (0.26 #2066, 0.14 #2831, 0.09 #2576), Ukrainian (0.26 #2298, 0.23 #2808, 0.22 #2043)
>> best conf = 0.89
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1284 for best value:
>> intensional similarity = 9
>> extensional distance = 16
>> proper extension:  C; ROU;
>> query: (?x789, European) <- ?x789[ has ethnicGroup ?x1672[ is ethnicGroup of ?x272<CDN>; is ethnicGroup of ?x297[ has dependentOf ?x78; is locatedIn of ?x282;];]; has language ?x796<Spanish>; has religion ?x352;]

*> Best rule #1282 for first EXPECTED value:
*> intensional similarity = 9
*> extensional distance = 16
*> proper extension:  C; ROU;
*> query: (?x789, African) <- ?x789[ has ethnicGroup ?x1672[ is ethnicGroup of ?x272<CDN>; is ethnicGroup of ?x297[ has dependentOf ?x78; is locatedIn of ?x282;];]; has language ?x796<Spanish>; has religion ?x352;]
*> conf = 0.44
ranks of expected_values: 4
EVAL	AND	ethnicGroup	Andorran	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	35.000	35.000	233.000	0.889	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	AND	ethnicGroup	African	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.250	35.000	35.000	233.000	0.889	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: African Andorran
=> 94 concepts (94 used for prediction)
PRED predicted values (max 10 best out of 237): European (0.82 #8690, 0.72 #9969, 0.67 #6646), Chinese (0.77 #7931, 0.60 #9465, 0.31 #9706), Mestizo (0.52 #6674, 0.48 #7440, 0.39 #8462), Italian (0.50 #1743, 0.43 #3531, 0.40 #2254), Amerindian (0.43 #6640, 0.36 #7406, 0.32 #8428), African (0.41 #13035, 0.36 #13547, 0.36 #8688), German (0.40 #2052, 0.33 #2308, 0.33 #1031), MediterraneanNordic (0.34 #9450, 0.33 #253, 0.30 #15588), BritishIsles (0.33 #698, 0.31 #9706, 0.21 #20450), Inuit (0.33 #524, 0.31 #9706, 0.21 #20450)
>> best conf = 0.82
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #8690 for best value:
>> intensional similarity = 13
>> extensional distance = 26
>> proper extension:  C; AUS; SLB; BDS;
>> query: (?x789, European) <- ?x789[ a Country; has encompassed ?x195; has ethnicGroup ?x1672[ a EthnicGroup; is ethnicGroup of ?x272<CDN>; is ethnicGroup of ?x297[ a Country; has dependentOf ?x78;]; is ethnicGroup of ?x439[ a Country; is locatedIn of ?x282;];]; has language ?x51;]

*> Best rule #13035 for first EXPECTED value:
*> intensional similarity = 10
*> extensional distance = 44
*> proper extension:  Q;
*> query: (?x789, African) <- ?x789[ a Country; has encompassed ?x195; has ethnicGroup ?x1672[ is ethnicGroup of ?x297[ has language ?x51;]; is ethnicGroup of ?x439[ has religion ?x713<Anglican>;];]; has neighbor ?x78; has religion ?x352;]
*> conf = 0.41
ranks of expected_values: 6
EVAL	AND	ethnicGroup	Andorran	CNN-1.+1._MA	0.000	0.000	0.000	0.000	94.000	94.000	237.000	0.821	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	AND	ethnicGroup	African	CNN-1.+1._MA	0.000	0.000	1.000	0.167	94.000	94.000	237.000	0.821	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#952-PuncakJaya
PRED entity: PuncakJaya

PRED relation: inMountains
PRED expected values: SudirmanRange
=> 41 concepts (41 used for prediction)
PRED predicted values (max 10 best out of 31): CanaryIslands (0.10 #230, 0.06 #752, 0.06 #839), Andes (0.08 #1316, 0.05 #1490, 0.04 #1925), Hawaii (0.08 #416, 0.08 #329, 0.07 #242), Alps (0.06 #1309, 0.05 #1483, 0.04 #1570), SnowyMountains (0.05 #978, 0.02 #1413, 0.02 #1674), Himalaya (0.05 #1311, 0.04 #1485, 0.03 #1572), RockyMountains (0.05 #1486, 0.04 #1573, 0.03 #1660), CordilleraVolcanica (0.04 #1370, 0.02 #1544), Azores (0.03 #214, 0.03 #388, 0.03 #301), Crete (0.03 #210, 0.03 #297, 0.02 #906)
>> best conf = 0.10
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #230 for best value:
>> intensional similarity = 4
>> extensional distance = 27
>> proper extension:  Etna; PicoBasile; Psiloritis;
>> query: (?x1807, CanaryIslands) <- ?x1807[ a Mountain; has locatedIn ?x217[ is neighbor of ?x376;]; has locatedOnIsland ?x1074;]

No rule for expected values
ranks of expected_values: 
EVAL	PuncakJaya	inMountains	SudirmanRange	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	41.000	41.000	31.000	0.103	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains
PRED expected values: SudirmanRange
=> 138 concepts (138 used for prediction)
PRED predicted values (max 10 best out of 73): Hawaii (0.33 #155, 0.14 #503, 0.12 #1373), RockyMountains (0.25 #2095, 0.21 #2530, 0.20 #2965), Alps (0.22 #2701, 0.16 #2179, 0.14 #4702), CanaryIslands (0.15 #926, 0.13 #1187, 0.13 #1100), SnowyMountains (0.14 #543, 0.04 #5850, 0.03 #4023), Andes (0.14 #4883, 0.14 #2186, 0.11 #6362), Himalaya (0.12 #3660, 0.07 #5487, 0.05 #5922), EliasRange (0.08 #2103, 0.07 #2538, 0.07 #2973), Pamir (0.08 #4454, 0.07 #4802, 0.07 #4976), CordilleraVolcanica (0.08 #4589, 0.07 #4676, 0.07 #4937)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #155 for best value:
>> intensional similarity = 10
>> extensional distance = 7
>> proper extension:  MaunaLoa; Asahi-Dake;
>> query: (?x1807, Hawaii) <- ?x1807[ a Mountain; has locatedOnIsland ?x1074[ a Island; has locatedIn ?x853[ has language ?x247; has religion ?x95;]; has locatedInWater ?x282<PacificOcean>; has locatedInWater ?x770[ is mergesWith of ?x60;];];]

No rule for expected values
ranks of expected_values: 
EVAL	PuncakJaya	inMountains	SudirmanRange	CNN-1.+1._MA	0.000	0.000	0.000	0.000	138.000	138.000	73.000	0.333	http://www.semwebtech.org/mondial/10/meta#inMountains

#951-USA
PRED entity: USA

PRED relation: ethnicGroup
PRED expected values: African
=> 26 concepts (26 used for prediction)
PRED predicted values (max 10 best out of 223): Indian (0.33 #326, 0.20 #581, 0.04 #2111), Chinese (0.33 #523, 0.11 #778, 0.10 #5623), Pakistani (0.33 #382, 0.07 #637, 0.03 #892), NorthernIrish (0.33 #485, 0.07 #740, 0.03 #995), English (0.33 #482, 0.07 #737, 0.03 #992), Welsh (0.33 #410, 0.07 #665, 0.03 #920), Scottish (0.33 #368, 0.07 #623, 0.03 #878), French (0.33 #122, 0.05 #887, 0.04 #1142), BritishIsles (0.33 #188, 0.03 #953, 0.02 #1208), Inuit (0.33 #12, 0.03 #777, 0.02 #1032)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #326 for best value:
>> intensional similarity = 5
>> extensional distance = 1
>> proper extension:  GB;
>> query: (?x315, Indian) <- ?x315[ has neighbor ?x482; has religion ?x462<Buddhist>; is locatedIn of ?x182<AtlanticOcean>;]

*> Best rule #4085 for first EXPECTED value:
*> intensional similarity = 3
*> extensional distance = 132
*> proper extension:  ARM;
*> query: (?x315, African) <- ?x315[ a Country; has ethnicGroup ?x79; is neighbor of ?x482;]
*> conf = 0.17
ranks of expected_values: 12
EVAL	USA	ethnicGroup	African	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.083	26.000	26.000	223.000	0.333	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: African
=> 90 concepts (90 used for prediction)
PRED predicted values (max 10 best out of 254): Mestizo (0.60 #2838, 0.45 #2073, 0.41 #7173), African (0.41 #7145, 0.36 #5615, 0.33 #13010), French (0.33 #377, 0.12 #1142, 0.11 #3437), BritishIsles (0.33 #443, 0.09 #19128, 0.09 #2228), Inuit (0.33 #267, 0.09 #19128, 0.09 #2052), Russian (0.23 #16901, 0.17 #14096, 0.17 #13840), Polynesian (0.22 #3401, 0.20 #9266, 0.19 #9521), Chinese (0.21 #4093, 0.16 #12763, 0.14 #4858), German (0.21 #9953, 0.20 #10718, 0.20 #4598), Ukrainian (0.20 #4591, 0.17 #13771, 0.16 #13516)
>> best conf = 0.60
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2838 for best value:
>> intensional similarity = 8
>> extensional distance = 13
>> proper extension:  ES; PA; HCA;
>> query: (?x315, Mestizo) <- ?x315[ has encompassed ?x521; has ethnicGroup ?x79; has neighbor ?x482; has religion ?x95; is locatedIn of ?x282<PacificOcean>; is locatedIn of ?x733[ is flowsInto of ?x1246;];]

*> Best rule #7145 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 27
*> proper extension:  BZ;
*> query: (?x315, African) <- ?x315[ has encompassed ?x521<America>; has ethnicGroup ?x380[ a EthnicGroup; is ethnicGroup of ?x196[ is locatedIn of ?x60;];]; has language ?x796; has religion ?x95;]
*> conf = 0.41
ranks of expected_values: 2
EVAL	USA	ethnicGroup	African	CNN-1.+1._MA	0.000	1.000	1.000	0.500	90.000	90.000	254.000	0.600	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#950-Donau
PRED entity: Donau

PRED relation: flowsInto!
PRED expected values: Enns Raab
=> 31 concepts (28 used for prediction)
PRED predicted values (max 10 best out of 344): Dnister (0.33 #210, 0.25 #498, 0.10 #1075), Dnjestr (0.33 #194, 0.25 #482, 0.10 #1059), Dnepr (0.33 #87, 0.25 #375, 0.10 #952), Donau (0.33 #5, 0.25 #293, 0.10 #870), Drina (0.17 #679, 0.10 #968, 0.05 #1256), Chire (0.17 #846, 0.05 #1423, 0.04 #1712), LakeCabora-Bassa (0.17 #611, 0.05 #1188, 0.04 #1477), Nile (0.10 #1140, 0.05 #1428, 0.04 #1717), Arno (0.10 #1111, 0.05 #1399, 0.04 #1688), Ebro (0.10 #1099, 0.05 #1387, 0.04 #1676)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #210 for best value:
>> intensional similarity = 6
>> extensional distance = 1
>> proper extension:  BlackSea;
>> query: (?x133, Dnister) <- ?x133[ has locatedIn ?x177<BG>; has locatedIn ?x886[ a Country; has religion ?x56;]; is flowsInto of ?x132;]

*> Best rule #2019 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 36
*> proper extension:  JavaSea; ArcticOcean; SeaofJapan; LakeNicaragua; NorwegianSea; SulawesiSea; BandaSea; PersianGulf; GulfofOman; LakeToba; ...
*> query: (?x133, ?x98) <- ?x133[ has locatedIn ?x177[ has neighbor ?x185; has religion ?x56; is locatedIn of ?x98;]; is locatedInWater of ?x151;]
*> conf = 0.02
ranks of expected_values: 126, 155
EVAL	Donau	flowsInto!	Raab	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.008	31.000	28.000	344.000	0.333	http://www.semwebtech.org/mondial/10/meta#flowsInto
EVAL	Donau	flowsInto!	Enns	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.006	31.000	28.000	344.000	0.333	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto!
PRED expected values: Enns Raab
=> 126 concepts (126 used for prediction)
PRED predicted values (max 10 best out of 603): Mosel (0.33 #1255, 0.20 #2128, 0.17 #2708), Main (0.33 #1240, 0.20 #2113, 0.17 #2693), Bodensee (0.33 #1384, 0.20 #2257, 0.17 #2837), Aare (0.33 #1286, 0.20 #2159, 0.17 #2739), Donau (0.33 #875, 0.20 #2329, 0.12 #11888), Dnepr (0.33 #957, 0.20 #2411, 0.12 #11888), Dnister (0.33 #1080, 0.20 #2534, 0.12 #11888), Dnjestr (0.33 #1064, 0.20 #2518, 0.12 #11888), Salzach (0.20 #2234, 0.17 #2814, 0.12 #3973), Alz (0.20 #2189, 0.17 #2769, 0.12 #3928)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1255 for best value:
>> intensional similarity = 12
>> extensional distance = 1
>> proper extension:  Rhein;
>> query: (?x133, Mosel) <- ?x133[ has hasEstuary ?x1556; has hasSource ?x1190; has locatedIn ?x163[ is locatedIn of ?x1097<March>;]; has locatedIn ?x904[ has wasDependentOf ?x1197;]; is flowsInto of ?x955[ has hasEstuary ?x1751; has hasSource ?x1716;]; is flowsInto of ?x1124[ is flowsInto of ?x558;];]

*> Best rule #1160 for first EXPECTED value:
*> intensional similarity = 12
*> extensional distance = 1
*> proper extension:  BlackSea;
*> query: (?x133, ?x360) <- ?x133[ has locatedIn ?x163[ has encompassed ?x195; has ethnicGroup ?x237<German>; has neighbor ?x194; is locatedIn of ?x360;]; has locatedIn ?x236[ has ethnicGroup ?x517; has religion ?x95;]; has locatedIn ?x303<UA>; is flowsInto of ?x132;]
*> conf = 0.11
ranks of expected_values: 70, 71
EVAL	Donau	flowsInto!	Raab	CNN-1.+1._MA	0.000	0.000	0.000	0.014	126.000	126.000	603.000	0.333	http://www.semwebtech.org/mondial/10/meta#flowsInto
EVAL	Donau	flowsInto!	Enns	CNN-1.+1._MA	0.000	0.000	0.000	0.014	126.000	126.000	603.000	0.333	http://www.semwebtech.org/mondial/10/meta#flowsInto

#949-A
PRED entity: A

PRED relation: government
PRED expected values: "federal republic"
=> 34 concepts (34 used for prediction)
PRED predicted values (max 10 best out of 53): "republic" (0.40 #6, 0.35 #150, 0.34 #294), "hereditary constitutional monarchy" (0.20 #37, 0.15 #1729, 0.02 #253), "parliamentary democracy" (0.15 #1729, 0.14 #509, 0.14 #437), "federal republic" (0.15 #1729, 0.11 #147, 0.10 #363), "parliamentary republic" (0.15 #1729, 0.04 #523, 0.04 #451), "formally a confederation but similar in structure to a federal republic" (0.15 #1729, 0.02 #130, 0.02 #202), "British Overseas Territories" (0.07 #727, 0.06 #1447, 0.06 #1087), "constitutional monarchy" (0.07 #1658, 0.07 #1586, 0.07 #1731), "constitutional republic" (0.05 #225, 0.04 #729, 0.03 #945), "parliamentary democracy and a Commonwealth realm" (0.04 #972, 0.04 #1044, 0.04 #1116)
>> best conf = 0.40
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #6 for best value:
>> intensional similarity = 6
>> extensional distance = 3
>> proper extension:  F; I; FL;
>> query: (?x424, "republic") <- ?x424[ has language ?x511; has neighbor ?x234<CH>; has neighbor ?x471[ has ethnicGroup ?x164;]; is locatedIn of ?x133;]

*> Best rule #1729 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 132
*> proper extension:  SD; BHT;
*> query: (?x424, ?x1952) <- ?x424[ has ethnicGroup ?x160; has neighbor ?x236[ a Country;]; has neighbor ?x423[ has government ?x1952;];]
*> conf = 0.15
ranks of expected_values: 4
EVAL	A	government	"federal republic"	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.250	34.000	34.000	53.000	0.400	http://www.semwebtech.org/mondial/10/meta#government

PRED relation: government
PRED expected values: "federal republic"
=> 73 concepts (73 used for prediction)
PRED predicted values (max 10 best out of 68): "parliamentary democracy" (0.50 #581, 0.50 #437, 0.40 #1086), "republic" (0.40 #1087, 0.38 #870, 0.35 #3609), "federal republic" (0.33 #291, 0.25 #363, 0.16 #4902), "hereditary constitutional monarchy" (0.33 #109, 0.22 #2235, 0.17 #1370), "formally a confederation but similar in structure to a federal republic" (0.33 #202, 0.16 #4902, 0.16 #3820), "constitutional monarchy" (0.25 #794, 0.22 #1010, 0.14 #2813), "parliamentary republic" (0.22 #2235, 0.20 #667, 0.17 #1370), "theocratic republic" (0.12 #986, 0.03 #2140, 0.03 #2357), "parliamentary monarchy" (0.11 #1036, 0.07 #1398, 0.06 #1470), "republic; authoritarian presidential rule, with little power outside the executive branch" (0.09 #1215, 0.08 #1287, 0.07 #1359)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #581 for best value:
>> intensional similarity = 18
>> extensional distance = 2
>> proper extension:  CZ;
>> query: (?x424, "parliamentary democracy") <- ?x424[ a Country; has encompassed ?x195<Europe>; has ethnicGroup ?x160; has language ?x511; has neighbor ?x423[ has wasDependentOf ?x2516;]; has religion ?x95; is locatedIn of ?x133[ is flowsInto of ?x132;]; is locatedIn of ?x256[ a River;]; is locatedIn of ?x1096<March>; is locatedIn of ?x1097[ a Estuary;]; is locatedIn of ?x1738[ has inMountains ?x261;];]
>> Best rule #437 for best value:
>> intensional similarity = 17
>> extensional distance = 2
>> proper extension:  HR;
>> query: (?x424, "parliamentary democracy") <- ?x424[ a Country; has ethnicGroup ?x2136[ is ethnicGroup of ?x575[ is wasDependentOf of ?x179;];]; has language ?x684[ is language of ?x904<SRB>;]; is locatedIn of ?x475[ has hasEstuary ?x2313;]; is locatedIn of ?x614<Drau>; is neighbor of ?x234[ a Country; has language ?x51; is locatedIn of ?x233;]; is neighbor of ?x446<SLO>;]

*> Best rule #291 for first EXPECTED value:
*> intensional similarity = 22
*> extensional distance = 1
*> proper extension:  D;
*> query: (?x424, "federal republic") <- ?x424[ has ethnicGroup ?x160; is locatedIn of ?x155[ a River;]; is locatedIn of ?x475<Iller>; is locatedIn of ?x756<Inn>; is locatedIn of ?x889<Lech>; is locatedIn of ?x1111[ a Source; has inMountains ?x261;]; is locatedIn of ?x1124<Isar>; is locatedIn of ?x1278<Inn>; is locatedIn of ?x1440<Salzach>; is neighbor of ?x234<CH>; is neighbor of ?x471<CZ>;]
*> conf = 0.33
ranks of expected_values: 3
EVAL	A	government	"federal republic"	CNN-1.+1._MA	0.000	1.000	1.000	0.333	73.000	73.000	68.000	0.500	http://www.semwebtech.org/mondial/10/meta#government

#948-AZ
PRED entity: AZ

PRED relation: language
PRED expected values: Russian Azeri
=> 40 concepts (40 used for prediction)
PRED predicted values (max 10 best out of 86): Russian (0.50 #203, 0.42 #395, 0.33 #299), Kazakh (0.33 #34, 0.17 #322, 0.04 #418), English (0.30 #484, 0.25 #1542, 0.20 #1061), Spanish (0.27 #1079, 0.26 #791, 0.23 #983), Uzbek (0.25 #234, 0.17 #330, 0.12 #426), Turkmen (0.25 #259, 0.17 #355, 0.04 #451), Turkish (0.20 #1442, 0.17 #296, 0.14 #673), Arabic (0.20 #1442, 0.17 #345, 0.14 #673), Balochi (0.20 #1442, 0.17 #361, 0.14 #673), Turkic (0.20 #1442, 0.17 #378, 0.14 #673)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #203 for best value:
>> intensional similarity = 6
>> extensional distance = 2
>> proper extension:  TM;
>> query: (?x332, Russian) <- ?x332[ has language ?x843; has religion ?x56; has wasDependentOf ?x903; is locatedIn of ?x1337<CaspianSea>; is neighbor of ?x73;]
ranks of expected_values: 1, 14
EVAL	AZ	language	Azeri	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.077	40.000	40.000	86.000	0.500	http://www.semwebtech.org/mondial/10/meta#language
EVAL	AZ	language	Russian	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	40.000	40.000	86.000	0.500	http://www.semwebtech.org/mondial/10/meta#language

PRED relation: language
PRED expected values: Russian Azeri
=> 87 concepts (87 used for prediction)
PRED predicted values (max 10 best out of 96): Russian (0.75 #782, 0.67 #493, 0.50 #1169), Pashtu (0.50 #122, 0.17 #4341, 0.15 #3082), Spanish (0.45 #1564, 0.31 #2141, 0.29 #1948), Azeri (0.42 #965, 0.42 #964, 0.33 #97), Georgian (0.42 #965, 0.42 #964, 0.33 #97), Turkish (0.33 #97, 0.27 #3275, 0.25 #201), Balochi (0.33 #97, 0.27 #3275, 0.25 #170), Arabic (0.33 #97, 0.27 #3275, 0.24 #4051), Turkic (0.33 #97, 0.27 #3275, 0.24 #4051), Persian (0.33 #97, 0.27 #3275, 0.24 #4051)
>> best conf = 0.75
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #782 for best value:
>> intensional similarity = 11
>> extensional distance = 10
>> proper extension:  KGZ; UZB; MD;
>> query: (?x332, Russian) <- ?x332[ has ethnicGroup ?x908; has government ?x435; has language ?x843; has neighbor ?x73[ has ethnicGroup ?x58; is locatedIn of ?x72; is locatedIn of ?x720[ a Estuary;];]; has religion ?x56; has wasDependentOf ?x903<SovietUnion>;]
ranks of expected_values: 1, 4
EVAL	AZ	language	Azeri	CNN-1.+1._MA	0.000	1.000	1.000	0.333	87.000	87.000	96.000	0.750	http://www.semwebtech.org/mondial/10/meta#language
EVAL	AZ	language	Russian	CNN-1.+1._MA	1.000	1.000	1.000	1.000	87.000	87.000	96.000	0.750	http://www.semwebtech.org/mondial/10/meta#language

#947-XMAS
PRED entity: XMAS

PRED relation: locatedIn!
PRED expected values: ChristmasIsland
=> 33 concepts (29 used for prediction)
PRED predicted values (max 10 best out of 972): PacificOcean (0.86 #12896, 0.86 #11472, 0.75 #10049), SouthChinaSea (0.60 #2986, 0.42 #7256, 0.16 #18644), AtlanticOcean (0.50 #8581, 0.46 #17122, 0.42 #15699), SulawesiSea (0.40 #3127, 0.11 #7397, 0.10 #31315), Borneo (0.40 #2982, 0.11 #7252, 0.08 #37013), GrandeTerre (0.33 #915, 0.08 #37013, 0.06 #38437), PulauPanjang (0.33 #2704, 0.06 #6973, 0.05 #14092), CaribbeanSea (0.30 #8645, 0.25 #17186, 0.23 #15763), GulfofBengal (0.25 #4341, 0.10 #27045, 0.10 #31317), LakeMalawi (0.25 #5203, 0.08 #37013, 0.06 #38437)
>> best conf = 0.86
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #12896 for best value:
>> intensional similarity = 6
>> extensional distance = 19
>> proper extension:  PAL;
>> query: (?x1731, PacificOcean) <- ?x1731[ a Country; has encompassed ?x211<Australia-Oceania>; has government ?x907; is locatedIn of ?x60[ is mergesWith of ?x182;];]
>> Best rule #11472 for best value:
>> intensional similarity = 6
>> extensional distance = 19
>> proper extension:  NORF; TO;
>> query: (?x1731, PacificOcean) <- ?x1731[ a Country; has encompassed ?x211<Australia-Oceania>; has religion ?x116; is locatedIn of ?x60[ is locatedInWater of ?x226;];]

*> Best rule #12811 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 19
*> proper extension:  NORF; TO;
*> query: (?x1731, ?x226) <- ?x1731[ a Country; has encompassed ?x211<Australia-Oceania>; has religion ?x116; is locatedIn of ?x60[ is locatedInWater of ?x226;];]
*> conf = 0.03
ranks of expected_values: 514
EVAL	XMAS	locatedIn!	ChristmasIsland	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.002	33.000	29.000	972.000	0.857	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: ChristmasIsland
=> 64 concepts (61 used for prediction)
PRED predicted values (max 10 best out of 1287): PacificOcean (0.86 #24317, 0.75 #14333, 0.69 #15758), AtlanticOcean (0.49 #49961, 0.49 #47107, 0.48 #44255), CaribbeanSea (0.42 #14353, 0.38 #15778, 0.26 #50025), MediterraneanSea (0.33 #82, 0.32 #21459, 0.21 #51428), Nauru (0.33 #2651, 0.25 #5501, 0.25 #4076), ArabianSea (0.27 #12128, 0.19 #74185, 0.19 #17829), GulfofBengal (0.25 #17170, 0.25 #8620, 0.19 #74185), LakeMalawi (0.25 #9482, 0.25 #5208, 0.09 #12331), Popomanaseu (0.25 #3419, 0.20 #7694, 0.09 #18522), AndamanSea (0.25 #17216, 0.19 #74185, 0.18 #11515)
>> best conf = 0.86
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #24317 for best value:
>> intensional similarity = 15
>> extensional distance = 19
>> proper extension:  PAL;
>> query: (?x1731, PacificOcean) <- ?x1731[ a Country; has encompassed ?x211<Australia-Oceania>; has government ?x907; is locatedIn of ?x60[ a Sea; has locatedIn ?x192[ has neighbor ?x193;]; has locatedIn ?x924[ has language ?x2392; has religion ?x116; is neighbor of ?x83;]; is flowsInto of ?x242; is locatedInWater of ?x226; is mergesWith of ?x182;];]

*> Best rule #52775 for first EXPECTED value:
*> intensional similarity = 13
*> extensional distance = 54
*> proper extension:  G; I; RCB; OM; DK;
*> query: (?x1731, ?x226) <- ?x1731[ a Country; has encompassed ?x211[ is encompassed of ?x461[ has language ?x51;];]; has government ?x907; has religion ?x187<Muslim>; is locatedIn of ?x60[ a Sea; is locatedInWater of ?x226; is mergesWith of ?x282[ is locatedInWater of ?x205; is mergesWith of ?x271;];];]
*> conf = 0.05
ranks of expected_values: 584
EVAL	XMAS	locatedIn!	ChristmasIsland	CNN-1.+1._MA	0.000	0.000	0.000	0.002	64.000	61.000	1287.000	0.857	http://www.semwebtech.org/mondial/10/meta#locatedIn

#946-R
PRED entity: R

PRED relation: government
PRED expected values: "federation"
=> 30 concepts (30 used for prediction)
PRED predicted values (max 10 best out of 51): "republic" (0.37 #510, 0.36 #1086, 0.35 #582), "republic; authoritarian presidential rule, with little power outside the executive branch" (0.33 #62, 0.25 #134, 0.17 #1657), "parliamentary democracy" (0.33 #221, 0.17 #1657, 0.12 #941), "constitutional monarchy" (0.25 #146, 0.17 #1657, 0.10 #362), "Communist state" (0.25 #85, 0.17 #1657, 0.05 #373), "territory of Norway administered by the Ministry of Industry" (0.25 #165), "parliamentary republic" (0.17 #1657, 0.03 #1820, 0.03 #1315), "parliamentary" (0.17 #1657, 0.02 #1264), "republic in name, although in fact a dictatorship" (0.17 #1657), "theocratic republic" (0.17 #338, 0.02 #554, 0.01 #626)
>> best conf = 0.37
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #510 for best value:
>> intensional similarity = 4
>> extensional distance = 50
>> proper extension:  DJI; KGZ; SA;
>> query: (?x73, "republic") <- ?x73[ has ethnicGroup ?x58; has neighbor ?x170; is locatedIn of ?x1938[ has type ?x706;];]

No rule for expected values
ranks of expected_values: 
EVAL	R	government	"federation"	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	30.000	30.000	51.000	0.365	http://www.semwebtech.org/mondial/10/meta#government

PRED relation: government
PRED expected values: "federation"
=> 104 concepts (104 used for prediction)
PRED predicted values (max 10 best out of 68): "republic" (0.46 #1736, 0.44 #2024, 0.43 #1232), "republic; authoritarian presidential rule, with little power outside the executive branch" (0.33 #567, 0.25 #856, 0.25 #784), "constitutional monarchy" (0.33 #218, 0.20 #1444, 0.19 #6066), "parliamentary republic" (0.33 #451, 0.19 #6066, 0.18 #4766), "Communist state" (0.33 #157, 0.18 #7006, 0.18 #7295), "republic; authoritarian presidential rule with little power outside the executive branch" (0.25 #827, 0.16 #4257, 0.08 #6573), "authoritarian presidential rule" (0.25 #785, 0.16 #4257, 0.08 #6573), "a parliamentary democracy, a federation, and a constitutional monarchy" (0.25 #921, 0.07 #1857, 0.06 #2073), "parliamentary democracy" (0.23 #4189, 0.20 #1879, 0.19 #6066), "federal republic" (0.20 #1517, 0.20 #1013, 0.17 #1085)
>> best conf = 0.46
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1736 for best value:
>> intensional similarity = 11
>> extensional distance = 11
>> proper extension:  ARM;
>> query: (?x73, "republic") <- ?x73[ has ethnicGroup ?x58; has language ?x555; has religion ?x56; has wasDependentOf ?x903<SovietUnion>; is neighbor of ?x353[ a Country; is locatedIn of ?x98;]; is neighbor of ?x448[ has encompassed ?x195; has ethnicGroup ?x516;];]

No rule for expected values
ranks of expected_values: 
EVAL	R	government	"federation"	CNN-1.+1._MA	0.000	0.000	0.000	0.000	104.000	104.000	68.000	0.462	http://www.semwebtech.org/mondial/10/meta#government

#945-HELX
PRED entity: HELX

PRED relation: ethnicGroup
PRED expected values: Africandescent
=> 41 concepts (41 used for prediction)
PRED predicted values (max 10 best out of 236): African (0.41 #3334, 0.39 #4870, 0.32 #3590), European (0.41 #3336, 0.34 #4104, 0.33 #3848), Amerindian (0.37 #4098, 0.17 #2, 0.14 #514), Malay (0.33 #1121, 0.29 #353, 0.24 #2913), Black (0.29 #568, 0.25 #824, 0.22 #2360), Mestizo (0.20 #4131, 0.13 #5411, 0.13 #9217), Mulatto (0.18 #3386, 0.12 #3898, 0.11 #4410), Asian (0.18 #3090, 0.09 #3346, 0.09 #4626), Indian (0.17 #1096, 0.14 #2888, 0.14 #328), African-white-Indian (0.17 #63, 0.14 #319, 0.13 #9217)
>> best conf = 0.41
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3334 for best value:
>> intensional similarity = 6
>> extensional distance = 20
>> proper extension:  ROU; RA; RG;
>> query: (?x212, African) <- ?x212[ a Country; has encompassed ?x213; has ethnicGroup ?x298; has language ?x247; is locatedIn of ?x182<AtlanticOcean>;]

No rule for expected values
ranks of expected_values: 
EVAL	HELX	ethnicGroup	Africandescent	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	41.000	41.000	236.000	0.409	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: Africandescent
=> 99 concepts (99 used for prediction)
PRED predicted values (max 10 best out of 238): Amerindian (0.78 #6669, 0.39 #10260, 0.38 #9491), European (0.67 #6675, 0.64 #14111, 0.62 #5649), African (0.67 #13595, 0.53 #14621, 0.50 #15391), Black (0.60 #3644, 0.50 #4928, 0.35 #3845), Mestizo (0.56 #6702, 0.35 #3845, 0.30 #14138), Mixed (0.40 #3714, 0.35 #3845, 0.29 #15899), African-white-Indian (0.35 #3845, 0.33 #575, 0.29 #15899), Indian (0.35 #3845, 0.33 #840, 0.27 #1281), PacificIslander (0.35 #3845, 0.33 #74, 0.27 #1281), Creole (0.35 #3845, 0.33 #390, 0.27 #1281)
>> best conf = 0.78
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #6669 for best value:
>> intensional similarity = 10
>> extensional distance = 7
>> proper extension:  CO; PE;
>> query: (?x212, Amerindian) <- ?x212[ a Country; has encompassed ?x213; has ethnicGroup ?x298[ is ethnicGroup of ?x179<SME>;]; has language ?x247; is locatedIn of ?x283[ a Mountain; has type ?x706<"volcano">;];]

No rule for expected values
ranks of expected_values: 
EVAL	HELX	ethnicGroup	Africandescent	CNN-1.+1._MA	0.000	0.000	0.000	0.000	99.000	99.000	238.000	0.778	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#944-Muslim
PRED entity: Muslim

PRED relation: religion!
PRED expected values: BIH F PK DJI TN IL USA RN RWA MV DZ L TL MAYO BEN BF SRB COCO
=> 14 concepts (14 used for prediction)
PRED predicted values (max 10 best out of 132): UA (0.50 #756, 0.48 #484, 0.45 #967), SRB (0.50 #821, 0.48 #484, 0.45 #967), LT (0.50 #825, 0.48 #484, 0.45 #967), MACX (0.50 #797, 0.48 #484, 0.45 #967), BY (0.50 #749, 0.48 #484, 0.45 #967), RO (0.50 #742, 0.48 #484, 0.45 #967), BIH (0.50 #728, 0.48 #484, 0.45 #967), PNG (0.50 #815, 0.48 #484, 0.45 #967), F (0.50 #731, 0.48 #484, 0.45 #967), B (0.50 #787, 0.48 #484, 0.45 #967)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #756 for best value:
>> intensional similarity = 8
>> extensional distance = 2
>> proper extension:  RomanCatholic;
>> query: (?x187, UA) <- ?x187[ is religion of ?x466[ a Country; has ethnicGroup ?x244; is locatedIn of ?x275;]; is religion of ?x639[ has government ?x640;]; is religion of ?x819<MW>;]

*> Best rule #821 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 2
*> proper extension:  RomanCatholic;
*> query: (?x187, SRB) <- ?x187[ is religion of ?x466[ a Country; has ethnicGroup ?x244; is locatedIn of ?x275;]; is religion of ?x639[ has government ?x640;]; is religion of ?x819<MW>;]
*> conf = 0.50
ranks of expected_values: 2, 7, 9, 12, 14, 20, 55, 60, 61, 62, 66, 67, 70, 77, 81, 101
EVAL	Muslim	religion!	COCO	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.012	14.000	14.000	132.000	0.500	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Muslim	religion!	SRB	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	14.000	14.000	132.000	0.500	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Muslim	religion!	BF	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.018	14.000	14.000	132.000	0.500	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Muslim	religion!	BEN	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.018	14.000	14.000	132.000	0.500	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Muslim	religion!	MAYO	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	14.000	14.000	132.000	0.500	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Muslim	religion!	TL	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.020	14.000	14.000	132.000	0.500	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Muslim	religion!	L	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.111	14.000	14.000	132.000	0.500	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Muslim	religion!	DZ	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.019	14.000	14.000	132.000	0.500	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Muslim	religion!	MV	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	14.000	14.000	132.000	0.500	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Muslim	religion!	RWA	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.100	14.000	14.000	132.000	0.500	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Muslim	religion!	RN	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.015	14.000	14.000	132.000	0.500	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Muslim	religion!	USA	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.067	14.000	14.000	132.000	0.500	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Muslim	religion!	IL	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.019	14.000	14.000	132.000	0.500	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Muslim	religion!	TN	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.019	14.000	14.000	132.000	0.500	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Muslim	religion!	DJI	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.017	14.000	14.000	132.000	0.500	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Muslim	religion!	PK	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.016	14.000	14.000	132.000	0.500	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Muslim	religion!	F	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.143	14.000	14.000	132.000	0.500	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Muslim	religion!	BIH	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.167	14.000	14.000	132.000	0.500	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion!
PRED expected values: BIH F PK DJI TN IL USA RN RWA MV DZ L TL MAYO BEN BF SRB COCO
=> 32 concepts (32 used for prediction)
PRED predicted values (max 10 best out of 152): UA (0.71 #1140, 0.61 #983, 0.58 #985), USA (0.71 #1264, 0.44 #1513, 0.43 #1141), TL (0.64 #1727, 0.61 #983, 0.58 #985), ER (0.64 #1727, 0.61 #983, 0.58 #985), B (0.64 #1727, 0.61 #983, 0.58 #985), SF (0.64 #1727, 0.61 #983, 0.58 #985), L (0.64 #1727, 0.58 #985, 0.58 #1233), SGP (0.64 #1727, 0.32 #1355, 0.32 #1480), PL (0.61 #983, 0.58 #985, 0.58 #1233), PK (0.61 #983, 0.58 #985, 0.58 #1233)
>> best conf = 0.71
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1140 for best value:
>> intensional similarity = 18
>> extensional distance = 5
>> proper extension:  UkrainianGreekCatholic;
>> query: (?x187, UA) <- ?x187[ a Religion; is religion of ?x73[ has ethnicGroup ?x58; has neighbor ?x194; is locatedIn of ?x2264[ is hasEstuary of ?x1761;];]; is religion of ?x177[ has ethnicGroup ?x1780<Bulgarian>; has wasDependentOf ?x1656;]; is religion of ?x204[ a Country; is locatedIn of ?x104;]; is religion of ?x217[ has encompassed ?x211; is locatedIn of ?x60;]; is religion of ?x692[ is neighbor of ?x904;];]

*> Best rule #1264 for first EXPECTED value:
*> intensional similarity = 23
*> extensional distance = 5
*> proper extension:  Mormon;
*> query: (?x187, USA) <- ?x187[ is religion of ?x73[ has ethnicGroup ?x58; is locatedIn of ?x72; is locatedIn of ?x293[ a River;]; is locatedIn of ?x920[ a Estuary;];]; is religion of ?x177[ has ethnicGroup ?x164; has wasDependentOf ?x1656;]; is religion of ?x204[ has language ?x1251; is locatedIn of ?x104;]; is religion of ?x272[ has ethnicGroup ?x197; is locatedIn of ?x733<YukonRiver>;]; is religion of ?x688[ a Country; is locatedIn of ?x600;]; is religion of ?x692[ has encompassed ?x195; is neighbor of ?x904;];]
*> conf = 0.71
ranks of expected_values: 2, 3, 7, 10, 12, 21, 22, 24, 25, 28, 30, 39, 40, 48, 49, 116, 129, 144
EVAL	Muslim	religion!	COCO	CNN-1.+1._MA	0.000	0.000	0.000	0.009	32.000	32.000	152.000	0.714	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Muslim	religion!	SRB	CNN-1.+1._MA	0.000	0.000	0.000	0.062	32.000	32.000	152.000	0.714	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Muslim	religion!	BF	CNN-1.+1._MA	0.000	0.000	0.000	0.036	32.000	32.000	152.000	0.714	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Muslim	religion!	BEN	CNN-1.+1._MA	0.000	0.000	0.000	0.053	32.000	32.000	152.000	0.714	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Muslim	religion!	MAYO	CNN-1.+1._MA	0.000	0.000	0.000	0.008	32.000	32.000	152.000	0.714	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Muslim	religion!	TL	CNN-1.+1._MA	0.000	1.000	1.000	0.500	32.000	32.000	152.000	0.714	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Muslim	religion!	L	CNN-1.+1._MA	0.000	0.000	1.000	0.200	32.000	32.000	152.000	0.714	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Muslim	religion!	DZ	CNN-1.+1._MA	0.000	0.000	0.000	0.059	32.000	32.000	152.000	0.714	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Muslim	religion!	MV	CNN-1.+1._MA	0.000	0.000	0.000	0.010	32.000	32.000	152.000	0.714	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Muslim	religion!	RWA	CNN-1.+1._MA	0.000	0.000	0.000	0.059	32.000	32.000	152.000	0.714	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Muslim	religion!	RN	CNN-1.+1._MA	0.000	0.000	0.000	0.050	32.000	32.000	152.000	0.714	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Muslim	religion!	USA	CNN-1.+1._MA	0.000	1.000	1.000	0.500	32.000	32.000	152.000	0.714	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Muslim	religion!	IL	CNN-1.+1._MA	0.000	0.000	1.000	0.125	32.000	32.000	152.000	0.714	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Muslim	religion!	TN	CNN-1.+1._MA	0.000	0.000	0.000	0.036	32.000	32.000	152.000	0.714	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Muslim	religion!	DJI	CNN-1.+1._MA	0.000	0.000	0.000	0.029	32.000	32.000	152.000	0.714	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Muslim	religion!	PK	CNN-1.+1._MA	0.000	0.000	1.000	0.143	32.000	32.000	152.000	0.714	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Muslim	religion!	F	CNN-1.+1._MA	0.000	0.000	0.000	0.029	32.000	32.000	152.000	0.714	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Muslim	religion!	BIH	CNN-1.+1._MA	0.000	0.000	0.000	0.062	32.000	32.000	152.000	0.714	http://www.semwebtech.org/mondial/10/meta#religion

#943-ETH
PRED entity: ETH

PRED relation: locatedIn!
PRED expected values: LakeTurkana
=> 29 concepts (23 used for prediction)
PRED predicted values (max 10 best out of 1384): AtlanticOcean (0.42 #9886, 0.33 #18322, 0.32 #15510), WhiteNile (0.33 #505, 0.08 #23907, 0.06 #9844), Sobat (0.33 #1324, 0.08 #23907), Pibor (0.33 #1233, 0.08 #23907), Bahrel-Djebel-Albert-Nil (0.33 #1209, 0.08 #23907), WhiteNile (0.33 #1192, 0.08 #23907), Bahrel-Djebel-Albert-Nil (0.33 #973, 0.08 #23907), Bahrel-Ghasal (0.33 #625, 0.08 #23907), Bahrel-Ghasal (0.33 #110, 0.08 #23907), Sobat (0.33 #67, 0.08 #23907)
>> best conf = 0.42
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #9886 for best value:
>> intensional similarity = 5
>> extensional distance = 41
>> proper extension:  MEL;
>> query: (?x476, AtlanticOcean) <- ?x476[ a Country; has encompassed ?x213<Africa>; has neighbor ?x94; is locatedIn of ?x228;]

*> Best rule #23907 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 127
*> proper extension:  RN;
*> query: (?x476, ?x415) <- ?x476[ a Country; has ethnicGroup ?x1179; has neighbor ?x94[ a Country; is locatedIn of ?x415;]; is locatedIn of ?x228;]
*> conf = 0.08
ranks of expected_values: 129
EVAL	ETH	locatedIn!	LakeTurkana	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.008	29.000	23.000	1384.000	0.419	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: LakeTurkana
=> 78 concepts (73 used for prediction)
PRED predicted values (max 10 best out of 1404): IndianOcean (0.79 #9858, 0.73 #43644, 0.73 #1407), Nile (0.79 #9858, 0.73 #43644, 0.73 #1407), Sobat (0.79 #9858, 0.73 #43644, 0.73 #1407), AtlanticOcean (0.76 #53549, 0.70 #59182, 0.50 #5676), RedSea (0.50 #3689, 0.35 #12673, 0.25 #5100), Shabelle (0.44 #8450, 0.35 #12673, 0.25 #5552), Jubba (0.44 #8450, 0.35 #12673, 0.25 #4445), Atbara (0.44 #8450, 0.35 #12673, 0.25 #3885), BlueNile (0.44 #8450, 0.35 #12673, 0.25 #3083), MediterraneanSea (0.43 #14163, 0.35 #12673, 0.33 #16978)
>> best conf = 0.79
=> the first rule below is the first best rule for 3 predicted values
>> Best rule #9858 for best value:
>> intensional similarity = 18
>> extensional distance = 3
>> proper extension:  ZRE;
>> query: (?x476, ?x252) <- ?x476[ a Country; has ethnicGroup ?x1179; has religion ?x95<Protestant>; has religion ?x187<Muslim>; is locatedIn of ?x655[ a Volcano;]; is locatedIn of ?x747[ has flowsInto ?x252;]; is locatedIn of ?x964[ a Source;]; is locatedIn of ?x1597[ a River;]; is locatedIn of ?x1635[ has type ?x762;]; is neighbor of ?x474[ has ethnicGroup ?x244;];]

*> Best rule #12673 for first EXPECTED value:
*> intensional similarity = 15
*> extensional distance = 4
*> proper extension:  GB;
*> query: (?x476, ?x388) <- ?x476[ a Country; has ethnicGroup ?x1179; has government ?x140; has neighbor ?x229[ is locatedIn of ?x53; is neighbor of ?x736[ has ethnicGroup ?x992; is locatedIn of ?x388;];]; has religion ?x95<Protestant>; is locatedIn of ?x655[ has type ?x150;]; is locatedIn of ?x1917[ a Source;]; is wasDependentOf of ?x629;]
*> conf = 0.35
ranks of expected_values: 95
EVAL	ETH	locatedIn!	LakeTurkana	CNN-1.+1._MA	0.000	0.000	0.000	0.011	78.000	73.000	1404.000	0.792	http://www.semwebtech.org/mondial/10/meta#locatedIn

#942-NOK
PRED entity: NOK

PRED relation: neighbor
PRED expected values: R CN
=> 41 concepts (41 used for prediction)
PRED predicted values (max 10 best out of 169): CN (0.92 #969, 0.91 #2262, 0.90 #2261), NOK (0.50 #218, 0.33 #380, 0.25 #1291), R (0.25 #1291, 0.25 #5176, 0.25 #163), J (0.25 #1291, 0.12 #1938, 0.12 #2260), IND (0.25 #5176, 0.25 #622, 0.25 #298), PK (0.25 #5176, 0.25 #489, 0.25 #165), AFG (0.25 #5176, 0.25 #228, 0.22 #5992), KAZ (0.25 #5176, 0.25 #231, 0.22 #5992), KGZ (0.25 #5176, 0.25 #176, 0.22 #5992), TAD (0.25 #5176, 0.25 #175, 0.22 #5992)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #969 for best value:
>> intensional similarity = 8
>> extensional distance = 15
>> proper extension:  THA; SP; HONX;
>> query: (?x334, ?x232) <- ?x334[ a Country; has government ?x1979; is locatedIn of ?x270[ a Sea;]; is locatedIn of ?x271[ has mergesWith ?x282<PacificOcean>;]; is neighbor of ?x232;]
ranks of expected_values: 1, 3
EVAL	NOK	neighbor	CN	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	41.000	41.000	169.000	0.917	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	NOK	neighbor	R	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	41.000	41.000	169.000	0.917	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: R CN
=> 108 concepts (108 used for prediction)
PRED predicted values (max 10 best out of 203): CN (0.95 #9355, 0.91 #13676, 0.91 #11674), PK (0.50 #1146, 0.50 #988, 0.50 #164), R (0.50 #3273, 0.50 #164, 0.41 #2617), MYA (0.50 #164, 0.40 #166, 0.40 #165), IND (0.50 #164, 0.40 #166, 0.40 #165), KGZ (0.50 #164, 0.40 #166, 0.40 #165), KAZ (0.50 #164, 0.40 #166, 0.40 #165), AFG (0.50 #164, 0.40 #166, 0.40 #165), HONX (0.50 #164, 0.40 #166, 0.40 #165), MACX (0.50 #164, 0.40 #166, 0.40 #165)
>> best conf = 0.95
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #9355 for best value:
>> intensional similarity = 13
>> extensional distance = 25
>> proper extension:  TR; USA;
>> query: (?x334, ?x232) <- ?x334[ a Country; has encompassed ?x175; has language ?x2244; has neighbor ?x626[ has government ?x435; has religion ?x116; is locatedIn of ?x619;]; has wasDependentOf ?x117; is locatedIn of ?x2111[ has inMountains ?x898;]; is neighbor of ?x232[ is locatedIn of ?x386[ a Lake;];];]
ranks of expected_values: 1, 3
EVAL	NOK	neighbor	CN	CNN-1.+1._MA	1.000	1.000	1.000	1.000	108.000	108.000	203.000	0.952	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	NOK	neighbor	R	CNN-1.+1._MA	0.000	1.000	1.000	0.500	108.000	108.000	203.000	0.952	http://www.semwebtech.org/mondial/10/meta#neighbor

#941-PK
PRED entity: PK

PRED relation: locatedIn!
PRED expected values: Indus Indus ArabianSea GasherbrumI
=> 44 concepts (40 used for prediction)
PRED predicted values (max 10 best out of 1352): PersianGulf (0.75 #13206, 0.60 #10371, 0.43 #14167), PacificOcean (0.44 #14252, 0.26 #29838, 0.25 #25588), ArabianSea (0.43 #14167, 0.33 #2145, 0.25 #21250), RubAlChali (0.40 #10201, 0.38 #13036, 0.06 #18702), MediterraneanSea (0.38 #17081, 0.26 #24169, 0.25 #25585), CaspianSea (0.38 #12061, 0.33 #14896, 0.33 #4979), Amudarja (0.38 #11429, 0.33 #2931, 0.19 #18513), AtlanticOcean (0.34 #51043, 0.33 #46793, 0.33 #25544), SchattalArab (0.33 #6451, 0.33 #5035, 0.25 #13535), Brahmaputra (0.33 #2536, 0.33 #1119, 0.14 #11332)
>> best conf = 0.75
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #13206 for best value:
>> intensional similarity = 6
>> extensional distance = 6
>> proper extension:  BRN;
>> query: (?x83, PersianGulf) <- ?x83[ a Country; has government ?x140; is locatedIn of ?x926[ has locatedIn ?x304<IR>; is mergesWith of ?x918;];]

*> Best rule #14167 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 6
*> proper extension:  BRN;
*> query: (?x83, ?x918) <- ?x83[ a Country; has government ?x140; is locatedIn of ?x926[ has locatedIn ?x304<IR>; is mergesWith of ?x918;];]
*> conf = 0.43
ranks of expected_values: 3, 11, 72, 518
EVAL	PK	locatedIn!	GasherbrumI	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.014	44.000	40.000	1352.000	0.750	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	PK	locatedIn!	ArabianSea	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.333	44.000	40.000	1352.000	0.750	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	PK	locatedIn!	Indus	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.100	44.000	40.000	1352.000	0.750	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	PK	locatedIn!	Indus	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.002	44.000	40.000	1352.000	0.750	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Indus Indus ArabianSea GasherbrumI
=> 79 concepts (79 used for prediction)
PRED predicted values (max 10 best out of 1327): IndianOcean (0.90 #44026, 0.57 #4262, 0.47 #95159), PersianGulf (0.78 #23174, 0.75 #21753, 0.50 #24139), ArabianSea (0.57 #4262, 0.47 #95159, 0.43 #22717), GulfofBengal (0.57 #4262, 0.47 #95159, 0.40 #8595), AndamanSea (0.57 #4262, 0.47 #95159, 0.40 #8641), Amudarja (0.57 #4262, 0.43 #19873, 0.43 #18551), Ganges (0.57 #4262, 0.39 #36910, 0.38 #21291), Murgab (0.57 #4262, 0.33 #36914, 0.33 #14506), Pjandsh (0.57 #4262, 0.33 #14293, 0.33 #5681), Pjandsh (0.57 #4262, 0.33 #14662, 0.33 #4727)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #44026 for best value:
>> intensional similarity = 13
>> extensional distance = 19
>> proper extension:  REUN; RM; MV; SY; MAYO; MS; COCO; COM; XMAS;
>> query: (?x83, IndianOcean) <- ?x83[ a Country; has government ?x140; is locatedIn of ?x926[ a Sea; has mergesWith ?x918[ a Sea; has locatedIn ?x174; is locatedInWater of ?x1443;]; has mergesWith ?x1333<ArabianSea>;]; is locatedIn of ?x1877[ has locatedIn ?x924<IND>;];]

*> Best rule #4262 for first EXPECTED value:
*> intensional similarity = 16
*> extensional distance = 1
*> proper extension:  IND;
*> query: (?x83, ?x276) <- ?x83[ a Country; has government ?x140; has language ?x559[ a Language;]; is locatedIn of ?x1877<Thar>; is neighbor of ?x381[ has government ?x2442; has wasDependentOf ?x81; is locatedIn of ?x276; is locatedIn of ?x682[ a River; has flowsInto ?x592; has hasSource ?x1106; is flowsInto of ?x683;]; is neighbor of ?x129;];]
*> conf = 0.57
ranks of expected_values: 3, 15, 250, 1041
EVAL	PK	locatedIn!	GasherbrumI	CNN-1.+1._MA	0.000	0.000	0.000	0.004	79.000	79.000	1327.000	0.905	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	PK	locatedIn!	ArabianSea	CNN-1.+1._MA	0.000	1.000	1.000	0.333	79.000	79.000	1327.000	0.905	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	PK	locatedIn!	Indus	CNN-1.+1._MA	0.000	0.000	0.000	0.071	79.000	79.000	1327.000	0.905	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	PK	locatedIn!	Indus	CNN-1.+1._MA	0.000	0.000	0.000	0.001	79.000	79.000	1327.000	0.905	http://www.semwebtech.org/mondial/10/meta#locatedIn

#940-Portuguese
PRED entity: Portuguese

PRED relation: language!
PRED expected values: STP P
=> 22 concepts (22 used for prediction)
PRED predicted values (max 10 best out of 212): NLSM (0.50 #120, 0.40 #239, 0.33 #587), NZ (0.50 #188, 0.40 #307, 0.33 #423), BZ (0.50 #670, 0.40 #322, 0.33 #438), CDN (0.50 #157, 0.40 #276, 0.33 #392), TUCA (0.50 #236, 0.33 #75, 0.25 #194), CAYM (0.50 #236, 0.33 #97, 0.25 #216), HONX (0.50 #236, 0.33 #90, 0.25 #209), FALK (0.50 #236, 0.33 #106, 0.25 #225), AXA (0.50 #236, 0.33 #61, 0.25 #180), HELX (0.50 #236, 0.33 #30, 0.25 #149)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #120 for best value:
>> intensional similarity = 11
>> extensional distance = 2
>> proper extension:  French;
>> query: (?x539, NLSM) <- ?x539[ a Language; is language of ?x641[ is neighbor of ?x232;]; is language of ?x643[ has dependentOf ?x81[ has religion ?x95; is locatedIn of ?x121; is wasDependentOf of ?x63;]; is locatedIn of ?x642;]; is language of ?x718<L>;]

*> Best rule #354 for first EXPECTED value:
*> intensional similarity = 11
*> extensional distance = 3
*> proper extension:  German;
*> query: (?x539, ?x678) <- ?x539[ a Language; is language of ?x643[ a Country; has ethnicGroup ?x1242[ is ethnicGroup of ?x678;]; is locatedIn of ?x1211[ has mergesWith ?x121; is locatedInWater of ?x495<GreatBritain>;];]; is language of ?x718<L>;]
*> conf = 0.21
ranks of expected_values: 86, 129
EVAL	Portuguese	language!	P	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.008	22.000	22.000	212.000	0.500	http://www.semwebtech.org/mondial/10/meta#language
EVAL	Portuguese	language!	STP	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.012	22.000	22.000	212.000	0.500	http://www.semwebtech.org/mondial/10/meta#language

PRED relation: language!
PRED expected values: STP P
=> 43 concepts (43 used for prediction)
PRED predicted values (max 10 best out of 235): BOL (0.68 #481, 0.62 #957, 0.60 #567), PE (0.68 #481, 0.62 #957, 0.60 #523), PY (0.68 #481, 0.62 #957, 0.59 #722), CO (0.68 #481, 0.62 #957, 0.59 #722), YV (0.68 #481, 0.62 #957, 0.56 #718), ROU (0.68 #481, 0.58 #1086, 0.53 #1806), SME (0.68 #481, 0.53 #1806, 0.53 #598), F (0.62 #957, 0.62 #358, 0.59 #722), E (0.62 #957, 0.62 #358, 0.50 #978), KAZ (0.62 #237, 0.60 #236, 0.40 #119)
>> best conf = 0.68
=> the first rule below is the first best rule for 7 predicted values
>> Best rule #481 for best value:
>> intensional similarity = 22
>> extensional distance = 3
>> proper extension:  Slovenian; Albanian;
>> query: (?x539, ?x179) <- ?x539[ a Language; is language of ?x542[ a Country; has encompassed ?x521; has ethnicGroup ?x162; has neighbor ?x179; is locatedIn of ?x182[ has mergesWith ?x60; is flowsInto of ?x137; is locatedInWater of ?x112; is mergesWith of ?x121;]; is locatedIn of ?x1578[ a River; is flowsInto of ?x432;]; is locatedIn of ?x2500[ has inMountains ?x2515;];]; is language of ?x789[ has encompassed ?x195; has language ?x51<French>; has religion ?x352; is neighbor of ?x78;];]

*> Best rule #119 for first EXPECTED value:
*> intensional similarity = 21
*> extensional distance = 1
*> proper extension:  Spanish;
*> query: (?x539, ?x55) <- ?x539[ a Language; is language of ?x641[ a Country; has dependentOf ?x232[ has neighbor ?x83; has religion ?x187<Muslim>; is locatedIn of ?x231;]; has encompassed ?x175; has ethnicGroup ?x298; has religion ?x95;]; is language of ?x643[ a Country; has dependentOf ?x81; has encompassed ?x195[ is encompassed of ?x55;]; has ethnicGroup ?x1242; has government ?x254; is locatedIn of ?x642;]; is language of ?x789<AND>;]
*> conf = 0.40
ranks of expected_values: 44, 93
EVAL	Portuguese	language!	P	CNN-1.+1._MA	0.000	0.000	0.000	0.023	43.000	43.000	235.000	0.680	http://www.semwebtech.org/mondial/10/meta#language
EVAL	Portuguese	language!	STP	CNN-1.+1._MA	0.000	0.000	0.000	0.011	43.000	43.000	235.000	0.680	http://www.semwebtech.org/mondial/10/meta#language

#939-COCO
PRED entity: COCO

PRED relation: religion
PRED expected values: Muslim
=> 29 concepts (29 used for prediction)
PRED predicted values (max 10 best out of 28): Muslim (0.86 #209, 0.71 #127, 0.67 #250), RomanCatholic (0.52 #583, 0.52 #376, 0.51 #500), Hindu (0.43 #132, 0.29 #214, 0.22 #255), Protestant (0.42 #578, 0.41 #824, 0.41 #783), Buddhist (0.29 #216, 0.29 #134, 0.22 #257), Seventh-DayAdventist (0.18 #174, 0.16 #905, 0.16 #1070), Anglican (0.16 #905, 0.16 #1070, 0.15 #535), ChristianOrthodox (0.16 #905, 0.16 #1070, 0.15 #535), UnitingChurchAustralia (0.16 #905, 0.16 #1070, 0.09 #188), ChristianCongregationalist (0.16 #905, 0.16 #1070, 0.09 #185)
>> best conf = 0.86
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #209 for best value:
>> intensional similarity = 7
>> extensional distance = 12
>> proper extension:  AUS; RI; SP; YE; COM;
>> query: (?x906, Muslim) <- ?x906[ a Country; has government ?x907; has religion ?x116[ is religion of ?x416<SN>;]; is locatedIn of ?x60<IndianOcean>;]
ranks of expected_values: 1
EVAL	COCO	religion	Muslim	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	29.000	29.000	28.000	0.857	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion
PRED expected values: Muslim
=> 80 concepts (80 used for prediction)
PRED predicted values (max 10 best out of 39): Muslim (0.83 #2267, 0.83 #838, 0.76 #1762), RomanCatholic (0.67 #2398, 0.66 #1392, 0.64 #1385), Protestant (0.53 #2393, 0.49 #1718, 0.48 #2181), Hindu (0.36 #718, 0.36 #969, 0.33 #843), Buddhist (0.34 #1438, 0.33 #845, 0.33 #11), Seventh-DayAdventist (0.33 #386, 0.33 #1128, 0.21 #1224), Jewish (0.33 #3, 0.26 #1340, 0.18 #1002), Mormon (0.33 #25, 0.21 #3193, 0.20 #83), ChristianCongregationalist (0.33 #1128, 0.25 #188, 0.21 #3193), Anglican (0.31 #1019, 0.28 #1146, 0.27 #960)
>> best conf = 0.83
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2267 for best value:
>> intensional similarity = 25
>> extensional distance = 101
>> proper extension:  BG;
>> query: (?x906, Muslim) <- ?x906[ has government ?x907; has religion ?x116[ is religion of ?x115<RL>; is religion of ?x186<SUD>; is religion of ?x192<MOC>; is religion of ?x232<CN>; is religion of ?x302<IRQ>; is religion of ?x376<MAL>; is religion of ?x434<RM>; is religion of ?x538<BRU>; is religion of ?x568<WEST>; is religion of ?x797<MS>; is religion of ?x1731<XMAS>;]; is locatedIn of ?x60;]
ranks of expected_values: 1
EVAL	COCO	religion	Muslim	CNN-1.+1._MA	1.000	1.000	1.000	1.000	80.000	80.000	39.000	0.835	http://www.semwebtech.org/mondial/10/meta#religion

#938-GUAD
PRED entity: GUAD

PRED relation: language
PRED expected values: French
=> 43 concepts (43 used for prediction)
PRED predicted values (max 10 best out of 86): English (0.50 #102, 0.40 #788, 0.33 #200), Spanish (0.40 #1590, 0.38 #1492, 0.35 #904), French (0.33 #1, 0.17 #393, 0.13 #589), Dutch (0.12 #108, 0.11 #206, 0.09 #304), Papiamento (0.12 #112, 0.11 #210, 0.09 #308), Russian (0.09 #2853, 0.07 #3343, 0.07 #3441), German (0.08 #2073, 0.08 #2269, 0.07 #1779), Samoan (0.08 #395, 0.08 #493, 0.07 #591), Tongan (0.08 #483, 0.08 #581, 0.07 #679), Polynesian (0.08 #490, 0.07 #686, 0.03 #1274)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #102 for best value:
>> intensional similarity = 6
>> extensional distance = 6
>> proper extension:  CUR;
>> query: (?x633, English) <- ?x633[ a Country; has dependentOf ?x78; has encompassed ?x521; has religion ?x95; is locatedIn of ?x317<CaribbeanSea>;]

*> Best rule #1 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 4
*> proper extension:  SMAR;
*> query: (?x633, French) <- ?x633[ a Country; has dependentOf ?x78<F>; has encompassed ?x521<America>; has government ?x828; is locatedIn of ?x182;]
*> conf = 0.33
ranks of expected_values: 3
EVAL	GUAD	language	French	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.333	43.000	43.000	86.000	0.500	http://www.semwebtech.org/mondial/10/meta#language

PRED relation: language
PRED expected values: French
=> 65 concepts (65 used for prediction)
PRED predicted values (max 10 best out of 83): Spanish (0.55 #2671, 0.45 #2082, 0.43 #2180), English (0.53 #1574, 0.50 #887, 0.44 #1378), French (0.29 #1473, 0.25 #1276, 0.20 #1571), Portuguese (0.17 #793, 0.14 #1971, 0.10 #5890), Dutch (0.17 #893, 0.13 #1580, 0.07 #5989), Papiamento (0.17 #897, 0.13 #1584, 0.07 #5989), Chinese (0.17 #848, 0.12 #1339, 0.10 #2453), Polynesian (0.17 #882, 0.07 #1570, 0.05 #1962), Russian (0.14 #3546, 0.09 #4624, 0.07 #4428), German (0.12 #2369, 0.12 #1290, 0.10 #5890)
>> best conf = 0.55
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2671 for best value:
>> intensional similarity = 19
>> extensional distance = 27
>> proper extension:  ES;
>> query: (?x633, Spanish) <- ?x633[ a Country; has encompassed ?x521<America>; has religion ?x410; is locatedIn of ?x317[ has locatedIn ?x80[ has ethnicGroup ?x79; has religion ?x95;]; has locatedIn ?x181<GCA>; has locatedIn ?x408<NIC>; has locatedIn ?x783<PA>; has locatedIn ?x865[ has dependentOf ?x81;]; has locatedIn ?x899[ has government ?x2535;]; is locatedInWater of ?x123;];]

*> Best rule #1473 for first EXPECTED value:
*> intensional similarity = 17
*> extensional distance = 12
*> proper extension:  CEU;
*> query: (?x633, French) <- ?x633[ has dependentOf ?x78[ has government ?x435; has religion ?x95; is locatedIn of ?x742[ a River;]; is locatedIn of ?x1969[ a Source; has inMountains ?x261;]; is wasDependentOf of ?x94[ has neighbor ?x220;]; is wasDependentOf of ?x169[ a Country; is locatedIn of ?x168;]; is wasDependentOf of ?x736[ a Country; is neighbor of ?x186;];]; is locatedIn of ?x182;]
*> conf = 0.29
ranks of expected_values: 3
EVAL	GUAD	language	French	CNN-1.+1._MA	0.000	1.000	1.000	0.333	65.000	65.000	83.000	0.552	http://www.semwebtech.org/mondial/10/meta#language

#937-Uruguay
PRED entity: Uruguay

PRED relation: flowsInto
PRED expected values: AtlanticOcean
=> 34 concepts (24 used for prediction)
PRED predicted values (max 10 best out of 77): AtlanticOcean (0.14 #509, 0.14 #343, 0.10 #1669), Donau (0.08 #1499, 0.07 #1830, 0.07 #2326), BalticSea (0.05 #1667, 0.05 #2328, 0.04 #1832), Amazonas (0.05 #344, 0.04 #510, 0.02 #1657), Parana (0.05 #382, 0.04 #548, 0.02 #1657), RioSaoFrancisco (0.05 #479, 0.04 #645, 0.02 #1657), MediterraneanSea (0.04 #1680, 0.04 #2507, 0.04 #2176), Zaire (0.04 #1086, 0.04 #1251, 0.03 #1416), CaribbeanSea (0.04 #529, 0.02 #695, 0.02 #861), IndianOcean (0.03 #664, 0.03 #830, 0.02 #1161)
>> best conf = 0.14
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #509 for best value:
>> intensional similarity = 6
>> extensional distance = 26
>> proper extension:  Orinoco; Orinoco; LakeMaracaibo; PicoBolivar;
>> query: (?x1055, AtlanticOcean) <- ?x1055[ has locatedIn ?x363[ has government ?x700;]; has locatedIn ?x542[ has neighbor ?x179; is neighbor of ?x351<GUY>;];]
ranks of expected_values: 1
EVAL	Uruguay	flowsInto	AtlanticOcean	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	34.000	24.000	77.000	0.143	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto
PRED expected values: AtlanticOcean
=> 103 concepts (103 used for prediction)
PRED predicted values (max 10 best out of 142): AtlanticOcean (0.38 #664, 0.38 #509, 0.36 #998), Parana (0.33 #51, 0.20 #382, 0.12 #548), Tocantins (0.20 #420, 0.08 #8405, 0.07 #7055), Amazonas (0.12 #510, 0.08 #8405, 0.08 #6885), Donau (0.11 #5382, 0.11 #4365, 0.10 #3860), Zaire (0.10 #3271, 0.05 #6473, 0.05 #5465), PacificOcean (0.09 #1696, 0.07 #2365, 0.06 #2029), BalticSea (0.08 #3862, 0.07 #3357, 0.06 #6898), RioSaoFrancisco (0.08 #8405, 0.08 #6885, 0.07 #7055), RioMadeira (0.08 #8405, 0.07 #7055, 0.06 #2338)
>> best conf = 0.38
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #664 for best value:
>> intensional similarity = 11
>> extensional distance = 6
>> proper extension:  Amazonas; Tocantins; RioNegro; RioMadeira; RioSaoFrancisco;
>> query: (?x1055, ?x182) <- ?x1055[ a River; has locatedIn ?x363[ has encompassed ?x521; has ethnicGroup ?x162<African>; has language ?x796; is locatedIn of ?x182<AtlanticOcean>;]; has locatedIn ?x379[ has neighbor ?x202; has religion ?x95;];]
>> Best rule #509 for best value:
>> intensional similarity = 11
>> extensional distance = 6
>> proper extension:  Amazonas; Tocantins; RioNegro; RioMadeira; RioSaoFrancisco;
>> query: (?x1055, AtlanticOcean) <- ?x1055[ a River; has locatedIn ?x363[ has encompassed ?x521; has ethnicGroup ?x162<African>; has language ?x796; is locatedIn of ?x182<AtlanticOcean>;]; has locatedIn ?x379[ has neighbor ?x202; has religion ?x95;];]
ranks of expected_values: 1
EVAL	Uruguay	flowsInto	AtlanticOcean	CNN-1.+1._MA	1.000	1.000	1.000	1.000	103.000	103.000	142.000	0.375	http://www.semwebtech.org/mondial/10/meta#flowsInto

#936-RSA
PRED entity: RSA

PRED relation: neighbor
PRED expected values: SD
=> 39 concepts (39 used for prediction)
PRED predicted values (max 10 best out of 214): SD (0.90 #4087, 0.89 #4403, 0.89 #3455), RSA (0.60 #829, 0.40 #1143, 0.33 #358), EAT (0.50 #599, 0.40 #1070, 0.33 #285), Z (0.40 #872, 0.33 #401, 0.33 #244), ANG (0.40 #1236, 0.33 #451, 0.26 #1099), MW (0.33 #284, 0.27 #4561, 0.26 #1099), ZRE (0.25 #528, 0.20 #1943, 0.20 #1156), RCB (0.25 #716, 0.20 #1344, 0.12 #1659), RCA (0.20 #1373, 0.13 #2003, 0.09 #1531), SSD (0.20 #1925, 0.11 #2395, 0.10 #1768)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4087 for best value:
>> intensional similarity = 6
>> extensional distance = 99
>> proper extension:  DJI;
>> query: (?x243, ?x193) <- ?x243[ has ethnicGroup ?x2226; has neighbor ?x89; has wasDependentOf ?x81; is locatedIn of ?x182; is neighbor of ?x193[ has ethnicGroup ?x162;];]
ranks of expected_values: 1
EVAL	RSA	neighbor	SD	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	39.000	39.000	214.000	0.898	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: SD
=> 87 concepts (87 used for prediction)
PRED predicted values (max 10 best out of 219): SD (0.92 #7698, 0.91 #10935, 0.90 #10127), RG (0.50 #1119, 0.50 #1117, 0.38 #1118), ROU (0.50 #1119, 0.50 #1117, 0.38 #1118), BR (0.50 #1119, 0.50 #1117, 0.38 #1118), RA (0.50 #1119, 0.50 #1117, 0.38 #1118), RCH (0.50 #1119, 0.50 #1117, 0.38 #1118), RH (0.50 #1119, 0.50 #1117, 0.38 #1118), DOM (0.50 #1119, 0.50 #1117, 0.38 #1118), USA (0.50 #1119, 0.50 #1117, 0.38 #1118), IRL (0.50 #1119, 0.50 #1117, 0.38 #1118)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #7698 for best value:
>> intensional similarity = 16
>> extensional distance = 53
>> proper extension:  AND;
>> query: (?x243, ?x193) <- ?x243[ a Country; has encompassed ?x213; has ethnicGroup ?x2226; has neighbor ?x89[ has ethnicGroup ?x2491; is locatedIn of ?x137[ is flowsInto of ?x1054;]; is locatedIn of ?x2139[ a Source; has inMountains ?x2374;];]; has religion ?x187[ is religion of ?x701<MK>; is religion of ?x736<RCA>;]; is neighbor of ?x193;]
ranks of expected_values: 1
EVAL	RSA	neighbor	SD	CNN-1.+1._MA	1.000	1.000	1.000	1.000	87.000	87.000	219.000	0.918	http://www.semwebtech.org/mondial/10/meta#neighbor

#935-ChangbaiShan
PRED entity: ChangbaiShan

PRED relation: locatedIn
PRED expected values: CN
=> 37 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 186): R (0.90 #2132, 0.30 #5672, 0.29 #711), J (0.71 #490, 0.33 #471, 0.33 #254), ROK (0.67 #372, 0.50 #137, 0.29 #608), CN (0.57 #762, 0.33 #291, 0.33 #4073), CDN (0.33 #4788, 0.11 #6204, 0.11 #6441), USA (0.24 #1961, 0.21 #2436, 0.20 #6213), TR (0.23 #1221, 0.20 #1457, 0.11 #4058), PE (0.17 #4792, 0.16 #1010, 0.13 #1720), RC (0.17 #458, 0.14 #694, 0.04 #929), AUS (0.16 #4770, 0.07 #516, 0.06 #1698)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2132 for best value:
>> intensional similarity = 7
>> extensional distance = 110
>> proper extension:  Selenge; SeaofAzov; Suchona; Lena; BarentsSea; ArcticOcean; Swir; Swir; NorthernDwina; KuybyshevReservoir; ...
>> query: (?x2111, R) <- ?x2111[ has locatedIn ?x334[ a Country; has encompassed ?x175[ is encompassed of ?x185<TR>;]; is locatedIn of ?x271<SeaofJapan>;];]

*> Best rule #762 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 26
*> proper extension:  YuShan;
*> query: (?x2111, CN) <- ?x2111[ a Mountain; has locatedIn ?x334[ a Country; has encompassed ?x175; is locatedIn of ?x270[ has locatedIn ?x626<ROK>;];];]
*> conf = 0.57
ranks of expected_values: 4
EVAL	ChangbaiShan	locatedIn	CN	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.250	37.000	31.000	186.000	0.902	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: CN
=> 137 concepts (136 used for prediction)
PRED predicted values (max 10 best out of 203): R (0.97 #18794, 0.86 #19978, 0.86 #19746), CN (0.95 #14089, 0.89 #4572, 0.85 #3146), ROK (0.89 #7371, 0.84 #5470, 0.67 #1085), SF (0.81 #7266, 0.20 #840, 0.11 #23672), J (0.77 #3348, 0.33 #1186, 0.33 #967), SUD (0.62 #5275, 0.33 #42, 0.09 #11460), RI (0.58 #14800, 0.25 #8852, 0.22 #21932), N (0.55 #2409, 0.17 #20252, 0.15 #20728), USA (0.52 #24570, 0.36 #15769, 0.34 #15058), TR (0.47 #9080, 0.36 #3607, 0.18 #2655)
>> best conf = 0.97
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #18794 for best value:
>> intensional similarity = 12
>> extensional distance = 102
>> proper extension:  SeaofAzov; BlackSea; Suchona; Lena; Swir; Swir; NorthernDwina; KuybyshevReservoir; Volga; EastSibirianSea; ...
>> query: (?x2111, R) <- ?x2111[ has locatedIn ?x334[ a Country; has encompassed ?x175; has language ?x2244[ a Language;]; has wasDependentOf ?x117; is locatedIn of ?x270[ a Sea; is mergesWith of ?x620<EastChinaSea>;]; is locatedIn of ?x271<SeaofJapan>;];]

*> Best rule #14089 for first EXPECTED value:
*> intensional similarity = 12
*> extensional distance = 58
*> proper extension:  Hwangho; Ili; Zhoushan; Ordos; LopNor; Tarim-Yarkend; Tarim-Yarkend; Jangtse; Gobi; Hainan; ...
*> query: (?x2111, CN) <- ?x2111[ has locatedIn ?x334[ a Country; is locatedIn of ?x270<YellowSea>; is locatedIn of ?x271[ has mergesWith ?x282;]; is neighbor of ?x626[ has government ?x435<"republic">; has religion ?x116; has wasDependentOf ?x117; is locatedIn of ?x619;];];]
*> conf = 0.95
ranks of expected_values: 2
EVAL	ChangbaiShan	locatedIn	CN	CNN-1.+1._MA	0.000	1.000	1.000	0.500	137.000	136.000	203.000	0.971	http://www.semwebtech.org/mondial/10/meta#locatedIn

#934-AtlanticOcean
PRED entity: AtlanticOcean

PRED relation: flowsInto!
PRED expected values: Niger Uruguay Thjorsa
=> 25 concepts (25 used for prediction)
PRED predicted values (max 10 best out of 618): LakeMaracaibo (0.33 #556, 0.25 #1962, 0.05 #2526), RioSanJuan (0.33 #306, 0.25 #1712, 0.05 #2276), Ebro (0.33 #227, 0.05 #2478, 0.04 #2759), Rhone (0.33 #165, 0.05 #2416, 0.04 #2697), Nile (0.33 #268, 0.05 #2519, 0.04 #2800), Arno (0.33 #239, 0.05 #2490, 0.04 #2771), Tiber (0.33 #125, 0.05 #2376, 0.04 #2657), Etsch (0.33 #107, 0.05 #2358, 0.04 #2639), Po (0.33 #91, 0.05 #2342, 0.04 #2623), Drin (0.33 #90, 0.05 #2341, 0.04 #2622)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #556 for best value:
>> intensional similarity = 5
>> extensional distance = 1
>> proper extension:  CaribbeanSea;
>> query: (?x182, LakeMaracaibo) <- ?x182[ has locatedIn ?x745<MART>; is locatedInWater of ?x112; is mergesWith of ?x275[ is locatedInWater of ?x68;];]

*> Best rule #1970 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 2
*> proper extension:  St.Martin;
*> query: (?x182, ?x350) <- ?x182[ has locatedIn ?x628<SMAR>; has locatedIn ?x1206[ has religion ?x187; is locatedIn of ?x350;];]
*> conf = 0.03
ranks of expected_values: 329, 389, 455
EVAL	AtlanticOcean	flowsInto!	Thjorsa	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.002	25.000	25.000	618.000	0.333	http://www.semwebtech.org/mondial/10/meta#flowsInto
EVAL	AtlanticOcean	flowsInto!	Uruguay	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.003	25.000	25.000	618.000	0.333	http://www.semwebtech.org/mondial/10/meta#flowsInto
EVAL	AtlanticOcean	flowsInto!	Niger	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.003	25.000	25.000	618.000	0.333	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto!
PRED expected values: Niger Uruguay Thjorsa
=> 88 concepts (87 used for prediction)
PRED predicted values (max 10 best out of 665): LakeMaracaibo (0.33 #558, 0.25 #1689, 0.20 #3674), RioSanJuan (0.33 #308, 0.25 #1439, 0.20 #3424), Ebro (0.33 #227, 0.11 #4765, 0.10 #5332), Rhone (0.33 #165, 0.11 #4703, 0.10 #5270), Nile (0.33 #268, 0.11 #4806, 0.10 #5373), Arno (0.33 #239, 0.11 #4777, 0.10 #5344), Tiber (0.33 #125, 0.11 #4663, 0.10 #5230), Etsch (0.33 #107, 0.11 #4645, 0.10 #5212), Po (0.33 #91, 0.11 #4629, 0.10 #5196), Drin (0.33 #90, 0.11 #4628, 0.10 #5195)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #558 for best value:
>> intensional similarity = 13
>> extensional distance = 1
>> proper extension:  CaribbeanSea;
>> query: (?x182, LakeMaracaibo) <- ?x182[ has locatedIn ?x272[ a Country;]; has locatedIn ?x633<GUAD>; is locatedInWater of ?x477<Tobago>; is locatedInWater of ?x703<Barbuda>; is locatedInWater of ?x1000[ a Island; has type ?x150;]; is locatedInWater of ?x1117<Trinidad>;]

*> Best rule #283 for first EXPECTED value:
*> intensional similarity = 13
*> extensional distance = 1
*> proper extension:  MediterraneanSea;
*> query: (?x182, ?x771) <- ?x182[ has locatedIn ?x315[ a Country; has ethnicGroup ?x79; has religion ?x95; is neighbor of ?x482;]; has locatedIn ?x851<MA>; has locatedIn ?x1408[ is locatedIn of ?x771;]; has mergesWith ?x60; is flowsInto of ?x137; is locatedInWater of ?x1000[ has type ?x150;];]
*> conf = 0.04
ranks of expected_values: 133, 231, 356
EVAL	AtlanticOcean	flowsInto!	Thjorsa	CNN-1.+1._MA	0.000	0.000	0.000	0.003	88.000	87.000	665.000	0.333	http://www.semwebtech.org/mondial/10/meta#flowsInto
EVAL	AtlanticOcean	flowsInto!	Uruguay	CNN-1.+1._MA	0.000	0.000	0.000	0.008	88.000	87.000	665.000	0.333	http://www.semwebtech.org/mondial/10/meta#flowsInto
EVAL	AtlanticOcean	flowsInto!	Niger	CNN-1.+1._MA	0.000	0.000	0.000	0.004	88.000	87.000	665.000	0.333	http://www.semwebtech.org/mondial/10/meta#flowsInto

#933-AppalachianMountains
PRED entity: AppalachianMountains

PRED relation: inMountains!
PRED expected values: Tennessee ClingmansDome
=> 23 concepts (23 used for prediction)
PRED predicted values (max 10 best out of 352): MtMarcy (0.33 #185, 0.12 #441, 0.11 #698), HudsonRiver (0.33 #60, 0.12 #316, 0.11 #573), BarredesEcrins (0.12 #511, 0.05 #2052, 0.05 #2309), Grossglockner (0.12 #510, 0.05 #2051, 0.05 #2308), GranParadiso (0.12 #502, 0.05 #2043, 0.05 #2300), Marmolata (0.12 #499, 0.05 #2040, 0.05 #2297), CrapSognGion (0.12 #498, 0.05 #2039, 0.05 #2296), Ammer (0.12 #497, 0.05 #2038, 0.05 #2295), Matterhorn (0.12 #484, 0.05 #2025, 0.05 #2282), Finsteraarhorn (0.12 #472, 0.05 #2013, 0.05 #2270)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #185 for best value:
>> intensional similarity = 9
>> extensional distance = 1
>> proper extension:  Adirondacks;
>> query: (?x1262, MtMarcy) <- ?x1262[ a Mountains; is inMountains of ?x664[ a Mountain; has locatedIn ?x315<USA>;]; is inMountains of ?x1261[ is hasSource of ?x2118[ a River; has hasEstuary ?x1176;];];]

*> Best rule #2824 for first EXPECTED value:
*> intensional similarity = 9
*> extensional distance = 20
*> proper extension:  BlackForest; Apennin; Alps; Karakorum; Andes; Zagros; Pamir; Balkan; SnowyMountains; Altai; ...
*> query: (?x1262, ?x182) <- ?x1262[ a Mountains; is inMountains of ?x664[ a Mountain; has locatedIn ?x315[ is locatedIn of ?x182;];]; is inMountains of ?x1261[ is hasSource of ?x2118[ a River; has hasEstuary ?x1176;];];]
*> conf = 0.09
ranks of expected_values: 126, 178
EVAL	AppalachianMountains	inMountains!	ClingmansDome	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.008	23.000	23.000	352.000	0.333	http://www.semwebtech.org/mondial/10/meta#inMountains
EVAL	AppalachianMountains	inMountains!	Tennessee	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.006	23.000	23.000	352.000	0.333	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains!
PRED expected values: Tennessee ClingmansDome
=> 56 concepts (56 used for prediction)
PRED predicted values (max 10 best out of 352): MtMarcy (0.33 #185, 0.25 #441, 0.20 #1469), HudsonRiver (0.33 #60, 0.25 #316, 0.20 #1344), MurrayRiver (0.25 #443, 0.20 #700, 0.08 #5582), SnowyRiver (0.25 #438, 0.20 #695, 0.08 #5577), MurrumbidgeeRiver (0.25 #420, 0.20 #677, 0.08 #5559), EucumbeneRiver (0.25 #395, 0.20 #652, 0.08 #5534), Mt.Kosciuszko (0.25 #353, 0.20 #610, 0.08 #5492), Mt.Bogong (0.25 #323, 0.20 #580, 0.08 #5462), Mantaro (0.20 #1022, 0.17 #2564, 0.17 #2050), PicoBolivar (0.20 #1021, 0.17 #2563, 0.17 #2049)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #185 for best value:
>> intensional similarity = 12
>> extensional distance = 1
>> proper extension:  Adirondacks;
>> query: (?x1262, MtMarcy) <- ?x1262[ a Mountains; is inMountains of ?x664[ a Mountain; has locatedIn ?x315<USA>;]; is inMountains of ?x1261[ a Source; has locatedIn ?x315<USA>; is hasSource of ?x2118[ a River; has flowsInto ?x759; has hasEstuary ?x1176;];];]

*> Best rule #3083 for first EXPECTED value:
*> intensional similarity = 15
*> extensional distance = 6
*> proper extension:  Karpaten;
*> query: (?x1262, ?x182) <- ?x1262[ a Mountains; is inMountains of ?x664[ has locatedIn ?x315[ a Country; has ethnicGroup ?x79; has language ?x796; has religion ?x95<Protestant>; has wasDependentOf ?x81; is locatedIn of ?x182; is neighbor of ?x482;];]; is inMountains of ?x1261[ a Source;]; is inMountains of ?x1828[ a Mountain;];]
*> conf = 0.12
ranks of expected_values: 169, 221
EVAL	AppalachianMountains	inMountains!	ClingmansDome	CNN-1.+1._MA	0.000	0.000	0.000	0.006	56.000	56.000	352.000	0.333	http://www.semwebtech.org/mondial/10/meta#inMountains
EVAL	AppalachianMountains	inMountains!	Tennessee	CNN-1.+1._MA	0.000	0.000	0.000	0.005	56.000	56.000	352.000	0.333	http://www.semwebtech.org/mondial/10/meta#inMountains

#932-TOK
PRED entity: TOK

PRED relation: locatedIn!
PRED expected values: Fakaofo
=> 33 concepts (30 used for prediction)
PRED predicted values (max 10 best out of 1056): AtlanticOcean (0.50 #18544, 0.45 #11428, 0.45 #24239), CaribbeanSea (0.46 #7220, 0.44 #4374, 0.33 #14337), Rarotonga (0.33 #994, 0.14 #2417, 0.12 #3840), Tahiti (0.14 #2581, 0.12 #4004, 0.08 #8273), Uvea (0.14 #2122, 0.12 #3545, 0.08 #7814), Futuna (0.14 #1605, 0.12 #3028, 0.08 #7297), MontOrohena (0.14 #2644, 0.12 #4067, 0.08 #8336), SouthChinaSea (0.14 #8678, 0.11 #4409, 0.11 #31316), Pitcairn (0.14 #2179, 0.08 #6448, 0.08 #7871), GrandeTerre (0.14 #2338, 0.08 #8030, 0.05 #13724)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #18544 for best value:
>> intensional similarity = 5
>> extensional distance = 24
>> proper extension:  GROX; FALK;
>> query: (?x1819, AtlanticOcean) <- ?x1819[ has dependentOf ?x461[ has language ?x51; is locatedIn of ?x587;]; has ethnicGroup ?x1335; is locatedIn of ?x282;]

*> Best rule #34165 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 162
*> proper extension:  WV; NOK; MV; M; AG; MNTS; SBAR; MEL;
*> query: (?x1819, ?x205) <- ?x1819[ a Country; has encompassed ?x211; is locatedIn of ?x282[ has mergesWith ?x60; is locatedInWater of ?x205;];]
*> conf = 0.03
ranks of expected_values: 511
EVAL	TOK	locatedIn!	Fakaofo	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.002	33.000	30.000	1056.000	0.500	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Fakaofo
=> 60 concepts (58 used for prediction)
PRED predicted values (max 10 best out of 1330): AtlanticOcean (0.50 #58496, 0.46 #18565, 0.45 #62783), CaribbeanSea (0.46 #32886, 0.28 #38591, 0.28 #35742), IndianOcean (0.35 #34211, 0.23 #17099, 0.20 #2851), Tahiti (0.26 #28504, 0.25 #2582, 0.17 #6855), Uvea (0.26 #28504, 0.25 #2123, 0.17 #6396), Futuna (0.26 #28504, 0.25 #1606, 0.17 #5879), MontOrohena (0.26 #28504, 0.25 #2645, 0.17 #6918), Rarotonga (0.20 #3842, 0.14 #9540, 0.12 #12391), NorwegianSea (0.20 #2983, 0.05 #37197, 0.05 #38621), BarentsSea (0.20 #2915, 0.05 #32781, 0.04 #31424)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #58496 for best value:
>> intensional similarity = 10
>> extensional distance = 132
>> proper extension:  PNG;
>> query: (?x1819, AtlanticOcean) <- ?x1819[ is locatedIn of ?x282[ has locatedIn ?x196[ has religion ?x713<Anglican>;]; has locatedIn ?x315[ has ethnicGroup ?x79; has religion ?x95;]; has locatedIn ?x1364[ has encompassed ?x521; has government ?x1535;];];]

*> Best rule #18523 for first EXPECTED value:
*> intensional similarity = 16
*> extensional distance = 11
*> proper extension:  XMAS;
*> query: (?x1819, ?x205) <- ?x1819[ has encompassed ?x211<Australia-Oceania>; has ethnicGroup ?x1335; has religion ?x352[ is religion of ?x217<RI>; is religion of ?x351<GUY>; is religion of ?x690[ has government ?x2135; is locatedIn of ?x274;]; is religion of ?x793<DK>;]; is locatedIn of ?x282[ is locatedInWater of ?x205; is mergesWith of ?x60;];]
*> conf = 0.05
ranks of expected_values: 505
EVAL	TOK	locatedIn!	Fakaofo	CNN-1.+1._MA	0.000	0.000	0.000	0.002	60.000	58.000	1330.000	0.500	http://www.semwebtech.org/mondial/10/meta#locatedIn

#931-Perene
PRED entity: Perene

PRED relation: flowsInto
PRED expected values: Tambo
=> 47 concepts (32 used for prediction)
PRED predicted values (max 10 best out of 76): Amazonas (0.25 #508, 0.25 #13, 0.22 #178), Ucayali (0.25 #96, 0.22 #261, 0.20 #426), Tambo (0.20 #440, 0.17 #605, 0.12 #110), Maranon (0.11 #257, 0.10 #422, 0.08 #587), AtlanticOcean (0.09 #2163, 0.09 #838, 0.09 #1997), RioMadeira (0.08 #634), Donau (0.07 #1164, 0.07 #2159, 0.07 #1662), BalticSea (0.06 #836, 0.05 #1001, 0.04 #2161), MediterraneanSea (0.05 #1179, 0.04 #849, 0.04 #1345), Zaire (0.04 #1082, 0.03 #2242, 0.03 #1413)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #508 for best value:
>> intensional similarity = 5
>> extensional distance = 10
>> proper extension:  RioMamore; RioMadeira;
>> query: (?x1350, Amazonas) <- ?x1350[ a River; has hasEstuary ?x2043; has locatedIn ?x296[ is locatedIn of ?x480<LakeTiticaca>;];]
>> Best rule #13 for best value:
>> intensional similarity = 6
>> extensional distance = 6
>> proper extension:  Maranon; Ucayali; Ene; Tambo; Urubamba; Apurimac;
>> query: (?x1350, Amazonas) <- ?x1350[ a River; has hasEstuary ?x2043; has hasSource ?x1351[ has inMountains ?x431;]; has locatedIn ?x296<PE>;]

*> Best rule #440 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 8
*> proper extension:  Mantaro;
*> query: (?x1350, Tambo) <- ?x1350[ a River; has hasEstuary ?x2043[ a Estuary;]; has locatedIn ?x296<PE>;]
*> conf = 0.20
ranks of expected_values: 3
EVAL	Perene	flowsInto	Tambo	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.333	47.000	32.000	76.000	0.250	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto
PRED expected values: Tambo
=> 105 concepts (105 used for prediction)
PRED predicted values (max 10 best out of 132): Amazonas (0.29 #13, 0.25 #840, 0.25 #673), Ucayali (0.25 #261, 0.20 #591, 0.17 #923), Tambo (0.20 #605, 0.17 #770, 0.14 #110), AtlanticOcean (0.19 #1843, 0.19 #1510, 0.12 #1175), Donau (0.17 #2175, 0.16 #3510, 0.15 #3677), Maranon (0.12 #422, 0.12 #7185, 0.11 #5843), PacificOcean (0.11 #5843, 0.08 #1497, 0.08 #1354), Mantaro (0.11 #5843, 0.07 #994, 0.06 #1665), RioDesaguadero (0.11 #1495, 0.07 #1998, 0.03 #1714), RioMadeira (0.08 #966, 0.08 #799, 0.04 #1302)
>> best conf = 0.29
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #13 for best value:
>> intensional similarity = 11
>> extensional distance = 5
>> proper extension:  Maranon; Ucayali; Ene; Tambo; Apurimac;
>> query: (?x1350, Amazonas) <- ?x1350[ a River; has hasEstuary ?x2043[ a Estuary; has locatedIn ?x296<PE>;]; has hasSource ?x1351[ a Source; has inMountains ?x431<Andes>; has locatedIn ?x296<PE>;]; has locatedIn ?x296<PE>;]

*> Best rule #605 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 8
*> proper extension:  Mantaro;
*> query: (?x1350, Tambo) <- ?x1350[ a River; has hasEstuary ?x2043[ a Estuary; has locatedIn ?x296<PE>;]; has locatedIn ?x296<PE>;]
*> conf = 0.20
ranks of expected_values: 3
EVAL	Perene	flowsInto	Tambo	CNN-1.+1._MA	0.000	1.000	1.000	0.333	105.000	105.000	132.000	0.286	http://www.semwebtech.org/mondial/10/meta#flowsInto

#930-SRB
PRED entity: SRB

PRED relation: neighbor!
PRED expected values: HR KOS MK
=> 47 concepts (47 used for prediction)
PRED predicted values (max 10 best out of 210): KOS (0.91 #3457, 0.91 #3456, 0.91 #2986), MK (0.91 #3457, 0.91 #3456, 0.91 #2986), SK (0.50 #20, 0.33 #177, 0.26 #4558), UA (0.50 #205, 0.26 #4558, 0.25 #48), A (0.33 #229, 0.26 #4558, 0.25 #72), SLO (0.33 #231, 0.26 #4558, 0.25 #74), CZ (0.33 #235, 0.10 #5660, 0.10 #866), R (0.27 #634, 0.25 #2, 0.23 #790), HR (0.26 #4558, 0.25 #19, 0.17 #5661), SRB (0.26 #4558, 0.17 #5661, 0.17 #291)
>> best conf = 0.91
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #3457 for best value:
>> intensional similarity = 5
>> extensional distance = 82
>> proper extension:  PK;
>> query: (?x904, ?x55) <- ?x904[ a Country; has language ?x684; has neighbor ?x55[ has religion ?x56;]; is locatedIn of ?x132;]
ranks of expected_values: 1, 2, 9
EVAL	SRB	neighbor!	MK	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	47.000	47.000	210.000	0.914	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	SRB	neighbor!	KOS	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	47.000	47.000	210.000	0.914	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	SRB	neighbor!	HR	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.143	47.000	47.000	210.000	0.914	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: HR KOS MK
=> 96 concepts (96 used for prediction)
PRED predicted values (max 10 best out of 218): KOS (0.94 #6592, 0.94 #8543, 0.93 #7078), MK (0.94 #6592, 0.94 #8543, 0.93 #7078), SRB (0.50 #1116, 0.50 #931, 0.49 #7894), SK (0.50 #1116, 0.50 #977, 0.49 #7894), A (0.50 #1116, 0.49 #7894, 0.46 #3681), HR (0.50 #1116, 0.49 #7894, 0.46 #3681), SLO (0.50 #551, 0.35 #7896, 0.33 #234), UA (0.49 #7894, 0.46 #3681, 0.40 #160), MD (0.49 #7894, 0.46 #3681, 0.40 #160), D (0.49 #7894, 0.46 #3681, 0.40 #160)
>> best conf = 0.94
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #6592 for best value:
>> intensional similarity = 15
>> extensional distance = 47
>> proper extension:  GCA; IL; SLO; B; RG; PA; MNG; HCA;
>> query: (?x904, ?x177) <- ?x904[ has government ?x435; has language ?x684; has neighbor ?x55[ a Country; has language ?x1241;]; has neighbor ?x176[ a Country; is locatedIn of ?x98;]; has neighbor ?x177[ has ethnicGroup ?x164; has neighbor ?x185; has wasDependentOf ?x1656;]; has religion ?x56; has wasDependentOf ?x1197; is locatedIn of ?x132;]
ranks of expected_values: 1, 2, 6
EVAL	SRB	neighbor!	MK	CNN-1.+1._MA	1.000	1.000	1.000	1.000	96.000	96.000	218.000	0.941	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	SRB	neighbor!	KOS	CNN-1.+1._MA	1.000	1.000	1.000	1.000	96.000	96.000	218.000	0.941	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	SRB	neighbor!	HR	CNN-1.+1._MA	0.000	0.000	1.000	0.250	96.000	96.000	218.000	0.941	http://www.semwebtech.org/mondial/10/meta#neighbor

#929-Ural
PRED entity: Ural

PRED relation: hasSource!
PRED expected values: Ural
=> 26 concepts (21 used for prediction)
PRED predicted values (max 10 best out of 106): Katun (0.04 #219, 0.03 #447, 0.01 #457), Schilka (0.04 #216, 0.03 #444, 0.01 #457), Kama (0.04 #200, 0.03 #428, 0.01 #457), Amur (0.04 #184, 0.03 #412, 0.01 #457), Oka (0.04 #182, 0.03 #410, 0.01 #457), WesternDwina (0.04 #170, 0.03 #398, 0.01 #457), Kolyma (0.04 #164, 0.03 #392, 0.01 #457), Don (0.04 #147, 0.03 #375, 0.01 #457), Petschora (0.04 #139, 0.03 #367, 0.01 #457), Lena (0.04 #109, 0.03 #337, 0.01 #457)
>> best conf = 0.04
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #219 for best value:
>> intensional similarity = 3
>> extensional distance = 22
>> proper extension:  NorthernDwina; Volga; Chatanga; Tobol; Schilka; WesternDwina; Lena; Ob; Katun; Suchona; ...
>> query: (?x1507, Katun) <- ?x1507[ a Source; has locatedIn ?x73<R>;]

*> Best rule #457 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 27
*> proper extension:  Kura; Murat; Euphrat; Tigris; Karasu;
*> query: (?x1507, ?x72) <- ?x1507[ a Source; has locatedIn ?x73[ has neighbor ?x353<GE>; is locatedIn of ?x72;];]
*> conf = 0.01
ranks of expected_values: 75
EVAL	Ural	hasSource!	Ural	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.013	26.000	21.000	106.000	0.042	http://www.semwebtech.org/mondial/10/meta#hasSource

PRED relation: hasSource!
PRED expected values: Ural
=> 89 concepts (89 used for prediction)
PRED predicted values (max 10 best out of 225): Dnepr (0.07 #5059, 0.07 #6677, 0.07 #1836), Angara (0.07 #5059, 0.07 #6677, 0.07 #1836), Volga (0.07 #5059, 0.07 #6677, 0.07 #1836), Amur (0.07 #5059, 0.07 #6677, 0.07 #1836), Narva (0.07 #5059, 0.07 #6677, 0.07 #1836), Jenissej (0.07 #5059, 0.07 #6677, 0.07 #1836), Newa (0.07 #5059, 0.07 #6677, 0.07 #1836), Swir (0.07 #5059, 0.07 #6677, 0.07 #1836), Katun (0.07 #5059, 0.07 #6677, 0.07 #1836), Schilka (0.07 #5059, 0.07 #6677, 0.07 #1836)
>> best conf = 0.07
=> the first rule below is the first best rule for 27 predicted values
>> Best rule #5059 for best value:
>> intensional similarity = 9
>> extensional distance = 137
>> proper extension:  Buna; Moraca; Piva; Bani; Senegal; WhiteDrin; Tara; Drina; Niger; Orinoco; ...
>> query: (?x1507, ?x72) <- ?x1507[ a Source; has locatedIn ?x73[ has neighbor ?x403; has wasDependentOf ?x903; is locatedIn of ?x72[ a River;]; is neighbor of ?x591[ has encompassed ?x195; has ethnicGroup ?x58;];];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 22
EVAL	Ural	hasSource!	Ural	CNN-1.+1._MA	0.000	0.000	0.000	0.045	89.000	89.000	225.000	0.073	http://www.semwebtech.org/mondial/10/meta#hasSource

#928-GasherbrumI
PRED entity: GasherbrumI

PRED relation: inMountains
PRED expected values: Karakorum
=> 37 concepts (37 used for prediction)
PRED predicted values (max 10 best out of 45): Himalaya (0.30 #180, 0.21 #93, 0.18 #6), RockyMountains (0.13 #877, 0.06 #1661, 0.06 #1487), Pamir (0.12 #365, 0.06 #17, 0.05 #104), Kunlun (0.12 #10, 0.11 #97, 0.10 #184), Alps (0.07 #1571, 0.07 #1658, 0.07 #1745), Andes (0.07 #1491, 0.06 #1752, 0.06 #1839), TianShan (0.06 #31, 0.05 #118, 0.05 #205), Karakorum (0.06 #8, 0.05 #95, 0.05 #182), Transhimalaya (0.06 #24, 0.05 #111, 0.05 #198), Hindukusch (0.05 #165, 0.01 #1471)
>> best conf = 0.30
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #180 for best value:
>> intensional similarity = 7
>> extensional distance = 18
>> proper extension:  Annapurna; Kangchendzonga; Dhaulagiri;
>> query: (?x2471, Himalaya) <- ?x2471[ a Mountain; has locatedIn ?x232[ has neighbor ?x73; has neighbor ?x83[ a Country;]; is locatedIn of ?x1771<Makalu>;];]

*> Best rule #8 for first EXPECTED value:
*> intensional similarity = 3
*> extensional distance = 15
*> proper extension:  Lhotse; UlugMuztag; GasherbrumII; ChoOyu; Muztagata; Kongur; Kailash; LiushiShan; K2; PikChan-Tengri; ...
*> query: (?x2471, Karakorum) <- ?x2471[ a Mountain; has locatedIn ?x232<CN>;]
*> conf = 0.06
ranks of expected_values: 8
EVAL	GasherbrumI	inMountains	Karakorum	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.125	37.000	37.000	45.000	0.300	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains
PRED expected values: Karakorum
=> 89 concepts (89 used for prediction)
PRED predicted values (max 10 best out of 66): Himalaya (0.30 #528, 0.26 #267, 0.24 #2616), EastAfricanRift (0.24 #813, 0.07 #901, 0.05 #2556), Pamir (0.24 #2616, 0.22 #2354, 0.22 #627), Kunlun (0.24 #2616, 0.22 #2354, 0.21 #3577), TianShan (0.24 #2616, 0.22 #2354, 0.21 #3577), Karakorum (0.24 #2616, 0.22 #2354, 0.21 #3577), Transhimalaya (0.24 #2616, 0.22 #2354, 0.21 #3577), Kaukasus (0.16 #716, 0.10 #1067, 0.07 #1328), Andes (0.10 #1668, 0.08 #2976, 0.08 #2539), RockyMountains (0.10 #2448, 0.10 #2361, 0.10 #2623)
>> best conf = 0.30
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #528 for best value:
>> intensional similarity = 11
>> extensional distance = 18
>> proper extension:  Annapurna; Dhaulagiri;
>> query: (?x2471, Himalaya) <- ?x2471[ a Mountain; has locatedIn ?x232[ has encompassed ?x175; has religion ?x187<Muslim>; is locatedIn of ?x576<ChoOyu>; is locatedIn of ?x1375[ has inMountains ?x368;]; is neighbor of ?x617[ has religion ?x95;];];]

*> Best rule #2616 for first EXPECTED value:
*> intensional similarity = 13
*> extensional distance = 90
*> proper extension:  Illampu; Illimani;
*> query: (?x2471, ?x950) <- ?x2471[ a Mountain; has locatedIn ?x232[ has government ?x831; has religion ?x116; is locatedIn of ?x472[ a River;]; is locatedIn of ?x818[ has inMountains ?x950;]; is locatedIn of ?x1881[ has type ?x762;]; is neighbor of ?x924[ has religion ?x410; is locatedIn of ?x60;];];]
*> conf = 0.24
ranks of expected_values: 6
EVAL	GasherbrumI	inMountains	Karakorum	CNN-1.+1._MA	0.000	0.000	1.000	0.167	89.000	89.000	66.000	0.300	http://www.semwebtech.org/mondial/10/meta#inMountains

#927-Vaesterdalaelv
PRED entity: Vaesterdalaelv

PRED relation: locatedIn
PRED expected values: S
=> 41 concepts (41 used for prediction)
PRED predicted values (max 10 best out of 94): S (0.92 #8538, 0.92 #4977, 0.91 #7825), R (0.57 #1426, 0.17 #4982, 0.16 #4744), D (0.26 #1205, 0.17 #2390, 0.17 #2626), CH (0.26 #1242, 0.12 #2190, 0.09 #5746), N (0.25 #982, 0.14 #4028, 0.13 #7113), SF (0.18 #1553, 0.14 #4028, 0.13 #7113), ZRE (0.17 #1739, 0.16 #3396, 0.15 #1976), USA (0.13 #4811, 0.12 #5287, 0.11 #6237), I (0.11 #1233, 0.07 #3839, 0.06 #5263), AUS (0.11 #1230, 0.05 #2178, 0.04 #7159)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #8538 for best value:
>> intensional similarity = 7
>> extensional distance = 180
>> proper extension:  Leine; Neckar; Niger; VictoriaNile; Hwangho; SchattalArab; Okavango; Weser; Luapula;
>> query: (?x1118, ?x402) <- ?x1118[ a River; has hasEstuary ?x1119[ a Estuary; has locatedIn ?x402[ a Country; has ethnicGroup ?x1473; is neighbor of ?x170;];];]
ranks of expected_values: 1
EVAL	Vaesterdalaelv	locatedIn	S	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	41.000	41.000	94.000	0.921	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: S
=> 133 concepts (133 used for prediction)
PRED predicted values (max 10 best out of 104): S (0.96 #27890, 0.94 #23356, 0.94 #21210), R (0.78 #7153, 0.75 #7631, 0.67 #10253), SF (0.42 #3941, 0.38 #4417, 0.33 #132), USA (0.41 #9604, 0.34 #9842, 0.29 #6266), N (0.33 #273, 0.29 #2410, 0.27 #3365), CH (0.30 #8873, 0.28 #6489, 0.25 #4105), SRB (0.29 #6140, 0.26 #7094, 0.25 #6855), D (0.27 #20753, 0.26 #6929, 0.25 #20992), A (0.26 #7008, 0.20 #11298, 0.18 #14393), CDN (0.25 #7928, 0.24 #11025, 0.21 #10788)
>> best conf = 0.96
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #27890 for best value:
>> intensional similarity = 16
>> extensional distance = 83
>> proper extension:  Schari;
>> query: (?x1118, ?x402) <- ?x1118[ a River; has flowsInto ?x1328[ has locatedIn ?x402[ a Country; has ethnicGroup ?x1473; has government ?x92; has neighbor ?x170; has religion ?x95; is locatedIn of ?x1119[ a Estuary;]; is locatedIn of ?x1327; is neighbor of ?x170;]; is flowsInto of ?x1327;]; has hasEstuary ?x1119; has hasSource ?x1870[ a Source;];]
ranks of expected_values: 1
EVAL	Vaesterdalaelv	locatedIn	S	CNN-1.+1._MA	1.000	1.000	1.000	1.000	133.000	133.000	104.000	0.957	http://www.semwebtech.org/mondial/10/meta#locatedIn

#926-Muztagata
PRED entity: Muztagata

PRED relation: inMountains
PRED expected values: Pamir
=> 34 concepts (34 used for prediction)
PRED predicted values (max 10 best out of 43): Himalaya (0.21 #958, 0.21 #93, 0.18 #6), Kunlun (0.21 #958, 0.12 #10, 0.11 #97), Pamir (0.21 #958, 0.06 #17, 0.05 #104), TianShan (0.21 #958, 0.06 #31, 0.05 #118), Karakorum (0.21 #958, 0.06 #8, 0.05 #95), Transhimalaya (0.21 #958, 0.06 #24, 0.05 #111), RockyMountains (0.12 #355, 0.06 #1052, 0.06 #877), Alps (0.08 #787, 0.08 #874, 0.06 #1136), Andes (0.06 #881, 0.06 #969, 0.05 #1143), Kaukasus (0.05 #367, 0.03 #802, 0.03 #889)
>> best conf = 0.21
=> the first rule below is the first best rule for 6 predicted values
>> Best rule #958 for best value:
>> intensional similarity = 5
>> extensional distance = 154
>> proper extension:  Annapurna; GranSasso; MontBlanc; PuyDeDome; Etna; MonteFalterona; Pico; PicoRuivo; MonteCinto; PuydeSancy; ...
>> query: (?x686, ?x368) <- ?x686[ a Mountain; has locatedIn ?x232[ is locatedIn of ?x1375[ has inMountains ?x368;]; is neighbor of ?x73;];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 3
EVAL	Muztagata	inMountains	Pamir	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.333	34.000	34.000	43.000	0.212	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains
PRED expected values: Pamir
=> 83 concepts (83 used for prediction)
PRED predicted values (max 10 best out of 56): Himalaya (0.21 #93, 0.18 #6, 0.17 #2617), Kunlun (0.17 #2617, 0.13 #1137, 0.12 #437), Pamir (0.17 #2617, 0.13 #1137, 0.12 #437), TianShan (0.17 #2617, 0.13 #1137, 0.12 #437), Karakorum (0.17 #2617, 0.13 #1137, 0.12 #437), Transhimalaya (0.17 #2617, 0.13 #1137, 0.12 #437), RockyMountains (0.17 #1056, 0.16 #356, 0.13 #1579), Alps (0.09 #1228, 0.08 #1663, 0.08 #1750), Andes (0.08 #1060, 0.07 #2715, 0.06 #3238), Kaukasus (0.07 #368, 0.06 #1068, 0.06 #1243)
>> best conf = 0.21
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #93 for best value:
>> intensional similarity = 13
>> extensional distance = 17
>> proper extension:  NangaParbat; TirichMir;
>> query: (?x686, Himalaya) <- ?x686[ a Mountain; has locatedIn ?x232[ a Country; has neighbor ?x73[ has ethnicGroup ?x58;]; is locatedIn of ?x384[ is locatedInWater of ?x518;]; is locatedIn of ?x484<GasherbrumII>; is locatedIn of ?x748[ a Mountain; has inMountains ?x749;]; is wasDependentOf of ?x1010;];]

*> Best rule #2617 for first EXPECTED value:
*> intensional similarity = 12
*> extensional distance = 109
*> proper extension:  Olympos; Fogo; PicoTurquino;
*> query: (?x686, ?x309) <- ?x686[ a Mountain; has locatedIn ?x232[ is locatedIn of ?x270[ a Sea; is mergesWith of ?x271;]; is locatedIn of ?x576[ a Mountain; has inMountains ?x309;]; is locatedIn of ?x620[ has mergesWith ?x282;]; is locatedIn of ?x873[ a Island;];];]
*> conf = 0.17
ranks of expected_values: 3
EVAL	Muztagata	inMountains	Pamir	CNN-1.+1._MA	0.000	1.000	1.000	0.333	83.000	83.000	56.000	0.211	http://www.semwebtech.org/mondial/10/meta#inMountains

#925-Aare
PRED entity: Aare

PRED relation: inMountains
PRED expected values: Alps
=> 36 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 24): Alps (0.27 #4, 0.13 #178, 0.11 #91), EastAfricanRift (0.12 #289, 0.08 #463, 0.08 #550), Andes (0.09 #98, 0.07 #185, 0.07 #707), SnowyMountains (0.05 #369, 0.04 #543, 0.04 #630), Balkan (0.05 #716, 0.03 #1325, 0.03 #1151), Pamir (0.05 #104, 0.04 #191, 0.03 #626), Vogesen (0.04 #222, 0.02 #135, 0.02 #744), Karpaten (0.03 #748, 0.03 #922, 0.03 #1009), SudetyMountains (0.02 #145, 0.02 #754, 0.02 #232), BlackForest (0.02 #88, 0.02 #175, 0.02 #871)
>> best conf = 0.27
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4 for best value:
>> intensional similarity = 2
>> extensional distance = 28
>> proper extension:  Aare; Inn; MonteRosa; Limmat; Limmat; Aare; Reuss; Doubs; PizBernina; Vierwaldstattersee; ...
>> query: (?x1641, Alps) <- ?x1641[ has locatedIn ?x234<CH>;]
ranks of expected_values: 1
EVAL	Aare	inMountains	Alps	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	36.000	36.000	24.000	0.267	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains
PRED expected values: Alps
=> 123 concepts (123 used for prediction)
PRED predicted values (max 10 best out of 41): Alps (0.53 #787, 0.50 #613, 0.44 #1396), CordilleraIberica (0.27 #577, 0.10 #1795, 0.06 #1882), BlackForest (0.25 #88, 0.08 #1393, 0.07 #1480), Vogesen (0.20 #744, 0.13 #1353, 0.06 #1875), Balkan (0.19 #1760, 0.07 #3066, 0.07 #2804), SudetyMountains (0.18 #1015, 0.03 #2755, 0.03 #2929), Apennin (0.14 #612, 0.13 #786, 0.09 #1308), SnowyMountains (0.13 #891, 0.10 #1065, 0.07 #1587), EastAfricanRift (0.13 #2029, 0.06 #3248, 0.05 #3857), Beskides (0.12 #987, 0.05 #1248, 0.03 #1944)
>> best conf = 0.53
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #787 for best value:
>> intensional similarity = 14
>> extensional distance = 13
>> proper extension:  Mincio;
>> query: (?x1641, Alps) <- ?x1641[ a Source; has locatedIn ?x234[ has encompassed ?x195; has language ?x51<French>; has neighbor ?x78; is locatedIn of ?x958[ a River;]; is locatedIn of ?x1123<PizBernina>; is locatedIn of ?x1840<LagoMaggiore>; is locatedIn of ?x2011[ a Lake;];];]
ranks of expected_values: 1
EVAL	Aare	inMountains	Alps	CNN-1.+1._MA	1.000	1.000	1.000	1.000	123.000	123.000	41.000	0.533	http://www.semwebtech.org/mondial/10/meta#inMountains

#924-BIH
PRED entity: BIH

PRED relation: locatedIn!
PRED expected values: Tara
=> 42 concepts (32 used for prediction)
PRED predicted values (max 10 best out of 1354): AtlanticOcean (0.36 #32611, 0.36 #8537, 0.35 #15618), Donau (0.33 #26, 0.21 #7106, 0.21 #4274), Theiss (0.33 #323, 0.09 #4571, 0.08 #5987), SouthernMorava (0.33 #832, 0.07 #7912, 0.05 #10745), VelikiRatnoOstrvo (0.33 #32, 0.03 #4280, 0.03 #9913), Theiss (0.33 #1402, 0.03 #5650, 0.03 #7066), Morava (0.33 #1318, 0.03 #5566, 0.03 #6982), SouthernMorava (0.33 #1242, 0.03 #5490, 0.03 #6906), Save (0.33 #1236, 0.03 #5484, 0.03 #6900), WesternMorava (0.33 #731, 0.03 #4979, 0.03 #6395)
>> best conf = 0.36
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #32611 for best value:
>> intensional similarity = 4
>> extensional distance = 108
>> proper extension:  WV; MV; AG; PAL;
>> query: (?x55, AtlanticOcean) <- ?x55[ has wasDependentOf ?x1197; is locatedIn of ?x275[ is locatedInWater of ?x68; is mergesWith of ?x182;];]
>> Best rule #8537 for best value:
>> intensional similarity = 5
>> extensional distance = 42
>> proper extension:  BS; WD;
>> query: (?x55, AtlanticOcean) <- ?x55[ has religion ?x95<Protestant>; has wasDependentOf ?x1197; is locatedIn of ?x275[ is locatedInWater of ?x68;];]

*> Best rule #2809 for first EXPECTED value:
*> intensional similarity = 4
*> extensional distance = 14
*> proper extension:  M;
*> query: (?x55, Tara) <- ?x55[ has encompassed ?x195; has wasDependentOf ?x1197; is locatedIn of ?x275<MediterraneanSea>;]
*> conf = 0.06
ranks of expected_values: 93
EVAL	BIH	locatedIn!	Tara	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.011	42.000	32.000	1354.000	0.364	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Tara
=> 105 concepts (96 used for prediction)
PRED predicted values (max 10 best out of 1381): Donau (0.75 #9925, 0.73 #25547, 0.64 #52478), Tara (0.74 #52479, 0.73 #66663, 0.70 #73754), Piva (0.74 #52479, 0.73 #66663, 0.70 #73754), Save (0.74 #52479, 0.73 #66663, 0.70 #73754), AtlanticOcean (0.73 #117776, 0.66 #83724, 0.47 #104997), Drau (0.50 #7362, 0.50 #4255, 0.33 #13035), Mur (0.50 #7124, 0.50 #4255, 0.33 #5706), PacificOcean (0.50 #41214, 0.41 #36957, 0.38 #73839), Mur (0.50 #4255, 0.33 #6068, 0.33 #3234), Drau (0.50 #4255, 0.33 #3979, 0.33 #2838)
>> best conf = 0.75
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #9925 for best value:
>> intensional similarity = 17
>> extensional distance = 2
>> proper extension:  F; I;
>> query: (?x55, ?x133) <- ?x55[ a Country; has government ?x2074; has neighbor ?x106[ a Country; has ethnicGroup ?x775; has neighbor ?x204; is locatedIn of ?x104;]; has religion ?x95<Protestant>; is locatedIn of ?x152[ a River; has flowsInto ?x133; has hasSource ?x1363;]; is locatedIn of ?x275<MediterraneanSea>; is locatedIn of ?x825[ has inMountains ?x785;];]

*> Best rule #56733 for first EXPECTED value:
*> intensional similarity = 14
*> extensional distance = 25
*> proper extension:  ROU;
*> query: (?x55, ?x2462) <- ?x55[ a Country; has encompassed ?x195[ is encompassed of ?x78[ is locatedIn of ?x121; is wasDependentOf of ?x94;];]; has ethnicGroup ?x160; has government ?x2074; has religion ?x352<RomanCatholic>; has wasDependentOf ?x1197; is locatedIn of ?x473[ a River; has hasEstuary ?x2462;]; is neighbor of ?x156;]
*> conf = 0.47
ranks of expected_values: 12
EVAL	BIH	locatedIn!	Tara	CNN-1.+1._MA	0.000	0.000	0.000	0.083	105.000	96.000	1381.000	0.750	http://www.semwebtech.org/mondial/10/meta#locatedIn

#923-LakeTurkana
PRED entity: LakeTurkana

PRED relation: locatedIn
PRED expected values: ETH
=> 47 concepts (39 used for prediction)
PRED predicted values (max 10 best out of 151): MOC (0.45 #1695, 0.25 #1220, 0.14 #985), ZRE (0.43 #1493, 0.38 #6924, 0.34 #7633), EAT (0.43 #1588, 0.29 #1116, 0.25 #1413), RI (0.38 #2647, 0.14 #994, 0.12 #5478), IR (0.36 #3137, 0.34 #3373, 0.11 #1959), EAU (0.33 #1328, 0.29 #1093, 0.25 #623), ETH (0.33 #114, 0.25 #586, 0.25 #471), SUD (0.28 #3108, 0.26 #3344, 0.10 #8733), DJI (0.25 #248, 0.10 #8733, 0.04 #1901), AUS (0.23 #2640, 0.14 #987, 0.09 #2876)
>> best conf = 0.45
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1695 for best value:
>> intensional similarity = 6
>> extensional distance = 18
>> proper extension:  Anjouan; GrandComoro;
>> query: (?x2230, MOC) <- ?x2230[ has locatedIn ?x474[ has encompassed ?x213<Africa>; has wasDependentOf ?x81; is locatedIn of ?x60<IndianOcean>;];]

*> Best rule #114 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 1
*> proper extension:  ChewBahir;
*> query: (?x2230, ETH) <- ?x2230[ a Lake; has locatedIn ?x474<EAK>; has type ?x762<"salt">;]
*> conf = 0.33
ranks of expected_values: 7
EVAL	LakeTurkana	locatedIn	ETH	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.143	47.000	39.000	151.000	0.450	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: ETH
=> 117 concepts (100 used for prediction)
PRED predicted values (max 10 best out of 227): EAU (0.87 #7379, 0.81 #4034, 0.77 #9281), ZRE (0.82 #12940, 0.43 #2686, 0.33 #3161), ETH (0.81 #4034, 0.44 #2005, 0.40 #2483), EAT (0.71 #4447, 0.57 #5167, 0.50 #6597), RI (0.68 #10054, 0.63 #10766, 0.54 #7195), SSD (0.67 #4806, 0.23 #4988, 0.23 #3319), CL (0.65 #13099, 0.31 #4987, 0.27 #13100), SUD (0.65 #13099, 0.26 #9562, 0.24 #9802), IR (0.65 #13099, 0.16 #9758, 0.13 #6017), IL (0.65 #13099, 0.16 #9758, 0.12 #2364)
>> best conf = 0.87
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #7379 for best value:
>> intensional similarity = 12
>> extensional distance = 26
>> proper extension:  Mayotte;
>> query: (?x2230, ?x688) <- ?x2230[ has locatedIn ?x474[ a Country; has government ?x435; is locatedIn of ?x60<IndianOcean>; is locatedIn of ?x730[ has locatedIn ?x688; has type ?x150<"volcanic">;]; is locatedIn of ?x1195[ is flowsInto of ?x1194;];]; has type ?x762;]

*> Best rule #4034 for first EXPECTED value:
*> intensional similarity = 13
*> extensional distance = 14
*> proper extension:  LakeHume;
*> query: (?x2230, ?x688) <- ?x2230[ a Lake; has locatedIn ?x474[ has ethnicGroup ?x244; has ethnicGroup ?x1775[ a EthnicGroup;]; has government ?x435; is locatedIn of ?x60<IndianOcean>; is locatedIn of ?x598[ a Mountain;]; is locatedIn of ?x730[ has locatedIn ?x688; has type ?x150;];];]
*> conf = 0.81
ranks of expected_values: 3
EVAL	LakeTurkana	locatedIn	ETH	CNN-1.+1._MA	0.000	1.000	1.000	0.333	117.000	100.000	227.000	0.871	http://www.semwebtech.org/mondial/10/meta#locatedIn

#922-SuluSea
PRED entity: SuluSea

PRED relation: locatedIn
PRED expected values: RP
=> 40 concepts (24 used for prediction)
PRED predicted values (max 10 best out of 223): RI (0.75 #288, 0.50 #52, 0.39 #236), IND (0.46 #1365, 0.14 #1600, 0.12 #423), THA (0.39 #236, 0.22 #483, 0.20 #718), BRU (0.39 #236, 0.22 #594, 0.20 #829), TL (0.38 #394, 0.25 #158, 0.11 #2829), CN (0.35 #2885, 0.29 #3122, 0.26 #3359), R (0.31 #3546, 0.25 #3783, 0.25 #5), USA (0.30 #3850, 0.25 #72, 0.20 #4324), SGP (0.30 #921, 0.22 #686, 0.12 #451), RC (0.25 #459, 0.25 #223, 0.12 #2356)
>> best conf = 0.75
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #288 for best value:
>> intensional similarity = 8
>> extensional distance = 6
>> proper extension:  IndianOcean; SouthChinaSea; SulawesiSea; BandaSea;
>> query: (?x677, RI) <- ?x677[ has locatedIn ?x376; has mergesWith ?x625[ has mergesWith ?x241; is locatedInWater of ?x1005<Sulawesi>; is locatedInWater of ?x2129[ has belongsToIslands ?x370;];]; is mergesWith of ?x384;]

*> Best rule #344 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 6
*> proper extension:  IndianOcean; SouthChinaSea; SulawesiSea; BandaSea;
*> query: (?x677, RP) <- ?x677[ has locatedIn ?x376; has mergesWith ?x625[ has mergesWith ?x241; is locatedInWater of ?x1005<Sulawesi>; is locatedInWater of ?x2129[ has belongsToIslands ?x370;];]; is mergesWith of ?x384;]
*> conf = 0.25
ranks of expected_values: 12
EVAL	SuluSea	locatedIn	RP	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.083	40.000	24.000	223.000	0.750	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: RP
=> 101 concepts (93 used for prediction)
PRED predicted values (max 10 best out of 235): I (0.84 #6995, 0.22 #12255, 0.14 #14184), CN (0.81 #7957, 0.73 #8196, 0.61 #8676), GB (0.76 #6239, 0.75 #7186, 0.57 #6480), NL (0.75 #7186, 0.12 #5983, 0.12 #5259), RP (0.71 #3692, 0.60 #236, 0.50 #237), RI (0.67 #52, 0.60 #236, 0.50 #237), BRU (0.50 #237, 0.43 #1186, 0.43 #1182), THA (0.46 #1679, 0.43 #1186, 0.43 #1182), R (0.43 #12690, 0.42 #12932, 0.41 #13175), IND (0.42 #5450, 0.19 #2331, 0.19 #7851)
>> best conf = 0.84
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #6995 for best value:
>> intensional similarity = 15
>> extensional distance = 54
>> proper extension:  Stromboli; GranSasso; Po; MontBlanc; LagodiBolsena; Salina; Mincio; LagodiComo; LagoTrasimeno; Etna; ...
>> query: (?x677, I) <- ?x677[ has locatedIn ?x376[ has neighbor ?x91[ has encompassed ?x175; has neighbor ?x366;]; has neighbor ?x538[ has wasDependentOf ?x81;]; has religion ?x462<Buddhist>; is locatedIn of ?x385[ a Sea; is locatedInWater of ?x740; is mergesWith of ?x339;]; is wasDependentOf of ?x1404[ has ethnicGroup ?x298; has government ?x1174;];];]

*> Best rule #3692 for first EXPECTED value:
*> intensional similarity = 18
*> extensional distance = 22
*> proper extension:  LagunadeBay; Mindoro; Pulog; Panay; MountApo; Leyte; Luzon; Samar; Mantalingajan; Negros; ...
*> query: (?x677, RP) <- ?x677[ has locatedIn ?x376[ has encompassed ?x175; has ethnicGroup ?x1487<Malay>; has government ?x92; has religion ?x187<Muslim>; has religion ?x410[ is religion of ?x111<NEP>; is religion of ?x508<CL>; is religion of ?x797<MS>;]; is locatedIn of ?x375[ a Island;]; is locatedIn of ?x384<SouthChinaSea>;];]
*> conf = 0.71
ranks of expected_values: 5
EVAL	SuluSea	locatedIn	RP	CNN-1.+1._MA	0.000	0.000	1.000	0.200	101.000	93.000	235.000	0.839	http://www.semwebtech.org/mondial/10/meta#locatedIn

#921-Donau
PRED entity: Donau

PRED relation: locatedIn
PRED expected values: D
=> 41 concepts (41 used for prediction)
PRED predicted values (max 10 best out of 75): UA (0.61 #5951, 0.60 #785, 0.58 #6190), RO (0.61 #5951, 0.58 #6190, 0.58 #8336), MD (0.61 #5951, 0.58 #6190, 0.58 #8336), SK (0.61 #5951, 0.58 #6190, 0.58 #8336), SRB (0.61 #5951, 0.58 #6190, 0.58 #8336), H (0.61 #5951, 0.58 #6190, 0.58 #8336), HR (0.61 #5951, 0.58 #6190, 0.58 #8336), BG (0.61 #5951, 0.58 #6190, 0.57 #6909), D (0.60 #977, 0.58 #8336, 0.57 #8338), A (0.58 #6190, 0.58 #8336, 0.57 #6909)
>> best conf = 0.61
=> the first rule below is the first best rule for 8 predicted values
>> Best rule #5951 for best value:
>> intensional similarity = 6
>> extensional distance = 121
>> proper extension:  Buna; Apurimac; RioNegro; OhioRiver; Niger; RioMagdalena; Perene; Uruguay; Saluen; Thjorsa; ...
>> query: (?x1190, ?x886) <- ?x1190[ is hasSource of ?x133[ has locatedIn ?x886[ a Country; has ethnicGroup ?x58; has language ?x555; has wasDependentOf ?x903;];];]

*> Best rule #977 for first EXPECTED value:
*> intensional similarity = 2
*> extensional distance = 3
*> proper extension:  Feldberg;
*> query: (?x1190, D) <- ?x1190[ has inMountains ?x71<BlackForest>;]
*> conf = 0.60
ranks of expected_values: 9
EVAL	Donau	locatedIn	D	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.111	41.000	41.000	75.000	0.613	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: D
=> 111 concepts (111 used for prediction)
PRED predicted values (max 10 best out of 82): SRB (0.69 #14953, 0.62 #4818, 0.60 #14951), CH (0.64 #7785, 0.58 #12065, 0.58 #12064), RO (0.62 #4818, 0.60 #14951, 0.60 #3136), SK (0.62 #4818, 0.60 #14951, 0.60 #3136), UA (0.62 #4818, 0.60 #14951, 0.60 #3136), HR (0.62 #4818, 0.60 #14951, 0.60 #3136), MD (0.62 #4818, 0.60 #14951, 0.60 #3136), H (0.62 #4818, 0.60 #3136, 0.60 #1692), BG (0.62 #4818, 0.60 #3136, 0.60 #1692), A (0.62 #4818, 0.60 #1692, 0.60 #1691)
>> best conf = 0.69
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #14953 for best value:
>> intensional similarity = 13
>> extensional distance = 31
>> proper extension:  Suchona;
>> query: (?x1190, ?x904) <- ?x1190[ a Source; is hasSource of ?x133[ a River; has locatedIn ?x886[ has encompassed ?x195<Europe>; has ethnicGroup ?x58; has language ?x555; has religion ?x56<ChristianOrthodox>;]; has locatedIn ?x904[ has ethnicGroup ?x164; is wasDependentOf of ?x106;];];]

*> Best rule #3401 for first EXPECTED value:
*> intensional similarity = 2
*> extensional distance = 3
*> proper extension:  Feldberg;
*> query: (?x1190, D) <- ?x1190[ has inMountains ?x71<BlackForest>;]
*> conf = 0.60
ranks of expected_values: 12
EVAL	Donau	locatedIn	D	CNN-1.+1._MA	0.000	0.000	0.000	0.083	111.000	111.000	82.000	0.686	http://www.semwebtech.org/mondial/10/meta#locatedIn

#920-Saone
PRED entity: Saone

PRED relation: locatedIn
PRED expected values: F
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 32): R (0.09 #5, 0.04 #241), ZRE (0.08 #79, 0.03 #315), D (0.08 #20, 0.03 #256), USA (0.06 #72, 0.05 #308), CDN (0.04 #63, 0.03 #299), PE (0.04 #67, 0.01 #303), F (0.03 #7, 0.01 #243), I (0.02 #48, 0.02 #284), S (0.02 #92, 0.01 #328), SRB (0.02 #185)
>> best conf = 0.09
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5 for best value:
>> intensional similarity = 1
>> extensional distance = 247
>> proper extension:  Leine; Moraca; Marne; Umeaelv; Lena; Pibor; Po; Hwangho; Aare; Euphrat; ...
>> query: (?x2517, R) <- ?x2517[ a Estuary;]

*> Best rule #7 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 247
*> proper extension:  Leine; Moraca; Marne; Umeaelv; Lena; Pibor; Po; Hwangho; Aare; Euphrat; ...
*> query: (?x2517, F) <- ?x2517[ a Estuary;]
*> conf = 0.03
ranks of expected_values: 7
EVAL	Saone	locatedIn	F	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.143	2.000	2.000	32.000	0.088	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: F
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 32): R (0.09 #5, 0.04 #241), ZRE (0.08 #79, 0.03 #315), D (0.08 #20, 0.03 #256), USA (0.06 #72, 0.05 #308), CDN (0.04 #63, 0.03 #299), PE (0.04 #67, 0.01 #303), F (0.03 #7, 0.01 #243), I (0.02 #48, 0.02 #284), S (0.02 #92, 0.01 #328), SRB (0.02 #185)
>> best conf = 0.09
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5 for best value:
>> intensional similarity = 1
>> extensional distance = 247
>> proper extension:  Leine; Moraca; Marne; Umeaelv; Lena; Pibor; Po; Hwangho; Aare; Euphrat; ...
>> query: (?x2517, R) <- ?x2517[ a Estuary;]

*> Best rule #7 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 247
*> proper extension:  Leine; Moraca; Marne; Umeaelv; Lena; Pibor; Po; Hwangho; Aare; Euphrat; ...
*> query: (?x2517, F) <- ?x2517[ a Estuary;]
*> conf = 0.03
ranks of expected_values: 7
EVAL	Saone	locatedIn	F	CNN-1.+1._MA	0.000	0.000	1.000	0.143	2.000	2.000	32.000	0.088	http://www.semwebtech.org/mondial/10/meta#locatedIn

#919-Lesbos
PRED entity: Lesbos

PRED relation: belongsToIslands
PRED expected values: Sporades
=> 45 concepts (45 used for prediction)
PRED predicted values (max 10 best out of 43): Sporades (0.43 #1502, 0.43 #1501, 0.38 #89), IonicIslands (0.43 #1502, 0.43 #1501, 0.33 #1364), Kyklades (0.43 #1502, 0.43 #1501, 0.33 #1364), LipariIslands (0.23 #274, 0.06 #614, 0.06 #682), SundaIslands (0.16 #558, 0.10 #762, 0.10 #694), HawaiiIslands (0.10 #573, 0.06 #777, 0.06 #709), Baleares (0.10 #311, 0.05 #1639, 0.01 #1609), LesserAntilles (0.08 #1447, 0.08 #1585, 0.08 #1722), CalifornianChannelIslands (0.07 #603, 0.05 #807, 0.05 #739), WestfriesischeInseln (0.07 #557, 0.05 #761, 0.04 #897)
>> best conf = 0.43
=> the first rule below is the first best rule for 3 predicted values
>> Best rule #1502 for best value:
>> intensional similarity = 6
>> extensional distance = 191
>> proper extension:  Saipan; Tongatapu; GrandBermuda; Futuna; Tobago; Tinian; Tutuila; Mayotte; Dominica; Jersey; ...
>> query: (?x1529, ?x1053) <- ?x1529[ a Island; has locatedIn ?x399[ has government ?x1174; is locatedIn of ?x1063[ a Island; has belongsToIslands ?x1053;];];]
>> Best rule #1501 for best value:
>> intensional similarity = 6
>> extensional distance = 191
>> proper extension:  Saipan; Tongatapu; GrandBermuda; Futuna; Tobago; Tinian; Tutuila; Mayotte; Dominica; Jersey; ...
>> query: (?x1529, ?x978) <- ?x1529[ a Island; has locatedIn ?x399[ has government ?x1174; is locatedIn of ?x977[ a Island; has belongsToIslands ?x978;];];]
ranks of expected_values: 1
EVAL	Lesbos	belongsToIslands	Sporades	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	45.000	45.000	43.000	0.430	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

PRED relation: belongsToIslands
PRED expected values: Sporades
=> 122 concepts (122 used for prediction)
PRED predicted values (max 10 best out of 59): Sporades (0.43 #3905, 0.42 #4864, 0.42 #4863), IonicIslands (0.43 #3905, 0.42 #4864, 0.42 #4863), Kyklades (0.43 #3905, 0.42 #4864, 0.42 #4863), LipariIslands (0.23 #821, 0.20 #753, 0.13 #1639), SundaIslands (0.22 #1855, 0.20 #2060, 0.20 #1583), CanadianArcticIslands (0.21 #691, 0.14 #1372, 0.10 #1849), LesserAntilles (0.18 #3437, 0.11 #1993, 0.10 #4740), Canares (0.11 #2001, 0.08 #2616, 0.08 #2686), WestfriesischeInseln (0.10 #1922, 0.10 #1786, 0.06 #1650), InnerHebrides (0.10 #1973, 0.10 #1359, 0.10 #1428)
>> best conf = 0.43
=> the first rule below is the first best rule for 3 predicted values
>> Best rule #3905 for best value:
>> intensional similarity = 13
>> extensional distance = 109
>> proper extension:  Male; Koror;
>> query: (?x1529, ?x1053) <- ?x1529[ a Island; has locatedIn ?x399[ a Country; has encompassed ?x195; has wasDependentOf ?x1656; is locatedIn of ?x275[ a Sea; has mergesWith ?x182; is flowsInto of ?x698; is locatedInWater of ?x68;]; is locatedIn of ?x2006[ a Island; has belongsToIslands ?x1053;];];]
ranks of expected_values: 1
EVAL	Lesbos	belongsToIslands	Sporades	CNN-1.+1._MA	1.000	1.000	1.000	1.000	122.000	122.000	59.000	0.429	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

#918-PicoRuivo
PRED entity: PicoRuivo

PRED relation: inMountains
PRED expected values: Madeira
=> 65 concepts (65 used for prediction)
PRED predicted values (max 10 best out of 47): Azores (0.33 #40, 0.09 #214, 0.08 #388), CanaryIslands (0.27 #230, 0.25 #404, 0.23 #491), Pyrenees (0.17 #410, 0.02 #2933, 0.01 #3107), Andes (0.12 #2186, 0.12 #1664, 0.09 #2621), CapeVerdes (0.09 #186, 0.08 #447, 0.04 #882), CordilleraIberica (0.08 #403, 0.02 #3622, 0.01 #3970), CordilleraCentral (0.08 #434), CordilleraCantabrica (0.08 #421), CordilleraBetica (0.08 #364), Cevennes (0.08 #773, 0.08 #860, 0.06 #1208)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #40 for best value:
>> intensional similarity = 6
>> extensional distance = 1
>> proper extension:  Pico;
>> query: (?x1036, Azores) <- ?x1036[ a Mountain; a Volcano; has locatedIn ?x1027<P>; has locatedOnIsland ?x1037; has type ?x150;]

No rule for expected values
ranks of expected_values: 
EVAL	PicoRuivo	inMountains	Madeira	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	65.000	65.000	47.000	0.333	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains
PRED expected values: Madeira
=> 165 concepts (165 used for prediction)
PRED predicted values (max 10 best out of 66): CanaryIslands (0.50 #230, 0.25 #926, 0.19 #1361), Azores (0.33 #40, 0.17 #214, 0.08 #910), Cevennes (0.29 #338, 0.25 #425, 0.20 #599), Kaukasus (0.25 #367, 0.20 #541, 0.13 #1150), RockyMountains (0.23 #4357, 0.18 #5314, 0.17 #5662), Alps (0.23 #5137, 0.10 #5485, 0.09 #5746), Hawaii (0.19 #1547, 0.14 #1982, 0.13 #2156), Pyrenees (0.17 #932, 0.04 #4412, 0.04 #4499), Andes (0.14 #272, 0.12 #4100, 0.10 #5231), Amhara (0.12 #415, 0.04 #2329, 0.03 #2938)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #230 for best value:
>> intensional similarity = 8
>> extensional distance = 4
>> proper extension:  RoquedelosMuchachos;
>> query: (?x1036, CanaryIslands) <- ?x1036[ a Mountain; a Volcano; has locatedIn ?x1027[ is locatedIn of ?x1739<Guadiana>;]; has locatedOnIsland ?x1037[ a Island;]; has type ?x150;]

No rule for expected values
ranks of expected_values: 
EVAL	PicoRuivo	inMountains	Madeira	CNN-1.+1._MA	0.000	0.000	0.000	0.000	165.000	165.000	66.000	0.500	http://www.semwebtech.org/mondial/10/meta#inMountains

#917-LV
PRED entity: LV

PRED relation: neighbor
PRED expected values: LT
=> 38 concepts (38 used for prediction)
PRED predicted values (max 10 best out of 188): LT (0.92 #2712, 0.92 #1756, 0.91 #318), UA (0.50 #50, 0.40 #209, 0.25 #368), PL (0.40 #192, 0.38 #351, 0.25 #33), LV (0.40 #237, 0.25 #78, 0.25 #3513), SK (0.25 #339, 0.25 #21, 0.20 #180), CN (0.25 #41, 0.25 #3513, 0.20 #200), GE (0.25 #60, 0.25 #3513, 0.20 #219), SF (0.25 #95, 0.25 #3513, 0.20 #254), KAZ (0.25 #69, 0.25 #3513, 0.20 #228), AZ (0.25 #55, 0.25 #3513, 0.20 #214)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2712 for best value:
>> intensional similarity = 6
>> extensional distance = 101
>> proper extension:  LB; AND;
>> query: (?x448, ?x962) <- ?x448[ has ethnicGroup ?x1322[ a EthnicGroup;]; is neighbor of ?x962[ has language ?x555; has religion ?x56; is locatedIn of ?x146;];]
ranks of expected_values: 1
EVAL	LV	neighbor	LT	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	38.000	38.000	188.000	0.919	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: LT
=> 82 concepts (82 used for prediction)
PRED predicted values (max 10 best out of 234): LT (0.91 #8383, 0.90 #8218, 0.90 #7559), LV (0.50 #241, 0.40 #401, 0.33 #1050), PL (0.40 #356, 0.33 #1005, 0.33 #518), TR (0.36 #2153, 0.35 #2650, 0.29 #2319), CN (0.35 #2992, 0.29 #11697, 0.28 #11696), SK (0.33 #506, 0.29 #831, 0.23 #3273), UA (0.32 #1301, 0.31 #1136, 0.31 #1135), GE (0.31 #645, 0.29 #11697, 0.28 #11696), SF (0.31 #645, 0.28 #11696, 0.27 #970), UZB (0.31 #645, 0.27 #970, 0.27 #969)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #8383 for best value:
>> intensional similarity = 13
>> extensional distance = 70
>> proper extension:  PY; A; ES; L;
>> query: (?x448, ?x73) <- ?x448[ has ethnicGroup ?x58; has ethnicGroup ?x516[ a EthnicGroup;]; has language ?x555; is locatedIn of ?x885; is neighbor of ?x73[ has ethnicGroup ?x1326; has religion ?x56; is locatedIn of ?x72; is neighbor of ?x170[ has ethnicGroup ?x979; has government ?x92; is locatedIn of ?x121;];];]
ranks of expected_values: 1
EVAL	LV	neighbor	LT	CNN-1.+1._MA	1.000	1.000	1.000	1.000	82.000	82.000	234.000	0.909	http://www.semwebtech.org/mondial/10/meta#neighbor

#916-K2
PRED entity: K2

PRED relation: inMountains
PRED expected values: Karakorum
=> 35 concepts (35 used for prediction)
PRED predicted values (max 10 best out of 45): Himalaya (0.30 #789, 0.30 #93, 0.21 #1742), Karakorum (0.25 #8, 0.21 #1742, 0.21 #1741), Kunlun (0.21 #1742, 0.21 #1741, 0.12 #184), Pamir (0.21 #1742, 0.21 #1741, 0.11 #365), TianShan (0.21 #1742, 0.21 #1741, 0.10 #118), Transhimalaya (0.21 #1742, 0.21 #1741, 0.06 #198), Hindukusch (0.21 #1742, 0.21 #1741), Alps (0.10 #1222, 0.08 #1657, 0.07 #2007), RockyMountains (0.08 #1486, 0.07 #1836, 0.06 #2097), Andes (0.06 #1664, 0.06 #1753, 0.06 #1927)
>> best conf = 0.30
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #789 for best value:
>> intensional similarity = 5
>> extensional distance = 18
>> proper extension:  Annapurna; Kangchendzonga; Dhaulagiri;
>> query: (?x1040, Himalaya) <- ?x1040[ a Mountain; has locatedIn ?x232[ has neighbor ?x73; is locatedIn of ?x1771<Makalu>;];]
>> Best rule #93 for best value:
>> intensional similarity = 6
>> extensional distance = 8
>> proper extension:  Lhotse; ChoOyu; PikChan-Tengri; MountEverest; Makalu; PikPobeda;
>> query: (?x1040, Himalaya) <- ?x1040[ a Mountain; has locatedIn ?x83[ has language ?x559; has neighbor ?x381;]; has locatedIn ?x232<CN>;]

*> Best rule #8 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 2
*> proper extension:  GasherbrumII; BroadPeak;
*> query: (?x1040, Karakorum) <- ?x1040[ a Mountain; has locatedIn ?x83<PK>; has locatedIn ?x232<CN>;]
*> conf = 0.25
ranks of expected_values: 2
EVAL	K2	inMountains	Karakorum	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	35.000	35.000	45.000	0.300	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains
PRED expected values: Karakorum
=> 105 concepts (105 used for prediction)
PRED predicted values (max 10 best out of 66): Himalaya (0.30 #617, 0.26 #1491, 0.21 #4887), Karakorum (0.25 #8, 0.21 #4887, 0.20 #95), TianShan (0.21 #4887, 0.20 #3928, 0.19 #4277), Kunlun (0.21 #4887, 0.20 #3928, 0.19 #4277), Pamir (0.21 #4887, 0.20 #3928, 0.19 #4277), Transhimalaya (0.21 #4887, 0.20 #3928, 0.19 #4277), Hindukusch (0.21 #4887, 0.13 #1047, 0.11 #1485), Kaukasus (0.16 #1591, 0.06 #2290, 0.05 #2551), RockyMountains (0.16 #2103, 0.11 #2800, 0.11 #2539), Alps (0.09 #2275, 0.09 #3582, 0.08 #4803)
>> best conf = 0.30
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #617 for best value:
>> intensional similarity = 9
>> extensional distance = 8
>> proper extension:  Lhotse; ChoOyu; MountEverest; Makalu;
>> query: (?x1040, Himalaya) <- ?x1040[ a Mountain; has locatedIn ?x83[ has language ?x559; has neighbor ?x381[ has ethnicGroup ?x2116; has religion ?x187; is locatedIn of ?x82;];]; has locatedIn ?x232<CN>;]

*> Best rule #8 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 2
*> proper extension:  GasherbrumII; BroadPeak;
*> query: (?x1040, Karakorum) <- ?x1040[ a Mountain; has locatedIn ?x83<PK>; has locatedIn ?x232<CN>;]
*> conf = 0.25
ranks of expected_values: 2
EVAL	K2	inMountains	Karakorum	CNN-1.+1._MA	0.000	1.000	1.000	0.500	105.000	105.000	66.000	0.300	http://www.semwebtech.org/mondial/10/meta#inMountains

#915-LakeSakakawea
PRED entity: LakeSakakawea

PRED relation: locatedIn
PRED expected values: USA
=> 45 concepts (45 used for prediction)
PRED predicted values (max 10 best out of 112): USA (0.82 #3077, 0.81 #2840, 0.80 #3551), CDN (0.33 #3140, 0.25 #5984, 0.25 #6458), D (0.25 #729, 0.21 #1203, 0.17 #2386), ZRE (0.24 #5763, 0.23 #6947, 0.17 #6237), CH (0.22 #2423, 0.16 #3844, 0.14 #1240), UA (0.18 #2200, 0.18 #1964, 0.18 #1727), AUS (0.18 #2175, 0.18 #1939, 0.17 #2648), I (0.17 #757, 0.14 #1231, 0.11 #7626), SUD (0.15 #3356, 0.06 #2172, 0.06 #1936), RI (0.12 #1709, 0.08 #3366, 0.06 #9764)
>> best conf = 0.82
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3077 for best value:
>> intensional similarity = 8
>> extensional distance = 17
>> proper extension:  KoliSarez;
>> query: (?x2384, ?x315) <- ?x2384[ a Lake; has type ?x136; is flowsThrough of ?x1366[ a River; has flowsInto ?x361; has flowsThrough ?x1113[ has locatedIn ?x315;]; has hasSource ?x2450;];]
ranks of expected_values: 1
EVAL	LakeSakakawea	locatedIn	USA	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	45.000	45.000	112.000	0.818	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: USA
=> 106 concepts (106 used for prediction)
PRED predicted values (max 10 best out of 118): USA (0.94 #16617, 0.93 #20189, 0.92 #16143), ZRE (0.62 #9094, 0.22 #6959, 0.19 #22170), AUS (0.40 #991, 0.29 #1941, 0.25 #281), CDN (0.38 #3144, 0.33 #6467, 0.29 #6230), R (0.26 #20669, 0.20 #10210, 0.18 #13775), PE (0.26 #11937, 0.16 #13837, 0.15 #12174), F (0.25 #4275, 0.23 #9260, 0.21 #9736), SUD (0.24 #5734, 0.11 #12623, 0.09 #14049), UA (0.20 #5051, 0.20 #4814, 0.18 #6000), S (0.20 #801, 0.09 #13624, 0.07 #13149)
>> best conf = 0.94
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #16617 for best value:
>> intensional similarity = 11
>> extensional distance = 54
>> proper extension:  LakeNicaragua; KuybyshevReservoir; LakeManagua; OzeroLadoga; Vaenern; Inari; LagodeChapala; AtlinLake; LacLeman; Oulujaervi; ...
>> query: (?x2384, ?x315) <- ?x2384[ a Lake; has flowsInto ?x1366[ a River; has flowsInto ?x361; has hasEstuary ?x1254[ a Estuary;]; has hasSource ?x2450[ a Source;]; is flowsInto of ?x1113[ a Lake; has locatedIn ?x315;];];]
ranks of expected_values: 1
EVAL	LakeSakakawea	locatedIn	USA	CNN-1.+1._MA	1.000	1.000	1.000	1.000	106.000	106.000	118.000	0.944	http://www.semwebtech.org/mondial/10/meta#locatedIn

#914-Baleares
PRED entity: Baleares

PRED relation: belongsToIslands!
PRED expected values: Menorca
=> 14 concepts (14 used for prediction)
PRED predicted values (max 10 best out of 231): Lipari (0.33 #183, 0.17 #380, 0.15 #788), Alicudi (0.33 #153, 0.17 #350, 0.15 #788), Filicudi (0.33 #144, 0.17 #341, 0.15 #788), Vulcano (0.33 #138, 0.17 #335, 0.15 #788), Panarea (0.33 #107, 0.17 #304, 0.15 #788), Salina (0.33 #12, 0.17 #209, 0.15 #788), Stromboli (0.33 #2, 0.17 #199, 0.15 #788), Chios (0.17 #368, 0.15 #788, 0.15 #1182), Kos (0.17 #351, 0.15 #788, 0.15 #1182), Mykonos (0.17 #326, 0.15 #788, 0.15 #1182)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #183 for best value:
>> intensional similarity = 11
>> extensional distance = 1
>> proper extension:  LipariIslands;
>> query: (?x1715, Lipari) <- ?x1715[ a Islands; is belongsToIslands of ?x1714[ a Island; has locatedInWater ?x275<MediterraneanSea>;]; is belongsToIslands of ?x2304[ a Island; has locatedIn ?x149[ has government ?x1657; is neighbor of ?x78; is wasDependentOf of ?x148;];];]

*> Best rule #591 for first EXPECTED value:
*> intensional similarity = 12
*> extensional distance = 5
*> proper extension:  Malta;
*> query: (?x1715, ?x68) <- ?x1715[ a Islands; is belongsToIslands of ?x1714[ a Island; has locatedInWater ?x275<MediterraneanSea>;]; is belongsToIslands of ?x2304[ a Island; has locatedIn ?x149[ has encompassed ?x195<Europe>; has language ?x790; is locatedIn of ?x68;];];]
*> conf = 0.17
ranks of expected_values: 18
EVAL	Baleares	belongsToIslands!	Menorca	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.056	14.000	14.000	231.000	0.333	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

PRED relation: belongsToIslands!
PRED expected values: Menorca
=> 16 concepts (16 used for prediction)
PRED predicted values (max 10 best out of 231): Fuerteventura (0.33 #157, 0.25 #551, 0.19 #788), Gomera (0.33 #150, 0.25 #544, 0.19 #788), Teneriffa (0.33 #140, 0.25 #534, 0.19 #788), Lanzarote (0.33 #130, 0.25 #524, 0.19 #788), Hierro (0.33 #84, 0.25 #478, 0.19 #788), GranCanaria (0.33 #76, 0.25 #470, 0.19 #788), Lipari (0.33 #380, 0.16 #1773, 0.15 #1379), Alicudi (0.33 #350, 0.16 #1773, 0.15 #1379), Filicudi (0.33 #341, 0.16 #1773, 0.15 #1379), Vulcano (0.33 #335, 0.16 #1773, 0.15 #1379)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #157 for best value:
>> intensional similarity = 15
>> extensional distance = 1
>> proper extension:  Canares;
>> query: (?x1715, Fuerteventura) <- ?x1715[ a Islands; is belongsToIslands of ?x2304[ a Island; has locatedIn ?x149<E>; has locatedInWater ?x275[ a Sea; has locatedIn ?x78<F>; has locatedIn ?x149<E>; has locatedIn ?x851<MA>; has mergesWith ?x182; is flowsInto of ?x698; is mergesWith of ?x182;];];]

*> Best rule #788 for first EXPECTED value:
*> intensional similarity = 18
*> extensional distance = 3
*> proper extension:  IonicIslands; Sporades; Kyklades;
*> query: (?x1715, ?x68) <- ?x1715[ a Islands; is belongsToIslands of ?x2304[ a Island; has locatedIn ?x149[ a Country; has encompassed ?x195<Europe>; has ethnicGroup ?x2540; has government ?x1657; has language ?x790; has neighbor ?x1027[ a Country;]; has religion ?x352; is locatedIn of ?x68; is locatedIn of ?x275<MediterraneanSea>; is neighbor of ?x1027;]; has locatedInWater ?x275<MediterraneanSea>;];]
*> conf = 0.19
ranks of expected_values: 25
EVAL	Baleares	belongsToIslands!	Menorca	CNN-1.+1._MA	0.000	0.000	0.000	0.040	16.000	16.000	231.000	0.333	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

#913-CAM
PRED entity: CAM

PRED relation: ethnicGroup
PRED expected values: NorthwesternBantu
=> 36 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 243): European (0.47 #1014, 0.32 #1518, 0.30 #2022), Russian (0.17 #2336, 0.16 #5108, 0.15 #3596), Amerindian (0.17 #1010, 0.16 #2018, 0.11 #5294), Mestizo (0.17 #1041, 0.12 #2049, 0.09 #5325), Mulatto (0.17 #1062, 0.09 #1566, 0.09 #1314), German (0.15 #3032, 0.15 #2780, 0.11 #5048), Arab (0.14 #765, 0.12 #7561, 0.12 #1773), Tuareg (0.14 #916, 0.12 #7561, 0.03 #3940), Beja (0.14 #972, 0.12 #7561, 0.02 #1980), Mandjia (0.14 #950, 0.12 #7561, 0.02 #3218)
>> best conf = 0.47
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1014 for best value:
>> intensional similarity = 6
>> extensional distance = 28
>> proper extension:  C; KN; JA; CV; TUCA; WG; BDS; WL;
>> query: (?x536, European) <- ?x536[ a Country; has ethnicGroup ?x122[ a EthnicGroup;]; has ethnicGroup ?x162<African>; is locatedIn of ?x182;]

No rule for expected values
ranks of expected_values: 
EVAL	CAM	ethnicGroup	NorthwesternBantu	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	36.000	36.000	243.000	0.467	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: NorthwesternBantu
=> 86 concepts (86 used for prediction)
PRED predicted values (max 10 best out of 246): European (0.71 #9868, 0.67 #507, 0.67 #506), Amerindian (0.67 #507, 0.67 #506, 0.40 #7588), Mulatto (0.67 #507, 0.67 #506, 0.40 #2583), Wolof (0.67 #507, 0.67 #506, 0.33 #728), EuropeanLebanese (0.67 #507, 0.67 #506, 0.33 #722), Diola (0.67 #507, 0.67 #506, 0.33 #552), Toucouleur (0.67 #507, 0.67 #506, 0.33 #539), Mandingo (0.67 #507, 0.67 #506, 0.33 #538), Serer (0.67 #507, 0.67 #506, 0.33 #525), Mestizo (0.67 #507, 0.67 #506, 0.30 #7619)
>> best conf = 0.71
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #9868 for best value:
>> intensional similarity = 16
>> extensional distance = 29
>> proper extension:  TN; GCA; RCH; PE; RA; MEX; DZ; BOL; PA;
>> query: (?x536, European) <- ?x536[ has ethnicGroup ?x162[ is ethnicGroup of ?x148<C>; is ethnicGroup of ?x1364<HCA>;]; has government ?x1721; is locatedIn of ?x182; is neighbor of ?x736[ has neighbor ?x229[ a Country; has government ?x435; has neighbor ?x474; is locatedIn of ?x53;]; has religion ?x187; has wasDependentOf ?x78; is locatedIn of ?x388;];]

No rule for expected values
ranks of expected_values: 
EVAL	CAM	ethnicGroup	NorthwesternBantu	CNN-1.+1._MA	0.000	0.000	0.000	0.000	86.000	86.000	246.000	0.710	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#912-YE
PRED entity: YE

PRED relation: neighbor!
PRED expected values: OM SA
=> 34 concepts (34 used for prediction)
PRED predicted values (max 10 best out of 189): SA (0.91 #2941, 0.90 #3600, 0.90 #2123), OM (0.90 #2123, 0.90 #1470, 0.89 #2286), IL (0.40 #373, 0.33 #47, 0.21 #490), LAR (0.40 #476, 0.33 #150, 0.10 #4092), SUD (0.33 #32, 0.21 #490, 0.20 #358), GAZA (0.33 #156, 0.20 #482, 0.10 #4092), UAE (0.28 #3763, 0.28 #3108, 0.26 #4258), KWT (0.28 #3763, 0.26 #4258, 0.26 #2942), YE (0.28 #3763, 0.26 #4258, 0.25 #275), JOR (0.28 #3763, 0.26 #4258, 0.21 #490)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2941 for best value:
>> intensional similarity = 7
>> extensional distance = 111
>> proper extension:  F; LS; THA; NEP; D; TAD; E; PL; I; SP; ...
>> query: (?x668, ?x751) <- ?x668[ has encompassed ?x175; has government ?x435; has neighbor ?x751[ has ethnicGroup ?x244; has neighbor ?x107;]; has religion ?x187; is locatedIn of ?x60;]
ranks of expected_values: 1, 2
EVAL	YE	neighbor!	SA	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	34.000	34.000	189.000	0.910	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	YE	neighbor!	OM	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	34.000	34.000	189.000	0.910	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: OM SA
=> 84 concepts (84 used for prediction)
PRED predicted values (max 10 best out of 235): SA (0.92 #3841, 0.92 #9051, 0.92 #9052), OM (0.92 #3841, 0.92 #9051, 0.92 #9052), UAE (0.64 #1664, 0.55 #832, 0.53 #6182), KWT (0.64 #1664, 0.55 #832, 0.48 #1999), IRQ (0.64 #1664, 0.28 #11081, 0.25 #1552), YE (0.55 #832, 0.53 #6182, 0.53 #5017), JOR (0.55 #832, 0.48 #1999, 0.33 #1331), Q (0.55 #832, 0.48 #1999, 0.29 #11079), GAZA (0.50 #1991, 0.29 #3157, 0.25 #3659), EAK (0.33 #1331, 0.33 #998, 0.33 #328)
>> best conf = 0.92
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #3841 for best value:
>> intensional similarity = 15
>> extensional distance = 9
>> proper extension:  THA;
>> query: (?x668, ?x639) <- ?x668[ has neighbor ?x639; is locatedIn of ?x60[ a Sea; has locatedIn ?x217<RI>; has locatedIn ?x787[ has government ?x2534;]; has locatedIn ?x924<IND>; has mergesWith ?x262<GulfofBengal>; has mergesWith ?x770[ is locatedInWater of ?x216;]; is locatedInWater of ?x1555[ is locatedOnIsland of ?x1167;];];]
ranks of expected_values: 1, 2
EVAL	YE	neighbor!	SA	CNN-1.+1._MA	1.000	1.000	1.000	1.000	84.000	84.000	235.000	0.923	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	YE	neighbor!	OM	CNN-1.+1._MA	1.000	1.000	1.000	1.000	84.000	84.000	235.000	0.923	http://www.semwebtech.org/mondial/10/meta#neighbor

#911-SRB
PRED entity: SRB

PRED relation: language
PRED expected values: Roma
=> 36 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 87): Roma (0.40 #235, 0.22 #1522, 0.20 #1141), Turkish (0.40 #198, 0.22 #1522, 0.20 #1141), Ukrainian (0.30 #409, 0.25 #124, 0.09 #599), Spanish (0.25 #781, 0.24 #1257, 0.22 #1733), English (0.24 #2288, 0.23 #2573, 0.21 #954), Romanian (0.22 #1522, 0.20 #1141, 0.20 #2094), Albanian (0.22 #1522, 0.20 #1141, 0.20 #2094), Montenegrin (0.22 #1522, 0.20 #1141, 0.20 #2094), Serbo-Croatian (0.22 #1522, 0.20 #1141, 0.20 #2094), Bulgarian (0.22 #1522, 0.20 #1141, 0.20 #2094)
>> best conf = 0.40
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #235 for best value:
>> intensional similarity = 6
>> extensional distance = 3
>> proper extension:  RO; TR; MK;
>> query: (?x904, Roma) <- ?x904[ has ethnicGroup ?x164; has wasDependentOf ?x1197; is locatedIn of ?x708[ a River;]; is neighbor of ?x177<BG>;]
ranks of expected_values: 1
EVAL	SRB	language	Roma	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	36.000	36.000	87.000	0.400	http://www.semwebtech.org/mondial/10/meta#language

PRED relation: language
PRED expected values: Roma
=> 112 concepts (112 used for prediction)
PRED predicted values (max 10 best out of 95): Albanian (0.60 #901, 0.50 #516, 0.50 #383), Roma (0.50 #383, 0.40 #1005, 0.38 #1534), Turkish (0.50 #383, 0.40 #968, 0.33 #295), Macedonian (0.50 #383, 0.33 #367, 0.31 #5080), Romanian (0.50 #383, 0.33 #47, 0.31 #5080), Bulgarian (0.50 #383, 0.31 #5080, 0.30 #1438), Serbo-Croatian (0.50 #383, 0.31 #5080, 0.30 #1438), Montenegrin (0.50 #383, 0.31 #1633, 0.30 #1438), Spanish (0.39 #3189, 0.33 #5005, 0.31 #5101), Greek (0.33 #240, 0.25 #1486, 0.22 #5466)
>> best conf = 0.60
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #901 for best value:
>> intensional similarity = 17
>> extensional distance = 3
>> proper extension:  MNE;
>> query: (?x904, Albanian) <- ?x904[ has ethnicGroup ?x164; has language ?x684; has neighbor ?x106[ has ethnicGroup ?x1472; has government ?x435; has neighbor ?x156; is locatedIn of ?x104;]; has neighbor ?x177[ a Country; has ethnicGroup ?x354; has government ?x254; has neighbor ?x185; has religion ?x56;]; has neighbor ?x692<KOS>; is locatedIn of ?x132; is neighbor of ?x236;]

*> Best rule #383 for first EXPECTED value:
*> intensional similarity = 16
*> extensional distance = 1
*> proper extension:  MK;
*> query: (?x904, ?x1241) <- ?x904[ a Country; has ethnicGroup ?x164; has government ?x435; has language ?x1296<Serbian>; has neighbor ?x55[ has language ?x1241; is locatedIn of ?x275;]; has neighbor ?x177<BG>; has religion ?x56; has wasDependentOf ?x1197<Yugoslavia>; is locatedIn of ?x1489<SouthernMorava>; is neighbor of ?x236;]
*> conf = 0.50
ranks of expected_values: 2
EVAL	SRB	language	Roma	CNN-1.+1._MA	0.000	1.000	1.000	0.500	112.000	112.000	95.000	0.600	http://www.semwebtech.org/mondial/10/meta#language

#910-LakeMaiNdombe
PRED entity: LakeMaiNdombe

PRED relation: locatedIn
PRED expected values: ZRE
=> 27 concepts (27 used for prediction)
PRED predicted values (max 10 best out of 85): ZRE (0.88 #3321, 0.82 #1183, 0.82 #1025), CH (0.31 #766, 0.25 #530, 0.10 #2904), USA (0.19 #3867, 0.18 #4105, 0.18 #2682), D (0.17 #4290, 0.15 #729, 0.12 #3579), RCA (0.14 #1106, 0.12 #1343, 0.11 #396), RCB (0.14 #1068, 0.12 #1305, 0.10 #5218), ANG (0.14 #1135, 0.12 #1372, 0.10 #5217), R (0.11 #5699, 0.10 #5461, 0.10 #5936), F (0.11 #3802, 0.11 #4040, 0.08 #480), SSD (0.10 #5218, 0.10 #4979, 0.10 #5217)
>> best conf = 0.88
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3321 for best value:
>> intensional similarity = 7
>> extensional distance = 65
>> proper extension:  StarnbergerSee; LakeVolta; LakeHuron; ChickamaugaLake; LakeTanganjika; LakeNicaragua; LakeMweru; Franklin.D.RooseveltLake; KakhovkaReservoir; OzeroBaikal; ...
>> query: (?x1604, ?x348) <- ?x1604[ a Lake; has flowsInto ?x1244[ a River; has flowsInto ?x113; has hasSource ?x441; is flowsInto of ?x1671[ has locatedIn ?x348;];];]
ranks of expected_values: 1
EVAL	LakeMaiNdombe	locatedIn	ZRE	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	27.000	27.000	85.000	0.879	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: ZRE
=> 86 concepts (86 used for prediction)
PRED predicted values (max 10 best out of 97): ZRE (0.94 #11431, 0.92 #6185, 0.88 #9763), CH (0.44 #2675, 0.40 #3388, 0.33 #2914), USA (0.43 #7454, 0.36 #3879, 0.36 #15564), EAT (0.40 #885, 0.25 #947, 0.25 #648), RCB (0.33 #122, 0.25 #947, 0.19 #5596), ANG (0.33 #1853, 0.21 #4951, 0.20 #5188), Z (0.25 #947, 0.25 #594, 0.20 #1071), BI (0.25 #947, 0.25 #556, 0.20 #1033), RCA (0.25 #947, 0.19 #5634, 0.17 #3805), EAU (0.25 #947, 0.17 #3805, 0.17 #1339)
>> best conf = 0.94
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #11431 for best value:
>> intensional similarity = 13
>> extensional distance = 47
>> proper extension:  Baro; Pibor;
>> query: (?x1604, ?x348) <- ?x1604[ has flowsInto ?x1244[ a River; has hasEstuary ?x1878[ a Estuary; has locatedIn ?x348[ is locatedIn of ?x441[ a Source;]; is locatedIn of ?x1671[ a River; has hasEstuary ?x888; has hasSource ?x2447;];];]; has hasSource ?x441; is flowsInto of ?x1671;];]
ranks of expected_values: 1
EVAL	LakeMaiNdombe	locatedIn	ZRE	CNN-1.+1._MA	1.000	1.000	1.000	1.000	86.000	86.000	97.000	0.939	http://www.semwebtech.org/mondial/10/meta#locatedIn

#909-UAE
PRED entity: UAE

PRED relation: wasDependentOf
PRED expected values: GB
=> 29 concepts (29 used for prediction)
PRED predicted values (max 10 best out of 24): GB (0.38 #183, 0.32 #213, 0.31 #244), UnitedNations (0.33 #44, 0.20 #105, 0.17 #164), P (0.20 #143, 0.20 #81, 0.09 #735), MergerofNorth-SouthYemen (0.20 #147, 0.04 #236, 0.04 #267), F (0.15 #305, 0.15 #335, 0.13 #458), E (0.12 #339, 0.11 #462, 0.11 #493), SovietUnion (0.10 #416, 0.09 #384, 0.08 #507), Yugoslavia (0.09 #295, 0.05 #326, 0.04 #387), OttomanEmpire (0.09 #297, 0.04 #358, 0.04 #389), NL (0.04 #227, 0.04 #258, 0.03 #350)
>> best conf = 0.38
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #183 for best value:
>> intensional similarity = 5
>> extensional distance = 6
>> proper extension:  Q; KWT;
>> query: (?x107, GB) <- ?x107[ a Country; has ethnicGroup ?x1595; has religion ?x187; is locatedIn of ?x918<PersianGulf>;]
ranks of expected_values: 1
EVAL	UAE	wasDependentOf	GB	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	29.000	29.000	24.000	0.375	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

PRED relation: wasDependentOf
PRED expected values: GB
=> 73 concepts (73 used for prediction)
PRED predicted values (max 10 best out of 45): GB (0.50 #159, 0.37 #769, 0.36 #1100), P (0.47 #554, 0.35 #731, 0.33 #82), SovietUnion (0.33 #542, 0.33 #508, 0.25 #371), UnitedNations (0.23 #607, 0.22 #433, 0.20 #199), F (0.22 #1507, 0.19 #1581, 0.19 #1431), OttomanEmpire (0.17 #1160, 0.17 #547, 0.15 #1608), E (0.15 #1399, 0.14 #1804, 0.14 #1733), PK (0.14 #288, 0.08 #1908, 0.08 #2364), RI (0.11 #395, 0.08 #1908, 0.08 #2364), MergerofNorth-SouthYemen (0.11 #412, 0.08 #1908, 0.08 #2364)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #159 for best value:
>> intensional similarity = 19
>> extensional distance = 2
>> proper extension:  Q; KWT;
>> query: (?x107, GB) <- ?x107[ a Country; has encompassed ?x175<Asia>; has ethnicGroup ?x1595[ a EthnicGroup;]; has neighbor ?x639[ has government ?x640<"monarchy">; has religion ?x187<Muslim>;]; has neighbor ?x751<SA>; is locatedIn of ?x918<PersianGulf>; is locatedIn of ?x926[ a Sea; has locatedIn ?x304<IR>; is locatedInWater of ?x2355;];]
ranks of expected_values: 1
EVAL	UAE	wasDependentOf	GB	CNN-1.+1._MA	1.000	1.000	1.000	1.000	73.000	73.000	45.000	0.500	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

#908-TeWaka-a-Maui-SouthIsland-
PRED entity: TeWaka-a-Maui-SouthIsland-

PRED relation: locatedOnIsland!
PRED expected values: Mt.Cook
=> 49 concepts (48 used for prediction)
PRED predicted values (max 10 best out of 41): Ruapehu (0.33 #52, 0.17 #116, 0.12 #708), Haleakala (0.03 #181, 0.03 #1485, 0.02 #245), MaunaKea (0.03 #139, 0.03 #1485, 0.02 #203), MaunaLoa (0.03 #137, 0.03 #1485, 0.02 #201), Mt.Wilhelm (0.03 #190, 0.03 #1485, 0.02 #382), PuncakJaya (0.03 #184, 0.03 #1485, 0.02 #376), Mt.Giluwe (0.03 #179, 0.03 #1485, 0.02 #371), Asahi-Dake (0.03 #1485, 0.02 #230, 0.02 #294), Mt.Balbi (0.03 #1485, 0.02 #316, 0.02 #510), Popomanaseu (0.03 #1485, 0.02 #278, 0.02 #472)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #52 for best value:
>> intensional similarity = 6
>> extensional distance = 1
>> proper extension:  TeIka-a-Maui-NorthIsland-;
>> query: (?x587, Ruapehu) <- ?x587[ a Island; has belongsToIslands ?x1523<NewZealand>; has locatedIn ?x461<NZ>; has locatedInWater ?x282;]

No rule for expected values
ranks of expected_values: 
EVAL	TeWaka-a-Maui-SouthIsland-	locatedOnIsland!	Mt.Cook	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	49.000	48.000	41.000	0.333	http://www.semwebtech.org/mondial/10/meta#locatedOnIsland

PRED relation: locatedOnIsland!
PRED expected values: Mt.Cook
=> 144 concepts (144 used for prediction)
PRED predicted values (max 10 best out of 58): Ruapehu (0.40 #325, 0.33 #52, 0.27 #326), Mt.Cook (0.27 #326, 0.20 #195, 0.20 #129), TeIka-a-Maui-NorthIsland- (0.27 #326, 0.20 #195, 0.11 #260), TeWaka-a-Maui-SouthIsland- (0.27 #326, 0.20 #195, 0.11 #260), PacificOcean (0.27 #326, 0.20 #195, 0.11 #260), Mt.Wilhelm (0.25 #322, 0.04 #1752, 0.03 #2664), PuncakJaya (0.25 #316, 0.04 #1746, 0.03 #2658), Mt.Giluwe (0.25 #311, 0.04 #1741, 0.03 #2653), Mt.Victoria (0.25 #160, 0.03 #2113), BarbeauPeak (0.10 #631, 0.04 #1997, 0.03 #2453)
>> best conf = 0.40
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #325 for best value:
>> intensional similarity = 14
>> extensional distance = 2
>> proper extension:  NewGuinea;
>> query: (?x587, ?x1711) <- ?x587[ a Island; has locatedIn ?x461[ has encompassed ?x211<Australia-Oceania>; has language ?x51; has religion ?x352<RomanCatholic>; has religion ?x713<Anglican>; is locatedIn of ?x1711[ a Volcano; has type ?x706;];]; has locatedInWater ?x282<PacificOcean>;]

*> Best rule #326 for first EXPECTED value:
*> intensional similarity = 15
*> extensional distance = 2
*> proper extension:  NewGuinea;
*> query: (?x587, ?x897) <- ?x587[ a Island; has locatedIn ?x461[ has encompassed ?x211<Australia-Oceania>; has language ?x51; has religion ?x352<RomanCatholic>; has religion ?x713<Anglican>; is locatedIn of ?x897; is locatedIn of ?x1711[ a Volcano; has type ?x706;];]; has locatedInWater ?x282<PacificOcean>;]
*> conf = 0.27
ranks of expected_values: 2
EVAL	TeWaka-a-Maui-SouthIsland-	locatedOnIsland!	Mt.Cook	CNN-1.+1._MA	0.000	1.000	1.000	0.500	144.000	144.000	58.000	0.400	http://www.semwebtech.org/mondial/10/meta#locatedOnIsland

#907-Wallisian
PRED entity: Wallisian

PRED relation: language!
PRED expected values: WAFU
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 60): PK (0.08 #8), SF (0.07 #79), IR (0.07 #45), BZ (0.06 #88), AND (0.05 #97), L (0.05 #93), MK (0.05 #92), NZ (0.05 #70), I (0.05 #30), NLSM (0.05 #1)
>> best conf = 0.08
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #8 for best value:
>> intensional similarity = 1
>> extensional distance = 102
>> proper extension:  French; Japanese; Samoan; English; Tajik; KhalkhaMongol; Italian; Turkish; Portuguese; Dutch; ...
>> query: (?x2538, PK) <- ?x2538[ a Language;]

*> Best rule #78 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 102
*> proper extension:  French; Japanese; Samoan; English; Tajik; KhalkhaMongol; Italian; Turkish; Portuguese; Dutch; ...
*> query: (?x2538, WAFU) <- ?x2538[ a Language;]
*> conf = 0.02
ranks of expected_values: 44
EVAL	Wallisian	language!	WAFU	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.023	2.000	2.000	60.000	0.077	http://www.semwebtech.org/mondial/10/meta#language

PRED relation: language!
PRED expected values: WAFU
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 60): PK (0.08 #8), SF (0.07 #79), IR (0.07 #45), BZ (0.06 #88), AND (0.05 #97), L (0.05 #93), MK (0.05 #92), NZ (0.05 #70), I (0.05 #30), NLSM (0.05 #1)
>> best conf = 0.08
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #8 for best value:
>> intensional similarity = 1
>> extensional distance = 102
>> proper extension:  French; Japanese; Samoan; English; Tajik; KhalkhaMongol; Italian; Turkish; Portuguese; Dutch; ...
>> query: (?x2538, PK) <- ?x2538[ a Language;]

*> Best rule #78 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 102
*> proper extension:  French; Japanese; Samoan; English; Tajik; KhalkhaMongol; Italian; Turkish; Portuguese; Dutch; ...
*> query: (?x2538, WAFU) <- ?x2538[ a Language;]
*> conf = 0.02
ranks of expected_values: 44
EVAL	Wallisian	language!	WAFU	CNN-1.+1._MA	0.000	0.000	0.000	0.023	2.000	2.000	60.000	0.077	http://www.semwebtech.org/mondial/10/meta#language

#906-NelsonRiver
PRED entity: NelsonRiver

PRED relation: flowsInto!
PRED expected values: LakeWinnipeg
=> 39 concepts (35 used for prediction)
PRED predicted values (max 10 best out of 189): RiviereRichelieu (0.10 #295, 0.06 #598, 0.03 #901), Manicouagan (0.10 #190, 0.06 #493, 0.03 #796), AtlinLake (0.10 #182, 0.06 #485, 0.03 #788), LakeOntario (0.10 #155, 0.06 #458, 0.03 #761), Franklin.D.RooseveltLake (0.10 #41, 0.06 #344, 0.03 #647), LakeSuperior (0.10 #153, 0.06 #456, 0.03 #759), LakeChamplain (0.10 #291, 0.06 #594, 0.03 #897), LakeHuron (0.10 #12, 0.06 #315, 0.03 #618), SaintMarysRiver (0.06 #367, 0.03 #670, 0.02 #8488), NiagaraRiver (0.06 #457, 0.03 #760, 0.02 #10612)
>> best conf = 0.10
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #295 for best value:
>> intensional similarity = 5
>> extensional distance = 8
>> proper extension:  DetroitRiver;
>> query: (?x1025, RiviereRichelieu) <- ?x1025[ a River; has flowsInto ?x248; has hasSource ?x2425; has locatedIn ?x272<CDN>;]

*> Best rule #5454 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 190
*> proper extension:  Kwa; RioDesaguadero; Kasai; Murgab; Baro; WhiteDrin; Reuss; Saone; Vuoksi; Benue;
*> query: (?x1025, ?x182) <- ?x1025[ a River; has flowsInto ?x248; has hasSource ?x2425; has locatedIn ?x272[ is locatedIn of ?x182;];]
*> conf = 0.01
ranks of expected_values: 178
EVAL	NelsonRiver	flowsInto!	LakeWinnipeg	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.006	39.000	35.000	189.000	0.100	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto!
PRED expected values: LakeWinnipeg
=> 144 concepts (144 used for prediction)
PRED predicted values (max 10 best out of 250): RiviereRichelieu (0.25 #295, 0.20 #597, 0.17 #1203), Manicouagan (0.25 #190, 0.20 #492, 0.17 #1098), LakeOntario (0.25 #155, 0.20 #457, 0.17 #1063), Franklin.D.RooseveltLake (0.25 #41, 0.20 #343, 0.17 #949), AtlinLake (0.20 #484, 0.17 #1090, 0.10 #2302), LakeSuperior (0.17 #1061, 0.14 #1364, 0.12 #1667), LakeWinnipesaukee (0.17 #886, 0.10 #2098, 0.08 #3007), LakeChamplain (0.14 #1502, 0.12 #1805, 0.10 #2411), GreatSlaveLake (0.12 #1617, 0.05 #4649, 0.04 #7283), LakePowell (0.10 #2076, 0.08 #2985, 0.05 #4200)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #295 for best value:
>> intensional similarity = 10
>> extensional distance = 2
>> proper extension:  SaintLawrenceRiver; ColumbiaRiver;
>> query: (?x1025, RiviereRichelieu) <- ?x1025[ has flowsInto ?x248[ a Sea; is locatedInWater of ?x869[ is locatedOnIsland of ?x1586;]; is mergesWith of ?x263[ is mergesWith of ?x251;];]; has hasSource ?x2425[ a Source;]; has locatedIn ?x272<CDN>;]

*> Best rule #7283 for first EXPECTED value:
*> intensional similarity = 10
*> extensional distance = 43
*> proper extension:  Goetaaelv;
*> query: (?x1025, ?x1077) <- ?x1025[ has flowsInto ?x248[ is mergesWith of ?x249;]; has hasSource ?x2425[ a Source;]; has locatedIn ?x272[ has religion ?x95; is locatedIn of ?x866[ a Island;]; is locatedIn of ?x1077[ a Lake;];];]
*> conf = 0.04
ranks of expected_values: 69
EVAL	NelsonRiver	flowsInto!	LakeWinnipeg	CNN-1.+1._MA	0.000	0.000	0.000	0.014	144.000	144.000	250.000	0.250	http://www.semwebtech.org/mondial/10/meta#flowsInto

#905-LakeKivu
PRED entity: LakeKivu

PRED relation: locatedIn
PRED expected values: RWA
=> 33 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 135): RWA (0.65 #237, 0.65 #236, 0.56 #1650), BI (0.65 #237, 0.65 #236, 0.56 #1650), USA (0.25 #5506, 0.17 #1959, 0.17 #309), EAU (0.17 #1802, 0.12 #4724, 0.08 #2359), ANG (0.14 #1365, 0.12 #188, 0.12 #4725), CDN (0.14 #5497, 0.12 #1950, 0.08 #2422), RCB (0.12 #121, 0.12 #4724, 0.12 #4725), RCA (0.12 #159, 0.12 #4724, 0.12 #4725), EAT (0.12 #4724, 0.12 #4725, 0.08 #2359), Z (0.12 #4724, 0.12 #4725, 0.08 #2359)
>> best conf = 0.65
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #237 for best value:
>> intensional similarity = 7
>> extensional distance = 23
>> proper extension:  Kwa; Ruki; Lukuga; Uelle; Luvua; Ubangi; Cuango; Kasai; Lualaba; Busira; ...
>> query: (?x1776, ?x546) <- ?x1776[ has flowsInto ?x1060[ a River; has locatedIn ?x348<ZRE>; has locatedIn ?x359[ has encompassed ?x213;]; has locatedIn ?x546;];]
>> Best rule #236 for best value:
>> intensional similarity = 6
>> extensional distance = 23
>> proper extension:  Kwa; Ruki; Lukuga; Uelle; Luvua; Ubangi; Cuango; Kasai; Lualaba; Busira; ...
>> query: (?x1776, ?x359) <- ?x1776[ has flowsInto ?x1060[ a River; has locatedIn ?x348<ZRE>; has locatedIn ?x359[ has encompassed ?x213;];];]
ranks of expected_values: 1
EVAL	LakeKivu	locatedIn	RWA	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	33.000	31.000	135.000	0.650	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: RWA
=> 94 concepts (94 used for prediction)
PRED predicted values (max 10 best out of 202): RWA (0.87 #2612, 0.79 #1898, 0.74 #2373), BI (0.74 #2373, 0.69 #1899, 0.68 #2610), EAT (0.58 #2374, 0.33 #1361, 0.26 #2137), Z (0.58 #2374, 0.30 #13311, 0.26 #2137), EAU (0.55 #12126, 0.33 #1423, 0.33 #1338), USA (0.36 #12671, 0.34 #12909, 0.34 #13147), RCB (0.30 #13311, 0.26 #2137, 0.25 #359), ANG (0.30 #13311, 0.26 #2137, 0.22 #1612), RCA (0.30 #13311, 0.26 #2137, 0.21 #1185), SSD (0.21 #1185, 0.18 #1424, 0.15 #2136)
>> best conf = 0.87
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2612 for best value:
>> intensional similarity = 9
>> extensional distance = 19
>> proper extension:  Kwa; Ruki; Ubangi; Cuango; Lualaba; Cuilo; Aruwimi; Lomami;
>> query: (?x1776, ?x546) <- ?x1776[ has flowsInto ?x1060[ a River; has flowsInto ?x284[ has locatedIn ?x525;]; has hasSource ?x1434[ has locatedIn ?x546;]; has locatedIn ?x359;]; has locatedIn ?x348<ZRE>;]
ranks of expected_values: 1
EVAL	LakeKivu	locatedIn	RWA	CNN-1.+1._MA	1.000	1.000	1.000	1.000	94.000	94.000	202.000	0.870	http://www.semwebtech.org/mondial/10/meta#locatedIn

#904-KotelnyIsland
PRED entity: KotelnyIsland

PRED relation: locatedInWater
PRED expected values: ArcticOcean
=> 39 concepts (29 used for prediction)
PRED predicted values (max 10 best out of 133): PacificOcean (0.96 #231, 0.43 #790, 0.41 #609), BarentsSea (0.43 #790, 0.41 #609, 0.25 #12), SeaofOkhotsk (0.43 #790, 0.41 #609, 0.25 #25), ArcticOcean (0.43 #790, 0.41 #609, 0.25 #14), BeringSea (0.43 #790, 0.41 #609, 0.20 #881), SeaofJapan (0.43 #790, 0.41 #609, 0.07 #789), Donau (0.38 #175, 0.04 #263, 0.03 #348), AtlanticOcean (0.30 #617, 0.29 #663, 0.27 #891), KaraSea (0.25 #29, 0.20 #114, 0.20 #71), BalticSea (0.20 #47, 0.12 #176, 0.07 #435)
>> best conf = 0.96
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #231 for best value:
>> intensional similarity = 5
>> extensional distance = 55
>> proper extension:  Fakaofo; Guadalcanal; Babelthuap; Bougainville; VanuaLevu; NorfolkIsland;
>> query: (?x2150, PacificOcean) <- ?x2150[ a Island; has locatedInWater ?x452[ has mergesWith ?x809<BeringSea>; is flowsInto of ?x919;];]

*> Best rule #790 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 249
*> proper extension:  IsleofMan;
*> query: (?x2150, ?x263) <- ?x2150[ a Island; has locatedIn ?x73[ is locatedIn of ?x263[ is locatedInWater of ?x478; is mergesWith of ?x248;];];]
*> conf = 0.43
ranks of expected_values: 4
EVAL	KotelnyIsland	locatedInWater	ArcticOcean	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.250	39.000	29.000	133.000	0.965	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater
PRED expected values: ArcticOcean
=> 78 concepts (78 used for prediction)
PRED predicted values (max 10 best out of 271): AtlanticOcean (0.91 #367, 0.38 #188, 0.36 #1132), PacificOcean (0.77 #287, 0.36 #768, 0.34 #543), ArcticOcean (0.50 #132, 0.50 #130, 0.50 #101), BeringSea (0.50 #132, 0.43 #403, 0.36 #768), BarentsSea (0.43 #403, 0.36 #768, 0.34 #543), HudsonBay (0.43 #403, 0.25 #97, 0.20 #131), GreenlandSea (0.43 #403, 0.25 #126, 0.20 #131), LabradorSea (0.43 #403, 0.25 #98, 0.20 #131), KaraSea (0.43 #403, 0.20 #131, 0.20 #29), SeaofOkhotsk (0.38 #70, 0.36 #768, 0.34 #543)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #367 for best value:
>> intensional similarity = 13
>> extensional distance = 75
>> proper extension:  SaintPierre; SaintVincent; Ireland; Pico; Flores; GrandBermuda; Fogo; Arran; Tobago; GreatBritain; ...
>> query: (?x2150, AtlanticOcean) <- ?x2150[ a Island; has locatedInWater ?x452[ a Sea; is flowsInto of ?x919; is mergesWith of ?x263[ has locatedIn ?x73; is locatedInWater of ?x1075<Greenland>; is locatedInWater of ?x1238<EllesmereIsland>; is locatedInWater of ?x1949<DevonIsland>; is mergesWith of ?x248;];];]

*> Best rule #132 for first EXPECTED value:
*> intensional similarity = 13
*> extensional distance = 6
*> proper extension:  Iceland; BaffinIsland; NowajaSemlja; Svalbard; Greenland; SouthamptonIsland;
*> query: (?x2150, ?x809) <- ?x2150[ a Island; has locatedInWater ?x452[ is flowsInto of ?x919; is mergesWith of ?x263<ArcticOcean>; is mergesWith of ?x809[ a Sea; has locatedIn ?x73<R>; has locatedIn ?x315<USA>; is locatedInWater of ?x1687; is mergesWith of ?x282;];];]
*> conf = 0.50
ranks of expected_values: 3
EVAL	KotelnyIsland	locatedInWater	ArcticOcean	CNN-1.+1._MA	0.000	1.000	1.000	0.333	78.000	78.000	271.000	0.909	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#903-Kazakh
PRED entity: Kazakh

PRED relation: ethnicGroup!
PRED expected values: KAZ
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 96): EAU (0.03 #132), ETH (0.03 #100), UA (0.03 #57), EAK (0.03 #99), CZ (0.02 #98), SN (0.02 #83), MYA (0.02 #70), IR (0.02 #58), SK (0.02 #25), RCA (0.02 #139)
>> best conf = 0.03
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #132 for best value:
>> intensional similarity = 1
>> extensional distance = 279
>> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
>> query: (?x2489, EAU) <- ?x2489[ a EthnicGroup;]

*> Best rule #78 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 279
*> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
*> query: (?x2489, KAZ) <- ?x2489[ a EthnicGroup;]
*> conf = 0.02
ranks of expected_values: 15
EVAL	Kazakh	ethnicGroup!	KAZ	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.067	2.000	2.000	96.000	0.032	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup!
PRED expected values: KAZ
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 96): EAU (0.03 #132), ETH (0.03 #100), UA (0.03 #57), EAK (0.03 #99), CZ (0.02 #98), SN (0.02 #83), MYA (0.02 #70), IR (0.02 #58), SK (0.02 #25), RCA (0.02 #139)
>> best conf = 0.03
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #132 for best value:
>> intensional similarity = 1
>> extensional distance = 279
>> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
>> query: (?x2489, EAU) <- ?x2489[ a EthnicGroup;]

*> Best rule #78 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 279
*> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
*> query: (?x2489, KAZ) <- ?x2489[ a EthnicGroup;]
*> conf = 0.02
ranks of expected_values: 15
EVAL	Kazakh	ethnicGroup!	KAZ	CNN-1.+1._MA	0.000	0.000	0.000	0.067	2.000	2.000	96.000	0.032	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#902-Melanesian
PRED entity: Melanesian

PRED relation: ethnicGroup!
PRED expected values: VU
=> 31 concepts (20 used for prediction)
PRED predicted values (max 10 best out of 196): GB (0.55 #1745, 0.29 #2333, 0.10 #964), CR (0.50 #252, 0.40 #445, 0.39 #1350), NAU (0.50 #373, 0.40 #566, 0.39 #1350), XMAS (0.50 #381, 0.40 #574, 0.39 #1350), NZ (0.40 #1638, 0.39 #1350, 0.33 #94), FPOL (0.39 #1350, 0.37 #1932, 0.33 #825), TT (0.39 #1350, 0.37 #1932, 0.33 #1091), MAL (0.39 #1350, 0.37 #1932, 0.29 #2203), HELX (0.39 #1350, 0.37 #1932, 0.27 #1738), SGP (0.39 #1350, 0.37 #1932, 0.27 #1738)
>> best conf = 0.55
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1745 for best value:
>> intensional similarity = 13
>> extensional distance = 9
>> proper extension:  Indian; Scottish; Pakistani; Welsh; English; NorthernIrish;
>> query: (?x2071, GB) <- ?x2071[ a EthnicGroup; is ethnicGroup of ?x390[ a Country; has ethnicGroup ?x298[ is ethnicGroup of ?x366<MYA>;]; has government ?x1947; has language ?x247; has religion ?x429[ is religion of ?x1008<BERM>;]; has religion ?x713<Anglican>;];]

*> Best rule #1437 for first EXPECTED value:
*> intensional similarity = 14
*> extensional distance = 7
*> proper extension:  French;
*> query: (?x2071, VU) <- ?x2071[ a EthnicGroup; is ethnicGroup of ?x390[ a Country; has government ?x1947; has religion ?x95[ is religion of ?x461<NZ>;]; has religion ?x429<Seventh-DayAdventist>; has religion ?x713<Anglican>; has religion ?x1082[ a Religion;]; is locatedIn of ?x1083;];]
*> conf = 0.11
ranks of expected_values: 79
EVAL	Melanesian	ethnicGroup!	VU	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.013	31.000	20.000	196.000	0.545	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup!
PRED expected values: VU
=> 50 concepts (48 used for prediction)
PRED predicted values (max 10 best out of 210): HONX (0.57 #386, 0.47 #1170, 0.41 #776), NL (0.57 #386, 0.36 #582, 0.18 #6520), CR (0.56 #1624, 0.56 #1427, 0.50 #252), NAU (0.50 #373, 0.47 #1170, 0.45 #580), XMAS (0.50 #381, 0.47 #1170, 0.41 #776), NZ (0.50 #1266, 0.45 #580, 0.41 #776), FPOL (0.47 #1170, 0.45 #580, 0.41 #776), RC (0.47 #1170, 0.45 #580, 0.41 #776), TT (0.47 #1170, 0.41 #776, 0.39 #1169), HELX (0.47 #1170, 0.41 #776, 0.39 #1169)
>> best conf = 0.57
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #386 for best value:
>> intensional similarity = 19
>> extensional distance = 2
>> proper extension:  Chinese;
>> query: (?x2071, ?x575) <- ?x2071[ a EthnicGroup; is ethnicGroup of ?x390<SLB>; is ethnicGroup of ?x1002[ a Country; has encompassed ?x211; has ethnicGroup ?x197<European>; has ethnicGroup ?x774[ is ethnicGroup of ?x575; is ethnicGroup of ?x773<HONX>;]; has government ?x916; has religion ?x95<Protestant>; has religion ?x352<RomanCatholic>; is locatedIn of ?x282<PacificOcean>;];]

*> Best rule #580 for first EXPECTED value:
*> intensional similarity = 19
*> extensional distance = 3
*> proper extension:  Micronesian;
*> query: (?x2071, ?x73) <- ?x2071[ a EthnicGroup; is ethnicGroup of ?x390<SLB>; is ethnicGroup of ?x1002[ a Country; has encompassed ?x211<Australia-Oceania>; has ethnicGroup ?x197<European>; has ethnicGroup ?x1250[ is ethnicGroup of ?x871<K>;]; has government ?x916; has religion ?x95<Protestant>; has religion ?x352<RomanCatholic>; is locatedIn of ?x282[ has locatedIn ?x73;];];]
*> conf = 0.45
ranks of expected_values: 50
EVAL	Melanesian	ethnicGroup!	VU	CNN-1.+1._MA	0.000	0.000	0.000	0.020	50.000	48.000	210.000	0.568	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#901-SYR
PRED entity: SYR

PRED relation: religion
PRED expected values: Christian
=> 29 concepts (29 used for prediction)
PRED predicted values (max 10 best out of 21): Christian (0.54 #535, 0.51 #577, 0.49 #701), Jewish (0.54 #535, 0.51 #577, 0.49 #701), Druze (0.54 #535, 0.51 #577, 0.49 #701), RomanCatholic (0.49 #500, 0.49 #542, 0.48 #459), Protestant (0.44 #454, 0.43 #495, 0.42 #537), ChristianOrthodox (0.43 #206, 0.36 #288, 0.33 #247), Bahai (0.20 #195, 0.20 #154, 0.14 #236), Hindu (0.14 #948, 0.10 #710, 0.09 #999), Buddhist (0.14 #948, 0.10 #917, 0.10 #712), CopticChristian (0.14 #948, 0.06 #358, 0.06 #399)
>> best conf = 0.54
=> the first rule below is the first best rule for 3 predicted values
>> Best rule #535 for best value:
>> intensional similarity = 5
>> extensional distance = 104
>> proper extension:  IRL;
>> query: (?x466, ?x187) <- ?x466[ is locatedIn of ?x419; is neighbor of ?x185[ has language ?x511; has religion ?x187; is locatedIn of ?x98;];]
ranks of expected_values: 1
EVAL	SYR	religion	Christian	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	29.000	29.000	21.000	0.545	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion
PRED expected values: Christian
=> 69 concepts (69 used for prediction)
PRED predicted values (max 10 best out of 33): Christian (0.67 #215, 0.62 #1333, 0.61 #1000), Jewish (0.62 #1333, 0.61 #1000, 0.57 #1500), Druze (0.61 #1000, 0.57 #1500, 0.56 #1083), RomanCatholic (0.57 #882, 0.56 #1090, 0.55 #1422), Protestant (0.50 #1085, 0.47 #877, 0.47 #1626), Bahai (0.44 #2082, 0.17 #284, 0.17 #127), ChristianOrthodox (0.43 #625, 0.40 #171, 0.38 #378), Buddhist (0.33 #253, 0.30 #2250, 0.30 #1998), Hindu (0.33 #253, 0.30 #2250, 0.30 #1998), CopticChristian (0.33 #253, 0.30 #1998, 0.29 #1914)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #215 for best value:
>> intensional similarity = 14
>> extensional distance = 4
>> proper extension:  WEST;
>> query: (?x466, Christian) <- ?x466[ has neighbor ?x185[ is locatedIn of ?x98;]; is locatedIn of ?x275[ has locatedIn ?x63[ has encompassed ?x175;]; has locatedIn ?x78[ has neighbor ?x234; has religion ?x95;]; is flowsInto of ?x699[ is flowsInto of ?x983;]; is flowsInto of ?x836[ a River;];]; is locatedIn of ?x419<Jordan>;]
ranks of expected_values: 1
EVAL	SYR	religion	Christian	CNN-1.+1._MA	1.000	1.000	1.000	1.000	69.000	69.000	33.000	0.667	http://www.semwebtech.org/mondial/10/meta#religion

#900-Asian
PRED entity: Asian

PRED relation: ethnicGroup!
PRED expected values: PR
=> 31 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 230): NL (0.50 #1044, 0.43 #1233, 0.33 #1609), NAU (0.50 #923, 0.40 #737, 0.33 #363), Z (0.34 #1119, 0.33 #287, 0.33 #101), LB (0.34 #1119, 0.33 #116, 0.29 #373), MEX (0.34 #1119, 0.33 #282, 0.25 #3366), RG (0.34 #1119, 0.29 #373, 0.25 #3366), RSA (0.34 #1119, 0.25 #3366, 0.24 #3554), RB (0.34 #1119, 0.25 #3366, 0.23 #3930), CR (0.33 #243, 0.33 #57, 0.31 #1367), CO (0.33 #225, 0.33 #39, 0.25 #1349)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1044 for best value:
>> intensional similarity = 10
>> extensional distance = 10
>> proper extension:  Amerindian; Moroccan; Indonesian; Norwegian; Dutch; Caribbean; Turkish; Surinamese; Sami;
>> query: (?x380, NL) <- ?x380[ is ethnicGroup of ?x154[ a Country;]; is ethnicGroup of ?x461[ has language ?x51; has religion ?x95; has wasDependentOf ?x81; is dependentOf of ?x1334; is locatedIn of ?x282;]; is ethnicGroup of ?x1072[ has neighbor ?x621;];]

*> Best rule #3741 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 104
*> proper extension:  Yezidi;
*> query: (?x380, ?x108) <- ?x380[ is ethnicGroup of ?x461[ has ethnicGroup ?x197[ is ethnicGroup of ?x108;]; has language ?x51; has religion ?x95; has wasDependentOf ?x81;];]
*> conf = 0.16
ranks of expected_values: 59
EVAL	Asian	ethnicGroup!	PR	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.017	31.000	31.000	230.000	0.500	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup!
PRED expected values: PR
=> 80 concepts (80 used for prediction)
PRED predicted values (max 10 best out of 228): CR (0.71 #1000, 0.62 #1561, 0.50 #433), CO (0.62 #1543, 0.57 #982, 0.50 #415), MEX (0.61 #1693, 0.56 #1130, 0.50 #472), EC (0.57 #1096, 0.50 #1657, 0.50 #529), CDN (0.57 #1366, 0.33 #942, 0.33 #237), R (0.56 #1885, 0.33 #942, 0.26 #3198), NIC (0.50 #453, 0.43 #1020, 0.38 #1581), ES (0.50 #498, 0.43 #1065, 0.38 #1626), GCA (0.50 #406, 0.43 #973, 0.38 #1534), C (0.50 #1523, 0.43 #962, 0.33 #207)
>> best conf = 0.71
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1000 for best value:
>> intensional similarity = 13
>> extensional distance = 5
>> proper extension:  African; Chinese; Mestizo;
>> query: (?x380, CR) <- ?x380[ a EthnicGroup; is ethnicGroup of ?x315[ a Country; has religion ?x95<Protestant>; is locatedIn of ?x219[ a River;]; is locatedIn of ?x282<PacificOcean>; is neighbor of ?x482;]; is ethnicGroup of ?x461[ a Country; has language ?x51;];]

*> Best rule #5258 for first EXPECTED value:
*> intensional similarity = 13
*> extensional distance = 29
*> proper extension:  None; Christian;
*> query: (?x380, ?x63) <- ?x380[ is ethnicGroup of ?x196[ has language ?x247; is locatedIn of ?x371;]; is ethnicGroup of ?x315[ has ethnicGroup ?x197[ is ethnicGroup of ?x63;]; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x282[ is locatedInWater of ?x205; is mergesWith of ?x60;]; is locatedIn of ?x1325[ is flowsInto of ?x1288;];];]
*> conf = 0.26
ranks of expected_values: 68
EVAL	Asian	ethnicGroup!	PR	CNN-1.+1._MA	0.000	0.000	0.000	0.015	80.000	80.000	228.000	0.714	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#899-Guam
PRED entity: Guam

PRED relation: locatedInWater
PRED expected values: PacificOcean
=> 41 concepts (41 used for prediction)
PRED predicted values (max 10 best out of 31): PacificOcean (0.71 #303, 0.71 #276, 0.70 #347), AtlanticOcean (0.38 #571, 0.37 #310, 0.37 #485), Guam (0.38 #173, 0.04 #912, 0.03 #824), IndianOcean (0.17 #175, 0.12 #480, 0.12 #566), MediterraneanSea (0.16 #796, 0.11 #710, 0.11 #449), CaribbeanSea (0.14 #583, 0.13 #497, 0.10 #409), JavaSea (0.12 #268, 0.09 #487, 0.08 #573), NorthSea (0.10 #740, 0.08 #871, 0.08 #915), EastChinaSea (0.06 #477, 0.06 #286, 0.04 #460), SeaofJapan (0.06 #477, 0.06 #274, 0.03 #318)
>> best conf = 0.71
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #303 for best value:
>> intensional similarity = 7
>> extensional distance = 32
>> proper extension:  Samosir; EasterIsland; Banaba;
>> query: (?x1401, ?x282) <- ?x1401[ a Island; has locatedIn ?x1154[ a Country; has ethnicGroup ?x1064; is locatedIn of ?x282<PacificOcean>;]; has type ?x1402;]
>> Best rule #276 for best value:
>> intensional similarity = 7
>> extensional distance = 32
>> proper extension:  Samosir; EasterIsland; Banaba;
>> query: (?x1401, PacificOcean) <- ?x1401[ a Island; has locatedIn ?x1154[ a Country; has ethnicGroup ?x1064; is locatedIn of ?x282<PacificOcean>;]; has type ?x1402;]
ranks of expected_values: 1
EVAL	Guam	locatedInWater	PacificOcean	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	41.000	41.000	31.000	0.706	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater
PRED expected values: PacificOcean
=> 106 concepts (106 used for prediction)
PRED predicted values (max 10 best out of 41): PacificOcean (0.84 #923, 0.84 #896, 0.83 #655), AtlanticOcean (0.48 #1995, 0.48 #662, 0.47 #1462), Guam (0.38 #260, 0.33 #173, 0.09 #1145), IndianOcean (0.26 #1810, 0.22 #1324, 0.21 #925), CaribbeanSea (0.25 #763, 0.25 #148, 0.19 #853), MediterraneanSea (0.25 #1028, 0.20 #2540, 0.20 #2586), JavaSea (0.18 #532, 0.17 #1154, 0.17 #576), ArcticOcean (0.18 #758, 0.15 #982, 0.14 #1292), EastChinaSea (0.17 #374, 0.11 #418, 0.10 #833), SeaofJapan (0.17 #362, 0.11 #406, 0.10 #494)
>> best conf = 0.84
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #923 for best value:
>> intensional similarity = 8
>> extensional distance = 29
>> proper extension:  Fakaofo;
>> query: (?x1401, ?x282) <- ?x1401[ a Island; has belongsToIslands ?x66[ a Islands; is belongsToIslands of ?x504[ a Island; has locatedInWater ?x282<PacificOcean>;];]; has type ?x1402;]
>> Best rule #896 for best value:
>> intensional similarity = 8
>> extensional distance = 29
>> proper extension:  Fakaofo;
>> query: (?x1401, PacificOcean) <- ?x1401[ a Island; has belongsToIslands ?x66[ a Islands; is belongsToIslands of ?x504[ a Island; has locatedInWater ?x282<PacificOcean>;];]; has type ?x1402;]
ranks of expected_values: 1
EVAL	Guam	locatedInWater	PacificOcean	CNN-1.+1._MA	1.000	1.000	1.000	1.000	106.000	106.000	41.000	0.839	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#898-MNE
PRED entity: MNE

PRED relation: locatedIn!
PRED expected values: Tara Moraca
=> 41 concepts (39 used for prediction)
PRED predicted values (max 10 best out of 1338): Drina (0.50 #2830, 0.33 #1798, 0.33 #383), Donau (0.50 #5683, 0.33 #1439, 0.20 #11339), Tara (0.50 #1415, 0.33 #185, 0.17 #5844), Moraca (0.50 #1415), SouthernMorava (0.40 #3660, 0.33 #5074, 0.33 #2245), LakePrespa (0.40 #3170, 0.33 #4584, 0.13 #5659), AtlanticOcean (0.35 #53786, 0.33 #39640, 0.30 #12770), Save (0.33 #1446, 0.33 #31, 0.22 #7104), Drina (0.33 #2527, 0.33 #1112, 0.22 #8185), WhiteDrin (0.33 #4675, 0.20 #3261, 0.13 #5659)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2830 for best value:
>> intensional similarity = 6
>> extensional distance = 1
>> proper extension:  SRB;
>> query: (?x106, ?x813) <- ?x106[ has ethnicGroup ?x2300<Montenegrin>; has language ?x1251; has neighbor ?x156; is locatedIn of ?x306[ has flowsInto ?x813;];]

*> Best rule #1415 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 1
*> proper extension:  BIH;
*> query: (?x106, ?x2296) <- ?x106[ has ethnicGroup ?x775; has language ?x1251; is locatedIn of ?x105[ is hasEstuary of ?x2296;]; is locatedIn of ?x306<Piva>; is neighbor of ?x55;]
*> conf = 0.50
ranks of expected_values: 3, 4
EVAL	MNE	locatedIn!	Moraca	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.333	41.000	39.000	1338.000	0.500	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	MNE	locatedIn!	Tara	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.333	41.000	39.000	1338.000	0.500	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Tara Moraca
=> 93 concepts (90 used for prediction)
PRED predicted values (max 10 best out of 1414): Tara (0.92 #51000, 0.33 #11327, 0.33 #5664), Moraca (0.92 #51000, 0.33 #11327, 0.33 #5664), AtlanticOcean (0.70 #87876, 0.67 #69458, 0.60 #48206), Drina (0.70 #39664, 0.69 #29749, 0.35 #2831), Buna (0.67 #39663, 0.65 #29748, 0.35 #2831), Donau (0.62 #21268, 0.57 #22686, 0.50 #14187), PacificOcean (0.47 #25578, 0.32 #70915, 0.31 #62413), SouthernMorava (0.43 #16409, 0.35 #2831, 0.33 #10742), MontBlanc (0.40 #12850, 0.35 #14163, 0.22 #19930), Drau (0.35 #14163, 0.35 #2831, 0.33 #20097)
>> best conf = 0.92
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #51000 for best value:
>> intensional similarity = 14
>> extensional distance = 43
>> proper extension:  USA; PY; WEST;
>> query: (?x106, ?x473) <- ?x106[ a Country; has language ?x1251; has neighbor ?x204[ has ethnicGroup ?x595; has religion ?x56; is locatedIn of ?x183;]; has neighbor ?x904[ has encompassed ?x195; has religion ?x95; is locatedIn of ?x132;]; is locatedIn of ?x203[ a River;]; is locatedIn of ?x2462[ is hasEstuary of ?x473;];]
ranks of expected_values: 1, 2
EVAL	MNE	locatedIn!	Moraca	CNN-1.+1._MA	1.000	1.000	1.000	1.000	93.000	90.000	1414.000	0.921	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	MNE	locatedIn!	Tara	CNN-1.+1._MA	1.000	1.000	1.000	1.000	93.000	90.000	1414.000	0.921	http://www.semwebtech.org/mondial/10/meta#locatedIn

#897-ET
PRED entity: ET

PRED relation: neighbor
PRED expected values: SUD
=> 36 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 195): SUD (0.91 #4009, 0.90 #2561, 0.90 #2562), TCH (0.50 #23, 0.33 #184, 0.31 #2564), SSD (0.50 #203, 0.31 #2564, 0.27 #3689), ET (0.33 #163, 0.31 #2564, 0.27 #3689), SYR (0.31 #2564, 0.29 #402, 0.27 #3689), JOR (0.31 #2564, 0.29 #446, 0.27 #3689), DZ (0.31 #2564, 0.27 #3689, 0.27 #4011), TN (0.31 #2564, 0.27 #3689, 0.27 #4011), ER (0.31 #2564, 0.27 #3689, 0.27 #4011), RCA (0.31 #2564, 0.27 #3689, 0.27 #4011)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4009 for best value:
>> intensional similarity = 7
>> extensional distance = 128
>> proper extension:  ARM;
>> query: (?x63, ?x239) <- ?x63[ has ethnicGroup ?x197; has religion ?x187; is neighbor of ?x239[ has government ?x254; is locatedIn of ?x238;]; is neighbor of ?x1184[ is neighbor of ?x108;];]
ranks of expected_values: 1
EVAL	ET	neighbor	SUD	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	36.000	36.000	195.000	0.911	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: SUD
=> 113 concepts (113 used for prediction)
PRED predicted values (max 10 best out of 210): SUD (0.90 #15036, 0.90 #12232, 0.90 #16020), ET (0.60 #4710, 0.50 #8140, 0.49 #3735), IRQ (0.57 #2326, 0.45 #4114, 0.40 #3953), SYR (0.50 #3984, 0.43 #2435, 0.43 #2357), TR (0.50 #2467, 0.40 #1488, 0.40 #487), KAZ (0.50 #1043, 0.33 #558, 0.25 #1205), NAM (0.50 #1638, 0.25 #5379, 0.19 #5217), CY (0.46 #650, 0.46 #648, 0.43 #651), ZW (0.46 #650, 0.46 #648, 0.43 #651), EAT (0.46 #650, 0.46 #648, 0.43 #651)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #15036 for best value:
>> intensional similarity = 11
>> extensional distance = 98
>> proper extension:  MACX; HONX;
>> query: (?x63, ?x186) <- ?x63[ a Country; has neighbor ?x239[ a Country; has ethnicGroup ?x244; has religion ?x109; is locatedIn of ?x238;]; has religion ?x187; is locatedIn of ?x275[ is locatedInWater of ?x68;]; is neighbor of ?x186[ is neighbor of ?x169;];]
ranks of expected_values: 1
EVAL	ET	neighbor	SUD	CNN-1.+1._MA	1.000	1.000	1.000	1.000	113.000	113.000	210.000	0.900	http://www.semwebtech.org/mondial/10/meta#neighbor

#896-L
PRED entity: L

PRED relation: ethnicGroup
PRED expected values: French Luxembourgish
=> 47 concepts (47 used for prediction)
PRED predicted values (max 10 best out of 244): French (0.67 #1909, 0.50 #889, 0.43 #1144), European (0.38 #2558, 0.33 #518, 0.30 #4344), Chinese (0.33 #269, 0.22 #1799, 0.17 #524), Spanish (0.33 #38, 0.17 #803, 0.17 #548), Russian (0.27 #3898, 0.26 #3643, 0.24 #2877), Ukrainian (0.26 #3572, 0.20 #3827, 0.18 #2806), African (0.23 #6892, 0.20 #8932, 0.19 #3066), Polish (0.22 #1734, 0.18 #3775, 0.18 #2244), Turkish (0.22 #1715, 0.18 #2225, 0.13 #3756), Asian (0.19 #3078, 0.19 #2568, 0.14 #3334)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1909 for best value:
>> intensional similarity = 4
>> extensional distance = 7
>> proper extension:  VU;
>> query: (?x718, French) <- ?x718[ has ethnicGroup ?x1673[ a EthnicGroup; is ethnicGroup of ?x789<AND>;];]
ranks of expected_values: 1
EVAL	L	ethnicGroup	Luxembourgish	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	47.000	47.000	244.000	0.667	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	L	ethnicGroup	French	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	47.000	47.000	244.000	0.667	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: French Luxembourgish
=> 104 concepts (104 used for prediction)
PRED predicted values (max 10 best out of 253): European (0.60 #256, 0.50 #13320, 0.50 #6405), French (0.60 #256, 0.40 #2425, 0.33 #3705), Mestizo (0.60 #256, 0.40 #2592, 0.33 #3873), Russian (0.60 #256, 0.33 #8520, 0.32 #10312), Indonesian (0.60 #256, 0.33 #39, 0.26 #18949), African (0.60 #256, 0.31 #13318, 0.29 #21518), Amerindian (0.60 #256, 0.27 #11263, 0.26 #2557), Indian (0.60 #256, 0.26 #18692, 0.26 #2557), Pakistani (0.60 #256, 0.26 #18692, 0.26 #2557), NorthernIrish (0.60 #256, 0.26 #18692, 0.26 #2557)
>> best conf = 0.60
=> the first rule below is the first best rule for 32 predicted values
>> Best rule #256 for best value:
>> intensional similarity = 17
>> extensional distance = 1
>> proper extension:  NL;
>> query: (?x718, ?x380) <- ?x718[ has encompassed ?x195; has language ?x247[ a Language; is language of ?x246<CUR>; is language of ?x322[ has ethnicGroup ?x380; is locatedIn of ?x65;]; is language of ?x621[ has religion ?x116;];]; is neighbor of ?x78[ is locatedIn of ?x829<Maas>; is wasDependentOf of ?x871[ is neighbor of ?x91;];]; is neighbor of ?x543<B>;]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 2
EVAL	L	ethnicGroup	Luxembourgish	CNN-1.+1._MA	0.000	0.000	0.000	0.000	104.000	104.000	253.000	0.600	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	L	ethnicGroup	French	CNN-1.+1._MA	0.000	1.000	1.000	0.500	104.000	104.000	253.000	0.600	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#895-AtlanticOcean
PRED entity: AtlanticOcean

PRED relation: locatedIn
PRED expected values: LB WD WG GBZ
=> 32 concepts (32 used for prediction)
PRED predicted values (max 10 best out of 160): WD (0.88 #3726, 0.33 #138, 0.25 #814), WG (0.88 #3726, 0.33 #145, 0.25 #821), CO (0.67 #4236, 0.67 #4235, 0.67 #4234), RMM (0.67 #4236, 0.67 #4235, 0.67 #4234), BF (0.67 #4236, 0.67 #4235, 0.67 #4234), PY (0.67 #4236, 0.67 #4235, 0.67 #4234), LS (0.67 #4236, 0.67 #4235, 0.67 #4234), MEX (0.50 #762, 0.33 #1778, 0.33 #86), R (0.38 #2034, 0.33 #1696, 0.24 #2712), NIC (0.33 #69, 0.25 #745, 0.20 #1592)
>> best conf = 0.88
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #3726 for best value:
>> intensional similarity = 3
>> extensional distance = 41
>> proper extension:  Araguaia;
>> query: (?x182, ?x792) <- ?x182[ is locatedInWater of ?x1075[ a Island; has locatedIn ?x792;];]
ranks of expected_values: 1, 2, 34, 44
EVAL	AtlanticOcean	locatedIn	GBZ	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.024	32.000	32.000	160.000	0.877	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	AtlanticOcean	locatedIn	WG	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	32.000	32.000	160.000	0.877	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	AtlanticOcean	locatedIn	WD	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	32.000	32.000	160.000	0.877	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	AtlanticOcean	locatedIn	LB	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.031	32.000	32.000	160.000	0.877	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: LB WD WG GBZ
=> 82 concepts (82 used for prediction)
PRED predicted values (max 10 best out of 177): WD (0.91 #5276, 0.91 #5275, 0.90 #6299), WG (0.91 #5276, 0.91 #5275, 0.90 #6299), CO (0.77 #6641, 0.75 #2380, 0.70 #10058), PY (0.77 #6641, 0.75 #2380, 0.70 #10058), RMM (0.77 #6641, 0.75 #2380, 0.70 #10058), BF (0.77 #6641, 0.75 #2380, 0.70 #10058), LS (0.77 #6641, 0.75 #2380, 0.70 #10058), RB (0.67 #680, 0.60 #1019, 0.58 #170), MEX (0.67 #680, 0.60 #1019, 0.58 #170), Z (0.67 #680, 0.60 #1019, 0.58 #170)
>> best conf = 0.91
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #5276 for best value:
>> intensional similarity = 9
>> extensional distance = 17
>> proper extension:  HudsonBay;
>> query: (?x182, ?x315) <- ?x182[ is flowsInto of ?x137; is locatedInWater of ?x1149[ has belongsToIslands ?x200;]; is locatedInWater of ?x1361[ has locatedIn ?x315;]; is mergesWith of ?x121[ is locatedInWater of ?x634;]; is mergesWith of ?x1419[ has locatedIn ?x455;];]
>> Best rule #5275 for best value:
>> intensional similarity = 9
>> extensional distance = 17
>> proper extension:  HudsonBay;
>> query: (?x182, ?x149) <- ?x182[ is flowsInto of ?x137; is locatedInWater of ?x1149[ has belongsToIslands ?x200;]; is locatedInWater of ?x1935[ has locatedIn ?x149;]; is mergesWith of ?x121[ is locatedInWater of ?x634;]; is mergesWith of ?x1419[ has locatedIn ?x455;];]
ranks of expected_values: 1, 2, 14, 29
EVAL	AtlanticOcean	locatedIn	GBZ	CNN-1.+1._MA	0.000	0.000	0.000	0.038	82.000	82.000	177.000	0.909	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	AtlanticOcean	locatedIn	WG	CNN-1.+1._MA	1.000	1.000	1.000	1.000	82.000	82.000	177.000	0.909	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	AtlanticOcean	locatedIn	WD	CNN-1.+1._MA	1.000	1.000	1.000	1.000	82.000	82.000	177.000	0.909	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	AtlanticOcean	locatedIn	LB	CNN-1.+1._MA	0.000	0.000	0.000	0.083	82.000	82.000	177.000	0.909	http://www.semwebtech.org/mondial/10/meta#locatedIn

#894-UA
PRED entity: UA

PRED relation: locatedIn!
PRED expected values: Dnjestr
=> 31 concepts (23 used for prediction)
PRED predicted values (max 10 best out of 1252): PacificOcean (0.34 #9888, 0.17 #25301, 0.15 #11289), Olt (0.33 #821, 0.15 #16811, 0.12 #28020), Pruth (0.33 #1172, 0.12 #28020, 0.10 #23818), Olt (0.33 #1175, 0.12 #28020, 0.10 #23818), Olt (0.33 #658, 0.12 #28020, 0.10 #23818), AtlanticOcean (0.32 #9845, 0.26 #25258, 0.24 #22456), WesternDwina (0.23 #8403, 0.23 #7802, 0.22 #3602), Narew (0.22 #3528, 0.18 #4928, 0.17 #6328), BalticSea (0.22 #2830, 0.18 #4230, 0.17 #5630), Drau (0.20 #1672, 0.15 #16811, 0.12 #28020)
>> best conf = 0.34
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #9888 for best value:
>> intensional similarity = 6
>> extensional distance = 71
>> proper extension:  REUN; COCO;
>> query: (?x303, PacificOcean) <- ?x303[ a Country; has religion ?x56; is locatedIn of ?x98[ has mergesWith ?x1633;]; is locatedIn of ?x1702[ has type ?x136;];]

No rule for expected values
ranks of expected_values: 
EVAL	UA	locatedIn!	Dnjestr	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	31.000	23.000	1252.000	0.342	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Dnjestr
=> 99 concepts (99 used for prediction)
PRED predicted values (max 10 best out of 1410): PacificOcean (0.81 #40767, 0.53 #36556, 0.50 #2805), Pruth (0.69 #19625, 0.69 #15417, 0.69 #15416), WesternBug (0.69 #19625, 0.69 #15417, 0.69 #15416), Theiss (0.69 #19625, 0.69 #15417, 0.69 #15416), Dnjestr (0.69 #19625, 0.69 #15417, 0.69 #15416), MediterraneanSea (0.67 #23916, 0.40 #15498, 0.33 #1480), Dnepr (0.54 #78581, 0.50 #2805, 0.38 #16822), Donau (0.54 #78581, 0.06 #78583, 0.05 #91212), Narew (0.50 #9134, 0.50 #2805, 0.38 #16822), BalticSea (0.50 #2805, 0.47 #109462, 0.46 #64548)
>> best conf = 0.81
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #40767 for best value:
>> intensional similarity = 14
>> extensional distance = 19
>> proper extension:  PAL;
>> query: (?x303, PacificOcean) <- ?x303[ has government ?x435; has wasDependentOf ?x903; is locatedIn of ?x98[ has locatedIn ?x176[ has ethnicGroup ?x58<Ukrainian>; has language ?x684; is neighbor of ?x236;]; has locatedIn ?x185[ has encompassed ?x175; has neighbor ?x302;]; is mergesWith of ?x1633;]; is locatedIn of ?x457[ has type ?x136;];]

*> Best rule #19625 for first EXPECTED value:
*> intensional similarity = 15
*> extensional distance = 3
*> proper extension:  HR;
*> query: (?x303, ?x2078) <- ?x303[ has encompassed ?x195; has ethnicGroup ?x517<Hungarian>; has language ?x1108; has neighbor ?x176[ has language ?x684;]; has neighbor ?x194[ has neighbor ?x120; is locatedIn of ?x146;]; has religion ?x56<ChristianOrthodox>; is locatedIn of ?x133<Donau>; is locatedIn of ?x1292[ has hasEstuary ?x2078;];]
*> conf = 0.69
ranks of expected_values: 5
EVAL	UA	locatedIn!	Dnjestr	CNN-1.+1._MA	0.000	0.000	1.000	0.200	99.000	99.000	1410.000	0.810	http://www.semwebtech.org/mondial/10/meta#locatedIn

#893-A
PRED entity: A

PRED relation: language
PRED expected values: German Croatian
=> 25 concepts (25 used for prediction)
PRED predicted values (max 10 best out of 82): Ukrainian (0.33 #122, 0.30 #404, 0.03 #592), Roma (0.33 #138, 0.20 #420, 0.14 #326), French (0.33 #1, 0.13 #753, 0.12 #1129), Slovak (0.33 #133, 0.10 #415, 0.02 #603), German (0.33 #14, 0.09 #578, 0.08 #766), Italian (0.33 #7, 0.03 #571, 0.03 #477), Romansch (0.33 #49, 0.02 #519, 0.01 #613), English (0.24 #1132, 0.24 #850, 0.23 #756), Spanish (0.24 #584, 0.22 #772, 0.21 #866), Romanian (0.20 #422, 0.02 #610, 0.02 #704)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #122 for best value:
>> intensional similarity = 6
>> extensional distance = 1
>> proper extension:  SK;
>> query: (?x424, Ukrainian) <- ?x424[ has encompassed ?x195; has ethnicGroup ?x160; is locatedIn of ?x133<Donau>; is locatedIn of ?x1097<March>;]

*> Best rule #14 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 1
*> proper extension:  CH;
*> query: (?x424, German) <- ?x424[ has encompassed ?x195; has language ?x511; is locatedIn of ?x1602<Bodensee>; is neighbor of ?x120;]
*> conf = 0.33
ranks of expected_values: 5, 16
EVAL	A	language	Croatian	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.067	25.000	25.000	82.000	0.333	http://www.semwebtech.org/mondial/10/meta#language
EVAL	A	language	German	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.200	25.000	25.000	82.000	0.333	http://www.semwebtech.org/mondial/10/meta#language

PRED relation: language
PRED expected values: German Croatian
=> 85 concepts (85 used for prediction)
PRED predicted values (max 10 best out of 93): French (0.50 #379, 0.40 #1134, 0.38 #1040), Slovak (0.50 #511, 0.33 #228, 0.30 #284), Croatian (0.50 #306, 0.30 #284, 0.29 #1228), German (0.40 #1147, 0.38 #1053, 0.33 #109), Spanish (0.39 #2664, 0.36 #1816, 0.35 #1626), Ukrainian (0.33 #878, 0.33 #690, 0.33 #217), Roma (0.33 #894, 0.33 #706, 0.33 #233), Italian (0.33 #102, 0.30 #1235, 0.30 #284), Romansch (0.33 #144, 0.30 #284, 0.29 #1228), Slovenian (0.30 #284, 0.29 #1228, 0.25 #395)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #379 for best value:
>> intensional similarity = 16
>> extensional distance = 2
>> proper extension:  F; I;
>> query: (?x424, French) <- ?x424[ a Country; has language ?x511; has neighbor ?x234<CH>; is locatedIn of ?x133[ is flowsInto of ?x132; is locatedInWater of ?x151;]; is locatedIn of ?x260[ a Mountain; has inMountains ?x261<Alps>;]; is locatedIn of ?x1388[ a Source;]; is locatedIn of ?x1838[ a River; has hasEstuary ?x1265;];]

*> Best rule #306 for first EXPECTED value:
*> intensional similarity = 15
*> extensional distance = 2
*> proper extension:  HR;
*> query: (?x424, Croatian) <- ?x424[ a Country; has ethnicGroup ?x245<Slovene>; has language ?x511; has neighbor ?x120[ has neighbor ?x793[ has government ?x92; has religion ?x95; is locatedIn of ?x754;]; is locatedIn of ?x70; is locatedIn of ?x313[ a Estuary;];]; has neighbor ?x236<H>; is locatedIn of ?x133;]
*> conf = 0.50
ranks of expected_values: 3, 4
EVAL	A	language	Croatian	CNN-1.+1._MA	0.000	1.000	1.000	0.333	85.000	85.000	93.000	0.500	http://www.semwebtech.org/mondial/10/meta#language
EVAL	A	language	German	CNN-1.+1._MA	0.000	1.000	1.000	0.333	85.000	85.000	93.000	0.500	http://www.semwebtech.org/mondial/10/meta#language

#892-OjosdelSalado
PRED entity: OjosdelSalado

PRED relation: type
PRED expected values: "volcano"
=> 49 concepts (49 used for prediction)
PRED predicted values (max 10 best out of 6): "volcano" (0.66 #134, 0.64 #161, 0.64 #86), "volcanic" (0.64 #161, 0.35 #291, 0.35 #146), "crater" (0.11 #77, 0.07 #578), "salt" (0.03 #568, 0.02 #729, 0.02 #713), "dam" (0.02 #514, 0.02 #498, 0.02 #530), "granite" (0.02 #367, 0.01 #431, 0.01 #463)
>> best conf = 0.66
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #134 for best value:
>> intensional similarity = 6
>> extensional distance = 45
>> proper extension:  MtAdams; Fogo; Karisimbi; Leuser; NevadodelHuila; MaunaKea; Elgon; HumphreysPeak; Tambora; MtHood; ...
>> query: (?x995, "volcano") <- ?x995[ a Mountain; a Volcano; has locatedIn ?x202[ has religion ?x352<RomanCatholic>; has wasDependentOf ?x149;];]
ranks of expected_values: 1
EVAL	OjosdelSalado	type	"volcano"	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	49.000	49.000	6.000	0.660	http://www.semwebtech.org/mondial/10/meta#type

PRED relation: type
PRED expected values: "volcano"
=> 128 concepts (128 used for prediction)
PRED predicted values (max 10 best out of 9): "volcano" (0.72 #375, 0.71 #166, 0.70 #278), "volcanic" (0.64 #549, 0.49 #663, 0.41 #760), "crater" (0.41 #760, 0.36 #386, 0.15 #630), "granite" (0.12 #126, 0.02 #725, 0.02 #432), "salt" (0.05 #425, 0.03 #1475, 0.02 #1427), "dam" (0.03 #337, 0.03 #1162, 0.03 #1274), "monolith" (0.02 #576, 0.02 #592, 0.02 #641), "sand" (0.02 #1181, 0.01 #1472, 0.01 #1277), "lime" (0.01 #1408, 0.01 #1101)
>> best conf = 0.72
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #375 for best value:
>> intensional similarity = 10
>> extensional distance = 37
>> proper extension:  Soufriere;
>> query: (?x995, "volcano") <- ?x995[ a Mountain; a Volcano; has locatedIn ?x202[ has wasDependentOf ?x149; is locatedIn of ?x282[ has locatedIn ?x783<PA>; is locatedInWater of ?x205;]; is locatedIn of ?x2259[ has type ?x2402;];];]
ranks of expected_values: 1
EVAL	OjosdelSalado	type	"volcano"	CNN-1.+1._MA	1.000	1.000	1.000	1.000	128.000	128.000	9.000	0.718	http://www.semwebtech.org/mondial/10/meta#type

#891-LakeKariba
PRED entity: LakeKariba

PRED relation: type
PRED expected values: "dam"
=> 38 concepts (38 used for prediction)
PRED predicted values (max 10 best out of 10): "dam" (0.34 #65, 0.13 #209, 0.12 #161), "salt" (0.24 #167, 0.23 #183, 0.23 #199), "sand" (0.08 #84, 0.01 #244, 0.01 #292), "volcanic" (0.08 #546, 0.07 #578, 0.06 #482), "caldera" (0.05 #195, 0.05 #227, 0.04 #163), "volcano" (0.05 #278, 0.04 #262, 0.04 #342), "impact" (0.03 #74, 0.02 #218, 0.02 #234), "naturaldam" (0.03 #80), "lime" (0.02 #85, 0.02 #245), "atoll" (0.01 #248)
>> best conf = 0.34
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #65 for best value:
>> intensional similarity = 5
>> extensional distance = 36
>> proper extension:  Saimaa; LakeOahe; LakeSakakawea;
>> query: (?x1676, "dam") <- ?x1676[ is flowsThrough of ?x1977[ has hasSource ?x1596; has locatedIn ?x138[ is neighbor of ?x243;]; is flowsInto of ?x387;];]
ranks of expected_values: 1
EVAL	LakeKariba	type	"dam"	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	38.000	38.000	10.000	0.342	http://www.semwebtech.org/mondial/10/meta#type

PRED relation: type
PRED expected values: "dam"
=> 105 concepts (105 used for prediction)
PRED predicted values (max 10 best out of 13): "dam" (0.54 #257, 0.38 #449, 0.33 #161), "salt" (0.40 #215, 0.30 #551, 0.29 #583), "impact" (0.20 #58, 0.11 #170, 0.08 #250), "volcanic" (0.17 #82, 0.11 #1170, 0.10 #850), "caldera" (0.14 #99, 0.08 #243, 0.07 #403), "sand" (0.08 #500, 0.05 #340, 0.03 #788), "acid" (0.08 #255, 0.03 #479, 0.01 #655), "volcano" (0.07 #678, 0.07 #1046, 0.07 #1206), "naturaldam" (0.03 #416, 0.03 #432, 0.03 #464), "crater" (0.03 #413, 0.03 #445, 0.03 #477)
>> best conf = 0.54
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #257 for best value:
>> intensional similarity = 10
>> extensional distance = 11
>> proper extension:  LakeOahe; LakeSakakawea;
>> query: (?x1676, "dam") <- ?x1676[ a Lake; is flowsThrough of ?x1977[ has flowsInto ?x60; has hasSource ?x1596; has locatedIn ?x138[ has language ?x247; is locatedIn of ?x182<AtlanticOcean>;]; is flowsInto of ?x2061[ has locatedIn ?x192;];];]
ranks of expected_values: 1
EVAL	LakeKariba	type	"dam"	CNN-1.+1._MA	1.000	1.000	1.000	1.000	105.000	105.000	13.000	0.538	http://www.semwebtech.org/mondial/10/meta#type

#890-Akagera
PRED entity: Akagera

PRED relation: locatedIn
PRED expected values: EAU
=> 34 concepts (34 used for prediction)
PRED predicted values (max 10 best out of 84): ZRE (0.89 #2252, 0.50 #1691, 0.50 #1530), RWA (0.85 #2411, 0.78 #2415, 0.76 #2414), EAU (0.78 #2415, 0.76 #2414, 0.71 #2173), EAT (0.78 #2415, 0.76 #2414, 0.71 #2173), EAK (0.53 #1930, 0.35 #5544, 0.20 #238), SSD (0.43 #1020, 0.19 #1690, 0.19 #1688), MOC (0.40 #524, 0.33 #963, 0.33 #766), BI (0.33 #83, 0.20 #238, 0.19 #1690), AUS (0.33 #286, 0.10 #1738, 0.04 #3666), D (0.25 #2677, 0.21 #3160, 0.20 #4120)
>> best conf = 0.89
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2252 for best value:
>> intensional similarity = 17
>> extensional distance = 17
>> proper extension:  Uelle;
>> query: (?x647, ZRE) <- ?x647[ is hasEstuary of ?x1194[ a River; has locatedIn ?x546[ has encompassed ?x213; has ethnicGroup ?x1946; has religion ?x95; is locatedIn of ?x545<Karisimbi>; is locatedIn of ?x1060<Ruzizi>; is locatedIn of ?x1434<Ruzizi>; is neighbor of ?x359;]; has locatedIn ?x688[ has ethnicGroup ?x529; has neighbor ?x229; has wasDependentOf ?x81;];];]

*> Best rule #2415 for first EXPECTED value:
*> intensional similarity = 18
*> extensional distance = 17
*> proper extension:  Uelle;
*> query: (?x647, ?x820) <- ?x647[ is hasEstuary of ?x1194[ a River; has locatedIn ?x546[ has encompassed ?x213; has ethnicGroup ?x1946; has religion ?x95; is locatedIn of ?x545<Karisimbi>; is locatedIn of ?x1060<Ruzizi>; is locatedIn of ?x1434<Ruzizi>; is neighbor of ?x359;]; has locatedIn ?x688[ has ethnicGroup ?x529; has neighbor ?x229; has wasDependentOf ?x81;]; has locatedIn ?x820;];]
*> conf = 0.78
ranks of expected_values: 3
EVAL	Akagera	locatedIn	EAU	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.333	34.000	34.000	84.000	0.895	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: EAU
=> 118 concepts (118 used for prediction)
PRED predicted values (max 10 best out of 84): EAU (0.89 #16462, 0.88 #15008, 0.77 #17190), ZRE (0.89 #16302, 0.88 #14845, 0.71 #9040), EAT (0.88 #15005, 0.77 #17190, 0.77 #17188), RWA (0.84 #16464, 0.77 #17190, 0.77 #17188), EAK (0.75 #8474, 0.53 #11863, 0.35 #27101), USA (0.70 #17018, 0.67 #16052, 0.43 #8792), D (0.56 #16485, 0.52 #19630, 0.48 #20356), I (0.50 #12879, 0.38 #13606, 0.20 #11432), MOC (0.50 #2950, 0.33 #1255, 0.25 #2464), CDN (0.45 #12169, 0.44 #14588, 0.43 #14347)
>> best conf = 0.89
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #16462 for best value:
>> intensional similarity = 30
>> extensional distance = 16
>> proper extension:  Uelle;
>> query: (?x647, ?x688) <- ?x647[ a Estuary; is hasEstuary of ?x1194[ a River; has flowsInto ?x1195; has locatedIn ?x546[ a Country; has ethnicGroup ?x1946; is locatedIn of ?x545<Karisimbi>; is locatedIn of ?x1060<Ruzizi>; is locatedIn of ?x1434<Ruzizi>; is neighbor of ?x359;]; has locatedIn ?x688[ a Country; has encompassed ?x213; has ethnicGroup ?x529; has neighbor ?x229; has religion ?x95<Protestant>; has wasDependentOf ?x81; is locatedIn of ?x600<Rutanzige-Eduardsee>; is locatedIn of ?x1188<Semliki>; is locatedIn of ?x1538<Ruwenzori>; is locatedIn of ?x1770<LakeSeseSeko-Albertsee>;];];]
ranks of expected_values: 1
EVAL	Akagera	locatedIn	EAU	CNN-1.+1._MA	1.000	1.000	1.000	1.000	118.000	118.000	84.000	0.889	http://www.semwebtech.org/mondial/10/meta#locatedIn

#889-Nung
PRED entity: Nung

PRED relation: ethnicGroup!
PRED expected values: VN
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 96): EAU (0.03 #132), ETH (0.03 #100), UA (0.03 #57), EAK (0.03 #99), CZ (0.02 #98), SN (0.02 #83), MYA (0.02 #70), IR (0.02 #58), SK (0.02 #25), RCA (0.02 #139)
>> best conf = 0.03
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #132 for best value:
>> intensional similarity = 1
>> extensional distance = 279
>> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
>> query: (?x597, EAU) <- ?x597[ a EthnicGroup;]

*> Best rule #120 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 279
*> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
*> query: (?x597, VN) <- ?x597[ a EthnicGroup;]
*> conf = 0.02
ranks of expected_values: 26
EVAL	Nung	ethnicGroup!	VN	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.038	2.000	2.000	96.000	0.032	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup!
PRED expected values: VN
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 96): EAU (0.03 #132), ETH (0.03 #100), UA (0.03 #57), EAK (0.03 #99), CZ (0.02 #98), SN (0.02 #83), MYA (0.02 #70), IR (0.02 #58), SK (0.02 #25), RCA (0.02 #139)
>> best conf = 0.03
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #132 for best value:
>> intensional similarity = 1
>> extensional distance = 279
>> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
>> query: (?x597, EAU) <- ?x597[ a EthnicGroup;]

*> Best rule #120 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 279
*> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
*> query: (?x597, VN) <- ?x597[ a EthnicGroup;]
*> conf = 0.02
ranks of expected_values: 26
EVAL	Nung	ethnicGroup!	VN	CNN-1.+1._MA	0.000	0.000	0.000	0.038	2.000	2.000	96.000	0.032	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#888-Leine
PRED entity: Leine

PRED relation: flowsInto
PRED expected values: Aller
=> 26 concepts (22 used for prediction)
PRED predicted values (max 10 best out of 123): Donau (0.33 #1002, 0.32 #505, 0.30 #670), Weser (0.25 #303, 0.20 #138, 0.16 #468), AtlanticOcean (0.12 #2005, 0.09 #2669, 0.08 #2171), MediterraneanSea (0.11 #1850, 0.04 #2016, 0.04 #2182), Rhein (0.11 #1513, 0.10 #681, 0.10 #846), Inn (0.11 #577, 0.10 #742, 0.10 #80), Rhone (0.11 #443, 0.03 #1773, 0.03 #1940), Isar (0.10 #105, 0.08 #270, 0.05 #602), BalticSea (0.09 #1328, 0.09 #1171, 0.05 #507), Po (0.09 #1902, 0.04 #1403, 0.02 #2068)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1002 for best value:
>> intensional similarity = 10
>> extensional distance = 19
>> proper extension:  Enns; March;
>> query: (?x100, Donau) <- ?x100[ a River; has hasEstuary ?x101[ a Estuary; has locatedIn ?x120[ has neighbor ?x78; is locatedIn of ?x756<Inn>; is locatedIn of ?x1440<Salzach>;];]; has hasSource ?x2415;]

*> Best rule #2325 for first EXPECTED value:
*> intensional similarity = 9
*> extensional distance = 140
*> proper extension:  Sobat; Bahrel-Ghasal; Bahrel-Djebel-Albert-Nil;
*> query: (?x100, ?x70) <- ?x100[ a River; has hasEstuary ?x101[ a Estuary; has locatedIn ?x120[ a Country; has government ?x140; has neighbor ?x78; is locatedIn of ?x70;];]; has hasSource ?x2415;]
*> conf = 0.03
ranks of expected_values: 49
EVAL	Leine	flowsInto	Aller	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.020	26.000	22.000	123.000	0.333	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto
PRED expected values: Aller
=> 78 concepts (78 used for prediction)
PRED predicted values (max 10 best out of 173): Donau (0.40 #4824, 0.40 #4665, 0.33 #3993), Weser (0.25 #303, 0.23 #468, 0.20 #635), MediterraneanSea (0.22 #1685, 0.12 #4847, 0.10 #2851), Nile (0.13 #659, 0.12 #1158, 0.12 #825), AtlanticOcean (0.13 #3340, 0.10 #2840, 0.09 #11311), Rhein (0.12 #3679, 0.12 #4510, 0.10 #2682), Zaire (0.12 #6908, 0.11 #7240, 0.10 #7738), Po (0.12 #4899, 0.06 #1737, 0.06 #1570), Inn (0.11 #2163, 0.11 #2075, 0.10 #2743), BalticSea (0.11 #7491, 0.09 #6993, 0.08 #497)
>> best conf = 0.40
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4824 for best value:
>> intensional similarity = 14
>> extensional distance = 23
>> proper extension:  Donau;
>> query: (?x100, ?x133) <- ?x100[ a River; has hasEstuary ?x101[ has locatedIn ?x120[ has encompassed ?x195<Europe>; has ethnicGroup ?x237<German>; has government ?x140; has neighbor ?x78; has religion ?x95; is locatedIn of ?x133<Donau>; is neighbor of ?x194;];]; has hasSource ?x2415;]
>> Best rule #4665 for best value:
>> intensional similarity = 14
>> extensional distance = 23
>> proper extension:  Donau;
>> query: (?x100, Donau) <- ?x100[ a River; has hasEstuary ?x101[ has locatedIn ?x120[ has encompassed ?x195<Europe>; has ethnicGroup ?x237<German>; has government ?x140; has neighbor ?x78; has religion ?x95; is locatedIn of ?x133<Donau>; is neighbor of ?x194;];]; has hasSource ?x2415;]

*> Best rule #11133 for first EXPECTED value:
*> intensional similarity = 14
*> extensional distance = 71
*> proper extension:  Oesterdalaelv;
*> query: (?x100, ?x70) <- ?x100[ a River; has hasEstuary ?x101[ a Estuary; has locatedIn ?x120[ a Country; has ethnicGroup ?x237; has government ?x140; has neighbor ?x78; has religion ?x352<RomanCatholic>; is locatedIn of ?x70; is neighbor of ?x194;];]; has hasSource ?x2415[ a Source;];]
*> conf = 0.03
ranks of expected_values: 100
EVAL	Leine	flowsInto	Aller	CNN-1.+1._MA	0.000	0.000	0.000	0.010	78.000	78.000	173.000	0.400	http://www.semwebtech.org/mondial/10/meta#flowsInto

#887-EAT
PRED entity: EAT

PRED relation: encompassed
PRED expected values: Africa
=> 34 concepts (34 used for prediction)
PRED predicted values (max 10 best out of 5): Africa (0.81 #143, 0.77 #56, 0.74 #72), Asia (0.37 #51, 0.37 #57, 0.36 #46), Europe (0.31 #27, 0.28 #37, 0.26 #124), America (0.28 #66, 0.27 #153, 0.25 #163), Australia-Oceania (0.23 #18, 0.21 #64, 0.20 #75)
>> best conf = 0.81
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #143 for best value:
>> intensional similarity = 7
>> extensional distance = 145
>> proper extension:  GBZ;
>> query: (?x820, ?x213) <- ?x820[ is locatedIn of ?x60; is neighbor of ?x546[ has encompassed ?x213; has ethnicGroup ?x1946;]; is neighbor of ?x819[ has government ?x2064; has religion ?x95;];]
ranks of expected_values: 1
EVAL	EAT	encompassed	Africa	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	34.000	34.000	5.000	0.812	http://www.semwebtech.org/mondial/10/meta#encompassed

PRED relation: encompassed
PRED expected values: Africa
=> 96 concepts (96 used for prediction)
PRED predicted values (max 10 best out of 5): Africa (0.90 #285, 0.89 #153, 0.88 #191), Asia (0.47 #216, 0.44 #102, 0.41 #274), Europe (0.43 #211, 0.39 #263, 0.35 #406), America (0.43 #85, 0.38 #141, 0.33 #243), Australia-Oceania (0.40 #304, 0.39 #261, 0.36 #531)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #285 for best value:
>> intensional similarity = 15
>> extensional distance = 39
>> proper extension:  CO; P;
>> query: (?x820, ?x213) <- ?x820[ has wasDependentOf ?x81[ is locatedIn of ?x121;]; is locatedIn of ?x284[ has flowsInto ?x285;]; is neighbor of ?x192[ has encompassed ?x213; is locatedIn of ?x1048[ has type ?x762;]; is neighbor of ?x193;]; is neighbor of ?x525[ has government ?x435; has religion ?x187[ is religion of ?x234<CH>;]; is locatedIn of ?x709;];]
ranks of expected_values: 1
EVAL	EAT	encompassed	Africa	CNN-1.+1._MA	1.000	1.000	1.000	1.000	96.000	96.000	5.000	0.905	http://www.semwebtech.org/mondial/10/meta#encompassed

#886-CH
PRED entity: CH

PRED relation: locatedIn!
PRED expected values: Rhein Matterhorn
=> 33 concepts (33 used for prediction)
PRED predicted values (max 10 best out of 1308): Ticino (0.64 #12557, 0.61 #26509, 0.33 #917), Inn (0.64 #12557, 0.61 #26509, 0.18 #7657), Doubs (0.64 #12557, 0.61 #26509, 0.17 #5178), Rhone (0.64 #12557, 0.61 #26509, 0.17 #4891), PacificOcean (0.50 #2875, 0.40 #1480, 0.28 #9851), Donau (0.45 #7001, 0.12 #5606, 0.12 #15373), MediterraneanSea (0.33 #4267, 0.33 #82, 0.20 #1477), Drau (0.33 #274, 0.18 #7249, 0.17 #4459), MontBlanc (0.33 #108, 0.17 #4293, 0.09 #37672), GranParadiso (0.33 #1333, 0.09 #37672, 0.08 #33485)
>> best conf = 0.64
=> the first rule below is the first best rule for 4 predicted values
>> Best rule #12557 for best value:
>> intensional similarity = 5
>> extensional distance = 53
>> proper extension:  SSD;
>> query: (?x234, ?x1784) <- ?x234[ is locatedIn of ?x1114[ has hasEstuary ?x1784;]; is locatedIn of ?x1641[ a Source;]; is neighbor of ?x78;]

*> Best rule #1225 for first EXPECTED value:
*> intensional similarity = 4
*> extensional distance = 1
*> proper extension:  I;
*> query: (?x234, Matterhorn) <- ?x234[ has language ?x51; has religion ?x56; is locatedIn of ?x1201<Ticino>;]
*> conf = 0.33
ranks of expected_values: 13, 119
EVAL	CH	locatedIn!	Matterhorn	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.077	33.000	33.000	1308.000	0.636	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	CH	locatedIn!	Rhein	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.008	33.000	33.000	1308.000	0.636	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Rhein Matterhorn
=> 95 concepts (95 used for prediction)
PRED predicted values (max 10 best out of 1406): Inn (0.66 #68476, 0.50 #4873, 0.33 #682), Ticino (0.66 #68476, 0.33 #3711, 0.25 #6505), Doubs (0.66 #68476, 0.17 #40528, 0.14 #57295), Rhone (0.66 #68476, 0.17 #40528, 0.14 #57295), Donau (0.57 #13978, 0.50 #4217, 0.40 #20991), Rhein (0.57 #13978, 0.50 #4260, 0.33 #69), MediterraneanSea (0.57 #13978, 0.47 #39213, 0.42 #37730), Po (0.57 #13978, 0.33 #3115, 0.25 #5909), Saone (0.57 #13978, 0.22 #67076, 0.22 #75464), Salzach (0.50 #4975, 0.33 #784, 0.22 #67076)
>> best conf = 0.66
=> the first rule below is the first best rule for 4 predicted values
>> Best rule #68476 for best value:
>> intensional similarity = 14
>> extensional distance = 37
>> proper extension:  PE; FL;
>> query: (?x234, ?x1278) <- ?x234[ has ethnicGroup ?x237; has religion ?x95<Protestant>; has religion ?x187[ is religion of ?x170<N>; is religion of ?x508[ has government ?x435; is locatedIn of ?x262;]; is religion of ?x1826<GBZ>;]; is locatedIn of ?x756[ has hasEstuary ?x1278;]; is neighbor of ?x78;]

*> Best rule #13978 for first EXPECTED value:
*> intensional similarity = 13
*> extensional distance = 5
*> proper extension:  AZ; MNG;
*> query: (?x234, ?x256) <- ?x234[ has ethnicGroup ?x237; has language ?x51; has religion ?x187<Muslim>; is locatedIn of ?x1602[ has flowsInto ?x256;]; is neighbor of ?x207[ has encompassed ?x195; has language ?x738; is locatedIn of ?x86; is locatedIn of ?x2067[ has type ?x150;]; is wasDependentOf of ?x1165;];]
*> conf = 0.57
ranks of expected_values: 6, 51
EVAL	CH	locatedIn!	Matterhorn	CNN-1.+1._MA	0.000	0.000	0.000	0.020	95.000	95.000	1406.000	0.665	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	CH	locatedIn!	Rhein	CNN-1.+1._MA	0.000	0.000	1.000	0.167	95.000	95.000	1406.000	0.665	http://www.semwebtech.org/mondial/10/meta#locatedIn

#885-VU
PRED entity: VU

PRED relation: ethnicGroup
PRED expected values: Melanesian
=> 27 concepts (27 used for prediction)
PRED predicted values (max 10 best out of 118): European (0.67 #779, 0.59 #1036, 0.57 #1293), Amerindian (0.44 #773, 0.32 #1801, 0.30 #1287), Asian (0.40 #276, 0.17 #790, 0.12 #533), Mestizo (0.39 #807, 0.35 #1321, 0.32 #1835), African (0.23 #1034, 0.22 #777, 0.22 #1291), Polynesian (0.21 #1888, 0.20 #2145, 0.19 #2916), Chinese (0.21 #3099, 0.13 #2328, 0.13 #2071), PacificIslander (0.20 #333, 0.12 #590, 0.07 #6687), Maori (0.20 #363, 0.07 #6687, 0.07 #6428), Russian (0.10 #4185, 0.09 #3671, 0.08 #5471)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #779 for best value:
>> intensional similarity = 7
>> extensional distance = 16
>> proper extension:  USA; ES;
>> query: (?x439, European) <- ?x439[ has religion ?x429[ is religion of ?x1008<BERM>;]; has wasDependentOf ?x78[ is dependentOf of ?x61;]; is locatedIn of ?x282<PacificOcean>;]

*> Best rule #6687 for first EXPECTED value:
*> intensional similarity = 4
*> extensional distance = 234
*> proper extension:  NMIS;
*> query: (?x439, ?x1196) <- ?x439[ is locatedIn of ?x282[ has locatedIn ?x158[ has ethnicGroup ?x1196; has government ?x435;];];]
*> conf = 0.07
ranks of expected_values: 29
EVAL	VU	ethnicGroup	Melanesian	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.034	27.000	27.000	118.000	0.667	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: Melanesian
=> 80 concepts (80 used for prediction)
PRED predicted values (max 10 best out of 223): European (0.87 #5674, 0.70 #3098, 0.67 #4385), Mestizo (0.60 #3126, 0.58 #4413, 0.47 #5702), Amerindian (0.60 #3092, 0.50 #4379, 0.47 #5668), African (0.41 #9025, 0.40 #3096, 0.40 #1552), Madurese (0.33 #216, 0.11 #9792, 0.10 #3563), Javanese (0.33 #42, 0.11 #9792, 0.10 #3389), Sundanese (0.33 #21, 0.11 #9792, 0.10 #3368), Asian (0.29 #2337, 0.27 #3880, 0.23 #4653), Italian (0.29 #2789, 0.21 #1289, 0.10 #5925), Portuguese (0.27 #5924, 0.21 #1289, 0.14 #2700)
>> best conf = 0.87
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5674 for best value:
>> intensional similarity = 16
>> extensional distance = 13
>> proper extension:  ES;
>> query: (?x439, European) <- ?x439[ a Country; has ethnicGroup ?x1672[ is ethnicGroup of ?x272<CDN>; is ethnicGroup of ?x789[ a Country; has encompassed ?x195; has language ?x539<Portuguese>;];]; has religion ?x396[ a Religion; is religion of ?x853<PNG>;]; has wasDependentOf ?x78; is locatedIn of ?x282<PacificOcean>;]

*> Best rule #1205 for first EXPECTED value:
*> intensional similarity = 15
*> extensional distance = 2
*> proper extension:  BERM;
*> query: (?x439, Melanesian) <- ?x439[ a Country; has ethnicGroup ?x1672[ is ethnicGroup of ?x1577[ has language ?x51; has religion ?x352;];]; has government ?x1174; has religion ?x396[ a Religion; is religion of ?x853<PNG>;]; has religion ?x429<Seventh-DayAdventist>; has religion ?x713<Anglican>; is locatedIn of ?x282;]
*> conf = 0.25
ranks of expected_values: 17
EVAL	VU	ethnicGroup	Melanesian	CNN-1.+1._MA	0.000	0.000	0.000	0.059	80.000	80.000	223.000	0.867	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#884-Sachalin
PRED entity: Sachalin

PRED relation: locatedInWater
PRED expected values: SeaofJapan
=> 31 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 129): PacificOcean (0.96 #365, 0.94 #232, 0.82 #408), AtlanticOcean (0.67 #311, 0.51 #918, 0.40 #530), SeaofJapan (0.50 #57, 0.44 #85, 0.33 #15), IndianOcean (0.38 #130, 0.28 #347, 0.23 #214), JavaSea (0.31 #137, 0.12 #180, 0.10 #269), MediterraneanSea (0.30 #800, 0.12 #971, 0.12 #1014), EastChinaSea (0.28 #347, 0.25 #68, 0.23 #214), SulawesiSea (0.28 #347, 0.23 #214, 0.21 #566), BeringSea (0.28 #347, 0.23 #214, 0.21 #566), BandaSea (0.28 #347, 0.23 #214, 0.21 #566)
>> best conf = 0.96
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #365 for best value:
>> intensional similarity = 11
>> extensional distance = 55
>> proper extension:  Tongatapu; Tasmania; Tinian; Guadalcanal; TeWaka-a-Maui-SouthIsland-; Oahu; Taiwan; Leyte; TeIka-a-Maui-NorthIsland-; NewGuinea; ...
>> query: (?x1816, PacificOcean) <- ?x1816[ a Island; has locatedInWater ?x507[ a Sea; has locatedIn ?x73<R>; has locatedIn ?x117<J>; has mergesWith ?x282; is flowsInto of ?x1585; is locatedInWater of ?x1411<Paramuschir>;];]

*> Best rule #57 for first EXPECTED value:
*> intensional similarity = 10
*> extensional distance = 2
*> proper extension:  Kyushu;
*> query: (?x1816, SeaofJapan) <- ?x1816[ a Island; has locatedInWater ?x507[ a Sea; has mergesWith ?x282<PacificOcean>; is locatedInWater of ?x451<Hokkaido>; is mergesWith of ?x271;]; has type ?x150<"volcanic">;]
*> conf = 0.50
ranks of expected_values: 3
EVAL	Sachalin	locatedInWater	SeaofJapan	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.333	31.000	31.000	129.000	0.965	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater
PRED expected values: SeaofJapan
=> 49 concepts (49 used for prediction)
PRED predicted values (max 10 best out of 138): PacificOcean (0.96 #795, 0.95 #888, 0.95 #841), AtlanticOcean (0.91 #552, 0.84 #1287, 0.72 #1467), IndianOcean (0.82 #500, 0.62 #225, 0.57 #733), SouthChinaSea (0.75 #429, 0.54 #1324, 0.45 #637), SeaofJapan (0.60 #220, 0.58 #172, 0.54 #1324), EastChinaSea (0.56 #295, 0.54 #1324, 0.50 #110), BeringSea (0.54 #1324, 0.45 #637, 0.45 #590), SulawesiSea (0.54 #1324, 0.45 #637, 0.45 #590), BandaSea (0.54 #1324, 0.45 #637, 0.45 #590), SuluSea (0.54 #1324, 0.45 #637, 0.45 #590)
>> best conf = 0.96
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #795 for best value:
>> intensional similarity = 21
>> extensional distance = 55
>> proper extension:  Tongatapu; Tasmania; Tinian; Guadalcanal; TeWaka-a-Maui-SouthIsland-; Oahu; Taiwan; Leyte; TeIka-a-Maui-NorthIsland-; NewGuinea; ...
>> query: (?x1816, PacificOcean) <- ?x1816[ a Island; has locatedInWater ?x507[ a Sea; has locatedIn ?x73<R>; has locatedIn ?x117<J>; has mergesWith ?x282[ has locatedIn ?x158; has mergesWith ?x60; is locatedInWater of ?x716<Taiwan>;]; is flowsInto of ?x1585[ a River; has hasEstuary ?x2105; has locatedIn ?x232; is flowsInto of ?x472;]; is locatedInWater of ?x451<Hokkaido>; is mergesWith of ?x271<SeaofJapan>;];]

*> Best rule #220 for first EXPECTED value:
*> intensional similarity = 19
*> extensional distance = 5
*> proper extension:  NowajaSemlja; Svalbard;
*> query: (?x1816, ?x271) <- ?x1816[ a Island; has locatedInWater ?x507[ a Sea; has locatedIn ?x73; has mergesWith ?x282[ has locatedIn ?x158; is locatedInWater of ?x205; is mergesWith of ?x809<BeringSea>;]; is flowsInto of ?x1585[ a River; has hasEstuary ?x2105; has hasSource ?x2148; has locatedIn ?x232; is flowsInto of ?x472;]; is locatedInWater of ?x451[ has belongsToIslands ?x1212;]; is mergesWith of ?x271[ has locatedIn ?x334;];];]
*> conf = 0.60
ranks of expected_values: 5
EVAL	Sachalin	locatedInWater	SeaofJapan	CNN-1.+1._MA	0.000	0.000	1.000	0.200	49.000	49.000	138.000	0.965	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#883-PAL
PRED entity: PAL

PRED relation: locatedIn!
PRED expected values: Babelthuap
=> 32 concepts (22 used for prediction)
PRED predicted values (max 10 best out of 664): Babelthuap (0.80 #2845, 0.03 #17069, 0.03 #18494), AtlanticOcean (0.63 #21386, 0.61 #22812, 0.40 #19962), CaribbeanSea (0.49 #18600, 0.28 #21449, 0.27 #10060), IndianOcean (0.37 #15648, 0.14 #19923, 0.13 #24199), Upolu (0.20 #967, 0.11 #12800, 0.11 #14223), Savaii (0.20 #652, 0.11 #12800, 0.11 #14223), Ponape (0.20 #853, 0.11 #12800, 0.11 #14223), Nauru (0.20 #1226, 0.11 #12800, 0.11 #14223), MediterraneanSea (0.17 #20003, 0.16 #24279, 0.15 #25705), ArcticOcean (0.12 #11451, 0.12 #12874, 0.08 #15719)
>> best conf = 0.80
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2845 for best value:
>> intensional similarity = 8
>> extensional distance = 13
>> proper extension:  GUAM;
>> query: (?x2188, ?x1203) <- ?x2188[ a Country; is locatedIn of ?x282<PacificOcean>; is locatedIn of ?x1168[ a Island; has belongsToIslands ?x1169[ is belongsToIslands of ?x1203;]; has type ?x150;];]
ranks of expected_values: 1
EVAL	PAL	locatedIn!	Babelthuap	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	32.000	22.000	664.000	0.795	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Babelthuap
=> 72 concepts (72 used for prediction)
PRED predicted values (max 10 best out of 1300): Babelthuap (0.82 #24211, 0.75 #25634, 0.70 #11388), AtlanticOcean (0.65 #72752, 0.63 #62763, 0.62 #75609), CaribbeanSea (0.50 #61395, 0.44 #52830, 0.39 #48559), IndianOcean (0.42 #38472, 0.21 #55581, 0.20 #57011), Upolu (0.33 #967, 0.25 #5237, 0.20 #6659), Savaii (0.33 #652, 0.25 #4922, 0.20 #6344), Ponape (0.33 #2276, 0.25 #3699, 0.17 #7969), Nauru (0.25 #5496, 0.17 #8342, 0.13 #41323), Majuro (0.25 #3546, 0.13 #41323, 0.11 #41324), SouthChinaSea (0.20 #31481, 0.20 #5832, 0.18 #31338)
>> best conf = 0.82
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #24211 for best value:
>> intensional similarity = 12
>> extensional distance = 12
>> proper extension:  NCA;
>> query: (?x2188, ?x1203) <- ?x2188[ a Country; has encompassed ?x211<Australia-Oceania>; has government ?x2126; is locatedIn of ?x282<PacificOcean>; is locatedIn of ?x1168[ a Island; has belongsToIslands ?x1169[ a Islands; is belongsToIslands of ?x1203;]; has locatedInWater ?x282<PacificOcean>;];]
ranks of expected_values: 1
EVAL	PAL	locatedIn!	Babelthuap	CNN-1.+1._MA	1.000	1.000	1.000	1.000	72.000	72.000	1300.000	0.818	http://www.semwebtech.org/mondial/10/meta#locatedIn

#882-Sajama
PRED entity: Sajama

PRED relation: locatedIn
PRED expected values: BOL
=> 30 concepts (30 used for prediction)
PRED predicted values (max 10 best out of 77): PE (0.42 #5689, 0.42 #5688, 0.42 #5687), BOL (0.42 #5689, 0.42 #5688, 0.42 #5687), CO (0.42 #5689, 0.42 #5688, 0.42 #5687), RA (0.42 #5689, 0.42 #5688, 0.42 #5687), RCH (0.42 #5689, 0.42 #5688, 0.42 #5687), EC (0.42 #5689, 0.42 #5688, 0.42 #5687), YV (0.42 #5689, 0.42 #5688, 0.42 #5687), USA (0.21 #3625, 0.21 #3863, 0.17 #1727), MEX (0.18 #1297, 0.10 #1771, 0.09 #2007), I (0.13 #2889, 0.13 #3126, 0.13 #3362)
>> best conf = 0.42
=> the first rule below is the first best rule for 7 predicted values
>> Best rule #5689 for best value:
>> intensional similarity = 4
>> extensional distance = 153
>> proper extension:  JabalKatrina; Annapurna; Demirkazik; Olympos; Manaslu; MtSt.Elias; UlugMuztag; Kangchendzonga; TorredeCerredo; ChoOyu; ...
>> query: (?x914, ?x215) <- ?x914[ a Mountain; has inMountains ?x431[ is inMountains of ?x1153[ has locatedIn ?x215;];];]
>> Best rule #5688 for best value:
>> intensional similarity = 4
>> extensional distance = 153
>> proper extension:  JabalKatrina; Annapurna; Demirkazik; Olympos; Manaslu; MtSt.Elias; UlugMuztag; Kangchendzonga; TorredeCerredo; ChoOyu; ...
>> query: (?x914, ?x202) <- ?x914[ a Mountain; has inMountains ?x431[ is inMountains of ?x995[ has locatedIn ?x202;];];]
>> Best rule #5687 for best value:
>> intensional similarity = 4
>> extensional distance = 153
>> proper extension:  JabalKatrina; Annapurna; Demirkazik; Olympos; Manaslu; MtSt.Elias; UlugMuztag; Kangchendzonga; TorredeCerredo; ChoOyu; ...
>> query: (?x914, ?x296) <- ?x914[ a Mountain; has inMountains ?x431[ is inMountains of ?x430[ has locatedIn ?x296;];];]
>> Best rule #5686 for best value:
>> intensional similarity = 4
>> extensional distance = 153
>> proper extension:  JabalKatrina; Annapurna; Demirkazik; Olympos; Manaslu; MtSt.Elias; UlugMuztag; Kangchendzonga; TorredeCerredo; ChoOyu; ...
>> query: (?x914, ?x902) <- ?x914[ a Mountain; has inMountains ?x431[ is inMountains of ?x1774[ has locatedIn ?x902;];];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 2
EVAL	Sajama	locatedIn	BOL	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	30.000	30.000	77.000	0.420	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: BOL
=> 41 concepts (41 used for prediction)
PRED predicted values (max 10 best out of 77): RA (0.45 #8395, 0.45 #8394, 0.45 #8393), PE (0.45 #8395, 0.45 #8394, 0.45 #8393), BOL (0.45 #8395, 0.45 #8394, 0.45 #8393), RCH (0.45 #8395, 0.45 #8394, 0.45 #8393), EC (0.45 #8395, 0.45 #8394, 0.45 #8393), CO (0.45 #8395, 0.45 #8394, 0.45 #8393), YV (0.45 #8395, 0.45 #8394, 0.45 #8393), MEX (0.29 #1783, 0.25 #1306, 0.20 #2751), USA (0.21 #6305, 0.18 #3904, 0.17 #4145), ZRE (0.16 #4388, 0.11 #3193, 0.10 #551)
>> best conf = 0.45
=> the first rule below is the first best rule for 7 predicted values
>> Best rule #8395 for best value:
>> intensional similarity = 6
>> extensional distance = 152
>> proper extension:  JabalKatrina; Annapurna; Demirkazik; Olympos; Manaslu; MtSt.Elias; UlugMuztag; Kangchendzonga; TorredeCerredo; ChoOyu; ...
>> query: (?x914, ?x902) <- ?x914[ a Mountain; has inMountains ?x431[ a Mountains; is inMountains of ?x1362[ a Mountain; has locatedIn ?x902;];];]
>> Best rule #8394 for best value:
>> intensional similarity = 6
>> extensional distance = 152
>> proper extension:  JabalKatrina; Annapurna; Demirkazik; Olympos; Manaslu; MtSt.Elias; UlugMuztag; Kangchendzonga; TorredeCerredo; ChoOyu; ...
>> query: (?x914, ?x690) <- ?x914[ a Mountain; has inMountains ?x431[ a Mountains; is inMountains of ?x1626[ a Mountain; has locatedIn ?x690;];];]
>> Best rule #8393 for best value:
>> intensional similarity = 6
>> extensional distance = 152
>> proper extension:  JabalKatrina; Annapurna; Demirkazik; Olympos; Manaslu; MtSt.Elias; UlugMuztag; Kangchendzonga; TorredeCerredo; ChoOyu; ...
>> query: (?x914, ?x345) <- ?x914[ a Mountain; has inMountains ?x431[ a Mountains; is inMountains of ?x2451[ a Mountain; has locatedIn ?x345;];];]
>> Best rule #8390 for best value:
>> intensional similarity = 9
>> extensional distance = 152
>> proper extension:  JabalKatrina; Annapurna; Demirkazik; Olympos; Manaslu; MtSt.Elias; UlugMuztag; Kangchendzonga; TorredeCerredo; ChoOyu; ...
>> query: (?x914, ?x379) <- ?x914[ a Mountain; has inMountains ?x431[ a Mountains; is inMountains of ?x295; is inMountains of ?x995[ a Mountain;]; is inMountains of ?x1161[ a Mountain; has locatedIn ?x379;];];]
>> Best rule #8389 for best value:
>> intensional similarity = 7
>> extensional distance = 152
>> proper extension:  JabalKatrina; Annapurna; Demirkazik; Olympos; Manaslu; MtSt.Elias; UlugMuztag; Kangchendzonga; TorredeCerredo; ChoOyu; ...
>> query: (?x914, ?x202) <- ?x914[ a Mountain; has inMountains ?x431[ a Mountains; is inMountains of ?x295; is inMountains of ?x995[ a Mountain; has locatedIn ?x202;];];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 3
EVAL	Sajama	locatedIn	BOL	CNN-1.+1._MA	0.000	1.000	1.000	0.333	41.000	41.000	77.000	0.453	http://www.semwebtech.org/mondial/10/meta#locatedIn

#881-Tigrinya
PRED entity: Tigrinya

PRED relation: ethnicGroup!
PRED expected values: ER
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 96): EAU (0.03 #132), ETH (0.03 #100), UA (0.03 #57), EAK (0.03 #99), CZ (0.02 #98), SN (0.02 #83), MYA (0.02 #70), IR (0.02 #58), SK (0.02 #25), RCA (0.02 #139)
>> best conf = 0.03
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #132 for best value:
>> intensional similarity = 1
>> extensional distance = 279
>> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
>> query: (?x2422, EAU) <- ?x2422[ a EthnicGroup;]

*> Best rule #122 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 279
*> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
*> query: (?x2422, ER) <- ?x2422[ a EthnicGroup;]
*> conf = 0.02
ranks of expected_values: 25
EVAL	Tigrinya	ethnicGroup!	ER	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.040	2.000	2.000	96.000	0.032	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup!
PRED expected values: ER
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 96): EAU (0.03 #132), ETH (0.03 #100), UA (0.03 #57), EAK (0.03 #99), CZ (0.02 #98), SN (0.02 #83), MYA (0.02 #70), IR (0.02 #58), SK (0.02 #25), RCA (0.02 #139)
>> best conf = 0.03
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #132 for best value:
>> intensional similarity = 1
>> extensional distance = 279
>> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
>> query: (?x2422, EAU) <- ?x2422[ a EthnicGroup;]

*> Best rule #122 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 279
*> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
*> query: (?x2422, ER) <- ?x2422[ a EthnicGroup;]
*> conf = 0.02
ranks of expected_values: 25
EVAL	Tigrinya	ethnicGroup!	ER	CNN-1.+1._MA	0.000	0.000	0.000	0.040	2.000	2.000	96.000	0.032	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#880-HCA
PRED entity: HCA

PRED relation: neighbor
PRED expected values: ES
=> 48 concepts (48 used for prediction)
PRED predicted values (max 10 best out of 210): ES (0.93 #641, 0.91 #5788, 0.91 #5304), PE (0.40 #210, 0.40 #49, 0.33 #370), PA (0.33 #442, 0.20 #282, 0.20 #121), HCA (0.28 #4336, 0.27 #5789, 0.27 #4821), CR (0.28 #4336, 0.27 #5789, 0.27 #4821), MEX (0.28 #4336, 0.27 #5789, 0.27 #4821), BOL (0.24 #915, 0.16 #1235, 0.15 #593), CO (0.23 #518, 0.20 #198, 0.20 #37), BR (0.20 #253, 0.20 #92, 0.17 #413), EC (0.20 #296, 0.20 #135, 0.17 #456)
>> best conf = 0.93
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #641 for best value:
>> intensional similarity = 6
>> extensional distance = 11
>> proper extension:  R; GCA; RCH; PE; USA; MEX; ES; PA;
>> query: (?x1364, ?x181) <- ?x1364[ has ethnicGroup ?x162; has language ?x796; has wasDependentOf ?x149; is locatedIn of ?x282<PacificOcean>; is neighbor of ?x181;]
ranks of expected_values: 1
EVAL	HCA	neighbor	ES	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	48.000	48.000	210.000	0.925	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: ES
=> 87 concepts (87 used for prediction)
PRED predicted values (max 10 best out of 223): ES (0.90 #10679, 0.90 #11510, 0.90 #7550), PE (0.38 #541, 0.30 #1355, 0.30 #1191), HCA (0.33 #315, 0.26 #2776, 0.25 #9682), MEX (0.33 #248, 0.26 #2776, 0.25 #9682), PA (0.33 #121, 0.25 #613, 0.20 #1427), YV (0.27 #1529, 0.25 #550, 0.20 #1364), CR (0.26 #2776, 0.25 #9682, 0.25 #326), BZ (0.26 #2776, 0.25 #9682, 0.25 #326), CO (0.25 #1673, 0.25 #692, 0.25 #529), BR (0.25 #1728, 0.22 #907, 0.21 #1799)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #10679 for best value:
>> intensional similarity = 14
>> extensional distance = 94
>> proper extension:  MW;
>> query: (?x1364, ?x654) <- ?x1364[ is locatedIn of ?x282; is neighbor of ?x408[ has wasDependentOf ?x149;]; is neighbor of ?x654[ has language ?x796; has religion ?x95[ is religion of ?x81<GB>; is religion of ?x348<ZRE>; is religion of ?x476<ETH>; is religion of ?x819<MW>;];];]
ranks of expected_values: 1
EVAL	HCA	neighbor	ES	CNN-1.+1._MA	1.000	1.000	1.000	1.000	87.000	87.000	223.000	0.905	http://www.semwebtech.org/mondial/10/meta#neighbor

#879-RioDesaguadero
PRED entity: RioDesaguadero

PRED relation: hasEstuary!
PRED expected values: RioDesaguadero
=> 33 concepts (21 used for prediction)
PRED predicted values (max 10 best out of 23): RioMamore (0.33 #36, 0.06 #262, 0.02 #488), RioMadeira (0.02 #632, 0.02 #906, 0.01 #679), RioSaoFrancisco (0.02 #642), RioNegro (0.02 #584), Uruguay (0.02 #570), Tocantins (0.02 #541), Parana (0.02 #500), Paraguay (0.02 #499), Amazonas (0.02 #461), Araguaia (0.02 #453)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #36 for best value:
>> intensional similarity = 3
>> extensional distance = 1
>> proper extension:  RioMamore;
>> query: (?x947, RioMamore) <- ?x947[ a Estuary; has locatedIn ?x690<BOL>;]

*> Best rule #906 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 114
*> proper extension:  EucumbeneRiver; SaintLawrenceRiver; Manicouagan; Thjorsa; DarlingRiver; JoekulsaaFjoellum; MurrayRiver; MackenzieRiver; RiviereRichelieu; SnowyRiver; ...
*> query: (?x947, ?x274) <- ?x947[ a Estuary; has locatedIn ?x690[ has ethnicGroup ?x197; has language ?x1742[ a Language;]; has wasDependentOf ?x149; is locatedIn of ?x274;];]
*> conf = 0.02
ranks of expected_values: 21
EVAL	RioDesaguadero	hasEstuary!	RioDesaguadero	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.048	33.000	21.000	23.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasEstuary

PRED relation: hasEstuary!
PRED expected values: RioDesaguadero
=> 123 concepts (123 used for prediction)
PRED predicted values (max 10 best out of 110): RioMamore (0.33 #36, 0.08 #262, 0.08 #4562), Apurimac (0.08 #432, 0.02 #1571, 0.02 #1799), Urubamba (0.08 #423, 0.02 #1562, 0.02 #1790), Perene (0.08 #381, 0.02 #1520, 0.02 #1748), Mantaro (0.08 #379, 0.02 #1518, 0.02 #1746), Tambo (0.08 #362, 0.02 #1501, 0.02 #1729), Ene (0.08 #342, 0.02 #1481, 0.02 #1709), Ucayali (0.08 #335, 0.02 #1474, 0.02 #1702), Maranon (0.08 #327, 0.02 #1466, 0.02 #1694), Huallaga (0.08 #326, 0.02 #1465, 0.02 #1693)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #36 for best value:
>> intensional similarity = 3
>> extensional distance = 1
>> proper extension:  RioMamore;
>> query: (?x947, RioMamore) <- ?x947[ a Estuary; has locatedIn ?x690<BOL>;]

*> Best rule #3649 for first EXPECTED value:
*> intensional similarity = 13
*> extensional distance = 103
*> proper extension:  Rhein; Morava; WesternMorava; Kemijoki; Mur; WesternDwina; Maas; Kymijoki; Raab; Glomma; ...
*> query: (?x947, ?x432) <- ?x947[ a Estuary; has locatedIn ?x690[ has language ?x702; has wasDependentOf ?x149; is locatedIn of ?x432[ a River;]; is neighbor of ?x202[ a Country; has encompassed ?x521; has religion ?x95;]; is neighbor of ?x296[ is locatedIn of ?x264; is neighbor of ?x215;];];]
*> conf = 0.07
ranks of expected_values: 12
EVAL	RioDesaguadero	hasEstuary!	RioDesaguadero	CNN-1.+1._MA	0.000	0.000	0.000	0.083	123.000	123.000	110.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasEstuary

#878-OstfriesischeInseln
PRED entity: OstfriesischeInseln

PRED relation: belongsToIslands!
PRED expected values: Baltrum Wangerooge
=> 15 concepts (15 used for prediction)
PRED predicted values (max 10 best out of 263): Sylt (0.33 #133, 0.25 #327, 0.15 #1560), Amrum (0.33 #104, 0.25 #298, 0.15 #1560), Pellworm (0.33 #63, 0.25 #257, 0.15 #1560), Vlieland (0.25 #365, 0.14 #560, 0.10 #1364), Terschelling (0.25 #351, 0.14 #546, 0.10 #1364), Schiermonnikoog (0.25 #283, 0.14 #478, 0.10 #1364), Texel (0.25 #247, 0.14 #442, 0.05 #637), Ameland (0.25 #244, 0.14 #439, 0.05 #634), Wangerooge (0.15 #1560, 0.13 #2148, 0.13 #2344), Baltrum (0.15 #1560, 0.13 #2148, 0.13 #2344)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #133 for best value:
>> intensional similarity = 7
>> extensional distance = 1
>> proper extension:  NordfriesischeInseln;
>> query: (?x1856, Sylt) <- ?x1856[ a Islands; is belongsToIslands of ?x1100[ a Island; has locatedIn ?x120<D>; has locatedInWater ?x121<NorthSea>;];]

*> Best rule #1560 for first EXPECTED value:
*> intensional similarity = 9
*> extensional distance = 37
*> proper extension:  BahamaIslands;
*> query: (?x1856, ?x70) <- ?x1856[ a Islands; is belongsToIslands of ?x1100[ a Island; has locatedIn ?x120[ a Country; has government ?x140; has religion ?x352<RomanCatholic>; is locatedIn of ?x70;];];]
*> conf = 0.15
ranks of expected_values: 9, 10
EVAL	OstfriesischeInseln	belongsToIslands!	Wangerooge	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.111	15.000	15.000	263.000	0.333	http://www.semwebtech.org/mondial/10/meta#belongsToIslands
EVAL	OstfriesischeInseln	belongsToIslands!	Baltrum	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.111	15.000	15.000	263.000	0.333	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

PRED relation: belongsToIslands!
PRED expected values: Baltrum Wangerooge
=> 40 concepts (40 used for prediction)
PRED predicted values (max 10 best out of 263): Sylt (0.53 #7450, 0.50 #5489, 0.48 #6665), Amrum (0.53 #7450, 0.50 #5489, 0.48 #6665), Pellworm (0.53 #7450, 0.50 #5489, 0.48 #6665), Wangerooge (0.53 #7450, 0.50 #5489, 0.48 #6665), Baltrum (0.53 #7450, 0.50 #5489, 0.48 #6665), Fohr (0.53 #7450, 0.50 #5489, 0.48 #6665), Helgoland (0.53 #7450, 0.50 #5489, 0.48 #6665), Vlieland (0.25 #561, 0.23 #3329, 0.22 #3721), Terschelling (0.25 #547, 0.23 #3329, 0.22 #3721), Schiermonnikoog (0.25 #479, 0.23 #3329, 0.22 #3721)
>> best conf = 0.53
=> the first rule below is the first best rule for 7 predicted values
>> Best rule #7450 for best value:
>> intensional similarity = 17
>> extensional distance = 52
>> proper extension:  Carolines;
>> query: (?x1856, ?x1589) <- ?x1856[ a Islands; is belongsToIslands of ?x1100[ a Island; has locatedInWater ?x121[ a Sea; has locatedIn ?x78; has mergesWith ?x1664; is flowsInto of ?x829; is locatedInWater of ?x1589[ has locatedIn ?x120[ a Country; has encompassed ?x195; has government ?x140; is locatedIn of ?x1359[ a Island;];];]; is mergesWith of ?x1664;];]; is belongsToIslands of ?x1359;]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 4, 5
EVAL	OstfriesischeInseln	belongsToIslands!	Wangerooge	CNN-1.+1._MA	0.000	0.000	1.000	0.250	40.000	40.000	263.000	0.526	http://www.semwebtech.org/mondial/10/meta#belongsToIslands
EVAL	OstfriesischeInseln	belongsToIslands!	Baltrum	CNN-1.+1._MA	0.000	0.000	1.000	0.250	40.000	40.000	263.000	0.526	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

#877-Karakorum
PRED entity: Karakorum

PRED relation: inMountains!
PRED expected values: GasherbrumII K2 GasherbrumI
=> 22 concepts (22 used for prediction)
PRED predicted values (max 10 best out of 306): PikChan-Tengri (0.33 #109, 0.14 #880, 0.12 #1654), Ili (0.33 #75, 0.14 #846, 0.12 #1620), Naryn (0.33 #208, 0.14 #979, 0.12 #1753), Makalu (0.33 #438, 0.14 #952, 0.12 #1726), MountEverest (0.33 #383, 0.14 #897, 0.12 #1671), ChoOyu (0.33 #298, 0.14 #812, 0.12 #1586), NangaParbat (0.33 #396, 0.14 #910, 0.12 #1684), Dhaulagiri (0.33 #379, 0.14 #893, 0.12 #1667), NandaDevi (0.33 #336, 0.14 #850, 0.12 #1624), Kangchendzonga (0.33 #292, 0.14 #806, 0.12 #1580)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #109 for best value:
>> intensional similarity = 11
>> extensional distance = 1
>> proper extension:  TianShan;
>> query: (?x368, PikChan-Tengri) <- ?x368[ a Mountains; is inMountains of ?x1375[ a Mountain; has locatedIn ?x83;]; is inMountains of ?x1936[ has locatedIn ?x232<CN>; is hasSource of ?x497[ a River; has flowsInto ?x386; has hasEstuary ?x498;];];]

*> Best rule #3867 for first EXPECTED value:
*> intensional similarity = 11
*> extensional distance = 44
*> proper extension:  Ahaggar; CordilleradeTalamanca; CordilleraBlanca; CordilleraVilcanota; SierraMadre; CordilleraReal; CordilleraVolcanica; CordilleraNegra;
*> query: (?x368, ?x231) <- ?x368[ a Mountains; is inMountains of ?x1375[ a Mountain;]; is inMountains of ?x1936[ has locatedIn ?x232[ has ethnicGroup ?x2285; has government ?x831; has neighbor ?x73; has religion ?x116; is locatedIn of ?x231; is neighbor of ?x463;];];]
*> conf = 0.09
ranks of expected_values: 108, 131, 140
EVAL	Karakorum	inMountains!	GasherbrumI	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.009	22.000	22.000	306.000	0.333	http://www.semwebtech.org/mondial/10/meta#inMountains
EVAL	Karakorum	inMountains!	K2	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.008	22.000	22.000	306.000	0.333	http://www.semwebtech.org/mondial/10/meta#inMountains
EVAL	Karakorum	inMountains!	GasherbrumII	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.007	22.000	22.000	306.000	0.333	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains!
PRED expected values: GasherbrumII K2 GasherbrumI
=> 49 concepts (49 used for prediction)
PRED predicted values (max 10 best out of 306): PikChan-Tengri (0.33 #109, 0.25 #1142, 0.20 #1658), Ili (0.33 #75, 0.25 #1108, 0.20 #1624), Naryn (0.33 #208, 0.25 #1241, 0.20 #1757), NangaParbat (0.33 #397, 0.18 #2323, 0.17 #7485), Makalu (0.33 #439, 0.17 #2246, 0.14 #3021), MountEverest (0.33 #384, 0.17 #2191, 0.14 #2966), ChoOyu (0.33 #299, 0.17 #2106, 0.14 #2881), Dhaulagiri (0.33 #380, 0.17 #2187, 0.14 #2962), NandaDevi (0.33 #337, 0.17 #2144, 0.14 #2919), Kangchendzonga (0.33 #293, 0.17 #2100, 0.14 #2875)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #109 for best value:
>> intensional similarity = 15
>> extensional distance = 1
>> proper extension:  TianShan;
>> query: (?x368, PikChan-Tengri) <- ?x368[ a Mountains; is inMountains of ?x1375[ a Mountain; has locatedIn ?x83[ a Country; has government ?x140; has language ?x559; is neighbor of ?x304;];]; is inMountains of ?x1936[ a Source; has locatedIn ?x232<CN>; is hasSource of ?x497[ has flowsInto ?x386; has hasEstuary ?x498;];];]

*> Best rule #2323 for first EXPECTED value:
*> intensional similarity = 14
*> extensional distance = 4
*> proper extension:  SierraMaestra;
*> query: (?x368, ?x82) <- ?x368[ a Mountains; is inMountains of ?x1375[ a Mountain; has locatedIn ?x83[ has language ?x559; is locatedIn of ?x82;];]; is inMountains of ?x1936[ has locatedIn ?x232[ a Country; has encompassed ?x175; has ethnicGroup ?x2285; has government ?x831<"Communist state">; has religion ?x116;];];]
*> conf = 0.18
ranks of expected_values: 42, 43, 132
EVAL	Karakorum	inMountains!	GasherbrumI	CNN-1.+1._MA	0.000	0.000	0.000	0.008	49.000	49.000	306.000	0.333	http://www.semwebtech.org/mondial/10/meta#inMountains
EVAL	Karakorum	inMountains!	K2	CNN-1.+1._MA	0.000	0.000	0.000	0.024	49.000	49.000	306.000	0.333	http://www.semwebtech.org/mondial/10/meta#inMountains
EVAL	Karakorum	inMountains!	GasherbrumII	CNN-1.+1._MA	0.000	0.000	0.000	0.024	49.000	49.000	306.000	0.333	http://www.semwebtech.org/mondial/10/meta#inMountains

#876-Karamojong
PRED entity: Karamojong

PRED relation: ethnicGroup!
PRED expected values: EAU
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 96): EAU (0.03 #132), ETH (0.03 #100), UA (0.03 #57), EAK (0.03 #99), CZ (0.02 #98), SN (0.02 #83), MYA (0.02 #70), IR (0.02 #58), SK (0.02 #25), RCA (0.02 #139)
>> best conf = 0.03
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #132 for best value:
>> intensional similarity = 1
>> extensional distance = 279
>> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
>> query: (?x1358, EAU) <- ?x1358[ a EthnicGroup;]
ranks of expected_values: 1
EVAL	Karamojong	ethnicGroup!	EAU	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	2.000	2.000	96.000	0.032	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup!
PRED expected values: EAU
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 96): EAU (0.03 #132), ETH (0.03 #100), UA (0.03 #57), EAK (0.03 #99), CZ (0.02 #98), SN (0.02 #83), MYA (0.02 #70), IR (0.02 #58), SK (0.02 #25), RCA (0.02 #139)
>> best conf = 0.03
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #132 for best value:
>> intensional similarity = 1
>> extensional distance = 279
>> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
>> query: (?x1358, EAU) <- ?x1358[ a EthnicGroup;]
ranks of expected_values: 1
EVAL	Karamojong	ethnicGroup!	EAU	CNN-1.+1._MA	1.000	1.000	1.000	1.000	2.000	2.000	96.000	0.032	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#875-E
PRED entity: E

PRED relation: dependentOf!
PRED expected values: MEL
=> 45 concepts (45 used for prediction)
PRED predicted values (max 10 best out of 42): GBZ (0.08 #158, 0.08 #117, 0.08 #78), FALK (0.08 #151, 0.08 #110, 0.08 #71), GBM (0.08 #150, 0.08 #109, 0.08 #70), BERM (0.08 #149, 0.08 #108, 0.08 #69), CAYM (0.08 #144, 0.08 #103, 0.08 #64), GBG (0.08 #138, 0.08 #97, 0.08 #58), GBJ (0.08 #137, 0.08 #96, 0.08 #57), TUCA (0.08 #132, 0.08 #91, 0.08 #52), PITC (0.08 #131, 0.08 #90, 0.08 #51), AXA (0.08 #130, 0.08 #89, 0.08 #50)
>> best conf = 0.08
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #158 for best value:
>> intensional similarity = 6
>> extensional distance = 10
>> proper extension:  PR;
>> query: (?x149, GBZ) <- ?x149[ is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x1020[ has belongsToIslands ?x1068;]; is locatedIn of ?x1166[ a Mountain;];]
>> Best rule #117 for best value:
>> intensional similarity = 6
>> extensional distance = 10
>> proper extension:  SMAR;
>> query: (?x149, GBZ) <- ?x149[ has neighbor ?x789[ has encompassed ?x195;]; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x1020[ a Island;];]
>> Best rule #78 for best value:
>> intensional similarity = 4
>> extensional distance = 10
>> proper extension:  UnitedNations; SovietUnion; OttomanEmpire;
>> query: (?x149, GBZ) <- ?x149[ is wasDependentOf of ?x1027[ a Country; is locatedIn of ?x199[ has belongsToIslands ?x200;];];]

No rule for expected values
ranks of expected_values: 
EVAL	E	dependentOf!	MEL	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	45.000	45.000	42.000	0.083	http://www.semwebtech.org/mondial/10/meta#dependentOf

PRED relation: dependentOf!
PRED expected values: MEL
=> 132 concepts (132 used for prediction)
PRED predicted values (max 10 best out of 148): MART (0.33 #20, 0.25 #99, 0.17 #297), SPMI (0.33 #3, 0.25 #82, 0.17 #280), GUAD (0.33 #16, 0.25 #95, 0.17 #293), SMAR (0.33 #15, 0.25 #94, 0.17 #292), FGU (0.33 #24, 0.25 #103, 0.17 #301), NCA (0.33 #29, 0.25 #108, 0.17 #306), WAFU (0.33 #14, 0.25 #93, 0.17 #291), FPOL (0.33 #8, 0.25 #87, 0.17 #285), MAYO (0.33 #22, 0.25 #101, 0.17 #299), REUN (0.33 #2, 0.25 #81, 0.17 #279)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #20 for best value:
>> intensional similarity = 12
>> extensional distance = 1
>> proper extension:  F;
>> query: (?x149, MART) <- ?x149[ has government ?x1657; has language ?x790; has religion ?x352; is dependentOf of ?x2084; is locatedIn of ?x275<MediterraneanSea>; is locatedIn of ?x1293[ a Mountain;]; is locatedIn of ?x1935[ has type ?x150;]; is neighbor of ?x78; is wasDependentOf of ?x148;]

*> Best rule #396 for first EXPECTED value:
*> intensional similarity = 10
*> extensional distance = 5
*> proper extension:  CN;
*> query: (?x149, ?x55) <- ?x149[ has neighbor ?x78; is dependentOf of ?x2084; is locatedIn of ?x275[ has locatedIn ?x55; is flowsInto of ?x698; is locatedInWater of ?x86[ a Island;];]; is wasDependentOf of ?x148; is wasDependentOf of ?x1027[ is locatedIn of ?x199;];]
*> conf = 0.07
ranks of expected_values: 123
EVAL	E	dependentOf!	MEL	CNN-1.+1._MA	0.000	0.000	0.000	0.008	132.000	132.000	148.000	0.333	http://www.semwebtech.org/mondial/10/meta#dependentOf

#874-SMAR
PRED entity: SMAR

PRED relation: neighbor!
PRED expected values: NLSM
=> 22 concepts (22 used for prediction)
PRED predicted values (max 10 best out of 202): BR (0.12 #255, 0.06 #3395, 0.06 #2584), CO (0.10 #200, 0.06 #3395, 0.06 #2584), PE (0.10 #212, 0.05 #3072, 0.04 #2096), RA (0.08 #228, 0.06 #3395, 0.06 #2584), BOL (0.08 #275, 0.05 #435, 0.05 #596), CN (0.08 #1492, 0.06 #2954, 0.06 #3277), CAM (0.07 #413, 0.06 #574, 0.06 #2584), RMM (0.07 #453, 0.06 #614, 0.06 #774), R (0.06 #1451, 0.06 #3076, 0.06 #3236), RCH (0.06 #3395, 0.06 #2584, 0.06 #2747)
>> best conf = 0.12
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #255 for best value:
>> intensional similarity = 4
>> extensional distance = 46
>> proper extension:  BZ;
>> query: (?x628, BR) <- ?x628[ a Country; has encompassed ?x521<America>; has government ?x1503;]

*> Best rule #3395 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 223
*> proper extension:  DJI; MNG;
*> query: (?x628, ?x1087) <- ?x628[ is locatedIn of ?x182[ has locatedIn ?x667[ has religion ?x95;]; has locatedIn ?x1087[ a Country; has language ?x247;];];]
*> conf = 0.06
ranks of expected_values: 42
EVAL	SMAR	neighbor!	NLSM	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.024	22.000	22.000	202.000	0.125	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: NLSM
=> 62 concepts (62 used for prediction)
PRED predicted values (max 10 best out of 212): RH (0.17 #1570, 0.11 #9605, 0.09 #9777), DOM (0.17 #1543, 0.11 #9605, 0.09 #9777), NLSM (0.17 #1615, 0.07 #4924, 0.06 #9610), ZRE (0.15 #6755, 0.11 #9605, 0.09 #9777), SME (0.14 #1804, 0.11 #2299, 0.11 #2131), BR (0.12 #5018, 0.11 #9605, 0.09 #4188), CO (0.11 #9605, 0.10 #4963, 0.09 #9777), RA (0.11 #9605, 0.09 #4161, 0.09 #9777), GUY (0.11 #9605, 0.09 #4156, 0.09 #9777), RG (0.11 #9605, 0.09 #9777, 0.08 #9267)
>> best conf = 0.17
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1570 for best value:
>> intensional similarity = 18
>> extensional distance = 4
>> proper extension:  DOM; RH;
>> query: (?x628, RH) <- ?x628[ a Country; has encompassed ?x521<America>; has government ?x1503; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x317<CaribbeanSea>; is locatedIn of ?x1380[ a Island; has locatedIn ?x50[ a Country; has government ?x2058; has language ?x51; has religion ?x95<Protestant>; has religion ?x352<RomanCatholic>;];];]

*> Best rule #1615 for first EXPECTED value:
*> intensional similarity = 18
*> extensional distance = 4
*> proper extension:  DOM; RH;
*> query: (?x628, ?x50) <- ?x628[ a Country; has encompassed ?x521<America>; has government ?x1503; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x317<CaribbeanSea>; is locatedIn of ?x1380[ a Island; has locatedIn ?x50[ a Country; has government ?x2058; has language ?x51; has religion ?x95<Protestant>; has religion ?x352<RomanCatholic>;];];]
*> conf = 0.17
ranks of expected_values: 3
EVAL	SMAR	neighbor!	NLSM	CNN-1.+1._MA	0.000	1.000	1.000	0.333	62.000	62.000	212.000	0.167	http://www.semwebtech.org/mondial/10/meta#neighbor

#873-NLSM
PRED entity: NLSM

PRED relation: language
PRED expected values: Creole
=> 40 concepts (40 used for prediction)
PRED predicted values (max 10 best out of 66): German (0.20 #476, 0.15 #1686, 0.11 #2152), Creole (0.20 #540, 0.09 #2141, 0.05 #1005), Garifuna (0.20 #522, 0.09 #2141, 0.04 #1546), MayanDialects (0.20 #500, 0.09 #2141, 0.04 #1524), Russian (0.11 #2428, 0.11 #2521, 0.10 #2614), Quechua (0.10 #1072, 0.09 #2141, 0.08 #1351), Aymara (0.10 #1037, 0.09 #2141, 0.08 #1316), Portuguese (0.09 #2141, 0.09 #2148, 0.08 #658), Samoan (0.09 #2141, 0.08 #653, 0.05 #1118), Chinese (0.09 #2141, 0.08 #710, 0.04 #2013)
>> best conf = 0.20
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #476 for best value:
>> intensional similarity = 8
>> extensional distance = 3
>> proper extension:  BZ;
>> query: (?x50, German) <- ?x50[ has government ?x2058; has language ?x247<English>; has language ?x796<Spanish>; has religion ?x109[ is religion of ?x177<BG>;];]

*> Best rule #540 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 3
*> proper extension:  BZ;
*> query: (?x50, Creole) <- ?x50[ has government ?x2058; has language ?x247<English>; has language ?x796<Spanish>; has religion ?x109[ is religion of ?x177<BG>;];]
*> conf = 0.20
ranks of expected_values: 2
EVAL	NLSM	language	Creole	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	40.000	40.000	66.000	0.200	http://www.semwebtech.org/mondial/10/meta#language

PRED relation: language
PRED expected values: Creole
=> 69 concepts (69 used for prediction)
PRED predicted values (max 10 best out of 92): German (0.33 #383, 0.18 #3083, 0.18 #2999), Portuguese (0.33 #379, 0.17 #3082, 0.12 #745), Luxembourgish (0.33 #461, 0.17 #3082, 0.12 #745), Ukrainian (0.20 #1237, 0.14 #1610, 0.14 #771), Creole (0.17 #3082, 0.14 #914, 0.12 #745), Garifuna (0.17 #3082, 0.12 #745, 0.08 #2894), MayanDialects (0.17 #3082, 0.12 #745, 0.08 #2894), Italian (0.17 #3082, 0.10 #2988, 0.08 #2430), Romansch (0.17 #3082, 0.10 #2988, 0.08 #2894), Monegasque (0.17 #3082, 0.10 #2988, 0.08 #2894)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #383 for best value:
>> intensional similarity = 20
>> extensional distance = 1
>> proper extension:  L;
>> query: (?x50, German) <- ?x50[ has language ?x51<French>; has language ?x247<English>; has religion ?x109<Jewish>; is locatedIn of ?x317[ has locatedIn ?x407[ has ethnicGroup ?x1009; has religion ?x280;]; has locatedIn ?x408[ a Country; has government ?x435;]; has locatedIn ?x865[ a Country; has religion ?x713;]; has locatedIn ?x1073[ a Country; has ethnicGroup ?x162;]; is flowsInto of ?x2241;];]

*> Best rule #3082 for first EXPECTED value:
*> intensional similarity = 20
*> extensional distance = 31
*> proper extension:  NMIS;
*> query: (?x50, ?x2160) <- ?x50[ a Country; has language ?x51[ a Language; is language of ?x461<NZ>; is language of ?x564[ has encompassed ?x211<Australia-Oceania>; has ethnicGroup ?x1335; is locatedIn of ?x282;]; is language of ?x1577[ has encompassed ?x195; has ethnicGroup ?x1672; has language ?x635<German>; has language ?x2160;];]; has language ?x796[ is language of ?x671<BZ>;]; is locatedIn of ?x182;]
*> conf = 0.17
ranks of expected_values: 5
EVAL	NLSM	language	Creole	CNN-1.+1._MA	0.000	0.000	1.000	0.200	69.000	69.000	92.000	0.333	http://www.semwebtech.org/mondial/10/meta#language

#872-LakeOahe
PRED entity: LakeOahe

PRED relation: type
PRED expected values: "dam"
=> 40 concepts (40 used for prediction)
PRED predicted values (max 10 best out of 7): "dam" (0.86 #131, 0.85 #98, 0.76 #115), "salt" (0.20 #491, 0.01 #635), "impact" (0.05 #74, 0.05 #91, 0.05 #124), "naturaldam" (0.05 #97, 0.05 #130, 0.02 #276), "caldera" (0.05 #487, 0.03 #375, 0.03 #359), "volcanic" (0.04 #630), "volcano" (0.02 #634)
>> best conf = 0.86
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #131 for best value:
>> intensional similarity = 7
>> extensional distance = 19
>> proper extension:  LakeVolta; LakeCabora-Bassa; Franklin.D.RooseveltLake; KakhovkaReservoir; LakeNasser; KoliSarez; BarragedeMbakaou; LakeBurleyGriffin; KremenchukReservoir; LakeMead; ...
>> query: (?x1989, ?x136) <- ?x1989[ a Lake; is flowsThrough of ?x1366[ a River; has flowsInto ?x361; is flowsInto of ?x2384[ a Lake; has type ?x136;];];]
ranks of expected_values: 1
EVAL	LakeOahe	type	"dam"	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	40.000	40.000	7.000	0.857	http://www.semwebtech.org/mondial/10/meta#type

PRED relation: type
PRED expected values: "dam"
=> 104 concepts (104 used for prediction)
PRED predicted values (max 10 best out of 7): "dam" (0.88 #588, 0.88 #572, 0.82 #361), "salt" (0.20 #1588, 0.01 #1668), "naturaldam" (0.17 #66, 0.17 #49, 0.09 #327), "impact" (0.17 #60, 0.06 #532, 0.04 #695), "caldera" (0.10 #640, 0.05 #960, 0.05 #976), "volcanic" (0.04 #1663), "volcano" (0.02 #1667)
>> best conf = 0.88
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #588 for best value:
>> intensional similarity = 12
>> extensional distance = 15
>> proper extension:  LakeKainji;
>> query: (?x1989, ?x136) <- ?x1989[ a Lake; has flowsInto ?x1366[ a River; has hasEstuary ?x1254[ a Estuary;]; has hasSource ?x2450[ a Source;]; is flowsInto of ?x1113[ a Lake; has locatedIn ?x315; has type ?x136<"dam">;];];]
>> Best rule #572 for best value:
>> intensional similarity = 12
>> extensional distance = 15
>> proper extension:  LakeKainji;
>> query: (?x1989, "dam") <- ?x1989[ a Lake; has flowsInto ?x1366[ a River; has hasEstuary ?x1254[ a Estuary;]; has hasSource ?x2450[ a Source;]; is flowsInto of ?x1113[ a Lake; has locatedIn ?x315; has type ?x136<"dam">;];];]
ranks of expected_values: 1
EVAL	LakeOahe	type	"dam"	CNN-1.+1._MA	1.000	1.000	1.000	1.000	104.000	104.000	7.000	0.882	http://www.semwebtech.org/mondial/10/meta#type

#871-UA
PRED entity: UA

PRED relation: language
PRED expected values: Russian
=> 42 concepts (42 used for prediction)
PRED predicted values (max 10 best out of 90): Russian (0.75 #496, 0.67 #399, 0.40 #108), Hungarian (0.40 #308, 0.29 #1069, 0.25 #17), Spanish (0.38 #993, 0.35 #702, 0.31 #604), Slovak (0.29 #1069, 0.25 #41, 0.24 #1749), Roma (0.29 #1069, 0.25 #46, 0.24 #1749), Romanian (0.29 #1069, 0.25 #48, 0.24 #1749), Gagauz (0.29 #1069, 0.25 #54, 0.24 #1749), Polish (0.29 #1069, 0.24 #1749, 0.20 #2332), Belorussian (0.29 #1069, 0.24 #1749, 0.17 #455), Uzbek (0.25 #528, 0.07 #917, 0.04 #1306)
>> best conf = 0.75
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #496 for best value:
>> intensional similarity = 6
>> extensional distance = 10
>> proper extension:  R; KGZ; BY; UZB; TM; AZ; GE; LV; LT;
>> query: (?x303, Russian) <- ?x303[ has ethnicGroup ?x58; has language ?x1108; has religion ?x56; has wasDependentOf ?x903<SovietUnion>; is locatedIn of ?x97;]
ranks of expected_values: 1
EVAL	UA	language	Russian	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	42.000	42.000	90.000	0.750	http://www.semwebtech.org/mondial/10/meta#language

PRED relation: language
PRED expected values: Russian
=> 116 concepts (116 used for prediction)
PRED predicted values (max 10 best out of 88): Russian (0.45 #2247, 0.40 #1276, 0.40 #1179), Spanish (0.45 #3521, 0.43 #5951, 0.40 #4785), Albanian (0.43 #1690, 0.27 #2272, 0.21 #2660), Hungarian (0.40 #991, 0.33 #213, 0.33 #114), Roma (0.40 #1020, 0.33 #195, 0.33 #143), Romanian (0.33 #633, 0.33 #145, 0.30 #6027), Gagauz (0.33 #639, 0.30 #6027, 0.27 #4083), Belorussian (0.33 #360, 0.30 #6027, 0.25 #943), English (0.33 #394, 0.29 #5155, 0.27 #6712), Serbian (0.33 #234, 0.29 #1692, 0.25 #8361)
>> best conf = 0.45
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2247 for best value:
>> intensional similarity = 12
>> extensional distance = 9
>> proper extension:  KGZ; UZB; TM; AZ;
>> query: (?x303, Russian) <- ?x303[ has ethnicGroup ?x58; has language ?x1108; has religion ?x56<ChristianOrthodox>; has wasDependentOf ?x903; is locatedIn of ?x457[ a Lake;]; is locatedIn of ?x1292[ a River;]; is neighbor of ?x194[ has government ?x435; is locatedIn of ?x146;];]
ranks of expected_values: 1
EVAL	UA	language	Russian	CNN-1.+1._MA	1.000	1.000	1.000	1.000	116.000	116.000	88.000	0.455	http://www.semwebtech.org/mondial/10/meta#language

#870-Dychtau
PRED entity: Dychtau

PRED relation: locatedIn
PRED expected values: R
=> 17 concepts (17 used for prediction)
PRED predicted values (max 10 best out of 54): R (0.80 #949, 0.80 #717, 0.78 #473), GE (0.73 #950, 0.71 #474, 0.70 #712), USA (0.21 #1259, 0.21 #1734, 0.21 #1496), KAZ (0.15 #567, 0.13 #805, 0.11 #329), PE (0.11 #2203, 0.06 #2914, 0.05 #3152), ZRE (0.08 #2215, 0.04 #1029, 0.03 #1266), MEX (0.08 #1066, 0.06 #1303, 0.06 #1778), RA (0.08 #1037, 0.06 #1274, 0.06 #1749), I (0.06 #2895, 0.06 #3133, 0.06 #3607), CN (0.06 #3379, 0.06 #2429, 0.06 #2667)
>> best conf = 0.80
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #949 for best value:
>> intensional similarity = 5
>> extensional distance = 13
>> proper extension:  WesternDwina; Dnepr;
>> query: (?x2417, ?x73) <- ?x2417[ has inMountains ?x781[ a Mountains; is inMountains of ?x141[ has locatedIn ?x73<R>;];];]
>> Best rule #717 for best value:
>> intensional similarity = 5
>> extensional distance = 13
>> proper extension:  WesternDwina; Dnepr;
>> query: (?x2417, R) <- ?x2417[ has inMountains ?x781[ a Mountains; is inMountains of ?x141[ has locatedIn ?x73<R>;];];]
ranks of expected_values: 1
EVAL	Dychtau	locatedIn	R	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	17.000	17.000	54.000	0.800	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: R
=> 19 concepts (19 used for prediction)
PRED predicted values (max 10 best out of 54): R (0.80 #1187, 0.80 #955, 0.78 #711), GE (0.73 #1188, 0.71 #712, 0.70 #949), USA (0.21 #1498, 0.21 #1974, 0.21 #1736), KAZ (0.15 #805, 0.13 #1043, 0.11 #567), PE (0.13 #2443, 0.06 #3393, 0.05 #3631), ZRE (0.09 #2455, 0.04 #1267, 0.03 #1505), MEX (0.08 #1304, 0.06 #1542, 0.06 #2018), RA (0.08 #1275, 0.06 #1513, 0.06 #1989), I (0.06 #3374, 0.06 #3612, 0.06 #4086), CN (0.06 #3858, 0.06 #2670, 0.06 #2908)
>> best conf = 0.80
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1187 for best value:
>> intensional similarity = 5
>> extensional distance = 13
>> proper extension:  WesternDwina; Dnepr;
>> query: (?x2417, ?x73) <- ?x2417[ has inMountains ?x781[ a Mountains; is inMountains of ?x141[ has locatedIn ?x73<R>;];];]
>> Best rule #955 for best value:
>> intensional similarity = 5
>> extensional distance = 13
>> proper extension:  WesternDwina; Dnepr;
>> query: (?x2417, R) <- ?x2417[ has inMountains ?x781[ a Mountains; is inMountains of ?x141[ has locatedIn ?x73<R>;];];]
ranks of expected_values: 1
EVAL	Dychtau	locatedIn	R	CNN-1.+1._MA	1.000	1.000	1.000	1.000	19.000	19.000	54.000	0.800	http://www.semwebtech.org/mondial/10/meta#locatedIn

#869-Jordan
PRED entity: Jordan

PRED relation: hasSource!
PRED expected values: Jordan
=> 39 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 99): Jordan (0.04 #4121, 0.02 #914, 0.02 #4122), MediterraneanSea (0.04 #4121, 0.02 #914, 0.02 #4122), Arno (0.03 #886, 0.02 #4120, 0.01 #1343), Tiber (0.03 #792, 0.02 #4120, 0.01 #1249), Etsch (0.03 #772, 0.02 #4120, 0.01 #1229), Po (0.03 #759, 0.02 #4120, 0.01 #1216), Ebro (0.03 #878, 0.02 #4120), Moraca (0.03 #909, 0.01 #1138, 0.01 #1366), Karasu (0.03 #846, 0.01 #1075, 0.01 #1303), Drina (0.03 #771, 0.01 #1000, 0.01 #1228)
>> best conf = 0.04
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #4121 for best value:
>> intensional similarity = 5
>> extensional distance = 215
>> proper extension:  Bahrel-Ghasal; Sobat; WhiteNile; Pibor;
>> query: (?x1564, ?x275) <- ?x1564[ a Source; has locatedIn ?x115[ is locatedIn of ?x275[ is flowsInto of ?x698;]; is neighbor of ?x239;];]
ranks of expected_values: 1
EVAL	Jordan	hasSource!	Jordan	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	39.000	31.000	99.000	0.043	http://www.semwebtech.org/mondial/10/meta#hasSource

PRED relation: hasSource!
PRED expected values: Jordan
=> 119 concepts (119 used for prediction)
PRED predicted values (max 10 best out of 207): Karasu (0.20 #1304, 0.12 #2224, 0.12 #1763), Tigris (0.20 #1213, 0.12 #2133, 0.12 #1672), Kura (0.20 #1184, 0.12 #2104, 0.12 #1643), SchattalArab (0.20 #1079, 0.08 #3377, 0.06 #3608), Benue (0.20 #1117, 0.08 #3415, 0.03 #6407), Sanaga (0.20 #1092, 0.08 #3390, 0.03 #6382), Jordan (0.15 #2979, 0.14 #4362, 0.12 #10816), Save (0.12 #1611, 0.09 #2758, 0.08 #2989), Drina (0.12 #1688, 0.09 #2835, 0.08 #3066), MediterraneanSea (0.12 #10816, 0.10 #3441, 0.08 #13351)
>> best conf = 0.20
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1304 for best value:
>> intensional similarity = 12
>> extensional distance = 3
>> proper extension:  Kura; Tigris; Karasu;
>> query: (?x1564, Karasu) <- ?x1564[ a Source; has inMountains ?x2473[ a Mountains;]; has locatedIn ?x115[ has encompassed ?x175<Asia>; has wasDependentOf ?x485; is locatedIn of ?x275<MediterraneanSea>; is locatedIn of ?x419[ has flowsInto ?x567; is flowsInto of ?x1999;];];]

*> Best rule #2979 for first EXPECTED value:
*> intensional similarity = 11
*> extensional distance = 9
*> proper extension:  Moraca; Piva; Tara;
*> query: (?x1564, ?x419) <- ?x1564[ a Source; has inMountains ?x2473[ a Mountains;]; has locatedIn ?x115[ a Country; has encompassed ?x175; has wasDependentOf ?x485; is locatedIn of ?x275<MediterraneanSea>; is locatedIn of ?x419[ has flowsInto ?x567;];];]
*> conf = 0.15
ranks of expected_values: 7
EVAL	Jordan	hasSource!	Jordan	CNN-1.+1._MA	0.000	0.000	1.000	0.143	119.000	119.000	207.000	0.200	http://www.semwebtech.org/mondial/10/meta#hasSource

#868-Fula
PRED entity: Fula

PRED relation: ethnicGroup!
PRED expected values: RN
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 96): EAU (0.03 #132), ETH (0.03 #100), UA (0.03 #57), EAK (0.03 #99), CZ (0.02 #98), SN (0.02 #83), MYA (0.02 #70), IR (0.02 #58), SK (0.02 #25), RCA (0.02 #139)
>> best conf = 0.03
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #132 for best value:
>> intensional similarity = 1
>> extensional distance = 279
>> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
>> query: (?x2481, EAU) <- ?x2481[ a EthnicGroup;]

*> Best rule #86 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 279
*> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
*> query: (?x2481, RN) <- ?x2481[ a EthnicGroup;]
*> conf = 0.02
ranks of expected_values: 28
EVAL	Fula	ethnicGroup!	RN	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.036	2.000	2.000	96.000	0.032	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup!
PRED expected values: RN
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 96): EAU (0.03 #132), ETH (0.03 #100), UA (0.03 #57), EAK (0.03 #99), CZ (0.02 #98), SN (0.02 #83), MYA (0.02 #70), IR (0.02 #58), SK (0.02 #25), RCA (0.02 #139)
>> best conf = 0.03
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #132 for best value:
>> intensional similarity = 1
>> extensional distance = 279
>> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
>> query: (?x2481, EAU) <- ?x2481[ a EthnicGroup;]

*> Best rule #86 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 279
*> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
*> query: (?x2481, RN) <- ?x2481[ a EthnicGroup;]
*> conf = 0.02
ranks of expected_values: 28
EVAL	Fula	ethnicGroup!	RN	CNN-1.+1._MA	0.000	0.000	0.000	0.036	2.000	2.000	96.000	0.032	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#867-AndamanSea
PRED entity: AndamanSea

PRED relation: mergesWith!
PRED expected values: IndianOcean
=> 35 concepts (27 used for prediction)
PRED predicted values (max 10 best out of 146): IndianOcean (0.82 #429, 0.80 #430, 0.46 #627), AndamanSea (0.46 #627, 0.46 #626, 0.46 #625), JavaSea (0.46 #627, 0.46 #626, 0.46 #625), SouthChinaSea (0.46 #627, 0.46 #626, 0.46 #625), GulfofAden (0.40 #116, 0.16 #431, 0.14 #271), PacificOcean (0.30 #171, 0.29 #248, 0.23 #327), BandaSea (0.30 #181, 0.20 #103, 0.16 #431), SulawesiSea (0.30 #179, 0.12 #454, 0.11 #531), AtlanticOcean (0.29 #357, 0.27 #318, 0.26 #514), EastChinaSea (0.21 #255, 0.20 #178, 0.12 #453)
>> best conf = 0.82
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #429 for best value:
>> intensional similarity = 6
>> extensional distance = 30
>> proper extension:  HudsonBay; KaraSea;
>> query: (?x339, ?x60) <- ?x339[ a Sea; has mergesWith ?x60; has mergesWith ?x385[ has locatedIn ?x91; is locatedInWater of ?x1299;]; is locatedInWater of ?x740;]
ranks of expected_values: 1
EVAL	AndamanSea	mergesWith!	IndianOcean	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	35.000	27.000	146.000	0.815	http://www.semwebtech.org/mondial/10/meta#mergesWith

PRED relation: mergesWith!
PRED expected values: IndianOcean
=> 123 concepts (123 used for prediction)
PRED predicted values (max 10 best out of 86): IndianOcean (0.86 #1180, 0.84 #1631, 0.83 #1797), JavaSea (0.50 #1096, 0.50 #411, 0.47 #1509), AndamanSea (0.50 #1096, 0.47 #1509, 0.47 #1508), SouthChinaSea (0.50 #1096, 0.47 #1509, 0.47 #1508), SulawesiSea (0.50 #185, 0.38 #469, 0.33 #104), PacificOcean (0.50 #660, 0.38 #419, 0.29 #785), BandaSea (0.33 #106, 0.29 #351, 0.29 #324), EastChinaSea (0.33 #63, 0.29 #348, 0.29 #324), GulfofAden (0.33 #322, 0.29 #324, 0.26 #323), AtlanticOcean (0.32 #1392, 0.32 #1103, 0.29 #1061)
>> best conf = 0.86
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1180 for best value:
>> intensional similarity = 7
>> extensional distance = 20
>> proper extension:  SeaofAzov; BlackSea;
>> query: (?x339, ?x60) <- ?x339[ has mergesWith ?x60[ has locatedIn ?x820[ has ethnicGroup ?x1233;]; is flowsInto of ?x242; is mergesWith of ?x241[ a Sea;];]; is flowsInto of ?x338;]
ranks of expected_values: 1
EVAL	AndamanSea	mergesWith!	IndianOcean	CNN-1.+1._MA	1.000	1.000	1.000	1.000	123.000	123.000	86.000	0.860	http://www.semwebtech.org/mondial/10/meta#mergesWith

#866-Illimani
PRED entity: Illimani

PRED relation: type
PRED expected values: "granite"
=> 44 concepts (44 used for prediction)
PRED predicted values (max 10 best out of 9): "granite" (0.49 #164, 0.40 #33, 0.33 #14), "volcano" (0.41 #153, 0.35 #121, 0.30 #186), "volcanic" (0.25 #149, 0.19 #182, 0.15 #328), "salt" (0.15 #393, 0.13 #557, 0.13 #556), "crater" (0.15 #393, 0.13 #557, 0.13 #556), "dam" (0.03 #262, 0.02 #377, 0.02 #310), "sand" (0.02 #313, 0.02 #380, 0.01 #346), "lime" (0.02 #314, 0.01 #381), "atoll" (0.01 #384)
>> best conf = 0.49
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #164 for best value:
>> intensional similarity = 6
>> extensional distance = 67
>> proper extension:  Cayambe; MaunaLoa; MtRedoubt; Sajama; MontGreboun; GranitePeak; Dychtau;
>> query: (?x2274, ?x2437) <- ?x2274[ a Mountain; has inMountains ?x1862[ a Mountains; is inMountains of ?x689[ a Mountain; has type ?x2437;];];]
ranks of expected_values: 1
EVAL	Illimani	type	"granite"	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	44.000	44.000	9.000	0.489	http://www.semwebtech.org/mondial/10/meta#type

PRED relation: type
PRED expected values: "granite"
=> 155 concepts (155 used for prediction)
PRED predicted values (max 10 best out of 10): "granite" (0.56 #166, 0.53 #117, 0.51 #586), "volcano" (0.50 #56, 0.47 #106, 0.45 #206), "volcanic" (0.36 #151, 0.30 #571, 0.27 #554), "salt" (0.33 #369, 0.33 #368, 0.33 #367), "crater" (0.33 #369, 0.33 #368, 0.33 #367), "dam" (0.04 #1012, 0.03 #1339, 0.03 #1699), "sand" (0.02 #1179, 0.02 #1293, 0.02 #1390), "lime" (0.02 #1294, 0.02 #1637, 0.01 #1838), "monolith" (0.01 #747, 0.01 #876), "atoll" (0.01 #1841)
>> best conf = 0.56
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #166 for best value:
>> intensional similarity = 10
>> extensional distance = 23
>> proper extension:  Tahat; Tamgak;
>> query: (?x2274, ?x2437) <- ?x2274[ a Mountain; has inMountains ?x1862[ a Mountains; is inMountains of ?x689[ has type ?x2437;];]; has locatedIn ?x690[ has neighbor ?x379[ has religion ?x109<Jewish>; is locatedIn of ?x182;];];]
ranks of expected_values: 1
EVAL	Illimani	type	"granite"	CNN-1.+1._MA	1.000	1.000	1.000	1.000	155.000	155.000	10.000	0.559	http://www.semwebtech.org/mondial/10/meta#type

#865-WaldaiHills
PRED entity: WaldaiHills

PRED relation: inMountains!
PRED expected values: Volga
=> 19 concepts (19 used for prediction)
PRED predicted values (max 10 best out of 346): Narodnaja (0.25 #222, 0.17 #479, 0.11 #736), Petschora (0.25 #135, 0.17 #392, 0.11 #649), Katun (0.25 #90, 0.17 #347, 0.11 #604), Tobol (0.25 #50, 0.17 #307, 0.11 #564), Bjelucha (0.25 #5, 0.17 #262, 0.11 #519), Irtysch (0.25 #147, 0.17 #404, 0.11 #661), Zachwoa (0.17 #465, 0.11 #722, 0.08 #1804), KljutschewskajaSopka (0.17 #460, 0.11 #717, 0.08 #1804), Kasbek (0.17 #332, 0.11 #589, 0.08 #1804), Elbrus (0.17 #270, 0.11 #527, 0.08 #1804)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #222 for best value:
>> intensional similarity = 8
>> extensional distance = 2
>> proper extension:  Altai; Ural;
>> query: (?x1649, Narodnaja) <- ?x1649[ a Mountains; is inMountains of ?x868[ a Source; has locatedIn ?x73<R>;]; is inMountains of ?x2023[ a Source; is hasSource of ?x679;];]

*> Best rule #1804 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 29
*> proper extension:  BlackForest; Apennin; Alps; Karakorum; GreatDividingRange; Andes; Zagros; Pamir; Adirondacks; Balkan; ...
*> query: (?x1649, ?x72) <- ?x1649[ a Mountains; is inMountains of ?x868[ a Source; has locatedIn ?x73[ is locatedIn of ?x72;];]; is inMountains of ?x2023[ a Source; is hasSource of ?x679;];]
*> conf = 0.08
ranks of expected_values: 89
EVAL	WaldaiHills	inMountains!	Volga	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.011	19.000	19.000	346.000	0.250	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains!
PRED expected values: Volga
=> 48 concepts (48 used for prediction)
PRED predicted values (max 10 best out of 346): Narodnaja (0.25 #480, 0.22 #258, 0.20 #737), Petschora (0.25 #393, 0.22 #258, 0.20 #650), Katun (0.25 #348, 0.22 #258, 0.20 #605), Tobol (0.25 #308, 0.22 #258, 0.20 #565), Bjelucha (0.25 #263, 0.22 #258, 0.20 #520), Irtysch (0.25 #405, 0.20 #662, 0.17 #1436), Schneekoppe (0.25 #227, 0.09 #4095, 0.08 #5643), March (0.25 #212, 0.09 #4080, 0.08 #5628), Weichsel (0.25 #210, 0.09 #4078, 0.08 #5626), Elbe (0.25 #177, 0.09 #4045, 0.08 #5593)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #480 for best value:
>> intensional similarity = 10
>> extensional distance = 2
>> proper extension:  Altai; Ural;
>> query: (?x1649, Narodnaja) <- ?x1649[ a Mountains; is inMountains of ?x868[ a Source; has locatedIn ?x73<R>; is hasSource of ?x1457[ a River; has flowsInto ?x146; has hasEstuary ?x885; has locatedIn ?x222;];];]

*> Best rule #258 for first EXPECTED value:
*> intensional similarity = 13
*> extensional distance = 2
*> proper extension:  Beskides; SudetyMountains;
*> query: (?x1649, ?x72) <- ?x1649[ a Mountains; is inMountains of ?x868[ a Source; has locatedIn ?x73[ has ethnicGroup ?x58; has language ?x555; has neighbor ?x170; is locatedIn of ?x72;]; is hasSource of ?x1457[ a River; has flowsInto ?x146<BalticSea>; has locatedIn ?x222;];];]
*> conf = 0.22
ranks of expected_values: 89
EVAL	WaldaiHills	inMountains!	Volga	CNN-1.+1._MA	0.000	0.000	0.000	0.011	48.000	48.000	346.000	0.250	http://www.semwebtech.org/mondial/10/meta#inMountains

#864-Ceram
PRED entity: Ceram

PRED relation: locatedOnIsland!
PRED expected values: GunungBinaiya
=> 49 concepts (44 used for prediction)
PRED predicted values (max 10 best out of 77): Rantekombola (0.17 #162, 0.09 #841, 0.07 #226), PuncakJaya (0.17 #184, 0.06 #312, 0.05 #376), Mt.Wilhelm (0.17 #190, 0.06 #318, 0.05 #382), Mt.Giluwe (0.17 #179, 0.06 #307, 0.05 #371), Merapi (0.09 #841, 0.07 #255, 0.06 #319), Krakatau (0.09 #841, 0.07 #237, 0.06 #301), GunungAgung (0.09 #841, 0.07 #235, 0.06 #299), Rinjani (0.09 #841, 0.07 #234, 0.06 #298), Semeru (0.09 #841, 0.07 #232, 0.06 #296), Kerinci (0.09 #841, 0.07 #209, 0.06 #273)
>> best conf = 0.17
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #162 for best value:
>> intensional similarity = 5
>> extensional distance = 4
>> proper extension:  NewGuinea;
>> query: (?x1965, Rantekombola) <- ?x1965[ a Island; has locatedIn ?x217<RI>; has locatedInWater ?x770<BandaSea>;]

No rule for expected values
ranks of expected_values: 
EVAL	Ceram	locatedOnIsland!	GunungBinaiya	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	49.000	44.000	77.000	0.167	http://www.semwebtech.org/mondial/10/meta#locatedOnIsland

PRED relation: locatedOnIsland!
PRED expected values: GunungBinaiya
=> 111 concepts (111 used for prediction)
PRED predicted values (max 10 best out of 82): Rantekombola (0.20 #162, 0.17 #226, 0.12 #2225), PuncakJaya (0.17 #248, 0.12 #2225, 0.08 #312), Mt.Wilhelm (0.17 #254, 0.12 #2225, 0.08 #318), Mt.Giluwe (0.17 #243, 0.12 #2225, 0.08 #307), Merapi (0.12 #2225, 0.09 #2226, 0.09 #2223), Krakatau (0.12 #2225, 0.09 #2226, 0.09 #2223), GunungAgung (0.12 #2225, 0.09 #2226, 0.09 #2223), Rinjani (0.12 #2225, 0.09 #2226, 0.09 #2223), Semeru (0.12 #2225, 0.09 #2226, 0.09 #2223), Kerinci (0.12 #2225, 0.09 #2226, 0.09 #2223)
>> best conf = 0.20
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #162 for best value:
>> intensional similarity = 7
>> extensional distance = 3
>> proper extension:  Sulawesi; Timor;
>> query: (?x1965, Rantekombola) <- ?x1965[ a Island; has belongsToIslands ?x1099[ a Islands;]; has locatedIn ?x217<RI>; has locatedInWater ?x770<BandaSea>;]

No rule for expected values
ranks of expected_values: 
EVAL	Ceram	locatedOnIsland!	GunungBinaiya	CNN-1.+1._MA	0.000	0.000	0.000	0.000	111.000	111.000	82.000	0.200	http://www.semwebtech.org/mondial/10/meta#locatedOnIsland

#863-America
PRED entity: America

PRED relation: encompassed!
PRED expected values: NLSM GROX FALK
=> 5 concepts (5 used for prediction)
PRED predicted values (max 10 best out of 171): NL (0.44 #693, 0.38 #519, 0.37 #692), GB (0.44 #693, 0.38 #519, 0.37 #692), F (0.44 #693, 0.38 #519, 0.37 #692), E (0.37 #692, 0.33 #694, 0.25 #540), PAL (0.33 #171, 0.25 #691, 0.25 #518), TO (0.33 #168, 0.25 #688, 0.25 #515), TOK (0.33 #166, 0.25 #686, 0.25 #513), XMAS (0.33 #164, 0.25 #684, 0.25 #511), FSM (0.33 #158, 0.25 #678, 0.25 #505), NAU (0.33 #156, 0.25 #676, 0.25 #503)
>> best conf = 0.44
=> the first rule below is the first best rule for 3 predicted values
>> Best rule #693 for best value:
>> intensional similarity = 36
>> extensional distance = 2
>> proper extension:  Europe;
>> query: (?x521, ?x78) <- ?x521[ is encompassed of ?x181[ has language ?x796; has religion ?x352<RomanCatholic>;]; is encompassed of ?x215[ a Country; has ethnicGroup ?x162; has government ?x1377; is locatedIn of ?x214;]; is encompassed of ?x272[ a Country; has ethnicGroup ?x273; is locatedIn of ?x282<PacificOcean>; is locatedIn of ?x356[ a Island;]; is locatedIn of ?x2359[ a Estuary;];]; is encompassed of ?x379[ has ethnicGroup ?x197[ a EthnicGroup;]; has wasDependentOf ?x149; is locatedIn of ?x512;]; is encompassed of ?x542[ is locatedIn of ?x48[ is hasSource of ?x47;];]; is encompassed of ?x633[ a Country; has dependentOf ?x78; has religion ?x410;]; is encompassed of ?x865[ has government ?x254; has religion ?x713;]; is encompassed of ?x1209[ a Country; has wasDependentOf ?x81;];]

No rule for expected values
ranks of expected_values: 
EVAL	America	encompassed!	FALK	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	5.000	5.000	171.000	0.444	http://www.semwebtech.org/mondial/10/meta#encompassed
EVAL	America	encompassed!	GROX	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	5.000	5.000	171.000	0.444	http://www.semwebtech.org/mondial/10/meta#encompassed
EVAL	America	encompassed!	NLSM	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	5.000	5.000	171.000	0.444	http://www.semwebtech.org/mondial/10/meta#encompassed

PRED relation: encompassed!
PRED expected values: NLSM GROX FALK
=> 5 concepts (5 used for prediction)
PRED predicted values (max 10 best out of 190): NZ (0.62 #361, 0.45 #544, 0.38 #172), AUS (0.62 #361, 0.45 #544, 0.38 #172), GH (0.62 #361, 0.45 #544, 0.38 #172), ET (0.62 #361, 0.45 #544, 0.33 #733), SLB (0.62 #361, 0.45 #544, 0.33 #733), Z (0.62 #361, 0.45 #544, 0.33 #733), SD (0.62 #361, 0.45 #544, 0.33 #733), BRU (0.62 #361, 0.45 #544, 0.33 #455), MAL (0.62 #361, 0.45 #544, 0.33 #426), MYA (0.62 #361, 0.45 #544, 0.33 #425)
>> best conf = 0.62
=> the first rule below is the first best rule for 37 predicted values
>> Best rule #361 for best value:
>> intensional similarity = 109
>> extensional distance = 1
>> proper extension:  Africa;
>> query: (?x521, ?x63) <- ?x521[ a Continent; is encompassed of ?x202[ has government ?x435; is locatedIn of ?x2259[ has type ?x2402;];]; is encompassed of ?x215[ a Country; has ethnicGroup ?x79[ a EthnicGroup;]; has government ?x1377; has language ?x796; has wasDependentOf ?x149; is locatedIn of ?x214;]; is encompassed of ?x272[ a Country; is locatedIn of ?x356; is locatedIn of ?x1142[ a Estuary;]; is locatedIn of ?x1421[ a Island;];]; is encompassed of ?x315[ a Country; has ethnicGroup ?x380; has religion ?x462; is locatedIn of ?x218[ a Lake;]; is locatedIn of ?x294[ a Mountain; has type ?x706;]; is locatedIn of ?x314[ a Mountain; has inMountains ?x337;]; is locatedIn of ?x895[ a Source;]; is locatedIn of ?x1796[ a Estuary;]; is locatedIn of ?x2018[ a River; has hasEstuary ?x2245;];]; is encompassed of ?x318[ has ethnicGroup ?x298; has government ?x711; is locatedIn of ?x496[ a Volcano;];]; is encompassed of ?x321[ a Country; has ethnicGroup ?x162<African>; has ethnicGroup ?x374[ a EthnicGroup;]; has government ?x854; is locatedIn of ?x1410[ a Mountain;];]; is encompassed of ?x404[ a Country; has language ?x2456; has religion ?x95<Protestant>; has religion ?x352[ is religion of ?x543<B>; is religion of ?x575<NL>; is religion of ?x1514<FSM>;]; is locatedIn of ?x513[ a River;];]; is encompassed of ?x520[ a Country; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x329[ a Mountain;];]; is encompassed of ?x561[ has government ?x562; has religion ?x280[ a Religion;]; is locatedIn of ?x1995[ a Island;];]; is encompassed of ?x671[ a Country; has ethnicGroup ?x1728<Creole>;]; is encompassed of ?x745[ has dependentOf ?x78; has religion ?x410;]; is encompassed of ?x816[ has language ?x51<French>;]; is encompassed of ?x865[ has ethnicGroup ?x1147; has government ?x254; has religion ?x713; is locatedIn of ?x687[ a Island;];]; is encompassed of ?x1130[ has language ?x247[ a Language; is language of ?x718<L>;]; has wasDependentOf ?x81[ a Country; has encompassed ?x195; has government ?x1854; has religion ?x187; is dependentOf of ?x212; is locatedIn of ?x121; is wasDependentOf of ?x63;];];]

*> Best rule #542 for first EXPECTED value:
*> intensional similarity = 130
*> extensional distance = 1
*> proper extension:  Asia;
*> query: (?x521, ?x212) <- ?x521[ is encompassed of ?x80[ a Country; has dependentOf ?x81[ has ethnicGroup ?x1196; is dependentOf of ?x212; is locatedIn of ?x121; is wasDependentOf of ?x158;]; has government ?x562;]; is encompassed of ?x148[ a Country; has ethnicGroup ?x1052; is locatedIn of ?x1371[ a Sea;];]; is encompassed of ?x179[ has ethnicGroup ?x2342[ a EthnicGroup;]; has government ?x180; has religion ?x410<Hindu>;]; is encompassed of ?x215[ a Country; has government ?x1377; has wasDependentOf ?x149; is locatedIn of ?x282[ is locatedInWater of ?x205; is mergesWith of ?x60;]; is locatedIn of ?x344[ a River; has hasEstuary ?x427;]; is locatedIn of ?x729[ a Source;]; is locatedIn of ?x1717[ a Mountain; has inMountains ?x431;];]; is encompassed of ?x315[ has religion ?x109; is locatedIn of ?x294[ a Mountain; a Volcano; has inMountains ?x1405;]; is locatedIn of ?x406[ a Lake;]; is locatedIn of ?x541[ a Island;]; is locatedIn of ?x782[ a Mountain; a Volcano; has type ?x150<"volcanic">;]; is locatedIn of ?x832[ a Source;]; is locatedIn of ?x1273[ a River;]; is locatedIn of ?x1325[ has hasEstuary ?x1142; is flowsInto of ?x1288;];]; is encompassed of ?x318[ a Country; has ethnicGroup ?x79[ a EthnicGroup;]; has ethnicGroup ?x197[ is ethnicGroup of ?x390<SLB>;]; has ethnicGroup ?x298<Chinese>; has government ?x711; has religion ?x1151; is locatedIn of ?x726[ a Lake;];]; is encompassed of ?x321[ a Country; has ethnicGroup ?x162[ is ethnicGroup of ?x1072<WAL>; is ethnicGroup of ?x1576<ZW>;]; is locatedIn of ?x1017;]; is encompassed of ?x379[ a Country; is locatedIn of ?x1055[ has hasSource ?x1486;];]; is encompassed of ?x404[ a Country; has religion ?x95<Protestant>; has religion ?x352<RomanCatholic>; is locatedIn of ?x512;]; is encompassed of ?x408[ a Country; has government ?x435; has language ?x2508; is locatedIn of ?x560[ a Lake;];]; is encompassed of ?x520[ a Country; is locatedIn of ?x317[ a Sea; is locatedInWater of ?x477;];]; is encompassed of ?x671[ a Country; has ethnicGroup ?x1728; has language ?x247<English>;]; is encompassed of ?x816[ a Country; has government ?x828; has language ?x51[ a Language; is language of ?x50<NLSM>; is language of ?x461<NZ>; is language of ?x718<L>;];]; is encompassed of ?x865[ a Country; has government ?x254<"parliamentary democracy">; has religion ?x1667[ a Religion;];]; is encompassed of ?x1008[ a Country; has ethnicGroup ?x1683; has religion ?x429[ is religion of ?x210<NORF>; is religion of ?x853<PNG>;]; is locatedIn of ?x405;];]
*> conf = 0.50
ranks of expected_values: 42, 44, 98
EVAL	America	encompassed!	FALK	CNN-1.+1._MA	0.000	0.000	0.000	0.023	5.000	5.000	190.000	0.625	http://www.semwebtech.org/mondial/10/meta#encompassed
EVAL	America	encompassed!	GROX	CNN-1.+1._MA	0.000	0.000	0.000	0.010	5.000	5.000	190.000	0.625	http://www.semwebtech.org/mondial/10/meta#encompassed
EVAL	America	encompassed!	NLSM	CNN-1.+1._MA	0.000	0.000	0.000	0.024	5.000	5.000	190.000	0.625	http://www.semwebtech.org/mondial/10/meta#encompassed

#862-Korab
PRED entity: Korab

PRED relation: inMountains
PRED expected values: Balkan
=> 44 concepts (44 used for prediction)
PRED predicted values (max 10 best out of 30): Balkan (0.33 #20, 0.14 #281, 0.12 #542), Kurdistan (0.20 #383, 0.17 #470, 0.14 #296), Alps (0.20 #352, 0.17 #439, 0.08 #1048), Elburs (0.14 #283, 0.10 #370, 0.08 #457), Zagros (0.14 #274, 0.10 #361, 0.08 #448), Troodos (0.14 #266, 0.08 #440, 0.03 #701), RockyMountains (0.11 #790, 0.08 #964, 0.08 #1138), Taurus (0.10 #374, 0.08 #461), Andes (0.09 #881, 0.09 #794, 0.07 #1142), Beskides (0.08 #465)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #20 for best value:
>> intensional similarity = 6
>> extensional distance = 1
>> proper extension:  Musala;
>> query: (?x1516, Balkan) <- ?x1516[ a Mountain; has locatedIn ?x701[ has language ?x511<Turkish>; is neighbor of ?x904<SRB>;];]
ranks of expected_values: 1
EVAL	Korab	inMountains	Balkan	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	44.000	44.000	30.000	0.333	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains
PRED expected values: Balkan
=> 107 concepts (107 used for prediction)
PRED predicted values (max 10 best out of 72): Balkan (0.33 #107, 0.33 #20, 0.20 #194), Alps (0.29 #439, 0.23 #1483, 0.14 #2614), Kurdistan (0.20 #209, 0.14 #470, 0.11 #1079), Taurus (0.20 #200, 0.14 #461, 0.04 #1070), Andes (0.17 #1664, 0.13 #2534, 0.12 #2447), Crete (0.17 #384, 0.14 #558, 0.03 #1341), Troodos (0.14 #527, 0.03 #1136, 0.01 #2267), RockyMountains (0.10 #3400, 0.09 #3835, 0.07 #4444), CordilleraVolcanica (0.09 #1718, 0.08 #1892, 0.08 #2153), Himalaya (0.08 #3051, 0.08 #3921, 0.08 #3312)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #107 for best value:
>> intensional similarity = 13
>> extensional distance = 1
>> proper extension:  Jezerce;
>> query: (?x1516, Balkan) <- ?x1516[ a Mountain; has locatedIn ?x204<AL>; has locatedIn ?x701[ has encompassed ?x195; has ethnicGroup ?x354; has government ?x254; has language ?x511; has religion ?x56; has wasDependentOf ?x1197; is locatedIn of ?x2099[ a Source;]; is neighbor of ?x177;];]
>> Best rule #20 for best value:
>> intensional similarity = 12
>> extensional distance = 1
>> proper extension:  Musala;
>> query: (?x1516, Balkan) <- ?x1516[ a Mountain; has locatedIn ?x204[ has language ?x1567; has neighbor ?x106; has religion ?x56; is locatedIn of ?x887[ a River;];]; has locatedIn ?x701[ has ethnicGroup ?x354<Macedonian>; has government ?x254; has language ?x511;];]
ranks of expected_values: 1
EVAL	Korab	inMountains	Balkan	CNN-1.+1._MA	1.000	1.000	1.000	1.000	107.000	107.000	72.000	0.333	http://www.semwebtech.org/mondial/10/meta#inMountains

#861-Katla
PRED entity: Katla

PRED relation: type
PRED expected values: "volcano"
=> 25 concepts (25 used for prediction)
PRED predicted values (max 10 best out of 4): "volcano" (0.81 #234, 0.81 #223, 0.81 #184), "volcanic" (0.80 #269, 0.77 #167, 0.76 #235), "granite" (0.03 #283, 0.02 #348, 0.02 #364), "salt" (0.01 #405)
>> best conf = 0.81
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #234 for best value:
>> intensional similarity = 11
>> extensional distance = 30
>> proper extension:  Concepcion;
>> query: (?x1236, ?x706) <- ?x1236[ a Mountain; a Volcano; has locatedOnIsland ?x807[ a Island; has locatedInWater ?x373[ has locatedIn ?x81;]; is locatedOnIsland of ?x1622[ a Mountain; a Volcano; has type ?x706<"volcano">;];];]
>> Best rule #223 for best value:
>> intensional similarity = 11
>> extensional distance = 30
>> proper extension:  Concepcion;
>> query: (?x1236, "volcano") <- ?x1236[ a Mountain; a Volcano; has locatedOnIsland ?x807[ a Island; has locatedInWater ?x373[ has locatedIn ?x81;]; is locatedOnIsland of ?x1622[ a Mountain; a Volcano; has type ?x706<"volcano">;];];]
ranks of expected_values: 1
EVAL	Katla	type	"volcano"	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	25.000	25.000	4.000	0.812	http://www.semwebtech.org/mondial/10/meta#type

PRED relation: type
PRED expected values: "volcano"
=> 47 concepts (47 used for prediction)
PRED predicted values (max 10 best out of 4): "volcano" (0.72 #694, 0.72 #682, 0.67 #693), "volcanic" (0.67 #693, 0.67 #608, 0.67 #592), "granite" (0.05 #708), "salt" (0.01 #799)
>> best conf = 0.72
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #694 for best value:
>> intensional similarity = 19
>> extensional distance = 16
>> proper extension:  Karthala;
>> query: (?x1236, ?x706) <- ?x1236[ a Mountain; a Volcano; has locatedOnIsland ?x807[ a Island; has locatedIn ?x455[ a Country; has encompassed ?x195; has government ?x700; has religion ?x352<RomanCatholic>; has wasDependentOf ?x793; is locatedIn of ?x373; is locatedIn of ?x1622[ a Mountain; a Volcano; has type ?x706<"volcano">;];]; has locatedInWater ?x373; is locatedOnIsland of ?x1622;];]
>> Best rule #682 for best value:
>> intensional similarity = 19
>> extensional distance = 16
>> proper extension:  Karthala;
>> query: (?x1236, "volcano") <- ?x1236[ a Mountain; a Volcano; has locatedOnIsland ?x807[ a Island; has locatedIn ?x455[ a Country; has encompassed ?x195; has government ?x700; has religion ?x352<RomanCatholic>; has wasDependentOf ?x793; is locatedIn of ?x373; is locatedIn of ?x1622[ a Mountain; a Volcano; has type ?x706<"volcano">;];]; has locatedInWater ?x373; is locatedOnIsland of ?x1622;];]
ranks of expected_values: 1
EVAL	Katla	type	"volcano"	CNN-1.+1._MA	1.000	1.000	1.000	1.000	47.000	47.000	4.000	0.722	http://www.semwebtech.org/mondial/10/meta#type

#860-Curacao
PRED entity: Curacao

PRED relation: belongsToIslands
PRED expected values: LesserAntilles
=> 38 concepts (38 used for prediction)
PRED predicted values (max 10 best out of 43): LesserAntilles (0.58 #219, 0.57 #151, 0.24 #423), CanadianArcticIslands (0.12 #280, 0.06 #484, 0.04 #552), InnerHebrides (0.10 #336, 0.06 #472, 0.05 #540), GreaterAntilles (0.10 #183, 0.08 #251, 0.06 #115), Azores (0.09 #412, 0.04 #752, 0.04 #820), CaymanIslands (0.08 #234, 0.05 #166, 0.03 #438), Canares (0.08 #431, 0.06 #499, 0.04 #635), HawaiiIslands (0.07 #505, 0.05 #641, 0.04 #709), BritishIsles (0.06 #291, 0.04 #495, 0.03 #427), OuterHebrides (0.06 #303, 0.04 #439, 0.03 #507)
>> best conf = 0.58
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #219 for best value:
>> intensional similarity = 3
>> extensional distance = 24
>> proper extension:  CaymanBrac; Grande-Terre; SanAndres; Basse-Terre; St.Barthelemy;
>> query: (?x506, LesserAntilles) <- ?x506[ a Island; has locatedInWater ?x317<CaribbeanSea>;]
ranks of expected_values: 1
EVAL	Curacao	belongsToIslands	LesserAntilles	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	38.000	38.000	43.000	0.577	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

PRED relation: belongsToIslands
PRED expected values: LesserAntilles
=> 98 concepts (98 used for prediction)
PRED predicted values (max 10 best out of 61): WestfriesischeInseln (0.71 #489, 0.42 #1033, 0.05 #2735), LesserAntilles (0.58 #1715, 0.55 #1579, 0.44 #831), HawaiiIslands (0.28 #1661, 0.12 #2001, 0.09 #2137), CaymanIslands (0.20 #234, 0.17 #438, 0.14 #574), CalifornianChannelIslands (0.20 #1691, 0.09 #2031, 0.07 #2167), TurksandCaicosIslands (0.18 #1003, 0.15 #1139, 0.12 #1411), MarianaIslands (0.16 #1429, 0.06 #2041, 0.02 #2381), GreaterAntilles (0.13 #1271, 0.12 #659, 0.11 #931), FalklandIslands (0.12 #1425, 0.11 #1493, 0.10 #1561), SundaIslands (0.11 #2326, 0.10 #2532, 0.05 #2940)
>> best conf = 0.71
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #489 for best value:
>> intensional similarity = 12
>> extensional distance = 5
>> proper extension:  Ameland; Texel;
>> query: (?x506, WestfriesischeInseln) <- ?x506[ a Island; has locatedIn ?x246[ a Country; has encompassed ?x521; has language ?x544<Dutch>; has religion ?x352<RomanCatholic>; is locatedIn of ?x317[ a Sea; has mergesWith ?x182; is locatedInWater of ?x123;];];]

*> Best rule #1715 for first EXPECTED value:
*> intensional similarity = 3
*> extensional distance = 24
*> proper extension:  CaymanBrac; Grande-Terre; SanAndres; Basse-Terre; St.Barthelemy;
*> query: (?x506, LesserAntilles) <- ?x506[ a Island; has locatedInWater ?x317<CaribbeanSea>;]
*> conf = 0.58
ranks of expected_values: 2
EVAL	Curacao	belongsToIslands	LesserAntilles	CNN-1.+1._MA	0.000	1.000	1.000	0.500	98.000	98.000	61.000	0.714	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

#859-Garonne
PRED entity: Garonne

PRED relation: locatedIn
PRED expected values: E
=> 39 concepts (39 used for prediction)
PRED predicted values (max 10 best out of 126): E (0.87 #1899, 0.87 #1688, 0.82 #1898), F (0.82 #1898, 0.76 #3565, 0.75 #3088), USA (0.54 #2921, 0.38 #3875, 0.31 #1185), D (0.32 #3108, 0.08 #3585, 0.08 #5012), CDN (0.31 #1185, 0.18 #2912, 0.13 #3866), ZRE (0.31 #1185, 0.15 #1978, 0.10 #6736), RG (0.31 #1185, 0.10 #2283, 0.06 #7369), BR (0.31 #1185, 0.06 #7369, 0.06 #5229), YV (0.31 #1185, 0.06 #7369, 0.06 #5229), RSA (0.31 #1185, 0.06 #7369, 0.06 #5229)
>> best conf = 0.87
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1899 for best value:
>> intensional similarity = 6
>> extensional distance = 13
>> proper extension:  TorredeCerredo; PicodelosNieves; PicodeTeide; Moncayo; PicodeAlmanzor; RoquedelosMuchachos; Mulhacen;
>> query: (?x1863, ?x149) <- ?x1863[ has inMountains ?x1864[ a Mountains; is inMountains of ?x2440[ a Mountain; has locatedIn ?x149<E>;];];]
>> Best rule #1688 for best value:
>> intensional similarity = 6
>> extensional distance = 13
>> proper extension:  TorredeCerredo; PicodelosNieves; PicodeTeide; Moncayo; PicodeAlmanzor; RoquedelosMuchachos; Mulhacen;
>> query: (?x1863, E) <- ?x1863[ has inMountains ?x1864[ a Mountains; is inMountains of ?x2440[ a Mountain; has locatedIn ?x149<E>;];];]
ranks of expected_values: 1
EVAL	Garonne	locatedIn	E	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	39.000	39.000	126.000	0.867	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: E
=> 103 concepts (103 used for prediction)
PRED predicted values (max 10 best out of 117): E (0.87 #7648, 0.87 #7438, 0.82 #7649), F (0.82 #7649, 0.75 #14104, 0.75 #6452), CDN (0.67 #4535, 0.67 #4360, 0.31 #14407), USA (0.54 #12740, 0.54 #13937, 0.31 #6764), CZ (0.43 #2736, 0.27 #5847, 0.20 #5367), CH (0.41 #8423, 0.33 #57, 0.29 #11055), ZRE (0.40 #5732, 0.40 #5573, 0.31 #2383), R (0.39 #18171, 0.38 #19126, 0.13 #18411), A (0.35 #8226, 0.30 #10140, 0.30 #5353), I (0.33 #8654, 0.30 #9849, 0.29 #7697)
>> best conf = 0.87
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #7648 for best value:
>> intensional similarity = 6
>> extensional distance = 13
>> proper extension:  TorredeCerredo; PicodelosNieves; PicodeTeide; Moncayo; PicodeAlmanzor; RoquedelosMuchachos; Mulhacen;
>> query: (?x1863, ?x149) <- ?x1863[ has inMountains ?x1864[ a Mountains; is inMountains of ?x1293[ a Mountain; has locatedIn ?x149<E>;];];]
>> Best rule #7438 for best value:
>> intensional similarity = 6
>> extensional distance = 13
>> proper extension:  TorredeCerredo; PicodelosNieves; PicodeTeide; Moncayo; PicodeAlmanzor; RoquedelosMuchachos; Mulhacen;
>> query: (?x1863, E) <- ?x1863[ has inMountains ?x1864[ a Mountains; is inMountains of ?x1293[ a Mountain; has locatedIn ?x149<E>;];];]
ranks of expected_values: 1
EVAL	Garonne	locatedIn	E	CNN-1.+1._MA	1.000	1.000	1.000	1.000	103.000	103.000	117.000	0.867	http://www.semwebtech.org/mondial/10/meta#locatedIn

#858-Reunion
PRED entity: Reunion

PRED relation: locatedInWater
PRED expected values: IndianOcean
=> 60 concepts (56 used for prediction)
PRED predicted values (max 10 best out of 38): IndianOcean (0.53 #651, 0.53 #609, 0.52 #606), AtlanticOcean (0.45 #397, 0.41 #483, 0.40 #1052), CaribbeanSea (0.40 #105, 0.20 #888, 0.17 #1020), PacificOcean (0.36 #1193, 0.32 #1454, 0.31 #1368), JavaSea (0.28 #571, 0.27 #616, 0.23 #528), PitondelaFournaise (0.19 #302, 0.18 #914, 0.16 #1045), Reunion (0.19 #302, 0.18 #914, 0.16 #1045), PitondesNeiges (0.19 #302, 0.18 #914, 0.16 #1045), BandaSea (0.17 #591, 0.17 #636, 0.06 #681), SouthChinaSea (0.14 #584, 0.13 #629, 0.09 #674)
>> best conf = 0.53
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #651 for best value:
>> intensional similarity = 6
>> extensional distance = 28
>> proper extension:  Mahe;
>> query: (?x951, ?x60) <- ?x951[ a Island; has locatedIn ?x61[ has encompassed ?x213; has religion ?x352; is locatedIn of ?x60<IndianOcean>;];]
>> Best rule #609 for best value:
>> intensional similarity = 6
>> extensional distance = 28
>> proper extension:  Mahe;
>> query: (?x951, IndianOcean) <- ?x951[ a Island; has locatedIn ?x61[ has encompassed ?x213; has religion ?x352; is locatedIn of ?x60<IndianOcean>;];]
ranks of expected_values: 1
EVAL	Reunion	locatedInWater	IndianOcean	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	60.000	56.000	38.000	0.533	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater
PRED expected values: IndianOcean
=> 169 concepts (169 used for prediction)
PRED predicted values (max 10 best out of 76): IndianOcean (0.85 #842, 0.64 #793, 0.64 #751), PacificOcean (0.60 #678, 0.45 #4649, 0.44 #589), AtlanticOcean (0.50 #139, 0.49 #3004, 0.48 #2327), CaribbeanSea (0.50 #239, 0.40 #326, 0.33 #19), JavaSea (0.44 #625, 0.44 #492, 0.40 #1972), PitondelaFournaise (0.39 #843, 0.39 #705, 0.38 #616), Reunion (0.39 #843, 0.39 #705, 0.38 #616), PitondesNeiges (0.39 #843, 0.39 #705, 0.38 #616), BandaSea (0.25 #1992, 0.23 #2169, 0.22 #2213), EastChinaSea (0.25 #957, 0.07 #2394, 0.06 #3473)
>> best conf = 0.85
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #842 for best value:
>> intensional similarity = 10
>> extensional distance = 9
>> proper extension:  St.Martin;
>> query: (?x951, ?x60) <- ?x951[ a Island; has locatedIn ?x61[ a Country; has dependentOf ?x78<F>; is locatedIn of ?x60[ has mergesWith ?x182; is flowsInto of ?x242; is locatedInWater of ?x226; is mergesWith of ?x241;];];]
ranks of expected_values: 1
EVAL	Reunion	locatedInWater	IndianOcean	CNN-1.+1._MA	1.000	1.000	1.000	1.000	169.000	169.000	76.000	0.846	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#857-RT
PRED entity: RT

PRED relation: neighbor
PRED expected values: BEN
=> 41 concepts (41 used for prediction)
PRED predicted values (max 10 best out of 211): BEN (0.89 #2888, 0.89 #2887, 0.89 #5623), RMM (0.36 #289, 0.33 #449, 0.26 #3210), RN (0.36 #237, 0.26 #3210, 0.26 #2889), CI (0.27 #307, 0.26 #3210, 0.26 #2889), DZ (0.27 #258, 0.22 #418, 0.10 #1058), RIM (0.27 #246, 0.17 #406, 0.10 #886), RT (0.26 #3210, 0.26 #2889, 0.25 #5622), WAN (0.26 #2889, 0.25 #18, 0.14 #4016), RG (0.25 #109, 0.18 #268, 0.17 #428), LB (0.25 #102, 0.14 #4016, 0.13 #4017)
>> best conf = 0.89
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2888 for best value:
>> intensional similarity = 5
>> extensional distance = 104
>> proper extension:  IRL; ROU;
>> query: (?x1307, ?x810) <- ?x1307[ has ethnicGroup ?x162; has religion ?x116; has wasDependentOf ?x78; is neighbor of ?x810[ a Country;];]
>> Best rule #2887 for best value:
>> intensional similarity = 6
>> extensional distance = 104
>> proper extension:  IRL; ROU;
>> query: (?x1307, ?x483) <- ?x1307[ has ethnicGroup ?x162; has religion ?x116; has wasDependentOf ?x78; is neighbor of ?x483; is neighbor of ?x810[ a Country;];]
ranks of expected_values: 1
EVAL	RT	neighbor	BEN	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	41.000	41.000	211.000	0.894	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: BEN
=> 71 concepts (71 used for prediction)
PRED predicted values (max 10 best out of 234): BEN (0.92 #2632, 0.90 #9082, 0.89 #6430), RMM (0.50 #783, 0.33 #2268, 0.33 #457), CI (0.35 #3291, 0.33 #313, 0.29 #8416), RT (0.35 #3291, 0.29 #8416, 0.29 #4451), RG (0.33 #274, 0.33 #109, 0.29 #4451), LB (0.33 #102, 0.29 #4451, 0.28 #4449), RN (0.29 #8416, 0.29 #4450, 0.29 #4451), WAN (0.29 #4451, 0.28 #4449, 0.27 #8415), SN (0.28 #4449, 0.27 #8415, 0.26 #2711), DZ (0.28 #4449, 0.27 #8415, 0.22 #2237)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2632 for best value:
>> intensional similarity = 10
>> extensional distance = 17
>> proper extension:  DJI;
>> query: (?x1307, ?x810) <- ?x1307[ has neighbor ?x483[ a Country;]; has religion ?x116; has wasDependentOf ?x78<F>; is locatedIn of ?x182; is neighbor of ?x810[ a Country; has encompassed ?x213; has ethnicGroup ?x162;];]
ranks of expected_values: 1
EVAL	RT	neighbor	BEN	CNN-1.+1._MA	1.000	1.000	1.000	1.000	71.000	71.000	234.000	0.915	http://www.semwebtech.org/mondial/10/meta#neighbor

#856-LAO
PRED entity: LAO

PRED relation: wasDependentOf
PRED expected values: F
=> 32 concepts (29 used for prediction)
PRED predicted values (max 10 best out of 31): F (0.30 #344, 0.30 #506, 0.29 #542), GB (0.30 #344, 0.30 #506, 0.29 #542), E (0.20 #38, 0.18 #129, 0.11 #551), SovietUnion (0.13 #143, 0.11 #175, 0.10 #300), UnitedNations (0.08 #487, 0.08 #523, 0.08 #107), CN (0.08 #91, 0.03 #90, 0.03 #576), J (0.07 #736, 0.05 #98, 0.02 #160), OttomanEmpire (0.07 #736, 0.04 #604, 0.04 #570), P (0.07 #736, 0.04 #207, 0.04 #334), PK (0.07 #736, 0.03 #97, 0.01 #159)
>> best conf = 0.30
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #344 for best value:
>> intensional similarity = 7
>> extensional distance = 111
>> proper extension:  ROK;
>> query: (?x463, ?x78) <- ?x463[ has encompassed ?x175; has government ?x831; has neighbor ?x617[ a Country;]; has religion ?x462; is neighbor of ?x871[ has wasDependentOf ?x78;];]
ranks of expected_values: 1
EVAL	LAO	wasDependentOf	F	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	32.000	29.000	31.000	0.300	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

PRED relation: wasDependentOf
PRED expected values: F
=> 81 concepts (81 used for prediction)
PRED predicted values (max 10 best out of 62): GB (0.58 #497, 0.55 #457, 0.55 #424), PK (0.40 #100, 0.33 #207, 0.32 #600), F (0.33 #207, 0.33 #3, 0.32 #600), E (0.22 #1211, 0.22 #1174, 0.21 #1248), SovietUnion (0.22 #2463, 0.17 #1116, 0.17 #1081), J (0.22 #2463, 0.08 #1379, 0.07 #2651), UnitedNations (0.13 #971, 0.11 #933, 0.10 #2186), NL (0.12 #296, 0.09 #438, 0.08 #622), CN (0.12 #1024, 0.11 #1127, 0.08 #1562), OttomanEmpire (0.10 #1086, 0.09 #2866, 0.08 #944)
>> best conf = 0.58
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #497 for best value:
>> intensional similarity = 17
>> extensional distance = 10
>> proper extension:  SD;
>> query: (?x463, GB) <- ?x463[ has ethnicGroup ?x1647; has neighbor ?x232; has religion ?x462[ is religion of ?x81[ a Country; is locatedIn of ?x121; is wasDependentOf of ?x63;];]; is neighbor of ?x91[ a Country; has religion ?x116<Christian>; is locatedIn of ?x339;]; is neighbor of ?x366[ has encompassed ?x175; has ethnicGroup ?x1196<Indian>; is locatedIn of ?x262;];]

*> Best rule #207 for first EXPECTED value:
*> intensional similarity = 20
*> extensional distance = 3
*> proper extension:  MYA; MAL; K;
*> query: (?x463, ?x83) <- ?x463[ has ethnicGroup ?x1647; has ethnicGroup ?x1696[ a EthnicGroup;]; has neighbor ?x232; has religion ?x462<Buddhist>; is locatedIn of ?x1152; is neighbor of ?x91<THA>; is neighbor of ?x366[ has encompassed ?x175; has ethnicGroup ?x298; has neighbor ?x943[ has ethnicGroup ?x2119; has religion ?x187; has wasDependentOf ?x83;]; is locatedIn of ?x262;]; is neighbor of ?x871[ a Country; has ethnicGroup ?x872;];]
*> conf = 0.33
ranks of expected_values: 3
EVAL	LAO	wasDependentOf	F	CNN-1.+1._MA	0.000	1.000	1.000	0.333	81.000	81.000	62.000	0.583	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

#855-Turkish
PRED entity: Turkish

PRED relation: ethnicGroup!
PRED expected values: RO
=> 29 concepts (20 used for prediction)
PRED predicted values (max 10 best out of 228): NZ (0.50 #376, 0.40 #467, 0.33 #753), USA (0.50 #376, 0.40 #432, 0.33 #753), AUS (0.50 #376, 0.40 #411, 0.33 #753), MNE (0.50 #198, 0.33 #764, 0.33 #575), ET (0.50 #376, 0.33 #753, 0.21 #1132), CDN (0.50 #376, 0.33 #753, 0.21 #1132), IRL (0.50 #376, 0.33 #753, 0.21 #1132), SLB (0.50 #376, 0.33 #753, 0.21 #1132), BDS (0.50 #376, 0.33 #753, 0.21 #1132), ZW (0.50 #376, 0.33 #753, 0.21 #1132)
>> best conf = 0.50
=> the first rule below is the first best rule for 48 predicted values
>> Best rule #376 for best value:
>> intensional similarity = 11
>> extensional distance = 2
>> proper extension:  Serb; Albanian;
>> query: (?x2136, ?x63) <- ?x2136[ a EthnicGroup; is ethnicGroup of ?x235[ a Country; has religion ?x56<ChristianOrthodox>; has wasDependentOf ?x81[ is locatedIn of ?x121; is wasDependentOf of ?x63;]; is locatedIn of ?x275;]; is ethnicGroup of ?x701<MK>;]

*> Best rule #942 for first EXPECTED value:
*> intensional similarity = 10
*> extensional distance = 10
*> proper extension:  Russian; Finn; Bosniak; Montenegrin; Swede;
*> query: (?x2136, ?x204) <- ?x2136[ a EthnicGroup; is ethnicGroup of ?x235[ a Country; has religion ?x56<ChristianOrthodox>; has wasDependentOf ?x81[ is locatedIn of ?x121;]; is locatedIn of ?x275;]; is ethnicGroup of ?x701[ has neighbor ?x204;];]
*> conf = 0.39
ranks of expected_values: 53
EVAL	Turkish	ethnicGroup!	RO	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.019	29.000	20.000	228.000	0.500	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup!
PRED expected values: RO
=> 65 concepts (65 used for prediction)
PRED predicted values (max 10 best out of 229): H (0.67 #2354, 0.50 #1964, 0.45 #2108), SK (0.62 #1944, 0.58 #3110, 0.45 #2108), CZ (0.62 #2013, 0.45 #2108, 0.44 #2403), RO (0.57 #382, 0.50 #2113, 0.50 #1947), SRB (0.57 #382, 0.47 #766, 0.44 #2462), AL (0.57 #382, 0.47 #766, 0.44 #767), GR (0.57 #382, 0.47 #766, 0.44 #767), SLO (0.57 #382, 0.45 #2108, 0.38 #381), HR (0.57 #382, 0.42 #575, 0.33 #213), BIH (0.57 #382, 0.31 #765, 0.28 #1534)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2354 for best value:
>> intensional similarity = 19
>> extensional distance = 7
>> proper extension:  Romanian;
>> query: (?x2136, H) <- ?x2136[ is ethnicGroup of ?x177[ has language ?x2511; has neighbor ?x176<RO>; has religion ?x109;]; is ethnicGroup of ?x235[ a Country; has religion ?x56;]; is ethnicGroup of ?x424[ has language ?x684; is locatedIn of ?x155<Mur>;]; is ethnicGroup of ?x701[ has encompassed ?x195; has language ?x511; has wasDependentOf ?x1197; is neighbor of ?x692[ has ethnicGroup ?x223; is locatedIn of ?x784;];];]

*> Best rule #382 for first EXPECTED value:
*> intensional similarity = 20
*> extensional distance = 1
*> proper extension:  Serb;
*> query: (?x2136, ?x204) <- ?x2136[ is ethnicGroup of ?x177[ has ethnicGroup ?x354<Macedonian>; has language ?x511<Turkish>; has neighbor ?x176; has religion ?x56; has wasDependentOf ?x1656[ is wasDependentOf of ?x204;]; is locatedIn of ?x1941[ a Mountain;];]; is ethnicGroup of ?x424[ has neighbor ?x163; has religion ?x95; is locatedIn of ?x155<Mur>; is locatedIn of ?x657<Neusiedlersee>;]; is ethnicGroup of ?x701<MK>;]
*> conf = 0.57
ranks of expected_values: 4
EVAL	Turkish	ethnicGroup!	RO	CNN-1.+1._MA	0.000	0.000	1.000	0.250	65.000	65.000	229.000	0.667	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#854-Kymijoki
PRED entity: Kymijoki

PRED relation: hasEstuary
PRED expected values: Kymijoki
=> 44 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 169): Oulujoki (0.10 #161, 0.08 #387, 0.05 #4300), Kemijoki (0.10 #43, 0.08 #269, 0.05 #4300), Kokemaeenjoki (0.10 #184, 0.08 #410, 0.05 #4300), Newa (0.10 #125, 0.02 #803, 0.02 #1029), Weichsel (0.10 #107, 0.02 #785, 0.02 #1011), Umeaelv (0.10 #4, 0.02 #682, 0.02 #908), WesternDwina (0.10 #51, 0.02 #729, 0.02 #955), Oder (0.10 #45, 0.02 #723, 0.02 #949), Ounasjoki (0.08 #402, 0.05 #4300, 0.04 #628), Paatsjoki (0.08 #267, 0.04 #493, 0.02 #719)
>> best conf = 0.10
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #161 for best value:
>> intensional similarity = 6
>> extensional distance = 8
>> proper extension:  Newa; Kemijoki; Oder; Weichsel; WesternDwina; Kokemaeenjoki; Oulujoki; Umeaelv;
>> query: (?x2041, Oulujoki) <- ?x2041[ a River; has flowsInto ?x146<BalticSea>; has hasSource ?x2026[ a Source;]; has locatedIn ?x565;]

*> Best rule #4300 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 154
*> proper extension:  LakeWinnipeg; GreenlandSea;
*> query: (?x2041, ?x1959) <- ?x2041[ has locatedIn ?x565[ is locatedIn of ?x1462[ a River;]; is locatedIn of ?x1959[ a Estuary;];]; is flowsInto of ?x1534;]
*> conf = 0.05
ranks of expected_values: 11
EVAL	Kymijoki	hasEstuary	Kymijoki	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.091	44.000	36.000	169.000	0.100	http://www.semwebtech.org/mondial/10/meta#hasEstuary

PRED relation: hasEstuary
PRED expected values: Kymijoki
=> 149 concepts (149 used for prediction)
PRED predicted values (max 10 best out of 193): Oulujoki (0.33 #161, 0.25 #387, 0.24 #10446), Kemijoki (0.25 #269, 0.24 #10446, 0.20 #721), Kokemaeenjoki (0.24 #10446, 0.20 #862, 0.20 #17949), Umeaelv (0.24 #10446, 0.20 #17949, 0.19 #16581), Newa (0.24 #10446, 0.20 #17949, 0.19 #16581), Weichsel (0.24 #10446, 0.20 #17949, 0.19 #16581), WesternDwina (0.24 #10446, 0.20 #17949, 0.17 #1358), Oder (0.24 #10446, 0.20 #17949, 0.17 #1358), Paatsjoki (0.20 #493, 0.14 #945, 0.13 #7036), MurrumbidgeeRiver (0.14 #1062, 0.09 #1971, 0.07 #2881)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #161 for best value:
>> intensional similarity = 10
>> extensional distance = 1
>> proper extension:  Oulujoki;
>> query: (?x2041, Oulujoki) <- ?x2041[ a River; has flowsInto ?x146<BalticSea>; has hasSource ?x2026[ a Source;]; has locatedIn ?x565<SF>; is flowsInto of ?x1534[ a Lake; has locatedIn ?x565<SF>;];]

*> Best rule #10900 for first EXPECTED value:
*> intensional similarity = 10
*> extensional distance = 61
*> proper extension:  SeaofAzov; BlackSea; LakeSkutari; BalticSea; BarentsSea; Poopo; OzeroBalchash; GulfofMexico; OzeroAral;
*> query: (?x2041, ?x1959) <- ?x2041[ has locatedIn ?x565[ has ethnicGroup ?x1193; has government ?x435; has language ?x247; is locatedIn of ?x1959[ a Estuary;]; is locatedIn of ?x2026[ a Source;]; is neighbor of ?x73;]; is flowsInto of ?x1534;]
*> conf = 0.10
ranks of expected_values: 13
EVAL	Kymijoki	hasEstuary	Kymijoki	CNN-1.+1._MA	0.000	0.000	0.000	0.077	149.000	149.000	193.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasEstuary

#853-Croat
PRED entity: Croat

PRED relation: ethnicGroup!
PRED expected values: SRB
=> 29 concepts (13 used for prediction)
PRED predicted values (max 10 best out of 210): SRB (0.38 #538, 0.33 #346, 0.32 #383), MK (0.38 #515, 0.17 #706, 0.17 #323), H (0.33 #237, 0.32 #383, 0.32 #765), MNE (0.32 #383, 0.32 #765, 0.31 #392), UA (0.30 #629, 0.20 #820, 0.19 #1014), SK (0.23 #597, 0.15 #788, 0.15 #982), BG (0.23 #410, 0.20 #601, 0.13 #792), CZ (0.20 #668, 0.17 #285, 0.15 #477), RO (0.17 #600, 0.17 #217, 0.15 #409), LV (0.17 #660, 0.13 #851, 0.12 #1045)
>> best conf = 0.38
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #538 for best value:
>> intensional similarity = 9
>> extensional distance = 11
>> proper extension:  Roma; Macedonian; Albanian; Turkish; Bosniak; Gypsy; Montenegrin;
>> query: (?x160, SRB) <- ?x160[ is ethnicGroup of ?x156[ has religion ?x56<ChristianOrthodox>; has religion ?x352<RomanCatholic>; has wasDependentOf ?x1197<Yugoslavia>; is locatedIn of ?x133; is neighbor of ?x106;];]
ranks of expected_values: 1
EVAL	Croat	ethnicGroup!	SRB	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	29.000	13.000	210.000	0.385	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup!
PRED expected values: SRB
=> 69 concepts (69 used for prediction)
PRED predicted values (max 10 best out of 227): UA (0.60 #3166, 0.53 #3360, 0.36 #4143), SK (0.55 #2720, 0.55 #2550, 0.54 #2941), CZ (0.55 #2621, 0.48 #2721, 0.41 #3399), H (0.50 #2769, 0.50 #2184, 0.40 #2332), D (0.50 #400, 0.40 #2332, 0.40 #2331), PL (0.50 #1977, 0.33 #1394, 0.33 #228), CH (0.40 #2376, 0.40 #2332, 0.40 #2331), FL (0.40 #2332, 0.40 #2331, 0.38 #1555), SRB (0.40 #1124, 0.40 #931, 0.38 #2293), MNE (0.40 #785, 0.38 #1748, 0.38 #385)
>> best conf = 0.60
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3166 for best value:
>> intensional similarity = 16
>> extensional distance = 13
>> proper extension:  Moldovan; Russian; Belorussian; Bulgarian; CrimeanTatar;
>> query: (?x160, UA) <- ?x160[ is ethnicGroup of ?x156[ has religion ?x56<ChristianOrthodox>; is locatedIn of ?x275[ has mergesWith ?x182; is flowsInto of ?x698;];]; is ethnicGroup of ?x424[ a Country; has language ?x511; has neighbor ?x163<SK>; has neighbor ?x471[ has ethnicGroup ?x164; is locatedIn of ?x442;]; is locatedIn of ?x1837[ a Source;];];]

*> Best rule #1124 for first EXPECTED value:
*> intensional similarity = 21
*> extensional distance = 3
*> proper extension:  Hungarian;
*> query: (?x160, SRB) <- ?x160[ is ethnicGroup of ?x156<HR>; is ethnicGroup of ?x424[ a Country; has ethnicGroup ?x1913[ a EthnicGroup;]; has language ?x511; has neighbor ?x163<SK>; has neighbor ?x423[ has encompassed ?x195; has government ?x1952;]; has neighbor ?x471[ a Country; is locatedIn of ?x442;]; is locatedIn of ?x155<Mur>; is locatedIn of ?x475[ a River;]; is locatedIn of ?x614<Drau>;];]
*> conf = 0.40
ranks of expected_values: 9
EVAL	Croat	ethnicGroup!	SRB	CNN-1.+1._MA	0.000	0.000	1.000	0.111	69.000	69.000	227.000	0.600	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#852-Tajik
PRED entity: Tajik

PRED relation: ethnicGroup!
PRED expected values: AFG
=> 24 concepts (24 used for prediction)
PRED predicted values (max 10 best out of 170): KGZ (0.60 #208, 0.50 #401, 0.50 #385), KAZ (0.55 #654, 0.50 #385, 0.50 #76), TM (0.50 #385, 0.50 #51, 0.40 #243), AFG (0.50 #385, 0.26 #578, 0.26 #772), R (0.45 #581, 0.25 #3, 0.21 #2132), RO (0.27 #605, 0.25 #27, 0.21 #2132), CN (0.26 #578, 0.26 #772, 0.24 #2327), UA (0.25 #55, 0.21 #2132, 0.20 #247), EW (0.25 #117, 0.21 #2132, 0.20 #309), LV (0.25 #88, 0.21 #2132, 0.20 #280)
>> best conf = 0.60
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #208 for best value:
>> intensional similarity = 10
>> extensional distance = 3
>> proper extension:  Kyrgyz;
>> query: (?x1630, KGZ) <- ?x1630[ a EthnicGroup; is ethnicGroup of ?x129<TAD>; is ethnicGroup of ?x277[ has government ?x1815; has language ?x1430<Uzbek>; has neighbor ?x130; is locatedIn of ?x1971[ a Lake;];];]

*> Best rule #385 for first EXPECTED value:
*> intensional similarity = 10
*> extensional distance = 3
*> proper extension:  Kyrgyz;
*> query: (?x1630, ?x130) <- ?x1630[ a EthnicGroup; is ethnicGroup of ?x129<TAD>; is ethnicGroup of ?x277[ has government ?x1815; has language ?x1430<Uzbek>; has neighbor ?x130; is locatedIn of ?x1971[ a Lake;];];]
*> conf = 0.50
ranks of expected_values: 4
EVAL	Tajik	ethnicGroup!	AFG	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.250	24.000	24.000	170.000	0.600	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup!
PRED expected values: AFG
=> 73 concepts (73 used for prediction)
PRED predicted values (max 10 best out of 208): KGZ (0.60 #1186, 0.60 #208, 0.55 #387), KAZ (0.60 #463, 0.55 #387, 0.55 #386), TM (0.55 #387, 0.55 #386, 0.50 #51), AFG (0.55 #387, 0.55 #386, 0.47 #778), R (0.45 #1368, 0.40 #390, 0.33 #782), CN (0.42 #974, 0.38 #1950, 0.38 #1168), IR (0.35 #2008, 0.30 #2145, 0.25 #973), LV (0.32 #1755, 0.30 #1845, 0.28 #6277), MD (0.32 #1755, 0.28 #6277, 0.27 #3519), BY (0.32 #1755, 0.28 #6277, 0.27 #3519)
>> best conf = 0.60
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1186 for best value:
>> intensional similarity = 16
>> extensional distance = 8
>> proper extension:  Ukrainian; Dungan; Uighur;
>> query: (?x1630, KGZ) <- ?x1630[ a EthnicGroup; is ethnicGroup of ?x277[ has ethnicGroup ?x1948<Uzbek>; has language ?x278[ a Language;]; has language ?x1430<Uzbek>; has religion ?x187<Muslim>; has wasDependentOf ?x903; is locatedIn of ?x289; is neighbor of ?x130[ a Country; is locatedIn of ?x961<PikLenina>;];];]
>> Best rule #208 for best value:
>> intensional similarity = 17
>> extensional distance = 3
>> proper extension:  Kyrgyz;
>> query: (?x1630, KGZ) <- ?x1630[ a EthnicGroup; is ethnicGroup of ?x129<TAD>; is ethnicGroup of ?x277[ a Country; has encompassed ?x175; has language ?x555<Russian>; has religion ?x56; has wasDependentOf ?x903<SovietUnion>; is locatedIn of ?x1019[ has hasSource ?x2157;]; is locatedIn of ?x2336<Naryn>; is neighbor of ?x130[ has ethnicGroup ?x58;];];]

*> Best rule #387 for first EXPECTED value:
*> intensional similarity = 18
*> extensional distance = 3
*> proper extension:  Kyrgyz;
*> query: (?x1630, ?x290) <- ?x1630[ a EthnicGroup; is ethnicGroup of ?x129<TAD>; is ethnicGroup of ?x277[ a Country; has encompassed ?x175; has language ?x555<Russian>; has religion ?x56; has wasDependentOf ?x903<SovietUnion>; is locatedIn of ?x1019[ has hasSource ?x2157;]; is locatedIn of ?x2336<Naryn>; is neighbor of ?x130[ has ethnicGroup ?x58;]; is neighbor of ?x290;];]
*> conf = 0.55
ranks of expected_values: 4
EVAL	Tajik	ethnicGroup!	AFG	CNN-1.+1._MA	0.000	0.000	1.000	0.250	73.000	73.000	208.000	0.600	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#851-DK
PRED entity: DK

PRED relation: language
PRED expected values: Danish
=> 38 concepts (38 used for prediction)
PRED predicted values (max 10 best out of 91): French (0.33 #293, 0.25 #195, 0.18 #973), Dutch (0.33 #302, 0.25 #204, 0.14 #497), Norwegian (0.33 #37, 0.25 #134, 0.14 #524), English (0.27 #976, 0.19 #2240, 0.16 #2046), Spanish (0.27 #1869, 0.23 #2548, 0.22 #799), Polish (0.25 #624, 0.17 #332, 0.08 #1109), Swedish (0.25 #177, 0.11 #761, 0.06 #1344), Finnish (0.25 #110, 0.11 #694, 0.06 #1277), Russian (0.25 #595, 0.08 #1956, 0.08 #1080), Icelandic (0.18 #292, 0.14 #1264, 0.11 #487)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #293 for best value:
>> intensional similarity = 5
>> extensional distance = 4
>> proper extension:  B;
>> query: (?x793, French) <- ?x793[ has government ?x92; is locatedIn of ?x146[ is locatedInWater of ?x145;]; is neighbor of ?x120<D>;]

No rule for expected values
ranks of expected_values: 
EVAL	DK	language	Danish	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	38.000	38.000	91.000	0.333	http://www.semwebtech.org/mondial/10/meta#language

PRED relation: language
PRED expected values: Danish
=> 79 concepts (79 used for prediction)
PRED predicted values (max 10 best out of 97): Polish (0.50 #623, 0.23 #1654, 0.14 #1596), French (0.47 #2529, 0.43 #1363, 0.33 #293), English (0.43 #4962, 0.33 #2532, 0.29 #2434), Spanish (0.34 #4687, 0.26 #4491, 0.25 #5661), Dutch (0.33 #205, 0.25 #2052, 0.23 #1654), Italian (0.33 #299, 0.20 #881, 0.14 #1563), Romansch (0.33 #343, 0.14 #1607, 0.14 #1413), Monegasque (0.33 #173, 0.12 #1827, 0.07 #6714), Portuguese (0.27 #2537, 0.14 #1371, 0.08 #2342), Russian (0.25 #594, 0.23 #1654, 0.21 #4384)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #623 for best value:
>> intensional similarity = 17
>> extensional distance = 2
>> proper extension:  LT;
>> query: (?x793, Polish) <- ?x793[ has encompassed ?x195<Europe>; has government ?x92; has language ?x635; has religion ?x95; is locatedIn of ?x121[ a Sea; has mergesWith ?x182; is locatedInWater of ?x495;]; is locatedIn of ?x146<BalticSea>; is neighbor of ?x120[ is locatedIn of ?x558[ a River;]; is locatedIn of ?x1094<Oder>; is neighbor of ?x194;];]

No rule for expected values
ranks of expected_values: 
EVAL	DK	language	Danish	CNN-1.+1._MA	0.000	0.000	0.000	0.000	79.000	79.000	97.000	0.500	http://www.semwebtech.org/mondial/10/meta#language

#850-Madeira
PRED entity: Madeira

PRED relation: belongsToIslands!
PRED expected values: PortoSanto
=> 26 concepts (26 used for prediction)
PRED predicted values (max 10 best out of 224): SaoJorge (0.33 #596, 0.33 #91, 0.27 #3783), SantaMaria (0.33 #596, 0.33 #74, 0.27 #3783), Graciosa (0.33 #596, 0.33 #59, 0.27 #3783), Terceira (0.33 #596, 0.33 #55, 0.27 #3783), Corvo (0.33 #596, 0.33 #45, 0.27 #3783), Pico (0.33 #596, 0.33 #5, 0.27 #3783), SaoMiguel (0.33 #596, 0.33 #113, 0.27 #3783), PortoSanto (0.33 #596, 0.27 #3783, 0.26 #1791), Faial (0.33 #596, 0.27 #3783, 0.26 #1791), Tajo (0.33 #596, 0.27 #3783, 0.26 #1791)
>> best conf = 0.33
=> the first rule below is the first best rule for 17 predicted values
>> Best rule #596 for best value:
>> intensional similarity = 9
>> extensional distance = 2
>> proper extension:  Galapagos;
>> query: (?x1954, ?x182) <- ?x1954[ a Islands; is belongsToIslands of ?x1037[ a Island; has locatedIn ?x1027[ has wasDependentOf ?x149<E>; is locatedIn of ?x182;]; has type ?x150<"volcanic">;];]
>> Best rule #91 for best value:
>> intensional similarity = 10
>> extensional distance = 1
>> proper extension:  Azores;
>> query: (?x1954, SaoJorge) <- ?x1954[ a Islands; is belongsToIslands of ?x1037[ a Island; has locatedIn ?x1027<P>; has locatedInWater ?x182; has type ?x150<"volcanic">; is locatedOnIsland of ?x1036[ a Mountain;];];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 8
EVAL	Madeira	belongsToIslands!	PortoSanto	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.125	26.000	26.000	224.000	0.333	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

PRED relation: belongsToIslands!
PRED expected values: PortoSanto
=> 54 concepts (54 used for prediction)
PRED predicted values (max 10 best out of 224): SaoJorge (0.71 #10384, 0.71 #10383, 0.71 #10382), SantaMaria (0.71 #10384, 0.71 #10383, 0.71 #10382), Graciosa (0.71 #10384, 0.71 #10383, 0.71 #10382), Terceira (0.71 #10384, 0.71 #10383, 0.71 #10382), Corvo (0.71 #10384, 0.71 #10383, 0.71 #10382), Pico (0.71 #10384, 0.71 #10383, 0.71 #10382), PortoSanto (0.71 #10384, 0.71 #10383, 0.71 #10382), Faial (0.71 #10384, 0.71 #10383, 0.71 #10382), SaoMiguel (0.41 #1193, 0.38 #1392, 0.33 #2387), Tajo (0.41 #1193, 0.38 #1392, 0.33 #2387)
>> best conf = 0.71
=> the first rule below is the first best rule for 8 predicted values
>> Best rule #10384 for best value:
>> intensional similarity = 16
>> extensional distance = 23
>> proper extension:  Maldives;
>> query: (?x1954, ?x827) <- ?x1954[ a Islands; is belongsToIslands of ?x1037[ a Island; has locatedIn ?x1027[ a Country; has encompassed ?x195; has government ?x2551; is locatedIn of ?x182[ a Sea; has mergesWith ?x60; is flowsInto of ?x137; is locatedInWater of ?x827; is mergesWith of ?x60;]; is locatedIn of ?x827;]; has locatedInWater ?x182; has type ?x150;];]
>> Best rule #10383 for best value:
>> intensional similarity = 16
>> extensional distance = 23
>> proper extension:  Maldives;
>> query: (?x1954, ?x199) <- ?x1954[ a Islands; is belongsToIslands of ?x1037[ a Island; has locatedIn ?x1027[ a Country; has encompassed ?x195; has government ?x2551; is locatedIn of ?x182[ a Sea; has mergesWith ?x60; is flowsInto of ?x137; is locatedInWater of ?x199; is mergesWith of ?x60;]; is locatedIn of ?x199;]; has locatedInWater ?x182; has type ?x150;];]
>> Best rule #10382 for best value:
>> intensional similarity = 16
>> extensional distance = 23
>> proper extension:  Maldives;
>> query: (?x1954, ?x1000) <- ?x1954[ a Islands; is belongsToIslands of ?x1037[ a Island; has locatedIn ?x1027[ a Country; has encompassed ?x195; has government ?x2551; is locatedIn of ?x182[ a Sea; has mergesWith ?x60; is flowsInto of ?x137; is locatedInWater of ?x1000; is mergesWith of ?x60;]; is locatedIn of ?x1000;]; has locatedInWater ?x182; has type ?x150;];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 7
EVAL	Madeira	belongsToIslands!	PortoSanto	CNN-1.+1._MA	0.000	0.000	1.000	0.143	54.000	54.000	224.000	0.711	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

#849-Croatian
PRED entity: Croatian

PRED relation: language!
PRED expected values: A
=> 23 concepts (23 used for prediction)
PRED predicted values (max 10 best out of 210): A (0.60 #428, 0.50 #307, 0.50 #122), SRB (0.50 #347, 0.40 #468, 0.33 #104), I (0.50 #122, 0.33 #364, 0.33 #151), H (0.50 #122, 0.33 #364, 0.33 #363), V (0.50 #120, 0.14 #1812, 0.14 #1934), LAR (0.50 #120, 0.09 #2419, 0.08 #2178), MK (0.40 #454, 0.33 #90, 0.25 #333), MNE (0.33 #364, 0.33 #363, 0.33 #9), BIH (0.33 #364, 0.33 #363, 0.30 #484), KOS (0.33 #88, 0.25 #331, 0.20 #452)
>> best conf = 0.60
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #428 for best value:
>> intensional similarity = 12
>> extensional distance = 3
>> proper extension:  Turkish;
>> query: (?x878, A) <- ?x878[ is language of ?x156[ a Country; has ethnicGroup ?x160; is locatedIn of ?x133<Donau>; is locatedIn of ?x155<Mur>;]; is language of ?x446[ has encompassed ?x195; has government ?x1174; has neighbor ?x207; has religion ?x187;];]
ranks of expected_values: 1
EVAL	Croatian	language!	A	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	23.000	23.000	210.000	0.600	http://www.semwebtech.org/mondial/10/meta#language

PRED relation: language!
PRED expected values: A
=> 39 concepts (39 used for prediction)
PRED predicted values (max 10 best out of 213): SK (0.67 #512, 0.36 #881, 0.35 #3615), SRB (0.60 #474, 0.35 #863, 0.35 #3615), CH (0.57 #650, 0.40 #120, 0.31 #123), I (0.43 #646, 0.35 #863, 0.35 #3615), A (0.40 #434, 0.40 #120, 0.35 #863), H (0.40 #120, 0.35 #3615, 0.31 #1108), F (0.40 #120, 0.31 #123, 0.29 #616), RSM (0.40 #120, 0.31 #123, 0.16 #4494), V (0.40 #120, 0.31 #123, 0.16 #4494), MNE (0.35 #863, 0.33 #9, 0.31 #1108)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #512 for best value:
>> intensional similarity = 21
>> extensional distance = 4
>> proper extension:  Hungarian; Ukrainian; Slovak; Roma;
>> query: (?x878, SK) <- ?x878[ a Language; is language of ?x156[ a Country; has encompassed ?x195; has ethnicGroup ?x160; has religion ?x56<ChristianOrthodox>; has religion ?x352<RomanCatholic>; is locatedIn of ?x133<Donau>; is neighbor of ?x55;]; is language of ?x446[ a Country; has government ?x1174; has neighbor ?x207; is locatedIn of ?x275[ is flowsInto of ?x698; is locatedInWater of ?x68;]; is neighbor of ?x424<A>;];]

*> Best rule #434 for first EXPECTED value:
*> intensional similarity = 21
*> extensional distance = 3
*> proper extension:  Bosnian;
*> query: (?x878, A) <- ?x878[ a Language; is language of ?x156[ a Country; has encompassed ?x195; has ethnicGroup ?x160; has religion ?x56<ChristianOrthodox>; has religion ?x352<RomanCatholic>; is locatedIn of ?x1993[ a Estuary;]; is neighbor of ?x55<BIH>;]; is language of ?x446[ a Country; has ethnicGroup ?x2213; has neighbor ?x207; is locatedIn of ?x155; is neighbor of ?x424[ has ethnicGroup ?x237; is locatedIn of ?x256;];];]
*> conf = 0.40
ranks of expected_values: 5
EVAL	Croatian	language!	A	CNN-1.+1._MA	0.000	0.000	1.000	0.200	39.000	39.000	213.000	0.667	http://www.semwebtech.org/mondial/10/meta#language

#848-RT
PRED entity: RT

PRED relation: ethnicGroup
PRED expected values: EuropeanSyrian-Lebanese
=> 34 concepts (34 used for prediction)
PRED predicted values (max 10 best out of 180): European (0.37 #2834, 0.34 #1806, 0.32 #2063), Malinke (0.33 #196, 0.14 #453, 0.13 #7455), Baoule (0.33 #256, 0.13 #7455, 0.07 #513), Senoufou (0.33 #227, 0.13 #7455, 0.07 #484), Mestizo (0.15 #4147, 0.12 #2862, 0.10 #4404), Chinese (0.15 #1556, 0.14 #3355, 0.12 #1299), Tuareg (0.14 #421, 0.13 #7455, 0.03 #1706), Amerindian (0.14 #2829, 0.13 #4371, 0.12 #4114), Peuhl (0.14 #479, 0.06 #5912, 0.06 #1764), Gourmantche (0.13 #7455, 0.07 #508)
>> best conf = 0.37
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2834 for best value:
>> intensional similarity = 5
>> extensional distance = 47
>> proper extension:  GCA; CO; JA; NIC; MEX; PA; WD; HCA;
>> query: (?x1307, European) <- ?x1307[ has wasDependentOf ?x78; is locatedIn of ?x182[ has locatedIn ?x124<WV>; is locatedInWater of ?x112;];]

No rule for expected values
ranks of expected_values: 
EVAL	RT	ethnicGroup	EuropeanSyrian-Lebanese	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	34.000	34.000	180.000	0.367	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: EuropeanSyrian-Lebanese
=> 64 concepts (64 used for prediction)
PRED predicted values (max 10 best out of 247): European (0.39 #4910, 0.38 #7752, 0.32 #5167), Malinke (0.33 #196, 0.22 #971, 0.20 #1230), Baoule (0.33 #256, 0.20 #514, 0.17 #2580), Senoufou (0.33 #227, 0.20 #485, 0.17 #2580), Peuhl (0.20 #1256, 0.17 #2580, 0.15 #1513), Tuareg (0.20 #422, 0.17 #2580, 0.14 #680), Gourmantche (0.20 #509, 0.14 #14720, 0.04 #10328), Hausa (0.20 #464, 0.14 #14720, 0.04 #10328), Djerma (0.20 #431, 0.14 #14720, 0.04 #10328), BeriBeri (0.20 #319, 0.14 #14720, 0.04 #10328)
>> best conf = 0.39
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4910 for best value:
>> intensional similarity = 19
>> extensional distance = 34
>> proper extension:  HELX; SVAX;
>> query: (?x1307, European) <- ?x1307[ a Country; has encompassed ?x213; has ethnicGroup ?x162[ a EthnicGroup; is ethnicGroup of ?x154[ has encompassed ?x195; has government ?x2243; has language ?x247;]; is ethnicGroup of ?x408[ has religion ?x95;]; is ethnicGroup of ?x1051[ is neighbor of ?x416;]; is ethnicGroup of ?x1072[ a Country; has neighbor ?x651;]; is ethnicGroup of ?x1130[ has wasDependentOf ?x81;];]; is locatedIn of ?x182<AtlanticOcean>;]

No rule for expected values
ranks of expected_values: 
EVAL	RT	ethnicGroup	EuropeanSyrian-Lebanese	CNN-1.+1._MA	0.000	0.000	0.000	0.000	64.000	64.000	247.000	0.389	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#847-KAZ
PRED entity: KAZ

PRED relation: ethnicGroup
PRED expected values: Kazakh
=> 35 concepts (35 used for prediction)
PRED predicted values (max 10 best out of 224): Tajik (0.40 #618, 0.33 #114, 0.18 #4537), Karakalpak (0.33 #109, 0.20 #613, 0.18 #4537), Chuvash (0.33 #427, 0.18 #4537, 0.16 #6806), Bashkir (0.33 #350, 0.18 #4537, 0.16 #6806), European (0.31 #1771, 0.26 #2023, 0.24 #1519), Kyrgyz (0.20 #651, 0.18 #4537, 0.18 #903), HanChinese (0.20 #707, 0.18 #4537, 0.16 #6806), Polish (0.19 #1460, 0.12 #1712, 0.10 #2468), Dungan (0.18 #4537, 0.16 #6806, 0.09 #896), Turkmen (0.18 #4537, 0.16 #6806, 0.06 #1206)
>> best conf = 0.40
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #618 for best value:
>> intensional similarity = 6
>> extensional distance = 3
>> proper extension:  TAD; CN;
>> query: (?x403, Tajik) <- ?x403[ has encompassed ?x175; has ethnicGroup ?x58; has government ?x2502; is locatedIn of ?x127; is neighbor of ?x130<KGZ>;]

No rule for expected values
ranks of expected_values: 
EVAL	KAZ	ethnicGroup	Kazakh	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	35.000	35.000	224.000	0.400	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: Kazakh
=> 93 concepts (93 used for prediction)
PRED predicted values (max 10 best out of 251): European (0.53 #4295, 0.43 #3537, 0.41 #7330), Polish (0.50 #2217, 0.45 #2974, 0.35 #4236), Mestizo (0.43 #3564, 0.35 #4322, 0.33 #6093), Amerindian (0.43 #3531, 0.35 #4289, 0.33 #1514), Chinese (0.43 #5313, 0.22 #7589, 0.22 #12135), Turkmen (0.40 #954, 0.28 #5806, 0.28 #6819), Belorussian (0.36 #2857, 0.36 #2605, 0.30 #2100), Tajik (0.33 #618, 0.33 #114, 0.28 #5806), Kyrgyz (0.33 #147, 0.28 #5806, 0.28 #6819), Karakalpak (0.33 #613, 0.28 #5806, 0.28 #6819)
>> best conf = 0.53
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4295 for best value:
>> intensional similarity = 11
>> extensional distance = 15
>> proper extension:  NZ;
>> query: (?x403, European) <- ?x403[ a Country; has encompassed ?x175[ a Continent;]; has ethnicGroup ?x58; has government ?x2502; has religion ?x56; is locatedIn of ?x890[ has locatedIn ?x73<R>;]; is locatedIn of ?x1512[ has inMountains ?x1039;];]

No rule for expected values
ranks of expected_values: 
EVAL	KAZ	ethnicGroup	Kazakh	CNN-1.+1._MA	0.000	0.000	0.000	0.000	93.000	93.000	251.000	0.529	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#846-LAR
PRED entity: LAR

PRED relation: neighbor
PRED expected values: RN
=> 37 concepts (37 used for prediction)
PRED predicted values (max 10 best out of 187): RN (0.89 #3006, 0.89 #3005, 0.89 #2690), SSD (0.40 #38, 0.33 #196, 0.32 #318), CAM (0.40 #88, 0.33 #246, 0.32 #318), RCA (0.33 #273, 0.32 #318, 0.28 #1266), RCB (0.33 #245, 0.20 #87, 0.16 #5228), RMM (0.32 #318, 0.32 #762, 0.29 #445), RIM (0.32 #318, 0.29 #402, 0.28 #1266), BEN (0.32 #318, 0.29 #440, 0.28 #1266), LAR (0.32 #318, 0.28 #1266, 0.27 #1425), ER (0.32 #318, 0.28 #1266, 0.27 #1425)
>> best conf = 0.89
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3006 for best value:
>> intensional similarity = 7
>> extensional distance = 98
>> proper extension:  BIH; ET; R; DJI; MNE; TN; RL; KGZ; HR; SK; ...
>> query: (?x1184, ?x426) <- ?x1184[ a Country; has ethnicGroup ?x1215; has wasDependentOf ?x207; is locatedIn of ?x275; is neighbor of ?x169[ has wasDependentOf ?x78;]; is neighbor of ?x426;]
>> Best rule #3005 for best value:
>> intensional similarity = 7
>> extensional distance = 98
>> proper extension:  BIH; ET; R; DJI; MNE; TN; RL; KGZ; HR; SK; ...
>> query: (?x1184, ?x63) <- ?x1184[ a Country; has ethnicGroup ?x1215; has wasDependentOf ?x207; is locatedIn of ?x275; is neighbor of ?x63; is neighbor of ?x169[ has wasDependentOf ?x78;];]
ranks of expected_values: 1
EVAL	LAR	neighbor	RN	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	37.000	37.000	187.000	0.894	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: RN
=> 71 concepts (71 used for prediction)
PRED predicted values (max 10 best out of 222): RN (0.91 #8810, 0.90 #4388, 0.90 #7326), IL (0.75 #2638, 0.40 #161, 0.36 #3786), LAR (0.50 #1112, 0.40 #161, 0.33 #790), RCA (0.46 #4184, 0.33 #761, 0.33 #4229), TR (0.44 #2786, 0.40 #161, 0.22 #3115), SSD (0.42 #4065, 0.42 #3945, 0.36 #3740), ZRE (0.40 #1674, 0.36 #3636, 0.33 #3963), RIM (0.40 #161, 0.33 #248, 0.33 #84), RMM (0.40 #161, 0.33 #291, 0.31 #4881), MA (0.40 #161, 0.33 #292, 0.31 #4881)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #8810 for best value:
>> intensional similarity = 20
>> extensional distance = 108
>> proper extension:  E;
>> query: (?x1184, ?x426) <- ?x1184[ a Country; has ethnicGroup ?x1215; has government ?x1522; is locatedIn of ?x275; is neighbor of ?x186[ has government ?x140; has religion ?x187[ is religion of ?x170<N>; is religion of ?x575<NL>; is religion of ?x1826<GBZ>;]; is locatedIn of ?x531; is neighbor of ?x229;]; is neighbor of ?x426[ has government ?x435; is locatedIn of ?x535;]; is neighbor of ?x581[ has ethnicGroup ?x197;];]
ranks of expected_values: 1
EVAL	LAR	neighbor	RN	CNN-1.+1._MA	1.000	1.000	1.000	1.000	71.000	71.000	222.000	0.905	http://www.semwebtech.org/mondial/10/meta#neighbor

#845-KGZ
PRED entity: KGZ

PRED relation: locatedIn!
PRED expected values: Naryn
=> 44 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 1384): CaspianSea (0.40 #730, 0.33 #2150, 0.17 #3570), Amudarja (0.40 #98, 0.33 #1518, 0.13 #4357), Karakum (0.40 #92, 0.33 #1512, 0.12 #2840), Syrdarja (0.40 #527, 0.17 #1947, 0.13 #4786), OzeroAral (0.40 #1135, 0.17 #2555, 0.12 #2840), UstUrt (0.40 #1047, 0.17 #2467, 0.12 #2840), Kysylkum (0.40 #435, 0.17 #1855, 0.12 #2840), Donau (0.33 #2866, 0.27 #5706, 0.13 #17058), Irtysch (0.33 #2413, 0.20 #993, 0.17 #3833), Amur (0.33 #2314, 0.12 #2840, 0.11 #1420)
>> best conf = 0.40
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #730 for best value:
>> intensional similarity = 7
>> extensional distance = 3
>> proper extension:  KAZ;
>> query: (?x130, CaspianSea) <- ?x130[ a Country; has ethnicGroup ?x1948<Uzbek>; has language ?x555; has neighbor ?x129[ is locatedIn of ?x276;]; is locatedIn of ?x662;]

No rule for expected values
ranks of expected_values: 
EVAL	KGZ	locatedIn!	Naryn	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	44.000	36.000	1384.000	0.400	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Naryn
=> 107 concepts (107 used for prediction)
PRED predicted values (max 10 best out of 1408): Syrdarja (0.90 #1420, 0.72 #52565, 0.65 #82409), PacificOcean (0.90 #1420, 0.52 #55490, 0.50 #59753), CaspianSea (0.90 #1420, 0.40 #10670, 0.40 #9250), Amudarja (0.90 #1420, 0.40 #11459, 0.40 #10038), Karakum (0.90 #1420, 0.40 #11453, 0.33 #15714), Irtysch (0.90 #1420, 0.40 #9513, 0.33 #2413), Ural (0.90 #1420, 0.40 #8961, 0.33 #441), Tobol (0.90 #1420, 0.40 #9725, 0.33 #1205), Ryn (0.90 #1420, 0.40 #9922, 0.33 #1402), Bjelucha (0.90 #1420, 0.40 #8544, 0.33 #24)
>> best conf = 0.90
=> the first rule below is the first best rule for 145 predicted values
>> Best rule #1420 for best value:
>> intensional similarity = 14
>> extensional distance = 1
>> proper extension:  KAZ;
>> query: (?x130, ?x631) <- ?x130[ has ethnicGroup ?x58<Ukrainian>; has government ?x435; has language ?x555[ a Language; is language of ?x565[ is locatedIn of ?x631;]; is language of ?x962[ has neighbor ?x194;];]; has religion ?x187<Muslim>; is locatedIn of ?x1143<PikChan-Tengri>; is neighbor of ?x129;]

*> Best rule #49723 for first EXPECTED value:
*> intensional similarity = 12
*> extensional distance = 22
*> proper extension:  IR; MYA;
*> query: (?x130, ?x1975) <- ?x130[ has ethnicGroup ?x58[ a EthnicGroup; is ethnicGroup of ?x73[ has neighbor ?x170; is locatedIn of ?x72;];]; has government ?x435; has language ?x555; has religion ?x187<Muslim>; is locatedIn of ?x2336[ has hasSource ?x1975;]; is neighbor of ?x129;]
*> conf = 0.40
ranks of expected_values: 146
EVAL	KGZ	locatedIn!	Naryn	CNN-1.+1._MA	0.000	0.000	0.000	0.007	107.000	107.000	1408.000	0.905	http://www.semwebtech.org/mondial/10/meta#locatedIn

#844-WD
PRED entity: WD

PRED relation: ethnicGroup
PRED expected values: CaribIndians
=> 47 concepts (47 used for prediction)
PRED predicted values (max 10 best out of 217): African (0.53 #1296, 0.44 #780, 0.44 #522), European (0.47 #1298, 0.41 #3620, 0.33 #1556), Mestizo (0.35 #1326, 0.28 #1584, 0.24 #3906), Amerindian (0.33 #1550, 0.31 #3614, 0.24 #3872), Roma (0.25 #265, 0.16 #2071, 0.16 #1813), German (0.25 #268, 0.16 #2074, 0.11 #1816), Slovak (0.25 #470, 0.16 #2276, 0.11 #2018), Mulatto (0.24 #1349, 0.14 #3929, 0.11 #3413), EastIndian (0.22 #912, 0.22 #654, 0.17 #138), Polish (0.21 #2270, 0.12 #464, 0.11 #2012)
>> best conf = 0.53
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1296 for best value:
>> intensional similarity = 6
>> extensional distance = 15
>> proper extension:  DOM; PA;
>> query: (?x922, African) <- ?x922[ a Country; has encompassed ?x521; has wasDependentOf ?x81[ is locatedIn of ?x121;]; is locatedIn of ?x317<CaribbeanSea>;]

No rule for expected values
ranks of expected_values: 
EVAL	WD	ethnicGroup	CaribIndians	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	47.000	47.000	217.000	0.529	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: CaribIndians
=> 99 concepts (99 used for prediction)
PRED predicted values (max 10 best out of 241): Amerindian (0.57 #5681, 0.56 #6971, 0.33 #1293), African (0.50 #6459, 0.44 #7233, 0.44 #6717), European (0.44 #6977, 0.43 #5687, 0.43 #5171), Mestizo (0.43 #5715, 0.33 #7005, 0.24 #12175), Black (0.33 #1348, 0.33 #57, 0.30 #7542), EastIndian (0.33 #1687, 0.33 #1171, 0.25 #6591), Chinese (0.33 #1564, 0.25 #8016, 0.25 #2855), White (0.33 #1358, 0.25 #2907, 0.25 #2133), Mixed (0.33 #1419, 0.25 #2194, 0.17 #4775), Creole (0.33 #394, 0.25 #1943, 0.12 #517)
>> best conf = 0.57
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5681 for best value:
>> intensional similarity = 11
>> extensional distance = 5
>> proper extension:  CR;
>> query: (?x922, Amerindian) <- ?x922[ a Country; has encompassed ?x521<America>; has government ?x254; has religion ?x95<Protestant>; is locatedIn of ?x317<CaribbeanSea>; is locatedIn of ?x609[ has type ?x150<"volcanic">;];]

No rule for expected values
ranks of expected_values: 
EVAL	WD	ethnicGroup	CaribIndians	CNN-1.+1._MA	0.000	0.000	0.000	0.000	99.000	99.000	241.000	0.571	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#843-LakeWinnipeg
PRED entity: LakeWinnipeg

PRED relation: flowsInto
PRED expected values: NelsonRiver
=> 40 concepts (28 used for prediction)
PRED predicted values (max 10 best out of 161): SaintLawrenceRiver (0.14 #616, 0.08 #451, 0.08 #285), DetroitRiver (0.11 #15, 0.08 #347, 0.08 #181), NiagaraRiver (0.11 #103, 0.08 #435, 0.08 #269), AtlanticOcean (0.11 #12, 0.08 #178, 0.08 #1174), Colorado (0.09 #941, 0.03 #1942, 0.02 #2275), Tennessee (0.09 #850, 0.03 #1851, 0.02 #2184), LakeHuron (0.08 #180, 0.08 #1176, 0.07 #511), LakeErie (0.08 #329, 0.07 #660, 0.05 #4662), BeringSea (0.08 #252, 0.07 #583, 0.04 #1248), RiviereRichelieu (0.08 #496, 0.05 #4662, 0.04 #993)
>> best conf = 0.14
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #616 for best value:
>> intensional similarity = 4
>> extensional distance = 12
>> proper extension:  ColumbiaRiver;
>> query: (?x717, SaintLawrenceRiver) <- ?x717[ has locatedIn ?x272<CDN>; is flowsInto of ?x1688;]

*> Best rule #4162 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 132
*> proper extension:  Würm; Sanaga;
*> query: (?x717, ?x182) <- ?x717[ is flowsInto of ?x1688[ has locatedIn ?x272[ has government ?x2416; has religion ?x95; is locatedIn of ?x182;];];]
*> conf = 0.01
ranks of expected_values: 130
EVAL	LakeWinnipeg	flowsInto	NelsonRiver	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.008	40.000	28.000	161.000	0.143	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto
PRED expected values: NelsonRiver
=> 138 concepts (138 used for prediction)
PRED predicted values (max 10 best out of 197): DetroitRiver (0.33 #15, 0.25 #347, 0.25 #181), SaintLawrenceRiver (0.25 #451, 0.14 #1448, 0.09 #8895), NiagaraRiver (0.25 #269, 0.08 #1099, 0.08 #933), AtlanticOcean (0.17 #676, 0.17 #510, 0.11 #2179), LakeHuron (0.09 #8895, 0.08 #844, 0.08 #2013), SaintMarysRiver (0.09 #8895, 0.08 #1048, 0.08 #8896), RiviereRichelieu (0.09 #8895, 0.08 #1160, 0.08 #8896), YukonRiver (0.09 #8895, 0.08 #1073, 0.08 #8896), MackenzieRiver (0.09 #8895, 0.08 #1157, 0.08 #8896), LakeErie (0.09 #8895, 0.08 #993, 0.07 #1492)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #15 for best value:
>> intensional similarity = 9
>> extensional distance = 1
>> proper extension:  LakeHuron;
>> query: (?x717, DetroitRiver) <- ?x717[ a Lake; has locatedIn ?x272<CDN>; is flowsInto of ?x1688[ a River; has hasEstuary ?x2411[ a Estuary;]; has hasSource ?x1689[ a Source;];];]

*> Best rule #8896 for first EXPECTED value:
*> intensional similarity = 10
*> extensional distance = 88
*> proper extension:  KoliSarez; LakeKioga; LakeVictoria; ChewBahir; LakeAbaya; LakeTana; LakeAbbe;
*> query: (?x717, ?x733) <- ?x717[ a Lake; has locatedIn ?x272[ has ethnicGroup ?x197; has government ?x2416; is locatedIn of ?x282[ is flowsInto of ?x602;]; is locatedIn of ?x733[ has hasEstuary ?x864;]; is locatedIn of ?x2411[ a Estuary;];];]
*> conf = 0.08
ranks of expected_values: 22
EVAL	LakeWinnipeg	flowsInto	NelsonRiver	CNN-1.+1._MA	0.000	0.000	0.000	0.045	138.000	138.000	197.000	0.333	http://www.semwebtech.org/mondial/10/meta#flowsInto

#842-CanadianArcticIslands
PRED entity: CanadianArcticIslands

PRED relation: belongsToIslands!
PRED expected values: BaffinIsland
=> 25 concepts (23 used for prediction)
PRED predicted values (max 10 best out of 253): BaffinIsland (0.38 #776, 0.38 #1360, 0.18 #1944), MtRobson (0.38 #776, 0.38 #1360, 0.18 #1944), NelsonRiver (0.38 #776, 0.38 #1360, 0.18 #1944), NiagaraRiver (0.38 #776, 0.38 #1360, 0.18 #1944), NelsonRiver (0.38 #776, 0.38 #1360, 0.18 #1944), SaskatchewanRiver (0.38 #776, 0.38 #1360, 0.18 #1944), LakeNipigon (0.38 #776, 0.38 #1360, 0.18 #1944), RiviereRichelieu (0.38 #776, 0.38 #1360, 0.18 #1944), Manicouagan (0.38 #776, 0.38 #1360, 0.18 #1944), DetroitRiver (0.38 #776, 0.38 #1360, 0.18 #1944)
>> best conf = 0.38
=> the first rule below is the first best rule for 59 predicted values
>> Best rule #776 for best value:
>> intensional similarity = 12
>> extensional distance = 3
>> proper extension:  BahamaIslands;
>> query: (?x479, ?x182) <- ?x479[ a Islands; is belongsToIslands of ?x2220[ a Island; has locatedIn ?x272[ has government ?x2416; has religion ?x95<Protestant>; has religion ?x352<RomanCatholic>; has wasDependentOf ?x81<GB>; is locatedIn of ?x182;];];]
ranks of expected_values: 1
EVAL	CanadianArcticIslands	belongsToIslands!	BaffinIsland	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	25.000	23.000	253.000	0.385	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

PRED relation: belongsToIslands!
PRED expected values: BaffinIsland
=> 53 concepts (53 used for prediction)
PRED predicted values (max 10 best out of 254): BaffinIsland (0.75 #9352, 0.59 #8572, 0.56 #8181), MtRobson (0.50 #2922, 0.42 #5259, 0.39 #6816), NelsonRiver (0.50 #2922, 0.42 #5259, 0.39 #6816), NiagaraRiver (0.50 #2922, 0.42 #5259, 0.39 #6816), NelsonRiver (0.50 #2922, 0.42 #5259, 0.39 #6816), SaskatchewanRiver (0.50 #2922, 0.42 #5259, 0.39 #6816), LakeNipigon (0.50 #2922, 0.42 #5259, 0.39 #6816), RiviereRichelieu (0.50 #2922, 0.42 #5259, 0.39 #6816), Manicouagan (0.50 #2922, 0.42 #5259, 0.39 #6816), DetroitRiver (0.50 #2922, 0.42 #5259, 0.39 #6816)
>> best conf = 0.75
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #9352 for best value:
>> intensional similarity = 21
>> extensional distance = 13
>> proper extension:  Maldives;
>> query: (?x479, ?x869) <- ?x479[ a Islands; is belongsToIslands of ?x478[ a Island;]; is belongsToIslands of ?x866[ a Island; has locatedIn ?x272[ a Country; has encompassed ?x521; has government ?x2416; has wasDependentOf ?x81<GB>; is locatedIn of ?x263[ a Sea; has mergesWith ?x248; is locatedInWater of ?x869; is locatedInWater of ?x1238[ a Island;]; is mergesWith of ?x248;]; is locatedIn of ?x869; is locatedIn of ?x1238;];]; is belongsToIslands of ?x1238;]
ranks of expected_values: 1
EVAL	CanadianArcticIslands	belongsToIslands!	BaffinIsland	CNN-1.+1._MA	1.000	1.000	1.000	1.000	53.000	53.000	254.000	0.750	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

#841-SchattalArab
PRED entity: SchattalArab

PRED relation: flowsInto
PRED expected values: PersianGulf
=> 45 concepts (28 used for prediction)
PRED predicted values (max 10 best out of 98): SchattalArab (0.33 #133, 0.02 #1166, 0.02 #4670), AtlanticOcean (0.10 #2683, 0.10 #1011, 0.09 #3016), Donau (0.07 #2679, 0.07 #3012, 0.07 #3178), BalticSea (0.06 #675, 0.06 #1009, 0.06 #1342), MediterraneanSea (0.05 #854, 0.04 #2694, 0.04 #1522), Zaire (0.04 #1423, 0.04 #1590, 0.03 #3095), Amazonas (0.04 #678, 0.04 #844, 0.04 #1012), Po (0.04 #906, 0.03 #1241, 0.03 #1742), BlackSea (0.03 #2005, 0.03 #2674, 0.03 #668), IndianOcean (0.03 #1500, 0.02 #832, 0.02 #1836)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #133 for best value:
>> intensional similarity = 6
>> extensional distance = 1
>> proper extension:  Euphrat;
>> query: (?x1422, SchattalArab) <- ?x1422[ has locatedIn ?x302<IRQ>; has locatedIn ?x304[ has language ?x511; has neighbor ?x83;]; is flowsInto of ?x666;]

*> Best rule #1166 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 81
*> proper extension:  Bahrel-Djebel-Albert-Nil;
*> query: (?x1422, ?x573) <- ?x1422[ a River; has hasSource ?x596; has locatedIn ?x304[ has government ?x2318; is locatedIn of ?x573;]; is flowsInto of ?x666;]
*> conf = 0.02
ranks of expected_values: 27
EVAL	SchattalArab	flowsInto	PersianGulf	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.037	45.000	28.000	98.000	0.333	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto
PRED expected values: PersianGulf
=> 117 concepts (117 used for prediction)
PRED predicted values (max 10 best out of 156): SchattalArab (0.40 #1301, 0.33 #1637, 0.33 #299), Donau (0.40 #1008, 0.22 #2015, 0.14 #4041), DeadSea (0.33 #59, 0.14 #1731, 0.12 #1899), Euphrat (0.27 #1839, 0.25 #976, 0.11 #1840), LakeKeban (0.25 #1957, 0.06 #5335, 0.05 #1168), MediterraneanSea (0.20 #3548, 0.19 #3717, 0.19 #2701), BlackSea (0.20 #1003, 0.14 #1675, 0.11 #2010), KaraSea (0.20 #2427, 0.07 #4963, 0.07 #5133), OzeroAral (0.20 #1491, 0.06 #2833, 0.06 #5539), Pjandsh (0.20 #1365, 0.06 #2707, 0.05 #3554)
>> best conf = 0.40
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1301 for best value:
>> intensional similarity = 13
>> extensional distance = 3
>> proper extension:  Karun;
>> query: (?x1422, SchattalArab) <- ?x1422[ a River; has locatedIn ?x302[ has ethnicGroup ?x557; has government ?x254; has neighbor ?x185<TR>; has neighbor ?x466[ has ethnicGroup ?x244; is locatedIn of ?x275<MediterraneanSea>;]; has religion ?x116; is locatedIn of ?x918<PersianGulf>;];]

*> Best rule #1840 for first EXPECTED value:
*> intensional similarity = 14
*> extensional distance = 5
*> proper extension:  Kura;
*> query: (?x1422, ?x255) <- ?x1422[ a River; has locatedIn ?x302[ has government ?x254; has neighbor ?x185<TR>; has neighbor ?x803[ has encompassed ?x175<Asia>; has ethnicGroup ?x244;]; has religion ?x116; has wasDependentOf ?x485; is locatedIn of ?x255; is locatedIn of ?x1644[ is flowsInto of ?x1272;];];]
*> conf = 0.11
ranks of expected_values: 16
EVAL	SchattalArab	flowsInto	PersianGulf	CNN-1.+1._MA	0.000	0.000	0.000	0.062	117.000	117.000	156.000	0.400	http://www.semwebtech.org/mondial/10/meta#flowsInto

#840-Fulda
PRED entity: Fulda

PRED relation: locatedIn
PRED expected values: D
=> 32 concepts (32 used for prediction)
PRED predicted values (max 10 best out of 77): D (0.88 #1422, 0.88 #1205, 0.86 #1185), F (0.18 #2377, 0.08 #5218, 0.08 #3321), ZRE (0.16 #4586, 0.16 #4349, 0.15 #5298), A (0.15 #1995, 0.14 #1047, 0.12 #1284), R (0.14 #5224, 0.09 #5699, 0.09 #5462), USA (0.12 #5053, 0.09 #5529, 0.08 #5766), HR (0.10 #1925, 0.06 #2399, 0.04 #2162), PE (0.09 #3626, 0.07 #2913, 0.07 #3151), SF (0.09 #2741, 0.02 #5826, 0.02 #6064), PL (0.08 #5218, 0.08 #3321, 0.07 #3083)
>> best conf = 0.88
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1422 for best value:
>> intensional similarity = 9
>> extensional distance = 14
>> proper extension:  Main;
>> query: (?x1836, ?x120) <- ?x1836[ a Estuary; is hasEstuary of ?x1594[ a River; has flowsInto ?x1533[ a River; has hasEstuary ?x1252; has hasSource ?x1668;]; has locatedIn ?x120<D>;];]
>> Best rule #1205 for best value:
>> intensional similarity = 9
>> extensional distance = 14
>> proper extension:  Main;
>> query: (?x1836, D) <- ?x1836[ a Estuary; is hasEstuary of ?x1594[ a River; has flowsInto ?x1533[ a River; has hasEstuary ?x1252; has hasSource ?x1668;]; has locatedIn ?x120<D>;];]
ranks of expected_values: 1
EVAL	Fulda	locatedIn	D	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	32.000	32.000	77.000	0.875	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: D
=> 84 concepts (84 used for prediction)
PRED predicted values (max 10 best out of 82): D (0.88 #2133, 0.88 #1916, 0.88 #1896), R (0.38 #11911, 0.37 #12150, 0.09 #18824), ZRE (0.34 #13653, 0.29 #15316, 0.21 #15793), F (0.22 #7625, 0.10 #3569, 0.09 #14049), USA (0.20 #16500, 0.16 #17934, 0.09 #18891), A (0.18 #4854, 0.15 #3083, 0.15 #2944), BR (0.17 #9412, 0.10 #2732, 0.07 #16315), PL (0.17 #5992, 0.09 #14049, 0.09 #12859), I (0.16 #10048, 0.15 #12432, 0.08 #14336), PE (0.14 #14117, 0.13 #14829, 0.11 #16018)
>> best conf = 0.88
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2133 for best value:
>> intensional similarity = 10
>> extensional distance = 14
>> proper extension:  Main;
>> query: (?x1836, ?x120) <- ?x1836[ a Estuary; is hasEstuary of ?x1594[ a River; has flowsInto ?x1533[ a River; has hasEstuary ?x1252; has hasSource ?x1668; has locatedIn ?x120<D>;]; has locatedIn ?x120<D>;];]
>> Best rule #1916 for best value:
>> intensional similarity = 10
>> extensional distance = 14
>> proper extension:  Main;
>> query: (?x1836, D) <- ?x1836[ a Estuary; is hasEstuary of ?x1594[ a River; has flowsInto ?x1533[ a River; has hasEstuary ?x1252; has hasSource ?x1668; has locatedIn ?x120<D>;]; has locatedIn ?x120<D>;];]
>> Best rule #1896 for best value:
>> intensional similarity = 11
>> extensional distance = 14
>> proper extension:  Oder; Elbe;
>> query: (?x1836, ?x120) <- ?x1836[ a Estuary; is hasEstuary of ?x1594[ a River; has flowsInto ?x1533[ has locatedIn ?x120<D>;]; has hasSource ?x1797[ a Source; has inMountains ?x1690;]; has locatedIn ?x120<D>;];]
>> Best rule #1679 for best value:
>> intensional similarity = 11
>> extensional distance = 14
>> proper extension:  Oder; Elbe;
>> query: (?x1836, D) <- ?x1836[ a Estuary; is hasEstuary of ?x1594[ a River; has flowsInto ?x1533[ has locatedIn ?x120<D>;]; has hasSource ?x1797[ a Source; has inMountains ?x1690;]; has locatedIn ?x120<D>;];]
ranks of expected_values: 1
EVAL	Fulda	locatedIn	D	CNN-1.+1._MA	1.000	1.000	1.000	1.000	84.000	84.000	82.000	0.875	http://www.semwebtech.org/mondial/10/meta#locatedIn

#839-A
PRED entity: A

PRED relation: locatedIn!
PRED expected values: Zugspitze
=> 36 concepts (33 used for prediction)
PRED predicted values (max 10 best out of 1334): MediterraneanSea (0.66 #7066, 0.40 #4271, 0.25 #2874), Mosel (0.62 #5590, 0.50 #3135, 0.40 #4532), Breg (0.62 #5590, 0.33 #2470, 0.25 #3867), Alz (0.62 #5590, 0.33 #2014, 0.25 #3411), Brigach (0.62 #5590, 0.33 #1871, 0.25 #3268), Main (0.62 #5590, 0.33 #1666, 0.25 #3063), Ammer (0.62 #5590, 0.33 #1633, 0.25 #3030), Theiss (0.62 #5590, 0.33 #318, 0.21 #23762), Aare (0.62 #5590, 0.21 #23762, 0.20 #32151), Waag (0.62 #5590, 0.21 #23762, 0.20 #32151)
>> best conf = 0.66
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #7066 for best value:
>> intensional similarity = 5
>> extensional distance = 27
>> proper extension:  CY;
>> query: (?x424, MediterraneanSea) <- ?x424[ a Country; has ethnicGroup ?x160; is locatedIn of ?x614[ has locatedIn ?x156<HR>;];]

*> Best rule #1436 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 1
*> proper extension:  D;
*> query: (?x424, Zugspitze) <- ?x424[ has ethnicGroup ?x160; has religion ?x95; is locatedIn of ?x889<Lech>; is neighbor of ?x234<CH>;]
*> conf = 0.33
ranks of expected_values: 82
EVAL	A	locatedIn!	Zugspitze	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.012	36.000	33.000	1334.000	0.655	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Zugspitze
=> 85 concepts (85 used for prediction)
PRED predicted values (max 10 best out of 1346): MediterraneanSea (0.81 #26656, 0.40 #12661, 0.38 #16853), Isar (0.75 #36368, 0.72 #34968, 0.72 #37770), Drau (0.75 #36368, 0.72 #34968, 0.72 #37770), Donau (0.75 #36368, 0.72 #34968, 0.72 #37770), Rhein (0.75 #36368, 0.72 #34968, 0.72 #37770), Iller (0.75 #36368, 0.72 #34968, 0.72 #37770), Lech (0.75 #36368, 0.72 #34968, 0.72 #37770), Mur (0.75 #36368, 0.72 #34968, 0.72 #37770), Raab (0.75 #36368, 0.72 #34968, 0.72 #37770), Inn (0.73 #33568, 0.70 #41970, 0.62 #39170)
>> best conf = 0.81
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #26656 for best value:
>> intensional similarity = 11
>> extensional distance = 14
>> proper extension:  M;
>> query: (?x424, MediterraneanSea) <- ?x424[ has encompassed ?x195<Europe>; has language ?x684[ a Language;]; is locatedIn of ?x155[ has locatedIn ?x156<HR>; has locatedIn ?x446<SLO>;]; is locatedIn of ?x756[ is flowsInto of ?x1172;];]

*> Best rule #32170 for first EXPECTED value:
*> intensional similarity = 11
*> extensional distance = 22
*> proper extension:  IS;
*> query: (?x424, ?x70) <- ?x424[ has encompassed ?x195<Europe>; has ethnicGroup ?x160; has language ?x511; is locatedIn of ?x756[ a River;]; is locatedIn of ?x1440[ a Estuary; has locatedIn ?x120[ has religion ?x95; is locatedIn of ?x70;];];]
*> conf = 0.53
ranks of expected_values: 62
EVAL	A	locatedIn!	Zugspitze	CNN-1.+1._MA	0.000	0.000	0.000	0.016	85.000	85.000	1346.000	0.812	http://www.semwebtech.org/mondial/10/meta#locatedIn

#838-Austria-Hungary
PRED entity: Austria-Hungary

PRED relation: wasDependentOf!
PRED expected values: A
=> 16 concepts (16 used for prediction)
PRED predicted values (max 10 best out of 206): HR (0.40 #159, 0.40 #158, 0.37 #1756), SLO (0.40 #159, 0.40 #158, 0.37 #1756), SK (0.40 #159, 0.40 #158, 0.37 #1756), UA (0.40 #159, 0.40 #158, 0.37 #1756), RO (0.40 #159, 0.40 #158, 0.37 #1756), SRB (0.40 #159, 0.40 #158, 0.36 #478), A (0.37 #1756, 0.37 #1754, 0.35 #1755), BIH (0.33 #1, 0.26 #1915, 0.25 #319), MK (0.33 #108, 0.26 #1915, 0.25 #426), CZ (0.33 #237, 0.26 #1915, 0.24 #1433)
>> best conf = 0.40
=> the first rule below is the first best rule for 6 predicted values
>> Best rule #159 for best value:
>> intensional similarity = 12
>> extensional distance = 1
>> proper extension:  Yugoslavia;
>> query: (?x2352, ?x163) <- ?x2352[ is wasDependentOf of ?x236[ has ethnicGroup ?x164; has government ?x254; has language ?x684; has neighbor ?x156<HR>; has neighbor ?x163; has neighbor ?x446[ has language ?x738;]; has religion ?x95<Protestant>; is locatedIn of ?x133;];]
>> Best rule #158 for best value:
>> intensional similarity = 11
>> extensional distance = 1
>> proper extension:  Yugoslavia;
>> query: (?x2352, ?x446) <- ?x2352[ is wasDependentOf of ?x236[ has ethnicGroup ?x164; has government ?x254; has language ?x684; has neighbor ?x156<HR>; has neighbor ?x446[ has language ?x738;]; has religion ?x95<Protestant>; is locatedIn of ?x133;];]

*> Best rule #1756 for first EXPECTED value:
*> intensional similarity = 11
*> extensional distance = 18
*> proper extension:  BR;
*> query: (?x2352, ?x156) <- ?x2352[ is wasDependentOf of ?x236[ has ethnicGroup ?x164; has government ?x254; has religion ?x95; is locatedIn of ?x133[ is flowsInto of ?x132;]; is neighbor of ?x156; is neighbor of ?x163[ a Country;]; is neighbor of ?x176[ has language ?x684;];];]
*> conf = 0.37
ranks of expected_values: 7
EVAL	Austria-Hungary	wasDependentOf!	A	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.143	16.000	16.000	206.000	0.400	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

PRED relation: wasDependentOf!
PRED expected values: A
=> 26 concepts (26 used for prediction)
PRED predicted values (max 10 best out of 179): HR (0.52 #2960, 0.52 #1469, 0.50 #1474), SLO (0.52 #2960, 0.52 #1469, 0.50 #1474), UA (0.52 #2960, 0.52 #1469, 0.50 #1474), RO (0.52 #2960, 0.52 #1469, 0.50 #1474), SK (0.52 #2960, 0.52 #1469, 0.50 #1474), A (0.50 #653, 0.49 #1468, 0.44 #821), MNE (0.50 #653, 0.49 #1468, 0.38 #157), BIH (0.50 #653, 0.49 #1468, 0.38 #2788), I (0.50 #653, 0.44 #821, 0.39 #1476), SRB (0.49 #1468, 0.44 #821, 0.43 #649)
>> best conf = 0.52
=> the first rule below is the first best rule for 5 predicted values
>> Best rule #2960 for best value:
>> intensional similarity = 21
>> extensional distance = 11
>> proper extension:  F; PK; UnitedNations;
>> query: (?x2352, ?x446) <- ?x2352[ is wasDependentOf of ?x236[ a Country; has ethnicGroup ?x164; is locatedIn of ?x133[ has hasSource ?x1190; is flowsInto of ?x132;]; is locatedIn of ?x614[ a River;]; is neighbor of ?x163[ a Country; has encompassed ?x195; has ethnicGroup ?x58; has government ?x254; has language ?x684; has religion ?x56; is locatedIn of ?x360; is neighbor of ?x194;]; is neighbor of ?x446[ has ethnicGroup ?x160; has wasDependentOf ?x1197; is locatedIn of ?x275;];];]

*> Best rule #653 for first EXPECTED value:
*> intensional similarity = 32
*> extensional distance = 1
*> proper extension:  OttomanEmpire;
*> query: (?x2352, ?x207) <- ?x2352[ is wasDependentOf of ?x236[ a Country; has ethnicGroup ?x164<Roma>; has neighbor ?x904; is locatedIn of ?x133<Donau>; is locatedIn of ?x708[ a River; has hasEstuary ?x2495;]; is neighbor of ?x156[ has religion ?x95<Protestant>; is locatedIn of ?x152<Save>; is neighbor of ?x55<BIH>;]; is neighbor of ?x176<RO>; is neighbor of ?x446[ has encompassed ?x195; has ethnicGroup ?x160; has government ?x1174; has language ?x738; has neighbor ?x207; has religion ?x187<Muslim>; has religion ?x352<RomanCatholic>; has wasDependentOf ?x1197<Yugoslavia>; is locatedIn of ?x275;];];]
*> conf = 0.50
ranks of expected_values: 6
EVAL	Austria-Hungary	wasDependentOf!	A	CNN-1.+1._MA	0.000	0.000	1.000	0.167	26.000	26.000	179.000	0.524	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

#837-SUD
PRED entity: SUD

PRED relation: locatedIn!
PRED expected values: NubianDesert
=> 38 concepts (33 used for prediction)
PRED predicted values (max 10 best out of 1180): WhiteNile (0.69 #15529, 0.33 #1196, 0.10 #7058), BlueNile (0.69 #15529, 0.16 #5646, 0.14 #8321), Atbara (0.69 #15529, 0.16 #5646, 0.14 #8124), MediterraneanSea (0.50 #4317, 0.24 #15612, 0.18 #14200), Nile (0.50 #1412, 0.17 #5001, 0.08 #36707), PacificOcean (0.44 #9969, 0.43 #11381, 0.39 #17026), ChadLake (0.43 #6917, 0.17 #5505, 0.16 #5646), AtlanticOcean (0.40 #45224, 0.38 #43812, 0.35 #19805), Bahrel-Djebel-Albert-Nil (0.33 #973, 0.28 #11295, 0.24 #18352), Bahrel-Ghasal (0.33 #626, 0.28 #11295, 0.24 #18352)
>> best conf = 0.69
=> the first rule below is the first best rule for 3 predicted values
>> Best rule #15529 for best value:
>> intensional similarity = 7
>> extensional distance = 31
>> proper extension:  AUS; CDN;
>> query: (?x186, ?x2225) <- ?x186[ has ethnicGroup ?x244; has wasDependentOf ?x63; is locatedIn of ?x1587[ has inMountains ?x1927[ a Mountains;];]; is locatedIn of ?x1597[ has hasSource ?x2225;];]

No rule for expected values
ranks of expected_values: 
EVAL	SUD	locatedIn!	NubianDesert	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	38.000	33.000	1180.000	0.694	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: NubianDesert
=> 78 concepts (74 used for prediction)
PRED predicted values (max 10 best out of 1383): IndianOcean (0.71 #41026, 0.71 #42440, 0.50 #14147), AtlanticOcean (0.71 #45302, 0.67 #66483, 0.67 #65111), LakeTana (0.67 #22638, 0.66 #35365, 0.66 #29709), Bahrel-Djebel-Albert-Nil (0.67 #22638, 0.66 #35365, 0.66 #29709), Bahrel-Ghasal (0.67 #22638, 0.66 #35365, 0.66 #29709), MediterraneanSea (0.58 #52330, 0.58 #51001, 0.49 #80634), BlueNile (0.56 #72144, 0.55 #35364, 0.53 #29710), WhiteNile (0.56 #72144, 0.55 #35364, 0.53 #29710), Atbara (0.56 #72144, 0.45 #14143, 0.33 #6726), Ubangi (0.55 #33948, 0.45 #14143, 0.43 #28438)
>> best conf = 0.71
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #41026 for best value:
>> intensional similarity = 16
>> extensional distance = 12
>> proper extension:  MV; MAYO;
>> query: (?x186, IndianOcean) <- ?x186[ a Country; has encompassed ?x213; has government ?x140; is locatedIn of ?x531[ has type ?x136;]; is locatedIn of ?x1552[ has locatedIn ?x94[ has neighbor ?x220; has wasDependentOf ?x78;]; has locatedIn ?x668<YE>; has locatedIn ?x803[ has religion ?x116; is neighbor of ?x302;];]; is locatedIn of ?x1597[ is flowsInto of ?x1783;];]

No rule for expected values
ranks of expected_values: 
EVAL	SUD	locatedIn!	NubianDesert	CNN-1.+1._MA	0.000	0.000	0.000	0.000	78.000	74.000	1383.000	0.714	http://www.semwebtech.org/mondial/10/meta#locatedIn

#836-Savaii
PRED entity: Savaii

PRED relation: locatedInWater
PRED expected values: PacificOcean
=> 58 concepts (56 used for prediction)
PRED predicted values (max 10 best out of 36): PacificOcean (0.70 #908, 0.70 #952, 0.50 #60), AtlanticOcean (0.41 #136, 0.38 #871, 0.38 #1131), JavaSea (0.27 #310, 0.27 #267, 0.27 #224), IndianOcean (0.27 #303, 0.27 #260, 0.25 #346), CaribbeanSea (0.16 #578, 0.15 #666, 0.14 #1143), SulawesiSea (0.15 #243, 0.12 #329, 0.12 #286), SouthChinaSea (0.12 #323, 0.12 #280, 0.12 #237), MediterraneanSea (0.11 #880, 0.11 #749, 0.11 #792), NorthSea (0.10 #1256, 0.10 #1520, 0.10 #1563), Upolu (0.07 #603, 0.05 #1822, 0.04 #821)
>> best conf = 0.70
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #908 for best value:
>> intensional similarity = 6
>> extensional distance = 71
>> proper extension:  Grande-Terre;
>> query: (?x1205, ?x282) <- ?x1205[ a Island; has belongsToIslands ?x586[ is belongsToIslands of ?x585[ has locatedIn ?x1276; has locatedInWater ?x282;];]; has type ?x150;]
ranks of expected_values: 1
EVAL	Savaii	locatedInWater	PacificOcean	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	58.000	56.000	36.000	0.705	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater
PRED expected values: PacificOcean
=> 166 concepts (166 used for prediction)
PRED predicted values (max 10 best out of 44): PacificOcean (0.90 #2301, 0.90 #2274, 0.87 #1811), JavaSea (0.58 #804, 0.44 #582, 0.41 #1066), CaribbeanSea (0.50 #372, 0.40 #636, 0.22 #1253), IndianOcean (0.50 #797, 0.35 #1059, 0.35 #1414), AtlanticOcean (0.45 #2220, 0.44 #1241, 0.44 #4554), Donau (0.43 #269, 0.05 #4459, 0.04 #4184), Upolu (0.37 #397, 0.22 #265, 0.17 #176), Savaii (0.37 #397, 0.22 #265, 0.17 #176), SouthChinaSea (0.29 #332, 0.25 #463, 0.21 #1390), SulawesiSea (0.29 #338, 0.14 #1484, 0.13 #661)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2301 for best value:
>> intensional similarity = 9
>> extensional distance = 27
>> proper extension:  Fakaofo;
>> query: (?x1205, ?x282) <- ?x1205[ a Island; has belongsToIslands ?x586[ a Islands; is belongsToIslands of ?x585[ a Island; has locatedInWater ?x282<PacificOcean>; has type ?x150;];]; has type ?x150;]
>> Best rule #2274 for best value:
>> intensional similarity = 9
>> extensional distance = 27
>> proper extension:  Fakaofo;
>> query: (?x1205, PacificOcean) <- ?x1205[ a Island; has belongsToIslands ?x586[ a Islands; is belongsToIslands of ?x585[ a Island; has locatedInWater ?x282<PacificOcean>; has type ?x150;];]; has type ?x150;]
ranks of expected_values: 1
EVAL	Savaii	locatedInWater	PacificOcean	CNN-1.+1._MA	1.000	1.000	1.000	1.000	166.000	166.000	44.000	0.897	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#835-TorredeEstrela
PRED entity: TorredeEstrela

PRED relation: locatedIn
PRED expected values: P
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 33): USA (0.10 #72, 0.05 #308), CN (0.06 #56, 0.02 #292), I (0.04 #48, 0.02 #284), RI (0.04 #52, 0.02 #288), R (0.04 #241, 0.03 #5), CDN (0.04 #63, 0.03 #299), E (0.04 #27, 0.01 #263), D (0.03 #256, 0.02 #20), NEP (0.03 #17), ZRE (0.03 #315)
>> best conf = 0.10
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #72 for best value:
>> intensional similarity = 1
>> extensional distance = 250
>> proper extension:  JabalKatrina; Tahat; Annapurna; Bjelucha; Schchara; GranSasso; EmiKussi; Zugspitze; Tahan; Demirkazik; ...
>> query: (?x1710, USA) <- ?x1710[ a Mountain;]

No rule for expected values
ranks of expected_values: 
EVAL	TorredeEstrela	locatedIn	P	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	2.000	2.000	33.000	0.099	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: P
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 33): USA (0.10 #72, 0.05 #308), CN (0.06 #56, 0.02 #292), I (0.04 #48, 0.02 #284), RI (0.04 #52, 0.02 #288), R (0.04 #241, 0.03 #5), CDN (0.04 #63, 0.03 #299), E (0.04 #27, 0.01 #263), D (0.03 #256, 0.02 #20), NEP (0.03 #17), ZRE (0.03 #315)
>> best conf = 0.10
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #72 for best value:
>> intensional similarity = 1
>> extensional distance = 250
>> proper extension:  JabalKatrina; Tahat; Annapurna; Bjelucha; Schchara; GranSasso; EmiKussi; Zugspitze; Tahan; Demirkazik; ...
>> query: (?x1710, USA) <- ?x1710[ a Mountain;]

No rule for expected values
ranks of expected_values: 
EVAL	TorredeEstrela	locatedIn	P	CNN-1.+1._MA	0.000	0.000	0.000	0.000	2.000	2.000	33.000	0.099	http://www.semwebtech.org/mondial/10/meta#locatedIn

#834-PacificOcean
PRED entity: PacificOcean

PRED relation: mergesWith
PRED expected values: SouthChinaSea
=> 30 concepts (27 used for prediction)
PRED predicted values (max 10 best out of 450): SouthChinaSea (0.82 #428, 0.80 #395, 0.44 #624), PacificOcean (0.44 #624, 0.38 #178, 0.35 #279), AtlanticOcean (0.35 #366, 0.33 #334, 0.30 #399), JavaSea (0.33 #39, 0.15 #271, 0.14 #335), ArcticOcean (0.26 #405, 0.23 #175, 0.17 #471), NorwegianSea (0.17 #411, 0.16 #444, 0.15 #314), YellowSea (0.15 #176, 0.10 #277, 0.10 #341), EastSibirianSea (0.15 #184, 0.07 #480, 0.06 #512), GreenlandSea (0.15 #292, 0.14 #356, 0.13 #388), GulfofMexico (0.11 #156, 0.10 #323, 0.10 #291)
>> best conf = 0.82
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #428 for best value:
>> intensional similarity = 5
>> extensional distance = 21
>> proper extension:  KaraSea;
>> query: (?x282, ?x384) <- ?x282[ has mergesWith ?x60; is flowsInto of ?x602; is locatedInWater of ?x1643[ has locatedIn ?x202;]; is mergesWith of ?x384;]
ranks of expected_values: 1
EVAL	PacificOcean	mergesWith	SouthChinaSea	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	30.000	27.000	450.000	0.825	http://www.semwebtech.org/mondial/10/meta#mergesWith

PRED relation: mergesWith
PRED expected values: SouthChinaSea
=> 93 concepts (93 used for prediction)
PRED predicted values (max 10 best out of 456): SouthChinaSea (0.83 #748, 0.83 #747, 0.81 #1131), PacificOcean (0.50 #279, 0.45 #1198, 0.44 #1233), AtlanticOcean (0.33 #585, 0.32 #684, 0.32 #651), JavaSea (0.33 #41, 0.29 #319, 0.27 #70), GreenlandSea (0.33 #272, 0.14 #574, 0.14 #408), CaribbeanSea (0.29 #362, 0.27 #70, 0.25 #227), EastSibirianSea (0.29 #401, 0.27 #70, 0.17 #265), GulfofMexico (0.27 #70, 0.25 #205, 0.22 #440), AndamanSea (0.27 #70, 0.25 #160, 0.17 #496), ArcticOcean (0.27 #70, 0.21 #1143, 0.18 #657)
>> best conf = 0.83
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #748 for best value:
>> intensional similarity = 10
>> extensional distance = 20
>> proper extension:  SeaofAzov; BlackSea; Skagerrak;
>> query: (?x282, ?x620) <- ?x282[ has locatedIn ?x181[ a Country; has ethnicGroup ?x79;]; has locatedIn ?x217[ has ethnicGroup ?x425; has neighbor ?x376;]; is flowsInto of ?x602; is mergesWith of ?x620[ has locatedIn ?x232; is mergesWith of ?x270;];]
>> Best rule #747 for best value:
>> intensional similarity = 11
>> extensional distance = 20
>> proper extension:  SeaofAzov; BlackSea; Skagerrak;
>> query: (?x282, ?x60) <- ?x282[ has locatedIn ?x181[ a Country; has ethnicGroup ?x79;]; has locatedIn ?x217[ has ethnicGroup ?x425; has neighbor ?x376;]; is flowsInto of ?x602; is mergesWith of ?x60; is mergesWith of ?x620[ has locatedIn ?x232; is mergesWith of ?x270;];]
ranks of expected_values: 1
EVAL	PacificOcean	mergesWith	SouthChinaSea	CNN-1.+1._MA	1.000	1.000	1.000	1.000	93.000	93.000	456.000	0.833	http://www.semwebtech.org/mondial/10/meta#mergesWith

#833-SSD
PRED entity: SSD

PRED relation: wasDependentOf
PRED expected values: SUD
=> 21 concepts (21 used for prediction)
PRED predicted values (max 10 best out of 29): GB (0.40 #63, 0.33 #33, 0.27 #94), F (0.33 #3, 0.27 #123, 0.23 #184), UnitedNations (0.27 #105, 0.22 #44, 0.20 #135), B (0.17 #90, 0.09 #107, 0.07 #137), E (0.14 #188, 0.13 #219, 0.12 #313), SovietUnion (0.10 #296, 0.09 #265, 0.09 #327), P (0.07 #143, 0.05 #173, 0.05 #204), ET (0.07 #121, 0.02 #182, 0.01 #213), Yugoslavia (0.05 #268, 0.04 #299, 0.04 #236), NL (0.05 #199, 0.03 #324, 0.03 #262)
>> best conf = 0.40
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #63 for best value:
>> intensional similarity = 6
>> extensional distance = 8
>> proper extension:  EAK;
>> query: (?x229, GB) <- ?x229[ has government ?x435; has neighbor ?x348[ has wasDependentOf ?x543; is locatedIn of ?x1188<Semliki>;]; is locatedIn of ?x53;]

*> Best rule #306 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 118
*> proper extension:  ARM; BHT; LB; BZ; AND;
*> query: (?x229, ?x348) <- ?x229[ is neighbor of ?x348[ has encompassed ?x213; has ethnicGroup ?x2121; is locatedIn of ?x113; is locatedIn of ?x1434[ a Source;];];]
*> conf = 0.03
ranks of expected_values: 14
EVAL	SSD	wasDependentOf	SUD	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.071	21.000	21.000	29.000	0.400	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

PRED relation: wasDependentOf
PRED expected values: SUD
=> 83 concepts (83 used for prediction)
PRED predicted values (max 10 best out of 46): E (0.75 #166, 0.50 #399, 0.50 #365), F (0.42 #944, 0.30 #2410, 0.29 #1713), GB (0.33 #264, 0.33 #36, 0.30 #298), UnitedNations (0.33 #76, 0.30 #309, 0.13 #1870), ET (0.30 #2410, 0.29 #1713, 0.29 #1513), B (0.29 #1713, 0.29 #1513, 0.28 #2195), ETH (0.25 #159, 0.25 #139, 0.25 #125), Yugoslavia (0.25 #215, 0.10 #1133, 0.09 #1570), SovietUnion (0.24 #927, 0.22 #1263, 0.21 #996), EAK (0.12 #120, 0.11 #291, 0.10 #155)
>> best conf = 0.75
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #166 for best value:
>> intensional similarity = 16
>> extensional distance = 6
>> proper extension:  RA; PY;
>> query: (?x229, E) <- ?x229[ has neighbor ?x476[ has encompassed ?x213; has ethnicGroup ?x1179; has ethnicGroup ?x1418[ a EthnicGroup;]; has religion ?x95<Protestant>; is locatedIn of ?x1468[ a Lake;]; is locatedIn of ?x1875[ has inMountains ?x2477;]; is locatedIn of ?x1879[ has hasEstuary ?x1885;]; is wasDependentOf of ?x629;]; is locatedIn of ?x747[ a River;];]

*> Best rule #120 for first EXPECTED value:
*> intensional similarity = 16
*> extensional distance = 2
*> proper extension:  DJI;
*> query: (?x229, ?x186) <- ?x229[ a Country; has neighbor ?x186; has neighbor ?x476<ETH>; has neighbor ?x736[ a Country; has ethnicGroup ?x992; has neighbor ?x528[ a Country; is locatedIn of ?x182;]; has wasDependentOf ?x78[ has religion ?x95; is locatedIn of ?x121; is neighbor of ?x120;]; is locatedIn of ?x388;]; is locatedIn of ?x53;]
*> conf = 0.12
ranks of expected_values: 12
EVAL	SSD	wasDependentOf	SUD	CNN-1.+1._MA	0.000	0.000	0.000	0.083	83.000	83.000	46.000	0.750	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

#832-Hwangho
PRED entity: Hwangho

PRED relation: flowsInto
PRED expected values: YellowSea
=> 40 concepts (30 used for prediction)
PRED predicted values (max 10 best out of 114): EastChinaSea (0.20 #68, 0.17 #233, 0.08 #399), Amur (0.20 #140, 0.17 #305, 0.08 #471), LopNor (0.20 #39, 0.17 #204, 0.08 #370), AtlanticOcean (0.19 #508, 0.09 #2672, 0.09 #2174), SouthChinaSea (0.08 #368, 0.02 #533, 0.02 #4324), SeaofOkhotsk (0.08 #380, 0.02 #545, 0.02 #711), OzeroBalchash (0.08 #364, 0.02 #529, 0.02 #695), Ob (0.08 #484, 0.02 #1982, 0.02 #815), ArabianSea (0.08 #454, 0.02 #785, 0.02 #1117), Ganges (0.08 #447, 0.02 #778, 0.02 #1110)
>> best conf = 0.20
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #68 for best value:
>> intensional similarity = 6
>> extensional distance = 3
>> proper extension:  Argun; Tarim-Yarkend; Jangtse;
>> query: (?x1022, EastChinaSea) <- ?x1022[ a River; has hasEstuary ?x231[ a Estuary; has locatedIn ?x232<CN>;]; has hasSource ?x2525;]

*> Best rule #4324 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 923
*> proper extension:  Araguaia; JabalKatrina; Menorca; Breg; Stromboli; Leine; LakeSkutari; Moraca; StarnbergerSee; NorthSea; ...
*> query: (?x1022, ?x270) <- ?x1022[ has locatedIn ?x232[ has religion ?x116; is locatedIn of ?x270[ a Sea;]; is locatedIn of ?x338[ a River;]; is neighbor of ?x73;];]
*> conf = 0.02
ranks of expected_values: 34
EVAL	Hwangho	flowsInto	YellowSea	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.029	40.000	30.000	114.000	0.200	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto
PRED expected values: YellowSea
=> 86 concepts (82 used for prediction)
PRED predicted values (max 10 best out of 152): EastChinaSea (0.20 #68, 0.12 #5355, 0.11 #4847), Amur (0.20 #140, 0.11 #305, 0.10 #638), LopNor (0.20 #39, 0.11 #204, 0.10 #537), AtlanticOcean (0.19 #2011, 0.17 #1845, 0.17 #1008), MediterraneanSea (0.14 #1856, 0.09 #3027, 0.08 #1019), SouthChinaSea (0.12 #5355, 0.11 #4847, 0.11 #202), SeaofOkhotsk (0.11 #214, 0.10 #547, 0.10 #379), OzeroBalchash (0.11 #198, 0.10 #531, 0.10 #363), MalakkaStrait (0.10 #368, 0.04 #1034, 0.02 #1871), GulfofBengal (0.10 #518, 0.01 #3695)
>> best conf = 0.20
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #68 for best value:
>> intensional similarity = 8
>> extensional distance = 3
>> proper extension:  Argun; Tarim-Yarkend; Jangtse;
>> query: (?x1022, EastChinaSea) <- ?x1022[ a River; has hasEstuary ?x231[ a Estuary; has locatedIn ?x232<CN>;]; has hasSource ?x2525[ a Source;]; has locatedIn ?x232<CN>;]

*> Best rule #5523 for first EXPECTED value:
*> intensional similarity = 12
*> extensional distance = 161
*> proper extension:  Uelle; Jordan; Main; Drin; Maas; Mincio; Guadalquivir; Euphrat;
*> query: (?x1022, ?x384) <- ?x1022[ a River; has hasEstuary ?x231; has locatedIn ?x232[ has neighbor ?x334; has religion ?x116; is locatedIn of ?x384[ has mergesWith ?x241;]; is locatedIn of ?x1881[ a Lake;]; is neighbor of ?x641[ a Country; has religion ?x95;];];]
*> conf = 0.09
ranks of expected_values: 14
EVAL	Hwangho	flowsInto	YellowSea	CNN-1.+1._MA	0.000	0.000	0.000	0.071	86.000	82.000	152.000	0.200	http://www.semwebtech.org/mondial/10/meta#flowsInto

#831-Rwanda
PRED entity: Rwanda

PRED relation: ethnicGroup!
PRED expected values: EAU
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 96): EAU (0.03 #132), ETH (0.03 #100), UA (0.03 #57), EAK (0.03 #99), CZ (0.02 #98), SN (0.02 #83), MYA (0.02 #70), IR (0.02 #58), SK (0.02 #25), RCA (0.02 #139)
>> best conf = 0.03
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #132 for best value:
>> intensional similarity = 1
>> extensional distance = 279
>> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
>> query: (?x1598, EAU) <- ?x1598[ a EthnicGroup;]
ranks of expected_values: 1
EVAL	Rwanda	ethnicGroup!	EAU	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	2.000	2.000	96.000	0.032	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup!
PRED expected values: EAU
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 96): EAU (0.03 #132), ETH (0.03 #100), UA (0.03 #57), EAK (0.03 #99), CZ (0.02 #98), SN (0.02 #83), MYA (0.02 #70), IR (0.02 #58), SK (0.02 #25), RCA (0.02 #139)
>> best conf = 0.03
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #132 for best value:
>> intensional similarity = 1
>> extensional distance = 279
>> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
>> query: (?x1598, EAU) <- ?x1598[ a EthnicGroup;]
ranks of expected_values: 1
EVAL	Rwanda	ethnicGroup!	EAU	CNN-1.+1._MA	1.000	1.000	1.000	1.000	2.000	2.000	96.000	0.032	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#830-Petschora
PRED entity: Petschora

PRED relation: locatedIn
PRED expected values: R
=> 29 concepts (29 used for prediction)
PRED predicted values (max 10 best out of 72): R (0.79 #4748, 0.68 #1187, 0.68 #955), N (0.42 #713, 0.33 #34, 0.29 #474), SVAX (0.42 #713, 0.29 #474, 0.10 #5223), D (0.26 #2155, 0.19 #3819, 0.19 #3581), I (0.17 #761, 0.15 #1235, 0.13 #1709), UA (0.17 #783, 0.13 #1731, 0.10 #1257), CN (0.14 #1953, 0.07 #4510, 0.07 #5461), P (0.11 #910, 0.10 #1384, 0.09 #1858), USA (0.11 #2683, 0.11 #2921, 0.10 #3395), ZRE (0.10 #4590, 0.10 #5303, 0.10 #5778)
>> best conf = 0.79
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4748 for best value:
>> intensional similarity = 8
>> extensional distance = 153
>> proper extension:  Busira; Schari; Oesterdalaelv;
>> query: (?x2338, ?x73) <- ?x2338[ a Estuary; is hasEstuary of ?x1227[ a River; has flowsInto ?x251[ has locatedIn ?x73;]; has hasSource ?x1416[ a Source;]; has locatedIn ?x73;];]
ranks of expected_values: 1
EVAL	Petschora	locatedIn	R	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	29.000	29.000	72.000	0.791	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: R
=> 79 concepts (79 used for prediction)
PRED predicted values (max 10 best out of 83): R (0.78 #12416, 0.77 #14569, 0.77 #8110), D (0.64 #5495, 0.43 #11239, 0.41 #12196), N (0.62 #2854, 0.57 #2617, 0.45 #4521), SVAX (0.62 #2854, 0.57 #2617, 0.45 #4521), USA (0.36 #11771, 0.35 #11531, 0.31 #12488), AUS (0.33 #45, 0.25 #2900, 0.09 #3614), UA (0.33 #785, 0.20 #1734, 0.17 #2210), LV (0.33 #343, 0.20 #1769, 0.17 #2245), GB (0.33 #485, 0.03 #10512, 0.01 #18651), I (0.27 #3617, 0.23 #6002, 0.18 #8878)
>> best conf = 0.78
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #12416 for best value:
>> intensional similarity = 14
>> extensional distance = 39
>> proper extension:  Neckar; Weser; Aller;
>> query: (?x2338, ?x73) <- ?x2338[ a Estuary; is hasEstuary of ?x1227[ a River; has hasSource ?x1416[ a Source;]; has locatedIn ?x73[ a Country; has encompassed ?x195; has ethnicGroup ?x58; has neighbor ?x403; has religion ?x187<Muslim>; is neighbor of ?x194<PL>;];];]
ranks of expected_values: 1
EVAL	Petschora	locatedIn	R	CNN-1.+1._MA	1.000	1.000	1.000	1.000	79.000	79.000	83.000	0.780	http://www.semwebtech.org/mondial/10/meta#locatedIn

#829-BlackMaur
PRED entity: BlackMaur

PRED relation: ethnicGroup!
PRED expected values: RIM
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 96): EAU (0.03 #132), ETH (0.03 #100), UA (0.03 #57), EAK (0.03 #99), CZ (0.02 #98), SN (0.02 #83), MYA (0.02 #70), IR (0.02 #58), SK (0.02 #25), RCA (0.02 #139)
>> best conf = 0.03
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #132 for best value:
>> intensional similarity = 1
>> extensional distance = 279
>> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
>> query: (?x1222, EAU) <- ?x1222[ a EthnicGroup;]

No rule for expected values
ranks of expected_values: 
EVAL	BlackMaur	ethnicGroup!	RIM	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	2.000	2.000	96.000	0.032	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup!
PRED expected values: RIM
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 96): EAU (0.03 #132), ETH (0.03 #100), UA (0.03 #57), EAK (0.03 #99), CZ (0.02 #98), SN (0.02 #83), MYA (0.02 #70), IR (0.02 #58), SK (0.02 #25), RCA (0.02 #139)
>> best conf = 0.03
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #132 for best value:
>> intensional similarity = 1
>> extensional distance = 279
>> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
>> query: (?x1222, EAU) <- ?x1222[ a EthnicGroup;]

No rule for expected values
ranks of expected_values: 
EVAL	BlackMaur	ethnicGroup!	RIM	CNN-1.+1._MA	0.000	0.000	0.000	0.000	2.000	2.000	96.000	0.032	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#828-PE
PRED entity: PE

PRED relation: ethnicGroup
PRED expected values: European
=> 35 concepts (35 used for prediction)
PRED predicted values (max 10 best out of 242): European (0.79 #263, 0.73 #519, 0.63 #775), African (0.37 #2305, 0.37 #2053, 0.36 #261), Mulatto (0.30 #2306, 0.17 #3331, 0.16 #7429), Polynesian (0.21 #1879, 0.21 #1367, 0.19 #1623), Russian (0.17 #3402, 0.17 #4426, 0.17 #4682), Quechua (0.17 #3331, 0.16 #7429, 0.16 #7428), Aymara (0.17 #3331, 0.16 #7429, 0.16 #7428), Chinese (0.14 #2576, 0.14 #1294, 0.14 #6417), Ukrainian (0.14 #4356, 0.12 #3588, 0.12 #3075), German (0.13 #3083, 0.12 #3596, 0.11 #4364)
>> best conf = 0.79
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #263 for best value:
>> intensional similarity = 6
>> extensional distance = 12
>> proper extension:  R; GCA; RCH; CO; USA; CR; NIC; MEX; ES; PA; ...
>> query: (?x296, European) <- ?x296[ has encompassed ?x521; has ethnicGroup ?x79; has language ?x702; is locatedIn of ?x282<PacificOcean>; is neighbor of ?x202;]
ranks of expected_values: 1
EVAL	PE	ethnicGroup	European	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	35.000	35.000	242.000	0.786	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: European
=> 106 concepts (106 used for prediction)
PRED predicted values (max 10 best out of 255): European (0.88 #9485, 0.87 #8461, 0.86 #7179), African (0.59 #12302, 0.57 #6660, 0.51 #16401), Mulatto (0.57 #6660, 0.41 #10760, 0.33 #313), Quechua (0.57 #6660, 0.41 #10760, 0.33 #217), Aymara (0.57 #6660, 0.41 #10760, 0.33 #166), German (0.29 #7951, 0.28 #9743, 0.25 #8975), Chinese (0.27 #4368, 0.21 #4880, 0.18 #5905), White (0.27 #4163, 0.13 #21524, 0.11 #3650), Russian (0.27 #5705, 0.24 #15954, 0.20 #9805), Ukrainian (0.24 #7943, 0.20 #11530, 0.20 #9991)
>> best conf = 0.88
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #9485 for best value:
>> intensional similarity = 11
>> extensional distance = 22
>> proper extension:  BDS;
>> query: (?x296, European) <- ?x296[ a Country; has ethnicGroup ?x79[ a EthnicGroup; is ethnicGroup of ?x181<GCA>; is ethnicGroup of ?x902<EC>;]; has government ?x700; has language ?x702; has religion ?x95; is locatedIn of ?x264;]
ranks of expected_values: 1
EVAL	PE	ethnicGroup	European	CNN-1.+1._MA	1.000	1.000	1.000	1.000	106.000	106.000	255.000	0.875	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#827-Thjorsa
PRED entity: Thjorsa

PRED relation: flowsInto
PRED expected values: AtlanticOcean
=> 39 concepts (30 used for prediction)
PRED predicted values (max 10 best out of 95): GreenlandSea (0.33 #132, 0.11 #1662, 0.10 #996), AtlanticOcean (0.19 #663, 0.19 #508, 0.12 #841), NorwegianSea (0.11 #1662, 0.10 #996, 0.03 #3493), Donau (0.11 #671, 0.08 #1005, 0.07 #1671), Zaire (0.07 #587, 0.04 #1254, 0.03 #2250), BalticSea (0.06 #1007, 0.04 #2169, 0.04 #1173), MediterraneanSea (0.05 #852, 0.05 #686, 0.04 #1519), Kasai (0.04 #546, 0.02 #713, 0.02 #1879), BlackSea (0.03 #666, 0.03 #832, 0.03 #1000), PacificOcean (0.03 #521, 0.02 #2019, 0.02 #688)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #132 for best value:
>> intensional similarity = 7
>> extensional distance = 1
>> proper extension:  JoekulsaaFjoellum;
>> query: (?x2147, GreenlandSea) <- ?x2147[ a River; has hasEstuary ?x1369[ a Estuary;]; has hasSource ?x2272[ a Source;]; has locatedIn ?x455<IS>;]

*> Best rule #663 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 67
*> proper extension:  Sanaga;
*> query: (?x2147, ?x182) <- ?x2147[ a River; has hasSource ?x2272[ a Source; has locatedIn ?x455[ is locatedIn of ?x182<AtlanticOcean>;];];]
*> conf = 0.19
ranks of expected_values: 2
EVAL	Thjorsa	flowsInto	AtlanticOcean	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	39.000	30.000	95.000	0.333	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto
PRED expected values: AtlanticOcean
=> 94 concepts (94 used for prediction)
PRED predicted values (max 10 best out of 126): GreenlandSea (0.33 #132, 0.20 #3669, 0.16 #4501), AtlanticOcean (0.31 #1666, 0.31 #1512, 0.30 #1165), NorwegianSea (0.16 #4501, 0.13 #2001, 0.11 #6340), NorthSea (0.14 #171, 0.03 #1172, 0.02 #4500), Skagerrak (0.14 #311, 0.01 #2816, 0.01 #2982), Glomma (0.14 #300, 0.01 #2805, 0.01 #2971), Vaenern (0.14 #253, 0.01 #2758, 0.01 #2924), Zaire (0.13 #2093, 0.10 #1758, 0.09 #2260), Donau (0.09 #3678, 0.08 #3343, 0.08 #2678), Kasai (0.08 #882, 0.06 #1049, 0.06 #1717)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #132 for best value:
>> intensional similarity = 9
>> extensional distance = 1
>> proper extension:  JoekulsaaFjoellum;
>> query: (?x2147, GreenlandSea) <- ?x2147[ a River; has hasEstuary ?x1369[ a Estuary; has locatedIn ?x455<IS>;]; has hasSource ?x2272[ a Source; has locatedIn ?x455<IS>;]; has locatedIn ?x455<IS>;]

*> Best rule #1666 for first EXPECTED value:
*> intensional similarity = 9
*> extensional distance = 43
*> proper extension:  Garonne; Loire;
*> query: (?x2147, ?x182) <- ?x2147[ a River; has hasEstuary ?x1369; has hasSource ?x2272; has locatedIn ?x455[ has encompassed ?x195; has government ?x700; has religion ?x95; is locatedIn of ?x182<AtlanticOcean>;];]
*> conf = 0.31
ranks of expected_values: 2
EVAL	Thjorsa	flowsInto	AtlanticOcean	CNN-1.+1._MA	0.000	1.000	1.000	0.500	94.000	94.000	126.000	0.333	http://www.semwebtech.org/mondial/10/meta#flowsInto

#826-Danish
PRED entity: Danish

PRED relation: language!
PRED expected values: DK
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 60): PK (0.08 #8), SF (0.07 #79), IR (0.07 #45), BZ (0.06 #88), AND (0.05 #97), L (0.05 #93), MK (0.05 #92), NZ (0.05 #70), I (0.05 #30), NLSM (0.05 #1)
>> best conf = 0.08
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #8 for best value:
>> intensional similarity = 1
>> extensional distance = 102
>> proper extension:  French; Japanese; Samoan; English; Tajik; KhalkhaMongol; Italian; Turkish; Portuguese; Dutch; ...
>> query: (?x2443, PK) <- ?x2443[ a Language;]

No rule for expected values
ranks of expected_values: 
EVAL	Danish	language!	DK	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	2.000	2.000	60.000	0.077	http://www.semwebtech.org/mondial/10/meta#language

PRED relation: language!
PRED expected values: DK
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 60): PK (0.08 #8), SF (0.07 #79), IR (0.07 #45), BZ (0.06 #88), AND (0.05 #97), L (0.05 #93), MK (0.05 #92), NZ (0.05 #70), I (0.05 #30), NLSM (0.05 #1)
>> best conf = 0.08
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #8 for best value:
>> intensional similarity = 1
>> extensional distance = 102
>> proper extension:  French; Japanese; Samoan; English; Tajik; KhalkhaMongol; Italian; Turkish; Portuguese; Dutch; ...
>> query: (?x2443, PK) <- ?x2443[ a Language;]

No rule for expected values
ranks of expected_values: 
EVAL	Danish	language!	DK	CNN-1.+1._MA	0.000	0.000	0.000	0.000	2.000	2.000	60.000	0.077	http://www.semwebtech.org/mondial/10/meta#language

#825-BD
PRED entity: BD

PRED relation: neighbor!
PRED expected values: IND
=> 44 concepts (44 used for prediction)
PRED predicted values (max 10 best out of 180): IND (0.90 #3726, 0.90 #5679, 0.89 #3725), CN (0.50 #367, 0.50 #44, 0.40 #206), BD (0.50 #141, 0.40 #303, 0.33 #464), LAO (0.33 #403, 0.29 #3727, 0.28 #5680), THA (0.29 #3727, 0.28 #5680, 0.28 #3728), PK (0.29 #3727, 0.28 #5680, 0.28 #3728), BHT (0.29 #3727, 0.28 #5680, 0.28 #3728), NEP (0.29 #3727, 0.28 #5680, 0.26 #3075), IL (0.25 #1500, 0.17 #370, 0.16 #1984), CL (0.21 #2423, 0.18 #2424, 0.09 #2586)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3726 for best value:
>> intensional similarity = 5
>> extensional distance = 94
>> proper extension:  SSD;
>> query: (?x943, ?x924) <- ?x943[ a Country; has neighbor ?x924[ has neighbor ?x83;]; is locatedIn of ?x1258[ has hasSource ?x1478;];]
ranks of expected_values: 1
EVAL	BD	neighbor!	IND	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	44.000	44.000	180.000	0.901	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: IND
=> 112 concepts (112 used for prediction)
PRED predicted values (max 10 best out of 211): IND (0.94 #8260, 0.93 #10417, 0.93 #11253), CN (0.50 #4771, 0.50 #4654, 0.50 #1028), BD (0.50 #1125, 0.33 #471, 0.33 #9258), MNE (0.50 #666, 0.25 #2973, 0.25 #2808), R (0.44 #3954, 0.33 #166, 0.29 #2140), LAO (0.40 #4690, 0.38 #3375, 0.33 #3702), MOC (0.40 #4481, 0.33 #33, 0.15 #326), A (0.38 #3205, 0.38 #2708, 0.33 #4194), PL (0.38 #3164, 0.25 #2667, 0.22 #4153), PK (0.33 #1486, 0.33 #336, 0.33 #169)
>> best conf = 0.94
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #8260 for best value:
>> intensional similarity = 15
>> extensional distance = 37
>> proper extension:  NOK;
>> query: (?x943, ?x924) <- ?x943[ a Country; has encompassed ?x175; has government ?x254; has neighbor ?x924[ has religion ?x187[ is religion of ?x1072<WAL>; is religion of ?x1576<ZW>;]; is locatedIn of ?x60;]; has wasDependentOf ?x83[ has language ?x559; is locatedIn of ?x926[ is mergesWith of ?x918;];]; is locatedIn of ?x262;]
ranks of expected_values: 1
EVAL	BD	neighbor!	IND	CNN-1.+1._MA	1.000	1.000	1.000	1.000	112.000	112.000	211.000	0.935	http://www.semwebtech.org/mondial/10/meta#neighbor

#824-SBAR
PRED entity: SBAR

PRED relation: locatedIn!
PRED expected values: St.Barthelemy
=> 24 concepts (16 used for prediction)
PRED predicted values (max 10 best out of 765): PacificOcean (0.27 #8622, 0.26 #11467, 0.25 #12890), MediterraneanSea (0.15 #5690, 0.15 #17157, 0.14 #18582), IndianOcean (0.15 #5690, 0.11 #17078, 0.11 #18503), GulfofMexico (0.15 #5690, 0.11 #22774, 0.10 #19924), TheChannel (0.15 #5690, 0.11 #22774, 0.10 #19924), GreenlandSea (0.15 #5690, 0.11 #22774, 0.10 #19924), LabradorSea (0.15 #5690, 0.11 #22774, 0.10 #19924), NorthSea (0.15 #5690, 0.09 #15674, 0.04 #17097), Hispaniola (0.11 #1267, 0.11 #2689, 0.07 #4111), NorwegianSea (0.11 #22774, 0.10 #19924, 0.10 #18498)
>> best conf = 0.27
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #8622 for best value:
>> intensional similarity = 7
>> extensional distance = 46
>> proper extension:  ES;
>> query: (?x1502, PacificOcean) <- ?x1502[ has encompassed ?x521<America>; is locatedIn of ?x317[ a Sea; has locatedIn ?x246[ has language ?x247;]; is locatedInWater of ?x123;];]

*> Best rule #21350 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 177
*> proper extension:  BG;
*> query: (?x1502, ?x112) <- ?x1502[ a Country; is locatedIn of ?x182[ has locatedIn ?x379[ has neighbor ?x404;]; is flowsInto of ?x137; is locatedInWater of ?x112;];]
*> conf = 0.02
ranks of expected_values: 534
EVAL	SBAR	locatedIn!	St.Barthelemy	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.002	24.000	16.000	765.000	0.271	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: St.Barthelemy
=> 49 concepts (43 used for prediction)
PRED predicted values (max 10 best out of 1174): PacificOcean (0.59 #7198, 0.37 #25743, 0.35 #27173), St.Martin (0.33 #761, 0.11 #3605, 0.06 #2183), MediterraneanSea (0.27 #22888, 0.27 #21460, 0.25 #24314), IndianOcean (0.12 #35660, 0.12 #31370, 0.12 #38516), Orinoco (0.12 #57055, 0.08 #55632, 0.08 #55631), Amazonas (0.12 #57055, 0.08 #55632, 0.08 #55631), RioSanJuan (0.12 #57055, 0.08 #55632, 0.08 #55631), Tajo (0.12 #57055, 0.08 #55632, 0.08 #55631), Guadiana (0.12 #57055, 0.08 #55632, 0.08 #55631), Douro (0.12 #57055, 0.08 #55632, 0.08 #55631)
>> best conf = 0.59
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #7198 for best value:
>> intensional similarity = 12
>> extensional distance = 54
>> proper extension:  VU; GUAM;
>> query: (?x1502, PacificOcean) <- ?x1502[ a Country; has government ?x1503; is locatedIn of ?x182[ has mergesWith ?x60; is flowsInto of ?x137; is locatedInWater of ?x112;]; is locatedIn of ?x317[ a Sea; has locatedIn ?x408<NIC>; is flowsInto of ?x311; is locatedInWater of ?x506;];]

*> Best rule #41368 for first EXPECTED value:
*> intensional similarity = 12
*> extensional distance = 157
*> proper extension:  UAE; IR; OM;
*> query: (?x1502, ?x112) <- ?x1502[ has encompassed ?x521; is locatedIn of ?x182[ has locatedIn ?x81[ is wasDependentOf of ?x63;]; has locatedIn ?x154[ has encompassed ?x195; has government ?x2243; has religion ?x352;]; has mergesWith ?x60; is locatedInWater of ?x112; is locatedInWater of ?x495[ a Island;];];]
*> conf = 0.03
ranks of expected_values: 608
EVAL	SBAR	locatedIn!	St.Barthelemy	CNN-1.+1._MA	0.000	0.000	0.000	0.002	49.000	43.000	1174.000	0.589	http://www.semwebtech.org/mondial/10/meta#locatedIn

#823-OzeroChanka
PRED entity: OzeroChanka

PRED relation: locatedIn
PRED expected values: R
=> 33 concepts (30 used for prediction)
PRED predicted values (max 10 best out of 124): R (0.53 #714, 0.23 #4253, 0.17 #950), IR (0.40 #542, 0.18 #2194, 0.09 #1015), USA (0.34 #1016, 0.16 #2669, 0.14 #4556), AUS (0.29 #2881, 0.14 #4057, 0.11 #990), KAZ (0.21 #1979, 0.20 #2453, 0.20 #801), TAD (0.19 #1909, 0.18 #2383, 0.08 #2834), F (0.17 #4019, 0.09 #4255, 0.04 #4492), KGZ (0.17 #258, 0.08 #2834, 0.08 #3307), E (0.15 #4039, 0.08 #4275, 0.04 #4512), IND (0.14 #2311, 0.08 #2834, 0.08 #3307)
>> best conf = 0.53
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #714 for best value:
>> intensional similarity = 7
>> extensional distance = 13
>> proper extension:  OzeroBalchash; KuybyshevReservoir; OzeroBaikal; OzeroTschany; OzeroLadoga; OzeroTaimyr; CaspianSea; OzeroOnega; OzeroAral; OzeroPskovskoje;
>> query: (?x2488, R) <- ?x2488[ a Lake; has locatedIn ?x232[ is locatedIn of ?x1478[ a Source;]; is locatedIn of ?x1748<Irtysch>; is neighbor of ?x73;];]
ranks of expected_values: 1
EVAL	OzeroChanka	locatedIn	R	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	33.000	30.000	124.000	0.533	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: R
=> 94 concepts (92 used for prediction)
PRED predicted values (max 10 best out of 204): USA (0.62 #1984, 0.58 #9907, 0.57 #10146), R (0.62 #1678, 0.34 #2151, 0.34 #2150), KAZ (0.60 #14141, 0.59 #20614, 0.58 #20137), KGZ (0.60 #14141, 0.59 #20614, 0.58 #20137), PK (0.60 #14141, 0.59 #20614, 0.58 #20137), NEP (0.60 #14141, 0.59 #20614, 0.58 #20137), IR (0.44 #1504, 0.13 #7995, 0.12 #1264), RI (0.40 #7015, 0.32 #7499, 0.15 #10364), D (0.39 #9614, 0.20 #10568, 0.19 #10808), LAO (0.36 #3587, 0.34 #2151, 0.34 #2150)
>> best conf = 0.62
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1984 for best value:
>> intensional similarity = 17
>> extensional distance = 24
>> proper extension:  LakeBurleyGriffin; LakeEyre; LakeEucumbene; LakeJindabyne; LakeHume;
>> query: (?x2488, USA) <- ?x2488[ a Lake; has locatedIn ?x232[ is dependentOf of ?x641; is locatedIn of ?x338[ has hasEstuary ?x1481;]; is locatedIn of ?x791[ a Desert;]; is locatedIn of ?x1152[ a River;]; is locatedIn of ?x1256[ a Source;]; is locatedIn of ?x1320[ has inMountains ?x309;]; is locatedIn of ?x1793[ a Estuary;]; is locatedIn of ?x1950[ has flowsInto ?x1258;];];]

*> Best rule #1678 for first EXPECTED value:
*> intensional similarity = 16
*> extensional distance = 11
*> proper extension:  KuybyshevReservoir; OzeroBaikal; OzeroTschany; OzeroLadoga; OzeroTaimyr; CaspianSea; OzeroOnega; OzeroPskovskoje;
*> query: (?x2488, R) <- ?x2488[ a Lake; has locatedIn ?x232[ has ethnicGroup ?x2285; has neighbor ?x381[ is locatedIn of ?x82;]; has neighbor ?x641[ has religion ?x95;]; is locatedIn of ?x338[ a River;]; is locatedIn of ?x472<Argun>; is locatedIn of ?x1464[ has type ?x578;]; is locatedIn of ?x1585<Amur>; is neighbor of ?x463;];]
*> conf = 0.62
ranks of expected_values: 2
EVAL	OzeroChanka	locatedIn	R	CNN-1.+1._MA	0.000	1.000	1.000	0.500	94.000	92.000	204.000	0.615	http://www.semwebtech.org/mondial/10/meta#locatedIn

#822-P
PRED entity: P

PRED relation: language
PRED expected values: Portuguese
=> 54 concepts (54 used for prediction)
PRED predicted values (max 10 best out of 94): Spanish (0.76 #1002, 0.40 #120, 0.35 #1590), Catalan (0.33 #21, 0.06 #805, 0.05 #1197), Basque (0.33 #31, 0.06 #815, 0.05 #1207), English (0.25 #3826, 0.23 #3434, 0.22 #1082), Dutch (0.20 #500, 0.20 #108, 0.17 #206), Russian (0.17 #207, 0.14 #305, 0.14 #2461), Hungarian (0.17 #1095, 0.12 #409, 0.10 #1977), French (0.16 #1177, 0.15 #1275, 0.14 #2941), German (0.15 #1289, 0.12 #799, 0.11 #1191), Serbian (0.12 #431, 0.12 #2979, 0.10 #529)
>> best conf = 0.76
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1002 for best value:
>> intensional similarity = 4
>> extensional distance = 15
>> proper extension:  ES;
>> query: (?x1027, Spanish) <- ?x1027[ a Country; has wasDependentOf ?x149<E>; is locatedIn of ?x182;]

*> Best rule #793 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 15
*> proper extension:  DK;
*> query: (?x1027, Portuguese) <- ?x1027[ a Country; is locatedIn of ?x182; is locatedIn of ?x827[ a Island;]; is wasDependentOf of ?x192;]
*> conf = 0.06
ranks of expected_values: 35
EVAL	P	language	Portuguese	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.029	54.000	54.000	94.000	0.765	http://www.semwebtech.org/mondial/10/meta#language

PRED relation: language
PRED expected values: Portuguese
=> 141 concepts (141 used for prediction)
PRED predicted values (max 10 best out of 98): Spanish (0.61 #6480, 0.61 #6403, 0.50 #4834), French (0.50 #295, 0.33 #981, 0.33 #785), English (0.41 #8644, 0.40 #592, 0.39 #7663), Catalan (0.33 #21, 0.33 #6481, 0.25 #3732), Basque (0.33 #31, 0.33 #6481, 0.25 #3732), Portuguese (0.25 #303, 0.18 #3929, 0.12 #2364), Galician (0.25 #345, 0.12 #9621, 0.02 #4714), German (0.20 #603, 0.20 #505, 0.18 #3747), Dutch (0.20 #500, 0.18 #3742, 0.17 #4331), Albanian (0.20 #527, 0.17 #1115, 0.17 #1017)
>> best conf = 0.61
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #6480 for best value:
>> intensional similarity = 9
>> extensional distance = 16
>> proper extension:  HCA;
>> query: (?x1027, ?x796) <- ?x1027[ has government ?x2551; has neighbor ?x149[ has language ?x796<Spanish>; has neighbor ?x78; is locatedIn of ?x68;]; is locatedIn of ?x182[ is locatedInWater of ?x112; is mergesWith of ?x60;];]
>> Best rule #6403 for best value:
>> intensional similarity = 9
>> extensional distance = 16
>> proper extension:  HCA;
>> query: (?x1027, Spanish) <- ?x1027[ has government ?x2551; has neighbor ?x149[ has language ?x796<Spanish>; has neighbor ?x78; is locatedIn of ?x68;]; is locatedIn of ?x182[ is locatedInWater of ?x112; is mergesWith of ?x60;];]

*> Best rule #303 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 2
*> proper extension:  AND;
*> query: (?x1027, Portuguese) <- ?x1027[ a Country; has encompassed ?x195<Europe>; has neighbor ?x149<E>; has religion ?x352<RomanCatholic>; is neighbor of ?x149<E>;]
*> conf = 0.25
ranks of expected_values: 6
EVAL	P	language	Portuguese	CNN-1.+1._MA	0.000	0.000	1.000	0.167	141.000	141.000	98.000	0.611	http://www.semwebtech.org/mondial/10/meta#language

#821-LabradorSea
PRED entity: LabradorSea

PRED relation: mergesWith!
PRED expected values: ArcticOcean
=> 29 concepts (26 used for prediction)
PRED predicted values (max 10 best out of 102): ArcticOcean (0.83 #645, 0.83 #644, 0.83 #603), LabradorSea (0.51 #360, 0.51 #441, 0.51 #524), GreenlandSea (0.51 #360, 0.51 #441, 0.51 #524), PacificOcean (0.47 #254, 0.26 #333, 0.25 #455), BeringSea (0.33 #27, 0.25 #228, 0.19 #482), NorwegianSea (0.33 #58, 0.25 #98, 0.17 #140), BarentsSea (0.33 #8, 0.19 #482, 0.18 #604), EastSibirianSea (0.33 #20, 0.19 #482, 0.18 #604), KaraSea (0.33 #26, 0.19 #482, 0.18 #604), IndianOcean (0.25 #202, 0.25 #82, 0.19 #482)
>> best conf = 0.83
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #645 for best value:
>> intensional similarity = 7
>> extensional distance = 38
>> proper extension:  RedSea;
>> query: (?x249, ?x263) <- ?x249[ has mergesWith ?x182[ has locatedIn ?x179[ has government ?x180;]; has locatedIn ?x810[ has neighbor ?x426;]; has mergesWith ?x60;]; has mergesWith ?x263;]
>> Best rule #644 for best value:
>> intensional similarity = 7
>> extensional distance = 38
>> proper extension:  RedSea;
>> query: (?x249, ?x248) <- ?x249[ has mergesWith ?x182[ has locatedIn ?x179[ has government ?x180;]; has locatedIn ?x810[ has neighbor ?x426;]; has mergesWith ?x60;]; has mergesWith ?x248;]
ranks of expected_values: 1
EVAL	LabradorSea	mergesWith!	ArcticOcean	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	29.000	26.000	102.000	0.832	http://www.semwebtech.org/mondial/10/meta#mergesWith

PRED relation: mergesWith!
PRED expected values: ArcticOcean
=> 73 concepts (73 used for prediction)
PRED predicted values (max 10 best out of 194): ArcticOcean (0.82 #1131, 0.82 #958, 0.82 #576), GreenlandSea (0.61 #288, 0.61 #287, 0.61 #286), LabradorSea (0.61 #288, 0.61 #287, 0.61 #286), PacificOcean (0.54 #510, 0.47 #551, 0.42 #673), BeringSea (0.33 #27, 0.29 #274, 0.27 #828), BarentsSea (0.33 #8, 0.27 #828, 0.25 #42), EastSibirianSea (0.33 #20, 0.27 #828, 0.25 #42), KaraSea (0.33 #26, 0.27 #828, 0.25 #42), NorwegianSea (0.33 #102, 0.25 #143, 0.23 #412), JavaSea (0.31 #502, 0.27 #543, 0.21 #665)
>> best conf = 0.82
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1131 for best value:
>> intensional similarity = 10
>> extensional distance = 29
>> proper extension:  Skagerrak; GulfofAden;
>> query: (?x249, ?x182) <- ?x249[ has locatedIn ?x792; has mergesWith ?x182; has mergesWith ?x263[ has locatedIn ?x73[ has ethnicGroup ?x58; has language ?x555; has neighbor ?x170; has religion ?x56; has wasDependentOf ?x903;]; is locatedInWater of ?x478;];]
ranks of expected_values: 1
EVAL	LabradorSea	mergesWith!	ArcticOcean	CNN-1.+1._MA	1.000	1.000	1.000	1.000	73.000	73.000	194.000	0.823	http://www.semwebtech.org/mondial/10/meta#mergesWith

#820-MtWaddington
PRED entity: MtWaddington

PRED relation: locatedIn
PRED expected values: CDN
=> 4 concepts (4 used for prediction)
PRED predicted values (max 10 best out of 46): USA (0.15 #72, 0.10 #308, 0.10 #544), CN (0.06 #292, 0.06 #56, 0.04 #528), I (0.06 #520, 0.05 #48, 0.04 #284), E (0.06 #27, 0.05 #499, 0.04 #263), CDN (0.05 #63, 0.04 #299, 0.03 #535), PE (0.05 #539, 0.03 #67, 0.02 #303), R (0.05 #477, 0.04 #5, 0.04 #713), D (0.04 #492, 0.03 #728, 0.03 #20), RI (0.04 #288, 0.02 #760), F (0.04 #479, 0.03 #7, 0.02 #243)
>> best conf = 0.15
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #72 for best value:
>> intensional similarity = 3
>> extensional distance = 156
>> proper extension:  JabalKatrina; Tahat; Annapurna; Bjelucha; Schchara; GranSasso; EmiKussi; Zugspitze; Demirkazik; Cayambe; ...
>> query: (?x2015, USA) <- ?x2015[ a Mountain; has inMountains ?x2016[ a Mountains;];]

*> Best rule #63 for first EXPECTED value:
*> intensional similarity = 3
*> extensional distance = 156
*> proper extension:  JabalKatrina; Tahat; Annapurna; Bjelucha; Schchara; GranSasso; EmiKussi; Zugspitze; Demirkazik; Cayambe; ...
*> query: (?x2015, CDN) <- ?x2015[ a Mountain; has inMountains ?x2016[ a Mountains;];]
*> conf = 0.05
ranks of expected_values: 5
EVAL	MtWaddington	locatedIn	CDN	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.200	4.000	4.000	46.000	0.146	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: CDN
=> 4 concepts (4 used for prediction)
PRED predicted values (max 10 best out of 46): USA (0.15 #72, 0.10 #308, 0.10 #544), CN (0.06 #292, 0.06 #56, 0.04 #528), I (0.06 #520, 0.05 #48, 0.04 #284), E (0.06 #27, 0.05 #499, 0.04 #263), CDN (0.05 #63, 0.04 #299, 0.03 #535), PE (0.05 #539, 0.03 #67, 0.02 #303), R (0.05 #477, 0.04 #5, 0.04 #713), D (0.04 #492, 0.03 #728, 0.03 #20), RI (0.04 #288, 0.02 #760), F (0.04 #479, 0.03 #7, 0.02 #243)
>> best conf = 0.15
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #72 for best value:
>> intensional similarity = 3
>> extensional distance = 156
>> proper extension:  JabalKatrina; Tahat; Annapurna; Bjelucha; Schchara; GranSasso; EmiKussi; Zugspitze; Demirkazik; Cayambe; ...
>> query: (?x2015, USA) <- ?x2015[ a Mountain; has inMountains ?x2016[ a Mountains;];]

*> Best rule #63 for first EXPECTED value:
*> intensional similarity = 3
*> extensional distance = 156
*> proper extension:  JabalKatrina; Tahat; Annapurna; Bjelucha; Schchara; GranSasso; EmiKussi; Zugspitze; Demirkazik; Cayambe; ...
*> query: (?x2015, CDN) <- ?x2015[ a Mountain; has inMountains ?x2016[ a Mountains;];]
*> conf = 0.05
ranks of expected_values: 5
EVAL	MtWaddington	locatedIn	CDN	CNN-1.+1._MA	0.000	0.000	1.000	0.200	4.000	4.000	46.000	0.146	http://www.semwebtech.org/mondial/10/meta#locatedIn

#819-LaBreaPitchLake
PRED entity: LaBreaPitchLake

PRED relation: locatedIn
PRED expected values: TT
=> 4 concepts (4 used for prediction)
PRED predicted values (max 10 best out of 66): USA (0.11 #308, 0.10 #72, 0.08 #544), CDN (0.08 #299, 0.03 #771, 0.02 #63), AUS (0.07 #45, 0.03 #281, 0.03 #517), RI (0.06 #524, 0.03 #52, 0.02 #760), R (0.06 #241, 0.04 #713, 0.02 #5), EAT (0.05 #175, 0.04 #411, 0.02 #647), CN (0.05 #56, 0.03 #292, 0.02 #764), BOL (0.05 #153, 0.03 #389, 0.02 #625), IR (0.05 #71, 0.03 #307, 0.02 #543), ETH (0.05 #115, 0.03 #351, 0.02 #587)
>> best conf = 0.11
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #308 for best value:
>> intensional similarity = 1
>> extensional distance = 142
>> proper extension:  LakeSkutari; StarnbergerSee; LakeHuron; MaleboPool; ChickamaugaLake; LakeTanganjika; LakeNicaragua; LakeMweru; KuybyshevReservoir; LagunadeBay; ...
>> query: (?x1988, USA) <- ?x1988[ a Lake;]

No rule for expected values
ranks of expected_values: 
EVAL	LaBreaPitchLake	locatedIn	TT	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	4.000	4.000	66.000	0.111	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: TT
=> 4 concepts (4 used for prediction)
PRED predicted values (max 10 best out of 66): USA (0.11 #308, 0.10 #72, 0.08 #544), CDN (0.08 #299, 0.03 #771, 0.02 #63), AUS (0.07 #45, 0.03 #281, 0.03 #517), RI (0.06 #524, 0.03 #52, 0.02 #760), R (0.06 #241, 0.04 #713, 0.02 #5), EAT (0.05 #175, 0.04 #411, 0.02 #647), CN (0.05 #56, 0.03 #292, 0.02 #764), BOL (0.05 #153, 0.03 #389, 0.02 #625), IR (0.05 #71, 0.03 #307, 0.02 #543), ETH (0.05 #115, 0.03 #351, 0.02 #587)
>> best conf = 0.11
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #308 for best value:
>> intensional similarity = 1
>> extensional distance = 142
>> proper extension:  LakeSkutari; StarnbergerSee; LakeHuron; MaleboPool; ChickamaugaLake; LakeTanganjika; LakeNicaragua; LakeMweru; KuybyshevReservoir; LagunadeBay; ...
>> query: (?x1988, USA) <- ?x1988[ a Lake;]

No rule for expected values
ranks of expected_values: 
EVAL	LaBreaPitchLake	locatedIn	TT	CNN-1.+1._MA	0.000	0.000	0.000	0.000	4.000	4.000	66.000	0.111	http://www.semwebtech.org/mondial/10/meta#locatedIn

#818-GB
PRED entity: GB

PRED relation: locatedIn!
PRED expected values: Thames SouthRonaldsay Mull IsleofWight NorthUist
=> 41 concepts (39 used for prediction)
PRED predicted values (max 10 best out of 1346): PacificOcean (0.40 #4271, 0.40 #2875, 0.34 #28008), MediterraneanSea (0.40 #1476, 0.29 #5665, 0.27 #11252), Maas (0.40 #1785, 0.29 #5974, 0.23 #12957), Vignemale (0.40 #2758, 0.29 #6947, 0.20 #4154), CaribbeanSea (0.38 #26632, 0.38 #32217, 0.38 #8481), IndianOcean (0.29 #6985, 0.21 #11172, 0.11 #27928), GulfofBengal (0.29 #7052, 0.07 #19549, 0.03 #33584), NorthCaicos (0.24 #5585, 0.07 #11171, 0.06 #20726), Providenciales (0.24 #5585, 0.07 #11171, 0.06 #20677), Anguilla (0.24 #5585, 0.07 #11171, 0.06 #19878)
>> best conf = 0.40
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4271 for best value:
>> intensional similarity = 5
>> extensional distance = 3
>> proper extension:  N;
>> query: (?x81, PacificOcean) <- ?x81[ is dependentOf of ?x80[ has ethnicGroup ?x79; is locatedIn of ?x317;]; is locatedIn of ?x467[ has belongsToIslands ?x2364;];]
>> Best rule #2875 for best value:
>> intensional similarity = 6
>> extensional distance = 3
>> proper extension:  USA; NZ;
>> query: (?x81, PacificOcean) <- ?x81[ has religion ?x95; is dependentOf of ?x80; is locatedIn of ?x467[ has belongsToIslands ?x2364;]; is locatedIn of ?x674[ has type ?x150;];]

*> Best rule #43294 for first EXPECTED value:
*> intensional similarity = 3
*> extensional distance = 171
*> proper extension:  LS; TAD; SUD; BY; CH; UZB; TM; AZ; GE; AFG; ...
*> query: (?x81, ?x137) <- ?x81[ has ethnicGroup ?x1196; is locatedIn of ?x182[ is flowsInto of ?x137;];]
*> conf = 0.14
ranks of expected_values: 308, 616, 622, 623
EVAL	GB	locatedIn!	NorthUist	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	41.000	39.000	1346.000	0.400	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	GB	locatedIn!	IsleofWight	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.002	41.000	39.000	1346.000	0.400	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	GB	locatedIn!	Mull	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.002	41.000	39.000	1346.000	0.400	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	GB	locatedIn!	SouthRonaldsay	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.002	41.000	39.000	1346.000	0.400	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	GB	locatedIn!	Thames	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.003	41.000	39.000	1346.000	0.400	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Thames SouthRonaldsay Mull IsleofWight NorthUist
=> 102 concepts (102 used for prediction)
PRED predicted values (max 10 best out of 1429): Thames (0.88 #93692, 0.43 #8387, 0.35 #93691), MediterraneanSea (0.71 #65791, 0.68 #60196, 0.40 #53208), Donau (0.56 #55949, 0.21 #61515, 0.20 #67108), PacificOcean (0.50 #14067, 0.45 #42024, 0.40 #19654), CaribbeanSea (0.50 #16880, 0.43 #67212, 0.42 #85400), Maas (0.50 #11577, 0.43 #8387, 0.35 #93691), Tajo (0.43 #8387, 0.35 #93691, 0.33 #817), Guadiana (0.43 #8387, 0.35 #93691, 0.33 #636), Douro (0.43 #8387, 0.35 #93691, 0.33 #843), Guadalquivir (0.43 #8387, 0.35 #93691, 0.33 #602)
>> best conf = 0.88
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #93692 for best value:
>> intensional similarity = 11
>> extensional distance = 39
>> proper extension:  SSD;
>> query: (?x81, ?x1381) <- ?x81[ a Country; has neighbor ?x154[ has encompassed ?x195;]; is locatedIn of ?x121[ is flowsInto of ?x1631[ is flowsInto of ?x946;];]; is locatedIn of ?x1499[ a Estuary;]; is locatedIn of ?x1734[ a Source; is hasSource of ?x1381;];]
ranks of expected_values: 1, 809, 919, 925
EVAL	GB	locatedIn!	NorthUist	CNN-1.+1._MA	0.000	0.000	0.000	0.000	102.000	102.000	1429.000	0.884	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	GB	locatedIn!	IsleofWight	CNN-1.+1._MA	0.000	0.000	0.000	0.001	102.000	102.000	1429.000	0.884	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	GB	locatedIn!	Mull	CNN-1.+1._MA	0.000	0.000	0.000	0.001	102.000	102.000	1429.000	0.884	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	GB	locatedIn!	SouthRonaldsay	CNN-1.+1._MA	0.000	0.000	0.000	0.001	102.000	102.000	1429.000	0.884	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	GB	locatedIn!	Thames	CNN-1.+1._MA	1.000	1.000	1.000	1.000	102.000	102.000	1429.000	0.884	http://www.semwebtech.org/mondial/10/meta#locatedIn

#817-AUS
PRED entity: AUS

PRED relation: language
PRED expected values: Italian Greek Chinese
=> 46 concepts (46 used for prediction)
PRED predicted values (max 10 best out of 95): Spanish (0.47 #1066, 0.34 #2016, 0.30 #1446), Hindi (0.25 #182, 0.25 #87, 0.20 #277), French (0.25 #1, 0.21 #1046, 0.20 #286), Samoan (0.25 #3, 0.20 #193, 0.06 #1333), Chinese (0.25 #61, 0.20 #251, 0.06 #1011), German (0.20 #299, 0.12 #774, 0.12 #679), Albanian (0.20 #321, 0.12 #701, 0.11 #1746), Slovenian (0.20 #303, 0.12 #683, 0.07 #873), Italian (0.20 #291, 0.12 #671, 0.07 #861), Burmese (0.14 #614, 0.12 #709, 0.07 #899)
>> best conf = 0.47
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1066 for best value:
>> intensional similarity = 6
>> extensional distance = 17
>> proper extension:  GUAM;
>> query: (?x196, Spanish) <- ?x196[ a Country; has language ?x247; is locatedIn of ?x282<PacificOcean>; is locatedIn of ?x1953[ has type ?x704;];]

*> Best rule #61 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 2
*> proper extension:  USA; NZ;
*> query: (?x196, Chinese) <- ?x196[ has religion ?x462<Buddhist>; has wasDependentOf ?x81; is dependentOf of ?x210; is locatedIn of ?x60;]
*> conf = 0.25
ranks of expected_values: 5, 9, 19
EVAL	AUS	language	Chinese	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.200	46.000	46.000	95.000	0.474	http://www.semwebtech.org/mondial/10/meta#language
EVAL	AUS	language	Greek	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.059	46.000	46.000	95.000	0.474	http://www.semwebtech.org/mondial/10/meta#language
EVAL	AUS	language	Italian	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.125	46.000	46.000	95.000	0.474	http://www.semwebtech.org/mondial/10/meta#language

PRED relation: language
PRED expected values: Italian Greek Chinese
=> 105 concepts (105 used for prediction)
PRED predicted values (max 10 best out of 95): French (0.60 #1521, 0.56 #2188, 0.25 #2759), Spanish (0.54 #3065, 0.50 #3635, 0.50 #2779), Hebrew (0.50 #612, 0.33 #42, 0.15 #3901), Turkish (0.50 #672, 0.25 #3241, 0.25 #1812), German (0.40 #1534, 0.25 #1819, 0.25 #1059), Italian (0.40 #1526, 0.25 #1811, 0.22 #2193), Albanian (0.38 #1841, 0.20 #1556, 0.19 #4033), Russian (0.33 #295, 0.27 #2483, 0.25 #1910), Swedish (0.33 #363, 0.25 #1978, 0.15 #3901), Finnish (0.33 #297, 0.25 #1912, 0.15 #3901)
>> best conf = 0.60
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1521 for best value:
>> intensional similarity = 15
>> extensional distance = 3
>> proper extension:  CH;
>> query: (?x196, French) <- ?x196[ a Country; has language ?x247[ a Language; is language of ?x80[ has ethnicGroup ?x79;]; is language of ?x621[ has neighbor ?x1206<CI>;];]; has religion ?x56; is locatedIn of ?x1021[ a Lake;]; is locatedIn of ?x1820[ a Source; has inMountains ?x846; is hasSource of ?x1356;];]

*> Best rule #1526 for first EXPECTED value:
*> intensional similarity = 15
*> extensional distance = 3
*> proper extension:  CH;
*> query: (?x196, Italian) <- ?x196[ a Country; has language ?x247[ a Language; is language of ?x80[ has ethnicGroup ?x79;]; is language of ?x621[ has neighbor ?x1206<CI>;];]; has religion ?x56; is locatedIn of ?x1021[ a Lake;]; is locatedIn of ?x1820[ a Source; has inMountains ?x846; is hasSource of ?x1356;];]
*> conf = 0.40
ranks of expected_values: 6, 15, 21
EVAL	AUS	language	Chinese	CNN-1.+1._MA	0.000	0.000	0.000	0.053	105.000	105.000	95.000	0.600	http://www.semwebtech.org/mondial/10/meta#language
EVAL	AUS	language	Greek	CNN-1.+1._MA	0.000	0.000	0.000	0.071	105.000	105.000	95.000	0.600	http://www.semwebtech.org/mondial/10/meta#language
EVAL	AUS	language	Italian	CNN-1.+1._MA	0.000	0.000	1.000	0.167	105.000	105.000	95.000	0.600	http://www.semwebtech.org/mondial/10/meta#language

#816-TorredeEstrela
PRED entity: TorredeEstrela

PRED relation: inMountains
PRED expected values: CordilleraCentral
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 11): Alps (0.05 #4, 0.01 #91), Andes (0.05 #11), RockyMountains (0.05 #7), Himalaya (0.04 #6), EastAfricanRift (0.02 #28), CordilleraVolcanica (0.02 #65), Kaukasus (0.02 #19), Pamir (0.02 #17), EliasRange (0.02 #15), Hawaii (0.01 #68)
>> best conf = 0.05
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4 for best value:
>> intensional similarity = 1
>> extensional distance = 250
>> proper extension:  JabalKatrina; Tahat; Annapurna; Bjelucha; Schchara; GranSasso; EmiKussi; Zugspitze; Tahan; Demirkazik; ...
>> query: (?x1710, Alps) <- ?x1710[ a Mountain;]

No rule for expected values
ranks of expected_values: 
EVAL	TorredeEstrela	inMountains	CordilleraCentral	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	2.000	2.000	11.000	0.052	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains
PRED expected values: CordilleraCentral
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 11): Alps (0.05 #4, 0.01 #91), Andes (0.05 #11), RockyMountains (0.05 #7), Himalaya (0.04 #6), EastAfricanRift (0.02 #28), CordilleraVolcanica (0.02 #65), Kaukasus (0.02 #19), Pamir (0.02 #17), EliasRange (0.02 #15), Hawaii (0.01 #68)
>> best conf = 0.05
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4 for best value:
>> intensional similarity = 1
>> extensional distance = 250
>> proper extension:  JabalKatrina; Tahat; Annapurna; Bjelucha; Schchara; GranSasso; EmiKussi; Zugspitze; Tahan; Demirkazik; ...
>> query: (?x1710, Alps) <- ?x1710[ a Mountain;]

No rule for expected values
ranks of expected_values: 
EVAL	TorredeEstrela	inMountains	CordilleraCentral	CNN-1.+1._MA	0.000	0.000	0.000	0.000	2.000	2.000	11.000	0.052	http://www.semwebtech.org/mondial/10/meta#inMountains

#815-MNG
PRED entity: MNG

PRED relation: neighbor
PRED expected values: CN
=> 32 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 183): CN (0.27 #202, 0.27 #1286, 0.26 #2745), PL (0.27 #1286, 0.26 #2745, 0.26 #3230), UA (0.27 #1286, 0.26 #2745, 0.26 #3230), BY (0.27 #1286, 0.26 #2745, 0.26 #3230), LV (0.27 #1286, 0.26 #2745, 0.26 #3230), GE (0.27 #1286, 0.26 #2745, 0.26 #3230), N (0.27 #1286, 0.26 #2745, 0.26 #3230), LT (0.27 #1286, 0.26 #2745, 0.26 #3230), EW (0.27 #1286, 0.26 #2745, 0.26 #3230), MNG (0.27 #1286, 0.26 #2745, 0.26 #3716)
>> best conf = 0.27
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #202 for best value:
>> intensional similarity = 7
>> extensional distance = 9
>> proper extension:  GB; NEP; I; RI; MYA; MAL; BRU; VN; IND;
>> query: (?x1010, CN) <- ?x1010[ has government ?x2058; has neighbor ?x73; has religion ?x187<Muslim>; has religion ?x462<Buddhist>; is locatedIn of ?x72;]
ranks of expected_values: 1
EVAL	MNG	neighbor	CN	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	32.000	31.000	183.000	0.273	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: CN
=> 76 concepts (76 used for prediction)
PRED predicted values (max 10 best out of 204): TR (0.40 #687, 0.31 #2332, 0.30 #3478), CN (0.39 #2508, 0.33 #496, 0.33 #42), BY (0.36 #1852, 0.33 #1520, 0.33 #40), KAZ (0.33 #240, 0.33 #70, 0.27 #2210), SF (0.33 #429, 0.33 #96, 0.26 #10025), UA (0.33 #51, 0.32 #2679, 0.30 #8548), PL (0.33 #33, 0.30 #8548, 0.30 #1017), LV (0.33 #79, 0.30 #8548, 0.29 #1891), MNG (0.33 #143, 0.30 #8548, 0.27 #8058), AZ (0.33 #56, 0.30 #8548, 0.26 #7400)
>> best conf = 0.40
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #687 for best value:
>> intensional similarity = 15
>> extensional distance = 3
>> proper extension:  AZ;
>> query: (?x1010, TR) <- ?x1010[ a Country; has ethnicGroup ?x1553[ a EthnicGroup;]; has language ?x335; has neighbor ?x73<R>; has religion ?x187<Muslim>; has religion ?x462[ is religion of ?x871[ a Country; is neighbor of ?x91;];]; is locatedIn of ?x72[ a River; has flowsInto ?x464;];]

*> Best rule #2508 for first EXPECTED value:
*> intensional similarity = 13
*> extensional distance = 16
*> proper extension:  MACX;
*> query: (?x1010, CN) <- ?x1010[ a Country; has ethnicGroup ?x1553; has language ?x335; has neighbor ?x73[ has ethnicGroup ?x58; is locatedIn of ?x98[ has mergesWith ?x1633;]; is locatedIn of ?x1038[ a Source;]; is locatedIn of ?x1748<Irtysch>;]; has religion ?x116; is locatedIn of ?x72;]
*> conf = 0.39
ranks of expected_values: 2
EVAL	MNG	neighbor	CN	CNN-1.+1._MA	0.000	1.000	1.000	0.500	76.000	76.000	204.000	0.400	http://www.semwebtech.org/mondial/10/meta#neighbor

#814-ArcticOcean
PRED entity: ArcticOcean

PRED relation: locatedInWater!
PRED expected values: KotelnyIsland
=> 32 concepts (26 used for prediction)
PRED predicted values (max 10 best out of 497): SouthamptonIsland (0.56 #1057, 0.25 #735, 0.15 #1058), Newfoundland (0.56 #1057, 0.17 #1034, 0.15 #1058), PrinceEdwardIsland (0.56 #1057, 0.17 #940, 0.15 #1058), LongIsland (0.56 #1057, 0.17 #989, 0.15 #1058), Nantucket (0.56 #1057, 0.17 #936, 0.15 #1058), MarthasVineyard (0.56 #1057, 0.17 #931, 0.15 #1058), Paramuschir (0.56 #1057, 0.15 #1058, 0.12 #3971), VancouverIsland (0.56 #1057, 0.15 #1058, 0.12 #3971), KotelnyIsland (0.56 #1057, 0.15 #1058, 0.12 #3971), ReneLevasseurIsland (0.56 #1057, 0.15 #1058, 0.12 #3971)
>> best conf = 0.56
=> the first rule below is the first best rule for 24 predicted values
>> Best rule #1057 for best value:
>> intensional similarity = 6
>> extensional distance = 4
>> proper extension:  Greenland;
>> query: (?x263, ?x844) <- ?x263[ has locatedIn ?x315[ has religion ?x95; is locatedIn of ?x844[ a Island;];]; has locatedIn ?x792<GROX>;]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 9
EVAL	ArcticOcean	locatedInWater!	KotelnyIsland	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.111	32.000	26.000	497.000	0.556	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater!
PRED expected values: KotelnyIsland
=> 73 concepts (73 used for prediction)
PRED predicted values (max 10 best out of 522): Unalaska (0.33 #713, 0.25 #1325, 0.18 #2036), GreatBritain (0.31 #2149, 0.25 #1325, 0.20 #5561), Hokkaido (0.27 #1881, 0.25 #1325, 0.20 #2410), Kyushu (0.27 #1975, 0.25 #1325, 0.20 #2504), Taiwan (0.27 #1910, 0.25 #1325, 0.17 #1646), Streymoy (0.25 #1325, 0.25 #1026, 0.20 #5561), ShetlandMainland (0.25 #1325, 0.25 #844, 0.20 #5561), Iceland (0.25 #1325, 0.25 #861, 0.20 #5561), Aust-Vagoey (0.25 #1325, 0.25 #1007, 0.20 #5561), Cuba (0.25 #1325, 0.23 #2328, 0.20 #5561)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #713 for best value:
>> intensional similarity = 9
>> extensional distance = 1
>> proper extension:  BeringSea;
>> query: (?x263, Unalaska) <- ?x263[ has locatedIn ?x73; has mergesWith ?x251[ has mergesWith ?x373;]; has mergesWith ?x452<EastSibirianSea>; is locatedInWater of ?x1949[ a Island; has belongsToIslands ?x479; has locatedIn ?x272;];]

*> Best rule #1325 for first EXPECTED value:
*> intensional similarity = 11
*> extensional distance = 2
*> proper extension:  KaraSea;
*> query: (?x263, ?x205) <- ?x263[ a Sea; is locatedInWater of ?x866[ a Island;]; is locatedInWater of ?x931<NowajaSemlja>; is mergesWith of ?x809[ has mergesWith ?x282[ has locatedIn ?x73; has mergesWith ?x60; is locatedInWater of ?x205;]; is locatedInWater of ?x1687;];]
*> conf = 0.25
ranks of expected_values: 129
EVAL	ArcticOcean	locatedInWater!	KotelnyIsland	CNN-1.+1._MA	0.000	0.000	0.000	0.008	73.000	73.000	522.000	0.333	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#813-Maori
PRED entity: Maori

PRED relation: language!
PRED expected values: NZ
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 60): PK (0.08 #8), SF (0.07 #79), IR (0.07 #45), BZ (0.06 #88), AND (0.05 #97), L (0.05 #93), MK (0.05 #92), NZ (0.05 #70), I (0.05 #30), NLSM (0.05 #1)
>> best conf = 0.08
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #8 for best value:
>> intensional similarity = 1
>> extensional distance = 102
>> proper extension:  French; Japanese; Samoan; English; Tajik; KhalkhaMongol; Italian; Turkish; Portuguese; Dutch; ...
>> query: (?x2512, PK) <- ?x2512[ a Language;]

*> Best rule #70 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 102
*> proper extension:  French; Japanese; Samoan; English; Tajik; KhalkhaMongol; Italian; Turkish; Portuguese; Dutch; ...
*> query: (?x2512, NZ) <- ?x2512[ a Language;]
*> conf = 0.05
ranks of expected_values: 8
EVAL	Maori	language!	NZ	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.125	2.000	2.000	60.000	0.077	http://www.semwebtech.org/mondial/10/meta#language

PRED relation: language!
PRED expected values: NZ
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 60): PK (0.08 #8), SF (0.07 #79), IR (0.07 #45), BZ (0.06 #88), AND (0.05 #97), L (0.05 #93), MK (0.05 #92), NZ (0.05 #70), I (0.05 #30), NLSM (0.05 #1)
>> best conf = 0.08
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #8 for best value:
>> intensional similarity = 1
>> extensional distance = 102
>> proper extension:  French; Japanese; Samoan; English; Tajik; KhalkhaMongol; Italian; Turkish; Portuguese; Dutch; ...
>> query: (?x2512, PK) <- ?x2512[ a Language;]

*> Best rule #70 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 102
*> proper extension:  French; Japanese; Samoan; English; Tajik; KhalkhaMongol; Italian; Turkish; Portuguese; Dutch; ...
*> query: (?x2512, NZ) <- ?x2512[ a Language;]
*> conf = 0.05
ranks of expected_values: 8
EVAL	Maori	language!	NZ	CNN-1.+1._MA	0.000	0.000	1.000	0.125	2.000	2.000	60.000	0.077	http://www.semwebtech.org/mondial/10/meta#language

#812-Main
PRED entity: Main

PRED relation: hasSource
PRED expected values: Main
=> 41 concepts (29 used for prediction)
PRED predicted values (max 10 best out of 167): Mosel (0.25 #160, 0.05 #388, 0.05 #845), Ammer (0.05 #436, 0.05 #893, 0.05 #664), Brigach (0.05 #360, 0.05 #817, 0.05 #588), Alz (0.05 #310, 0.05 #767, 0.05 #538), Werra (0.05 #243, 0.05 #700, 0.05 #471), Breg (0.05 #231, 0.05 #688, 0.05 #459), Aller (0.05 #376, 0.05 #833, 0.05 #604), Iller (0.05 #379, 0.05 #836, 0.05 #607), Salzach (0.05 #334, 0.05 #791, 0.05 #562), Isar (0.05 #328, 0.05 #785, 0.05 #556)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #160 for best value:
>> intensional similarity = 4
>> extensional distance = 2
>> proper extension:  Bodensee;
>> query: (?x613, Mosel) <- ?x613[ has flowsInto ?x256<Rhein>; has locatedIn ?x120<D>;]

*> Best rule #5945 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 802
*> proper extension:  Menorca; Rigestan; Stromboli; SeaofAzov; BlackSea; LakeSkutari; Moraca; Bjelucha; Schchara; GranSasso; ...
*> query: (?x613, ?x1716) <- ?x613[ has locatedIn ?x120[ has neighbor ?x78; is locatedIn of ?x1716[ a Source; has inMountains ?x71;];];]
*> conf = 0.02
ranks of expected_values: 31
EVAL	Main	hasSource	Main	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.032	41.000	29.000	167.000	0.250	http://www.semwebtech.org/mondial/10/meta#hasSource

PRED relation: hasSource
PRED expected values: Main
=> 113 concepts (113 used for prediction)
PRED predicted values (max 10 best out of 253): Mosel (0.33 #160, 0.25 #846, 0.25 #618), Aare (0.25 #350, 0.18 #10327, 0.06 #4346), Oder (0.25 #479, 0.09 #13318, 0.08 #11018), Saar (0.20 #1232, 0.09 #13318, 0.08 #11018), Elbe (0.20 #1280, 0.09 #13318, 0.08 #11018), Alz (0.09 #13318, 0.08 #11018, 0.08 #1453), Brigach (0.09 #13318, 0.08 #11018, 0.08 #1503), Breg (0.09 #13318, 0.08 #11018, 0.08 #1374), Salzach (0.09 #13318, 0.08 #11018, 0.08 #1477), Iller (0.09 #13318, 0.08 #11018, 0.08 #1522)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #160 for best value:
>> intensional similarity = 8
>> extensional distance = 1
>> proper extension:  Mosel;
>> query: (?x613, Mosel) <- ?x613[ a River; has flowsInto ?x256<Rhein>; has hasEstuary ?x269[ a Estuary; has locatedIn ?x120<D>;]; has locatedIn ?x120<D>;]

*> Best rule #7796 for first EXPECTED value:
*> intensional similarity = 9
*> extensional distance = 93
*> proper extension:  Thjorsa;
*> query: (?x613, ?x1668) <- ?x613[ a River; has locatedIn ?x120[ has encompassed ?x195; has religion ?x352<RomanCatholic>; is locatedIn of ?x1100[ a Island;]; is locatedIn of ?x1668[ a Source;];];]
*> conf = 0.07
ranks of expected_values: 23
EVAL	Main	hasSource	Main	CNN-1.+1._MA	0.000	0.000	0.000	0.043	113.000	113.000	253.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasSource

#811-BaffinIsland
PRED entity: BaffinIsland

PRED relation: belongsToIslands
PRED expected values: CanadianArcticIslands
=> 36 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 33): CanadianArcticIslands (0.40 #76, 0.33 #8, 0.09 #212), SundaIslands (0.16 #150, 0.06 #422, 0.05 #490), LesserAntilles (0.07 #899, 0.07 #967, 0.07 #627), Philipines (0.06 #143, 0.04 #415, 0.04 #483), HawaiiIslands (0.04 #301, 0.04 #369, 0.04 #165), Canares (0.04 #159, 0.03 #431, 0.03 #499), GreaterAntilles (0.04 #183, 0.01 #455), SolomonIslands (0.04 #147), Azores (0.03 #480, 0.03 #616, 0.03 #820), CalifornianChannelIslands (0.03 #331, 0.03 #399, 0.03 #467)
>> best conf = 0.40
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #76 for best value:
>> intensional similarity = 6
>> extensional distance = 3
>> proper extension:  Greenland; EllesmereIsland; DevonIsland;
>> query: (?x869, CanadianArcticIslands) <- ?x869[ a Island; has locatedIn ?x272; has locatedInWater ?x249<LabradorSea>; has locatedInWater ?x263<ArcticOcean>;]
ranks of expected_values: 1
EVAL	BaffinIsland	belongsToIslands	CanadianArcticIslands	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	36.000	36.000	33.000	0.400	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

PRED relation: belongsToIslands
PRED expected values: CanadianArcticIslands
=> 105 concepts (105 used for prediction)
PRED predicted values (max 10 best out of 63): CanadianArcticIslands (0.50 #417, 0.50 #144, 0.45 #485), SundaIslands (0.40 #763, 0.20 #1035, 0.19 #967), Svalbard (0.33 #205, 0.17 #276, 0.10 #412), LesserAntilles (0.23 #1852, 0.13 #2602, 0.13 #2875), HawaiiIslands (0.19 #846, 0.15 #574, 0.14 #1390), Canares (0.15 #568, 0.12 #636, 0.11 #704), Philipines (0.15 #756, 0.08 #960, 0.07 #1028), NewZealand (0.14 #376, 0.06 #852, 0.05 #716), CalifornianChannelIslands (0.14 #876, 0.10 #1420, 0.10 #1488), GreaterAntilles (0.12 #660, 0.08 #592, 0.06 #932)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #417 for best value:
>> intensional similarity = 10
>> extensional distance = 8
>> proper extension:  VictoriaIsland; PrinceofWalesIsland; NowajaSemlja; BanksIsland;
>> query: (?x869, CanadianArcticIslands) <- ?x869[ a Island; has locatedInWater ?x249[ a Sea; is mergesWith of ?x182[ has locatedIn ?x50; is flowsInto of ?x137; is locatedInWater of ?x112; is mergesWith of ?x60;];]; has locatedInWater ?x263<ArcticOcean>;]
>> Best rule #144 for best value:
>> intensional similarity = 12
>> extensional distance = 2
>> proper extension:  DevonIsland;
>> query: (?x869, CanadianArcticIslands) <- ?x869[ a Island; has locatedIn ?x272<CDN>; has locatedInWater ?x248[ a Sea; is locatedInWater of ?x1891[ a Island; has belongsToIslands ?x479;];]; has locatedInWater ?x249<LabradorSea>; has locatedInWater ?x263<ArcticOcean>;]
ranks of expected_values: 1
EVAL	BaffinIsland	belongsToIslands	CanadianArcticIslands	CNN-1.+1._MA	1.000	1.000	1.000	1.000	105.000	105.000	63.000	0.500	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

#810-Sanga
PRED entity: Sanga

PRED relation: hasEstuary!
PRED expected values: Sanga
=> 23 concepts (19 used for prediction)
PRED predicted values (max 10 best out of 75): Ubangi (0.33 #30, 0.12 #256, 0.05 #482), Luapula (0.05 #672, 0.01 #899, 0.01 #1126), Oranje (0.05 #457, 0.01 #684, 0.01 #911), Zaire (0.02 #679, 0.02 #1360, 0.01 #906), Sanga (0.02 #679, 0.02 #1360, 0.01 #906), Sanga (0.02 #679, 0.02 #1360, 0.01 #906), Ubangi (0.02 #679, 0.02 #1360, 0.01 #906), MaleboPool (0.02 #679, 0.02 #1360, 0.01 #906), AtlanticOcean (0.02 #679, 0.02 #1360, 0.01 #906), Lomami (0.01 #903, 0.01 #1130)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #30 for best value:
>> intensional similarity = 3
>> extensional distance = 1
>> proper extension:  Ubangi;
>> query: (?x2248, Ubangi) <- ?x2248[ a Estuary; has locatedIn ?x528<RCB>;]

*> Best rule #679 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 20
*> proper extension:  Oranje; LakeTanganjika; Namib; Luapula; Kalahari; EtoschaSaltPan; Okavango; Luapula; Zambezi; Oranje; ...
*> query: (?x2248, ?x182) <- ?x2248[ has locatedIn ?x528[ has government ?x435; has neighbor ?x934<ANG>; has religion ?x116; is locatedIn of ?x182;];]
*> conf = 0.02
ranks of expected_values: 6
EVAL	Sanga	hasEstuary!	Sanga	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.167	23.000	19.000	75.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasEstuary

PRED relation: hasEstuary!
PRED expected values: Sanga
=> 55 concepts (50 used for prediction)
PRED predicted values (max 10 best out of 28): Ubangi (0.33 #454, 0.33 #30, 0.25 #256), Zaire (0.33 #454, 0.11 #455, 0.06 #1365), Oranje (0.25 #231, 0.09 #686, 0.06 #1142), Sanga (0.11 #455, 0.07 #453, 0.03 #4357), MaleboPool (0.11 #455, 0.07 #453, 0.02 #1366), AtlanticOcean (0.11 #455, 0.03 #3434, 0.03 #3432), Sanga (0.11 #455, 0.02 #1366, 0.02 #1593), Ubangi (0.11 #455, 0.02 #1366, 0.02 #1593), Bomu (0.09 #772, 0.07 #1000, 0.07 #453), Pibor (0.09 #886, 0.03 #1800, 0.02 #2256)
>> best conf = 0.33
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #454 for best value:
>> intensional similarity = 15
>> extensional distance = 2
>> proper extension:  Oranje;
>> query: (?x2248, ?x388) <- ?x2248[ a Estuary; has locatedIn ?x528[ a Country; has encompassed ?x213; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x388[ a River; is flowsInto of ?x343;]; is neighbor of ?x172[ has government ?x1721;]; is neighbor of ?x536[ is neighbor of ?x139;]; is neighbor of ?x934<ANG>;];]
>> Best rule #30 for best value:
>> intensional similarity = 3
>> extensional distance = 1
>> proper extension:  Ubangi;
>> query: (?x2248, Ubangi) <- ?x2248[ a Estuary; has locatedIn ?x528<RCB>;]

*> Best rule #455 for first EXPECTED value:
*> intensional similarity = 16
*> extensional distance = 2
*> proper extension:  Oranje;
*> query: (?x2248, ?x265) <- ?x2248[ a Estuary; has locatedIn ?x528[ a Country; has encompassed ?x213; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x265; is locatedIn of ?x388[ a River; is flowsInto of ?x343;]; is neighbor of ?x172[ has government ?x1721;]; is neighbor of ?x536[ is neighbor of ?x139;]; is neighbor of ?x934<ANG>;];]
*> conf = 0.11
ranks of expected_values: 4
EVAL	Sanga	hasEstuary!	Sanga	CNN-1.+1._MA	0.000	0.000	1.000	0.250	55.000	50.000	28.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasEstuary

#809-D
PRED entity: D

PRED relation: locatedIn!
PRED expected values: Leine Würm Donau Fulda Usedom Würm
=> 23 concepts (23 used for prediction)
PRED predicted values (max 10 best out of 1312): AtlanticOcean (0.38 #14858, 0.36 #5425, 0.34 #16207), Drau (0.33 #1603, 0.33 #255, 0.17 #2951), MediterraneanSea (0.33 #1421, 0.30 #9509, 0.24 #6813), March (0.33 #549, 0.17 #1897, 0.11 #1348), Mur (0.33 #29, 0.17 #1377, 0.11 #1348), March (0.33 #550, 0.17 #1898, 0.11 #1348), Neusiedlersee (0.33 #276, 0.17 #1624, 0.11 #1348), Grossglockner (0.33 #1341, 0.17 #2689, 0.11 #1348), Iller (0.33 #1005, 0.17 #2353, 0.11 #1348), Raab (0.33 #988, 0.17 #2336, 0.11 #1348)
>> best conf = 0.38
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #14858 for best value:
>> intensional similarity = 4
>> extensional distance = 66
>> proper extension:  FALK; GUAM;
>> query: (?x120, AtlanticOcean) <- ?x120[ is locatedIn of ?x558[ is flowsInto of ?x394;]; is locatedIn of ?x1589[ has belongsToIslands ?x1590;];]

*> Best rule #1348 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 1
*> proper extension:  A;
*> query: (?x120, ?x121) <- ?x120[ has neighbor ?x234<CH>; has neighbor ?x793[ is locatedIn of ?x121;]; has religion ?x95; is locatedIn of ?x1124<Isar>;]
*> conf = 0.11
ranks of expected_values: 139, 172
EVAL	D	locatedIn!	Würm	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	23.000	23.000	1312.000	0.382	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	D	locatedIn!	Usedom	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.007	23.000	23.000	1312.000	0.382	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	D	locatedIn!	Fulda	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	23.000	23.000	1312.000	0.382	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	D	locatedIn!	Donau	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	23.000	23.000	1312.000	0.382	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	D	locatedIn!	Würm	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.006	23.000	23.000	1312.000	0.382	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	D	locatedIn!	Leine	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	23.000	23.000	1312.000	0.382	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Leine Würm Donau Fulda Usedom Würm
=> 87 concepts (87 used for prediction)
PRED predicted values (max 10 best out of 1355): Leine (0.91 #47220, 0.90 #62071, 0.90 #8095), Würm (0.91 #47220, 0.90 #62071, 0.90 #8095), PacificOcean (0.81 #58095, 0.37 #72940, 0.35 #89133), Iller (0.76 #32379, 0.72 #21585, 0.71 #36428), Salzach (0.76 #32379, 0.72 #21585, 0.71 #36428), Isar (0.76 #32379, 0.72 #21585, 0.71 #36428), Lech (0.76 #32379, 0.72 #21585, 0.71 #36428), Mosel (0.76 #32379, 0.72 #21585, 0.71 #36428), Saar (0.76 #32379, 0.72 #21585, 0.71 #36428), Rhein (0.76 #32379, 0.72 #21585, 0.71 #36428)
>> best conf = 0.91
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #47220 for best value:
>> intensional similarity = 15
>> extensional distance = 30
>> proper extension:  SUD;
>> query: (?x120, ?x394) <- ?x120[ has encompassed ?x195; has ethnicGroup ?x237; has neighbor ?x194[ has ethnicGroup ?x58; is locatedIn of ?x221;]; has neighbor ?x575[ has religion ?x187; is locatedIn of ?x257;]; is locatedIn of ?x395[ a Source; is hasSource of ?x394;]; is locatedIn of ?x475[ a River;]; is locatedIn of ?x1105[ a Mountain;];]
ranks of expected_values: 1, 2, 14, 18, 284
EVAL	D	locatedIn!	Würm	CNN-1.+1._MA	0.000	0.000	0.000	0.000	87.000	87.000	1355.000	0.914	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	D	locatedIn!	Usedom	CNN-1.+1._MA	0.000	0.000	0.000	0.004	87.000	87.000	1355.000	0.914	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	D	locatedIn!	Fulda	CNN-1.+1._MA	0.000	0.000	0.000	0.067	87.000	87.000	1355.000	0.914	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	D	locatedIn!	Donau	CNN-1.+1._MA	0.000	0.000	0.000	0.083	87.000	87.000	1355.000	0.914	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	D	locatedIn!	Würm	CNN-1.+1._MA	1.000	1.000	1.000	1.000	87.000	87.000	1355.000	0.914	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	D	locatedIn!	Leine	CNN-1.+1._MA	1.000	1.000	1.000	1.000	87.000	87.000	1355.000	0.914	http://www.semwebtech.org/mondial/10/meta#locatedIn

#808-SouthRonaldsay
PRED entity: SouthRonaldsay

PRED relation: locatedIn
PRED expected values: GB
=> 28 concepts (28 used for prediction)
PRED predicted values (max 10 best out of 69): GB (0.79 #710, 0.73 #1183, 0.73 #955), D (0.52 #730, 0.48 #1203, 0.44 #1678), NL (0.21 #1555, 0.18 #607, 0.15 #1421), N (0.15 #1421, 0.14 #1896, 0.14 #1895), DK (0.15 #1421, 0.14 #1896, 0.14 #1895), B (0.15 #1421, 0.14 #1896, 0.14 #1895), F (0.15 #1421, 0.14 #1896, 0.14 #1895), P (0.10 #2093, 0.10 #2330, 0.05 #3279), E (0.09 #1923, 0.08 #2160, 0.04 #2397), USA (0.08 #3154, 0.08 #2442, 0.07 #2679)
>> best conf = 0.79
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #710 for best value:
>> intensional similarity = 8
>> extensional distance = 15
>> proper extension:  Pellworm; Langeoog; Schiermonnikoog; Amrum; Norderney; Sylt; Borkum; Terschelling; Juist; Vlieland;
>> query: (?x1882, ?x81) <- ?x1882[ a Island; has belongsToIslands ?x921[ a Islands; is belongsToIslands of ?x1029[ a Island; has locatedIn ?x81;];]; has locatedInWater ?x121<NorthSea>;]
ranks of expected_values: 1
EVAL	SouthRonaldsay	locatedIn	GB	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	28.000	28.000	69.000	0.789	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: GB
=> 44 concepts (44 used for prediction)
PRED predicted values (max 10 best out of 103): GB (0.88 #950, 0.83 #2625, 0.82 #710), D (0.52 #1693, 0.48 #2166, 0.44 #3105), NL (0.34 #2388, 0.26 #1431, 0.21 #2522), F (0.26 #1431, 0.20 #1190, 0.15 #2385), DK (0.26 #1431, 0.20 #1190, 0.15 #2385), N (0.20 #1190, 0.18 #3110, 0.15 #2385), GR (0.19 #3201, 0.13 #3923, 0.10 #4652), B (0.18 #3110, 0.15 #2385, 0.15 #2384), I (0.17 #3159, 0.12 #3881, 0.09 #4610), USA (0.14 #4390, 0.09 #5114, 0.08 #6554)
>> best conf = 0.88
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #950 for best value:
>> intensional similarity = 9
>> extensional distance = 15
>> proper extension:  Pellworm; Langeoog; Schiermonnikoog; Amrum; Norderney; Sylt; Borkum; Terschelling; Juist; Vlieland;
>> query: (?x1882, ?x81) <- ?x1882[ a Island; has belongsToIslands ?x921[ a Islands; is belongsToIslands of ?x755[ a Island; has locatedIn ?x81; has locatedInWater ?x121<NorthSea>;];]; has locatedInWater ?x121<NorthSea>;]
ranks of expected_values: 1
EVAL	SouthRonaldsay	locatedIn	GB	CNN-1.+1._MA	1.000	1.000	1.000	1.000	44.000	44.000	103.000	0.882	http://www.semwebtech.org/mondial/10/meta#locatedIn

#807-Hindu
PRED entity: Hindu

PRED relation: religion!
PRED expected values: RI
=> 22 concepts (15 used for prediction)
PRED predicted values (max 10 best out of 222): MYA (0.75 #200, 0.63 #1001, 0.56 #600), CN (0.75 #200, 0.63 #1001, 0.56 #600), PK (0.75 #200, 0.63 #1001, 0.56 #600), IRL (0.75 #200, 0.63 #1001, 0.55 #2005), RI (0.63 #1001, 0.60 #844, 0.56 #600), BRU (0.63 #1001, 0.56 #600, 0.56 #599), BR (0.63 #1001, 0.56 #600, 0.56 #599), K (0.63 #1001, 0.56 #600, 0.56 #599), FGU (0.63 #1001, 0.56 #600, 0.56 #599), LAO (0.63 #1001, 0.56 #600, 0.56 #599)
>> best conf = 0.75
=> the first rule below is the first best rule for 4 predicted values
>> Best rule #200 for best value:
>> intensional similarity = 14
>> extensional distance = 1
>> proper extension:  RomanCatholic;
>> query: (?x410, ?x83) <- ?x410[ is religion of ?x81<GB>; is religion of ?x207<I>; is religion of ?x351<GUY>; is religion of ?x745<MART>; is religion of ?x924[ has encompassed ?x175; has ethnicGroup ?x1553; has language ?x2392; has neighbor ?x83; is locatedIn of ?x60;];]

*> Best rule #1001 for first EXPECTED value:
*> intensional similarity = 15
*> extensional distance = 3
*> proper extension:  Protestant;
*> query: (?x410, ?x217) <- ?x410[ is religion of ?x179<SME>; is religion of ?x351<GUY>; is religion of ?x376[ has ethnicGroup ?x298; has neighbor ?x217; is locatedIn of ?x178;]; is religion of ?x508[ has government ?x435; has language ?x1978; is locatedIn of ?x60;]; is religion of ?x745[ has encompassed ?x521; is locatedIn of ?x182;];]
*> conf = 0.63
ranks of expected_values: 5
EVAL	Hindu	religion!	RI	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.200	22.000	15.000	222.000	0.750	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion!
PRED expected values: RI
=> 35 concepts (35 used for prediction)
PRED predicted values (max 10 best out of 227): MYA (0.75 #408, 0.66 #406, 0.66 #405), BRU (0.75 #408, 0.66 #406, 0.66 #405), CN (0.75 #408, 0.66 #406, 0.66 #405), PK (0.75 #408, 0.66 #406, 0.66 #405), RI (0.75 #408, 0.66 #406, 0.66 #405), K (0.75 #408, 0.66 #406, 0.66 #405), LAO (0.75 #408, 0.66 #406, 0.66 #405), BR (0.75 #408, 0.66 #406, 0.66 #405), FGU (0.75 #408, 0.66 #406, 0.66 #405), F (0.75 #408, 0.66 #406, 0.66 #405)
>> best conf = 0.75
=> the first rule below is the first best rule for 17 predicted values
>> Best rule #408 for best value:
>> intensional similarity = 23
>> extensional distance = 1
>> proper extension:  RomanCatholic;
>> query: (?x410, ?x639) <- ?x410[ a Religion; is religion of ?x81<GB>; is religion of ?x91[ has government ?x92; is locatedIn of ?x384; is neighbor of ?x366;]; is religion of ?x207<I>; is religion of ?x668[ has encompassed ?x175; has neighbor ?x639; is locatedIn of ?x1333[ a Sea; is mergesWith of ?x926;];]; is religion of ?x745<MART>; is religion of ?x924[ has ethnicGroup ?x1553; is locatedIn of ?x339<AndamanSea>;]; is religion of ?x943[ a Country;];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 5
EVAL	Hindu	religion!	RI	CNN-1.+1._MA	0.000	0.000	1.000	0.200	35.000	35.000	227.000	0.746	http://www.semwebtech.org/mondial/10/meta#religion

#806-Kurdish
PRED entity: Kurdish

PRED relation: language!
PRED expected values: TR
=> 24 concepts (24 used for prediction)
PRED predicted values (max 10 best out of 180): PK (0.53 #745, 0.26 #1708, 0.25 #1466), GE (0.33 #415, 0.16 #2210, 0.15 #2457), SF (0.28 #938, 0.25 #1465, 0.18 #318), UZB (0.27 #280, 0.25 #529, 0.17 #734), TM (0.26 #1708, 0.25 #1466, 0.25 #530), TR (0.26 #1708, 0.25 #1466, 0.25 #1465), AZ (0.26 #1708, 0.25 #1466, 0.24 #2458), AFG (0.26 #1708, 0.25 #1466, 0.24 #2458), IRQ (0.25 #1466, 0.24 #1957, 0.23 #1962), AUS (0.25 #1465, 0.17 #887, 0.14 #2701)
>> best conf = 0.53
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #745 for best value:
>> intensional similarity = 13
>> extensional distance = 13
>> proper extension:  Punjabi; Siraiki; Pashtu; Urdu; Brahui; Sindhi; Hindko;
>> query: (?x1104, PK) <- ?x1104[ a Language; is language of ?x304[ a Country; is locatedIn of ?x1028[ a Desert;]; is locatedIn of ?x1693[ has inMountains ?x1303;]; is neighbor of ?x331[ a Country; has ethnicGroup ?x1193; has religion ?x670;]; is neighbor of ?x381<AFG>;];]

*> Best rule #1708 for first EXPECTED value:
*> intensional similarity = 9
*> extensional distance = 52
*> proper extension:  Nepali;
*> query: (?x1104, ?x83) <- ?x1104[ a Language; is language of ?x304[ has neighbor ?x83[ has language ?x559;]; is locatedIn of ?x1382[ has inMountains ?x574;]; is neighbor of ?x381[ has religion ?x187; is locatedIn of ?x82;];];]
*> conf = 0.26
ranks of expected_values: 6
EVAL	Kurdish	language!	TR	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.167	24.000	24.000	180.000	0.533	http://www.semwebtech.org/mondial/10/meta#language

PRED relation: language!
PRED expected values: TR
=> 54 concepts (54 used for prediction)
PRED predicted values (max 10 best out of 191): PK (0.53 #2539, 0.40 #3421, 0.37 #4696), MK (0.40 #3421, 0.38 #2111, 0.37 #4696), SF (0.40 #3421, 0.37 #4696, 0.37 #3801), TR (0.40 #3421, 0.37 #4696, 0.34 #4055), AUS (0.40 #3421, 0.37 #4696, 0.34 #4055), BG (0.40 #3421, 0.37 #4696, 0.34 #4055), IL (0.40 #3421, 0.37 #4696, 0.34 #4055), CY (0.40 #3421, 0.37 #4696, 0.34 #4055), WEST (0.40 #3421, 0.37 #4696, 0.34 #4055), GAZA (0.40 #3421, 0.37 #4696, 0.34 #4055)
>> best conf = 0.53
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2539 for best value:
>> intensional similarity = 22
>> extensional distance = 13
>> proper extension:  Punjabi; Siraiki; Urdu; Brahui; Sindhi; Hindko;
>> query: (?x1104, PK) <- ?x1104[ a Language; is language of ?x304[ a Country; has government ?x2318; has neighbor ?x185[ a Country; has encompassed ?x175; has ethnicGroup ?x638; is locatedIn of ?x98;]; has neighbor ?x332[ a Country; has ethnicGroup ?x908; has neighbor ?x73;]; has neighbor ?x381<AFG>; is locatedIn of ?x859[ has inMountains ?x860;]; is locatedIn of ?x918[ has locatedIn ?x107<UAE>; is locatedInWater of ?x1736;]; is neighbor of ?x331;];]

*> Best rule #3421 for first EXPECTED value:
*> intensional similarity = 18
*> extensional distance = 17
*> proper extension:  Icelandic;
*> query: (?x1104, ?x196) <- ?x1104[ a Language; is language of ?x304[ has encompassed ?x175[ a Continent; is encompassed of ?x185<TR>; is encompassed of ?x403<KAZ>;]; has ethnicGroup ?x244[ a EthnicGroup;]; has language ?x1848[ a Language; is language of ?x196;]; has religion ?x187; is locatedIn of ?x926[ has mergesWith ?x1333;]; is locatedIn of ?x1092[ has type ?x762;];];]
*> conf = 0.40
ranks of expected_values: 4
EVAL	Kurdish	language!	TR	CNN-1.+1._MA	0.000	0.000	1.000	0.250	54.000	54.000	191.000	0.533	http://www.semwebtech.org/mondial/10/meta#language

#805-Amazonas
PRED entity: Amazonas

PRED relation: flowsInto!
PRED expected values: RioNegro
=> 48 concepts (41 used for prediction)
PRED predicted values (max 10 best out of 322): RioMamore (0.20 #643, 0.12 #943, 0.03 #2744), Araguaia (0.20 #599, 0.12 #899, 0.03 #2700), LagodeSobradinho (0.20 #887, 0.12 #1187, 0.02 #7195), ColumbiaRiver (0.08 #1470, 0.02 #3571, 0.02 #4767), Colorado (0.08 #1371, 0.02 #3472, 0.02 #4668), SnowyRiver (0.08 #1341, 0.02 #3442, 0.02 #4638), RioLerma (0.08 #1277, 0.02 #3378, 0.02 #4574), LakeMaracaibo (0.08 #1490, 0.02 #4787, 0.02 #5088), RioSanJuan (0.08 #1224, 0.02 #4521, 0.02 #4822), Urubamba (0.03 #2051, 0.03 #1749, 0.03 #2351)
>> best conf = 0.20
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #643 for best value:
>> intensional similarity = 6
>> extensional distance = 3
>> proper extension:  Tocantins; RioMadeira; RioSaoFrancisco;
>> query: (?x214, RioMamore) <- ?x214[ a River; has flowsInto ?x182; has hasSource ?x2254; has locatedIn ?x542<BR>; is flowsInto of ?x949;]

*> Best rule #7195 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 95
*> proper extension:  AndamanSea; ArabianSea;
*> query: (?x214, ?x282) <- ?x214[ has locatedIn ?x215[ has ethnicGroup ?x79; is locatedIn of ?x282;]; is flowsInto of ?x987[ has hasEstuary ?x2048;];]
*> conf = 0.02
ranks of expected_values: 168
EVAL	Amazonas	flowsInto!	RioNegro	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.006	48.000	41.000	322.000	0.200	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto!
PRED expected values: RioNegro
=> 126 concepts (126 used for prediction)
PRED predicted values (max 10 best out of 421): LagodeSobradinho (0.39 #9021, 0.32 #21663, 0.32 #23776), Araguaia (0.39 #9021, 0.32 #21663, 0.32 #23776), RiviereRichelieu (0.39 #9021, 0.32 #21663, 0.32 #23776), Manicouagan (0.39 #9021, 0.32 #21663, 0.32 #23776), LakeOntario (0.39 #9021, 0.32 #21663, 0.32 #23776), Vaal (0.39 #9021, 0.32 #21663, 0.32 #23776), Sanga (0.39 #9021, 0.32 #21663, 0.32 #23776), Ubangi (0.39 #9021, 0.32 #21663, 0.32 #23776), MaleboPool (0.39 #9021, 0.32 #21663, 0.32 #23776), Lomami (0.39 #9021, 0.32 #21663, 0.32 #23776)
>> best conf = 0.39
=> the first rule below is the first best rule for 17 predicted values
>> Best rule #9021 for best value:
>> intensional similarity = 9
>> extensional distance = 33
>> proper extension:  LakeJindabyne;
>> query: (?x214, ?x1085) <- ?x214[ has flowsInto ?x182[ is flowsInto of ?x1325[ has locatedIn ?x272; is flowsInto of ?x1085;];]; is flowsInto of ?x987[ is flowsInto of ?x1207;]; is flowsInto of ?x1578[ has hasEstuary ?x1579; has locatedIn ?x542;];]

*> Best rule #1502 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 4
*> proper extension:  PacificOcean; CaribbeanSea;
*> query: (?x214, ?x1703) <- ?x214[ has locatedIn ?x542[ has neighbor ?x345<YV>; is locatedIn of ?x1703[ a River;];]; is flowsInto of ?x1578[ has hasSource ?x2164; has locatedIn ?x690;];]
*> conf = 0.12
ranks of expected_values: 47
EVAL	Amazonas	flowsInto!	RioNegro	CNN-1.+1._MA	0.000	0.000	0.000	0.021	126.000	126.000	421.000	0.387	http://www.semwebtech.org/mondial/10/meta#flowsInto

#804-Aruba
PRED entity: Aruba

PRED relation: locatedInWater
PRED expected values: CaribbeanSea
=> 40 concepts (40 used for prediction)
PRED predicted values (max 10 best out of 30): AtlanticOcean (0.88 #136, 0.86 #222, 0.83 #389), CaribbeanSea (0.76 #388, 0.71 #608, 0.71 #234), PacificOcean (0.27 #581, 0.27 #449, 0.27 #625), NorthSea (0.15 #654, 0.13 #697, 0.12 #740), IndianOcean (0.12 #434, 0.12 #566, 0.12 #610), MediterraneanSea (0.11 #1013, 0.11 #1142, 0.10 #926), SulawesiSea (0.10 #504, 0.09 #548, 0.06 #679), JavaSea (0.09 #441, 0.09 #485, 0.08 #529), BalticSea (0.06 #656, 0.06 #872, 0.06 #699), SouthChinaSea (0.06 #498, 0.06 #542, 0.06 #673)
>> best conf = 0.88
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #136 for best value:
>> intensional similarity = 6
>> extensional distance = 15
>> proper extension:  SaintVincent; Barbuda; Montserrat; Grenada; Nevis; Antigua;
>> query: (?x1865, AtlanticOcean) <- ?x1865[ a Island; has belongsToIslands ?x877<LesserAntilles>; has locatedIn ?x1171[ a Country; has government ?x254;];]

*> Best rule #388 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 61
*> proper extension:  GrandTurk; Streymoy; NorthUist;
*> query: (?x1865, ?x317) <- ?x1865[ a Island; has belongsToIslands ?x877[ is belongsToIslands of ?x817[ has locatedInWater ?x182<AtlanticOcean>; has locatedInWater ?x317;];];]
*> conf = 0.76
ranks of expected_values: 2
EVAL	Aruba	locatedInWater	CaribbeanSea	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	40.000	40.000	30.000	0.882	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater
PRED expected values: CaribbeanSea
=> 75 concepts (75 used for prediction)
PRED predicted values (max 10 best out of 42): AtlanticOcean (0.89 #1004, 0.89 #966, 0.89 #959), CaribbeanSea (0.87 #958, 0.86 #1003, 0.86 #1048), Donau (0.43 #440, 0.08 #875, 0.05 #1141), PacificOcean (0.33 #1111, 0.28 #1505, 0.28 #1681), ArcticOcean (0.21 #754, 0.13 #1108, 0.06 #1414), NorthSea (0.16 #1754, 0.15 #1797, 0.12 #1883), IndianOcean (0.14 #1358, 0.12 #1490, 0.12 #1710), Waag (0.14 #469, 0.03 #904, 0.02 #1170), MediterraneanSea (0.13 #1896, 0.11 #2155, 0.11 #2241), JavaSea (0.11 #1365, 0.09 #1453, 0.09 #1409)
>> best conf = 0.89
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1004 for best value:
>> intensional similarity = 16
>> extensional distance = 36
>> proper extension:  Pico; Flores; Fogo; Corvo; Terceira; Graciosa; SantaMaria; GranCanaria; Madeira; Hierro; ...
>> query: (?x1865, ?x182) <- ?x1865[ a Island; has belongsToIslands ?x877[ a Islands; is belongsToIslands of ?x123[ a Island; has locatedIn ?x124; is locatedOnIsland of ?x1806;]; is belongsToIslands of ?x817[ a Island; has locatedIn ?x1444; has type ?x150<"volcanic">;]; is belongsToIslands of ?x1219[ a Island; has locatedInWater ?x182<AtlanticOcean>;];];]
>> Best rule #966 for best value:
>> intensional similarity = 16
>> extensional distance = 36
>> proper extension:  Pico; Flores; Fogo; Corvo; Terceira; Graciosa; SantaMaria; GranCanaria; Madeira; Hierro; ...
>> query: (?x1865, AtlanticOcean) <- ?x1865[ a Island; has belongsToIslands ?x877[ a Islands; is belongsToIslands of ?x123[ a Island; has locatedIn ?x124; is locatedOnIsland of ?x1806;]; is belongsToIslands of ?x817[ a Island; has locatedIn ?x1444; has type ?x150<"volcanic">;]; is belongsToIslands of ?x1219[ a Island; has locatedInWater ?x182<AtlanticOcean>;];];]
>> Best rule #959 for best value:
>> intensional similarity = 12
>> extensional distance = 36
>> proper extension:  Arran; Rhum; Tiree; Jura; Skye;
>> query: (?x1865, ?x182) <- ?x1865[ a Island; has belongsToIslands ?x877[ a Islands; is belongsToIslands of ?x817[ a Island; has type ?x150<"volcanic">;]; is belongsToIslands of ?x1219[ a Island; has locatedInWater ?x182<AtlanticOcean>;];]; has locatedIn ?x1171;]
>> Best rule #921 for best value:
>> intensional similarity = 12
>> extensional distance = 36
>> proper extension:  Arran; Rhum; Tiree; Jura; Skye;
>> query: (?x1865, AtlanticOcean) <- ?x1865[ a Island; has belongsToIslands ?x877[ a Islands; is belongsToIslands of ?x817[ a Island; has type ?x150<"volcanic">;]; is belongsToIslands of ?x1219[ a Island; has locatedInWater ?x182<AtlanticOcean>;];]; has locatedIn ?x1171;]

*> Best rule #958 for first EXPECTED value:
*> intensional similarity = 13
*> extensional distance = 36
*> proper extension:  Arran; Rhum; Tiree; Jura; Skye;
*> query: (?x1865, ?x317) <- ?x1865[ a Island; has belongsToIslands ?x877[ a Islands; is belongsToIslands of ?x817[ a Island; has type ?x150<"volcanic">;]; is belongsToIslands of ?x1219[ a Island; has locatedInWater ?x182<AtlanticOcean>; has locatedInWater ?x317;];]; has locatedIn ?x1171;]
*> conf = 0.87
ranks of expected_values: 2
EVAL	Aruba	locatedInWater	CaribbeanSea	CNN-1.+1._MA	0.000	1.000	1.000	0.500	75.000	75.000	42.000	0.895	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#803-Zambezi
PRED entity: Zambezi

PRED relation: hasEstuary
PRED expected values: Zambezi
=> 50 concepts (32 used for prediction)
PRED predicted values (max 10 best out of 161): Luapula (0.33 #574, 0.11 #1479, 0.02 #5899), MurrayRiver (0.33 #849, 0.03 #2891, 0.03 #3344), Limpopo (0.33 #344, 0.03 #3292, 0.02 #3972), Donau (0.17 #1254, 0.04 #1934, 0.04 #2615), Waag (0.17 #1182, 0.03 #3450), Araguaia (0.17 #1283), Nile (0.05 #1691, 0.04 #2145, 0.03 #3053), RioSanJuan (0.05 #1621, 0.04 #2075, 0.03 #2983), Volta (0.05 #1732, 0.04 #2186, 0.03 #3094), ColumbiaRiver (0.05 #1731, 0.04 #2185, 0.03 #3093)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #574 for best value:
>> intensional similarity = 7
>> extensional distance = 1
>> proper extension:  Luapula;
>> query: (?x1977, Luapula) <- ?x1977[ has hasSource ?x1596; has locatedIn ?x525<Z>; has locatedIn ?x1239[ has ethnicGroup ?x2322; has government ?x1174; has wasDependentOf ?x81;];]

No rule for expected values
ranks of expected_values: 
EVAL	Zambezi	hasEstuary	Zambezi	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	50.000	32.000	161.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasEstuary

PRED relation: hasEstuary
PRED expected values: Zambezi
=> 131 concepts (131 used for prediction)
PRED predicted values (max 10 best out of 378): Limpopo (0.33 #345, 0.25 #3068, 0.25 #2161), Oranje (0.33 #582, 0.25 #1944, 0.10 #228), Luapula (0.33 #120, 0.11 #3526, 0.10 #228), MurrayRiver (0.33 #1078, 0.08 #4488, 0.03 #8125), Donau (0.33 #803, 0.05 #4666, 0.05 #5802), Okavango (0.25 #1952, 0.12 #3312, 0.10 #228), Colorado (0.25 #2739, 0.10 #3648, 0.09 #3876), ColumbiaRiver (0.25 #2870, 0.09 #4007, 0.08 #4464), Ganges (0.25 #2106, 0.04 #6197, 0.03 #8472), Waag (0.25 #2320, 0.04 #6638, 0.03 #8231)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #345 for best value:
>> intensional similarity = 12
>> extensional distance = 1
>> proper extension:  Limpopo;
>> query: (?x1977, Limpopo) <- ?x1977[ has flowsInto ?x60<IndianOcean>; has hasSource ?x1596; has locatedIn ?x138[ has encompassed ?x213<Africa>; is neighbor of ?x243;]; has locatedIn ?x525[ is neighbor of ?x348[ is locatedIn of ?x113;];]; has locatedIn ?x1576<ZW>;]

*> Best rule #1136 for first EXPECTED value:
*> intensional similarity = 11
*> extensional distance = 1
*> proper extension:  MurrayRiver;
*> query: (?x1977, ?x242) <- ?x1977[ a River; has flowsInto ?x60<IndianOcean>; has hasSource ?x1596[ a Source;]; is flowsInto of ?x2061[ has locatedIn ?x192[ has ethnicGroup ?x197; has language ?x539; is locatedIn of ?x242;]; is flowsInto of ?x1650;];]
*> conf = 0.03
ranks of expected_values: 133
EVAL	Zambezi	hasEstuary	Zambezi	CNN-1.+1._MA	0.000	0.000	0.000	0.008	131.000	131.000	378.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasEstuary

#802-CN
PRED entity: CN

PRED relation: neighbor!
PRED expected values: NOK MYA MNG
=> 26 concepts (26 used for prediction)
PRED predicted values (max 10 best out of 170): MYA (0.89 #2679, 0.89 #2976, 0.82 #745), NOK (0.89 #2679, 0.89 #2976, 0.82 #745), CN (0.50 #635, 0.49 #2232, 0.40 #784), BD (0.49 #2232, 0.33 #429, 0.25 #725), THA (0.49 #2232, 0.12 #2233, 0.12 #2231), K (0.49 #2232, 0.12 #2233, 0.12 #2231), UZB (0.40 #789, 0.25 #640, 0.07 #1680), TM (0.40 #790, 0.25 #641, 0.05 #1384), PL (0.33 #478, 0.29 #923, 0.11 #1369), BY (0.33 #485, 0.29 #930, 0.04 #1078)
>> best conf = 0.89
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #2679 for best value:
>> intensional similarity = 5
>> extensional distance = 134
>> proper extension:  ROK;
>> query: (?x232, ?x334) <- ?x232[ has neighbor ?x334; has religion ?x116; is locatedIn of ?x231; is neighbor of ?x409[ has wasDependentOf ?x81;];]
ranks of expected_values: 1, 2, 16
EVAL	CN	neighbor!	MNG	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.071	26.000	26.000	170.000	0.895	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	CN	neighbor!	MYA	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	26.000	26.000	170.000	0.895	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	CN	neighbor!	NOK	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	26.000	26.000	170.000	0.895	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: NOK MYA MNG
=> 77 concepts (77 used for prediction)
PRED predicted values (max 10 best out of 220): MYA (0.91 #6866, 0.91 #9777, 0.91 #9163), NOK (0.91 #5491, 0.91 #8090, 0.90 #8089), CN (0.72 #3670, 0.69 #1376, 0.69 #1375), UZB (0.72 #3670, 0.50 #1372, 0.50 #960), TM (0.72 #3670, 0.34 #1839, 0.34 #4883), K (0.69 #1376, 0.69 #1375, 0.62 #766), BD (0.69 #1376, 0.69 #1375, 0.62 #766), MAL (0.69 #1376, 0.69 #1375, 0.62 #766), BRU (0.69 #1376, 0.69 #1375, 0.62 #766), RI (0.69 #1376, 0.69 #1375, 0.62 #766)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #6866 for best value:
>> intensional similarity = 14
>> extensional distance = 51
>> proper extension:  MAL; DOM;
>> query: (?x232, ?x129) <- ?x232[ has ethnicGroup ?x2285; has neighbor ?x129[ a Country; has encompassed ?x175; has ethnicGroup ?x1193; has neighbor ?x277; is locatedIn of ?x276;]; has neighbor ?x641[ a Country; has religion ?x95;]; has religion ?x116; is locatedIn of ?x231; is locatedIn of ?x484[ a Mountain;];]
ranks of expected_values: 1, 2, 26
EVAL	CN	neighbor!	MNG	CNN-1.+1._MA	0.000	0.000	0.000	0.042	77.000	77.000	220.000	0.912	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	CN	neighbor!	MYA	CNN-1.+1._MA	1.000	1.000	1.000	1.000	77.000	77.000	220.000	0.912	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	CN	neighbor!	NOK	CNN-1.+1._MA	1.000	1.000	1.000	1.000	77.000	77.000	220.000	0.912	http://www.semwebtech.org/mondial/10/meta#neighbor

#801-FL
PRED entity: FL

PRED relation: religion
PRED expected values: RomanCatholic
=> 44 concepts (44 used for prediction)
PRED predicted values (max 10 best out of 32): RomanCatholic (0.84 #294, 0.83 #253, 0.83 #376), ChristianOrthodox (0.57 #206, 0.54 #1108, 0.49 #1479), Muslim (0.56 #1277, 0.55 #1359, 0.54 #1070), Jewish (0.30 #1232, 0.13 #371, 0.12 #412), Buddhist (0.30 #1232, 0.11 #380, 0.10 #1243), Hindu (0.30 #1232, 0.10 #788, 0.10 #1488), Christian (0.30 #1235, 0.29 #1358, 0.29 #1069), Anglican (0.13 #591, 0.12 #714, 0.12 #263), JehovasWitnesses (0.12 #266, 0.11 #307, 0.11 #389), UkrainianGreekCatholic (0.06 #202, 0.04 #243, 0.02 #284)
>> best conf = 0.84
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #294 for best value:
>> intensional similarity = 6
>> extensional distance = 42
>> proper extension:  AUS; CDN; JA; SLB; IS; NZ; BDS; FSM; WL;
>> query: (?x423, RomanCatholic) <- ?x423[ has encompassed ?x195; has ethnicGroup ?x2314; has government ?x1952; has religion ?x95<Protestant>; has wasDependentOf ?x2516;]
ranks of expected_values: 1
EVAL	FL	religion	RomanCatholic	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	44.000	44.000	32.000	0.841	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion
PRED expected values: RomanCatholic
=> 104 concepts (104 used for prediction)
PRED predicted values (max 10 best out of 36): RomanCatholic (0.88 #2715, 0.86 #2840, 0.85 #3174), Muslim (0.72 #1707, 0.68 #3460, 0.66 #3375), ChristianOrthodox (0.66 #660, 0.63 #2125, 0.62 #1247), Jewish (0.48 #1580, 0.46 #1955, 0.44 #1370), Buddhist (0.48 #1580, 0.46 #1955, 0.44 #1370), Hindu (0.48 #1580, 0.46 #1955, 0.44 #1370), Christian (0.40 #2458, 0.40 #2419, 0.33 #45), Anglican (0.38 #928, 0.37 #3085, 0.36 #677), Sikh (0.37 #3085, 0.26 #953, 0.24 #3757), JehovasWitnesses (0.32 #3000, 0.19 #1348, 0.15 #1307)
>> best conf = 0.88
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2715 for best value:
>> intensional similarity = 19
>> extensional distance = 56
>> proper extension:  SPMI; FPOL; WAFU; CAYM;
>> query: (?x423, RomanCatholic) <- ?x423[ a Country; has encompassed ?x195; has ethnicGroup ?x2314; has government ?x1952; has language ?x635; has religion ?x95[ is religion of ?x170<N>; is religion of ?x202<RCH>; is religion of ?x408<NIC>; is religion of ?x460<RP>; is religion of ?x542<BR>; is religion of ?x1364<HCA>;]; is locatedIn of ?x256;]
ranks of expected_values: 1
EVAL	FL	religion	RomanCatholic	CNN-1.+1._MA	1.000	1.000	1.000	1.000	104.000	104.000	36.000	0.879	http://www.semwebtech.org/mondial/10/meta#religion

#800-ANG
PRED entity: ANG

PRED relation: locatedIn!
PRED expected values: Zambezi
=> 29 concepts (24 used for prediction)
PRED predicted values (max 10 best out of 1104): Zambezi (0.40 #3960, 0.40 #2546, 0.33 #1132), Limpopo (0.40 #2892, 0.33 #64, 0.20 #1478), Oranje (0.40 #2327, 0.20 #3741, 0.13 #19800), PacificOcean (0.38 #8569, 0.36 #22717, 0.21 #28377), CaribbeanSea (0.33 #8589, 0.29 #18490, 0.27 #17075), MakarikariSaltPan (0.33 #1233, 0.20 #4061, 0.13 #19800), LakeNgami (0.33 #1085, 0.20 #3913, 0.13 #19800), Okavango (0.33 #994, 0.20 #3822, 0.13 #19800), Uruguay (0.30 #6205, 0.21 #7619, 0.12 #9033), MediterraneanSea (0.28 #21297, 0.25 #24130, 0.14 #25546)
>> best conf = 0.40
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3960 for best value:
>> intensional similarity = 7
>> extensional distance = 3
>> proper extension:  RSA; Z;
>> query: (?x934, Zambezi) <- ?x934[ a Country; has ethnicGroup ?x197; has neighbor ?x138; has religion ?x95; is locatedIn of ?x933<Kalahari>; is neighbor of ?x348;]
>> Best rule #2546 for best value:
>> intensional similarity = 7
>> extensional distance = 3
>> proper extension:  NAM;
>> query: (?x934, Zambezi) <- ?x934[ has religion ?x352[ a Religion; is religion of ?x156<HR>;]; is locatedIn of ?x933<Kalahari>; is neighbor of ?x138;]
ranks of expected_values: 1
EVAL	ANG	locatedIn!	Zambezi	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	29.000	24.000	1104.000	0.400	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Zambezi
=> 108 concepts (103 used for prediction)
PRED predicted values (max 10 best out of 1402): Cuango (0.83 #11340, 0.69 #9924, 0.66 #67987), Cuilo (0.83 #11340, 0.69 #9924, 0.66 #67987), Okavango (0.83 #11340, 0.69 #9924, 0.66 #67987), PacificOcean (0.65 #29829, 0.48 #45413, 0.40 #59577), Zaire (0.53 #9923, 0.33 #1879, 0.32 #14173), Lulua (0.53 #9923, 0.32 #14173, 0.20 #7088), Oranje (0.53 #9923, 0.22 #69405, 0.22 #69404), Orinoco (0.53 #9923, 0.15 #21374, 0.14 #14301), SaintLawrenceRiver (0.53 #9923, 0.14 #16314, 0.14 #14898), Parana (0.53 #9923, 0.14 #15813, 0.13 #39656)
>> best conf = 0.83
=> the first rule below is the first best rule for 3 predicted values
>> Best rule #11340 for best value:
>> intensional similarity = 15
>> extensional distance = 4
>> proper extension:  LS;
>> query: (?x934, ?x928) <- ?x934[ has ethnicGroup ?x197; has religion ?x95; is locatedIn of ?x927[ a River; has hasEstuary ?x928;]; is locatedIn of ?x1076[ a Source;]; is neighbor of ?x525[ has neighbor ?x820[ has neighbor ?x359; has wasDependentOf ?x81;]; has neighbor ?x1576<ZW>; is locatedIn of ?x2185[ a River;];];]

*> Best rule #5671 for first EXPECTED value:
*> intensional similarity = 13
*> extensional distance = 2
*> proper extension:  RWA;
*> query: (?x934, ?x709) <- ?x934[ has ethnicGroup ?x197; has religion ?x95<Protestant>; is locatedIn of ?x1076[ a Source;]; is neighbor of ?x525[ has government ?x435; has neighbor ?x820<EAT>; has wasDependentOf ?x81; is locatedIn of ?x284<LakeTanganjika>; is locatedIn of ?x709;];]
*> conf = 0.40
ranks of expected_values: 34
EVAL	ANG	locatedIn!	Zambezi	CNN-1.+1._MA	0.000	0.000	0.000	0.029	108.000	103.000	1402.000	0.826	http://www.semwebtech.org/mondial/10/meta#locatedIn

#799-Tatamailau
PRED entity: Tatamailau

PRED relation: locatedOnIsland
PRED expected values: Timor
=> 37 concepts (26 used for prediction)
PRED predicted values (max 10 best out of 45): NewGuinea (0.29 #82, 0.18 #135, 0.06 #242), Borneo (0.14 #58, 0.06 #111, 0.03 #218), Bougainville (0.14 #87, 0.06 #140, 0.03 #247), Sumatra (0.12 #120, 0.07 #173, 0.03 #280), Java (0.12 #108, 0.07 #161, 0.03 #268), Timor (0.10 #105, 0.06 #477, 0.03 #212), Tatamailau (0.10 #105, 0.03 #212, 0.01 #479), BandaSea (0.10 #105, 0.03 #212, 0.01 #479), JavaSea (0.10 #105, 0.03 #212, 0.01 #479), IndianOcean (0.10 #105, 0.03 #212, 0.01 #479)
>> best conf = 0.29
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #82 for best value:
>> intensional similarity = 6
>> extensional distance = 5
>> proper extension:  Tahan; Kinabalu; Mt.Giluwe; Mt.Balbi; Mt.Wilhelm;
>> query: (?x2095, NewGuinea) <- ?x2095[ a Mountain; has locatedIn ?x735[ has encompassed ?x175; has government ?x435; is neighbor of ?x217<RI>;];]

*> Best rule #105 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 5
*> proper extension:  Tahan; Kinabalu; Mt.Giluwe; Mt.Balbi; Mt.Wilhelm;
*> query: (?x2095, ?x60) <- ?x2095[ a Mountain; has locatedIn ?x735[ has encompassed ?x175; has government ?x435; is locatedIn of ?x60; is neighbor of ?x217<RI>;];]
*> conf = 0.10
ranks of expected_values: 6
EVAL	Tatamailau	locatedOnIsland	Timor	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.167	37.000	26.000	45.000	0.286	http://www.semwebtech.org/mondial/10/meta#locatedOnIsland

PRED relation: locatedOnIsland
PRED expected values: Timor
=> 101 concepts (89 used for prediction)
PRED predicted values (max 10 best out of 94): NewGuinea (0.40 #191, 0.20 #461, 0.08 #674), Luzon (0.29 #285, 0.17 #394, 0.04 #1148), Borneo (0.25 #59, 0.12 #328, 0.08 #382), Taiwan (0.25 #120, 0.01 #1848, 0.01 #1902), Bougainville (0.20 #196, 0.07 #466, 0.03 #626), Timor (0.14 #215, 0.14 #214, 0.10 #320), BandaSea (0.14 #215, 0.14 #214, 0.06 #161), JavaSea (0.14 #215, 0.14 #214, 0.06 #161), IndianOcean (0.14 #215, 0.14 #214, 0.06 #161), Tatamailau (0.14 #215, 0.14 #214, 0.06 #161)
>> best conf = 0.40
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #191 for best value:
>> intensional similarity = 10
>> extensional distance = 3
>> proper extension:  Mt.Giluwe; Mt.Balbi; Mt.Wilhelm;
>> query: (?x2095, NewGuinea) <- ?x2095[ a Mountain; has locatedIn ?x735[ a Country; has encompassed ?x175[ a Continent;]; has government ?x435; has neighbor ?x217<RI>; has religion ?x95<Protestant>;];]

*> Best rule #215 for first EXPECTED value:
*> intensional similarity = 11
*> extensional distance = 3
*> proper extension:  Mt.Giluwe; Mt.Balbi; Mt.Wilhelm;
*> query: (?x2095, ?x241) <- ?x2095[ a Mountain; has locatedIn ?x735[ a Country; has encompassed ?x175[ a Continent;]; has government ?x435; has neighbor ?x217<RI>; has religion ?x95<Protestant>; is locatedIn of ?x241;];]
*> conf = 0.14
ranks of expected_values: 6
EVAL	Tatamailau	locatedOnIsland	Timor	CNN-1.+1._MA	0.000	0.000	1.000	0.167	101.000	89.000	94.000	0.400	http://www.semwebtech.org/mondial/10/meta#locatedOnIsland

#798-MackenzieRiver
PRED entity: MackenzieRiver

PRED relation: flowsInto
PRED expected values: ArcticOcean
=> 42 concepts (34 used for prediction)
PRED predicted values (max 10 best out of 149): SaintLawrenceRiver (0.20 #285, 0.14 #616, 0.12 #119), AtlanticOcean (0.17 #841, 0.15 #1494, 0.15 #1340), LakeHuron (0.12 #14, 0.11 #3161, 0.10 #180), PacificOcean (0.12 #25, 0.11 #3161, 0.10 #191), BeringSea (0.12 #86, 0.10 #252, 0.08 #417), LakeErie (0.12 #163, 0.08 #494, 0.07 #660), ArcticOcean (0.11 #3161, 0.08 #3329, 0.02 #4335), LakeManicouagan (0.11 #3161, 0.01 #3662, 0.01 #3661), HudsonBay (0.10 #182, 0.04 #845, 0.02 #3328), LakeWinnipeg (0.10 #242, 0.04 #905, 0.02 #1238)
>> best conf = 0.20
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #285 for best value:
>> intensional similarity = 5
>> extensional distance = 8
>> proper extension:  NelsonRiver; Manicouagan; SaskatchewanRiver;
>> query: (?x2083, SaintLawrenceRiver) <- ?x2083[ a River; has hasEstuary ?x2268[ a Estuary;]; has locatedIn ?x272<CDN>;]

*> Best rule #3161 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 182
*> proper extension:  Kwa; Save; Kasai; WhiteDrin; Zaire; Saone; Tajo; Benue; Zambezi;
*> query: (?x2083, ?x282) <- ?x2083[ a River; has hasSource ?x2291; has locatedIn ?x272[ is locatedIn of ?x282[ is locatedInWater of ?x205;];];]
*> conf = 0.11
ranks of expected_values: 7
EVAL	MackenzieRiver	flowsInto	ArcticOcean	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.143	42.000	34.000	149.000	0.200	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto
PRED expected values: ArcticOcean
=> 125 concepts (125 used for prediction)
PRED predicted values (max 10 best out of 188): SaintLawrenceRiver (0.25 #783, 0.20 #119, 0.17 #285), AtlanticOcean (0.20 #12, 0.19 #3180, 0.19 #3023), LakeHuron (0.20 #14, 0.17 #180, 0.14 #1345), PacificOcean (0.17 #2505, 0.17 #2362, 0.15 #8234), LakeOntario (0.17 #270, 0.10 #8229, 0.08 #11445), ArcticOcean (0.15 #8234, 0.08 #10261, 0.08 #5873), BeringSea (0.14 #584, 0.14 #418, 0.09 #11276), LakeErie (0.14 #661, 0.10 #8229, 0.09 #11276), LakeWinnipeg (0.12 #740, 0.10 #8229, 0.09 #11276), HudsonBay (0.12 #680, 0.09 #11276, 0.09 #10430)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #783 for best value:
>> intensional similarity = 9
>> extensional distance = 6
>> proper extension:  NelsonRiver; Manicouagan; SaskatchewanRiver;
>> query: (?x2083, SaintLawrenceRiver) <- ?x2083[ a River; has hasEstuary ?x2268[ a Estuary; has locatedIn ?x272<CDN>;]; has hasSource ?x2291[ a Source; has locatedIn ?x272<CDN>;]; has locatedIn ?x272<CDN>;]

*> Best rule #8234 for first EXPECTED value:
*> intensional similarity = 9
*> extensional distance = 89
*> proper extension:  Isere; Etsch; Garonne; Tiber; Loire; Marne; Arno;
*> query: (?x2083, ?x282) <- ?x2083[ a River; has locatedIn ?x272[ has government ?x2416; is locatedIn of ?x218[ is flowsInto of ?x2018;]; is locatedIn of ?x282[ has mergesWith ?x60;]; is locatedIn of ?x2007[ has flowsThrough ?x406;];];]
*> conf = 0.15
ranks of expected_values: 6
EVAL	MackenzieRiver	flowsInto	ArcticOcean	CNN-1.+1._MA	0.000	0.000	1.000	0.167	125.000	125.000	188.000	0.250	http://www.semwebtech.org/mondial/10/meta#flowsInto

#797-KuybyshevReservoir
PRED entity: KuybyshevReservoir

PRED relation: type
PRED expected values: "dam"
=> 51 concepts (51 used for prediction)
PRED predicted values (max 10 best out of 7): "dam" (0.39 #225, 0.23 #321, 0.22 #337), "salt" (0.25 #23, 0.24 #439, 0.23 #455), "volcanic" (0.07 #786, 0.07 #770, 0.07 #754), "caldera" (0.06 #419, 0.05 #451, 0.05 #467), "volcano" (0.04 #790, 0.04 #774, 0.03 #758), "impact" (0.03 #426, 0.03 #234, 0.02 #490), "naturaldam" (0.03 #240, 0.01 #352)
>> best conf = 0.39
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #225 for best value:
>> intensional similarity = 5
>> extensional distance = 36
>> proper extension:  LakeKariba;
>> query: (?x444, "dam") <- ?x444[ a Lake; has locatedIn ?x73; is flowsThrough of ?x445[ has flowsInto ?x1337; has hasSource ?x492;];]
ranks of expected_values: 1
EVAL	KuybyshevReservoir	type	"dam"	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	51.000	51.000	7.000	0.395	http://www.semwebtech.org/mondial/10/meta#type

PRED relation: type
PRED expected values: "dam"
=> 143 concepts (143 used for prediction)
PRED predicted values (max 10 best out of 11): "dam" (0.55 #689, 0.44 #321, 0.40 #97), "salt" (0.50 #183, 0.37 #647, 0.32 #663), "volcanic" (0.11 #1958, 0.11 #1828, 0.10 #1877), "volcano" (0.09 #1973, 0.08 #1843, 0.08 #1908), "caldera" (0.09 #1092, 0.07 #1285, 0.06 #1124), "impact" (0.05 #698, 0.03 #762, 0.03 #826), "naturaldam" (0.04 #752, 0.03 #832, 0.03 #848), "crater" (0.02 #1005, 0.02 #1118, 0.02 #1150), "acid" (0.02 #1007, 0.02 #1152, 0.01 #1297), "sand" (0.01 #1638, 0.01 #1814, 0.01 #1830)
>> best conf = 0.55
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #689 for best value:
>> intensional similarity = 10
>> extensional distance = 20
>> proper extension:  LakeBurleyGriffin; LakeEucumbene;
>> query: (?x444, "dam") <- ?x444[ a Lake; has locatedIn ?x73[ a Country; has ethnicGroup ?x1550[ a EthnicGroup;]; has language ?x555; has religion ?x56; is locatedIn of ?x507[ a Sea;];]; is flowsThrough of ?x445;]
ranks of expected_values: 1
EVAL	KuybyshevReservoir	type	"dam"	CNN-1.+1._MA	1.000	1.000	1.000	1.000	143.000	143.000	11.000	0.545	http://www.semwebtech.org/mondial/10/meta#type

#796-MA
PRED entity: MA

PRED relation: encompassed
PRED expected values: Africa
=> 32 concepts (32 used for prediction)
PRED predicted values (max 10 best out of 5): Africa (0.89 #46, 0.81 #143, 0.71 #9), America (0.55 #87, 0.47 #76, 0.46 #20), Asia (0.41 #31, 0.36 #88, 0.34 #21), Europe (0.30 #82, 0.27 #114, 0.26 #139), Australia-Oceania (0.12 #146, 0.12 #156, 0.11 #151)
>> best conf = 0.89
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #46 for best value:
>> intensional similarity = 6
>> extensional distance = 34
>> proper extension:  WSA;
>> query: (?x851, ?x213) <- ?x851[ has neighbor ?x1588[ a Country; has encompassed ?x213;]; has religion ?x109; is locatedIn of ?x182<AtlanticOcean>;]
ranks of expected_values: 1
EVAL	MA	encompassed	Africa	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	32.000	32.000	5.000	0.889	http://www.semwebtech.org/mondial/10/meta#encompassed

PRED relation: encompassed
PRED expected values: Africa
=> 72 concepts (72 used for prediction)
PRED predicted values (max 10 best out of 5): Africa (0.89 #176, 0.82 #340, 0.82 #259), Europe (0.54 #119, 0.53 #222, 0.52 #310), America (0.50 #26, 0.44 #132, 0.42 #112), Asia (0.42 #247, 0.42 #242, 0.40 #38), Australia-Oceania (0.20 #165, 0.20 #198, 0.20 #299)
>> best conf = 0.89
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #176 for best value:
>> intensional similarity = 13
>> extensional distance = 33
>> proper extension:  NAM; YV; FGU; GQ;
>> query: (?x851, ?x213) <- ?x851[ has neighbor ?x1588[ has encompassed ?x213; is locatedIn of ?x275[ is flowsInto of ?x698;];]; has religion ?x187[ is religion of ?x120<D>; is religion of ?x158[ a Country;]; is religion of ?x196<AUS>;]; is locatedIn of ?x182<AtlanticOcean>;]
ranks of expected_values: 1
EVAL	MA	encompassed	Africa	CNN-1.+1._MA	1.000	1.000	1.000	1.000	72.000	72.000	5.000	0.886	http://www.semwebtech.org/mondial/10/meta#encompassed

#795-Halmahera
PRED entity: Halmahera

PRED relation: locatedInWater
PRED expected values: BandaSea
=> 41 concepts (41 used for prediction)
PRED predicted values (max 10 best out of 68): BandaSea (0.84 #300, 0.41 #1125, 0.41 #1124), IndianOcean (0.61 #170, 0.61 #128, 0.41 #1125), AtlanticOcean (0.56 #524, 0.30 #1133, 0.29 #1220), JavaSea (0.53 #51, 0.47 #93, 0.41 #1125), SouthChinaSea (0.41 #1125, 0.41 #1124, 0.40 #908), SulawesiSea (0.41 #1125, 0.41 #1124, 0.40 #908), AndamanSea (0.41 #1125, 0.41 #1124, 0.40 #908), MalakkaStrait (0.41 #1125, 0.41 #1124, 0.40 #908), LakeToba (0.41 #1125, 0.41 #1124, 0.39 #1080), MediterraneanSea (0.16 #575, 0.15 #705, 0.12 #446)
>> best conf = 0.84
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #300 for best value:
>> intensional similarity = 6
>> extensional distance = 44
>> proper extension:  Fakaofo; Guadalcanal; Babelthuap; Bougainville; VanuaLevu;
>> query: (?x1098, ?x770) <- ?x1098[ has belongsToIslands ?x1099[ a Islands; is belongsToIslands of ?x216[ has locatedInWater ?x770;];]; has locatedInWater ?x282<PacificOcean>;]
ranks of expected_values: 1
EVAL	Halmahera	locatedInWater	BandaSea	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	41.000	41.000	68.000	0.839	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater
PRED expected values: BandaSea
=> 124 concepts (124 used for prediction)
PRED predicted values (max 10 best out of 454): BandaSea (0.84 #709, 0.83 #577, 0.83 #534), IndianOcean (0.56 #269, 0.56 #226, 0.45 #2007), AtlanticOcean (0.52 #1340, 0.52 #1612, 0.52 #1569), SouthChinaSea (0.45 #2007, 0.41 #1516, 0.40 #621), MalakkaStrait (0.45 #2007, 0.41 #1516, 0.40 #621), AndamanSea (0.45 #2007, 0.41 #1516, 0.40 #621), JavaSea (0.41 #1516, 0.40 #621, 0.40 #1424), SulawesiSea (0.41 #1516, 0.40 #621, 0.40 #1424), LakeToba (0.37 #1423, 0.22 #177, 0.09 #4925), CaribbeanSea (0.31 #1218, 0.29 #1039, 0.15 #1351)
>> best conf = 0.84
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #709 for best value:
>> intensional similarity = 8
>> extensional distance = 44
>> proper extension:  Fakaofo; Guadalcanal; Bougainville;
>> query: (?x1098, ?x770) <- ?x1098[ a Island; has belongsToIslands ?x1099[ a Islands; is belongsToIslands of ?x216[ a Island; has locatedInWater ?x770;];]; has locatedInWater ?x282<PacificOcean>;]
ranks of expected_values: 1
EVAL	Halmahera	locatedInWater	BandaSea	CNN-1.+1._MA	1.000	1.000	1.000	1.000	124.000	124.000	454.000	0.839	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#794-Mulatto
PRED entity: Mulatto

PRED relation: ethnicGroup!
PRED expected values: AXA
=> 25 concepts (16 used for prediction)
PRED predicted values (max 10 best out of 205): EC (0.50 #342, 0.33 #153, 0.25 #1516), ROU (0.50 #256, 0.33 #67, 0.25 #1516), CR (0.50 #247, 0.33 #58, 0.21 #437), HCA (0.50 #365, 0.33 #176, 0.12 #555), NIC (0.50 #268, 0.33 #79, 0.12 #458), CV (0.50 #278, 0.33 #89, 0.12 #468), BDS (0.50 #357, 0.33 #168, 0.08 #547), GNB (0.50 #376, 0.33 #187, 0.08 #566), GH (0.50 #289, 0.33 #100, 0.08 #479), Z (0.50 #292, 0.33 #103, 0.08 #482)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #342 for best value:
>> intensional similarity = 9
>> extensional distance = 2
>> proper extension:  European;
>> query: (?x1052, EC) <- ?x1052[ a EthnicGroup; is ethnicGroup of ?x148<C>; is ethnicGroup of ?x697[ a Country; has religion ?x95; has wasDependentOf ?x78; is locatedIn of ?x2210<Hispaniola>;];]

*> Best rule #457 for first EXPECTED value:
*> intensional similarity = 9
*> extensional distance = 22
*> proper extension:  Black; African-white-Indian; White; Mixed; BasquesBretons;
*> query: (?x1052, AXA) <- ?x1052[ a EthnicGroup; is ethnicGroup of ?x520[ a Country; has encompassed ?x521<America>; has government ?x711; is locatedIn of ?x182;]; is ethnicGroup of ?x697[ has religion ?x95;];]
*> conf = 0.08
ranks of expected_values: 66
EVAL	Mulatto	ethnicGroup!	AXA	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.015	25.000	16.000	205.000	0.500	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup!
PRED expected values: AXA
=> 68 concepts (68 used for prediction)
PRED predicted values (max 10 best out of 218): EC (0.69 #1931, 0.67 #916, 0.64 #569), GUY (0.69 #1931, 0.64 #569, 0.50 #957), PE (0.69 #1931, 0.53 #1738, 0.52 #3102), CR (0.67 #821, 0.64 #569, 0.56 #1600), BOL (0.64 #569, 0.53 #1738, 0.52 #3102), RA (0.64 #569, 0.53 #1738, 0.52 #3102), PA (0.64 #569, 0.53 #1738, 0.52 #3102), HCA (0.64 #569, 0.50 #939, 0.50 #365), NIC (0.64 #569, 0.50 #842, 0.50 #268), ROU (0.64 #569, 0.50 #256, 0.47 #1929)
>> best conf = 0.69
=> the first rule below is the first best rule for 3 predicted values
>> Best rule #1931 for best value:
>> intensional similarity = 16
>> extensional distance = 8
>> proper extension:  Chinese; Asian;
>> query: (?x1052, ?x351) <- ?x1052[ is ethnicGroup of ?x542[ has neighbor ?x179; is locatedIn of ?x1305[ a Estuary;]; is locatedIn of ?x1703[ a River; has hasSource ?x1704;]; is neighbor of ?x345[ is locatedIn of ?x317;]; is neighbor of ?x351[ a Country; has encompassed ?x521<America>; has ethnicGroup ?x79<Amerindian>; has religion ?x95;];];]

*> Best rule #379 for first EXPECTED value:
*> intensional similarity = 19
*> extensional distance = 2
*> proper extension:  European;
*> query: (?x1052, ?x50) <- ?x1052[ a EthnicGroup; is ethnicGroup of ?x215<CO>; is ethnicGroup of ?x520<DOM>; is ethnicGroup of ?x542<BR>; is ethnicGroup of ?x697[ has encompassed ?x521; has government ?x435<"republic">; has religion ?x95; has wasDependentOf ?x78<F>; is locatedIn of ?x182[ has locatedIn ?x50; has mergesWith ?x373; is locatedInWater of ?x112; is mergesWith of ?x60;];];]
*> conf = 0.27
ranks of expected_values: 67
EVAL	Mulatto	ethnicGroup!	AXA	CNN-1.+1._MA	0.000	0.000	0.000	0.015	68.000	68.000	218.000	0.686	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#793-Basse-Terre
PRED entity: Basse-Terre

PRED relation: locatedIn
PRED expected values: GUAD
=> 45 concepts (45 used for prediction)
PRED predicted values (max 10 best out of 115): GUAD (0.90 #4731, 0.90 #3074, 0.88 #4968), MART (0.35 #9237, 0.35 #9236, 0.34 #8999), WV (0.35 #9237, 0.35 #9236, 0.34 #8999), WD (0.35 #9237, 0.35 #9236, 0.34 #8999), MNTS (0.35 #9237, 0.35 #9236, 0.34 #8999), WG (0.35 #9237, 0.35 #9236, 0.34 #8999), AG (0.35 #9237, 0.35 #9236, 0.34 #8999), TT (0.35 #9237, 0.35 #9236, 0.34 #8999), WL (0.35 #9237, 0.35 #9236, 0.34 #8999), KN (0.35 #9237, 0.35 #9236, 0.34 #8999)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4731 for best value:
>> intensional similarity = 6
>> extensional distance = 37
>> proper extension:  ReneLevasseurIsland; Taiwan; BaffinIsland; Cyprus; Jamaica; Corse; Hispaniola;
>> query: (?x2152, ?x633) <- ?x2152[ a Island; has locatedInWater ?x317; is locatedOnIsland of ?x1435[ has locatedIn ?x633[ has government ?x828; has religion ?x95;];];]
>> Best rule #3074 for best value:
>> intensional similarity = 6
>> extensional distance = 18
>> proper extension:  Reunion; SaoTome;
>> query: (?x2152, ?x633) <- ?x2152[ a Island; has type ?x150; is locatedOnIsland of ?x1435[ a Volcano; has locatedIn ?x633; has type ?x706;];]
ranks of expected_values: 1
EVAL	Basse-Terre	locatedIn	GUAD	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	45.000	45.000	115.000	0.900	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: GUAD
=> 85 concepts (85 used for prediction)
PRED predicted values (max 10 best out of 120): GUAD (0.92 #7913, 0.90 #5026, 0.87 #3328), MART (0.48 #3326, 0.47 #5024, 0.47 #5021), WV (0.48 #3326, 0.47 #5024, 0.47 #5021), WD (0.48 #3326, 0.47 #5024, 0.47 #5021), MNTS (0.47 #5024, 0.47 #5021, 0.43 #11319), WG (0.47 #5024, 0.47 #5021, 0.43 #11319), WL (0.47 #5024, 0.47 #5021, 0.43 #11319), KN (0.47 #5024, 0.47 #5021, 0.43 #11319), AG (0.47 #5024, 0.47 #5021, 0.43 #11319), BDS (0.47 #5024, 0.47 #5021, 0.43 #11319)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #7913 for best value:
>> intensional similarity = 9
>> extensional distance = 34
>> proper extension:  Kreta;
>> query: (?x2152, ?x633) <- ?x2152[ a Island; is locatedOnIsland of ?x1435[ a Mountain; has locatedIn ?x633[ a Country; has encompassed ?x521; has ethnicGroup ?x298; has government ?x828; has religion ?x95;];];]
ranks of expected_values: 1
EVAL	Basse-Terre	locatedIn	GUAD	CNN-1.+1._MA	1.000	1.000	1.000	1.000	85.000	85.000	120.000	0.917	http://www.semwebtech.org/mondial/10/meta#locatedIn

#792-Jubba
PRED entity: Jubba

PRED relation: hasSource!
PRED expected values: Jubba
=> 34 concepts (28 used for prediction)
PRED predicted values (max 10 best out of 57): Shabelle (0.17 #212, 0.05 #440, 0.04 #686), Atbara (0.17 #204, 0.05 #432, 0.04 #686), BlueNile (0.17 #186, 0.05 #414, 0.04 #686), Baro (0.17 #79, 0.05 #307, 0.03 #685), Pibor (0.04 #686, 0.03 #662, 0.02 #1833), Jubba (0.04 #686, 0.02 #1833, 0.02 #2062), Bahrel-Ghasal (0.03 #585), WhiteNile (0.03 #567), Sobat (0.03 #473), Saluen (0.02 #916)
>> best conf = 0.17
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #212 for best value:
>> intensional similarity = 3
>> extensional distance = 4
>> proper extension:  Baro; Atbara; Shabelle; BlueNile;
>> query: (?x2014, Shabelle) <- ?x2014[ a Source; has locatedIn ?x476<ETH>;]

*> Best rule #686 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 27
*> proper extension:  Bahrel-Ghasal; Bahrel-Ghasal; Bahrel-Djebel-Albert-Nil; Bahrel-Djebel-Albert-Nil; Sobat;
*> query: (?x2014, ?x750) <- ?x2014[ has locatedIn ?x476[ a Country; is locatedIn of ?x747<Baro>; is locatedIn of ?x750[ has hasEstuary ?x510;];];]
*> conf = 0.04
ranks of expected_values: 6
EVAL	Jubba	hasSource!	Jubba	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.167	34.000	28.000	57.000	0.167	http://www.semwebtech.org/mondial/10/meta#hasSource

PRED relation: hasSource!
PRED expected values: Jubba
=> 77 concepts (77 used for prediction)
PRED predicted values (max 10 best out of 158): Atbara (0.17 #204, 0.14 #457, 0.14 #432), BlueNile (0.17 #186, 0.14 #414, 0.14 #689), Shabelle (0.17 #212, 0.14 #440, 0.14 #689), Baro (0.17 #79, 0.14 #307, 0.14 #689), Nile (0.14 #446, 0.09 #688, 0.04 #1365), Pibor (0.14 #689, 0.10 #687, 0.10 #664), Jubba (0.14 #689, 0.09 #688, 0.07 #4823), LakeTana (0.14 #689, 0.06 #458, 0.06 #1147), Sobat (0.10 #475, 0.09 #688, 0.03 #1393), Bahrel-Ghasal (0.10 #587, 0.03 #1505)
>> best conf = 0.17
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #204 for best value:
>> intensional similarity = 3
>> extensional distance = 4
>> proper extension:  Baro; Atbara; Shabelle; BlueNile;
>> query: (?x2014, Atbara) <- ?x2014[ a Source; has locatedIn ?x476<ETH>;]

*> Best rule #689 for first EXPECTED value:
*> intensional similarity = 12
*> extensional distance = 8
*> proper extension:  Bahrel-Ghasal; Sobat; WhiteNile; Pibor;
*> query: (?x2014, ?x1597) <- ?x2014[ a Source; has locatedIn ?x476[ a Country; has neighbor ?x186<SUD>; is locatedIn of ?x1597[ has flowsInto ?x2124;]; is locatedIn of ?x1895<Pibor>; is locatedIn of ?x2436<Baro>; is neighbor of ?x94;];]
*> conf = 0.14
ranks of expected_values: 7
EVAL	Jubba	hasSource!	Jubba	CNN-1.+1._MA	0.000	0.000	1.000	0.143	77.000	77.000	158.000	0.167	http://www.semwebtech.org/mondial/10/meta#hasSource

#791-WSA
PRED entity: WSA

PRED relation: neighbor
PRED expected values: DZ
=> 27 concepts (27 used for prediction)
PRED predicted values (max 10 best out of 197): DZ (0.89 #3390, 0.89 #3067, 0.89 #2904), RMM (0.50 #455, 0.33 #293, 0.29 #2089), RG (0.33 #271, 0.33 #109, 0.25 #433), RN (0.33 #78, 0.29 #2089, 0.28 #3068), SN (0.33 #75, 0.29 #2089, 0.28 #3068), WAG (0.33 #306, 0.25 #468, 0.20 #162), CI (0.33 #148, 0.20 #162, 0.16 #2578), LAR (0.29 #2089, 0.28 #3068, 0.28 #2906), TN (0.29 #2089, 0.28 #3068, 0.28 #2906), WSA (0.29 #2089, 0.28 #3068, 0.28 #2906)
>> best conf = 0.89
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3390 for best value:
>> intensional similarity = 7
>> extensional distance = 151
>> proper extension:  AND; RSM; V;
>> query: (?x646, ?x581) <- ?x646[ a Country; has religion ?x187; is neighbor of ?x581[ a Country; has encompassed ?x213; is neighbor of ?x839[ is locatedIn of ?x456;];];]
ranks of expected_values: 1
EVAL	WSA	neighbor	DZ	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	27.000	27.000	197.000	0.890	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: DZ
=> 56 concepts (56 used for prediction)
PRED predicted values (max 10 best out of 227): DZ (0.89 #8292, 0.89 #6770, 0.89 #7113), SN (0.62 #1552, 0.58 #662, 0.58 #661), RG (0.60 #1914, 0.56 #1751, 0.50 #442), F (0.60 #1154, 0.50 #500, 0.17 #658), RMM (0.58 #662, 0.58 #661, 0.55 #659), WSA (0.58 #662, 0.58 #661, 0.55 #659), MEL (0.58 #662, 0.58 #661, 0.55 #659), A (0.50 #573, 0.40 #1227, 0.10 #3018), IL (0.43 #2015, 0.20 #2987, 0.12 #4146), RN (0.42 #660, 0.40 #1638, 0.33 #247)
>> best conf = 0.89
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #8292 for best value:
>> intensional similarity = 10
>> extensional distance = 144
>> proper extension:  ARM;
>> query: (?x646, ?x581) <- ?x646[ a Country; has neighbor ?x851[ has ethnicGroup ?x582;]; has religion ?x187[ is religion of ?x304[ has language ?x511;]; is religion of ?x1206[ has ethnicGroup ?x2201; is neighbor of ?x811;];]; is neighbor of ?x581;]
ranks of expected_values: 1
EVAL	WSA	neighbor	DZ	CNN-1.+1._MA	1.000	1.000	1.000	1.000	56.000	56.000	227.000	0.895	http://www.semwebtech.org/mondial/10/meta#neighbor

#790-Kasai
PRED entity: Kasai

PRED relation: hasEstuary
PRED expected values: Kasai
=> 43 concepts (41 used for prediction)
PRED predicted values (max 10 best out of 180): Cuango (0.25 #192, 0.09 #418, 0.08 #7707), Cuilo (0.25 #58, 0.09 #284, 0.08 #7707), Okavango (0.09 #360), Fimi (0.08 #7707, 0.07 #829, 0.07 #602), Busira (0.08 #7707, 0.07 #699, 0.07 #472), Ruki (0.08 #7707, 0.07 #846, 0.07 #619), Lualaba (0.08 #7707, 0.07 #726, 0.07 #499), Ubangi (0.08 #7707, 0.07 #704, 0.07 #477), Luvua (0.08 #7707, 0.07 #857, 0.07 #630), Lukuga (0.08 #7707, 0.07 #827, 0.07 #600)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #192 for best value:
>> intensional similarity = 6
>> extensional distance = 2
>> proper extension:  Cuango; Cuilo;
>> query: (?x509, Cuango) <- ?x509[ a River; has flowsInto ?x113; has hasSource ?x1076[ a Source;]; has locatedIn ?x934<ANG>;]

*> Best rule #7707 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 232
*> proper extension:  Irawaddy; Indus; Main; Maas; Ural; Guadalquivir; Raab; Brahmaputra; Tobol;
*> query: (?x509, ?x1785) <- ?x509[ a River; has locatedIn ?x348[ has religion ?x95; is locatedIn of ?x1785[ a Estuary;];];]
*> conf = 0.08
ranks of expected_values: 20
EVAL	Kasai	hasEstuary	Kasai	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.050	43.000	41.000	180.000	0.250	http://www.semwebtech.org/mondial/10/meta#hasEstuary

PRED relation: hasEstuary
PRED expected values: Kasai
=> 129 concepts (129 used for prediction)
PRED predicted values (max 10 best out of 242): Fimi (0.33 #149, 0.20 #15925, 0.20 #20023), Cuango (0.25 #876, 0.20 #1102, 0.14 #1329), Cuilo (0.25 #742, 0.20 #968, 0.14 #1195), Okavango (0.20 #1044, 0.14 #1271, 0.09 #1952), Luapula (0.14 #1257, 0.12 #1484, 0.07 #20250), Lukuga (0.12 #1738, 0.09 #2192, 0.08 #2418), Luvua (0.12 #1768, 0.09 #2222, 0.08 #2448), Bomu (0.12 #1639, 0.09 #2093, 0.07 #20250), Busira (0.12 #1610, 0.08 #2290, 0.07 #20250), Uelle (0.12 #1744, 0.07 #20250, 0.06 #20934)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #149 for best value:
>> intensional similarity = 9
>> extensional distance = 1
>> proper extension:  Fimi;
>> query: (?x509, Fimi) <- ?x509[ a River; has flowsInto ?x113<Kwa>; has hasSource ?x1076; has locatedIn ?x348; is flowsInto of ?x436; is flowsInto of ?x927[ has hasEstuary ?x928; has hasSource ?x1138;];]

*> Best rule #19795 for first EXPECTED value:
*> intensional similarity = 11
*> extensional distance = 175
*> proper extension:  Irawaddy; Indus; Main; Maas; Ural; Guadalquivir;
*> query: (?x509, ?x1187) <- ?x509[ a River; has flowsInto ?x113; has locatedIn ?x348[ has neighbor ?x229; is locatedIn of ?x600[ a Lake;]; is locatedIn of ?x732[ a Source;]; is locatedIn of ?x1187[ a Estuary;]; is neighbor of ?x359;];]
*> conf = 0.06
ranks of expected_values: 23
EVAL	Kasai	hasEstuary	Kasai	CNN-1.+1._MA	0.000	0.000	0.000	0.043	129.000	129.000	242.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasEstuary

#789-RI
PRED entity: RI

PRED relation: ethnicGroup
PRED expected values: Malay
=> 33 concepts (33 used for prediction)
PRED predicted values (max 10 best out of 228): European (0.57 #518, 0.40 #263, 0.35 #1028), Amerindian (0.43 #512, 0.40 #257, 0.18 #1787), African (0.29 #516, 0.23 #4341, 0.21 #1536), Polynesian (0.24 #852, 0.23 #1107, 0.08 #3402), Russian (0.20 #326, 0.16 #1856, 0.15 #2111), Mestizo (0.20 #290, 0.14 #545, 0.13 #1820), Asian (0.20 #274, 0.14 #529, 0.12 #1039), Ukrainian (0.20 #256, 0.14 #511, 0.08 #1786), Tatar (0.20 #340, 0.14 #595, 0.06 #2380), Chuvash (0.20 #434, 0.14 #689, 0.05 #5866)
>> best conf = 0.57
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #518 for best value:
>> intensional similarity = 9
>> extensional distance = 5
>> proper extension:  RCH; NIC;
>> query: (?x217, European) <- ?x217[ has neighbor ?x376; is locatedIn of ?x241[ a Sea;]; is locatedIn of ?x282<PacificOcean>; is locatedIn of ?x1101[ a Island;]; is locatedIn of ?x1768[ has type ?x150;];]

*> Best rule #3412 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 73
*> proper extension:  SPMI; BVIR; GB; THA; D; C; E; IRL; KN; N; ...
*> query: (?x217, Malay) <- ?x217[ has ethnicGroup ?x425; is locatedIn of ?x241[ a Sea;]; is locatedIn of ?x282[ is locatedInWater of ?x205;]; is locatedIn of ?x1101[ a Island;];]
*> conf = 0.05
ranks of expected_values: 24
EVAL	RI	ethnicGroup	Malay	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.042	33.000	33.000	228.000	0.571	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: Malay
=> 75 concepts (75 used for prediction)
PRED predicted values (max 10 best out of 253): European (0.73 #5878, 0.69 #4345, 0.69 #6133), Amerindian (0.62 #4339, 0.60 #5872, 0.56 #6127), Mestizo (0.60 #5905, 0.56 #6160, 0.47 #6927), Chinese (0.60 #2565, 0.42 #3585, 0.33 #525), African (0.40 #2301, 0.37 #6898, 0.33 #5876), Taiwanese (0.33 #615, 0.11 #7658, 0.10 #17131), HanChinese (0.33 #207, 0.10 #17131, 0.10 #17645), Polynesian (0.29 #5446, 0.25 #6468, 0.24 #7745), French (0.25 #1398, 0.20 #2928, 0.18 #3183), Russian (0.25 #7473, 0.15 #10030, 0.14 #15408)
>> best conf = 0.73
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5878 for best value:
>> intensional similarity = 13
>> extensional distance = 13
>> proper extension:  ES; PA; HCA;
>> query: (?x217, European) <- ?x217[ has encompassed ?x211; has ethnicGroup ?x425; has religion ?x95; is locatedIn of ?x60[ is flowsInto of ?x242; is locatedInWater of ?x226;]; is locatedIn of ?x282<PacificOcean>; is locatedIn of ?x384[ a Sea; is locatedInWater of ?x1575[ is locatedOnIsland of ?x991;];]; is neighbor of ?x376;]

*> Best rule #2647 for first EXPECTED value:
*> intensional similarity = 16
*> extensional distance = 8
*> proper extension:  THA; MAL; BRU; VN; MACX; HONX; K;
*> query: (?x217, Malay) <- ?x217[ has ethnicGroup ?x425; has neighbor ?x376; has religion ?x95; is locatedIn of ?x282[ has locatedIn ?x297[ has language ?x51;]; is flowsInto of ?x602; is locatedInWater of ?x205; is locatedInWater of ?x414[ a Island;]; is mergesWith of ?x507[ is flowsInto of ?x1585;]; is mergesWith of ?x620<EastChinaSea>;]; is locatedIn of ?x384<SouthChinaSea>;]
*> conf = 0.20
ranks of expected_values: 13
EVAL	RI	ethnicGroup	Malay	CNN-1.+1._MA	0.000	0.000	0.000	0.077	75.000	75.000	253.000	0.733	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#788-GasherbrumI
PRED entity: GasherbrumI

PRED relation: locatedIn
PRED expected values: PK
=> 37 concepts (33 used for prediction)
PRED predicted values (max 10 best out of 108): R (0.61 #4263, 0.32 #1185, 0.27 #5444), USA (0.35 #5273, 0.32 #5510, 0.25 #5747), NEP (0.35 #961, 0.26 #943, 0.26 #724), PK (0.26 #481, 0.18 #10, 0.16 #717), IR (0.19 #3383, 0.08 #7338, 0.07 #1487), IND (0.19 #3023, 0.17 #3262, 0.11 #894), KGZ (0.16 #258, 0.12 #23, 0.11 #730), RI (0.15 #2651, 0.08 #5728, 0.07 #3604), I (0.15 #2647, 0.07 #3600, 0.07 #3835), E (0.13 #2626, 0.10 #5229, 0.07 #3579)
>> best conf = 0.61
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4263 for best value:
>> intensional similarity = 7
>> extensional distance = 164
>> proper extension:  Selenge; SeaofAzov; BlackSea; Suchona; Lena; BarentsSea; ArcticOcean; SeaofJapan; PacificOcean; Swir; ...
>> query: (?x2471, R) <- ?x2471[ has locatedIn ?x232[ has religion ?x116; is locatedIn of ?x1748<Irtysch>; is neighbor of ?x73; is neighbor of ?x83[ a Country;];];]

*> Best rule #481 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 17
*> proper extension:  NangaParbat; TirichMir;
*> query: (?x2471, PK) <- ?x2471[ a Mountain; has locatedIn ?x232[ a Country; is locatedIn of ?x1040<K2>; is neighbor of ?x73;];]
*> conf = 0.26
ranks of expected_values: 4
EVAL	GasherbrumI	locatedIn	PK	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.250	37.000	33.000	108.000	0.608	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: PK
=> 87 concepts (85 used for prediction)
PRED predicted values (max 10 best out of 203): R (0.65 #8832, 0.34 #12419, 0.32 #1200), USA (0.58 #11776, 0.57 #12012, 0.44 #2703), PK (0.56 #19109, 0.51 #14320, 0.39 #6920), NEP (0.56 #19109, 0.51 #14320, 0.39 #6920), KAZ (0.56 #19109, 0.51 #14320, 0.39 #6920), KGZ (0.56 #19109, 0.51 #14320, 0.39 #6920), RI (0.34 #1726, 0.33 #6736, 0.21 #2445), I (0.27 #2203, 0.21 #2441, 0.14 #4584), D (0.26 #12434, 0.13 #14579, 0.12 #14821), UA (0.25 #5087, 0.23 #6279, 0.20 #6989)
>> best conf = 0.65
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #8832 for best value:
>> intensional similarity = 11
>> extensional distance = 153
>> proper extension:  Suchona; Lena; ArcticOcean; SeaofJapan; PacificOcean; Swir; Swir; NorthernDwina; KuybyshevReservoir; Volga; ...
>> query: (?x2471, R) <- ?x2471[ has locatedIn ?x232[ has neighbor ?x73[ has ethnicGroup ?x58; has neighbor ?x170; has religion ?x56; is locatedIn of ?x72; is wasDependentOf of ?x565;]; is locatedIn of ?x384[ has mergesWith ?x241;]; is locatedIn of ?x1585<Amur>;];]

*> Best rule #19109 for first EXPECTED value:
*> intensional similarity = 10
*> extensional distance = 979
*> proper extension:  Fogo; Reunion; Principe; Jamaica; PuertoRico; SaoTome; Santiago; Cuba;
*> query: (?x2471, ?x83) <- ?x2471[ has locatedIn ?x232[ has encompassed ?x175; has religion ?x116[ a Religion;]; is locatedIn of ?x484[ a Mountain; has locatedIn ?x83;]; is locatedIn of ?x620[ a Sea; is locatedInWater of ?x619;];];]
*> conf = 0.56
ranks of expected_values: 3
EVAL	GasherbrumI	locatedIn	PK	CNN-1.+1._MA	0.000	1.000	1.000	0.333	87.000	85.000	203.000	0.652	http://www.semwebtech.org/mondial/10/meta#locatedIn

#787-Kattegat
PRED entity: Kattegat

PRED relation: mergesWith
PRED expected values: BalticSea
=> 62 concepts (49 used for prediction)
PRED predicted values (max 10 best out of 47): BalticSea (0.88 #449, 0.87 #202, 0.86 #203), Kattegat (0.46 #860, 0.38 #532, 0.25 #78), NorthSea (0.38 #532, 0.25 #45, 0.25 #531), AtlanticOcean (0.33 #87, 0.33 #5, 0.32 #372), NorwegianSea (0.33 #100, 0.33 #18, 0.25 #59), TheChannel (0.33 #113, 0.33 #31, 0.25 #72), GreenlandSea (0.33 #116, 0.13 #237, 0.12 #278), PacificOcean (0.29 #300, 0.28 #465, 0.26 #341), ArcticOcean (0.25 #420, 0.23 #544, 0.21 #173), IndianOcean (0.22 #575, 0.20 #699, 0.19 #534)
>> best conf = 0.88
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #449 for best value:
>> intensional similarity = 7
>> extensional distance = 22
>> proper extension:  HudsonBay; KaraSea;
>> query: (?x1663, ?x146) <- ?x1663[ is flowsInto of ?x1069[ has hasSource ?x2265;]; is mergesWith of ?x146[ has locatedIn ?x120;]; is mergesWith of ?x1664[ has locatedIn ?x170; is mergesWith of ?x121;];]
ranks of expected_values: 1
EVAL	Kattegat	mergesWith	BalticSea	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	62.000	49.000	47.000	0.875	http://www.semwebtech.org/mondial/10/meta#mergesWith

PRED relation: mergesWith
PRED expected values: BalticSea
=> 146 concepts (146 used for prediction)
PRED predicted values (max 10 best out of 181): BalticSea (0.86 #1142, 0.85 #1186, 0.84 #1185), NorwegianSea (0.50 #303, 0.50 #138, 0.40 #262), AtlanticOcean (0.50 #290, 0.40 #778, 0.40 #249), TheChannel (0.50 #151, 0.40 #275, 0.33 #316), Kattegat (0.47 #1269, 0.46 #1436, 0.44 #1642), GreenlandSea (0.40 #196, 0.25 #154, 0.20 #278), NorthSea (0.33 #44, 0.30 #1270, 0.29 #1889), ArcticOcean (0.30 #1113, 0.22 #1655, 0.20 #1156), PacificOcean (0.29 #1328, 0.29 #1533, 0.28 #1575), IndianOcean (0.26 #980, 0.25 #121, 0.21 #1519)
>> best conf = 0.86
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1142 for best value:
>> intensional similarity = 12
>> extensional distance = 18
>> proper extension:  HudsonBay; KaraSea;
>> query: (?x1663, ?x146) <- ?x1663[ is flowsInto of ?x1069; is locatedInWater of ?x917; is mergesWith of ?x146[ has locatedIn ?x194;]; is mergesWith of ?x1664[ a Sea; has locatedIn ?x402[ has language ?x566;]; has locatedIn ?x793[ is dependentOf of ?x357; is neighbor of ?x120;]; is mergesWith of ?x121;];]
ranks of expected_values: 1
EVAL	Kattegat	mergesWith	BalticSea	CNN-1.+1._MA	1.000	1.000	1.000	1.000	146.000	146.000	181.000	0.860	http://www.semwebtech.org/mondial/10/meta#mergesWith

#786-Drin
PRED entity: Drin

PRED relation: hasSource
PRED expected values: Drin
=> 58 concepts (45 used for prediction)
PRED predicted values (max 10 best out of 179): BlackDrin (0.25 #368, 0.20 #596, 0.06 #1280), Buna (0.25 #234, 0.06 #1146, 0.03 #2514), Po (0.20 #737, 0.19 #9138, 0.19 #9137), Nile (0.20 #877, 0.19 #9138, 0.19 #9137), Rhone (0.20 #816, 0.19 #9138, 0.19 #9137), WhiteDrin (0.20 #497, 0.06 #1181, 0.03 #2549), Arno (0.19 #9138, 0.19 #9137, 0.15 #8451), Ebro (0.19 #9138, 0.19 #9137, 0.15 #8451), Etsch (0.19 #9138, 0.19 #9137, 0.15 #8451), Tiber (0.19 #9138, 0.19 #9137, 0.15 #8451)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #368 for best value:
>> intensional similarity = 5
>> extensional distance = 2
>> proper extension:  Buna;
>> query: (?x698, BlackDrin) <- ?x698[ a River; has hasEstuary ?x2260[ a Estuary;]; has locatedIn ?x204<AL>;]

*> Best rule #9595 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 200
*> proper extension:  Neckar; Enns; Hwangho; Uruguay; RioNegro; Perene; Okavango; Karun; Raab; Apurimac; ...
*> query: (?x698, ?x104) <- ?x698[ a River; has hasEstuary ?x2260; has locatedIn ?x204[ has ethnicGroup ?x595; is locatedIn of ?x104;];]
*> conf = 0.01
ranks of expected_values: 144
EVAL	Drin	hasSource	Drin	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.007	58.000	45.000	179.000	0.250	http://www.semwebtech.org/mondial/10/meta#hasSource

PRED relation: hasSource
PRED expected values: Drin
=> 166 concepts (155 used for prediction)
PRED predicted values (max 10 best out of 209): BlackDrin (0.33 #139, 0.25 #826, 0.20 #1054), Buna (0.25 #692, 0.20 #920, 0.07 #19258), SouthernMorava (0.20 #1092, 0.05 #4757, 0.03 #7736), Rhone (0.15 #21557, 0.09 #1735, 0.09 #1506), Po (0.15 #21557, 0.09 #1656, 0.09 #1427), Nile (0.15 #21557, 0.09 #1567, 0.08 #2025), Arno (0.15 #21557, 0.03 #6331, 0.03 #6559), Etsch (0.15 #21557, 0.03 #6251, 0.03 #6479), Tiber (0.15 #21557, 0.03 #6233, 0.03 #6461), Ebro (0.15 #21557, 0.03 #6319, 0.03 #6547)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #139 for best value:
>> intensional similarity = 11
>> extensional distance = 1
>> proper extension:  BlackDrin;
>> query: (?x698, BlackDrin) <- ?x698[ a River; has flowsInto ?x275[ has locatedIn ?x156[ has religion ?x56;]; has locatedIn ?x1495[ has ethnicGroup ?x852; has language ?x1398;];]; has locatedIn ?x204<AL>; is flowsInto of ?x656[ has locatedIn ?x701;];]

*> Best rule #19258 for first EXPECTED value:
*> intensional similarity = 11
*> extensional distance = 125
*> proper extension:  Enns; Tigris; ConnecticutRiver; Chatanga; Lena; Petschora; Murat; Don; HudsonRiver; Karasu; ...
*> query: (?x698, ?x2445) <- ?x698[ a River; has hasEstuary ?x2260; has locatedIn ?x204[ a Country; has encompassed ?x195; has language ?x1251; is locatedIn of ?x1004[ has inMountains ?x785;]; is locatedIn of ?x2445[ a Source;]; is neighbor of ?x106;];]
*> conf = 0.07
ranks of expected_values: 36
EVAL	Drin	hasSource	Drin	CNN-1.+1._MA	0.000	0.000	0.000	0.028	166.000	155.000	209.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasSource

#785-Apurimac
PRED entity: Apurimac

PRED relation: flowsInto
PRED expected values: Tambo
=> 37 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 88): Amazonas (0.22 #13, 0.20 #178, 0.06 #343), Ucayali (0.22 #96, 0.20 #261, 0.06 #426), Tambo (0.20 #275, 0.11 #110, 0.06 #440), Donau (0.14 #669, 0.07 #834, 0.07 #1496), Maranon (0.11 #92, 0.10 #257, 0.03 #422), AtlanticOcean (0.09 #1666, 0.09 #1003, 0.08 #1335), MediterraneanSea (0.05 #684, 0.04 #1014, 0.04 #1180), BalticSea (0.04 #1498, 0.04 #836, 0.04 #1664), BlackSea (0.04 #664, 0.02 #994, 0.02 #1326), Zaire (0.03 #1745, 0.03 #917, 0.02 #1082)
>> best conf = 0.22
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #13 for best value:
>> intensional similarity = 5
>> extensional distance = 7
>> proper extension:  Huallaga; Maranon; Ucayali; Ene; Tambo; Perene; Urubamba;
>> query: (?x1898, Amazonas) <- ?x1898[ a River; has hasEstuary ?x1759; has hasSource ?x295; has locatedIn ?x296<PE>;]

*> Best rule #275 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 8
*> proper extension:  Mantaro;
*> query: (?x1898, Tambo) <- ?x1898[ a River; has hasEstuary ?x1759[ a Estuary;]; has locatedIn ?x296<PE>;]
*> conf = 0.20
ranks of expected_values: 3
EVAL	Apurimac	flowsInto	Tambo	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.333	37.000	31.000	88.000	0.222	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto
PRED expected values: Tambo
=> 119 concepts (119 used for prediction)
PRED predicted values (max 10 best out of 147): Amazonas (0.29 #13, 0.25 #838, 0.25 #343), Ucayali (0.25 #261, 0.22 #591, 0.20 #756), AtlanticOcean (0.23 #1670, 0.15 #1169, 0.15 #5166), Tambo (0.20 #770, 0.14 #5489, 0.14 #110), Donau (0.18 #2830, 0.16 #2996, 0.16 #4332), Maranon (0.14 #5489, 0.13 #6663, 0.12 #422), PacificOcean (0.14 #5489, 0.12 #6495, 0.11 #5320), Mantaro (0.14 #5489, 0.07 #8512, 0.03 #3322), RioMadeira (0.11 #634, 0.08 #1130, 0.08 #964), BlackSea (0.09 #1992, 0.09 #2158, 0.07 #2825)
>> best conf = 0.29
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #13 for best value:
>> intensional similarity = 11
>> extensional distance = 5
>> proper extension:  Maranon; Ucayali; Ene; Tambo; Perene;
>> query: (?x1898, Amazonas) <- ?x1898[ a River; has hasEstuary ?x1759[ a Estuary; has locatedIn ?x296<PE>;]; has hasSource ?x295[ a Source; has inMountains ?x431<Andes>; has locatedIn ?x296<PE>;]; has locatedIn ?x296<PE>;]

*> Best rule #770 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 8
*> proper extension:  Mantaro;
*> query: (?x1898, Tambo) <- ?x1898[ a River; has hasEstuary ?x1759[ a Estuary; has locatedIn ?x296<PE>;]; has locatedIn ?x296<PE>;]
*> conf = 0.20
ranks of expected_values: 4
EVAL	Apurimac	flowsInto	Tambo	CNN-1.+1._MA	0.000	0.000	1.000	0.250	119.000	119.000	147.000	0.286	http://www.semwebtech.org/mondial/10/meta#flowsInto

#784-UA
PRED entity: UA

PRED relation: neighbor!
PRED expected values: BY
=> 33 concepts (33 used for prediction)
PRED predicted values (max 10 best out of 197): BY (0.91 #1093, 0.91 #1565, 0.91 #780), CN (0.45 #977, 0.33 #196, 0.29 #1251), UA (0.33 #360, 0.33 #204, 0.33 #48), LT (0.33 #449, 0.33 #293, 0.29 #1251), LV (0.33 #387, 0.33 #231, 0.29 #1251), AZ (0.33 #209, 0.29 #1251, 0.28 #2347), SRB (0.33 #133, 0.29 #1251, 0.28 #2347), EW (0.33 #252, 0.29 #1251, 0.28 #2347), GE (0.33 #214, 0.29 #1251, 0.28 #2347), BG (0.33 #25, 0.29 #1251, 0.28 #2347)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1093 for best value:
>> intensional similarity = 6
>> extensional distance = 18
>> proper extension:  PK; NEP;
>> query: (?x303, ?x163) <- ?x303[ has language ?x1108; has neighbor ?x73[ is locatedIn of ?x1493<Argun>;]; has neighbor ?x163; is locatedIn of ?x97;]
ranks of expected_values: 1
EVAL	UA	neighbor!	BY	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	33.000	33.000	197.000	0.914	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: BY
=> 101 concepts (101 used for prediction)
PRED predicted values (max 10 best out of 227): BY (0.94 #9589, 0.92 #9270, 0.92 #7983), A (0.60 #1507, 0.53 #1272, 0.50 #2142), SRB (0.53 #1272, 0.40 #1726, 0.34 #1116), TR (0.53 #1272, 0.40 #1621, 0.31 #3175), UA (0.53 #1272, 0.34 #1116, 0.33 #2276), BG (0.53 #1272, 0.34 #1116, 0.33 #343), GE (0.53 #1272, 0.34 #1116, 0.33 #697), HR (0.53 #1272, 0.34 #1116, 0.31 #4620), D (0.53 #1272, 0.34 #1116, 0.31 #4620), CZ (0.50 #2306, 0.50 #1036, 0.40 #1513)
>> best conf = 0.94
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #9589 for best value:
>> intensional similarity = 14
>> extensional distance = 51
>> proper extension:  DJI; SLO; EAK;
>> query: (?x303, ?x73) <- ?x303[ a Country; has government ?x435; has neighbor ?x73[ has ethnicGroup ?x58; has neighbor ?x170; is locatedIn of ?x72; is locatedIn of ?x972[ a Source;];]; has neighbor ?x222[ has encompassed ?x195;]; has religion ?x56; has wasDependentOf ?x903; is locatedIn of ?x98[ has mergesWith ?x1633;];]
ranks of expected_values: 1
EVAL	UA	neighbor!	BY	CNN-1.+1._MA	1.000	1.000	1.000	1.000	101.000	101.000	227.000	0.944	http://www.semwebtech.org/mondial/10/meta#neighbor

#783-Principe
PRED entity: Principe

PRED relation: locatedInWater
PRED expected values: AtlanticOcean
=> 49 concepts (49 used for prediction)
PRED predicted values (max 10 best out of 37): AtlanticOcean (0.79 #260, 0.79 #223, 0.66 #348), PacificOcean (0.49 #410, 0.43 #366, 0.32 #983), ArcticOcean (0.17 #275, 0.13 #363, 0.10 #451), CaribbeanSea (0.15 #456, 0.11 #323, 0.09 #985), SulawesiSea (0.13 #465, 0.05 #1256, 0.05 #1300), SouthChinaSea (0.11 #459, 0.06 #1250, 0.06 #1382), MediterraneanSea (0.11 #720, 0.11 #540, 0.10 #851), NorthSea (0.11 #1231, 0.11 #1275, 0.10 #1363), IndianOcean (0.09 #706, 0.08 #263, 0.08 #968), LabradorSea (0.08 #272, 0.07 #1624, 0.06 #360)
>> best conf = 0.79
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #260 for best value:
>> intensional similarity = 7
>> extensional distance = 26
>> proper extension:  Pico; GrandBermuda; Fogo; Rhum; Corvo; Terceira; Iceland; Graciosa; SantaMaria; GranCanaria; ...
>> query: (?x993, ?x182) <- ?x993[ a Island; has locatedIn ?x994[ has government ?x435; has religion ?x316; is locatedIn of ?x182<AtlanticOcean>;]; has type ?x150;]
>> Best rule #223 for best value:
>> intensional similarity = 7
>> extensional distance = 26
>> proper extension:  Pico; GrandBermuda; Fogo; Rhum; Corvo; Terceira; Iceland; Graciosa; SantaMaria; GranCanaria; ...
>> query: (?x993, AtlanticOcean) <- ?x993[ a Island; has locatedIn ?x994[ has government ?x435; has religion ?x316; is locatedIn of ?x182<AtlanticOcean>;]; has type ?x150;]
ranks of expected_values: 1
EVAL	Principe	locatedInWater	AtlanticOcean	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	49.000	49.000	37.000	0.786	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater
PRED expected values: AtlanticOcean
=> 111 concepts (111 used for prediction)
PRED predicted values (max 10 best out of 40): AtlanticOcean (0.82 #843, 0.82 #806, 0.78 #620), PacificOcean (0.53 #413, 0.41 #1178, 0.39 #1042), MediterraneanSea (0.32 #637, 0.14 #1356, 0.12 #2391), EastChinaSea (0.25 #379, 0.14 #246, 0.05 #1592), SaoTome (0.20 #396, 0.20 #263, 0.15 #307), Principe (0.20 #396, 0.20 #263, 0.15 #307), PicodeSaoTome (0.20 #396, 0.20 #263, 0.15 #307), CaribbeanSea (0.18 #2578, 0.16 #2216, 0.15 #863), IndianOcean (0.18 #1073, 0.14 #623, 0.14 #265), JavaSea (0.18 #1080, 0.14 #630, 0.09 #944)
>> best conf = 0.82
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #843 for best value:
>> intensional similarity = 8
>> extensional distance = 32
>> proper extension:  Barbuda;
>> query: (?x993, ?x182) <- ?x993[ a Island; has locatedIn ?x994[ a Country; has encompassed ?x213; has government ?x435; is locatedIn of ?x182<AtlanticOcean>;]; has type ?x150;]
>> Best rule #806 for best value:
>> intensional similarity = 8
>> extensional distance = 32
>> proper extension:  Barbuda;
>> query: (?x993, AtlanticOcean) <- ?x993[ a Island; has locatedIn ?x994[ a Country; has encompassed ?x213; has government ?x435; is locatedIn of ?x182<AtlanticOcean>;]; has type ?x150;]
ranks of expected_values: 1
EVAL	Principe	locatedInWater	AtlanticOcean	CNN-1.+1._MA	1.000	1.000	1.000	1.000	111.000	111.000	40.000	0.824	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#782-GreaterAntilles
PRED entity: GreaterAntilles

PRED relation: belongsToIslands!
PRED expected values: Jamaica Hispaniola
=> 16 concepts (16 used for prediction)
PRED predicted values (max 10 best out of 224): Martinique (0.33 #157, 0.29 #989, 0.28 #395), St.Martin (0.33 #119, 0.29 #989, 0.28 #395), St.Barthelemy (0.33 #182, 0.29 #989, 0.28 #395), Antigua (0.33 #153, 0.29 #989, 0.28 #395), SaintThomas (0.33 #120, 0.29 #989, 0.28 #395), Grenada (0.33 #101, 0.29 #989, 0.28 #395), Grande-Terre (0.33 #91, 0.29 #989, 0.28 #395), Trinidad (0.33 #89, 0.29 #989, 0.28 #395), Montserrat (0.33 #57, 0.29 #989, 0.28 #395), Anguilla (0.33 #49, 0.29 #989, 0.28 #395)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #157 for best value:
>> intensional similarity = 8
>> extensional distance = 1
>> proper extension:  LesserAntilles;
>> query: (?x1962, Martinique) <- ?x1962[ a Islands; is belongsToIslands of ?x1557[ a Island; has locatedInWater ?x317<CaribbeanSea>; is locatedOnIsland of ?x665[ a Mountain; has locatedIn ?x899;];];]

*> Best rule #989 for first EXPECTED value:
*> intensional similarity = 9
*> extensional distance = 5
*> proper extension:  CanadianArcticIslands;
*> query: (?x1962, ?x123) <- ?x1962[ a Islands; is belongsToIslands of ?x1557[ a Island; has locatedInWater ?x317[ has locatedIn ?x50; has mergesWith ?x182<AtlanticOcean>; is locatedInWater of ?x123;]; is locatedOnIsland of ?x665;];]
*> conf = 0.29
ranks of expected_values: 23, 26
EVAL	GreaterAntilles	belongsToIslands!	Hispaniola	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.043	16.000	16.000	224.000	0.333	http://www.semwebtech.org/mondial/10/meta#belongsToIslands
EVAL	GreaterAntilles	belongsToIslands!	Jamaica	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.040	16.000	16.000	224.000	0.333	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

PRED relation: belongsToIslands!
PRED expected values: Jamaica Hispaniola
=> 28 concepts (28 used for prediction)
PRED predicted values (max 10 best out of 224): CaribbeanSea (0.38 #1601, 0.27 #3404, 0.25 #602), AtlanticOcean (0.38 #1601, 0.27 #3404, 0.25 #602), PicoTurquino (0.38 #1601, 0.27 #3404, 0.25 #602), GulfofMexico (0.38 #1601, 0.27 #3404, 0.25 #602), SaintVincent (0.33 #407, 0.28 #1399, 0.28 #1398), St.Martin (0.33 #523, 0.28 #1399, 0.28 #1398), Martinique (0.33 #561, 0.28 #1399, 0.28 #1398), Montserrat (0.33 #461, 0.28 #1399, 0.28 #1398), Dominica (0.33 #442, 0.28 #1399, 0.28 #1398), Antigua (0.33 #557, 0.28 #1399, 0.28 #1398)
>> best conf = 0.38
=> the first rule below is the first best rule for 4 predicted values
>> Best rule #1601 for best value:
>> intensional similarity = 37
>> extensional distance = 2
>> proper extension:  CapeVerdes;
>> query: (?x1962, ?x1918) <- ?x1962[ a Islands; is belongsToIslands of ?x1557[ a Island; has locatedIn ?x899; is locatedOnIsland of ?x665[ a Mountain;];]; is belongsToIslands of ?x1928[ a Island; has locatedIn ?x148[ has encompassed ?x521; has ethnicGroup ?x197<European>; has government ?x831; is locatedIn of ?x1918;]; has locatedInWater ?x317[ a Sea; has locatedIn ?x50<NLSM>; has locatedIn ?x80<BVIR>; has locatedIn ?x161<KN>; has locatedIn ?x407<AXA>; has locatedIn ?x1209<AG>; has locatedIn ?x1444<MNTS>; has locatedIn ?x1502<SBAR>; is flowsInto of ?x311; is locatedInWater of ?x123<SaintVincent>; is locatedInWater of ?x1380<St.Martin>; is locatedInWater of ?x2210<Hispaniola>;];];]

*> Best rule #1399 for first EXPECTED value:
*> intensional similarity = 24
*> extensional distance = 2
*> proper extension:  CanadianArcticIslands;
*> query: (?x1962, ?x112) <- ?x1962[ a Islands; is belongsToIslands of ?x1557[ a Island; has locatedIn ?x899; is locatedOnIsland of ?x665[ a Mountain;];]; is belongsToIslands of ?x1928[ a Island; has locatedIn ?x148[ has encompassed ?x521<America>; has ethnicGroup ?x197<European>; has government ?x831; has religion ?x352<RomanCatholic>;]; has locatedInWater ?x182[ a Sea; has locatedIn ?x272<CDN>; has mergesWith ?x60; is locatedInWater of ?x112; is locatedInWater of ?x1075<Greenland>;];];]
*> conf = 0.28
ranks of expected_values: 48, 82
EVAL	GreaterAntilles	belongsToIslands!	Hispaniola	CNN-1.+1._MA	0.000	0.000	0.000	0.021	28.000	28.000	224.000	0.375	http://www.semwebtech.org/mondial/10/meta#belongsToIslands
EVAL	GreaterAntilles	belongsToIslands!	Jamaica	CNN-1.+1._MA	0.000	0.000	0.000	0.012	28.000	28.000	224.000	0.375	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

#781-SulawesiSea
PRED entity: SulawesiSea

PRED relation: mergesWith!
PRED expected values: BandaSea
=> 37 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 87): BandaSea (0.83 #466, 0.81 #426, 0.81 #427), SulawesiSea (0.53 #194, 0.46 #584, 0.46 #545), SouthChinaSea (0.46 #584, 0.46 #545, 0.46 #544), AtlanticOcean (0.36 #238, 0.35 #278, 0.31 #162), EastChinaSea (0.33 #60, 0.33 #22, 0.25 #100), MalakkaStrait (0.33 #57, 0.25 #97, 0.09 #251), IndianOcean (0.33 #1, 0.20 #118, 0.19 #272), SeaofJapan (0.33 #13, 0.20 #130, 0.19 #272), BeringSea (0.33 #26, 0.20 #143, 0.19 #272), SeaofOkhotsk (0.33 #21, 0.20 #138, 0.19 #272)
>> best conf = 0.83
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #466 for best value:
>> intensional similarity = 5
>> extensional distance = 35
>> proper extension:  SeaofAzov; BlackSea; YellowSea; RedSea; MarmaraSea; Skagerrak; GulfofAden;
>> query: (?x625, ?x241) <- ?x625[ a Sea; has locatedIn ?x376[ is neighbor of ?x91[ is neighbor of ?x366;];]; has mergesWith ?x241;]
ranks of expected_values: 1
EVAL	SulawesiSea	mergesWith!	BandaSea	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	37.000	31.000	87.000	0.827	http://www.semwebtech.org/mondial/10/meta#mergesWith

PRED relation: mergesWith!
PRED expected values: BandaSea
=> 100 concepts (100 used for prediction)
PRED predicted values (max 10 best out of 87): BandaSea (0.82 #992, 0.82 #913, 0.82 #754), SulawesiSea (0.46 #1312, 0.46 #1311, 0.46 #1313), SouthChinaSea (0.46 #1312, 0.46 #1311, 0.46 #1313), IndianOcean (0.40 #357, 0.33 #1, 0.29 #397), AtlanticOcean (0.32 #602, 0.29 #880, 0.28 #441), AndamanSea (0.29 #235, 0.28 #951, 0.28 #434), MalakkaStrait (0.29 #235, 0.28 #951, 0.28 #434), SeaofJapan (0.27 #329, 0.25 #290, 0.25 #89), EastChinaSea (0.27 #338, 0.25 #299, 0.23 #394), GulfofBengal (0.25 #48, 0.23 #394, 0.23 #234)
>> best conf = 0.82
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #992 for best value:
>> intensional similarity = 11
>> extensional distance = 30
>> proper extension:  KaraSea; PersianGulf;
>> query: (?x625, ?x770) <- ?x625[ a Sea; has mergesWith ?x770; is locatedInWater of ?x375[ has locatedIn ?x376;]; is mergesWith of ?x282[ a Sea; has locatedIn ?x73; is locatedInWater of ?x553[ a Island;]; is locatedInWater of ?x833[ has locatedIn ?x315;];];]
ranks of expected_values: 1
EVAL	SulawesiSea	mergesWith!	BandaSea	CNN-1.+1._MA	1.000	1.000	1.000	1.000	100.000	100.000	87.000	0.824	http://www.semwebtech.org/mondial/10/meta#mergesWith

#780-NubianDesert
PRED entity: NubianDesert

PRED relation: locatedIn
PRED expected values: SUD
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 49): AUS (0.13 #45, 0.01 #281), USA (0.11 #72, 0.05 #308), DZ (0.11 #135), CN (0.10 #56, 0.02 #292), LAR (0.06 #206), RIM (0.06 #119), KAZ (0.06 #93), RMM (0.05 #176), SA (0.05 #162), MEX (0.05 #116)
>> best conf = 0.13
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #45 for best value:
>> intensional similarity = 1
>> extensional distance = 61
>> proper extension:  Rigestan; Atacama; Negev; Karakum; Ordos; GreatSandyDesert; ErgIgidi; Namib; Darfur; RubAlChali; ...
>> query: (?x1497, AUS) <- ?x1497[ a Desert;]

*> Best rule #42 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 61
*> proper extension:  Rigestan; Atacama; Negev; Karakum; Ordos; GreatSandyDesert; ErgIgidi; Namib; Darfur; RubAlChali; ...
*> query: (?x1497, SUD) <- ?x1497[ a Desert;]
*> conf = 0.03
ranks of expected_values: 16
EVAL	NubianDesert	locatedIn	SUD	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.062	2.000	2.000	49.000	0.127	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: SUD
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 49): AUS (0.13 #45, 0.01 #281), USA (0.11 #72, 0.05 #308), DZ (0.11 #135), CN (0.10 #56, 0.02 #292), LAR (0.06 #206), RIM (0.06 #119), KAZ (0.06 #93), RMM (0.05 #176), SA (0.05 #162), MEX (0.05 #116)
>> best conf = 0.13
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #45 for best value:
>> intensional similarity = 1
>> extensional distance = 61
>> proper extension:  Rigestan; Atacama; Negev; Karakum; Ordos; GreatSandyDesert; ErgIgidi; Namib; Darfur; RubAlChali; ...
>> query: (?x1497, AUS) <- ?x1497[ a Desert;]

*> Best rule #42 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 61
*> proper extension:  Rigestan; Atacama; Negev; Karakum; Ordos; GreatSandyDesert; ErgIgidi; Namib; Darfur; RubAlChali; ...
*> query: (?x1497, SUD) <- ?x1497[ a Desert;]
*> conf = 0.03
ranks of expected_values: 16
EVAL	NubianDesert	locatedIn	SUD	CNN-1.+1._MA	0.000	0.000	0.000	0.062	2.000	2.000	49.000	0.127	http://www.semwebtech.org/mondial/10/meta#locatedIn

#779-Italian
PRED entity: Italian

PRED relation: language!
PRED expected values: AUS MC
=> 26 concepts (26 used for prediction)
PRED predicted values (max 10 best out of 211): L (0.50 #91, 0.33 #209, 0.29 #328), B (0.50 #74, 0.33 #192, 0.29 #311), MC (0.50 #118, 0.33 #236, 0.29 #355), A (0.40 #419, 0.27 #237, 0.23 #1902), MK (0.30 #446, 0.17 #208, 0.16 #683), SLO (0.29 #302, 0.27 #237, 0.26 #949), F (0.27 #237, 0.26 #949, 0.25 #5), V (0.27 #237, 0.26 #949, 0.24 #1426), FL (0.27 #237, 0.25 #62, 0.23 #1902), D (0.27 #237, 0.23 #1902, 0.22 #1664)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #91 for best value:
>> intensional similarity = 10
>> extensional distance = 2
>> proper extension:  French; German;
>> query: (?x355, L) <- ?x355[ a Language; is language of ?x207<I>; is language of ?x234<CH>; is language of ?x998[ a Country; has encompassed ?x195; has religion ?x352<RomanCatholic>;];]

*> Best rule #118 for first EXPECTED value:
*> intensional similarity = 10
*> extensional distance = 2
*> proper extension:  French; German;
*> query: (?x355, MC) <- ?x355[ a Language; is language of ?x207<I>; is language of ?x234<CH>; is language of ?x998[ a Country; has encompassed ?x195; has religion ?x352<RomanCatholic>;];]
*> conf = 0.50
ranks of expected_values: 3, 56
EVAL	Italian	language!	MC	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.333	26.000	26.000	211.000	0.500	http://www.semwebtech.org/mondial/10/meta#language
EVAL	Italian	language!	AUS	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.018	26.000	26.000	211.000	0.500	http://www.semwebtech.org/mondial/10/meta#language

PRED relation: language!
PRED expected values: AUS MC
=> 65 concepts (65 used for prediction)
PRED predicted values (max 10 best out of 218): L (0.56 #1663, 0.50 #209, 0.44 #3148), B (0.50 #192, 0.44 #3148, 0.41 #1328), MC (0.50 #236, 0.44 #3148, 0.41 #1328), NZ (0.50 #2003, 0.44 #3148, 0.41 #1328), F (0.48 #475, 0.44 #3148, 0.41 #1328), FL (0.48 #475, 0.44 #3148, 0.41 #1328), A (0.48 #475, 0.40 #2484, 0.33 #6941), D (0.48 #475, 0.33 #6941, 0.32 #7559), NLSM (0.44 #3148, 0.41 #1328, 0.38 #3151), AND (0.44 #3148, 0.41 #1328, 0.38 #4128)
>> best conf = 0.56
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1663 for best value:
>> intensional similarity = 20
>> extensional distance = 7
>> proper extension:  Luxembourgish;
>> query: (?x355, L) <- ?x355[ a Language; is language of ?x207[ has language ?x635<German>; has religion ?x95<Protestant>; has religion ?x352<RomanCatholic>; is locatedIn of ?x86; is neighbor of ?x446;]; is language of ?x234[ a Country; has ethnicGroup ?x237; has neighbor ?x78<F>; is locatedIn of ?x233;]; is language of ?x998[ a Country; has encompassed ?x195<Europe>;];]

*> Best rule #236 for first EXPECTED value:
*> intensional similarity = 14
*> extensional distance = 2
*> proper extension:  German;
*> query: (?x355, MC) <- ?x355[ a Language; is language of ?x207<I>; is language of ?x234<CH>; is language of ?x998[ a Country; has encompassed ?x195<Europe>; has government ?x435<"republic">; has religion ?x352<RomanCatholic>;];]
*> conf = 0.50
ranks of expected_values: 3, 31
EVAL	Italian	language!	MC	CNN-1.+1._MA	0.000	1.000	1.000	0.333	65.000	65.000	218.000	0.556	http://www.semwebtech.org/mondial/10/meta#language
EVAL	Italian	language!	AUS	CNN-1.+1._MA	0.000	0.000	0.000	0.033	65.000	65.000	218.000	0.556	http://www.semwebtech.org/mondial/10/meta#language

#778-SaintLucia
PRED entity: SaintLucia

PRED relation: locatedInWater
PRED expected values: CaribbeanSea
=> 44 concepts (44 used for prediction)
PRED predicted values (max 10 best out of 26): CaribbeanSea (0.91 #465, 0.75 #102, 0.74 #186), PacificOcean (0.36 #777, 0.35 #820, 0.33 #607), NorthSea (0.18 #1358, 0.09 #977, 0.09 #1104), IrishSea (0.18 #1358, 0.06 #1144, 0.04 #463), NorwegianSea (0.18 #1358, 0.06 #1144, 0.02 #1121), TheChannel (0.18 #1358, 0.06 #1144, 0.02 #458), GulfofMexico (0.18 #1358, 0.06 #1144, 0.02 #460), GreenlandSea (0.18 #1358, 0.06 #1144, 0.02 #588), LabradorSea (0.18 #1358, 0.06 #1144, 0.02 #559), ArcticOcean (0.18 #1358, 0.04 #987, 0.03 #1072)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #465 for best value:
>> intensional similarity = 5
>> extensional distance = 51
>> proper extension:  GrandTurk; Streymoy; Mull;
>> query: (?x1984, ?x317) <- ?x1984[ has belongsToIslands ?x877[ is belongsToIslands of ?x1380[ has locatedInWater ?x317;];]; has locatedInWater ?x182<AtlanticOcean>;]
ranks of expected_values: 1
EVAL	SaintLucia	locatedInWater	CaribbeanSea	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	44.000	44.000	26.000	0.911	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater
PRED expected values: CaribbeanSea
=> 109 concepts (108 used for prediction)
PRED predicted values (max 10 best out of 140): CaribbeanSea (0.91 #1316, 0.75 #1919, 0.75 #1832), PacificOcean (0.73 #695, 0.53 #950, 0.48 #2541), SaintLucia (0.49 #934, 0.48 #679, 0.48 #678), IndianOcean (0.43 #340, 0.24 #936, 0.20 #1619), ArcticOcean (0.27 #1159, 0.18 #3698, 0.18 #2955), JavaSea (0.24 #942, 0.19 #1112, 0.17 #1625), NorthSea (0.19 #2700, 0.18 #3698, 0.18 #2955), LabradorSea (0.18 #3698, 0.18 #2955, 0.14 #1156), IrishSea (0.18 #3698, 0.18 #2955, 0.11 #1017), GreenlandSea (0.18 #3698, 0.18 #2955, 0.10 #631)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1316 for best value:
>> intensional similarity = 10
>> extensional distance = 26
>> proper extension:  SaintHelena; Ascension;
>> query: (?x1984, ?x317) <- ?x1984[ has locatedIn ?x1554[ is locatedIn of ?x317[ has locatedIn ?x50<NLSM>; is locatedInWater of ?x1397<SaintThomas>;];]; has locatedInWater ?x182<AtlanticOcean>; has type ?x150<"volcanic">;]
ranks of expected_values: 1
EVAL	SaintLucia	locatedInWater	CaribbeanSea	CNN-1.+1._MA	1.000	1.000	1.000	1.000	109.000	108.000	140.000	0.909	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#777-FJI
PRED entity: FJI

PRED relation: locatedIn!
PRED expected values: VanuaLevu
=> 36 concepts (29 used for prediction)
PRED predicted values (max 10 best out of 1377): AtlanticOcean (0.66 #27048, 0.64 #25625, 0.37 #34154), IndianOcean (0.50 #3, 0.33 #1424, 0.27 #4266), ArcticOcean (0.33 #1495, 0.18 #2916, 0.12 #7179), GulfofBengal (0.27 #4336, 0.05 #22814, 0.05 #14284), CaribbeanSea (0.27 #27111, 0.26 #10052, 0.25 #11473), SouthChinaSea (0.25 #140, 0.18 #2982, 0.17 #1561), SulawesiSea (0.25 #280, 0.17 #1701, 0.11 #36960), BandaSea (0.25 #366, 0.17 #1787, 0.11 #36960), MalakkaStrait (0.25 #141, 0.17 #1562, 0.09 #4404), AndamanSea (0.25 #118, 0.17 #1539, 0.09 #4381)
>> best conf = 0.66
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #27048 for best value:
>> intensional similarity = 6
>> extensional distance = 92
>> proper extension:  SMAR; SBAR;
>> query: (?x158, AtlanticOcean) <- ?x158[ a Country; has government ?x435; is locatedIn of ?x282[ has locatedIn ?x272<CDN>; is locatedInWater of ?x205;];]

*> Best rule #19896 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 66
*> proper extension:  PK;
*> query: (?x158, ?x205) <- ?x158[ a Country; has government ?x435; is locatedIn of ?x282[ is locatedInWater of ?x205;]; is locatedIn of ?x1214[ a Mountain;];]
*> conf = 0.04
ranks of expected_values: 470
EVAL	FJI	locatedIn!	VanuaLevu	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.002	36.000	29.000	1377.000	0.660	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: VanuaLevu
=> 54 concepts (54 used for prediction)
PRED predicted values (max 10 best out of 1359): VanuaLevu (0.71 #17103, 0.42 #42740, 0.27 #7123), AtlanticOcean (0.61 #47062, 0.50 #17145, 0.45 #59888), IndianOcean (0.45 #7122, 0.43 #8552, 0.40 #7121), SuluSea (0.45 #7122, 0.33 #1425, 0.33 #306), BeringSea (0.45 #7122, 0.33 #1818, 0.30 #7124), SulawesiSea (0.45 #7122, 0.33 #280, 0.26 #18525), SeaofJapan (0.45 #7122, 0.30 #7124, 0.25 #8549), SeaofOkhotsk (0.45 #7122, 0.30 #7124, 0.25 #8549), EastChinaSea (0.45 #7122, 0.25 #8549, 0.25 #8548), BandaSea (0.45 #7122, 0.25 #8549, 0.25 #8548)
>> best conf = 0.71
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #17103 for best value:
>> intensional similarity = 13
>> extensional distance = 8
>> proper extension:  NMIS;
>> query: (?x158, ?x1778) <- ?x158[ a Country; has encompassed ?x211<Australia-Oceania>; has ethnicGroup ?x2287[ a EthnicGroup;]; has government ?x435; is locatedIn of ?x532[ a Island; has belongsToIslands ?x2001[ a Islands; is belongsToIslands of ?x1778;]; has type ?x150<"volcanic">;];]
ranks of expected_values: 1
EVAL	FJI	locatedIn!	VanuaLevu	CNN-1.+1._MA	1.000	1.000	1.000	1.000	54.000	54.000	1359.000	0.714	http://www.semwebtech.org/mondial/10/meta#locatedIn

#776-Asia
PRED entity: Asia

PRED relation: encompassed!
PRED expected values: R PK RI IL IRQ ARM AZ MNG
=> 5 concepts (5 used for prediction)
PRED predicted values (max 10 best out of 189): R (0.88 #722, 0.81 #719, 0.81 #358), PK (0.88 #722, 0.81 #719, 0.81 #358), AZ (0.88 #722, 0.81 #719, 0.81 #358), IRQ (0.88 #722, 0.81 #719, 0.81 #358), RI (0.81 #719, 0.81 #358, 0.60 #178), GR (0.81 #719, 0.81 #358, 0.33 #539), LAR (0.81 #719, 0.81 #358, 0.33 #539), ARM (0.81 #719, 0.81 #358, 0.33 #539), IL (0.81 #719, 0.81 #358, 0.33 #539), BG (0.81 #719, 0.81 #358, 0.33 #539)
>> best conf = 0.88
=> the first rule below is the first best rule for 4 predicted values
>> Best rule #722 for best value:
>> intensional similarity = 28
>> extensional distance = 2
>> proper extension:  America;
>> query: (?x175, ?x302) <- ?x175[ is encompassed of ?x130[ a Country; has ethnicGroup ?x58; has language ?x555; has wasDependentOf ?x903; is locatedIn of ?x662;]; is encompassed of ?x751[ a Country; has ethnicGroup ?x244; has government ?x640; has language ?x1848; has neighbor ?x302; is locatedIn of ?x637;]; is encompassed of ?x924[ has government ?x140; has language ?x2392; has religion ?x187<Muslim>; has religion ?x925[ a Religion;]; is locatedIn of ?x60[ a Sea; has mergesWith ?x182; is flowsInto of ?x242; is locatedInWater of ?x226; is mergesWith of ?x241;]; is locatedIn of ?x489[ has inMountains ?x309;];];]
ranks of expected_values: 1, 2, 3, 4, 5, 8, 9
EVAL	Asia	encompassed!	MNG	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	5.000	5.000	189.000	0.875	http://www.semwebtech.org/mondial/10/meta#encompassed
EVAL	Asia	encompassed!	AZ	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	5.000	5.000	189.000	0.875	http://www.semwebtech.org/mondial/10/meta#encompassed
EVAL	Asia	encompassed!	ARM	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.333	5.000	5.000	189.000	0.875	http://www.semwebtech.org/mondial/10/meta#encompassed
EVAL	Asia	encompassed!	IRQ	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	5.000	5.000	189.000	0.875	http://www.semwebtech.org/mondial/10/meta#encompassed
EVAL	Asia	encompassed!	IL	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.333	5.000	5.000	189.000	0.875	http://www.semwebtech.org/mondial/10/meta#encompassed
EVAL	Asia	encompassed!	RI	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	5.000	5.000	189.000	0.875	http://www.semwebtech.org/mondial/10/meta#encompassed
EVAL	Asia	encompassed!	PK	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	5.000	5.000	189.000	0.875	http://www.semwebtech.org/mondial/10/meta#encompassed
EVAL	Asia	encompassed!	R	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	5.000	5.000	189.000	0.875	http://www.semwebtech.org/mondial/10/meta#encompassed

PRED relation: encompassed!
PRED expected values: R PK RI IL IRQ ARM AZ MNG
=> 5 concepts (5 used for prediction)
PRED predicted values (max 10 best out of 199): AZ (0.92 #182, 0.90 #186, 0.89 #185), ARM (0.92 #182, 0.90 #186, 0.89 #185), PK (0.92 #182, 0.90 #186, 0.89 #185), IRQ (0.92 #182, 0.90 #186, 0.88 #191), R (0.92 #182, 0.90 #186, 0.78 #758), IL (0.92 #182, 0.88 #191, 0.69 #567), LAR (0.92 #182, 0.88 #191, 0.69 #567), SUD (0.92 #182, 0.88 #191, 0.69 #567), RI (0.92 #182, 0.69 #567, 0.69 #568), GR (0.90 #186, 0.89 #185, 0.88 #191)
>> best conf = 0.92
=> the first rule below is the first best rule for 9 predicted values
>> Best rule #182 for best value:
>> intensional similarity = 112
>> extensional distance = 1
>> proper extension:  America;
>> query: (?x175, ?x302) <- ?x175[ a Continent; is encompassed of ?x117[ has ethnicGroup ?x2391; has government ?x2476; is locatedIn of ?x2077[ a Volcano;];]; is encompassed of ?x129[ has ethnicGroup ?x1630; is locatedIn of ?x683[ a Lake; has type ?x2549;]; is locatedIn of ?x2157[ a Source;];]; is encompassed of ?x130[ a Country; has ethnicGroup ?x1802; has language ?x986;]; is encompassed of ?x232[ has neighbor ?x73; is locatedIn of ?x231[ a Estuary;]; is locatedIn of ?x340[ a Island;]; is locatedIn of ?x384[ a Sea; has mergesWith ?x282<PacificOcean>; is locatedInWater of ?x518;]; is locatedIn of ?x472[ a River;]; is locatedIn of ?x1022[ has hasSource ?x2525;]; is locatedIn of ?x1256[ a Source;]; is locatedIn of ?x1771[ a Mountain; has inMountains ?x309;]; is locatedIn of ?x2432[ a Desert;];]; is encompassed of ?x353[ has ethnicGroup ?x908[ a EthnicGroup;]; has language ?x555[ a Language;]; has wasDependentOf ?x903; is locatedIn of ?x98[ is flowsInto of ?x133; is mergesWith of ?x97;]; is neighbor of ?x331[ a Country; has government ?x435; has language ?x741;];]; is encompassed of ?x403[ has ethnicGroup ?x58[ a EthnicGroup;]; has language ?x1245; has religion ?x95<Protestant>; is locatedIn of ?x1019[ a River; has hasEstuary ?x2311;]; is locatedIn of ?x1512[ a Source;];]; is encompassed of ?x538[ a Country; has ethnicGroup ?x298<Chinese>; has wasDependentOf ?x81; is locatedIn of ?x375;]; is encompassed of ?x617[ has ethnicGroup ?x2363[ a EthnicGroup;]; has government ?x831; has wasDependentOf ?x78; is locatedIn of ?x975;]; is encompassed of ?x803[ a Country; has ethnicGroup ?x1235; has government ?x92; has wasDependentOf ?x485; is locatedIn of ?x1552[ has mergesWith ?x2407;]; is neighbor of ?x302;]; is encompassed of ?x924[ has ethnicGroup ?x1553[ a EthnicGroup;]; has government ?x140; has religion ?x187<Muslim>; has religion ?x410<Hindu>; has religion ?x925[ a Religion;]; is locatedIn of ?x339[ a Sea; is locatedInWater of ?x1181;];]; is encompassed of ?x943[ a Country; has ethnicGroup ?x2119; has government ?x254<"parliamentary democracy">; is locatedIn of ?x1951;]; is encompassed of ?x1495[ a Country; has ethnicGroup ?x852; is locatedIn of ?x275[ has locatedIn ?x851<MA>; is flowsInto of ?x698; is locatedInWater of ?x68;]; is neighbor of ?x239[ has religion ?x109;];];]
ranks of expected_values: 1, 2, 3, 4, 5, 6, 9, 49
EVAL	Asia	encompassed!	MNG	CNN-1.+1._MA	0.000	0.000	0.000	0.024	5.000	5.000	199.000	0.917	http://www.semwebtech.org/mondial/10/meta#encompassed
EVAL	Asia	encompassed!	AZ	CNN-1.+1._MA	1.000	1.000	1.000	1.000	5.000	5.000	199.000	0.917	http://www.semwebtech.org/mondial/10/meta#encompassed
EVAL	Asia	encompassed!	ARM	CNN-1.+1._MA	1.000	1.000	1.000	1.000	5.000	5.000	199.000	0.917	http://www.semwebtech.org/mondial/10/meta#encompassed
EVAL	Asia	encompassed!	IRQ	CNN-1.+1._MA	1.000	1.000	1.000	1.000	5.000	5.000	199.000	0.917	http://www.semwebtech.org/mondial/10/meta#encompassed
EVAL	Asia	encompassed!	IL	CNN-1.+1._MA	1.000	1.000	1.000	1.000	5.000	5.000	199.000	0.917	http://www.semwebtech.org/mondial/10/meta#encompassed
EVAL	Asia	encompassed!	RI	CNN-1.+1._MA	0.000	1.000	1.000	0.333	5.000	5.000	199.000	0.917	http://www.semwebtech.org/mondial/10/meta#encompassed
EVAL	Asia	encompassed!	PK	CNN-1.+1._MA	1.000	1.000	1.000	1.000	5.000	5.000	199.000	0.917	http://www.semwebtech.org/mondial/10/meta#encompassed
EVAL	Asia	encompassed!	R	CNN-1.+1._MA	1.000	1.000	1.000	1.000	5.000	5.000	199.000	0.917	http://www.semwebtech.org/mondial/10/meta#encompassed

#775-Main
PRED entity: Main

PRED relation: hasSource!
PRED expected values: Main
=> 35 concepts (29 used for prediction)
PRED predicted values (max 10 best out of 152): Breg (0.12 #208, 0.08 #436, 0.08 #664), Fulda (0.12 #185, 0.08 #413, 0.08 #641), Alz (0.12 #130, 0.08 #358, 0.08 #586), Brigach (0.12 #105, 0.08 #333, 0.08 #561), Ammer (0.12 #56, 0.08 #284, 0.08 #512), Werra (0.12 #33, 0.08 #261, 0.08 #489), Aller (0.08 #637, 0.07 #3666, 0.05 #865), Weser (0.08 #636, 0.07 #3666, 0.05 #864), Neckar (0.08 #467, 0.07 #3666, 0.05 #695), Würm (0.08 #490, 0.05 #718, 0.05 #947)
>> best conf = 0.12
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #208 for best value:
>> intensional similarity = 5
>> extensional distance = 6
>> proper extension:  Breg; Werra; Alz; Brigach; Fulda; Ammer;
>> query: (?x1482, Breg) <- ?x1482[ a Source; has inMountains ?x1483[ a Mountains;]; has locatedIn ?x120<D>;]

*> Best rule #3666 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 193
*> proper extension:  DarlingRiver; JoekulsaaFjoellum; EucumbeneRiver; MurrumbidgeeRiver; MurrayRiver; Thjorsa;
*> query: (?x1482, ?x475) <- ?x1482[ a Source; has locatedIn ?x120[ has ethnicGroup ?x237; is locatedIn of ?x475[ a River;]; is locatedIn of ?x894[ a Estuary;];];]
*> conf = 0.07
ranks of expected_values: 22
EVAL	Main	hasSource!	Main	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.045	35.000	29.000	152.000	0.125	http://www.semwebtech.org/mondial/10/meta#hasSource

PRED relation: hasSource!
PRED expected values: Main
=> 115 concepts (115 used for prediction)
PRED predicted values (max 10 best out of 238): Alz (0.12 #130, 0.11 #589, 0.11 #358), Ammer (0.12 #56, 0.11 #515, 0.11 #284), Breg (0.12 #208, 0.11 #667, 0.11 #436), Brigach (0.12 #105, 0.11 #564, 0.11 #333), Werra (0.12 #33, 0.11 #492, 0.11 #261), Fulda (0.12 #185, 0.11 #644, 0.11 #413), Waag (0.11 #317, 0.07 #1236, 0.05 #2399), Save (0.11 #468, 0.04 #2781, 0.04 #3013), Aller (0.08 #1100, 0.07 #11779, 0.07 #11316), Weser (0.08 #1099, 0.07 #11779, 0.07 #11316)
>> best conf = 0.12
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #130 for best value:
>> intensional similarity = 5
>> extensional distance = 6
>> proper extension:  Breg; Werra; Alz; Brigach; Fulda; Ammer;
>> query: (?x1482, Alz) <- ?x1482[ a Source; has inMountains ?x1483[ a Mountains;]; has locatedIn ?x120<D>;]

*> Best rule #11779 for first EXPECTED value:
*> intensional similarity = 12
*> extensional distance = 176
*> proper extension:  Ebro;
*> query: (?x1482, ?x157) <- ?x1482[ a Source; has locatedIn ?x120[ has neighbor ?x234[ is locatedIn of ?x233;]; has neighbor ?x424[ has encompassed ?x195; has ethnicGroup ?x160; has language ?x511;]; is locatedIn of ?x133[ is flowsInto of ?x132;]; is locatedIn of ?x157[ a River;];];]
*> conf = 0.07
ranks of expected_values: 25
EVAL	Main	hasSource!	Main	CNN-1.+1._MA	0.000	0.000	0.000	0.040	115.000	115.000	238.000	0.125	http://www.semwebtech.org/mondial/10/meta#hasSource

#774-Olkhon
PRED entity: Olkhon

PRED relation: locatedIn
PRED expected values: R
=> 27 concepts (27 used for prediction)
PRED predicted values (max 10 best out of 83): R (0.90 #3330, 0.89 #2854, 0.88 #4043), RI (0.62 #1714, 0.42 #2669, 0.42 #2429), CDN (0.33 #63, 0.17 #1488, 0.16 #2680), NIC (0.33 #334, 0.14 #808, 0.14 #571), SK (0.25 #981, 0.14 #744, 0.14 #507), MNG (0.25 #238, 0.10 #2615, 0.10 #1187), H (0.25 #1008), USA (0.21 #4351, 0.20 #1259, 0.19 #4589), SRB (0.14 #897, 0.14 #660, 0.12 #1134), BR (0.14 #837)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3330 for best value:
>> intensional similarity = 7
>> extensional distance = 37
>> proper extension:  SaoTome;
>> query: (?x2277, ?x73) <- ?x2277[ a Island; is locatedOnIsland of ?x1134[ a Mountain; has locatedIn ?x73[ a Country; has ethnicGroup ?x58; has religion ?x56;];];]
ranks of expected_values: 1
EVAL	Olkhon	locatedIn	R	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	27.000	27.000	83.000	0.897	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: R
=> 40 concepts (40 used for prediction)
PRED predicted values (max 10 best out of 102): R (0.90 #4065, 0.88 #4540, 0.86 #3101), RI (0.57 #2915, 0.33 #292, 0.32 #3639), MNG (0.48 #2862, 0.45 #2387, 0.23 #2150), CDN (0.33 #1262, 0.33 #542, 0.21 #2926), NIC (0.33 #815, 0.14 #1771, 0.07 #3197), J (0.33 #1456, 0.12 #1931, 0.08 #2407), GR (0.33 #90, 0.11 #7023, 0.07 #3191), I (0.33 #1006, 0.10 #6981, 0.07 #3149), E (0.25 #2177, 0.23 #2652, 0.20 #3128), USA (0.21 #4848, 0.21 #5086, 0.20 #5325)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4065 for best value:
>> intensional similarity = 8
>> extensional distance = 37
>> proper extension:  SaoTome;
>> query: (?x2277, ?x73) <- ?x2277[ a Island; is locatedOnIsland of ?x1134[ a Mountain; has locatedIn ?x73[ a Country; has encompassed ?x195; has ethnicGroup ?x58; has religion ?x56;];];]
ranks of expected_values: 1
EVAL	Olkhon	locatedIn	R	CNN-1.+1._MA	1.000	1.000	1.000	1.000	40.000	40.000	102.000	0.897	http://www.semwebtech.org/mondial/10/meta#locatedIn

#773-PacificOcean
PRED entity: PacificOcean

PRED relation: locatedInWater!
PRED expected values: Saipan VitiLevu Luzon Samar Isabela Savaii Guam Shikoku
=> 30 concepts (26 used for prediction)
PRED predicted values (max 10 best out of 580): Sumatra (0.56 #1139, 0.25 #219, 0.25 #702), Sulawesi (0.50 #728, 0.40 #946, 0.33 #1165), Cuba (0.33 #169, 0.25 #606, 0.19 #1699), Hispaniola (0.33 #203, 0.25 #640, 0.12 #1733), St.Barthelemy (0.33 #197, 0.25 #634, 0.12 #1727), Martinique (0.33 #162, 0.25 #599, 0.12 #1692), Antigua (0.33 #158, 0.25 #595, 0.12 #1688), SaintThomas (0.33 #119, 0.25 #556, 0.12 #1649), St.Martin (0.33 #116, 0.25 #553, 0.12 #1646), Grenada (0.33 #100, 0.25 #537, 0.12 #1630)
>> best conf = 0.56
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1139 for best value:
>> intensional similarity = 5
>> extensional distance = 7
>> proper extension:  IndianOcean; AndamanSea; SouthChinaSea; MalakkaStrait;
>> query: (?x282, Sumatra) <- ?x282[ has locatedIn ?x217<RI>; is locatedInWater of ?x205; is mergesWith of ?x625[ is locatedInWater of ?x369;];]

*> Best rule #282 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 1
*> proper extension:  SulawesiSea;
*> query: (?x282, Samar) <- ?x282[ a Sea; has locatedIn ?x73; is locatedInWater of ?x205; is mergesWith of ?x677<SuluSea>;]
*> conf = 0.33
ranks of expected_values: 31, 79, 265, 306, 366, 427, 449
EVAL	PacificOcean	locatedInWater!	Shikoku	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.013	30.000	26.000	580.000	0.556	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	PacificOcean	locatedInWater!	Guam	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.002	30.000	26.000	580.000	0.556	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	PacificOcean	locatedInWater!	Savaii	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.004	30.000	26.000	580.000	0.556	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	PacificOcean	locatedInWater!	Isabela	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.003	30.000	26.000	580.000	0.556	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	PacificOcean	locatedInWater!	Samar	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.032	30.000	26.000	580.000	0.556	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	PacificOcean	locatedInWater!	Luzon	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.002	30.000	26.000	580.000	0.556	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	PacificOcean	locatedInWater!	VitiLevu	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.003	30.000	26.000	580.000	0.556	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	PacificOcean	locatedInWater!	Saipan	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	30.000	26.000	580.000	0.556	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater!
PRED expected values: Saipan VitiLevu Luzon Samar Isabela Savaii Guam Shikoku
=> 78 concepts (78 used for prediction)
PRED predicted values (max 10 best out of 730): Greenland (0.50 #2278, 0.33 #2936, 0.33 #960), NowajaSemlja (0.40 #2477, 0.25 #220, 0.25 #219), Svalbard (0.40 #2494, 0.25 #2276, 0.21 #3813), Cuba (0.33 #1045, 0.33 #169, 0.25 #2363), Hispaniola (0.33 #1079, 0.33 #203, 0.25 #2397), St.Barthelemy (0.33 #1073, 0.33 #197, 0.25 #2391), Martinique (0.33 #1038, 0.33 #162, 0.25 #2356), Antigua (0.33 #1034, 0.33 #158, 0.25 #2352), SaintThomas (0.33 #995, 0.33 #119, 0.25 #2313), St.Martin (0.33 #992, 0.33 #116, 0.25 #2310)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2278 for best value:
>> intensional similarity = 11
>> extensional distance = 2
>> proper extension:  ArcticOcean;
>> query: (?x282, Greenland) <- ?x282[ has locatedIn ?x217[ has encompassed ?x211;]; has locatedIn ?x272<CDN>; has locatedIn ?x315<USA>; has locatedIn ?x1819[ has religion ?x352;]; is locatedInWater of ?x205[ has belongsToIslands ?x206;]; is mergesWith of ?x60;]

*> Best rule #720 for first EXPECTED value:
*> intensional similarity = 10
*> extensional distance = 1
*> proper extension:  SulawesiSea;
*> query: (?x282, Samar) <- ?x282[ has locatedIn ?x318[ has ethnicGroup ?x79; has religion ?x95;]; has locatedIn ?x408[ has wasDependentOf ?x149;]; is locatedInWater of ?x458[ has belongsToIslands ?x2100;]; is locatedInWater of ?x1158<Mindanao>; is mergesWith of ?x60;]
*> conf = 0.33
ranks of expected_values: 87, 405, 408, 475, 477, 478, 479, 511
EVAL	PacificOcean	locatedInWater!	Shikoku	CNN-1.+1._MA	0.000	0.000	0.000	0.002	78.000	78.000	730.000	0.500	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	PacificOcean	locatedInWater!	Guam	CNN-1.+1._MA	0.000	0.000	0.000	0.002	78.000	78.000	730.000	0.500	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	PacificOcean	locatedInWater!	Savaii	CNN-1.+1._MA	0.000	0.000	0.000	0.002	78.000	78.000	730.000	0.500	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	PacificOcean	locatedInWater!	Isabela	CNN-1.+1._MA	0.000	0.000	0.000	0.002	78.000	78.000	730.000	0.500	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	PacificOcean	locatedInWater!	Samar	CNN-1.+1._MA	0.000	0.000	0.000	0.011	78.000	78.000	730.000	0.500	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	PacificOcean	locatedInWater!	Luzon	CNN-1.+1._MA	0.000	0.000	0.000	0.002	78.000	78.000	730.000	0.500	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	PacificOcean	locatedInWater!	VitiLevu	CNN-1.+1._MA	0.000	0.000	0.000	0.002	78.000	78.000	730.000	0.500	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	PacificOcean	locatedInWater!	Saipan	CNN-1.+1._MA	0.000	0.000	0.000	0.002	78.000	78.000	730.000	0.500	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#772-Raab
PRED entity: Raab

PRED relation: hasSource!
PRED expected values: Raab
=> 32 concepts (24 used for prediction)
PRED predicted values (max 10 best out of 39): Salzach (0.12 #168, 0.11 #396, 0.08 #1604), Isar (0.12 #127, 0.11 #355, 0.08 #1604), Lech (0.12 #94, 0.11 #322, 0.08 #1604), Enns (0.12 #46, 0.11 #274, 0.08 #1604), Iller (0.12 #44, 0.11 #272, 0.08 #1604), Mur (0.12 #10, 0.11 #238, 0.08 #1604), Waag (0.11 #317), Raab (0.08 #1604, 0.07 #2523, 0.07 #2522), March (0.08 #1604, 0.07 #2523, 0.07 #2522), Inn (0.08 #1604, 0.07 #2523, 0.07 #2522)
>> best conf = 0.12
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #168 for best value:
>> intensional similarity = 3
>> extensional distance = 6
>> proper extension:  Lech; Isar; Salzach; Enns; Mur; Iller;
>> query: (?x1837, Salzach) <- ?x1837[ a Source; has locatedIn ?x424<A>;]

*> Best rule #1604 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 147
*> proper extension:  Mincio; Tiber; Po; Etsch; Doubs; Drau; Marne; Saar; Saone; Maas; ...
*> query: (?x1837, ?x155) <- ?x1837[ a Source; has locatedIn ?x424[ has language ?x511; is locatedIn of ?x155[ a River;]; is neighbor of ?x120;];]
*> conf = 0.08
ranks of expected_values: 8
EVAL	Raab	hasSource!	Raab	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.125	32.000	24.000	39.000	0.125	http://www.semwebtech.org/mondial/10/meta#hasSource

PRED relation: hasSource!
PRED expected values: Raab
=> 70 concepts (66 used for prediction)
PRED predicted values (max 10 best out of 76): Isar (0.12 #127, 0.08 #4381, 0.08 #355), Salzach (0.12 #168, 0.08 #4381, 0.08 #396), Lech (0.12 #94, 0.08 #4381, 0.08 #322), Enns (0.12 #46, 0.08 #4381, 0.08 #274), Iller (0.12 #44, 0.08 #4381, 0.08 #272), Mur (0.12 #10, 0.08 #4381, 0.08 #238), Inn (0.08 #4381, 0.04 #458, 0.03 #2533), Rhein (0.08 #4381, 0.04 #458, 0.03 #2533), Drau (0.08 #4381, 0.04 #458, 0.03 #2533), Donau (0.08 #4381, 0.04 #458, 0.03 #2533)
>> best conf = 0.12
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #127 for best value:
>> intensional similarity = 3
>> extensional distance = 6
>> proper extension:  Lech; Isar; Salzach; Enns; Mur; Iller;
>> query: (?x1837, Isar) <- ?x1837[ a Source; has locatedIn ?x424<A>;]

*> Best rule #4381 for first EXPECTED value:
*> intensional similarity = 10
*> extensional distance = 132
*> proper extension:  Selenge;
*> query: (?x1837, ?x256) <- ?x1837[ a Source; has locatedIn ?x424[ a Country; has ethnicGroup ?x160; has language ?x511; has neighbor ?x236; is locatedIn of ?x256[ a River;]; is neighbor of ?x234[ has language ?x51;];];]
*> conf = 0.08
ranks of expected_values: 12
EVAL	Raab	hasSource!	Raab	CNN-1.+1._MA	0.000	0.000	0.000	0.083	70.000	66.000	76.000	0.125	http://www.semwebtech.org/mondial/10/meta#hasSource

#771-Vuoksi
PRED entity: Vuoksi

PRED relation: hasEstuary
PRED expected values: Vuoksi
=> 49 concepts (45 used for prediction)
PRED predicted values (max 10 best out of 250): Swir (0.33 #25, 0.19 #7023, 0.11 #251), Paatsjoki (0.11 #267, 0.10 #494, 0.08 #720), Volga (0.11 #413, 0.10 #640, 0.07 #1094), Newa (0.11 #351, 0.10 #578, 0.07 #1032), Angara (0.11 #408, 0.10 #635, 0.07 #1089), Dnepr (0.11 #287, 0.10 #514, 0.07 #968), Narva (0.11 #407, 0.04 #1315, 0.04 #1541), Amur (0.10 #628, 0.07 #1082, 0.04 #1309), Jenissej (0.10 #550, 0.07 #1004, 0.04 #1231), Oulujoki (0.08 #840, 0.04 #1747, 0.03 #2200)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #25 for best value:
>> intensional similarity = 6
>> extensional distance = 1
>> proper extension:  Swir;
>> query: (?x1396, Swir) <- ?x1396[ has flowsInto ?x589<OzeroLadoga>; has hasSource ?x2282; has locatedIn ?x73<R>; is flowsInto of ?x1395;]

*> Best rule #6569 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 169
*> proper extension:  Raab;
*> query: (?x1396, ?x631) <- ?x1396[ a River; has locatedIn ?x565[ has language ?x247; is locatedIn of ?x631; is neighbor of ?x170;];]
*> conf = 0.01
ranks of expected_values: 148
EVAL	Vuoksi	hasEstuary	Vuoksi	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.007	49.000	45.000	250.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasEstuary

PRED relation: hasEstuary
PRED expected values: Vuoksi
=> 137 concepts (137 used for prediction)
PRED predicted values (max 10 best out of 302): Paatsjoki (0.33 #720, 0.25 #1172, 0.13 #6125), Swir (0.33 #25, 0.11 #2291, 0.11 #2064), Volga (0.33 #1092, 0.11 #2226, 0.09 #2679), Irtysch (0.33 #312, 0.09 #2577, 0.09 #17500), EucumbeneRiver (0.33 #487, 0.08 #3206, 0.08 #2980), Oulujoki (0.13 #6125, 0.12 #1519, 0.09 #17500), Kemijoki (0.13 #6125, 0.12 #1401, 0.09 #17500), Ounasjoki (0.13 #6125, 0.12 #1534, 0.09 #17500), Kokemaeenjoki (0.13 #6125, 0.12 #1542, 0.09 #17500), Dnepr (0.12 #1646, 0.11 #2100, 0.09 #2553)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #720 for best value:
>> intensional similarity = 8
>> extensional distance = 1
>> proper extension:  Paatsjoki;
>> query: (?x1396, Paatsjoki) <- ?x1396[ a River; has flowsInto ?x589; has locatedIn ?x73<R>; has locatedIn ?x565<SF>; is flowsInto of ?x1395[ a Lake;];]

*> Best rule #18864 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 176
*> proper extension:  Gambia;
*> query: (?x1396, ?x661) <- ?x1396[ a River; has flowsInto ?x589; has hasSource ?x2282; has locatedIn ?x565[ has ethnicGroup ?x1193; has religion ?x56; is locatedIn of ?x661[ a Estuary;];];]
*> conf = 0.07
ranks of expected_values: 40
EVAL	Vuoksi	hasEstuary	Vuoksi	CNN-1.+1._MA	0.000	0.000	0.000	0.025	137.000	137.000	302.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasEstuary

#770-Zambezi
PRED entity: Zambezi

PRED relation: locatedIn
PRED expected values: MOC ANG
=> 43 concepts (41 used for prediction)
PRED predicted values (max 10 best out of 222): ZRE (0.83 #2180, 0.33 #1012, 0.33 #466), MOC (0.65 #3970, 0.62 #2570, 0.61 #2569), MW (0.65 #3970, 0.62 #2570, 0.59 #3504), EAT (0.44 #2743, 0.33 #466, 0.21 #934), USA (0.43 #1238, 0.35 #1471, 0.27 #1939), R (0.36 #1873, 0.23 #2341, 0.21 #1172), CDN (0.36 #1229, 0.29 #1462, 0.23 #1930), ANG (0.33 #466, 0.33 #420, 0.20 #1167), RSA (0.33 #466, 0.33 #293, 0.20 #1167), HR (0.33 #727, 0.12 #2364, 0.07 #2834)
>> best conf = 0.83
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2180 for best value:
>> intensional similarity = 6
>> extensional distance = 21
>> proper extension:  Cuango; Bomu; Cuilo; Okavango; Lukenie; Tshuapa; Aruwimi; Lomami;
>> query: (?x1977, ZRE) <- ?x1977[ a River; has hasSource ?x1596; has locatedIn ?x525[ has neighbor ?x934<ANG>; has religion ?x116;];]

*> Best rule #3970 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 53
*> proper extension:  Sobat;
*> query: (?x1977, ?x192) <- ?x1977[ a River; has hasSource ?x1596[ a Source;]; is flowsInto of ?x2061[ a River; has locatedIn ?x192;];]
*> conf = 0.65
ranks of expected_values: 2, 8
EVAL	Zambezi	locatedIn	ANG	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.143	43.000	41.000	222.000	0.826	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	Zambezi	locatedIn	MOC	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	43.000	41.000	222.000	0.826	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: MOC ANG
=> 101 concepts (100 used for prediction)
PRED predicted values (max 10 best out of 236): ZRE (0.92 #8056, 0.76 #3605, 0.57 #8996), MOC (0.85 #5404, 0.85 #5403, 0.77 #6107), MW (0.65 #3996, 0.65 #4701, 0.65 #4700), RSA (0.60 #1472, 0.55 #1409, 0.55 #1412), BR (0.57 #2244, 0.35 #6931, 0.33 #7400), EAT (0.55 #1409, 0.55 #1412, 0.54 #3527), ANG (0.55 #1409, 0.55 #1412, 0.54 #1411), BI (0.54 #3527, 0.33 #552, 0.33 #233), CDN (0.53 #9922, 0.49 #10391, 0.40 #11099), USA (0.51 #13456, 0.37 #15811, 0.35 #16213)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #8056 for best value:
>> intensional similarity = 10
>> extensional distance = 70
>> proper extension:  Kwa; MaleboPool; Ruki; Lukuga; Uelle; Busira; Ruzizi; LakeMweru; Luvua; Ubangi; ...
>> query: (?x1977, ZRE) <- ?x1977[ has locatedIn ?x138[ a Country; has wasDependentOf ?x485;]; has locatedIn ?x525[ has encompassed ?x213; has ethnicGroup ?x162; has neighbor ?x192; has religion ?x116; is locatedIn of ?x1541<Luapula>;];]

*> Best rule #5404 for first EXPECTED value:
*> intensional similarity = 9
*> extensional distance = 27
*> proper extension:  Würm;
*> query: (?x1977, ?x192) <- ?x1977[ a River; has flowsInto ?x60; has flowsThrough ?x387[ a Lake; has locatedIn ?x192[ has encompassed ?x213;];]; has hasSource ?x1596[ a Source;]; is flowsInto of ?x2061;]
*> conf = 0.85
ranks of expected_values: 2, 7
EVAL	Zambezi	locatedIn	ANG	CNN-1.+1._MA	0.000	0.000	1.000	0.167	101.000	100.000	236.000	0.917	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	Zambezi	locatedIn	MOC	CNN-1.+1._MA	0.000	1.000	1.000	0.500	101.000	100.000	236.000	0.917	http://www.semwebtech.org/mondial/10/meta#locatedIn

#769-Meru
PRED entity: Meru

PRED relation: type
PRED expected values: "volcano"
=> 37 concepts (37 used for prediction)
PRED predicted values (max 10 best out of 8): "volcano" (0.61 #118, 0.60 #102, 0.58 #182), "volcanic" (0.35 #242, 0.34 #82, 0.33 #292), "salt" (0.23 #39, 0.20 #71, 0.15 #340), "dam" (0.05 #65, 0.04 #257, 0.04 #274), "sand" (0.04 #260, 0.04 #277, 0.04 #310), "caldera" (0.03 #211, 0.02 #227, 0.02 #259), "atoll" (0.02 #331), "lime" (0.01 #261)
>> best conf = 0.61
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #118 for best value:
>> intensional similarity = 5
>> extensional distance = 55
>> proper extension:  Irazu; Tamgak; RasDaschanTerara; Damavand; PicodelosNieves; PicodeTeide; CerroChirripo; Sabalan; RoquedelosMuchachos;
>> query: (?x1551, "volcano") <- ?x1551[ a Mountain; a Volcano; has locatedIn ?x820[ has ethnicGroup ?x1233; is neighbor of ?x192;];]
ranks of expected_values: 1
EVAL	Meru	type	"volcano"	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	37.000	37.000	8.000	0.614	http://www.semwebtech.org/mondial/10/meta#type

PRED relation: type
PRED expected values: "volcano"
=> 111 concepts (111 used for prediction)
PRED predicted values (max 10 best out of 12): "volcano" (0.76 #441, 0.76 #429, 0.64 #522), "volcanic" (0.64 #522, 0.57 #440, 0.44 #116), "salt" (0.38 #325, 0.30 #607, 0.29 #249), "sand" (0.08 #526, 0.08 #560, 0.04 #807), "dam" (0.06 #523, 0.06 #557, 0.05 #770), "atoll" (0.04 #349, 0.03 #615, 0.02 #1122), "granite" (0.03 #405, 0.02 #471, 0.02 #734), "monolith" (0.03 #402, 0.02 #468, 0.01 #731), "lime" (0.03 #1119, 0.02 #527, 0.02 #561), "caldera" (0.02 #806, 0.02 #856, 0.02 #1262)
>> best conf = 0.76
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #441 for best value:
>> intensional similarity = 12
>> extensional distance = 35
>> proper extension:  MtRedoubt;
>> query: (?x1551, ?x706) <- ?x1551[ a Mountain; a Volcano; has inMountains ?x1066[ a Mountains; is inMountains of ?x730[ a Mountain; a Volcano; has type ?x150;]; is inMountains of ?x1572[ a Mountain; has type ?x706<"volcano">;];];]
>> Best rule #429 for best value:
>> intensional similarity = 12
>> extensional distance = 35
>> proper extension:  MtRedoubt;
>> query: (?x1551, "volcano") <- ?x1551[ a Mountain; a Volcano; has inMountains ?x1066[ a Mountains; is inMountains of ?x730[ a Mountain; a Volcano; has type ?x150;]; is inMountains of ?x1572[ a Mountain; has type ?x706<"volcano">;];];]
ranks of expected_values: 1
EVAL	Meru	type	"volcano"	CNN-1.+1._MA	1.000	1.000	1.000	1.000	111.000	111.000	12.000	0.757	http://www.semwebtech.org/mondial/10/meta#type

#768-Tobol
PRED entity: Tobol

PRED relation: hasSource!
PRED expected values: Tobol
=> 35 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 216): Petschora (0.33 #139, 0.25 #367, 0.14 #2059), WesternDwina (0.07 #4121, 0.04 #626, 0.04 #913), Katun (0.07 #4121, 0.04 #675, 0.04 #903), Dnepr (0.07 #4121, 0.04 #527, 0.04 #755), Schilka (0.07 #4121, 0.04 #672, 0.04 #900), Kama (0.07 #4121, 0.04 #656, 0.04 #884), Amur (0.07 #4121, 0.04 #640, 0.04 #868), Oka (0.07 #4121, 0.04 #638, 0.04 #866), Kolyma (0.07 #4121, 0.04 #620, 0.04 #848), Don (0.07 #4121, 0.04 #603, 0.04 #831)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #139 for best value:
>> intensional similarity = 5
>> extensional distance = 1
>> proper extension:  Petschora;
>> query: (?x693, Petschora) <- ?x693[ a Source; has inMountains ?x2187<Ural>; has locatedIn ?x73<R>;]

*> Best rule #4121 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 179
*> proper extension:  JoekulsaaFjoellum; Thjorsa;
*> query: (?x693, ?x631) <- ?x693[ a Source; has locatedIn ?x73[ is locatedIn of ?x282[ is locatedInWater of ?x205;]; is locatedIn of ?x631[ a River;];];]
*> conf = 0.07
ranks of expected_values: 21
EVAL	Tobol	hasSource!	Tobol	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.048	35.000	31.000	216.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasSource

PRED relation: hasSource!
PRED expected values: Tobol
=> 90 concepts (90 used for prediction)
PRED predicted values (max 10 best out of 249): Petschora (0.50 #914, 0.50 #457, 0.33 #1143), Katun (0.25 #447, 0.17 #1133, 0.17 #904), Dnepr (0.17 #756, 0.11 #12201, 0.07 #1670), WesternDwina (0.17 #855, 0.11 #12201, 0.07 #1769), Amur (0.11 #12201, 0.06 #11970, 0.06 #11738), Swir (0.11 #12201, 0.06 #11970, 0.06 #11738), Jenissej (0.11 #12201, 0.06 #11970, 0.06 #11738), Newa (0.11 #12201, 0.06 #11970, 0.06 #11738), Angara (0.11 #12201, 0.06 #11970, 0.06 #11738), Volga (0.11 #12201, 0.06 #11970, 0.06 #11738)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #914 for best value:
>> intensional similarity = 9
>> extensional distance = 4
>> proper extension:  WesternDwina; Dnepr;
>> query: (?x693, ?x1227) <- ?x693[ a Source; has inMountains ?x2187[ a Mountains; is inMountains of ?x1416[ a Source; has locatedIn ?x73<R>; is hasSource of ?x1227;];]; has locatedIn ?x73<R>;]
>> Best rule #457 for best value:
>> intensional similarity = 10
>> extensional distance = 2
>> proper extension:  Katun;
>> query: (?x693, ?x1227) <- ?x693[ a Source; has inMountains ?x2187[ a Mountains; is inMountains of ?x1416[ a Source; is hasSource of ?x1227;]; is inMountains of ?x2107[ a Mountain;];]; has locatedIn ?x73<R>;]

*> Best rule #11970 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 216
*> proper extension:  Selenge;
*> query: (?x693, ?x1457) <- ?x693[ a Source; has locatedIn ?x73[ has neighbor ?x232[ a Country; has religion ?x116; is locatedIn of ?x231;]; is locatedIn of ?x1457[ has flowsInto ?x146;];];]
*> conf = 0.06
ranks of expected_values: 33
EVAL	Tobol	hasSource!	Tobol	CNN-1.+1._MA	0.000	0.000	0.000	0.030	90.000	90.000	249.000	0.500	http://www.semwebtech.org/mondial/10/meta#hasSource

#767-Gotland
PRED entity: Gotland

PRED relation: locatedInWater
PRED expected values: BalticSea
=> 47 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 61): BalticSea (0.67 #92, 0.33 #5, 0.25 #48), Skagerrak (0.42 #895, 0.38 #803, 0.34 #355), NorthSea (0.38 #177, 0.13 #581, 0.12 #492), AtlanticOcean (0.33 #226, 0.32 #272, 0.32 #316), NorwegianSea (0.25 #64, 0.08 #195, 0.03 #422), Klaraelv (0.25 #218, 0.07 #131, 0.05 #489), Sulitjelma (0.25 #218, 0.07 #131, 0.05 #489), PacificOcean (0.23 #729, 0.22 #418, 0.21 #774), Kattegat (0.22 #128, 0.09 #263, 0.09 #354), MediterraneanSea (0.20 #417, 0.19 #461, 0.16 #505)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #92 for best value:
>> intensional similarity = 6
>> extensional distance = 7
>> proper extension:  Falster; Fuenen; Seeland; Bornholm; Lolland; Langeland;
>> query: (?x997, BalticSea) <- ?x997[ a Island; has locatedIn ?x402[ has religion ?x95; is locatedIn of ?x1664<Skagerrak>; is wasDependentOf of ?x170;];]
ranks of expected_values: 1
EVAL	Gotland	locatedInWater	BalticSea	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	47.000	31.000	61.000	0.667	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater
PRED expected values: BalticSea
=> 124 concepts (124 used for prediction)
PRED predicted values (max 10 best out of 108): BalticSea (0.76 #508, 0.57 #271, 0.56 #223), AtlanticOcean (0.55 #282, 0.50 #516, 0.48 #919), Skagerrak (0.49 #2274, 0.44 #2225, 0.44 #959), NorthSea (0.44 #556, 0.40 #320, 0.33 #137), Kattegat (0.40 #320, 0.29 #178, 0.20 #1332), PacificOcean (0.39 #433, 0.21 #2294, 0.20 #1490), MediterraneanSea (0.39 #382, 0.33 #1169, 0.30 #432), Klaraelv (0.29 #598, 0.08 #275, 0.08 #272), Sulitjelma (0.29 #598, 0.08 #275, 0.08 #272), Dalaelv (0.29 #178, 0.26 #2276, 0.24 #2892)
>> best conf = 0.76
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #508 for best value:
>> intensional similarity = 12
>> extensional distance = 33
>> proper extension:  Falster; Fuenen; EasterIsland; Seeland; Bornholm; Lolland; Hispaniola; Langeland;
>> query: (?x997, ?x146) <- ?x997[ a Island; has locatedIn ?x402[ has government ?x92; has language ?x566; has religion ?x95<Protestant>; has religion ?x352<RomanCatholic>; is locatedIn of ?x2002[ has locatedInWater ?x146;]; is neighbor of ?x170[ is locatedIn of ?x121;];];]
ranks of expected_values: 1
EVAL	Gotland	locatedInWater	BalticSea	CNN-1.+1._MA	1.000	1.000	1.000	1.000	124.000	124.000	108.000	0.761	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#766-RH
PRED entity: RH

PRED relation: ethnicGroup
PRED expected values: European
=> 50 concepts (50 used for prediction)
PRED predicted values (max 10 best out of 239): European (0.76 #1031, 0.74 #4359, 0.70 #263), Mestizo (0.50 #291, 0.48 #1059, 0.33 #547), Amerindian (0.43 #1026, 0.40 #258, 0.33 #514), Russian (0.30 #1607, 0.25 #1863, 0.22 #2887), Black (0.25 #824, 0.10 #3896, 0.07 #4922), Ukrainian (0.22 #1537, 0.21 #1793, 0.16 #2817), Chinese (0.17 #782, 0.11 #11031, 0.11 #12055), White (0.17 #833, 0.10 #3905, 0.07 #4931), EastIndian (0.17 #904, 0.07 #3976, 0.07 #2440), Hungarian (0.13 #1559, 0.12 #1815, 0.09 #2839)
>> best conf = 0.76
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1031 for best value:
>> intensional similarity = 6
>> extensional distance = 19
>> proper extension:  GCA; RCH; CO; PE; USA; CR; ROU; RA; PY; NIC; ...
>> query: (?x697, European) <- ?x697[ has encompassed ?x521<America>; has language ?x2186; has religion ?x95; is locatedIn of ?x182; is neighbor of ?x520;]
ranks of expected_values: 1
EVAL	RH	ethnicGroup	European	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	50.000	50.000	239.000	0.762	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: European
=> 96 concepts (96 used for prediction)
PRED predicted values (max 10 best out of 240): European (0.90 #5897, 0.80 #6416, 0.75 #7444), Mestizo (0.60 #6444, 0.50 #7472, 0.45 #10808), Amerindian (0.50 #10775, 0.50 #6411, 0.47 #10007), Black (0.43 #4670, 0.40 #2876, 0.33 #568), White (0.40 #2885, 0.29 #4679, 0.22 #6218), French (0.33 #379, 0.12 #14111, 0.12 #14110), EastIndian (0.29 #4750, 0.20 #2442, 0.18 #7059), Chinese (0.27 #6937, 0.17 #9507, 0.17 #3348), Indian (0.25 #1097, 0.20 #2122, 0.17 #3148), Pakistani (0.25 #1153, 0.20 #2178, 0.17 #3204)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5897 for best value:
>> intensional similarity = 14
>> extensional distance = 7
>> proper extension:  WG;
>> query: (?x697, ?x197) <- ?x697[ a Country; has encompassed ?x521<America>; has ethnicGroup ?x162<African>; is locatedIn of ?x182[ is mergesWith of ?x60;]; is locatedIn of ?x317<CaribbeanSea>; is locatedIn of ?x2210[ a Island; has locatedIn ?x520[ has ethnicGroup ?x197; has government ?x711;];];]
ranks of expected_values: 1
EVAL	RH	ethnicGroup	European	CNN-1.+1._MA	1.000	1.000	1.000	1.000	96.000	96.000	240.000	0.900	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#765-Polynesian
PRED entity: Polynesian

PRED relation: ethnicGroup!
PRED expected values: SLB
=> 29 concepts (15 used for prediction)
PRED predicted values (max 10 best out of 201): NZ (0.50 #469, 0.33 #281, 0.25 #1032), CR (0.50 #246, 0.25 #809, 0.25 #434), USA (0.50 #245, 0.25 #433, 0.19 #808), CO (0.50 #228, 0.20 #1541, 0.19 #791), EC (0.50 #341, 0.19 #904, 0.16 #1654), ES (0.50 #311, 0.12 #874, 0.12 #499), MEX (0.50 #287, 0.12 #850, 0.12 #475), GCA (0.50 #218, 0.12 #781, 0.12 #406), NAU (0.38 #553, 0.25 #178, 0.23 #740), AUS (0.33 #224, 0.25 #412, 0.15 #599)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #469 for best value:
>> intensional similarity = 11
>> extensional distance = 6
>> proper extension:  Maori;
>> query: (?x1335, NZ) <- ?x1335[ a EthnicGroup; is ethnicGroup of ?x297[ has government ?x2145; has language ?x51<French>;]; is ethnicGroup of ?x550[ has religion ?x352; is locatedIn of ?x282;]; is ethnicGroup of ?x1334[ has encompassed ?x211<Australia-Oceania>;];]

*> Best rule #1011 for first EXPECTED value:
*> intensional similarity = 11
*> extensional distance = 14
*> proper extension:  Micronesian; Indian;
*> query: (?x1335, SLB) <- ?x1335[ a EthnicGroup; is ethnicGroup of ?x297[ has government ?x2145; has language ?x51;]; is ethnicGroup of ?x550[ a Country; has religion ?x352;]; is ethnicGroup of ?x1334[ a Country; has encompassed ?x211<Australia-Oceania>;];]
*> conf = 0.25
ranks of expected_values: 17
EVAL	Polynesian	ethnicGroup!	SLB	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.059	29.000	15.000	201.000	0.500	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup!
PRED expected values: SLB
=> 67 concepts (67 used for prediction)
PRED predicted values (max 10 best out of 220): CR (0.62 #1187, 0.42 #2130, 0.38 #2319), CO (0.50 #1169, 0.36 #2678, 0.33 #2112), EC (0.50 #1282, 0.33 #2225, 0.31 #2414), SLB (0.43 #1014, 0.40 #1578, 0.33 #261), NAU (0.43 #1119, 0.33 #366, 0.30 #1683), NZ (0.40 #1599, 0.34 #2448, 0.29 #2920), C (0.38 #1147, 0.29 #2656, 0.25 #3034), HCA (0.38 #1304, 0.25 #2247, 0.23 #2436), NIC (0.38 #1208, 0.25 #2151, 0.23 #2340), MEX (0.38 #1228, 0.25 #2171, 0.23 #2360)
>> best conf = 0.62
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1187 for best value:
>> intensional similarity = 17
>> extensional distance = 6
>> proper extension:  Amerindian; African; European; Mestizo;
>> query: (?x1335, CR) <- ?x1335[ is ethnicGroup of ?x297[ has ethnicGroup ?x298<Chinese>; has language ?x51;]; is ethnicGroup of ?x564[ has government ?x2145; has language ?x2321; has religion ?x352<RomanCatholic>; is locatedIn of ?x282<PacificOcean>;]; is ethnicGroup of ?x1514[ a Country; has encompassed ?x211; has government ?x2126; has religion ?x95; has wasDependentOf ?x485;];]

*> Best rule #1014 for first EXPECTED value:
*> intensional similarity = 18
*> extensional distance = 5
*> proper extension:  Samoan;
*> query: (?x1335, SLB) <- ?x1335[ is ethnicGroup of ?x297[ a Country; has language ?x51;]; is ethnicGroup of ?x564[ has government ?x2145; has religion ?x352; is locatedIn of ?x282<PacificOcean>; is locatedIn of ?x1279[ has type ?x150;];]; is ethnicGroup of ?x1514[ a Country; has encompassed ?x211; has government ?x2126; has religion ?x95; has wasDependentOf ?x485<UnitedNations>; is locatedIn of ?x1513;];]
*> conf = 0.43
ranks of expected_values: 4
EVAL	Polynesian	ethnicGroup!	SLB	CNN-1.+1._MA	0.000	0.000	1.000	0.250	67.000	67.000	220.000	0.625	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#764-African
PRED entity: African

PRED relation: ethnicGroup!
PRED expected values: GB SME USA AND
=> 21 concepts (21 used for prediction)
PRED predicted values (max 10 best out of 148): RMM (0.56 #788, 0.45 #953, 0.12 #2472), GCA (0.50 #356, 0.47 #494, 0.45 #328), ES (0.50 #437, 0.47 #494, 0.45 #329), MEX (0.50 #418, 0.40 #253, 0.33 #89), PA (0.47 #494, 0.45 #328, 0.45 #329), MOC (0.47 #494, 0.45 #328, 0.45 #329), BOL (0.47 #494, 0.45 #328, 0.45 #329), PE (0.47 #494, 0.45 #328, 0.45 #329), RG (0.47 #494, 0.45 #328, 0.45 #329), SME (0.47 #494, 0.45 #328, 0.45 #329)
>> best conf = 0.56
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #788 for best value:
>> intensional similarity = 11
>> extensional distance = 7
>> proper extension:  Songhai; Voltaic; Mande; Soussou; Tuareg; Malinke; Peuhl;
>> query: (?x162, RMM) <- ?x162[ a EthnicGroup; is ethnicGroup of ?x139[ has religion ?x116; is locatedIn of ?x580<Niger>;]; is ethnicGroup of ?x483[ a Country; has neighbor ?x1206;]; is ethnicGroup of ?x810[ a Country; is neighbor of ?x426;];]

*> Best rule #494 for first EXPECTED value:
*> intensional similarity = 13
*> extensional distance = 4
*> proper extension:  Amerindian; Chinese;
*> query: (?x162, ?x426) <- ?x162[ a EthnicGroup; is ethnicGroup of ?x139[ has religion ?x116; is locatedIn of ?x182;]; is ethnicGroup of ?x148[ has encompassed ?x521; has wasDependentOf ?x149;]; is ethnicGroup of ?x318<CR>; is ethnicGroup of ?x483[ a Country;]; is ethnicGroup of ?x810[ is neighbor of ?x426;];]
*> conf = 0.47
ranks of expected_values: 10, 26, 31, 118
EVAL	African	ethnicGroup!	AND	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.009	21.000	21.000	148.000	0.556	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	African	ethnicGroup!	USA	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.034	21.000	21.000	148.000	0.556	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	African	ethnicGroup!	SME	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.100	21.000	21.000	148.000	0.556	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	African	ethnicGroup!	GB	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.040	21.000	21.000	148.000	0.556	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup!
PRED expected values: GB SME USA AND
=> 67 concepts (67 used for prediction)
PRED predicted values (max 10 best out of 212): BVIR (0.57 #1341, 0.29 #2502, 0.25 #841), GCA (0.50 #1029, 0.50 #863, 0.50 #193), ES (0.50 #1110, 0.50 #944, 0.50 #274), MEX (0.50 #1091, 0.50 #925, 0.50 #255), USA (0.50 #889, 0.50 #721, 0.33 #1055), PA (0.50 #290, 0.48 #1167, 0.48 #1000), ANG (0.50 #1307, 0.48 #1167, 0.48 #1000), NZ (0.50 #751, 0.33 #83, 0.25 #919), AUS (0.50 #700, 0.33 #32, 0.25 #868), SME (0.48 #1167, 0.48 #1000, 0.47 #1166)
>> best conf = 0.57
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1341 for best value:
>> intensional similarity = 15
>> extensional distance = 5
>> proper extension:  Black; White; Mixed;
>> query: (?x162, BVIR) <- ?x162[ is ethnicGroup of ?x520[ has government ?x711; has language ?x796; is locatedIn of ?x182<AtlanticOcean>;]; is ethnicGroup of ?x561[ a Country; has dependentOf ?x81<GB>; is locatedIn of ?x1995;]; is ethnicGroup of ?x1051[ a Country; has encompassed ?x213[ a Continent;]; has religion ?x187;];]

*> Best rule #889 for first EXPECTED value:
*> intensional similarity = 19
*> extensional distance = 2
*> proper extension:  Amerindian;
*> query: (?x162, USA) <- ?x162[ is ethnicGroup of ?x139[ has neighbor ?x169; is locatedIn of ?x579;]; is ethnicGroup of ?x161[ has government ?x1947; has language ?x247<English>;]; is ethnicGroup of ?x215<CO>; is ethnicGroup of ?x810[ has wasDependentOf ?x78; is locatedIn of ?x182;]; is ethnicGroup of ?x902<EC>; is ethnicGroup of ?x1051[ a Country; has encompassed ?x213; has religion ?x187<Muslim>;];]
*> conf = 0.50
ranks of expected_values: 5, 10, 33, 72
EVAL	African	ethnicGroup!	AND	CNN-1.+1._MA	0.000	0.000	0.000	0.014	67.000	67.000	212.000	0.571	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	African	ethnicGroup!	USA	CNN-1.+1._MA	0.000	0.000	1.000	0.200	67.000	67.000	212.000	0.571	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	African	ethnicGroup!	SME	CNN-1.+1._MA	0.000	0.000	1.000	0.111	67.000	67.000	212.000	0.571	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	African	ethnicGroup!	GB	CNN-1.+1._MA	0.000	0.000	0.000	0.032	67.000	67.000	212.000	0.571	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#763-IND
PRED entity: IND

PRED relation: ethnicGroup
PRED expected values: Dravidian
=> 45 concepts (45 used for prediction)
PRED predicted values (max 10 best out of 222): European (0.45 #2568, 0.33 #1800, 0.32 #3592), Russian (0.36 #1353, 0.33 #1609, 0.23 #4169), Chinese (0.29 #783, 0.18 #1295, 0.18 #7681), Ukrainian (0.27 #1281, 0.25 #1537, 0.18 #4097), Uzbek (0.27 #1435, 0.25 #1691, 0.07 #4251), HanChinese (0.25 #464, 0.20 #720, 0.18 #7681), Somali (0.25 #109, 0.11 #1133, 0.02 #8302), Bengali (0.25 #438, 0.02 #3510, 0.01 #4534), African (0.21 #4358, 0.21 #2054, 0.20 #5638), Arab (0.20 #523, 0.13 #3339, 0.11 #1035)
>> best conf = 0.45
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2568 for best value:
>> intensional similarity = 5
>> extensional distance = 29
>> proper extension:  C; CV;
>> query: (?x924, European) <- ?x924[ has government ?x140; has wasDependentOf ?x81[ is locatedIn of ?x121;]; is locatedIn of ?x489[ has inMountains ?x309;];]

No rule for expected values
ranks of expected_values: 
EVAL	IND	ethnicGroup	Dravidian	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	45.000	45.000	222.000	0.452	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: Dravidian
=> 90 concepts (90 used for prediction)
PRED predicted values (max 10 best out of 253): European (0.50 #10524, 0.36 #4884, 0.34 #12320), Chinese (0.43 #2578, 0.34 #10787, 0.33 #7198), African (0.42 #5394, 0.38 #7445, 0.33 #6933), Indian (0.33 #1101, 0.33 #587, 0.29 #3153), Malay (0.33 #1126, 0.29 #2920, 0.20 #7282), Arab (0.33 #1294, 0.25 #1806, 0.25 #1550), Vedda (0.33 #1458, 0.25 #1970, 0.17 #2482), Tamil (0.33 #1455, 0.25 #1967, 0.17 #2479), Sinhalese (0.33 #1416, 0.25 #1928, 0.17 #2440), Shan (0.33 #757, 0.21 #13083, 0.16 #18991)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #10524 for best value:
>> intensional similarity = 12
>> extensional distance = 26
>> proper extension:  HELX;
>> query: (?x924, European) <- ?x924[ has encompassed ?x175; has ethnicGroup ?x1553; has government ?x140; has language ?x2392; is locatedIn of ?x262[ a Sea;]; is locatedIn of ?x339[ is flowsInto of ?x338; is locatedInWater of ?x740; is mergesWith of ?x385;]; is locatedIn of ?x489[ a Mountain;];]

No rule for expected values
ranks of expected_values: 
EVAL	IND	ethnicGroup	Dravidian	CNN-1.+1._MA	0.000	0.000	0.000	0.000	90.000	90.000	253.000	0.500	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#762-CordilleraVolcanica
PRED entity: CordilleraVolcanica

PRED relation: inMountains!
PRED expected values: NevadodeColima
=> 30 concepts (30 used for prediction)
PRED predicted values (max 10 best out of 288): PicoTurquino (0.33 #196, 0.02 #6058, 0.02 #6313), Mantaro (0.20 #505, 0.17 #3822, 0.16 #2038), Amazonas (0.20 #491, 0.17 #3822, 0.16 #2038), Maranon (0.20 #489, 0.17 #3822, 0.16 #2038), Ucayali (0.20 #419, 0.17 #3822, 0.16 #2038), Perene (0.20 #380, 0.17 #3822, 0.16 #2038), Ampato (0.20 #307, 0.17 #3822, 0.16 #2038), Tambo (0.20 #284, 0.17 #3822, 0.16 #2038), Apurimac (0.20 #273, 0.17 #3822, 0.16 #2038), Ene (0.20 #270, 0.17 #3822, 0.16 #2038)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #196 for best value:
>> intensional similarity = 10
>> extensional distance = 1
>> proper extension:  SierraMaestra;
>> query: (?x2010, PicoTurquino) <- ?x2010[ a Mountains; is inMountains of ?x1216[ a Mountain;]; is inMountains of ?x2270[ has locatedIn ?x482[ has ethnicGroup ?x79; has government ?x140; has religion ?x95; is locatedIn of ?x1371<GulfofMexico>;];];]

*> Best rule #3822 for first EXPECTED value:
*> intensional similarity = 10
*> extensional distance = 26
*> proper extension:  JabalLubnan;
*> query: (?x2010, ?x282) <- ?x2010[ a Mountains; is inMountains of ?x1888[ has locatedIn ?x482[ a Country; has ethnicGroup ?x79; has government ?x140; has religion ?x95; has wasDependentOf ?x149; is locatedIn of ?x282; is neighbor of ?x181;];];]
*> conf = 0.17
ranks of expected_values: 46
EVAL	CordilleraVolcanica	inMountains!	NevadodeColima	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.022	30.000	30.000	288.000	0.333	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains!
PRED expected values: NevadodeColima
=> 59 concepts (59 used for prediction)
PRED predicted values (max 10 best out of 288): Mantaro (0.33 #762, 0.29 #2556, 0.27 #3836), Amazonas (0.33 #748, 0.29 #2556, 0.27 #3836), Maranon (0.33 #746, 0.29 #2556, 0.27 #3836), Ucayali (0.33 #676, 0.29 #2556, 0.27 #3836), Perene (0.33 #637, 0.29 #2556, 0.27 #3836), Ampato (0.33 #564, 0.29 #2556, 0.27 #3836), Tambo (0.33 #541, 0.29 #2556, 0.27 #3836), Apurimac (0.33 #530, 0.29 #2556, 0.27 #3836), Ene (0.33 #527, 0.29 #2556, 0.27 #3836), PicoBolivar (0.33 #761, 0.25 #2039, 0.25 #1784)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #762 for best value:
>> intensional similarity = 23
>> extensional distance = 1
>> proper extension:  Andes;
>> query: (?x2010, Mantaro) <- ?x2010[ a Mountains; is inMountains of ?x710[ a Mountain; a Volcano; has locatedIn ?x296<PE>;]; is inMountains of ?x1888[ a Mountain; a Volcano; has locatedIn ?x482[ a Country; has encompassed ?x521<America>; has ethnicGroup ?x197<European>; has government ?x140; has neighbor ?x181; has religion ?x95;];]; is inMountains of ?x2270[ a Mountain; a Volcano; has type ?x706<"volcano">;];]

*> Best rule #2556 for first EXPECTED value:
*> intensional similarity = 23
*> extensional distance = 3
*> proper extension:  SierraMadre;
*> query: (?x2010, ?x264) <- ?x2010[ a Mountains; is inMountains of ?x710[ a Mountain; a Volcano; has locatedIn ?x296[ a Country; has government ?x700; has neighbor ?x202; is locatedIn of ?x264; is locatedIn of ?x282<PacificOcean>;];]; is inMountains of ?x1888[ a Mountain; a Volcano; has locatedIn ?x482[ a Country; has government ?x140; has neighbor ?x181; has religion ?x95;];]; is inMountains of ?x2270[ a Mountain; a Volcano; has type ?x706;];]
*> conf = 0.29
ranks of expected_values: 36
EVAL	CordilleraVolcanica	inMountains!	NevadodeColima	CNN-1.+1._MA	0.000	0.000	0.000	0.028	59.000	59.000	288.000	0.333	http://www.semwebtech.org/mondial/10/meta#inMountains

#761-SudirmanRange
PRED entity: SudirmanRange

PRED relation: inMountains!
PRED expected values: PuncakJaya
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 259): MtRobson (0.01 #259), BarredesEcrins (0.01 #258), Grossglockner (0.01 #257), PicodeAgulhasNegras (0.01 #256), Mantaro (0.01 #255), PicoBolivar (0.01 #254), Drin (0.01 #253), Vignemale (0.01 #252), Dychtau (0.01 #251), GranitePeak (0.01 #250)
>> best conf = 0.01
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #259 for best value:
>> intensional similarity = 1
>> extensional distance = 91
>> proper extension:  BlackForest; Ahaggar; Apennin; Alps; Troodos; Himalaya; RockyMountains; Karakorum; GreatDividingRange; Kunlun; ...
>> query: (?x1562, MtRobson) <- ?x1562[ a Mountains;]

No rule for expected values
ranks of expected_values: 
EVAL	SudirmanRange	inMountains!	PuncakJaya	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	2.000	2.000	259.000	0.011	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains!
PRED expected values: PuncakJaya
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 259): MtRobson (0.01 #259), BarredesEcrins (0.01 #258), Grossglockner (0.01 #257), PicodeAgulhasNegras (0.01 #256), Mantaro (0.01 #255), PicoBolivar (0.01 #254), Drin (0.01 #253), Vignemale (0.01 #252), Dychtau (0.01 #251), GranitePeak (0.01 #250)
>> best conf = 0.01
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #259 for best value:
>> intensional similarity = 1
>> extensional distance = 91
>> proper extension:  BlackForest; Ahaggar; Apennin; Alps; Troodos; Himalaya; RockyMountains; Karakorum; GreatDividingRange; Kunlun; ...
>> query: (?x1562, MtRobson) <- ?x1562[ a Mountains;]

No rule for expected values
ranks of expected_values: 
EVAL	SudirmanRange	inMountains!	PuncakJaya	CNN-1.+1._MA	0.000	0.000	0.000	0.000	2.000	2.000	259.000	0.011	http://www.semwebtech.org/mondial/10/meta#inMountains

#760-Niger
PRED entity: Niger

PRED relation: locatedIn
PRED expected values: RN
=> 41 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 205): CI (0.71 #3048, 0.71 #3282, 0.68 #234), CAM (0.71 #3048, 0.71 #3282, 0.68 #234), SN (0.51 #468, 0.43 #938, 0.40 #939), RIM (0.51 #468, 0.43 #938, 0.40 #939), BEN (0.51 #468, 0.43 #938, 0.40 #939), WAL (0.51 #468, 0.43 #938, 0.40 #939), GNB (0.51 #468, 0.43 #938, 0.40 #939), DZ (0.51 #468, 0.43 #938, 0.40 #939), TCH (0.51 #468, 0.43 #938, 0.40 #939), RN (0.51 #468, 0.40 #939, 0.35 #1176)
>> best conf = 0.71
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #3048 for best value:
>> intensional similarity = 4
>> extensional distance = 92
>> proper extension:  Sobat; Ob;
>> query: (?x580, ?x139) <- ?x580[ has hasEstuary ?x2393; is flowsInto of ?x579[ has locatedIn ?x139[ has neighbor ?x169;];];]

*> Best rule #468 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 2
*> proper extension:  ChadLake;
*> query: (?x580, ?x416) <- ?x580[ has locatedIn ?x139<WAN>; has locatedIn ?x651[ has ethnicGroup ?x1685; has neighbor ?x416;]; is flowsInto of ?x456;]
*> conf = 0.51
ranks of expected_values: 10
EVAL	Niger	locatedIn	RN	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.100	41.000	31.000	205.000	0.711	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: RN
=> 121 concepts (109 used for prediction)
PRED predicted values (max 10 best out of 233): CI (0.83 #4520, 0.78 #7588, 0.75 #6877), CAM (0.83 #4520, 0.78 #7588, 0.75 #6877), AUS (0.80 #5272, 0.15 #17564, 0.14 #4093), CDN (0.55 #13805, 0.30 #17582, 0.29 #18230), USA (0.55 #17826, 0.42 #4355, 0.36 #4827), DZ (0.54 #2612, 0.54 #948, 0.54 #713), SN (0.54 #2612, 0.54 #948, 0.54 #713), RIM (0.54 #2612, 0.54 #948, 0.54 #713), RN (0.54 #2612, 0.54 #948, 0.54 #713), BEN (0.54 #2612, 0.54 #713, 0.54 #711)
>> best conf = 0.83
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #4520 for best value:
>> intensional similarity = 12
>> extensional distance = 24
>> proper extension:  MackenzieRiver;
>> query: (?x580, ?x536) <- ?x580[ a River; has hasEstuary ?x2393[ a Estuary;]; has locatedIn ?x839[ has ethnicGroup ?x1600[ a EthnicGroup;]; has wasDependentOf ?x78;]; is flowsInto of ?x1858[ has locatedIn ?x536[ has wasDependentOf ?x485; is locatedIn of ?x182<AtlanticOcean>;];];]

*> Best rule #2612 for first EXPECTED value:
*> intensional similarity = 13
*> extensional distance = 11
*> proper extension:  Amazonas; PacificOcean; LakeTanganjika; RioSanJuan; CaribbeanSea; Volta; GulfofMexico; RioMadeira; Zambezi;
*> query: (?x580, ?x515) <- ?x580[ has locatedIn ?x139[ has ethnicGroup ?x162<African>;]; has locatedIn ?x839[ has encompassed ?x213; has ethnicGroup ?x1537; has government ?x435; has neighbor ?x515[ has neighbor ?x646; is locatedIn of ?x182;]; has religion ?x116; has wasDependentOf ?x78;]; is flowsInto of ?x579;]
*> conf = 0.54
ranks of expected_values: 9
EVAL	Niger	locatedIn	RN	CNN-1.+1._MA	0.000	0.000	1.000	0.111	121.000	109.000	233.000	0.833	http://www.semwebtech.org/mondial/10/meta#locatedIn

#759-Bioko
PRED entity: Bioko

PRED relation: locatedInWater
PRED expected values: AtlanticOcean
=> 35 concepts (35 used for prediction)
PRED predicted values (max 10 best out of 30): AtlanticOcean (0.71 #660, 0.71 #623, 0.71 #572), JavaSea (0.64 #801, 0.44 #1109, 0.39 #1153), IndianOcean (0.55 #794, 0.44 #1102, 0.42 #1234), MediterraneanSea (0.33 #500, 0.33 #60, 0.12 #676), PacificOcean (0.33 #897, 0.29 #589, 0.27 #1073), LakeNicaragua (0.33 #18, 0.25 #194, 0.17 #502), CaribbeanSea (0.29 #635, 0.22 #1207, 0.21 #1031), PicoBasile (0.20 #264, 0.14 #396, 0.09 #44), SulawesiSea (0.19 #1128, 0.17 #1172, 0.16 #1260), SouthChinaSea (0.19 #1122, 0.17 #1166, 0.16 #1254)
>> best conf = 0.71
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #660 for best value:
>> intensional similarity = 13
>> extensional distance = 5
>> proper extension:  Cuba;
>> query: (?x772, ?x182) <- ?x772[ a Island; is locatedOnIsland of ?x771[ a Mountain; has locatedIn ?x1408[ a Country; has encompassed ?x213; has government ?x435; has language ?x796<Spanish>; has religion ?x352<RomanCatholic>; is locatedIn of ?x182<AtlanticOcean>;];];]
>> Best rule #623 for best value:
>> intensional similarity = 13
>> extensional distance = 5
>> proper extension:  Cuba;
>> query: (?x772, AtlanticOcean) <- ?x772[ a Island; is locatedOnIsland of ?x771[ a Mountain; has locatedIn ?x1408[ a Country; has encompassed ?x213; has government ?x435; has language ?x796<Spanish>; has religion ?x352<RomanCatholic>; is locatedIn of ?x182<AtlanticOcean>;];];]
>> Best rule #572 for best value:
>> intensional similarity = 14
>> extensional distance = 5
>> proper extension:  Pico; Madeira;
>> query: (?x772, ?x182) <- ?x772[ a Island; is locatedOnIsland of ?x771[ a Mountain; a Volcano; has locatedIn ?x1408[ a Country; has encompassed ?x213; has government ?x435; has neighbor ?x172; has religion ?x352<RomanCatholic>; is locatedIn of ?x182<AtlanticOcean>; is neighbor of ?x172;];];]
>> Best rule #535 for best value:
>> intensional similarity = 14
>> extensional distance = 5
>> proper extension:  Pico; Madeira;
>> query: (?x772, AtlanticOcean) <- ?x772[ a Island; is locatedOnIsland of ?x771[ a Mountain; a Volcano; has locatedIn ?x1408[ a Country; has encompassed ?x213; has government ?x435; has neighbor ?x172; has religion ?x352<RomanCatholic>; is locatedIn of ?x182<AtlanticOcean>; is neighbor of ?x172;];];]
ranks of expected_values: 1
EVAL	Bioko	locatedInWater	AtlanticOcean	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	35.000	35.000	30.000	0.714	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater
PRED expected values: AtlanticOcean
=> 35 concepts (35 used for prediction)
PRED predicted values (max 10 best out of 30): AtlanticOcean (0.71 #660, 0.71 #623, 0.71 #616), JavaSea (0.64 #801, 0.44 #1109, 0.39 #1153), IndianOcean (0.55 #794, 0.44 #1102, 0.42 #1234), MediterraneanSea (0.33 #500, 0.33 #60, 0.12 #676), PacificOcean (0.33 #897, 0.29 #545, 0.27 #1073), LakeNicaragua (0.33 #18, 0.25 #194, 0.17 #502), CaribbeanSea (0.29 #635, 0.22 #1207, 0.21 #1031), PicoBasile (0.20 #308, 0.14 #396, 0.09 #44), SulawesiSea (0.19 #1128, 0.17 #1172, 0.16 #1260), SouthChinaSea (0.19 #1122, 0.17 #1166, 0.16 #1254)
>> best conf = 0.71
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #660 for best value:
>> intensional similarity = 13
>> extensional distance = 5
>> proper extension:  Cuba;
>> query: (?x772, ?x182) <- ?x772[ a Island; is locatedOnIsland of ?x771[ a Mountain; has locatedIn ?x1408[ a Country; has encompassed ?x213; has government ?x435; has language ?x796<Spanish>; has religion ?x352<RomanCatholic>; is locatedIn of ?x182<AtlanticOcean>;];];]
>> Best rule #623 for best value:
>> intensional similarity = 13
>> extensional distance = 5
>> proper extension:  Cuba;
>> query: (?x772, AtlanticOcean) <- ?x772[ a Island; is locatedOnIsland of ?x771[ a Mountain; has locatedIn ?x1408[ a Country; has encompassed ?x213; has government ?x435; has language ?x796<Spanish>; has religion ?x352<RomanCatholic>; is locatedIn of ?x182<AtlanticOcean>;];];]
>> Best rule #616 for best value:
>> intensional similarity = 14
>> extensional distance = 5
>> proper extension:  Pico; Madeira;
>> query: (?x772, ?x182) <- ?x772[ a Island; is locatedOnIsland of ?x771[ a Mountain; a Volcano; has locatedIn ?x1408[ a Country; has encompassed ?x213; has government ?x435; has neighbor ?x172; has religion ?x352<RomanCatholic>; is locatedIn of ?x182<AtlanticOcean>; is neighbor of ?x172;];];]
>> Best rule #579 for best value:
>> intensional similarity = 14
>> extensional distance = 5
>> proper extension:  Pico; Madeira;
>> query: (?x772, AtlanticOcean) <- ?x772[ a Island; is locatedOnIsland of ?x771[ a Mountain; a Volcano; has locatedIn ?x1408[ a Country; has encompassed ?x213; has government ?x435; has neighbor ?x172; has religion ?x352<RomanCatholic>; is locatedIn of ?x182<AtlanticOcean>; is neighbor of ?x172;];];]
ranks of expected_values: 1
EVAL	Bioko	locatedInWater	AtlanticOcean	CNN-1.+1._MA	1.000	1.000	1.000	1.000	35.000	35.000	30.000	0.714	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#758-ROK
PRED entity: ROK

PRED relation: religion
PRED expected values: Buddhist
=> 29 concepts (29 used for prediction)
PRED predicted values (max 10 best out of 28): Muslim (0.84 #748, 0.80 #787, 0.74 #240), RomanCatholic (0.54 #282, 0.51 #712, 0.49 #949), Protestant (0.41 #707, 0.41 #277, 0.40 #589), Taoist (0.25 #117, 0.20 #156, 0.19 #862), ChristianOrthodox (0.21 #393, 0.19 #666, 0.19 #823), Buddhist (0.20 #128, 0.19 #364, 0.19 #862), Hindu (0.16 #705, 0.15 #236, 0.15 #166), Jewish (0.16 #705, 0.15 #236, 0.13 #942), Sikh (0.16 #705, 0.15 #236, 0.13 #942), Jains (0.16 #705, 0.15 #236, 0.13 #942)
>> best conf = 0.84
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #748 for best value:
>> intensional similarity = 12
>> extensional distance = 114
>> proper extension:  ET; R; GB; MNE; UAE; NEP; D; TAD; KGZ; HR; ...
>> query: (?x626, Muslim) <- ?x626[ has religion ?x116[ is religion of ?x239[ has wasDependentOf ?x485; is locatedIn of ?x238;]; is religion of ?x434<RM>; is religion of ?x839<RMM>; is religion of ?x851<MA>; is religion of ?x1010<MNG>;];]

*> Best rule #128 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 3
*> proper extension:  J;
*> query: (?x626, Buddhist) <- ?x626[ has religion ?x116[ is religion of ?x169[ a Country;]; is religion of ?x376<MAL>;]; is locatedIn of ?x620<EastChinaSea>;]
*> conf = 0.20
ranks of expected_values: 6
EVAL	ROK	religion	Buddhist	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.167	29.000	29.000	28.000	0.836	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion
PRED expected values: Buddhist
=> 76 concepts (76 used for prediction)
PRED predicted values (max 10 best out of 36): Muslim (0.94 #1933, 0.89 #2330, 0.89 #673), RomanCatholic (0.67 #832, 0.63 #2372, 0.62 #1184), Protestant (0.62 #1179, 0.54 #1140, 0.54 #513), Buddhist (0.50 #551, 0.50 #285, 0.50 #246), ChristianOrthodox (0.41 #1850, 0.38 #1178, 0.34 #2127), Hindu (0.33 #639, 0.33 #322, 0.33 #283), Taoist (0.32 #393, 0.25 #235, 0.24 #1217), Jewish (0.29 #355, 0.26 #1695, 0.26 #1732), Sikh (0.26 #1732, 0.21 #2764, 0.19 #1890), Jains (0.26 #1732, 0.21 #2764, 0.19 #1890)
>> best conf = 0.94
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1933 for best value:
>> intensional similarity = 21
>> extensional distance = 51
>> proper extension:  UAE; D; TAD; I; CH; IR; KAZ; GBZ;
>> query: (?x626, Muslim) <- ?x626[ a Country; has encompassed ?x175; has religion ?x116[ is religion of ?x91<THA>; is religion of ?x139<WAN>; is religion of ?x192<MOC>; is religion of ?x416<SN>; is religion of ?x811[ has ethnicGroup ?x2156;]; is religion of ?x1072<WAL>; is religion of ?x1495<GAZA>;]; is locatedIn of ?x270; is neighbor of ?x334[ has language ?x2244; is locatedIn of ?x2111;];]

*> Best rule #551 for first EXPECTED value:
*> intensional similarity = 18
*> extensional distance = 11
*> proper extension:  SUD;
*> query: (?x626, ?x462) <- ?x626[ a Country; has encompassed ?x175; has religion ?x1655[ a Religion;]; has wasDependentOf ?x117[ has religion ?x462;]; is locatedIn of ?x619[ has type ?x150<"volcanic">;]; is locatedIn of ?x620[ has mergesWith ?x384[ a Sea; has locatedIn ?x91; has mergesWith ?x241;];]; is neighbor of ?x334[ has government ?x1979; is locatedIn of ?x2111; is neighbor of ?x232;];]
*> conf = 0.50
ranks of expected_values: 4
EVAL	ROK	religion	Buddhist	CNN-1.+1._MA	0.000	0.000	1.000	0.250	76.000	76.000	36.000	0.943	http://www.semwebtech.org/mondial/10/meta#religion

#757-JavaSea
PRED entity: JavaSea

PRED relation: mergesWith!
PRED expected values: IndianOcean
=> 34 concepts (34 used for prediction)
PRED predicted values (max 10 best out of 69): IndianOcean (0.85 #507, 0.81 #232, 0.81 #230), JavaSea (0.51 #427, 0.51 #466, 0.50 #311), AndamanSea (0.51 #427, 0.51 #466, 0.50 #311), MalakkaStrait (0.51 #427, 0.51 #466, 0.50 #311), PacificOcean (0.50 #55, 0.40 #133, 0.40 #94), SuluSea (0.40 #102, 0.22 #257, 0.20 #297), AtlanticOcean (0.35 #395, 0.33 #434, 0.33 #6), GulfofBengal (0.33 #11, 0.25 #50, 0.22 #244), GulfofAden (0.33 #37, 0.25 #76, 0.20 #154), LakeToba (0.27 #663, 0.25 #388, 0.06 #192)
>> best conf = 0.85
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #507 for best value:
>> intensional similarity = 5
>> extensional distance = 26
>> proper extension:  Kattegat;
>> query: (?x241, ?x60) <- ?x241[ has locatedIn ?x217; has mergesWith ?x60[ a Sea; is flowsInto of ?x242;]; is locatedInWater of ?x240;]
ranks of expected_values: 1
EVAL	JavaSea	mergesWith!	IndianOcean	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	34.000	34.000	69.000	0.847	http://www.semwebtech.org/mondial/10/meta#mergesWith

PRED relation: mergesWith!
PRED expected values: IndianOcean
=> 104 concepts (104 used for prediction)
PRED predicted values (max 10 best out of 77): IndianOcean (0.85 #844, 0.85 #1053, 0.84 #486), JavaSea (0.54 #526, 0.51 #565, 0.51 #965), AndamanSea (0.54 #526, 0.51 #565, 0.51 #965), MalakkaStrait (0.54 #526, 0.51 #565, 0.51 #965), PacificOcean (0.50 #375, 0.50 #254, 0.50 #215), AtlanticOcean (0.36 #571, 0.36 #532, 0.35 #653), GulfofBengal (0.33 #328, 0.33 #50, 0.25 #278), EastChinaSea (0.33 #143, 0.33 #23, 0.25 #278), GulfofAden (0.33 #76, 0.25 #481, 0.25 #278), SuluSea (0.33 #24, 0.25 #278, 0.25 #262)
>> best conf = 0.85
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #844 for best value:
>> intensional similarity = 11
>> extensional distance = 25
>> proper extension:  Kattegat;
>> query: (?x241, ?x384) <- ?x241[ has locatedIn ?x217; has mergesWith ?x384[ has locatedIn ?x617[ has ethnicGroup ?x872;]; has locatedIn ?x773[ a Country; has language ?x247;]; is flowsInto of ?x1152;]; is locatedInWater of ?x240; is locatedInWater of ?x740[ a Island;];]
ranks of expected_values: 1
EVAL	JavaSea	mergesWith!	IndianOcean	CNN-1.+1._MA	1.000	1.000	1.000	1.000	104.000	104.000	77.000	0.849	http://www.semwebtech.org/mondial/10/meta#mergesWith

#756-Sobat
PRED entity: Sobat

PRED relation: flowsInto
PRED expected values: WhiteNile
=> 34 concepts (25 used for prediction)
PRED predicted values (max 10 best out of 102): MediterraneanSea (0.33 #23, 0.25 #190, 0.20 #690), AtlanticOcean (0.29 #846, 0.11 #1348, 0.11 #1852), Nile (0.27 #1332, 0.20 #661, 0.18 #1164), IndianOcean (0.25 #168, 0.20 #668, 0.20 #500), WhiteNile (0.25 #431, 0.18 #1267, 0.09 #1099), Sobat (0.25 #352, 0.18 #1188), KaraSea (0.11 #1420, 0.05 #1756, 0.04 #2259), Donau (0.10 #2183, 0.09 #1848, 0.08 #2015), Lualaba (0.09 #1056, 0.03 #1558, 0.02 #3404), Zambezi (0.09 #1158, 0.03 #1660, 0.01 #2666)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #23 for best value:
>> intensional similarity = 8
>> extensional distance = 1
>> proper extension:  Nile;
>> query: (?x252, MediterraneanSea) <- ?x252[ a River; has hasEstuary ?x2339; has hasSource ?x253; is flowsInto of ?x747[ a River; has hasSource ?x964; has locatedIn ?x476<ETH>;];]

*> Best rule #431 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 2
*> proper extension:  Bahrel-Ghasal; Pibor;
*> query: (?x252, WhiteNile) <- ?x252[ a River; has hasEstuary ?x2339[ a Estuary; has locatedIn ?x229<SSD>;]; has hasSource ?x253[ a Source; has locatedIn ?x229<SSD>;];]
*> conf = 0.25
ranks of expected_values: 5
EVAL	Sobat	flowsInto	WhiteNile	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.200	34.000	25.000	102.000	0.333	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto
PRED expected values: WhiteNile
=> 79 concepts (79 used for prediction)
PRED predicted values (max 10 best out of 134): Nile (0.40 #1165, 0.33 #2501, 0.25 #497), WhiteNile (0.33 #1604, 0.33 #97, 0.25 #768), MediterraneanSea (0.33 #191, 0.25 #526, 0.25 #358), Zaire (0.30 #2596, 0.25 #4107, 0.25 #2096), Po (0.27 #2916, 0.07 #4761, 0.04 #9463), Sobat (0.25 #689, 0.22 #2357, 0.17 #1525), IndianOcean (0.25 #504, 0.20 #1172, 0.17 #1340), Lualaba (0.20 #2559, 0.10 #4070, 0.07 #4574), LakeSeseSeko-Albertsee (0.20 #988, 0.10 #2657, 0.05 #4168), Donau (0.17 #3017, 0.15 #3352, 0.15 #3855)
>> best conf = 0.40
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1165 for best value:
>> intensional similarity = 14
>> extensional distance = 3
>> proper extension:  BlueNile;
>> query: (?x252, Nile) <- ?x252[ a River; has hasEstuary ?x2339[ a Estuary; has locatedIn ?x229[ a Country; has government ?x435; is locatedIn of ?x990<WhiteNile>; is neighbor of ?x476<ETH>; is neighbor of ?x736<RCA>;];]; has hasSource ?x253; is flowsInto of ?x747;]

*> Best rule #1604 for first EXPECTED value:
*> intensional similarity = 13
*> extensional distance = 4
*> proper extension:  Schari;
*> query: (?x252, WhiteNile) <- ?x252[ a River; has hasEstuary ?x2339[ a Estuary; has locatedIn ?x229[ a Country; has government ?x435<"republic">; has neighbor ?x186<SUD>; has neighbor ?x736<RCA>;];]; has hasSource ?x253[ a Source;];]
*> conf = 0.33
ranks of expected_values: 2
EVAL	Sobat	flowsInto	WhiteNile	CNN-1.+1._MA	0.000	1.000	1.000	0.500	79.000	79.000	134.000	0.400	http://www.semwebtech.org/mondial/10/meta#flowsInto

#755-LakeTahoe
PRED entity: LakeTahoe

PRED relation: locatedIn
PRED expected values: USA
=> 35 concepts (35 used for prediction)
PRED predicted values (max 10 best out of 91): USA (0.78 #5456, 0.71 #1897, 0.68 #711), CDN (0.36 #1011, 0.33 #537, 0.26 #774), R (0.21 #242, 0.20 #3089, 0.15 #5), F (0.19 #1192, 0.11 #4040, 0.11 #1904), ZRE (0.18 #1502, 0.18 #1976, 0.15 #79), D (0.13 #1205, 0.12 #6186, 0.11 #4292), PE (0.11 #3151, 0.08 #67, 0.06 #4339), CH (0.10 #1242, 0.09 #3378, 0.09 #1717), MEX (0.09 #2608, 0.09 #6403, 0.06 #4271), BR (0.08 #1548, 0.05 #2259, 0.05 #2022)
>> best conf = 0.78
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5456 for best value:
>> intensional similarity = 6
>> extensional distance = 135
>> proper extension:  Baro; Pibor;
>> query: (?x1890, ?x315) <- ?x1890[ has flowsInto ?x1273[ a River; has hasEstuary ?x1282; has hasSource ?x2414[ a Source; has locatedIn ?x315;];];]
ranks of expected_values: 1
EVAL	LakeTahoe	locatedIn	USA	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	35.000	35.000	91.000	0.781	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: USA
=> 88 concepts (88 used for prediction)
PRED predicted values (max 10 best out of 101): USA (0.88 #14520, 0.84 #11662, 0.79 #6660), CDN (0.50 #63, 0.40 #537, 0.36 #5056), ZRE (0.38 #11978, 0.26 #15313, 0.24 #15791), R (0.27 #9762, 0.25 #7379, 0.24 #8094), F (0.21 #4524, 0.14 #8335, 0.12 #16434), CH (0.19 #6955, 0.17 #9337, 0.16 #6717), PE (0.18 #6253, 0.09 #15062, 0.08 #14825), MEX (0.16 #3564, 0.14 #4041, 0.13 #16188), D (0.16 #10254, 0.15 #18589, 0.14 #15971), UA (0.13 #10304, 0.12 #14590, 0.11 #5300)
>> best conf = 0.88
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #14520 for best value:
>> intensional similarity = 8
>> extensional distance = 82
>> proper extension:  Baro; Pibor;
>> query: (?x1890, ?x315) <- ?x1890[ has flowsInto ?x1273[ a River; has hasEstuary ?x1282[ a Estuary; has locatedIn ?x315[ is locatedIn of ?x2414[ a Source;];];]; has hasSource ?x2414;];]
ranks of expected_values: 1
EVAL	LakeTahoe	locatedIn	USA	CNN-1.+1._MA	1.000	1.000	1.000	1.000	88.000	88.000	101.000	0.884	http://www.semwebtech.org/mondial/10/meta#locatedIn

#754-Ovimbundu
PRED entity: Ovimbundu

PRED relation: ethnicGroup!
PRED expected values: ANG
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 96): EAU (0.03 #132), ETH (0.03 #100), UA (0.03 #57), EAK (0.03 #99), CZ (0.02 #98), SN (0.02 #83), MYA (0.02 #70), IR (0.02 #58), SK (0.02 #25), RCA (0.02 #139)
>> best conf = 0.03
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #132 for best value:
>> intensional similarity = 1
>> extensional distance = 279
>> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
>> query: (?x2044, EAU) <- ?x2044[ a EthnicGroup;]

*> Best rule #161 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 279
*> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
*> query: (?x2044, ANG) <- ?x2044[ a EthnicGroup;]
*> conf = 0.01
ranks of expected_values: 62
EVAL	Ovimbundu	ethnicGroup!	ANG	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.016	2.000	2.000	96.000	0.032	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup!
PRED expected values: ANG
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 96): EAU (0.03 #132), ETH (0.03 #100), UA (0.03 #57), EAK (0.03 #99), CZ (0.02 #98), SN (0.02 #83), MYA (0.02 #70), IR (0.02 #58), SK (0.02 #25), RCA (0.02 #139)
>> best conf = 0.03
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #132 for best value:
>> intensional similarity = 1
>> extensional distance = 279
>> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
>> query: (?x2044, EAU) <- ?x2044[ a EthnicGroup;]

*> Best rule #161 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 279
*> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
*> query: (?x2044, ANG) <- ?x2044[ a EthnicGroup;]
*> conf = 0.01
ranks of expected_values: 62
EVAL	Ovimbundu	ethnicGroup!	ANG	CNN-1.+1._MA	0.000	0.000	0.000	0.016	2.000	2.000	96.000	0.032	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#753-EAT
PRED entity: EAT

PRED relation: locatedIn!
PRED expected values: Pemba
=> 38 concepts (33 used for prediction)
PRED predicted values (max 10 best out of 1339): Elgon (0.50 #1752, 0.18 #4237, 0.12 #4577), AtlanticOcean (0.42 #12751, 0.34 #19814, 0.30 #43825), Luapula (0.40 #4065, 0.25 #1241, 0.18 #4237), Luapula (0.40 #3693, 0.25 #869, 0.18 #4237), Ruzizi (0.40 #3379, 0.19 #18361, 0.19 #18360), Ruzizi (0.40 #2951, 0.18 #4237, 0.10 #28249), PacificOcean (0.33 #7147, 0.28 #17032, 0.26 #21271), Zambezi (0.25 #1132, 0.20 #3956, 0.19 #18361), Kalahari (0.25 #467, 0.20 #3291, 0.18 #4237), LakeSeseSeko-Albertsee (0.25 #2415, 0.20 #3827, 0.18 #4237)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1752 for best value:
>> intensional similarity = 5
>> extensional distance = 2
>> proper extension:  EAK; EAU;
>> query: (?x820, Elgon) <- ?x820[ has ethnicGroup ?x1233; is locatedIn of ?x1195<LakeVictoria>; is neighbor of ?x819[ has encompassed ?x213;];]

*> Best rule #21186 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 99
*> proper extension:  PK; G; NOK; ROK; OM; WSA; FGU; GQ;
*> query: (?x820, ?x226) <- ?x820[ is locatedIn of ?x60[ is locatedInWater of ?x226;]; is neighbor of ?x359[ has encompassed ?x213; has wasDependentOf ?x485;];]
*> conf = 0.02
ranks of expected_values: 961
EVAL	EAT	locatedIn!	Pemba	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.001	38.000	33.000	1339.000	0.500	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Pemba
=> 97 concepts (95 used for prediction)
PRED predicted values (max 10 best out of 1423): AtlanticOcean (0.87 #80679, 0.81 #43877, 0.79 #46707), PacificOcean (0.83 #49583, 0.66 #62332, 0.62 #52417), VictoriaNile (0.60 #11315, 0.33 #4608, 0.30 #1416), Lukuga (0.60 #11315, 0.33 #87, 0.30 #1416), Chire (0.60 #11315, 0.30 #1416, 0.29 #26626), MediterraneanSea (0.50 #55251, 0.44 #56669, 0.33 #73647), Elgon (0.50 #11655, 0.33 #4583, 0.30 #1416), GulfofBengal (0.50 #15627, 0.25 #9974, 0.19 #131596), Jubba (0.40 #17317, 0.33 #49494, 0.18 #66490), Shabelle (0.40 #18131, 0.18 #66490, 0.17 #20957)
>> best conf = 0.87
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #80679 for best value:
>> intensional similarity = 13
>> extensional distance = 59
>> proper extension:  SPMI; FARX; AXA; TUCA; GROX; VIRG;
>> query: (?x820, AtlanticOcean) <- ?x820[ a Country; has government ?x435; has religion ?x116; is locatedIn of ?x1195[ has locatedIn ?x688[ has ethnicGroup ?x529; has neighbor ?x348; has religion ?x95; is locatedIn of ?x1188<Semliki>; is locatedIn of ?x1538<Ruwenzori>; is neighbor of ?x229;];];]

*> Best rule #33932 for first EXPECTED value:
*> intensional similarity = 14
*> extensional distance = 14
*> proper extension:  FJI; CDN; NZ; TUV; KIR; TO;
*> query: (?x820, ?x226) <- ?x820[ a Country; has wasDependentOf ?x81<GB>; is locatedIn of ?x60[ has locatedIn ?x61[ has dependentOf ?x78;]; has locatedIn ?x474[ is neighbor of ?x229;]; has locatedIn ?x508[ has ethnicGroup ?x244;]; is locatedInWater of ?x226; is locatedInWater of ?x433<Tasmania>; is mergesWith of ?x182;];]
*> conf = 0.05
ranks of expected_values: 645
EVAL	EAT	locatedIn!	Pemba	CNN-1.+1._MA	0.000	0.000	0.000	0.002	97.000	95.000	1423.000	0.869	http://www.semwebtech.org/mondial/10/meta#locatedIn

#752-GrandTurk
PRED entity: GrandTurk

PRED relation: locatedIn
PRED expected values: TUCA
=> 18 concepts (18 used for prediction)
PRED predicted values (max 10 best out of 96): TUCA (0.50 #130, 0.35 #1903, 0.34 #2377), GB (0.19 #245, 0.19 #482, 0.17 #954), P (0.13 #433, 0.13 #670, 0.13 #906), USA (0.12 #1497, 0.07 #2449, 0.07 #2688), E (0.12 #263, 0.11 #500, 0.10 #736), CDN (0.08 #1662, 0.08 #1488, 0.05 #3813), RI (0.07 #1718, 0.07 #1955, 0.07 #2192), D (0.05 #2875, 0.05 #2397, 0.05 #2636), HELX (0.05 #3813, 0.05 #3812, 0.05 #3811), TT (0.05 #3813, 0.05 #3812, 0.05 #3811)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #130 for best value:
>> intensional similarity = 5
>> extensional distance = 2
>> proper extension:  Providenciales; NorthCaicos;
>> query: (?x1491, TUCA) <- ?x1491[ a Island; has belongsToIslands ?x2092<TurksandCaicosIslands>; has locatedInWater ?x182<AtlanticOcean>;]
ranks of expected_values: 1
EVAL	GrandTurk	locatedIn	TUCA	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	18.000	18.000	96.000	0.500	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: TUCA
=> 38 concepts (38 used for prediction)
PRED predicted values (max 10 best out of 127): TUCA (0.50 #130, 0.35 #5578, 0.34 #6052), GB (0.20 #959, 0.19 #1448, 0.19 #245), P (0.13 #433, 0.13 #911, 0.13 #670), USA (0.12 #2726, 0.11 #2970, 0.11 #3701), E (0.12 #263, 0.11 #741, 0.11 #500), CDN (0.10 #2470, 0.06 #3692, 0.05 #8781), RI (0.08 #4657, 0.07 #5393, 0.07 #5630), HELX (0.07 #4118, 0.06 #2897, 0.06 #3628), CV (0.07 #4118, 0.06 #2897, 0.06 #3628), C (0.07 #4118, 0.06 #2897, 0.06 #3628)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #130 for best value:
>> intensional similarity = 5
>> extensional distance = 2
>> proper extension:  Providenciales; NorthCaicos;
>> query: (?x1491, TUCA) <- ?x1491[ a Island; has belongsToIslands ?x2092<TurksandCaicosIslands>; has locatedInWater ?x182<AtlanticOcean>;]
ranks of expected_values: 1
EVAL	GrandTurk	locatedIn	TUCA	CNN-1.+1._MA	1.000	1.000	1.000	1.000	38.000	38.000	127.000	0.500	http://www.semwebtech.org/mondial/10/meta#locatedIn

#751-Bosniak
PRED entity: Bosniak

PRED relation: ethnicGroup!
PRED expected values: BIH
=> 24 concepts (20 used for prediction)
PRED predicted values (max 10 best out of 216): HR (0.56 #596, 0.33 #404, 0.33 #21), UA (0.53 #1015, 0.29 #3081, 0.17 #439), H (0.50 #430, 0.38 #767, 0.35 #1006), MK (0.45 #901, 0.40 #325, 0.33 #134), RO (0.38 #767, 0.33 #410, 0.30 #575), BG (0.38 #767, 0.30 #575, 0.29 #3081), BIH (0.38 #767, 0.30 #575, 0.29 #3081), CZ (0.33 #479, 0.29 #1055, 0.22 #671), KOS (0.30 #575, 0.29 #3081, 0.28 #1151), A (0.30 #575, 0.29 #3081, 0.27 #959)
>> best conf = 0.56
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #596 for best value:
>> intensional similarity = 6
>> extensional distance = 7
>> proper extension:  Croat; Slovene; Muslim;
>> query: (?x2213, HR) <- ?x2213[ is ethnicGroup of ?x904[ has religion ?x56<ChristianOrthodox>; is locatedIn of ?x152<Save>; is neighbor of ?x55;];]

*> Best rule #767 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 7
*> proper extension:  Croat; Slovene; Muslim;
*> query: (?x2213, ?x55) <- ?x2213[ is ethnicGroup of ?x904[ has religion ?x56<ChristianOrthodox>; is locatedIn of ?x152<Save>; is neighbor of ?x55;];]
*> conf = 0.38
ranks of expected_values: 7
EVAL	Bosniak	ethnicGroup!	BIH	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.143	24.000	20.000	216.000	0.556	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup!
PRED expected values: BIH
=> 80 concepts (80 used for prediction)
PRED predicted values (max 10 best out of 227): HR (0.71 #1387, 0.60 #604, 0.56 #1582), H (0.50 #1363, 0.50 #581, 0.50 #579), A (0.50 #1365, 0.42 #1561, 0.42 #1560), UA (0.47 #2736, 0.47 #2598, 0.45 #2993), RO (0.43 #999, 0.40 #5688, 0.39 #778), BG (0.43 #1000, 0.40 #5688, 0.39 #778), I (0.42 #1561, 0.42 #1560, 0.42 #385), AL (0.40 #5688, 0.40 #1949, 0.40 #1755), BIH (0.40 #5688, 0.39 #778, 0.39 #777), MK (0.40 #5688, 0.39 #778, 0.39 #777)
>> best conf = 0.71
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1387 for best value:
>> intensional similarity = 19
>> extensional distance = 5
>> proper extension:  Muslim;
>> query: (?x2213, HR) <- ?x2213[ is ethnicGroup of ?x446[ has encompassed ?x195; has government ?x1174; has language ?x878<Croatian>; has neighbor ?x156[ has language ?x1296;]; has neighbor ?x236<H>; has wasDependentOf ?x1197; is locatedIn of ?x155<Mur>; is locatedIn of ?x275<MediterraneanSea>;]; is ethnicGroup of ?x904[ has neighbor ?x55; has religion ?x95<Protestant>; is locatedIn of ?x132;];]

*> Best rule #5688 for first EXPECTED value:
*> intensional similarity = 16
*> extensional distance = 35
*> proper extension:  Serbian;
*> query: (?x2213, ?x236) <- ?x2213[ is ethnicGroup of ?x446[ a Country; has encompassed ?x195; has government ?x1174; has language ?x738; has neighbor ?x236[ has ethnicGroup ?x164; has neighbor ?x163; has religion ?x352<RomanCatholic>; has wasDependentOf ?x2352; is locatedIn of ?x133;]; has religion ?x187; has wasDependentOf ?x1197; is locatedIn of ?x1363[ a Source;];];]
*> conf = 0.40
ranks of expected_values: 9
EVAL	Bosniak	ethnicGroup!	BIH	CNN-1.+1._MA	0.000	0.000	1.000	0.111	80.000	80.000	227.000	0.714	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#750-Mantaro
PRED entity: Mantaro

PRED relation: hasSource!
PRED expected values: Mantaro
=> 50 concepts (43 used for prediction)
PRED predicted values (max 10 best out of 121): Apurimac (0.14 #3195, 0.11 #207, 0.10 #663), Perene (0.14 #3195, 0.11 #154, 0.10 #610), Tambo (0.14 #3195, 0.11 #136, 0.10 #592), Ene (0.14 #3195, 0.11 #117, 0.10 #573), Ucayali (0.14 #3195, 0.11 #110, 0.10 #566), Maranon (0.14 #3195, 0.11 #103, 0.10 #559), Amazonas (0.14 #3195, 0.11 #13, 0.10 #469), RioMagdalena (0.14 #3195, 0.08 #1135, 0.08 #1364), RioNegro (0.14 #3195, 0.08 #1044, 0.08 #1273), RioMamore (0.14 #3195, 0.08 #948, 0.08 #1177)
>> best conf = 0.14
=> the first rule below is the first best rule for 11 predicted values
>> Best rule #3195 for best value:
>> intensional similarity = 7
>> extensional distance = 98
>> proper extension:  Murgab; Donau; SnowyRiver; Garonne; Naryn;
>> query: (?x2486, ?x1049) <- ?x2486[ a Source; has inMountains ?x431[ a Mountains; is inMountains of ?x264[ is hasSource of ?x1049;]; is inMountains of ?x1774[ has locatedIn ?x902;];];]

*> Best rule #3881 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 150
*> proper extension:  JoekulsaaFjoellum; Thjorsa;
*> query: (?x2486, ?x948) <- ?x2486[ a Source; has locatedIn ?x296[ has wasDependentOf ?x149; is locatedIn of ?x948[ a River;];];]
*> conf = 0.07
ranks of expected_values: 13
EVAL	Mantaro	hasSource!	Mantaro	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.077	50.000	43.000	121.000	0.138	http://www.semwebtech.org/mondial/10/meta#hasSource

PRED relation: hasSource!
PRED expected values: Mantaro
=> 123 concepts (123 used for prediction)
PRED predicted values (max 10 best out of 165): Tambo (0.14 #2058, 0.13 #5496, 0.12 #6647), Ucayali (0.14 #2058, 0.13 #5496, 0.12 #6647), Maranon (0.14 #2058, 0.13 #5496, 0.12 #6647), Ene (0.14 #2058, 0.13 #5496, 0.12 #6647), Apurimac (0.14 #2058, 0.13 #5496, 0.12 #6647), Perene (0.14 #2058, 0.13 #5496, 0.12 #6647), Urubamba (0.14 #2058, 0.13 #5496, 0.12 #6647), Huallaga (0.14 #2058, 0.13 #5496, 0.10 #558), Mantaro (0.14 #2058, 0.13 #5496, 0.10 #1600), Amazonas (0.12 #6647, 0.12 #6646, 0.11 #13)
>> best conf = 0.14
=> the first rule below is the first best rule for 9 predicted values
>> Best rule #2058 for best value:
>> intensional similarity = 10
>> extensional distance = 14
>> proper extension:  Orinoco;
>> query: (?x2486, ?x948) <- ?x2486[ a Source; has locatedIn ?x296[ is locatedIn of ?x948[ a River;]; is locatedIn of ?x1646[ has inMountains ?x1287;]; is locatedIn of ?x1759[ a Estuary;]; is neighbor of ?x542<BR>;];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 9
EVAL	Mantaro	hasSource!	Mantaro	CNN-1.+1._MA	0.000	0.000	1.000	0.111	123.000	123.000	165.000	0.141	http://www.semwebtech.org/mondial/10/meta#hasSource

#749-Bahrel-Djebel-Albert-Nil
PRED entity: Bahrel-Djebel-Albert-Nil

PRED relation: locatedIn
PRED expected values: EAU
=> 38 concepts (38 used for prediction)
PRED predicted values (max 10 best out of 181): EAU (0.90 #5658, 0.79 #2592, 0.78 #2356), ZRE (0.79 #2592, 0.78 #2356, 0.78 #471), SUD (0.48 #5894, 0.18 #4948, 0.17 #42), ETH (0.33 #114, 0.27 #821, 0.18 #4948), R (0.31 #477, 0.15 #4717, 0.15 #4953), D (0.22 #1670, 0.20 #3317, 0.20 #3082), RCB (0.22 #356, 0.18 #1063, 0.09 #8487), USA (0.20 #2663, 0.19 #1485, 0.19 #2898), RCA (0.18 #4948, 0.13 #6836, 0.12 #7072), EAK (0.18 #4948, 0.13 #6836, 0.12 #7072)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5658 for best value:
>> intensional similarity = 5
>> extensional distance = 169
>> proper extension:  Araguaia; WesternBug; Limpopo; WesternMorava; Cuango; Bani; Argun; Paraguay; JoekulsaaFjoellum; Prypjat; ...
>> query: (?x1727, ?x688) <- ?x1727[ a River; has flowsInto ?x990; has hasEstuary ?x2133; has hasSource ?x1880[ has locatedIn ?x688;];]
ranks of expected_values: 1
EVAL	Bahrel-Djebel-Albert-Nil	locatedIn	EAU	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	38.000	38.000	181.000	0.899	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: EAU
=> 75 concepts (75 used for prediction)
PRED predicted values (max 10 best out of 212): EAU (0.93 #7815, 0.91 #12313, 0.89 #13732), ZRE (0.86 #5216, 0.76 #1967, 0.76 #4977), SUD (0.71 #5453, 0.65 #9946, 0.57 #8051), ETH (0.62 #2716, 0.50 #1531, 0.38 #1294), USA (0.38 #5051, 0.26 #2436, 0.20 #5524), RCA (0.27 #9469, 0.26 #3000, 0.26 #9467), EAK (0.27 #9469, 0.26 #9467, 0.24 #9470), ET (0.26 #2845, 0.24 #3084, 0.15 #2131), CDN (0.25 #5042, 0.19 #2427, 0.11 #3381), D (0.21 #6185, 0.20 #5949, 0.17 #7835)
>> best conf = 0.93
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #7815 for best value:
>> intensional similarity = 8
>> extensional distance = 84
>> proper extension:  Araguaia; Vaesterdalaelv;
>> query: (?x1727, ?x688) <- ?x1727[ a River; has flowsInto ?x990[ a River;]; has hasEstuary ?x2133[ a Estuary; has locatedIn ?x229;]; has hasSource ?x1880[ has locatedIn ?x688;];]
ranks of expected_values: 1
EVAL	Bahrel-Djebel-Albert-Nil	locatedIn	EAU	CNN-1.+1._MA	1.000	1.000	1.000	1.000	75.000	75.000	212.000	0.933	http://www.semwebtech.org/mondial/10/meta#locatedIn

#748-Brahmaputra
PRED entity: Brahmaputra

PRED relation: hasSource!
PRED expected values: Brahmaputra
=> 27 concepts (25 used for prediction)
PRED predicted values (max 10 best out of 119): Saluen (0.09 #228, 0.03 #456, 0.03 #457), Mekong (0.09 #128, 0.03 #356, 0.03 #457), Jangtse (0.09 #76, 0.03 #304, 0.03 #457), Tarim-Yarkend (0.09 #47, 0.03 #275, 0.03 #457), Argun (0.09 #42, 0.03 #270, 0.03 #457), Ili (0.09 #28, 0.03 #256, 0.03 #457), Ganges (0.09 #143, 0.03 #371, 0.02 #600), Ischim (0.03 #425, 0.01 #1833), Klaraelv (0.03 #455), Kymijoki (0.03 #441)
>> best conf = 0.09
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #228 for best value:
>> intensional similarity = 3
>> extensional distance = 9
>> proper extension:  Ili; Jangtse; Ganges; Argun; Irawaddy; Saluen; Tarim-Yarkend; Indus; Mekong;
>> query: (?x2454, Saluen) <- ?x2454[ a Source; has locatedIn ?x232<CN>;]

*> Best rule #457 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 30
*> proper extension:  Selenge;
*> query: (?x2454, ?x231) <- ?x2454[ a Source; has locatedIn ?x232[ is locatedIn of ?x231; is neighbor of ?x73<R>; is neighbor of ?x617[ has religion ?x95;];];]
*> conf = 0.03
ranks of expected_values: 29
EVAL	Brahmaputra	hasSource!	Brahmaputra	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.034	27.000	25.000	119.000	0.091	http://www.semwebtech.org/mondial/10/meta#hasSource

PRED relation: hasSource!
PRED expected values: Brahmaputra
=> 89 concepts (89 used for prediction)
PRED predicted values (max 10 best out of 118): Argun (0.09 #42, 0.08 #6462, 0.07 #7386), Mekong (0.09 #128, 0.08 #6462, 0.07 #7386), Jangtse (0.09 #76, 0.08 #6462, 0.07 #7386), Tarim-Yarkend (0.09 #47, 0.08 #6462, 0.07 #7386), Ili (0.09 #28, 0.08 #6462, 0.07 #7386), Saluen (0.09 #228, 0.08 #6462, 0.07 #7386), Ganges (0.09 #143, 0.03 #371, 0.03 #602), Amur (0.08 #6462, 0.07 #7386, 0.07 #5075), Hwangho (0.08 #6462, 0.07 #7386, 0.07 #5075), Brahmaputra (0.07 #7386, 0.07 #7387, 0.07 #6231)
>> best conf = 0.09
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #42 for best value:
>> intensional similarity = 3
>> extensional distance = 9
>> proper extension:  Ili; Jangtse; Ganges; Argun; Irawaddy; Saluen; Tarim-Yarkend; Indus; Mekong;
>> query: (?x2454, Argun) <- ?x2454[ a Source; has locatedIn ?x232<CN>;]

*> Best rule #7386 for first EXPECTED value:
*> intensional similarity = 13
*> extensional distance = 215
*> proper extension:  Kasai; Cuilo; Tajo; Guadiana; Ebro; Okavango; Douro; Cuango; Guadalquivir;
*> query: (?x2454, ?x338) <- ?x2454[ a Source; has locatedIn ?x232[ has neighbor ?x366[ has encompassed ?x175; has wasDependentOf ?x81;]; has neighbor ?x403[ a Country; has religion ?x56; is locatedIn of ?x127;]; is locatedIn of ?x319[ a River;]; is locatedIn of ?x338[ has hasEstuary ?x1481;];];]
*> conf = 0.07
ranks of expected_values: 10
EVAL	Brahmaputra	hasSource!	Brahmaputra	CNN-1.+1._MA	0.000	0.000	1.000	0.100	89.000	89.000	118.000	0.091	http://www.semwebtech.org/mondial/10/meta#hasSource

#747-Kanlaon
PRED entity: Kanlaon

PRED relation: type
PRED expected values: "volcano"
=> 40 concepts (40 used for prediction)
PRED predicted values (max 10 best out of 8): "volcano" (0.63 #70, 0.61 #86, 0.57 #102), "volcanic" (0.50 #18, 0.41 #211, 0.41 #194), "caldera" (0.03 #147, 0.01 #360), "granite" (0.03 #62, 0.02 #126, 0.02 #142), "salt" (0.03 #542, 0.02 #559, 0.02 #608), "dam" (0.02 #358, 0.02 #455, 0.01 #536), "sand" (0.01 #361), "atoll" (0.01 #381)
>> best conf = 0.63
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #70 for best value:
>> intensional similarity = 5
>> extensional distance = 36
>> proper extension:  MaunaLoa; Katla; Asahi-Dake; MorneDiablotins;
>> query: (?x1056, "volcano") <- ?x1056[ a Volcano; has locatedOnIsland ?x1034[ has locatedIn ?x460; has locatedInWater ?x625[ has mergesWith ?x241;];];]
ranks of expected_values: 1
EVAL	Kanlaon	type	"volcano"	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	40.000	40.000	8.000	0.632	http://www.semwebtech.org/mondial/10/meta#type

PRED relation: type
PRED expected values: "volcano"
=> 95 concepts (95 used for prediction)
PRED predicted values (max 10 best out of 8): "volcano" (0.67 #231, 0.66 #198, 0.65 #247), "volcanic" (0.50 #34, 0.41 #209, 0.40 #258), "salt" (0.07 #813, 0.05 #748, 0.05 #863), "granite" (0.06 #110, 0.05 #142, 0.04 #174), "sand" (0.04 #827, 0.03 #860, 0.02 #810), "caldera" (0.03 #502, 0.03 #485, 0.02 #875), "dam" (0.02 #939, 0.02 #956, 0.02 #1281), "atoll" (0.01 #831)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #231 for best value:
>> intensional similarity = 8
>> extensional distance = 28
>> proper extension:  Silisili;
>> query: (?x1056, "volcano") <- ?x1056[ a Mountain; a Volcano; has locatedOnIsland ?x1034[ a Island; has belongsToIslands ?x370[ a Islands;]; has locatedIn ?x460[ a Country;];];]
ranks of expected_values: 1
EVAL	Kanlaon	type	"volcano"	CNN-1.+1._MA	1.000	1.000	1.000	1.000	95.000	95.000	8.000	0.667	http://www.semwebtech.org/mondial/10/meta#type

#746-SLB
PRED entity: SLB

PRED relation: locatedIn!
PRED expected values: PacificOcean Guadalcanal
=> 43 concepts (43 used for prediction)
PRED predicted values (max 10 best out of 1191): PacificOcean (0.73 #20009, 0.50 #2932, 0.33 #5778), AtlanticOcean (0.60 #4311, 0.56 #12850, 0.52 #28503), CaribbeanSea (0.44 #11491, 0.38 #10068, 0.38 #8645), SouthChinaSea (0.40 #25756, 0.39 #24333, 0.36 #15795), NewProvidence (0.33 #2744, 0.20 #5590, 0.07 #19821), Trinidad (0.33 #594, 0.12 #10556, 0.11 #11979), Tobago (0.33 #194, 0.12 #10156, 0.11 #11579), RioSanJuan (0.25 #3093, 0.17 #5939, 0.14 #7362), RioSanJuan (0.25 #2948, 0.17 #5794, 0.14 #7217), LakeNicaragua (0.25 #2947, 0.17 #5793, 0.14 #7216)
>> best conf = 0.73
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #20009 for best value:
>> intensional similarity = 6
>> extensional distance = 13
>> proper extension:  PAL;
>> query: (?x390, PacificOcean) <- ?x390[ a Country; has encompassed ?x211<Australia-Oceania>; has government ?x1947; has wasDependentOf ?x81; is locatedIn of ?x1083;]
ranks of expected_values: 1
EVAL	SLB	locatedIn!	Guadalcanal	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	43.000	43.000	1191.000	0.733	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	SLB	locatedIn!	PacificOcean	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	43.000	43.000	1191.000	0.733	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: PacificOcean Guadalcanal
=> 99 concepts (99 used for prediction)
PRED predicted values (max 10 best out of 1348): Guadalcanal (0.75 #18513), PacificOcean (0.69 #27141, 0.69 #32841, 0.69 #31416), AtlanticOcean (0.52 #44193, 0.50 #45616, 0.50 #25674), CaribbeanSea (0.50 #34285, 0.44 #18619, 0.40 #21466), SouthChinaSea (0.43 #15804, 0.42 #35745, 0.38 #37171), Banaba (0.33 #3805, 0.25 #9498, 0.11 #32755), Tarawa (0.33 #3305, 0.25 #8998, 0.11 #32755), Kiritimati (0.33 #3297, 0.25 #8990, 0.11 #32755), NewGuinea (0.33 #7683, 0.22 #11390, 0.15 #27619), Fongafale (0.33 #982, 0.22 #11390, 0.11 #32755)
>> best conf = 0.75
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #18513 for best value:
>> intensional similarity = 14
>> extensional distance = 5
>> proper extension:  MAL;
>> query: (?x390, ?x553) <- ?x390[ has encompassed ?x211; has ethnicGroup ?x197[ is ethnicGroup of ?x783[ has language ?x796<Spanish>;]; is ethnicGroup of ?x1364[ a Country; has government ?x1535; has wasDependentOf ?x149;];]; has religion ?x95; has wasDependentOf ?x81; is locatedIn of ?x1083[ has locatedOnIsland ?x553; has type ?x150;];]
ranks of expected_values: 1, 2
EVAL	SLB	locatedIn!	Guadalcanal	CNN-1.+1._MA	1.000	1.000	1.000	1.000	99.000	99.000	1348.000	0.750	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	SLB	locatedIn!	PacificOcean	CNN-1.+1._MA	1.000	1.000	1.000	1.000	99.000	99.000	1348.000	0.750	http://www.semwebtech.org/mondial/10/meta#locatedIn

#745-LakeWinnipesaukee
PRED entity: LakeWinnipesaukee

PRED relation: locatedIn
PRED expected values: USA
=> 27 concepts (27 used for prediction)
PRED predicted values (max 10 best out of 129): USA (0.81 #2848, 0.78 #3795, 0.68 #3085), ZRE (0.57 #951, 0.57 #792, 0.20 #553), CDN (0.28 #300, 0.25 #1727, 0.23 #63), BR (0.23 #125, 0.09 #599, 0.08 #2611), PE (0.15 #67, 0.06 #541, 0.05 #4572), D (0.12 #4288, 0.09 #3578, 0.09 #3815), R (0.10 #3090, 0.10 #1194, 0.10 #5934), CH (0.10 #3378, 0.10 #2668, 0.09 #2905), RCB (0.09 #596, 0.08 #2611, 0.07 #835), SF (0.08 #1321, 0.06 #2743, 0.06 #2980)
>> best conf = 0.81
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2848 for best value:
>> intensional similarity = 6
>> extensional distance = 61
>> proper extension:  StarnbergerSee; BarragedeMbakaou; LagodeChapala; LakeHume;
>> query: (?x2042, ?x315) <- ?x2042[ a Lake; has flowsInto ?x1887[ a River; has flowsInto ?x182; has hasSource ?x960[ has locatedIn ?x315;];];]
ranks of expected_values: 1
EVAL	LakeWinnipesaukee	locatedIn	USA	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	27.000	27.000	129.000	0.806	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: USA
=> 83 concepts (83 used for prediction)
PRED predicted values (max 10 best out of 150): USA (0.88 #15486, 0.78 #9528, 0.78 #3573), CDN (0.60 #538, 0.47 #4764, 0.47 #4588), ZRE (0.44 #9607, 0.20 #791, 0.17 #17473), NIC (0.29 #2238, 0.10 #6288, 0.10 #6051), R (0.26 #15729, 0.19 #7389, 0.14 #9296), BR (0.20 #362, 0.17 #1552, 0.17 #1313), GH (0.20 #354, 0.17 #1544, 0.17 #1305), RCB (0.20 #834, 0.17 #1549, 0.11 #8577), CAM (0.20 #835, 0.11 #8577, 0.08 #10956), F (0.18 #7868, 0.14 #1672, 0.12 #16209)
>> best conf = 0.88
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #15486 for best value:
>> intensional similarity = 8
>> extensional distance = 82
>> proper extension:  Baro; Pibor;
>> query: (?x2042, ?x315) <- ?x2042[ has flowsInto ?x1887[ a River; has hasEstuary ?x1764[ a Estuary; has locatedIn ?x315[ is locatedIn of ?x960[ a Source;];];]; has hasSource ?x960;];]
ranks of expected_values: 1
EVAL	LakeWinnipesaukee	locatedIn	USA	CNN-1.+1._MA	1.000	1.000	1.000	1.000	83.000	83.000	150.000	0.884	http://www.semwebtech.org/mondial/10/meta#locatedIn

#744-Luapula
PRED entity: Luapula

PRED relation: flowsInto!
PRED expected values: LakeBangweulu
=> 36 concepts (27 used for prediction)
PRED predicted values (max 10 best out of 102): Ruzizi (0.25 #151, 0.20 #453, 0.11 #757), LakeCabora-Bassa (0.11 #643, 0.06 #2731, 0.05 #1251), Chire (0.11 #890, 0.05 #1498, 0.04 #2408), MaleboPool (0.06 #2731, 0.05 #1534, 0.05 #1836), LakeKariba (0.06 #2731, 0.01 #4548, 0.01 #4245), Busira (0.06 #985, 0.05 #1591, 0.05 #1893), Luvua (0.06 #947, 0.05 #1553, 0.05 #1855), Lukuga (0.06 #933, 0.05 #1539, 0.05 #1841), Tshuapa (0.06 #1163, 0.05 #1769, 0.05 #2071), Lukenie (0.06 #1155, 0.05 #1761, 0.05 #2063)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #151 for best value:
>> intensional similarity = 4
>> extensional distance = 2
>> proper extension:  LakeTanganjika; Luapula;
>> query: (?x2185, Ruzizi) <- ?x2185[ has locatedIn ?x348<ZRE>; has locatedIn ?x525<Z>;]

No rule for expected values
ranks of expected_values: 
EVAL	Luapula	flowsInto!	LakeBangweulu	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	36.000	27.000	102.000	0.250	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto!
PRED expected values: LakeBangweulu
=> 85 concepts (85 used for prediction)
PRED predicted values (max 10 best out of 194): Ruzizi (0.25 #151, 0.20 #453, 0.05 #2123), Zaire (0.20 #431, 0.05 #2123, 0.05 #2733), Oranje (0.20 #310, 0.03 #9416, 0.02 #2428), MerrimackRiver (0.20 #567, 0.03 #9416, 0.01 #3605), RioSaoFrancisco (0.20 #548, 0.03 #9416, 0.01 #3586), Sanaga (0.20 #529, 0.03 #9416, 0.01 #3567), Douro (0.20 #528, 0.03 #9416, 0.01 #3566), Tajo (0.20 #523, 0.03 #9416, 0.01 #3561), HudsonRiver (0.20 #500, 0.03 #9416, 0.01 #3538), SaintLawrenceRiver (0.20 #494, 0.03 #9416, 0.01 #3532)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #151 for best value:
>> intensional similarity = 4
>> extensional distance = 2
>> proper extension:  LakeTanganjika; Luapula;
>> query: (?x2185, Ruzizi) <- ?x2185[ has locatedIn ?x348<ZRE>; has locatedIn ?x525<Z>;]

No rule for expected values
ranks of expected_values: 
EVAL	Luapula	flowsInto!	LakeBangweulu	CNN-1.+1._MA	0.000	0.000	0.000	0.000	85.000	85.000	194.000	0.250	http://www.semwebtech.org/mondial/10/meta#flowsInto

#743-GasherbrumII
PRED entity: GasherbrumII

PRED relation: inMountains
PRED expected values: Karakorum
=> 31 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 31): Karakorum (0.25 #8, 0.21 #958, 0.17 #95), Himalaya (0.21 #958, 0.17 #93, 0.16 #354), Hindukusch (0.21 #958, 0.17 #165, 0.02 #426), Pamir (0.21 #958, 0.07 #713, 0.03 #887), TianShan (0.21 #958, 0.05 #292, 0.03 #466), Kunlun (0.21 #958, 0.04 #358, 0.03 #445), Transhimalaya (0.21 #958, 0.02 #372, 0.02 #459), EastAfricanRift (0.08 #376, 0.04 #898, 0.03 #1247), Alps (0.08 #787, 0.08 #874, 0.07 #1049), RockyMountains (0.07 #965, 0.07 #790, 0.06 #877)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #8 for best value:
>> intensional similarity = 5
>> extensional distance = 2
>> proper extension:  K2; BroadPeak;
>> query: (?x484, Karakorum) <- ?x484[ a Mountain; has locatedIn ?x83<PK>; has locatedIn ?x232<CN>;]
ranks of expected_values: 1
EVAL	GasherbrumII	inMountains	Karakorum	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	31.000	31.000	31.000	0.250	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains
PRED expected values: Karakorum
=> 84 concepts (84 used for prediction)
PRED predicted values (max 10 best out of 64): Himalaya (0.30 #877, 0.22 #3575, 0.21 #3574), Karakorum (0.25 #8, 0.22 #3575, 0.21 #3574), Pamir (0.22 #3575, 0.21 #3574, 0.19 #3051), Kunlun (0.22 #3575, 0.21 #3574, 0.19 #3051), TianShan (0.22 #3575, 0.21 #3574, 0.19 #3051), Transhimalaya (0.22 #3575, 0.21 #3574, 0.19 #3051), Kaukasus (0.16 #1064, 0.10 #1848, 0.08 #1238), RockyMountains (0.15 #2271, 0.10 #2621, 0.08 #3319), CordilleraVolcanica (0.13 #1545, 0.07 #2329, 0.05 #2679), Alps (0.12 #2356, 0.09 #3229, 0.07 #3840)
>> best conf = 0.30
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #877 for best value:
>> intensional similarity = 9
>> extensional distance = 18
>> proper extension:  Annapurna; Dhaulagiri;
>> query: (?x484, Himalaya) <- ?x484[ a Mountain; has locatedIn ?x232[ a Country; has encompassed ?x175; has neighbor ?x73[ has neighbor ?x170; is locatedIn of ?x72;]; is locatedIn of ?x328<Lhotse>;];]

*> Best rule #8 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 2
*> proper extension:  K2; BroadPeak;
*> query: (?x484, Karakorum) <- ?x484[ a Mountain; has locatedIn ?x83<PK>; has locatedIn ?x232<CN>;]
*> conf = 0.25
ranks of expected_values: 2
EVAL	GasherbrumII	inMountains	Karakorum	CNN-1.+1._MA	0.000	1.000	1.000	0.500	84.000	84.000	64.000	0.300	http://www.semwebtech.org/mondial/10/meta#inMountains

#742-Oesterdalaelv
PRED entity: Oesterdalaelv

PRED relation: locatedIn
PRED expected values: S
=> 45 concepts (45 used for prediction)
PRED predicted values (max 10 best out of 55): S (0.81 #4509, 0.74 #3557, 0.69 #2845), R (0.57 #4275, 0.38 #3799, 0.20 #953), AUS (0.33 #45, 0.25 #519, 0.17 #2179), N (0.33 #1457, 0.17 #1694, 0.08 #2405), USA (0.27 #4104, 0.25 #546, 0.19 #4581), CH (0.23 #2428, 0.21 #2665, 0.20 #2902), SF (0.20 #4402, 0.07 #6304, 0.07 #6409), I (0.20 #996, 0.07 #2656, 0.07 #2893), ZRE (0.18 #3398, 0.13 #8625, 0.13 #8150), D (0.15 #2391, 0.15 #3577, 0.13 #2865)
>> best conf = 0.81
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4509 for best value:
>> intensional similarity = 8
>> extensional distance = 33
>> proper extension:  Suchona;
>> query: (?x1832, ?x402) <- ?x1832[ a Source; is hasSource of ?x1327[ a River; has locatedIn ?x402[ has ethnicGroup ?x1473; has neighbor ?x170<N>; has religion ?x95;];];]
ranks of expected_values: 1
EVAL	Oesterdalaelv	locatedIn	S	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	45.000	45.000	55.000	0.811	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: S
=> 142 concepts (142 used for prediction)
PRED predicted values (max 10 best out of 56): S (0.80 #14313, 0.79 #22196, 0.79 #20286), R (0.72 #16708, 0.71 #20052, 0.64 #11932), SF (0.50 #8956, 0.50 #8002, 0.33 #1325), CH (0.50 #5063, 0.40 #2915, 0.36 #12462), USA (0.44 #14863, 0.33 #4839, 0.33 #789), N (0.43 #6948, 0.33 #1703, 0.25 #2177), D (0.40 #3356, 0.30 #9083, 0.25 #15050), I (0.36 #12214, 0.27 #14361, 0.22 #8394), ZRE (0.33 #14153, 0.31 #22038, 0.24 #20843), AUS (0.33 #1001, 0.17 #5529, 0.14 #6483)
>> best conf = 0.80
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #14313 for best value:
>> intensional similarity = 20
>> extensional distance = 13
>> proper extension:  Ubangi; Lualaba; Ruki;
>> query: (?x1832, ?x402) <- ?x1832[ a Source; is hasSource of ?x1327[ a River; has flowsInto ?x1328[ a River; has hasSource ?x401[ has locatedIn ?x402[ a Country; has ethnicGroup ?x1473; has religion ?x95<Protestant>; is locatedIn of ?x1992; is locatedIn of ?x2292[ a Estuary;]; is neighbor of ?x170[ a Country;];];];]; has hasEstuary ?x2408[ a Estuary;]; has locatedIn ?x402; is flowsInto of ?x1992;];]
ranks of expected_values: 1
EVAL	Oesterdalaelv	locatedIn	S	CNN-1.+1._MA	1.000	1.000	1.000	1.000	142.000	142.000	56.000	0.800	http://www.semwebtech.org/mondial/10/meta#locatedIn

#741-Luapula
PRED entity: Luapula

PRED relation: flowsInto
PRED expected values: LakeMweru
=> 27 concepts (24 used for prediction)
PRED predicted values (max 10 best out of 116): IndianOcean (0.33 #1, 0.14 #331, 0.11 #662), Kasai (0.29 #380, 0.02 #877, 0.02 #1043), Lukuga (0.25 #192, 0.11 #522, 0.06 #688), Oranje (0.14 #339), Zambezi (0.11 #817, 0.02 #1490, 0.02 #1149), AtlanticOcean (0.09 #1502, 0.09 #2496, 0.09 #2330), Zaire (0.09 #918, 0.07 #1084, 0.03 #2575), Donau (0.08 #1332, 0.08 #1498, 0.07 #1167), Chire (0.06 #821, 0.01 #1153), MediterraneanSea (0.05 #1347, 0.04 #1182, 0.04 #1513)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1 for best value:
>> intensional similarity = 5
>> extensional distance = 1
>> proper extension:  Zambezi;
>> query: (?x2185, IndianOcean) <- ?x2185[ a River; has hasSource ?x709[ a Source;]; has locatedIn ?x525<Z>;]

*> Best rule #2152 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 194
*> proper extension:  Araguaia; Leine; Sobat; RioLerma; SnowyRiver; Vaesterdalaelv; MurrayRiver; Thames; Moraca;
*> query: (?x2185, ?x113) <- ?x2185[ a River; has hasEstuary ?x1541[ has locatedIn ?x348[ is locatedIn of ?x113;];]; has hasSource ?x709[ a Source;];]
*> conf = 0.02
ranks of expected_values: 81
EVAL	Luapula	flowsInto	LakeMweru	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.012	27.000	24.000	116.000	0.333	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto
PRED expected values: LakeMweru
=> 83 concepts (83 used for prediction)
PRED predicted values (max 10 best out of 211): IndianOcean (0.33 #836, 0.33 #1, 0.12 #1340), AtlanticOcean (0.31 #3862, 0.25 #179, 0.22 #3190), Zaire (0.30 #2599, 0.30 #2097, 0.29 #2265), Kalahari (0.29 #4016, 0.22 #3344, 0.12 #334), Lukuga (0.25 #361, 0.20 #695, 0.20 #526), Zambezi (0.17 #991, 0.12 #334, 0.09 #6031), Oranje (0.17 #844, 0.06 #1348, 0.04 #2853), LakeTanganjika (0.12 #334, 0.09 #6031, 0.08 #5358), Luapula (0.12 #334, 0.05 #2674, 0.04 #2675), LakeKariba (0.12 #334, 0.03 #4684, 0.03 #5692)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #836 for best value:
>> intensional similarity = 11
>> extensional distance = 4
>> proper extension:  Limpopo; Vaal; Chire;
>> query: (?x2185, IndianOcean) <- ?x2185[ a River; has hasSource ?x709; has locatedIn ?x348[ has ethnicGroup ?x2121; has neighbor ?x229;]; has locatedIn ?x525[ a Country; has encompassed ?x213<Africa>; has neighbor ?x192<MOC>;];]
>> Best rule #1 for best value:
>> intensional similarity = 14
>> extensional distance = 1
>> proper extension:  Zambezi;
>> query: (?x2185, IndianOcean) <- ?x2185[ a River; has hasSource ?x709; has locatedIn ?x348[ has encompassed ?x213; has ethnicGroup ?x2121; has neighbor ?x820<EAT>; has religion ?x95; is locatedIn of ?x182[ is locatedInWater of ?x112;]; is locatedIn of ?x1541<Luapula>;]; has locatedIn ?x525<Z>;]

*> Best rule #4684 for first EXPECTED value:
*> intensional similarity = 12
*> extensional distance = 60
*> proper extension:  Sobat;
*> query: (?x2185, ?x113) <- ?x2185[ a River; has hasEstuary ?x1541[ has locatedIn ?x348[ has neighbor ?x229; is locatedIn of ?x113; is neighbor of ?x359;]; has locatedIn ?x525[ has government ?x435<"republic">; is neighbor of ?x138;];]; has hasSource ?x709[ a Source;];]
*> conf = 0.03
ranks of expected_values: 56
EVAL	Luapula	flowsInto	LakeMweru	CNN-1.+1._MA	0.000	0.000	0.000	0.018	83.000	83.000	211.000	0.333	http://www.semwebtech.org/mondial/10/meta#flowsInto

#740-MEL
PRED entity: MEL

PRED relation: dependentOf
PRED expected values: E
=> 30 concepts (30 used for prediction)
PRED predicted values (max 10 best out of 8): E (0.25 #13, 0.05 #45, 0.04 #71), GB (0.07 #242, 0.07 #329, 0.07 #293), F (0.07 #241, 0.06 #280, 0.06 #292), AUS (0.02 #245, 0.02 #284, 0.02 #296), CN (0.02 #118, 0.02 #143, 0.02 #157), USA (0.02 #346, 0.01 #247, 0.01 #286), DK (0.01 #301, 0.01 #337), NZ (0.01 #310, 0.01 #322)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #13 for best value:
>> intensional similarity = 7
>> extensional distance = 2
>> proper extension:  CEU;
>> query: (?x1588, E) <- ?x1588[ a Country; has encompassed ?x213<Africa>; is locatedIn of ?x275<MediterraneanSea>; is neighbor of ?x851<MA>;]
ranks of expected_values: 1
EVAL	MEL	dependentOf	E	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	30.000	30.000	8.000	0.250	http://www.semwebtech.org/mondial/10/meta#dependentOf

PRED relation: dependentOf
PRED expected values: E
=> 59 concepts (59 used for prediction)
PRED predicted values (max 10 best out of 13): E (0.25 #13, 0.20 #23, 0.14 #49), GB (0.20 #33, 0.08 #932, 0.06 #947), F (0.07 #931, 0.07 #946, 0.06 #357), CN (0.02 #467, 0.02 #548, 0.02 #570), USA (0.02 #967, 0.02 #986, 0.01 #937), I (0.02 #958, 0.01 #598, 0.01 #478), OttomanEmpire (0.02 #958), Yugoslavia (0.02 #958), SRB (0.02 #958), UnitedNations (0.02 #958)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #13 for best value:
>> intensional similarity = 8
>> extensional distance = 2
>> proper extension:  CEU;
>> query: (?x1588, E) <- ?x1588[ a Country; has encompassed ?x213<Africa>; is locatedIn of ?x275<MediterraneanSea>; is neighbor of ?x851<MA>;]
ranks of expected_values: 1
EVAL	MEL	dependentOf	E	CNN-1.+1._MA	1.000	1.000	1.000	1.000	59.000	59.000	13.000	0.250	http://www.semwebtech.org/mondial/10/meta#dependentOf

#739-GR
PRED entity: GR

PRED relation: neighbor
PRED expected values: BG
=> 42 concepts (42 used for prediction)
PRED predicted values (max 10 best out of 172): BG (0.90 #3664, 0.90 #4783, 0.90 #3663), F (0.50 #4, 0.40 #320, 0.40 #162), MNE (0.43 #641, 0.29 #1269, 0.28 #1748), HR (0.43 #653, 0.21 #1130, 0.15 #1290), CH (0.40 #200, 0.29 #516, 0.25 #42), AND (0.40 #279, 0.29 #595, 0.25 #121), KOS (0.29 #1269, 0.29 #745, 0.28 #1748), SRB (0.29 #1269, 0.29 #767, 0.28 #1748), GR (0.29 #1269, 0.28 #1748, 0.28 #1109), SYR (0.29 #1269, 0.28 #1748, 0.28 #1109)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3664 for best value:
>> intensional similarity = 5
>> extensional distance = 82
>> proper extension:  ARM;
>> query: (?x399, ?x177) <- ?x399[ has wasDependentOf ?x1656; is neighbor of ?x177[ has ethnicGroup ?x164; has language ?x511; has religion ?x56;];]
ranks of expected_values: 1
EVAL	GR	neighbor	BG	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	42.000	42.000	172.000	0.903	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: BG
=> 108 concepts (108 used for prediction)
PRED predicted values (max 10 best out of 219): BG (0.95 #9748, 0.94 #12194, 0.94 #9746), KOS (0.50 #2871, 0.40 #1085, 0.37 #1620), MNE (0.50 #2767, 0.40 #1469, 0.37 #1620), CH (0.50 #852, 0.29 #2152, 0.25 #4579), AZ (0.43 #2327, 0.33 #2109, 0.33 #1783), GR (0.40 #1527, 0.37 #1620, 0.36 #6007), IRQ (0.40 #1185, 0.33 #2109, 0.33 #1783), F (0.40 #1302, 0.33 #1625, 0.25 #2924), IL (0.40 #1180, 0.23 #1621, 0.21 #6376), SRB (0.37 #1620, 0.36 #6007, 0.35 #6006)
>> best conf = 0.95
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #9748 for best value:
>> intensional similarity = 16
>> extensional distance = 35
>> proper extension:  RL;
>> query: (?x399, ?x204) <- ?x399[ has ethnicGroup ?x595; has government ?x1174; has religion ?x187; is locatedIn of ?x2467[ a Mountain;]; is neighbor of ?x204[ a Country; has ethnicGroup ?x1472; has government ?x254; has language ?x1251; is locatedIn of ?x104;]; is neighbor of ?x701[ has ethnicGroup ?x354; has language ?x511; is locatedIn of ?x1489; is neighbor of ?x692;];]
ranks of expected_values: 1
EVAL	GR	neighbor	BG	CNN-1.+1._MA	1.000	1.000	1.000	1.000	108.000	108.000	219.000	0.945	http://www.semwebtech.org/mondial/10/meta#neighbor

#738-MH
PRED entity: MH

PRED relation: locatedIn!
PRED expected values: PacificOcean
=> 55 concepts (52 used for prediction)
PRED predicted values (max 10 best out of 1374): PacificOcean (0.91 #49820, 0.91 #28465, 0.91 #41279), AtlanticOcean (0.38 #38472, 0.36 #37047, 0.34 #31353), Jordan (0.33 #7277, 0.29 #4430, 0.25 #8700), Ponape (0.33 #854, 0.08 #9393, 0.08 #10816), CaribbeanSea (0.23 #38536, 0.23 #37111, 0.19 #34264), MediterraneanSea (0.22 #7199, 0.20 #42785, 0.17 #41362), IndianOcean (0.22 #5695, 0.19 #11389, 0.19 #34161), SouthChinaSea (0.22 #5833, 0.19 #11527, 0.18 #20065), SyrianDesert (0.22 #7598, 0.17 #9021, 0.14 #4751), RedSea (0.22 #7995, 0.14 #5148, 0.11 #22226)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #49820 for best value:
>> intensional similarity = 6
>> extensional distance = 79
>> proper extension:  CUR;
>> query: (?x400, ?x282) <- ?x400[ has encompassed ?x211; has religion ?x116; is locatedIn of ?x1281[ a Island; has locatedInWater ?x282[ has locatedIn ?x73;];];]
ranks of expected_values: 1
EVAL	MH	locatedIn!	PacificOcean	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	55.000	52.000	1374.000	0.913	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: PacificOcean
=> 72 concepts (72 used for prediction)
PRED predicted values (max 10 best out of 1356): PacificOcean (0.89 #57073, 0.88 #64207, 0.82 #41362), AtlanticOcean (0.51 #25664, 0.45 #25667, 0.43 #59971), Zambezi (0.51 #25664, 0.26 #25666, 0.26 #25668), Kalahari (0.51 #25664, 0.26 #25666, 0.26 #25668), Okavango (0.51 #25664, 0.26 #25666, 0.26 #25668), Oranje (0.51 #25664, 0.26 #25666, 0.26 #25668), Oranje (0.51 #25664, 0.26 #25666, 0.26 #25668), Impalila (0.51 #25664, 0.26 #25666, 0.26 #25668), EtoschaSaltPan (0.51 #25664, 0.26 #25666, 0.26 #25668), Namib (0.51 #25664, 0.26 #25666, 0.26 #25668)
>> best conf = 0.89
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #57073 for best value:
>> intensional similarity = 11
>> extensional distance = 62
>> proper extension:  GBM;
>> query: (?x400, ?x282) <- ?x400[ a Country; has government ?x2126; is locatedIn of ?x1281[ a Island; has belongsToIslands ?x2487[ a Islands;]; has locatedInWater ?x282[ a Sea; has locatedIn ?x73; is mergesWith of ?x60[ a Sea;];];];]
ranks of expected_values: 1
EVAL	MH	locatedIn!	PacificOcean	CNN-1.+1._MA	1.000	1.000	1.000	1.000	72.000	72.000	1356.000	0.891	http://www.semwebtech.org/mondial/10/meta#locatedIn

#737-GROX
PRED entity: GROX

PRED relation: encompassed
PRED expected values: America
=> 29 concepts (29 used for prediction)
PRED predicted values (max 10 best out of 5): America (0.61 #15, 0.57 #10, 0.45 #40), Africa (0.34 #49, 0.34 #55, 0.33 #44), Europe (0.34 #58, 0.33 #63, 0.32 #73), Australia-Oceania (0.32 #18, 0.26 #23, 0.24 #28), Asia (0.20 #122, 0.20 #117, 0.20 #97)
>> best conf = 0.61
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #15 for best value:
>> intensional similarity = 7
>> extensional distance = 16
>> proper extension:  NLSM; SMAR; FGU; VIRG;
>> query: (?x792, America) <- ?x792[ has dependentOf ?x793; has government ?x2552; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x249[ has mergesWith ?x248; is locatedInWater of ?x869;];]
ranks of expected_values: 1
EVAL	GROX	encompassed	America	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	29.000	29.000	5.000	0.611	http://www.semwebtech.org/mondial/10/meta#encompassed

PRED relation: encompassed
PRED expected values: America
=> 66 concepts (66 used for prediction)
PRED predicted values (max 10 best out of 5): America (0.73 #45, 0.73 #40, 0.70 #35), Europe (0.50 #22, 0.40 #162, 0.37 #289), Africa (0.40 #162, 0.39 #135, 0.37 #289), Australia-Oceania (0.32 #64, 0.21 #119, 0.21 #129), Asia (0.21 #315, 0.20 #321, 0.20 #209)
>> best conf = 0.73
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #45 for best value:
>> intensional similarity = 15
>> extensional distance = 9
>> proper extension:  TUCA;
>> query: (?x792, America) <- ?x792[ a Country; has dependentOf ?x793; has ethnicGroup ?x2022; has government ?x2552; has religion ?x95[ is religion of ?x196<AUS>; is religion of ?x482[ has encompassed ?x521;];]; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x249[ is mergesWith of ?x248;]; is locatedIn of ?x263[ is locatedInWater of ?x478;];]
>> Best rule #40 for best value:
>> intensional similarity = 14
>> extensional distance = 9
>> proper extension:  CUR;
>> query: (?x792, America) <- ?x792[ a Country; has dependentOf ?x793[ has religion ?x187; is locatedIn of ?x121<NorthSea>;]; has religion ?x95; is locatedIn of ?x182[ has locatedIn ?x1554<WL>; is mergesWith of ?x60;]; is locatedIn of ?x249[ is locatedInWater of ?x869;]; is locatedIn of ?x1075[ a Island;];]
ranks of expected_values: 1
EVAL	GROX	encompassed	America	CNN-1.+1._MA	1.000	1.000	1.000	1.000	66.000	66.000	5.000	0.727	http://www.semwebtech.org/mondial/10/meta#encompassed

#736-Senegal
PRED entity: Senegal

PRED relation: locatedIn
PRED expected values: RIM
=> 37 concepts (32 used for prediction)
PRED predicted values (max 10 best out of 167): DZ (0.60 #1305, 0.49 #467, 0.25 #1069), WAG (0.49 #467, 0.43 #664, 0.33 #429), RIM (0.49 #467, 0.33 #351, 0.29 #586), CI (0.49 #467, 0.20 #468, 0.19 #5621), GNB (0.49 #467, 0.20 #468, 0.19 #5621), WAL (0.49 #467, 0.20 #468, 0.19 #5621), RN (0.49 #467, 0.16 #1874, 0.16 #1873), LB (0.49 #467, 0.16 #1874, 0.16 #1873), WAN (0.44 #1663, 0.33 #25, 0.29 #727), USA (0.33 #2344, 0.29 #2179, 0.25 #1477)
>> best conf = 0.60
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1305 for best value:
>> intensional similarity = 5
>> extensional distance = 13
>> proper extension:  Tahat; MediterraneanSea; ErgIgidi; GrandErgOuest; GrandErgEst; HamadaduDraa; ErgIsaouane;
>> query: (?x838, DZ) <- ?x838[ has locatedIn ?x839[ has neighbor ?x426; has religion ?x116; is locatedIn of ?x1860<ErgChech>;];]

*> Best rule #467 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 4
*> proper extension:  Senegal; Ferlo;
*> query: (?x838, ?x426) <- ?x838[ has locatedIn ?x416<SN>; has locatedIn ?x839[ has neighbor ?x426; is locatedIn of ?x580[ is flowsInto of ?x579;];];]
*> conf = 0.49
ranks of expected_values: 3
EVAL	Senegal	locatedIn	RIM	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.333	37.000	32.000	167.000	0.600	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: RIM
=> 108 concepts (108 used for prediction)
PRED predicted values (max 10 best out of 229): RIM (0.92 #12110, 0.91 #12109, 0.91 #13057), CAM (0.71 #2012, 0.25 #595, 0.17 #15421), USA (0.57 #1255, 0.52 #7350, 0.52 #7188), WAN (0.50 #498, 0.33 #262, 0.29 #1444), CI (0.47 #1182, 0.46 #1183, 0.37 #1654), DZ (0.47 #1182, 0.46 #1183, 0.37 #1654), GNB (0.47 #1182, 0.46 #1183, 0.37 #1654), WAL (0.47 #1182, 0.46 #1183, 0.37 #1654), WAG (0.47 #1182, 0.46 #1183, 0.36 #709), BF (0.47 #1182, 0.46 #1183, 0.36 #1181)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #12110 for best value:
>> intensional similarity = 11
>> extensional distance = 153
>> proper extension:  Leine;
>> query: (?x838, ?x515) <- ?x838[ a River; has hasEstuary ?x1801[ a Estuary; has locatedIn ?x416[ has ethnicGroup ?x122; has religion ?x116;]; has locatedIn ?x515[ a Country;];]; has hasSource ?x650[ has locatedIn ?x651[ has neighbor ?x621;];];]
ranks of expected_values: 1
EVAL	Senegal	locatedIn	RIM	CNN-1.+1._MA	1.000	1.000	1.000	1.000	108.000	108.000	229.000	0.917	http://www.semwebtech.org/mondial/10/meta#locatedIn

#735-RO
PRED entity: RO

PRED relation: locatedIn!
PRED expected values: Moldoveanu Pruth
=> 40 concepts (34 used for prediction)
PRED predicted values (max 10 best out of 1364): AtlanticOcean (0.38 #12796, 0.36 #25553, 0.34 #8543), March (0.33 #3415, 0.33 #580, 0.17 #1997), March (0.33 #3416, 0.33 #581, 0.17 #1998), Drau (0.33 #3109, 0.33 #1691, 0.14 #24095), Mur (0.33 #2868, 0.33 #1450, 0.14 #35439), MediterraneanSea (0.33 #1498, 0.24 #5750, 0.20 #8584), Waag (0.33 #409, 0.17 #3244, 0.17 #1826), MalyZitnyOstrov (0.33 #1398, 0.17 #4233, 0.17 #2815), ZitnyOstrov (0.33 #684, 0.17 #3519, 0.17 #2101), GerlachovskyStit (0.33 #568, 0.17 #3403, 0.17 #1985)
>> best conf = 0.38
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #12796 for best value:
>> intensional similarity = 6
>> extensional distance = 51
>> proper extension:  CUR;
>> query: (?x176, AtlanticOcean) <- ?x176[ has encompassed ?x195; has language ?x684; has religion ?x352<RomanCatholic>; is locatedIn of ?x133[ is locatedInWater of ?x151;];]

*> Best rule #3541 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 4
*> proper extension:  A;
*> query: (?x176, Pruth) <- ?x176[ has ethnicGroup ?x58; has language ?x684; has neighbor ?x236<H>; is locatedIn of ?x133<Donau>;]
*> conf = 0.17
ranks of expected_values: 20
EVAL	RO	locatedIn!	Pruth	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.050	40.000	34.000	1364.000	0.377	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	RO	locatedIn!	Moldoveanu	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	40.000	34.000	1364.000	0.377	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Moldoveanu Pruth
=> 99 concepts (96 used for prediction)
PRED predicted values (max 10 best out of 1407): MediterraneanSea (0.50 #7174, 0.50 #5754, 0.42 #27048), Piva (0.50 #7190, 0.40 #11447, 0.33 #12869), Piva (0.50 #8415, 0.40 #12672, 0.33 #14094), AtlanticOcean (0.42 #95139, 0.40 #130642, 0.37 #117856), MalyZitnyOstrov (0.40 #11329, 0.33 #15593, 0.33 #5654), Drau (0.40 #10205, 0.33 #3113, 0.33 #1419), March (0.40 #10511, 0.33 #4836, 0.33 #1419), Mur (0.40 #9964, 0.33 #2872, 0.26 #12771), March (0.40 #10512, 0.33 #4837, 0.26 #12771), Neusiedlersee (0.40 #10226, 0.33 #3134, 0.26 #12771)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #7174 for best value:
>> intensional similarity = 17
>> extensional distance = 2
>> proper extension:  MNE;
>> query: (?x176, MediterraneanSea) <- ?x176[ has encompassed ?x195; has ethnicGroup ?x58; has language ?x684; has religion ?x56; is locatedIn of ?x133[ is flowsInto of ?x132; is locatedInWater of ?x151;]; is locatedIn of ?x1556[ a Estuary;]; is neighbor of ?x303[ has ethnicGroup ?x852; has religion ?x109; is locatedIn of ?x97; is locatedIn of ?x1393[ a River;];]; is neighbor of ?x904<SRB>;]
>> Best rule #5754 for best value:
>> intensional similarity = 12
>> extensional distance = 2
>> proper extension:  TR; GR;
>> query: (?x176, MediterraneanSea) <- ?x176[ has encompassed ?x195; has ethnicGroup ?x58; has religion ?x56; is locatedIn of ?x1231; is neighbor of ?x177<BG>; is neighbor of ?x303[ has ethnicGroup ?x852[ a EthnicGroup;]; is locatedIn of ?x1702[ has type ?x136;]; is neighbor of ?x73;];]

*> Best rule #2126 for first EXPECTED value:
*> intensional similarity = 15
*> extensional distance = 1
*> proper extension:  UA;
*> query: (?x176, Pruth) <- ?x176[ has encompassed ?x195<Europe>; has ethnicGroup ?x1193<Russian>; has religion ?x56; is neighbor of ?x236<H>; is neighbor of ?x303[ a Country; has ethnicGroup ?x852[ a EthnicGroup;]; has ethnicGroup ?x2273<Polish>; is locatedIn of ?x97; is neighbor of ?x73;];]
*> conf = 0.33
ranks of expected_values: 19
EVAL	RO	locatedIn!	Pruth	CNN-1.+1._MA	0.000	0.000	0.000	0.053	99.000	96.000	1407.000	0.500	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	RO	locatedIn!	Moldoveanu	CNN-1.+1._MA	0.000	0.000	0.000	0.000	99.000	96.000	1407.000	0.500	http://www.semwebtech.org/mondial/10/meta#locatedIn

#734-VN
PRED entity: VN

PRED relation: religion
PRED expected values: RomanCatholic
=> 43 concepts (43 used for prediction)
PRED predicted values (max 10 best out of 28): RomanCatholic (0.82 #672, 0.81 #598, 0.81 #376), Christian (0.55 #114, 0.51 #1111, 0.50 #151), Hindu (0.40 #82, 0.35 #193, 0.33 #267), ChristianOrthodox (0.32 #556, 0.29 #297, 0.28 #223), Anglican (0.20 #88, 0.18 #199, 0.10 #1519), Jewish (0.20 #76, 0.15 #705, 0.12 #372), Sikh (0.20 #103, 0.12 #214, 0.10 #1519), JehovasWitnesses (0.14 #387, 0.14 #313, 0.10 #424), Jains (0.10 #1519, 0.06 #200, 0.06 #274), Mormon (0.10 #1519, 0.06 #207, 0.05 #392)
>> best conf = 0.82
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #672 for best value:
>> intensional similarity = 10
>> extensional distance = 76
>> proper extension:  BG;
>> query: (?x617, RomanCatholic) <- ?x617[ has neighbor ?x232; has religion ?x95[ is religion of ?x163<SK>; is religion of ?x222<BY>; is religion of ?x315<USA>; is religion of ?x1826<GBZ>;];]
ranks of expected_values: 1
EVAL	VN	religion	RomanCatholic	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	43.000	43.000	28.000	0.821	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion
PRED expected values: RomanCatholic
=> 108 concepts (108 used for prediction)
PRED predicted values (max 10 best out of 35): RomanCatholic (0.89 #1082, 0.83 #1386, 0.80 #2178), Christian (0.67 #485, 0.62 #2132, 0.60 #782), Hindu (0.48 #520, 0.48 #408, 0.44 #713), Taoist (0.48 #520, 0.33 #928, 0.27 #1151), ChristianOrthodox (0.48 #408, 0.43 #3870, 0.41 #2473), Sikh (0.48 #408, 0.41 #2473, 0.38 #3413), Jains (0.48 #408, 0.41 #2473, 0.38 #3413), Jewish (0.40 #1872, 0.23 #930, 0.23 #1152), Anglican (0.25 #3338, 0.23 #1152, 0.20 #1127), JehovasWitnesses (0.23 #982, 0.23 #1152, 0.20 #1397)
>> best conf = 0.89
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1082 for best value:
>> intensional similarity = 21
>> extensional distance = 17
>> proper extension:  COM;
>> query: (?x617, RomanCatholic) <- ?x617[ a Country; has government ?x831; has religion ?x95[ is religion of ?x50<NLSM>; is religion of ?x351<GUY>; is religion of ?x407<AXA>; is religion of ?x904<SRB>;]; has religion ?x187<Muslim>; has religion ?x462[ a Religion; is religion of ?x196<AUS>; is religion of ?x207<I>;]; has wasDependentOf ?x78; is locatedIn of ?x384;]
ranks of expected_values: 1
EVAL	VN	religion	RomanCatholic	CNN-1.+1._MA	1.000	1.000	1.000	1.000	108.000	108.000	35.000	0.895	http://www.semwebtech.org/mondial/10/meta#religion

#733-Shikoku
PRED entity: Shikoku

PRED relation: type
PRED expected values: "volcanic"
=> 48 concepts (48 used for prediction)
PRED predicted values (max 10 best out of 9): "volcanic" (0.67 #2, 0.61 #166, 0.61 #181), "volcano" (0.25 #49, 0.23 #344, 0.20 #98), "atoll" (0.07 #172, 0.06 #189, 0.04 #368), "salt" (0.04 #269, 0.02 #576, 0.02 #625), "lime" (0.04 #169, 0.04 #186, 0.03 #300), "coral" (0.03 #287, 0.02 #336, 0.02 #385), "dam" (0.02 #489, 0.02 #521, 0.02 #537), "sand" (0.01 #266, 0.01 #573, 0.01 #622), "caldera" (0.01 #314, 0.01 #475)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2 for best value:
>> intensional similarity = 5
>> extensional distance = 4
>> proper extension:  Hokkaido; Okinawa; Honshu; Kyushu;
>> query: (?x2398, "volcanic") <- ?x2398[ a Island; has belongsToIslands ?x1212<Japan>; has locatedIn ?x117<J>;]
ranks of expected_values: 1
EVAL	Shikoku	type	"volcanic"	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	48.000	48.000	9.000	0.667	http://www.semwebtech.org/mondial/10/meta#type

PRED relation: type
PRED expected values: "volcanic"
=> 124 concepts (124 used for prediction)
PRED predicted values (max 10 best out of 12): "volcanic" (0.90 #233, 0.83 #82, 0.83 #67), "volcano" (0.40 #148, 0.40 #147, 0.25 #530), "atoll" (0.16 #224, 0.07 #423, 0.06 #669), "salt" (0.09 #553, 0.06 #996, 0.03 #733), "coral" (0.05 #139, 0.03 #588, 0.03 #258), "lime" (0.05 #501, 0.04 #518, 0.04 #666), "caldera" (0.04 #401, 0.03 #482, 0.03 #892), "sand" (0.03 #911, 0.02 #1044, 0.02 #1294), "dam" (0.02 #1389, 0.02 #1458, 0.02 #1257), "granite" (0.02 #363)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #233 for best value:
>> intensional similarity = 10
>> extensional distance = 29
>> proper extension:  Fakaofo;
>> query: (?x2398, ?x150) <- ?x2398[ a Island; has belongsToIslands ?x1212[ a Islands; is belongsToIslands of ?x451[ has locatedInWater ?x271; has type ?x150;]; is belongsToIslands of ?x630[ a Island; has locatedInWater ?x282<PacificOcean>;];];]
ranks of expected_values: 1
EVAL	Shikoku	type	"volcanic"	CNN-1.+1._MA	1.000	1.000	1.000	1.000	124.000	124.000	12.000	0.903	http://www.semwebtech.org/mondial/10/meta#type

#732-TristanDaCunha
PRED entity: TristanDaCunha

PRED relation: type
PRED expected values: "volcanic"
=> 46 concepts (46 used for prediction)
PRED predicted values (max 10 best out of 8): "volcanic" (0.82 #117, 0.80 #100, 0.78 #83), "volcano" (0.36 #81, 0.22 #214, 0.17 #22), "coral" (0.11 #73, 0.11 #57, 0.07 #157), "lime" (0.04 #348, 0.04 #364, 0.03 #444), "atoll" (0.03 #189, 0.03 #479, 0.03 #527), "salt" (0.03 #607, 0.02 #703, 0.02 #719), "sand" (0.03 #443), "dam" (0.02 #568, 0.02 #584, 0.02 #408)
>> best conf = 0.82
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #117 for best value:
>> intensional similarity = 6
>> extensional distance = 9
>> proper extension:  Dominica;
>> query: (?x1390, "volcanic") <- ?x1390[ a Island; has locatedIn ?x212; has locatedInWater ?x182<AtlanticOcean>; is locatedOnIsland of ?x283[ has type ?x706;];]
ranks of expected_values: 1
EVAL	TristanDaCunha	type	"volcanic"	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	46.000	46.000	8.000	0.818	http://www.semwebtech.org/mondial/10/meta#type

PRED relation: type
PRED expected values: "volcanic"
=> 112 concepts (112 used for prediction)
PRED predicted values (max 10 best out of 13): "volcanic" (0.83 #179, 0.75 #437, 0.75 #422), "volcano" (0.43 #684, 0.39 #291, 0.38 #210), "coral" (0.16 #961, 0.16 #944, 0.16 #472), "lime" (0.16 #961, 0.16 #944, 0.16 #472), "acid" (0.06 #306, 0.04 #403, 0.03 #552), "salt" (0.05 #330, 0.03 #968, 0.03 #1309), "sand" (0.05 #327, 0.02 #982, 0.02 #720), "atoll" (0.04 #935, 0.03 #952, 0.03 #870), "granite" (0.03 #452, 0.02 #648, 0.02 #714), "dam" (0.03 #1336, 0.03 #717, 0.03 #1205)
>> best conf = 0.83
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #179 for best value:
>> intensional similarity = 11
>> extensional distance = 10
>> proper extension:  Basse-Terre;
>> query: (?x1390, "volcanic") <- ?x1390[ has locatedInWater ?x182[ has locatedIn ?x124<WV>; has locatedIn ?x1502<SBAR>; has mergesWith ?x60; is flowsInto of ?x137; is locatedInWater of ?x1117<Trinidad>;]; is locatedOnIsland of ?x283[ a Volcano;];]
ranks of expected_values: 1
EVAL	TristanDaCunha	type	"volcanic"	CNN-1.+1._MA	1.000	1.000	1.000	1.000	112.000	112.000	13.000	0.833	http://www.semwebtech.org/mondial/10/meta#type

#731-GB
PRED entity: GB

PRED relation: wasDependentOf!
PRED expected values: PK LS UAE SP TT WG
=> 41 concepts (41 used for prediction)
PRED predicted values (max 10 best out of 140): P (0.20 #313, 0.14 #530, 0.11 #747), NL (0.20 #282, 0.14 #499, 0.11 #716), RA (0.20 #258, 0.14 #475, 0.11 #692), YV (0.20 #253, 0.14 #470, 0.11 #687), RCH (0.20 #238, 0.14 #455, 0.11 #672), C (0.20 #225, 0.14 #442, 0.11 #659), HCA (0.20 #319, 0.14 #536, 0.11 #753), EC (0.20 #307, 0.14 #524, 0.11 #741), BOL (0.20 #291, 0.14 #508, 0.11 #725), ES (0.20 #289, 0.14 #506, 0.11 #723)
>> best conf = 0.20
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #313 for best value:
>> intensional similarity = 5
>> extensional distance = 3
>> proper extension:  E; BR; RH;
>> query: (?x81, P) <- ?x81[ has ethnicGroup ?x1196; has religion ?x95; is locatedIn of ?x182<AtlanticOcean>; is wasDependentOf of ?x63;]

*> Best rule #1083 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 15
*> proper extension:  ET; R; J; CO; RI; CN; MAL; S; ETH; B; ...
*> query: (?x81, ?x50) <- ?x81[ has ethnicGroup ?x1196; is locatedIn of ?x182[ has locatedIn ?x50; is flowsInto of ?x137;]; is wasDependentOf of ?x63;]
*> conf = 0.03
ranks of expected_values: 123
EVAL	GB	wasDependentOf!	WG	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	41.000	41.000	140.000	0.200	http://www.semwebtech.org/mondial/10/meta#wasDependentOf
EVAL	GB	wasDependentOf!	TT	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.008	41.000	41.000	140.000	0.200	http://www.semwebtech.org/mondial/10/meta#wasDependentOf
EVAL	GB	wasDependentOf!	SP	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	41.000	41.000	140.000	0.200	http://www.semwebtech.org/mondial/10/meta#wasDependentOf
EVAL	GB	wasDependentOf!	UAE	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	41.000	41.000	140.000	0.200	http://www.semwebtech.org/mondial/10/meta#wasDependentOf
EVAL	GB	wasDependentOf!	LS	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	41.000	41.000	140.000	0.200	http://www.semwebtech.org/mondial/10/meta#wasDependentOf
EVAL	GB	wasDependentOf!	PK	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	41.000	41.000	140.000	0.200	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

PRED relation: wasDependentOf!
PRED expected values: PK LS UAE SP TT WG
=> 98 concepts (98 used for prediction)
PRED predicted values (max 10 best out of 188): MEX (0.55 #218, 0.52 #654, 0.52 #219), RI (0.55 #218, 0.52 #654, 0.52 #219), SME (0.55 #218, 0.52 #654, 0.52 #219), CI (0.55 #218, 0.52 #654, 0.52 #219), SN (0.55 #218, 0.52 #654, 0.52 #219), RT (0.55 #218, 0.52 #654, 0.52 #219), BEN (0.55 #218, 0.52 #654, 0.52 #219), TCH (0.55 #218, 0.52 #654, 0.52 #219), LAR (0.55 #218, 0.52 #654, 0.52 #219), NAM (0.55 #218, 0.52 #654, 0.52 #219)
>> best conf = 0.55
=> the first rule below is the first best rule for 37 predicted values
>> Best rule #218 for best value:
>> intensional similarity = 11
>> extensional distance = 1
>> proper extension:  E;
>> query: (?x81, ?x546) <- ?x81[ has ethnicGroup ?x1196; has religion ?x95; is dependentOf of ?x80; is locatedIn of ?x1822[ a Lake;]; is wasDependentOf of ?x820[ is locatedIn of ?x60; is neighbor of ?x546[ is locatedIn of ?x545;];]; is wasDependentOf of ?x1051[ has encompassed ?x213;];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 15, 27, 30, 127, 186
EVAL	GB	wasDependentOf!	WG	CNN-1.+1._MA	0.000	0.000	0.000	0.000	98.000	98.000	188.000	0.545	http://www.semwebtech.org/mondial/10/meta#wasDependentOf
EVAL	GB	wasDependentOf!	TT	CNN-1.+1._MA	0.000	0.000	0.000	0.008	98.000	98.000	188.000	0.545	http://www.semwebtech.org/mondial/10/meta#wasDependentOf
EVAL	GB	wasDependentOf!	SP	CNN-1.+1._MA	0.000	0.000	0.000	0.038	98.000	98.000	188.000	0.545	http://www.semwebtech.org/mondial/10/meta#wasDependentOf
EVAL	GB	wasDependentOf!	UAE	CNN-1.+1._MA	0.000	0.000	0.000	0.005	98.000	98.000	188.000	0.545	http://www.semwebtech.org/mondial/10/meta#wasDependentOf
EVAL	GB	wasDependentOf!	LS	CNN-1.+1._MA	0.000	0.000	0.000	0.067	98.000	98.000	188.000	0.545	http://www.semwebtech.org/mondial/10/meta#wasDependentOf
EVAL	GB	wasDependentOf!	PK	CNN-1.+1._MA	0.000	0.000	0.000	0.036	98.000	98.000	188.000	0.545	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

#730-Guadalcanal
PRED entity: Guadalcanal

PRED relation: locatedIn
PRED expected values: SLB
=> 38 concepts (38 used for prediction)
PRED predicted values (max 10 best out of 105): SLB (0.92 #4743, 0.90 #3557, 0.89 #4980), USA (0.41 #1493, 0.35 #1729, 0.33 #545), RC (0.33 #224, 0.05 #8541, 0.03 #3544), J (0.29 #1440, 0.25 #256, 0.20 #1676), RI (0.29 #2182, 0.27 #1945, 0.27 #3609), E (0.25 #1211, 0.11 #2157, 0.10 #3584), RP (0.20 #1056, 0.13 #5562, 0.12 #2002), NZ (0.17 #583, 0.14 #820, 0.12 #1531), PNG (0.14 #889, 0.10 #1126, 0.08 #1363), GB (0.10 #6411, 0.09 #6650, 0.08 #6886)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4743 for best value:
>> intensional similarity = 8
>> extensional distance = 34
>> proper extension:  Kreta;
>> query: (?x553, ?x390) <- ?x553[ a Island; is locatedOnIsland of ?x1083[ a Mountain; has locatedIn ?x390[ has encompassed ?x211; has ethnicGroup ?x197; has government ?x1947; has religion ?x95;];];]
ranks of expected_values: 1
EVAL	Guadalcanal	locatedIn	SLB	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	38.000	38.000	105.000	0.917	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: SLB
=> 57 concepts (57 used for prediction)
PRED predicted values (max 10 best out of 135): SLB (0.83 #3571, 0.67 #3808, 0.67 #2371), USA (0.44 #4366, 0.41 #5089, 0.40 #1731), RI (0.41 #4826, 0.25 #1238, 0.24 #6030), NZ (0.33 #348, 0.20 #1769, 0.12 #4404), FJI (0.33 #30, 0.10 #2401, 0.05 #5012), RC (0.33 #1173, 0.08 #4032, 0.06 #4998), STP (0.33 #668), J (0.29 #5036, 0.25 #4313, 0.25 #1442), E (0.25 #3598, 0.13 #5516, 0.12 #5760), PNG (0.25 #1365, 0.12 #2076, 0.09 #3029)
>> best conf = 0.83
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3571 for best value:
>> intensional similarity = 11
>> extensional distance = 10
>> proper extension:  Kreta;
>> query: (?x553, ?x390) <- ?x553[ a Island; is locatedOnIsland of ?x1083[ a Mountain; has locatedIn ?x390[ a Country; has encompassed ?x211; has ethnicGroup ?x197; has government ?x1947; has language ?x247; has religion ?x95; has wasDependentOf ?x81;];];]
ranks of expected_values: 1
EVAL	Guadalcanal	locatedIn	SLB	CNN-1.+1._MA	1.000	1.000	1.000	1.000	57.000	57.000	135.000	0.833	http://www.semwebtech.org/mondial/10/meta#locatedIn

#729-Mawenzi
PRED entity: Mawenzi

PRED relation: inMountains
PRED expected values: EastAfricanRift
=> 37 concepts (37 used for prediction)
PRED predicted values (max 10 best out of 37): EastAfricanRift (0.50 #28, 0.32 #376, 0.15 #115), Andes (0.13 #446, 0.12 #533, 0.10 #881), CordilleraVolcanica (0.08 #500, 0.07 #587, 0.06 #674), Drakensberge (0.07 #340, 0.01 #1384, 0.01 #1471), RockyMountains (0.07 #1138, 0.06 #1312, 0.06 #1399), Alps (0.06 #1570, 0.06 #1657, 0.05 #1744), SnowyMountains (0.05 #804, 0.05 #978, 0.05 #1065), CanaryIslands (0.05 #491, 0.04 #578, 0.03 #665), Himalaya (0.04 #1659, 0.04 #1572, 0.04 #1746), Pamir (0.04 #1235, 0.04 #1148, 0.03 #1322)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #28 for best value:
>> intensional similarity = 4
>> extensional distance = 2
>> proper extension:  Meru; Kilimanjaro;
>> query: (?x2194, EastAfricanRift) <- ?x2194[ a Mountain; a Volcano; has locatedIn ?x820<EAT>;]
ranks of expected_values: 1
EVAL	Mawenzi	inMountains	EastAfricanRift	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	37.000	37.000	37.000	0.500	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains
PRED expected values: EastAfricanRift
=> 101 concepts (101 used for prediction)
PRED predicted values (max 10 best out of 57): EastAfricanRift (0.71 #376, 0.67 #289, 0.67 #115), Himalaya (0.33 #180, 0.17 #2094, 0.14 #1659), Andes (0.21 #1316, 0.10 #3580, 0.08 #3754), Darfur (0.14 #586, 0.07 #934, 0.04 #1978), Atlas (0.14 #569, 0.07 #917, 0.03 #2135), Drakensberge (0.14 #514, 0.02 #3387, 0.02 #3561), Alps (0.10 #3225, 0.09 #4443, 0.09 #2876), Pamir (0.10 #2540, 0.06 #2889, 0.05 #3238), Kunlun (0.09 #1663, 0.07 #2098, 0.04 #2533), Lebanon (0.09 #668, 0.07 #929, 0.03 #2147)
>> best conf = 0.71
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #376 for best value:
>> intensional similarity = 18
>> extensional distance = 5
>> proper extension:  MountKenia;
>> query: (?x2194, EastAfricanRift) <- ?x2194[ a Mountain; has locatedIn ?x820[ a Country; has religion ?x187[ is religion of ?x120<D>; is religion of ?x179<SME>; is religion of ?x196<AUS>; is religion of ?x272<CDN>; is religion of ?x688<EAU>;]; is locatedIn of ?x1195<LakeVictoria>; is locatedIn of ?x2263[ a Lake;];];]
ranks of expected_values: 1
EVAL	Mawenzi	inMountains	EastAfricanRift	CNN-1.+1._MA	1.000	1.000	1.000	1.000	101.000	101.000	57.000	0.714	http://www.semwebtech.org/mondial/10/meta#inMountains

#728-WSA
PRED entity: WSA

PRED relation: government
PRED expected values: "legal status of territory and question of sovereignty unresolved"
=> 31 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 59): "republic" (0.45 #727, 0.45 #655, 0.45 #583), "military junta" (0.25 #361, 0.21 #866, 0.17 #197), "constitutional monarchy" (0.25 #361, 0.21 #866, 0.17 #146), "dependent territory of Spain" (0.20 #136, 0.16 #1733, 0.03 #713), "operates under a transitional government" (0.17 #168, 0.16 #1733, 0.14 #312), "constitutional democracy" (0.11 #365, 0.11 #437, 0.10 #509), "parliamentary democracy" (0.10 #1376, 0.09 #1883, 0.09 #2027), "republic; multiparty presidential regime" (0.09 #606, 0.06 #2095, 0.06 #390), "British Overseas Territories" (0.09 #1162, 0.08 #1090, 0.07 #1234), "federal republic" (0.07 #1374, 0.07 #869, 0.07 #724)
>> best conf = 0.45
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #727 for best value:
>> intensional similarity = 8
>> extensional distance = 42
>> proper extension:  G; SP; BI; EAK; ETH; GH; RWA; EAU; RT; GQ; ...
>> query: (?x646, "republic") <- ?x646[ has encompassed ?x213<Africa>; is locatedIn of ?x182; is neighbor of ?x581[ is locatedIn of ?x84;]; is neighbor of ?x851[ has religion ?x109; has wasDependentOf ?x78;];]
>> Best rule #655 for best value:
>> intensional similarity = 8
>> extensional distance = 31
>> proper extension:  ET; LS; NAM; WAN; TCH; SUD; MOC; RSA; ZRE; SN; ...
>> query: (?x646, "republic") <- ?x646[ has encompassed ?x213<Africa>; is neighbor of ?x581[ is locatedIn of ?x1298[ a Desert;];]; is neighbor of ?x851[ has religion ?x109; has wasDependentOf ?x78;];]
>> Best rule #583 for best value:
>> intensional similarity = 5
>> extensional distance = 20
>> proper extension:  HELX;
>> query: (?x646, "republic") <- ?x646[ a Country; has encompassed ?x213<Africa>; is locatedIn of ?x182<AtlanticOcean>;]

No rule for expected values
ranks of expected_values: 
EVAL	WSA	government	"legal status of territory and question of sovereignty unresolved"	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	31.000	31.000	59.000	0.455	http://www.semwebtech.org/mondial/10/meta#government

PRED relation: government
PRED expected values: "legal status of territory and question of sovereignty unresolved"
=> 65 concepts (65 used for prediction)
PRED predicted values (max 10 best out of 66): "republic" (0.54 #1162, 0.48 #874, 0.47 #1669), "military junta" (0.33 #53, 0.25 #198, 0.25 #126), "constitutional monarchy" (0.25 #147, 0.21 #868, 0.18 #2027), "federal republic" (0.25 #76, 0.17 #293, 0.11 #3838), "operates under a transitional government" (0.21 #868, 0.20 #242, 0.15 #3911), "parliamentary democracy" (0.17 #1234, 0.17 #656, 0.12 #3191), "republic; multiparty presidential regime established 1960" (0.17 #428, 0.11 #3838, 0.08 #644), "dependent territory of Spain" (0.15 #3911, 0.04 #1148, 0.03 #1365), "republic; multiparty presidential regime" (0.11 #752, 0.11 #3838, 0.11 #824), "parliamentary republic" (0.11 #670, 0.07 #1248, 0.05 #1518)
>> best conf = 0.54
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1162 for best value:
>> intensional similarity = 15
>> extensional distance = 26
>> proper extension:  SD;
>> query: (?x646, "republic") <- ?x646[ has encompassed ?x213<Africa>; is neighbor of ?x581[ has ethnicGroup ?x197; has government ?x435; has religion ?x116<Christian>; has wasDependentOf ?x78; is locatedIn of ?x84[ has type ?x150;]; is locatedIn of ?x275[ is locatedInWater of ?x68; is mergesWith of ?x182;]; is neighbor of ?x839[ has religion ?x187;];];]

No rule for expected values
ranks of expected_values: 
EVAL	WSA	government	"legal status of territory and question of sovereignty unresolved"	CNN-1.+1._MA	0.000	0.000	0.000	0.000	65.000	65.000	66.000	0.536	http://www.semwebtech.org/mondial/10/meta#government

#727-S
PRED entity: S

PRED relation: ethnicGroup
PRED expected values: Swede
=> 34 concepts (34 used for prediction)
PRED predicted values (max 10 best out of 242): Russian (0.44 #1101, 0.22 #5398, 0.22 #2900), European (0.38 #1293, 0.30 #3863, 0.26 #3092), Norwegian (0.33 #53, 0.22 #5398, 0.17 #6941), Sami (0.33 #235, 0.22 #5398, 0.17 #6941), African (0.30 #263, 0.22 #1805, 0.20 #5918), Mulatto (0.30 #316, 0.15 #573, 0.13 #830), Ukrainian (0.22 #1029, 0.16 #2828, 0.13 #2571), Swede (0.22 #5398, 0.17 #6941, 0.04 #1256), Amerindian (0.17 #1287, 0.15 #3086, 0.15 #3857), Mestizo (0.17 #1321, 0.15 #3120, 0.12 #2863)
>> best conf = 0.44
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1101 for best value:
>> intensional similarity = 5
>> extensional distance = 25
>> proper extension:  ARM; LB; BZ;
>> query: (?x402, Russian) <- ?x402[ has language ?x2235[ a Language; is language of ?x565<SF>;]; is neighbor of ?x170;]

*> Best rule #5398 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 100
*> proper extension:  UAE; IRL; SME; FL; OM; P; GBZ; KWT;
*> query: (?x402, ?x1193) <- ?x402[ is locatedIn of ?x191; is neighbor of ?x565[ a Country; has ethnicGroup ?x1193; has language ?x247; is locatedIn of ?x631;];]
*> conf = 0.22
ranks of expected_values: 8
EVAL	S	ethnicGroup	Swede	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.125	34.000	34.000	242.000	0.444	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: Swede
=> 126 concepts (126 used for prediction)
PRED predicted values (max 10 best out of 249): European (0.64 #11576, 0.60 #5150, 0.44 #9520), Polish (0.50 #2004, 0.42 #5861, 0.25 #1233), Russian (0.50 #5729, 0.38 #6501, 0.33 #6244), Ukrainian (0.42 #5657, 0.33 #1800, 0.29 #12854), Belorussian (0.42 #5742, 0.17 #28546, 0.17 #12939), Norwegian (0.33 #310, 0.33 #14653, 0.32 #15168), Sami (0.33 #492, 0.33 #14653, 0.32 #15168), Swede (0.33 #228, 0.33 #14653, 0.32 #15168), German (0.33 #1809, 0.28 #13891, 0.25 #13377), African (0.31 #16976, 0.30 #12602, 0.29 #14916)
>> best conf = 0.64
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #11576 for best value:
>> intensional similarity = 10
>> extensional distance = 20
>> proper extension:  SLB;
>> query: (?x402, European) <- ?x402[ a Country; has ethnicGroup ?x1473; has government ?x92; has language ?x566; has religion ?x352<RomanCatholic>; is locatedIn of ?x815[ a Mountain;]; is locatedIn of ?x1992[ has type ?x1424;];]

*> Best rule #228 for first EXPECTED value:
*> intensional similarity = 16
*> extensional distance = 1
*> proper extension:  SF;
*> query: (?x402, Swede) <- ?x402[ a Country; has language ?x2235<Swedish>; has religion ?x95; is locatedIn of ?x401[ a Source;]; is locatedIn of ?x855[ a Lake;]; is locatedIn of ?x1119[ a Estuary;]; is locatedIn of ?x2331[ has flowsInto ?x146;]; is locatedIn of ?x2354[ a Mountain;]; is neighbor of ?x565[ is locatedIn of ?x631;];]
*> conf = 0.33
ranks of expected_values: 8
EVAL	S	ethnicGroup	Swede	CNN-1.+1._MA	0.000	0.000	1.000	0.125	126.000	126.000	249.000	0.636	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#726-RCB
PRED entity: RCB

PRED relation: neighbor
PRED expected values: CAM
=> 31 concepts (30 used for prediction)
PRED predicted values (max 10 best out of 220): CAM (0.94 #317, 0.89 #3182, 0.89 #3503), RCB (0.54 #476, 0.51 #477, 0.51 #475), NAM (0.54 #476, 0.51 #477, 0.51 #475), GQ (0.54 #476, 0.51 #477, 0.51 #475), WAN (0.54 #476, 0.51 #477, 0.51 #475), SN (0.54 #476, 0.51 #477, 0.51 #475), RG (0.54 #476, 0.51 #477, 0.51 #475), BR (0.54 #476, 0.51 #477, 0.51 #475), SME (0.54 #476, 0.51 #477, 0.51 #475), CI (0.54 #476, 0.51 #477, 0.51 #475)
>> best conf = 0.94
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #317 for best value:
>> intensional similarity = 7
>> extensional distance = 6
>> proper extension:  BI; Z; RWA; EAU; RCA; EAT;
>> query: (?x528, ?x536) <- ?x528[ has neighbor ?x348<ZRE>; has neighbor ?x736[ has ethnicGroup ?x992;]; has religion ?x116; is locatedIn of ?x182; is neighbor of ?x536;]
ranks of expected_values: 1
EVAL	RCB	neighbor	CAM	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	31.000	30.000	220.000	0.935	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: CAM
=> 86 concepts (86 used for prediction)
PRED predicted values (max 10 best out of 227): CAM (0.92 #957, 0.89 #8360, 0.89 #10969), RCB (0.57 #956, 0.47 #955, 0.33 #568), NAM (0.57 #956, 0.47 #955, 0.33 #336), GQ (0.57 #956, 0.47 #955, 0.33 #150), WAN (0.57 #956, 0.47 #955, 0.33 #18), BR (0.57 #956, 0.47 #955, 0.25 #1846), RSA (0.57 #956, 0.47 #955, 0.25 #684), SME (0.57 #956, 0.47 #955, 0.25 #1301), ROU (0.57 #956, 0.47 #955, 0.25 #1336), YV (0.57 #956, 0.47 #955, 0.25 #1332)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #957 for best value:
>> intensional similarity = 9
>> extensional distance = 5
>> proper extension:  SSD;
>> query: (?x528, ?x536) <- ?x528[ a Country; has government ?x435<"republic">; has neighbor ?x348<ZRE>; is locatedIn of ?x929[ has hasSource ?x2438; is flowsInto of ?x113;]; is neighbor of ?x536;]
ranks of expected_values: 1
EVAL	RCB	neighbor	CAM	CNN-1.+1._MA	1.000	1.000	1.000	1.000	86.000	86.000	227.000	0.923	http://www.semwebtech.org/mondial/10/meta#neighbor

#725-MountApo
PRED entity: MountApo

PRED relation: locatedOnIsland
PRED expected values: Mindanao
=> 63 concepts (52 used for prediction)
PRED predicted values (max 10 best out of 58): Luzon (0.40 #123, 0.33 #175, 0.25 #18), Palawan (0.25 #40, 0.20 #145, 0.20 #92), Sulawesi (0.20 #76, 0.07 #233, 0.07 #286), Negros (0.17 #184, 0.07 #289, 0.06 #342), Java (0.14 #212, 0.13 #265, 0.08 #370), Sumatra (0.13 #277, 0.07 #224, 0.07 #489), Madagaskar (0.11 #458, 0.09 #565, 0.09 #619), Lombok (0.07 #252, 0.07 #305, 0.04 #410), Sumbawa (0.07 #240, 0.07 #293, 0.04 #398), Bali (0.07 #238, 0.07 #291, 0.04 #396)
>> best conf = 0.40
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #123 for best value:
>> intensional similarity = 5
>> extensional distance = 3
>> proper extension:  Pinatubo;
>> query: (?x752, Luzon) <- ?x752[ a Mountain; a Volcano; has locatedIn ?x460<RP>; has type ?x150;]

*> Best rule #105 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 3
*> proper extension:  Rantekombola;
*> query: (?x752, ?x384) <- ?x752[ a Mountain; a Volcano; has locatedIn ?x460[ is locatedIn of ?x384; is locatedIn of ?x625<SulawesiSea>;]; has type ?x150<"volcanic">;]
*> conf = 0.04
ranks of expected_values: 23
EVAL	MountApo	locatedOnIsland	Mindanao	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.043	63.000	52.000	58.000	0.400	http://www.semwebtech.org/mondial/10/meta#locatedOnIsland

PRED relation: locatedOnIsland
PRED expected values: Mindanao
=> 120 concepts (112 used for prediction)
PRED predicted values (max 10 best out of 65): Luzon (0.40 #124, 0.33 #176, 0.25 #18), Palawan (0.25 #40, 0.20 #146, 0.20 #92), Sulawesi (0.20 #76, 0.09 #234, 0.07 #288), Negros (0.17 #185, 0.14 #529, 0.09 #105), Madagaskar (0.15 #514, 0.12 #946, 0.10 #1053), Java (0.14 #267, 0.13 #320, 0.11 #374), Bohol (0.14 #529, 0.09 #105, 0.09 #263), Mindanao (0.14 #529, 0.09 #105, 0.09 #263), Samar (0.14 #529, 0.09 #105, 0.09 #263), Panay (0.14 #529, 0.09 #105, 0.09 #263)
>> best conf = 0.40
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #124 for best value:
>> intensional similarity = 5
>> extensional distance = 3
>> proper extension:  Pinatubo;
>> query: (?x752, Luzon) <- ?x752[ a Mountain; a Volcano; has locatedIn ?x460<RP>; has type ?x150;]

*> Best rule #529 for first EXPECTED value:
*> intensional similarity = 10
*> extensional distance = 18
*> proper extension:  PuyDeDome; PitondesNeiges; PuydeSancy; Andringitra; Tsiafajavona; Tsaratanana;
*> query: (?x752, ?x880) <- ?x752[ a Mountain; a Volcano; has locatedIn ?x460[ has religion ?x95; is locatedIn of ?x625[ has mergesWith ?x241;]; is locatedIn of ?x880[ a Island;];]; has type ?x150<"volcanic">;]
*> conf = 0.14
ranks of expected_values: 8
EVAL	MountApo	locatedOnIsland	Mindanao	CNN-1.+1._MA	0.000	0.000	1.000	0.125	120.000	112.000	65.000	0.400	http://www.semwebtech.org/mondial/10/meta#locatedOnIsland

#724-Cebu
PRED entity: Cebu

PRED relation: locatedIn
PRED expected values: RP
=> 23 concepts (23 used for prediction)
PRED predicted values (max 10 best out of 77): RP (0.82 #1419, 0.80 #345, 0.75 #109), RI (0.23 #761, 0.15 #2367, 0.15 #2182), USA (0.23 #1018, 0.22 #1254, 0.21 #1491), J (0.16 #965, 0.08 #1201, 0.08 #1438), MAL (0.12 #946, 0.09 #795, 0.09 #559), BRU (0.09 #597, 0.05 #4979, 0.05 #4978), P (0.09 #2090, 0.06 #2565, 0.04 #3512), GB (0.08 #2614, 0.08 #2851, 0.08 #3088), NZ (0.06 #1056, 0.03 #1292, 0.03 #1529), WS (0.06 #1053, 0.03 #1289, 0.03 #1526)
>> best conf = 0.82
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1419 for best value:
>> intensional similarity = 9
>> extensional distance = 57
>> proper extension:  Saipan; Tongatapu; Ambon; Hokkaido; Futuna; Tinian; VitiLevu; Niihau; Tutuila; TeWaka-a-Maui-SouthIsland-; ...
>> query: (?x369, ?x460) <- ?x369[ has belongsToIslands ?x370[ a Islands; is belongsToIslands of ?x880[ a Island; has locatedIn ?x460;]; is belongsToIslands of ?x1158[ a Island; has locatedInWater ?x282<PacificOcean>;];];]
ranks of expected_values: 1
EVAL	Cebu	locatedIn	RP	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	23.000	23.000	77.000	0.818	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: RP
=> 57 concepts (57 used for prediction)
PRED predicted values (max 10 best out of 103): RP (0.92 #2622, 0.87 #5730, 0.83 #3581), RI (0.47 #1478, 0.37 #3341, 0.37 #3156), MAL (0.34 #1188, 0.25 #5491, 0.24 #2141), USA (0.30 #2217, 0.29 #2457, 0.26 #3416), BRU (0.24 #2141, 0.22 #6687, 0.13 #6205), J (0.21 #2404, 0.19 #3363, 0.17 #2164), GR (0.11 #7018, 0.05 #9661, 0.05 #9902), GB (0.10 #8854, 0.09 #9095, 0.09 #9580), CDN (0.09 #6030, 0.07 #7470, 0.07 #7947), P (0.09 #8081, 0.08 #7366, 0.08 #7604)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2622 for best value:
>> intensional similarity = 18
>> extensional distance = 22
>> proper extension:  Shikoku;
>> query: (?x369, ?x460) <- ?x369[ a Island; has belongsToIslands ?x370[ a Islands; is belongsToIslands of ?x1034[ a Island; has locatedInWater ?x625[ is locatedInWater of ?x1158[ a Island; has locatedIn ?x460[ is locatedIn of ?x1575[ a Island; has locatedInWater ?x384; is locatedOnIsland of ?x991;];]; has locatedInWater ?x282<PacificOcean>;];]; is locatedOnIsland of ?x1056;]; is belongsToIslands of ?x1158; is belongsToIslands of ?x1575;];]
ranks of expected_values: 1
EVAL	Cebu	locatedIn	RP	CNN-1.+1._MA	1.000	1.000	1.000	1.000	57.000	57.000	103.000	0.917	http://www.semwebtech.org/mondial/10/meta#locatedIn

#723-CO
PRED entity: CO

PRED relation: locatedIn!
PRED expected values: SanAndres
=> 38 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 1209): AtlanticOcean (0.60 #7103, 0.39 #14168, 0.38 #48067), IndianOcean (0.35 #24015, 0.14 #24012, 0.11 #48028), RioSanJuan (0.33 #100, 0.22 #1512, 0.17 #4336), RioSanJuan (0.33 #243, 0.22 #1655, 0.17 #4479), LakeNicaragua (0.33 #99, 0.22 #1511, 0.17 #4335), CerroChirripo (0.33 #836, 0.11 #2248, 0.08 #5072), LakeIrazu (0.33 #334, 0.11 #1746, 0.08 #4570), Irazu (0.33 #204, 0.11 #1616, 0.08 #4440), NorthSea (0.25 #5671, 0.17 #12735, 0.17 #15560), MediterraneanSea (0.24 #22681, 0.17 #15620, 0.16 #31158)
>> best conf = 0.60
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #7103 for best value:
>> intensional similarity = 5
>> extensional distance = 13
>> proper extension:  KN;
>> query: (?x215, AtlanticOcean) <- ?x215[ has ethnicGroup ?x162<African>; has language ?x796; is locatedIn of ?x214[ is flowsInto of ?x949;];]

*> Best rule #5649 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 10
*> proper extension:  RI;
*> query: (?x215, ?x123) <- ?x215[ is locatedIn of ?x282<PacificOcean>; is locatedIn of ?x317[ is locatedInWater of ?x123;]; is locatedIn of ?x663[ a Volcano;]; is neighbor of ?x296;]
*> conf = 0.04
ranks of expected_values: 798
EVAL	CO	locatedIn!	SanAndres	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.001	38.000	36.000	1209.000	0.600	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: SanAndres
=> 95 concepts (95 used for prediction)
PRED predicted values (max 10 best out of 1430): AtlanticOcean (0.82 #100452, 0.82 #80636, 0.82 #97623), Amazonas (0.70 #91914, 0.33 #1413, 0.20 #15533), RioNegro (0.70 #91914, 0.33 #1413, 0.20 #15402), Orinoco (0.70 #91914, 0.33 #2989, 0.16 #33930), Amazonas (0.69 #28270, 0.33 #1413, 0.25 #8350), Orinoco (0.69 #28270, 0.33 #3332, 0.16 #33930), Hispaniola (0.40 #11151, 0.25 #9739, 0.20 #15391), MediterraneanSea (0.38 #67947, 0.29 #104744, 0.28 #73605), BeringSea (0.38 #93329, 0.23 #69278, 0.21 #111741), SeaofJapan (0.38 #93329, 0.23 #69278, 0.21 #111741)
>> best conf = 0.82
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #100452 for best value:
>> intensional similarity = 13
>> extensional distance = 72
>> proper extension:  BS; VIRG;
>> query: (?x215, AtlanticOcean) <- ?x215[ a Country; has encompassed ?x521; is locatedIn of ?x317[ has locatedIn ?x321[ has wasDependentOf ?x81;]; has locatedIn ?x1502<SBAR>; has locatedIn ?x1554<WL>; is flowsInto of ?x311[ is flowsInto of ?x310;]; is locatedInWater of ?x1219<Grenada>;];]

*> Best rule #67865 for first EXPECTED value:
*> intensional similarity = 11
*> extensional distance = 32
*> proper extension:  RL; SK; BG; AL; CY; RSA; GR; MA; IND;
*> query: (?x215, ?x205) <- ?x215[ a Country; has ethnicGroup ?x79[ is ethnicGroup of ?x351[ has religion ?x95;];]; has government ?x1377; is locatedIn of ?x282[ is locatedInWater of ?x205;]; is locatedIn of ?x663[ a Mountain;]; is locatedIn of ?x2206[ has inMountains ?x2341;];]
*> conf = 0.05
ranks of expected_values: 979
EVAL	CO	locatedIn!	SanAndres	CNN-1.+1._MA	0.000	0.000	0.000	0.001	95.000	95.000	1430.000	0.824	http://www.semwebtech.org/mondial/10/meta#locatedIn

#722-Mayan
PRED entity: Mayan

PRED relation: religion!
PRED expected values: GCA
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 217): GB (0.18 #9), IND (0.13 #179), SLB (0.13 #84), BS (0.13 #61), I (0.13 #46), AUS (0.13 #43), BERM (0.11 #186), PNG (0.11 #170), TT (0.11 #140), VN (0.11 #131)
>> best conf = 0.18
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #9 for best value:
>> intensional similarity = 1
>> extensional distance = 43
>> proper extension:  ChristianOrthodox; Protestant; Jewish; Christian; Muslim; Methodist; Adventist; RomanCatholic; Presbyterian; Hindu; ...
>> query: (?x259, GB) <- ?x259[ a Religion;]

*> Best rule #37 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 43
*> proper extension:  ChristianOrthodox; Protestant; Jewish; Christian; Muslim; Methodist; Adventist; RomanCatholic; Presbyterian; Hindu; ...
*> query: (?x259, GCA) <- ?x259[ a Religion;]
*> conf = 0.04
ranks of expected_values: 141
EVAL	Mayan	religion!	GCA	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.007	2.000	2.000	217.000	0.178	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion!
PRED expected values: GCA
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 217): GB (0.18 #9), IND (0.13 #179), SLB (0.13 #84), BS (0.13 #61), I (0.13 #46), AUS (0.13 #43), BERM (0.11 #186), PNG (0.11 #170), TT (0.11 #140), VN (0.11 #131)
>> best conf = 0.18
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #9 for best value:
>> intensional similarity = 1
>> extensional distance = 43
>> proper extension:  ChristianOrthodox; Protestant; Jewish; Christian; Muslim; Methodist; Adventist; RomanCatholic; Presbyterian; Hindu; ...
>> query: (?x259, GB) <- ?x259[ a Religion;]

*> Best rule #37 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 43
*> proper extension:  ChristianOrthodox; Protestant; Jewish; Christian; Muslim; Methodist; Adventist; RomanCatholic; Presbyterian; Hindu; ...
*> query: (?x259, GCA) <- ?x259[ a Religion;]
*> conf = 0.04
ranks of expected_values: 141
EVAL	Mayan	religion!	GCA	CNN-1.+1._MA	0.000	0.000	0.000	0.007	2.000	2.000	217.000	0.178	http://www.semwebtech.org/mondial/10/meta#religion

#721-GB
PRED entity: GB

PRED relation: dependentOf!
PRED expected values: MNTS
=> 35 concepts (35 used for prediction)
PRED predicted values (max 10 best out of 28): CEU (0.14 #84, 0.08 #112, 0.07 #168), ARU (0.14 #78, 0.08 #106, 0.07 #162), CUR (0.14 #61, 0.08 #89, 0.07 #145), NLSM (0.14 #57, 0.08 #85, 0.07 #141), NCA (0.14 #77, 0.08 #105, 0.07 #161), FGU (0.14 #73, 0.08 #101, 0.07 #157), MAYO (0.14 #71, 0.08 #99, 0.07 #155), MART (0.14 #69, 0.08 #97, 0.07 #153), GUAD (0.14 #67, 0.08 #95, 0.07 #151), SMAR (0.14 #66, 0.08 #94, 0.07 #150)
>> best conf = 0.14
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #84 for best value:
>> intensional similarity = 5
>> extensional distance = 5
>> proper extension:  F; DK;
>> query: (?x81, CEU) <- ?x81[ has encompassed ?x195; is dependentOf of ?x80; is wasDependentOf of ?x797[ has ethnicGroup ?x1728; has religion ?x116;];]

No rule for expected values
ranks of expected_values: 
EVAL	GB	dependentOf!	MNTS	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	35.000	35.000	28.000	0.143	http://www.semwebtech.org/mondial/10/meta#dependentOf

PRED relation: dependentOf!
PRED expected values: MNTS
=> 100 concepts (100 used for prediction)
PRED predicted values (max 10 best out of 221): PR (0.33 #49, 0.20 #251, 0.17 #404), VIRG (0.33 #54, 0.20 #256, 0.17 #409), NMIS (0.33 #38, 0.20 #240, 0.17 #393), AMSA (0.33 #55, 0.20 #257, 0.17 #410), NLSM (0.25 #61, 0.20 #205, 0.20 #176), CUR (0.25 #65, 0.20 #209, 0.20 #180), ARU (0.25 #82, 0.20 #226, 0.20 #197), HONX (0.25 #74, 0.20 #189, 0.06 #945), MACX (0.25 #72, 0.20 #187, 0.06 #943), TOK (0.25 #143, 0.17 #351, 0.14 #476)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #49 for best value:
>> intensional similarity = 15
>> extensional distance = 1
>> proper extension:  USA;
>> query: (?x81, PR) <- ?x81[ a Country; has encompassed ?x195; has ethnicGroup ?x1196; has religion ?x109<Jewish>; is dependentOf of ?x80[ a Country;]; is dependentOf of ?x561[ has religion ?x280;]; is locatedIn of ?x373[ has locatedIn ?x455<IS>; is locatedInWater of ?x807;]; is locatedIn of ?x1211[ is flowsInto of ?x1383;];]

*> Best rule #60 for first EXPECTED value:
*> intensional similarity = 16
*> extensional distance = 1
*> proper extension:  USA;
*> query: (?x81, ?x170) <- ?x81[ a Country; has encompassed ?x195; has ethnicGroup ?x1196; has religion ?x109<Jewish>; is dependentOf of ?x80[ a Country;]; is dependentOf of ?x561[ has religion ?x280;]; is locatedIn of ?x373[ has locatedIn ?x170; has locatedIn ?x455<IS>; is locatedInWater of ?x807;]; is locatedIn of ?x1211[ is flowsInto of ?x1383;];]
*> conf = 0.03
ranks of expected_values: 74
EVAL	GB	dependentOf!	MNTS	CNN-1.+1._MA	0.000	0.000	0.000	0.014	100.000	100.000	221.000	0.333	http://www.semwebtech.org/mondial/10/meta#dependentOf

#720-Alps
PRED entity: Alps

PRED relation: inMountains!
PRED expected values: Reuss Aare Rhone Raab
=> 14 concepts (14 used for prediction)
PRED predicted values (max 10 best out of 372): Arno (0.09 #171, 0.08 #916, 0.08 #687), Tiber (0.09 #67, 0.08 #916, 0.08 #687), MonteFalterona (0.09 #47, 0.08 #916, 0.08 #687), GranSasso (0.09 #7, 0.08 #916, 0.08 #687), Vignemale (0.09 #223, 0.08 #916, 0.08 #687), PuydeSancy (0.09 #89, 0.08 #916, 0.08 #687), MonteCinto (0.09 #88, 0.08 #916, 0.08 #687), PuyDeDome (0.09 #27, 0.08 #916, 0.08 #687), Garonne (0.09 #172, 0.03 #1088, 0.02 #1317), PicodeAneto (0.09 #110, 0.03 #1026, 0.02 #1255)
>> best conf = 0.09
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #171 for best value:
>> intensional similarity = 9
>> extensional distance = 9
>> proper extension:  RockyMountains; EliasRange; NewZealandAlps; BritishEmpireRange; PennyHighlands; Pyrenees; Corse; Cevennes;
>> query: (?x261, Arno) <- ?x261[ a Mountains; is inMountains of ?x1202[ has locatedIn ?x234[ a Country; has language ?x51<French>;];]; is inMountains of ?x2191[ a Mountain; has locatedIn ?x207;];]

*> Best rule #916 for first EXPECTED value:
*> intensional similarity = 11
*> extensional distance = 20
*> proper extension:  BlackForest; EastAfricanRift; Drakensberge;
*> query: (?x261, ?x233) <- ?x261[ a Mountains; is inMountains of ?x1202[ has locatedIn ?x234[ a Country; is locatedIn of ?x233;];]; is inMountains of ?x2191[ a Mountain; has locatedIn ?x207;]; is inMountains of ?x2316[ a Source; has locatedIn ?x120;];]
*> conf = 0.08
ranks of expected_values: 33, 34, 50, 91
EVAL	Alps	inMountains!	Raab	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.011	14.000	14.000	372.000	0.091	http://www.semwebtech.org/mondial/10/meta#inMountains
EVAL	Alps	inMountains!	Rhone	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.030	14.000	14.000	372.000	0.091	http://www.semwebtech.org/mondial/10/meta#inMountains
EVAL	Alps	inMountains!	Aare	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.030	14.000	14.000	372.000	0.091	http://www.semwebtech.org/mondial/10/meta#inMountains
EVAL	Alps	inMountains!	Reuss	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.021	14.000	14.000	372.000	0.091	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains!
PRED expected values: Reuss Aare Rhone Raab
=> 46 concepts (46 used for prediction)
PRED predicted values (max 10 best out of 372): Mosel (0.33 #180, 0.25 #639, 0.25 #2531), Saone (0.33 #118, 0.25 #577, 0.25 #2531), Saar (0.33 #111, 0.25 #570, 0.25 #2531), Vignemale (0.33 #453, 0.25 #2531, 0.20 #912), Garonne (0.33 #402, 0.20 #861, 0.12 #3164), PicodeAneto (0.33 #340, 0.20 #799, 0.12 #3102), Doubs (0.25 #552, 0.25 #2531, 0.19 #4605), Bodensee (0.25 #2531, 0.21 #4603, 0.19 #4605), Inn (0.25 #2531, 0.21 #4603, 0.19 #4605), Rhein (0.25 #2531, 0.21 #4603, 0.19 #4605)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #180 for best value:
>> intensional similarity = 17
>> extensional distance = 1
>> proper extension:  Vogesen;
>> query: (?x261, Mosel) <- ?x261[ a Mountains; is inMountains of ?x260[ has locatedIn ?x424[ has language ?x511; has neighbor ?x120; is locatedIn of ?x256<Rhein>;];]; is inMountains of ?x911[ a Source; is hasSource of ?x699[ is flowsInto of ?x983;];]; is inMountains of ?x2385[ has locatedIn ?x207[ has government ?x435; has neighbor ?x78; has religion ?x352<RomanCatholic>;];];]

*> Best rule #2531 for first EXPECTED value:
*> intensional similarity = 18
*> extensional distance = 5
*> proper extension:  Andes; Pamir; Balkan; Beskides; SudetyMountains;
*> query: (?x261, ?x86) <- ?x261[ a Mountains; is inMountains of ?x171[ a Mountain; has locatedIn ?x120[ has neighbor ?x194;];]; is inMountains of ?x323[ a Mountain; has locatedIn ?x207[ has language ?x51; has religion ?x56; is locatedIn of ?x86; is neighbor of ?x446;];]; is inMountains of ?x2005[ a Source; is hasSource of ?x1016[ a River; has hasEstuary ?x1517; is flowsInto of ?x499;];];]
*> conf = 0.25
ranks of expected_values: 18, 19, 29, 44
EVAL	Alps	inMountains!	Raab	CNN-1.+1._MA	0.000	0.000	0.000	0.024	46.000	46.000	372.000	0.333	http://www.semwebtech.org/mondial/10/meta#inMountains
EVAL	Alps	inMountains!	Rhone	CNN-1.+1._MA	0.000	0.000	0.000	0.056	46.000	46.000	372.000	0.333	http://www.semwebtech.org/mondial/10/meta#inMountains
EVAL	Alps	inMountains!	Aare	CNN-1.+1._MA	0.000	0.000	0.000	0.056	46.000	46.000	372.000	0.333	http://www.semwebtech.org/mondial/10/meta#inMountains
EVAL	Alps	inMountains!	Reuss	CNN-1.+1._MA	0.000	0.000	0.000	0.037	46.000	46.000	372.000	0.333	http://www.semwebtech.org/mondial/10/meta#inMountains

#719-Saone
PRED entity: Saone

PRED relation: hasEstuary
PRED expected values: Saone
=> 54 concepts (44 used for prediction)
PRED predicted values (max 10 best out of 181): Isere (0.33 #110, 0.25 #336, 0.03 #2145), Rhone (0.20 #552, 0.17 #778, 0.12 #1004), Rhein (0.20 #463, 0.17 #689, 0.12 #915), Mosel (0.17 #744, 0.12 #970, 0.10 #1196), Seine (0.17 #889, 0.10 #1341, 0.04 #1567), Amur (0.04 #1531, 0.04 #1758, 0.03 #1984), Irtysch (0.04 #1441, 0.04 #1668, 0.03 #1894), Glomma (0.04 #1455, 0.01 #2813, 0.01 #3267), Lagen (0.04 #1582, 0.01 #2940, 0.01 #3394), MurrumbidgeeRiver (0.04 #1514, 0.01 #2872, 0.01 #3326)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #110 for best value:
>> intensional similarity = 5
>> extensional distance = 1
>> proper extension:  Isere;
>> query: (?x1385, Isere) <- ?x1385[ a River; has flowsInto ?x1225<Rhone>; has locatedIn ?x78<F>;]

No rule for expected values
ranks of expected_values: 
EVAL	Saone	hasEstuary	Saone	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	54.000	44.000	181.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasEstuary

PRED relation: hasEstuary
PRED expected values: Saone
=> 160 concepts (160 used for prediction)
PRED predicted values (max 10 best out of 279): Isere (0.57 #1588, 0.46 #19325, 0.44 #3862), Mosel (0.20 #518, 0.17 #745, 0.14 #1427), Seine (0.20 #663, 0.17 #890, 0.14 #1117), Rhone (0.20 #552, 0.17 #779, 0.11 #23197), Rhein (0.17 #690, 0.11 #23197, 0.09 #2055), Saar (0.14 #1376, 0.11 #23197, 0.10 #1831), Marne (0.14 #1364, 0.11 #23197, 0.10 #1819), Aare (0.14 #1370, 0.10 #1825, 0.09 #2053), Ammer (0.14 #1101, 0.08 #2921, 0.05 #4739), Alz (0.14 #985, 0.08 #2805, 0.05 #4623)
>> best conf = 0.57
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1588 for best value:
>> intensional similarity = 10
>> extensional distance = 5
>> proper extension:  Saar; Marne;
>> query: (?x1385, ?x1460) <- ?x1385[ a River; has flowsInto ?x1225[ a River; is flowsInto of ?x812[ has hasEstuary ?x1460; has hasSource ?x1969; has locatedIn ?x78<F>;];]; has hasSource ?x1386[ a Source;];]

No rule for expected values
ranks of expected_values: 
EVAL	Saone	hasEstuary	Saone	CNN-1.+1._MA	0.000	0.000	0.000	0.000	160.000	160.000	279.000	0.571	http://www.semwebtech.org/mondial/10/meta#hasEstuary

#718-OM
PRED entity: OM

PRED relation: locatedIn!
PRED expected values: ArabianSea
=> 33 concepts (26 used for prediction)
PRED predicted values (max 10 best out of 603): RedSea (0.56 #10825, 0.33 #2298, 0.33 #877), AtlanticOcean (0.50 #8569, 0.37 #28466, 0.27 #24201), PacificOcean (0.36 #12876, 0.22 #28510, 0.16 #24245), IndianOcean (0.33 #3, 0.23 #12793, 0.17 #8530), SyrianDesert (0.33 #1900, 0.22 #10427, 0.20 #7585), ArabianSea (0.33 #729, 0.16 #5685, 0.11 #10677), GulfofAden (0.33 #1370, 0.16 #5685, 0.11 #11318), JabalShuayb (0.33 #1421, 0.16 #5685, 0.11 #11369), Sokotra (0.33 #1272, 0.16 #5685, 0.11 #11220), Nefud (0.33 #2341, 0.16 #5685, 0.10 #22738)
>> best conf = 0.56
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #10825 for best value:
>> intensional similarity = 7
>> extensional distance = 7
>> proper extension:  BRN;
>> query: (?x639, RedSea) <- ?x639[ a Country; has encompassed ?x175; has government ?x640; has wasDependentOf ?x1027; is locatedIn of ?x637[ has locatedIn ?x751<SA>;];]

*> Best rule #729 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 1
*> proper extension:  YE;
*> query: (?x639, ArabianSea) <- ?x639[ has encompassed ?x175; has government ?x640; has neighbor ?x107; has religion ?x187; has wasDependentOf ?x1027; is locatedIn of ?x637<RubAlChali>;]
*> conf = 0.33
ranks of expected_values: 6
EVAL	OM	locatedIn!	ArabianSea	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.167	33.000	26.000	603.000	0.556	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: ArabianSea
=> 85 concepts (84 used for prediction)
PRED predicted values (max 10 best out of 1396): AtlanticOcean (0.70 #85487, 0.49 #39898, 0.46 #47023), MediterraneanSea (0.56 #34250, 0.42 #35672, 0.40 #21431), ArabianSea (0.46 #78325, 0.41 #98261, 0.40 #1426), IndianOcean (0.46 #78325, 0.41 #98261, 0.40 #1426), RedSea (0.46 #78325, 0.40 #1426, 0.38 #7120), GulfofAden (0.46 #78325, 0.40 #1426, 0.38 #7120), SyrianDesert (0.44 #19926, 0.38 #7120, 0.33 #7599), SchattalArab (0.44 #19926, 0.33 #7907, 0.25 #10753), Euphrat (0.44 #19926, 0.33 #8049, 0.23 #27045), Tigris (0.44 #19926, 0.33 #7425, 0.23 #27045)
>> best conf = 0.70
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #85487 for best value:
>> intensional similarity = 15
>> extensional distance = 86
>> proper extension:  HELX; SVAX; FALK; MNTS; SBAR;
>> query: (?x639, AtlanticOcean) <- ?x639[ a Country; has government ?x640; is locatedIn of ?x918[ has locatedIn ?x302[ a Country; has neighbor ?x185; has religion ?x116<Christian>; has wasDependentOf ?x485<UnitedNations>;]; has locatedIn ?x1963[ has ethnicGroup ?x2169;]; is locatedInWater of ?x1443;]; is locatedIn of ?x926[ has mergesWith ?x1333;];]

*> Best rule #78325 for first EXPECTED value:
*> intensional similarity = 16
*> extensional distance = 70
*> proper extension:  MEL;
*> query: (?x639, ?x2407) <- ?x639[ a Country; has encompassed ?x175; has government ?x640; has neighbor ?x107[ a Country; has ethnicGroup ?x1595[ a EthnicGroup;]; has government ?x1136; has religion ?x187;]; is locatedIn of ?x918[ a Sea; is locatedInWater of ?x1443[ a Island;];]; is neighbor of ?x668[ is locatedIn of ?x2407[ a Sea;];];]
*> conf = 0.46
ranks of expected_values: 3
EVAL	OM	locatedIn!	ArabianSea	CNN-1.+1._MA	0.000	1.000	1.000	0.333	85.000	84.000	1396.000	0.705	http://www.semwebtech.org/mondial/10/meta#locatedIn

#717-NL
PRED entity: NL

PRED relation: locatedIn!
PRED expected values: Rhein
=> 49 concepts (48 used for prediction)
PRED predicted values (max 10 best out of 1398): AtlanticOcean (0.50 #5701, 0.43 #11361, 0.39 #24101), CaribbeanSea (0.50 #2934, 0.35 #31240, 0.24 #35488), PacificOcean (0.34 #28389, 0.29 #29805, 0.29 #9989), MediterraneanSea (0.33 #8571, 0.33 #7156, 0.33 #5741), Vignemale (0.33 #9870, 0.33 #7040, 0.33 #1380), TheChannel (0.33 #6311, 0.33 #651, 0.20 #32554), Mosel (0.33 #346, 0.21 #60868, 0.20 #4591), Rhein (0.33 #69, 0.21 #60868, 0.20 #4314), Saar (0.33 #101, 0.20 #4346, 0.17 #8591), MontBlanc (0.33 #107, 0.17 #8597, 0.17 #7182)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5701 for best value:
>> intensional similarity = 6
>> extensional distance = 4
>> proper extension:  DK;
>> query: (?x575, AtlanticOcean) <- ?x575[ has language ?x544; has neighbor ?x120; is dependentOf of ?x50; is locatedIn of ?x731[ a Island;]; is wasDependentOf of ?x179;]

*> Best rule #69 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 1
*> proper extension:  F;
*> query: (?x575, Rhein) <- ?x575[ has neighbor ?x120; has religion ?x95; is dependentOf of ?x50; is locatedIn of ?x829<Maas>; is wasDependentOf of ?x179;]
*> conf = 0.33
ranks of expected_values: 8
EVAL	NL	locatedIn!	Rhein	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.125	49.000	48.000	1398.000	0.500	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Rhein
=> 118 concepts (118 used for prediction)
PRED predicted values (max 10 best out of 1439): AtlanticOcean (0.91 #120581, 0.86 #107817, 0.83 #80858), Rhein (0.84 #42524, 0.60 #5669, 0.44 #24088), CaribbeanSea (0.53 #102204, 0.50 #21357, 0.42 #35427), PacificOcean (0.50 #46866, 0.44 #24088, 0.42 #85155), MediterraneanSea (0.44 #24088, 0.43 #39767, 0.42 #46782), SouthChinaSea (0.44 #24088, 0.42 #46782, 0.40 #52595), Mosel (0.44 #24088, 0.42 #46782, 0.36 #8503), Saar (0.44 #24088, 0.42 #46782, 0.36 #8503), Oder (0.44 #24088, 0.40 #31752, 0.36 #8503), BalticSea (0.44 #24088, 0.38 #148901, 0.37 #153157)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #120581 for best value:
>> intensional similarity = 14
>> extensional distance = 53
>> proper extension:  FALK;
>> query: (?x575, AtlanticOcean) <- ?x575[ a Country; has ethnicGroup ?x734; is locatedIn of ?x121[ has locatedIn ?x543[ a Country; has government ?x1312; has language ?x635<German>;]; is flowsInto of ?x1381; is locatedInWater of ?x495<GreatBritain>; is locatedInWater of ?x1029[ a Island;]; is mergesWith of ?x1211;];]

*> Best rule #42524 for first EXPECTED value:
*> intensional similarity = 15
*> extensional distance = 5
*> proper extension:  CDN;
*> query: (?x575, ?x256) <- ?x575[ a Country; has ethnicGroup ?x734[ a EthnicGroup;]; has language ?x544; has religion ?x352<RomanCatholic>; is locatedIn of ?x121[ is locatedInWater of ?x495; is locatedInWater of ?x1100[ a Island;];]; is locatedIn of ?x257[ a Estuary; is hasEstuary of ?x256;]; is locatedIn of ?x1121[ has belongsToIslands ?x795;];]
*> conf = 0.84
ranks of expected_values: 2
EVAL	NL	locatedIn!	Rhein	CNN-1.+1._MA	0.000	1.000	1.000	0.500	118.000	118.000	1439.000	0.909	http://www.semwebtech.org/mondial/10/meta#locatedIn

#716-NorthernDwina
PRED entity: NorthernDwina

PRED relation: flowsInto!
PRED expected values: Suchona
=> 31 concepts (23 used for prediction)
PRED predicted values (max 10 best out of 195): Inari (0.25 #119, 0.17 #4250, 0.16 #3944), Schilka (0.03 #588, 0.02 #892, 0.01 #1197), Argun (0.03 #356, 0.02 #660, 0.01 #965), LakePowell (0.03 #562, 0.02 #866, 0.01 #1171), LakeJindabyne (0.03 #527, 0.02 #831, 0.01 #1136), AtlinLake (0.03 #484, 0.02 #788, 0.01 #1093), LakeMead (0.03 #479, 0.02 #783, 0.01 #1088), LagodeChapala (0.03 #426, 0.02 #730, 0.01 #1035), Franklin.D.RooseveltLake (0.03 #343, 0.02 #647, 0.01 #952), Breg (0.03 #570, 0.02 #874, 0.01 #1179)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #119 for best value:
>> intensional similarity = 5
>> extensional distance = 2
>> proper extension:  Paatsjoki; Petschora;
>> query: (?x648, Inari) <- ?x648[ a River; has flowsInto ?x251<BarentsSea>; has hasSource ?x418; has locatedIn ?x73;]

*> Best rule #5470 for first EXPECTED value:
*> intensional similarity = 4
*> extensional distance = 211
*> proper extension:  Leine; Sobat;
*> query: (?x648, ?x72) <- ?x648[ a River; has hasSource ?x418[ has locatedIn ?x73[ is locatedIn of ?x72;];];]
*> conf = 0.01
ranks of expected_values: 176
EVAL	NorthernDwina	flowsInto!	Suchona	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.006	31.000	23.000	195.000	0.250	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto!
PRED expected values: Suchona
=> 82 concepts (82 used for prediction)
PRED predicted values (max 10 best out of 234): Inari (0.33 #1514, 0.06 #1329, 0.05 #2549), Angara (0.20 #354, 0.09 #959, 0.05 #1568), Irtysch (0.20 #552, 0.09 #1157, 0.05 #1766), Katun (0.20 #592, 0.09 #1197, 0.05 #1806), Tobol (0.20 #589, 0.09 #1194, 0.05 #1803), Schilka (0.09 #1193, 0.09 #890, 0.06 #1496), Argun (0.09 #961, 0.09 #658, 0.06 #1264), OzeroLadoga (0.09 #982, 0.09 #679, 0.06 #1285), KievReservoir (0.09 #849, 0.06 #1455, 0.05 #1761), KremenchukReservoir (0.09 #761, 0.06 #1367, 0.05 #1673)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1514 for best value:
>> intensional similarity = 8
>> extensional distance = 15
>> proper extension:  OzeroBaikal; OzeroLadoga;
>> query: (?x648, ?x884) <- ?x648[ has flowsInto ?x251[ has locatedIn ?x973[ has encompassed ?x195;]; is flowsInto of ?x631[ has locatedIn ?x565; is flowsInto of ?x884;];]; has locatedIn ?x73<R>;]

*> Best rule #12463 for first EXPECTED value:
*> intensional similarity = 9
*> extensional distance = 204
*> proper extension:  Hwangho;
*> query: (?x648, ?x445) <- ?x648[ a River; has hasSource ?x418[ a Source;]; has locatedIn ?x73[ is locatedIn of ?x445[ a River;]; is locatedIn of ?x720[ a Estuary;]; is neighbor of ?x170;];]
*> conf = 0.03
ranks of expected_values: 40
EVAL	NorthernDwina	flowsInto!	Suchona	CNN-1.+1._MA	0.000	0.000	0.000	0.025	82.000	82.000	234.000	0.333	http://www.semwebtech.org/mondial/10/meta#flowsInto

#715-LAO
PRED entity: LAO

PRED relation: neighbor
PRED expected values: K
=> 31 concepts (18 used for prediction)
PRED predicted values (max 10 best out of 200): K (0.90 #2228, 0.90 #2869, 0.90 #2069), LAO (0.48 #1111, 0.40 #78, 0.28 #1429), IND (0.28 #1429, 0.28 #2229, 0.28 #953), BD (0.28 #1429, 0.28 #2229, 0.28 #953), MAL (0.28 #2229, 0.27 #1270, 0.27 #2710), PK (0.27 #1270, 0.27 #2710, 0.20 #163), BHT (0.27 #1270, 0.27 #2710, 0.20 #229), AFG (0.27 #1270, 0.27 #2710, 0.13 #699), R (0.27 #1270, 0.27 #2710, 0.11 #1273), KAZ (0.27 #1270, 0.27 #2710, 0.10 #702)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2228 for best value:
>> intensional similarity = 8
>> extensional distance = 121
>> proper extension:  E;
>> query: (?x463, ?x91) <- ?x463[ has ethnicGroup ?x1647; has religion ?x462; is locatedIn of ?x1152; is neighbor of ?x91[ has encompassed ?x175; has government ?x92; is locatedIn of ?x339; is neighbor of ?x376;];]
ranks of expected_values: 1
EVAL	LAO	neighbor	K	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	31.000	18.000	200.000	0.903	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: K
=> 76 concepts (76 used for prediction)
PRED predicted values (max 10 best out of 205): K (0.90 #5129, 0.90 #5289, 0.90 #6736), LAO (0.57 #5450, 0.51 #6414, 0.50 #6253), R (0.46 #2874, 0.42 #2715, 0.35 #795), UZB (0.40 #1639, 0.33 #2278, 0.25 #2438), IR (0.40 #2283, 0.23 #6576, 0.21 #10295), IND (0.37 #9812, 0.36 #5451, 0.35 #795), BD (0.36 #5451, 0.34 #4971, 0.33 #9813), KAZ (0.35 #795, 0.32 #6577, 0.32 #7060), KGZ (0.35 #795, 0.32 #6577, 0.32 #7060), AFG (0.35 #795, 0.32 #6577, 0.32 #7060)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5129 for best value:
>> intensional similarity = 16
>> extensional distance = 58
>> proper extension:  BIH; ET; R; MNE; D; E; HR; SK; RO; BG; ...
>> query: (?x463, ?x91) <- ?x463[ a Country; has ethnicGroup ?x1647; has religion ?x462; is locatedIn of ?x1152[ has flowsInto ?x384; has hasEstuary ?x975; has hasSource ?x2532[ a Source;];]; is neighbor of ?x91; is neighbor of ?x366[ a Country; has encompassed ?x175; has ethnicGroup ?x298; has language ?x1463; is neighbor of ?x232[ is locatedIn of ?x231;];];]
ranks of expected_values: 1
EVAL	LAO	neighbor	K	CNN-1.+1._MA	1.000	1.000	1.000	1.000	76.000	76.000	205.000	0.901	http://www.semwebtech.org/mondial/10/meta#neighbor

#714-TT
PRED entity: TT

PRED relation: wasDependentOf
PRED expected values: GB
=> 24 concepts (24 used for prediction)
PRED predicted values (max 10 best out of 22): GB (0.44 #62, 0.41 #91, 0.32 #151), F (0.16 #395, 0.14 #269, 0.10 #556), E (0.13 #368, 0.13 #338, 0.12 #7), NL (0.09 #194, 0.04 #253, 0.03 #379), UnitedNations (0.08 #407, 0.07 #568, 0.07 #633), SovietUnion (0.06 #287, 0.06 #606, 0.05 #446), RH (0.06 #137, 0.03 #225), P (0.05 #170, 0.03 #229, 0.03 #448), Yugoslavia (0.05 #200, 0.05 #355, 0.04 #385), S (0.05 #189, 0.01 #248)
>> best conf = 0.44
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #62 for best value:
>> intensional similarity = 6
>> extensional distance = 14
>> proper extension:  GB; IRL; AUS; NORF; BS; SLB; AXA; NZ; TUCA; SY; ...
>> query: (?x667, GB) <- ?x667[ a Country; has encompassed ?x521; has government ?x254; has religion ?x713<Anglican>; is locatedIn of ?x182;]
ranks of expected_values: 1
EVAL	TT	wasDependentOf	GB	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	24.000	24.000	22.000	0.438	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

PRED relation: wasDependentOf
PRED expected values: GB
=> 60 concepts (60 used for prediction)
PRED predicted values (max 10 best out of 161): GB (0.43 #158, 0.43 #130, 0.28 #1055), E (0.32 #256, 0.30 #293, 0.28 #399), F (0.17 #632, 0.12 #599, 0.12 #1594), NL (0.17 #114, 0.14 #144, 0.11 #2027), CO (0.13 #829, 0.11 #2027, 0.11 #1730), RH (0.11 #2027, 0.11 #1730, 0.10 #1948), BR (0.11 #2027, 0.11 #1730, 0.10 #1948), UnitedNations (0.11 #2027, 0.11 #1730, 0.10 #1948), P (0.11 #2027, 0.11 #1730, 0.10 #1948), DK (0.11 #2027, 0.11 #1730, 0.10 #1948)
>> best conf = 0.43
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #158 for best value:
>> intensional similarity = 17
>> extensional distance = 5
>> proper extension:  AUS; RI;
>> query: (?x667, ?x81) <- ?x667[ a Country; has religion ?x95<Protestant>; has religion ?x410[ is religion of ?x81<GB>; is religion of ?x797<MS>;]; is locatedIn of ?x317[ a Sea; has locatedIn ?x482<MEX>; has locatedIn ?x1444[ has encompassed ?x521;]; is locatedInWater of ?x727[ a Island; has belongsToIslands ?x877;];];]
>> Best rule #130 for best value:
>> intensional similarity = 17
>> extensional distance = 5
>> proper extension:  AUS; RI;
>> query: (?x667, GB) <- ?x667[ a Country; has religion ?x95<Protestant>; has religion ?x410[ is religion of ?x81<GB>; is religion of ?x797<MS>;]; is locatedIn of ?x317[ a Sea; has locatedIn ?x482<MEX>; has locatedIn ?x1444[ has encompassed ?x521;]; is locatedInWater of ?x727[ a Island; has belongsToIslands ?x877;];];]
ranks of expected_values: 1
EVAL	TT	wasDependentOf	GB	CNN-1.+1._MA	1.000	1.000	1.000	1.000	60.000	60.000	161.000	0.429	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

#713-Kefallinia
PRED entity: Kefallinia

PRED relation: belongsToIslands
PRED expected values: IonicIslands
=> 16 concepts (16 used for prediction)
PRED predicted values (max 10 best out of 33): LipariIslands (0.23 #2, 0.06 #273, 0.06 #683), Sporades (0.13 #21, 0.06 #683, 0.05 #342), LesserAntilles (0.10 #83, 0.10 #151, 0.10 #219), Baleares (0.10 #39, 0.06 #683, 0.05 #342), IonicIslands (0.10 #20, 0.06 #683, 0.05 #342), Kyklades (0.07 #50, 0.06 #683, 0.05 #342), Malta (0.06 #273, 0.06 #683, 0.05 #342), SundaIslands (0.05 #218, 0.05 #287, 0.04 #628), Azores (0.04 #72, 0.04 #140, 0.04 #208), HawaiiIslands (0.04 #97, 0.04 #165, 0.04 #233)
>> best conf = 0.23
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2 for best value:
>> intensional similarity = 3
>> extensional distance = 28
>> proper extension:  Menorca; Stromboli; Mallorca; Salina; Lefkas; Rhodos; Gozo; Malta; Zakynthos; Cyprus; ...
>> query: (?x1956, LipariIslands) <- ?x1956[ a Island; has locatedInWater ?x275<MediterraneanSea>;]

*> Best rule #20 for first EXPECTED value:
*> intensional similarity = 3
*> extensional distance = 28
*> proper extension:  Menorca; Stromboli; Mallorca; Salina; Lefkas; Rhodos; Gozo; Malta; Zakynthos; Cyprus; ...
*> query: (?x1956, IonicIslands) <- ?x1956[ a Island; has locatedInWater ?x275<MediterraneanSea>;]
*> conf = 0.10
ranks of expected_values: 5
EVAL	Kefallinia	belongsToIslands	IonicIslands	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.200	16.000	16.000	33.000	0.233	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

PRED relation: belongsToIslands
PRED expected values: IonicIslands
=> 26 concepts (26 used for prediction)
PRED predicted values (max 10 best out of 39): LipariIslands (0.23 #2, 0.21 #70, 0.07 #138), LesserAntilles (0.18 #151, 0.15 #219, 0.15 #289), Sporades (0.13 #21, 0.12 #89, 0.07 #274), Baleares (0.10 #39, 0.09 #107, 0.07 #274), IonicIslands (0.10 #20, 0.09 #88, 0.07 #274), Azores (0.07 #140, 0.06 #208, 0.06 #278), Kyklades (0.07 #274, 0.07 #273, 0.07 #482), Malta (0.07 #274, 0.07 #273, 0.07 #482), SundaIslands (0.06 #700, 0.05 #288, 0.05 #358), Canares (0.06 #159, 0.05 #227, 0.05 #297)
>> best conf = 0.23
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2 for best value:
>> intensional similarity = 3
>> extensional distance = 28
>> proper extension:  Menorca; Stromboli; Mallorca; Salina; Lefkas; Rhodos; Gozo; Malta; Zakynthos; Cyprus; ...
>> query: (?x1956, LipariIslands) <- ?x1956[ a Island; has locatedInWater ?x275<MediterraneanSea>;]

*> Best rule #20 for first EXPECTED value:
*> intensional similarity = 3
*> extensional distance = 28
*> proper extension:  Menorca; Stromboli; Mallorca; Salina; Lefkas; Rhodos; Gozo; Malta; Zakynthos; Cyprus; ...
*> query: (?x1956, IonicIslands) <- ?x1956[ a Island; has locatedInWater ?x275<MediterraneanSea>;]
*> conf = 0.10
ranks of expected_values: 5
EVAL	Kefallinia	belongsToIslands	IonicIslands	CNN-1.+1._MA	0.000	0.000	1.000	0.200	26.000	26.000	39.000	0.233	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

#712-BZ
PRED entity: BZ

PRED relation: ethnicGroup
PRED expected values: Garifuna
=> 52 concepts (52 used for prediction)
PRED predicted values (max 10 best out of 226): European (0.79 #2303, 0.79 #1283, 0.71 #1028), Amerindian (0.68 #2042, 0.50 #1022, 0.45 #2552), African (0.55 #4851, 0.51 #5106, 0.50 #261), Mulatto (0.18 #3118, 0.16 #2353, 0.14 #1333), EastIndian (0.17 #391, 0.14 #646, 0.07 #10459), Asian (0.15 #4099, 0.15 #3844, 0.14 #529), Chinese (0.14 #525, 0.13 #11740, 0.13 #12507), Micronesian (0.14 #574, 0.12 #9692, 0.07 #10459), Melanesian (0.14 #683, 0.12 #9692, 0.07 #10459), PacificIslander (0.14 #585, 0.12 #9692, 0.07 #10459)
>> best conf = 0.79
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2303 for best value:
>> intensional similarity = 7
>> extensional distance = 17
>> proper extension:  C; ROU;
>> query: (?x671, European) <- ?x671[ has encompassed ?x521<America>; has ethnicGroup ?x676; has language ?x796<Spanish>; has religion ?x95; has wasDependentOf ?x81;]

No rule for expected values
ranks of expected_values: 
EVAL	BZ	ethnicGroup	Garifuna	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	52.000	52.000	226.000	0.789	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: Garifuna
=> 116 concepts (116 used for prediction)
PRED predicted values (max 10 best out of 250): European (0.89 #9986, 0.78 #3073, 0.75 #4353), Amerindian (0.68 #4088, 0.65 #3321, 0.61 #6904), Asian (0.68 #4088, 0.65 #3321, 0.61 #6904), African (0.67 #1538, 0.50 #2560, 0.50 #1793), Ukrainian (0.46 #5113, 0.43 #5881, 0.40 #13563), Russian (0.44 #13634, 0.38 #5184, 0.36 #5952), German (0.31 #5122, 0.29 #5890, 0.26 #8706), Polish (0.28 #13765, 0.23 #5315, 0.21 #6083), EastIndian (0.25 #2690, 0.25 #1158, 0.17 #1923), Chinese (0.25 #270, 0.22 #2824, 0.20 #5624)
>> best conf = 0.89
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #9986 for best value:
>> intensional similarity = 20
>> extensional distance = 17
>> proper extension:  NCA;
>> query: (?x671, European) <- ?x671[ a Country; has encompassed ?x521; has ethnicGroup ?x676[ is ethnicGroup of ?x215<CO>; is ethnicGroup of ?x318<CR>; is ethnicGroup of ?x404[ has neighbor ?x379;]; is ethnicGroup of ?x482<MEX>; is ethnicGroup of ?x1364<HCA>;]; has ethnicGroup ?x2207[ a EthnicGroup;]; has government ?x1947; has religion ?x95<Protestant>; has religion ?x352<RomanCatholic>;]

No rule for expected values
ranks of expected_values: 
EVAL	BZ	ethnicGroup	Garifuna	CNN-1.+1._MA	0.000	0.000	0.000	0.000	116.000	116.000	250.000	0.895	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#711-Huallaga
PRED entity: Huallaga

PRED relation: inMountains
PRED expected values: Andes
=> 41 concepts (41 used for prediction)
PRED predicted values (max 10 best out of 31): Andes (0.80 #98, 0.78 #11, 0.28 #272), CordilleraVolcanica (0.14 #871, 0.06 #2874, 0.05 #1742), CordilleraNegra (0.14 #871, 0.06 #2874, 0.05 #1742), CordilleraVilcanota (0.14 #871, 0.06 #2874, 0.05 #1742), CordilleraBlanca (0.14 #871, 0.06 #2874, 0.05 #1742), Alps (0.13 #352, 0.11 #439, 0.10 #787), CordilleraIberica (0.08 #316, 0.02 #490, 0.02 #838), Balkan (0.05 #368, 0.05 #455, 0.04 #803), EastAfricanRift (0.04 #811, 0.04 #1160, 0.04 #724), RockyMountains (0.03 #1313, 0.03 #1400, 0.02 #1487)
>> best conf = 0.80
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #98 for best value:
>> intensional similarity = 3
>> extensional distance = 8
>> proper extension:  Mantaro;
>> query: (?x1909, Andes) <- ?x1909[ a Source; has locatedIn ?x296<PE>;]
ranks of expected_values: 1
EVAL	Huallaga	inMountains	Andes	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	41.000	41.000	31.000	0.800	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains
PRED expected values: Andes
=> 116 concepts (116 used for prediction)
PRED predicted values (max 10 best out of 42): Andes (0.80 #185, 0.75 #98, 0.73 #272), Alps (0.17 #1572, 0.16 #1659, 0.14 #2443), CordilleraVolcanica (0.16 #4531, 0.14 #3572, 0.14 #4008), CordilleraNegra (0.16 #4531, 0.14 #3572, 0.14 #4008), CordilleraVilcanota (0.16 #4531, 0.14 #3572, 0.14 #4008), CordilleraBlanca (0.16 #4531, 0.14 #3572, 0.14 #4008), EastAfricanRift (0.07 #2379, 0.07 #1857, 0.07 #1944), CordilleraIberica (0.07 #1099, 0.05 #925, 0.03 #2406), SierraParima (0.06 #428, 0.06 #515, 0.06 #602), Karpaten (0.06 #1881, 0.05 #2142, 0.05 #2316)
>> best conf = 0.80
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #185 for best value:
>> intensional similarity = 3
>> extensional distance = 8
>> proper extension:  Mantaro;
>> query: (?x1909, Andes) <- ?x1909[ a Source; has locatedIn ?x296<PE>;]
ranks of expected_values: 1
EVAL	Huallaga	inMountains	Andes	CNN-1.+1._MA	1.000	1.000	1.000	1.000	116.000	116.000	42.000	0.800	http://www.semwebtech.org/mondial/10/meta#inMountains

#710-CI
PRED entity: CI

PRED relation: religion
PRED expected values: Christian
=> 36 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 29): Christian (0.67 #169, 0.66 #415, 0.54 #456), RomanCatholic (0.62 #623, 0.61 #582, 0.55 #500), Protestant (0.54 #618, 0.49 #495, 0.48 #577), Hindu (0.20 #297, 0.18 #1357, 0.13 #666), Jewish (0.20 #209, 0.18 #1357, 0.11 #578), ChristianOrthodox (0.19 #781, 0.18 #1152, 0.18 #988), Anglican (0.18 #1357, 0.15 #551, 0.15 #510), Buddhist (0.18 #1357, 0.13 #217, 0.13 #668), JehovasWitnesses (0.18 #1357, 0.09 #636, 0.05 #308), Seventh-DayAdventist (0.18 #1357, 0.07 #216, 0.03 #872)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #169 for best value:
>> intensional similarity = 8
>> extensional distance = 4
>> proper extension:  RMM; WAL; GNB;
>> query: (?x1206, Christian) <- ?x1206[ has neighbor ?x483[ has neighbor ?x1307; has wasDependentOf ?x81[ is dependentOf of ?x80;]; is locatedIn of ?x135;]; has neighbor ?x651<RG>; is locatedIn of ?x182;]
ranks of expected_values: 1
EVAL	CI	religion	Christian	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	36.000	36.000	29.000	0.667	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion
PRED expected values: Christian
=> 80 concepts (80 used for prediction)
PRED predicted values (max 10 best out of 33): Christian (0.75 #419, 0.71 #381, 0.71 #923), RomanCatholic (0.65 #1554, 0.65 #1220, 0.60 #1472), Protestant (0.55 #1215, 0.53 #3168, 0.52 #1549), Jewish (0.47 #1422, 0.46 #880, 0.39 #2876), ChristianOrthodox (0.27 #1297, 0.27 #2252, 0.27 #2046), Catholic (0.27 #1297, 0.17 #1837, 0.16 #2959), CopticChristian (0.27 #1297, 0.17 #1837, 0.16 #2959), Hindu (0.20 #1846, 0.17 #1837, 0.16 #2959), Anglican (0.17 #1837, 0.17 #1272, 0.16 #1314), NewApostolic (0.17 #1837, 0.16 #2959, 0.15 #2960)
>> best conf = 0.75
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #419 for best value:
>> intensional similarity = 20
>> extensional distance = 5
>> proper extension:  TCH; CAM;
>> query: (?x1206, ?x116) <- ?x1206[ has government ?x2531; has wasDependentOf ?x78; is neighbor of ?x483[ a Country; has ethnicGroup ?x162<African>; is locatedIn of ?x135;]; is neighbor of ?x621[ is neighbor of ?x1072;]; is neighbor of ?x839[ a Country; has ethnicGroup ?x1537; has government ?x435; has neighbor ?x426<RN>; has neighbor ?x581[ has neighbor ?x108;]; has religion ?x116; is locatedIn of ?x1032[ a Estuary;];];]
ranks of expected_values: 1
EVAL	CI	religion	Christian	CNN-1.+1._MA	1.000	1.000	1.000	1.000	80.000	80.000	33.000	0.750	http://www.semwebtech.org/mondial/10/meta#religion

#709-Tigris
PRED entity: Tigris

PRED relation: locatedIn
PRED expected values: SYR
=> 39 concepts (38 used for prediction)
PRED predicted values (max 10 best out of 117): R (0.69 #1648, 0.17 #473, 0.15 #4468), IR (0.47 #2822, 0.46 #2115, 0.45 #2116), SYR (0.44 #703, 0.33 #578, 0.33 #344), JOR (0.44 #703, 0.17 #2587, 0.17 #2586), KWT (0.44 #703, 0.17 #2587, 0.17 #2586), SA (0.44 #703, 0.17 #2587, 0.17 #2586), GE (0.33 #547, 0.14 #782, 0.13 #1173), I (0.31 #2163, 0.17 #515, 0.06 #4510), F (0.25 #2123, 0.17 #475, 0.06 #4470), E (0.22 #2143, 0.17 #495, 0.05 #965)
>> best conf = 0.69
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1648 for best value:
>> intensional similarity = 7
>> extensional distance = 144
>> proper extension:  SeaofAzov; Bjelucha; Schchara; Lena; Elbrus; BarentsSea; ArcticOcean; SeaofJapan; PacificOcean; Swir; ...
>> query: (?x666, R) <- ?x666[ has locatedIn ?x185[ is locatedIn of ?x98<BlackSea>; is locatedIn of ?x1633[ a Sea;]; is neighbor of ?x466[ has encompassed ?x175;];];]

*> Best rule #703 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 4
*> proper extension:  BlackSea; MediterraneanSea;
*> query: (?x666, ?x304) <- ?x666[ has locatedIn ?x185<TR>; has locatedIn ?x302[ has government ?x254; is locatedIn of ?x1719[ a Estuary;]; is neighbor of ?x304;];]
*> conf = 0.44
ranks of expected_values: 3
EVAL	Tigris	locatedIn	SYR	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.333	39.000	38.000	117.000	0.692	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: SYR
=> 108 concepts (107 used for prediction)
PRED predicted values (max 10 best out of 191): R (0.83 #3089, 0.59 #3565, 0.36 #12095), SYR (0.68 #7578, 0.50 #344, 0.38 #471), IR (0.44 #9711, 0.43 #9710, 0.41 #8290), USA (0.43 #12160, 0.41 #12396, 0.36 #12868), AL (0.40 #4081, 0.31 #1231, 0.28 #5027), JOR (0.38 #471, 0.31 #470, 0.21 #1898), SA (0.38 #471, 0.31 #470, 0.21 #1898), KWT (0.38 #471, 0.21 #1898, 0.19 #6634), CN (0.36 #2903, 0.35 #2428, 0.15 #1002), GR (0.30 #5069, 0.25 #1663, 0.25 #322)
>> best conf = 0.83
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3089 for best value:
>> intensional similarity = 14
>> extensional distance = 27
>> proper extension:  Selenge; Suchona; NorthernDwina; Vuoksi;
>> query: (?x666, R) <- ?x666[ a River; has hasSource ?x1669[ a Source;]; has locatedIn ?x185[ has ethnicGroup ?x638; is neighbor of ?x331[ a Country; has government ?x435; has wasDependentOf ?x903;]; is neighbor of ?x353<GE>; is neighbor of ?x399[ a Country; is locatedIn of ?x398;];];]

*> Best rule #7578 for first EXPECTED value:
*> intensional similarity = 13
*> extensional distance = 96
*> proper extension:  Ob;
*> query: (?x666, ?x302) <- ?x666[ a River; has flowsInto ?x1422[ is flowsInto of ?x1644[ a River; has hasEstuary ?x255; has locatedIn ?x185[ is locatedIn of ?x1669[ a Source;];]; has locatedIn ?x302; is flowsInto of ?x1272;];]; has hasEstuary ?x1719[ a Estuary;]; has hasSource ?x1669;]
*> conf = 0.68
ranks of expected_values: 2
EVAL	Tigris	locatedIn	SYR	CNN-1.+1._MA	0.000	1.000	1.000	0.500	108.000	107.000	191.000	0.828	http://www.semwebtech.org/mondial/10/meta#locatedIn

#708-SnowyRiver
PRED entity: SnowyRiver

PRED relation: locatedIn
PRED expected values: AUS
=> 46 concepts (46 used for prediction)
PRED predicted values (max 10 best out of 91): AUS (0.71 #992, 0.63 #5927, 0.62 #4743), R (0.48 #3083, 0.48 #2850, 0.19 #3088), USA (0.39 #2133, 0.39 #1967, 0.38 #4032), PE (0.33 #1421, 0.33 #1250, 0.12 #3082), CN (0.29 #765, 0.06 #4799, 0.05 #5036), CDN (0.26 #1958, 0.22 #2371, 0.22 #2196), MEX (0.20 #352, 0.14 #825, 0.12 #3082), I (0.14 #3368, 0.10 #8108, 0.09 #8345), RI (0.12 #3082, 0.08 #1710, 0.06 #8771), NIC (0.12 #3082, 0.06 #8771, 0.06 #9244)
>> best conf = 0.71
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #992 for best value:
>> intensional similarity = 2
>> extensional distance = 5
>> proper extension:  Mt.Bogong; Mt.Kosciuszko;
>> query: (?x1782, AUS) <- ?x1782[ has inMountains ?x846<SnowyMountains>;]
ranks of expected_values: 1
EVAL	SnowyRiver	locatedIn	AUS	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	46.000	46.000	91.000	0.714	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: AUS
=> 98 concepts (98 used for prediction)
PRED predicted values (max 10 best out of 91): AUS (0.74 #15000, 0.71 #6000, 0.67 #4763), R (0.60 #2616, 0.60 #2383, 0.46 #9765), USA (0.50 #1192, 0.50 #1024, 0.47 #10954), CDN (0.50 #1191, 0.50 #1015, 0.36 #9526), PE (0.33 #7624, 0.33 #7452, 0.21 #2615), MEX (0.25 #1545, 0.21 #2615, 0.17 #4403), S (0.25 #569, 0.20 #1995, 0.10 #11759), RI (0.21 #2615, 0.20 #2378, 0.20 #2192), NIC (0.21 #2615, 0.17 #4142, 0.14 #5812), CO (0.21 #2615, 0.10 #12379, 0.09 #951)
>> best conf = 0.74
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #15000 for best value:
>> intensional similarity = 9
>> extensional distance = 25
>> proper extension:  Dalaelv; SchattalArab; OhioRiver;
>> query: (?x1782, ?x196) <- ?x1782[ a Source; is hasSource of ?x1041[ a River; has hasEstuary ?x2381[ a Estuary; has locatedIn ?x196[ is locatedIn of ?x1492[ is flowsInto of ?x1240;];];]; is flowsInto of ?x1492;];]
ranks of expected_values: 1
EVAL	SnowyRiver	locatedIn	AUS	CNN-1.+1._MA	1.000	1.000	1.000	1.000	98.000	98.000	91.000	0.741	http://www.semwebtech.org/mondial/10/meta#locatedIn

#707-SUD
PRED entity: SUD

PRED relation: neighbor!
PRED expected values: ET
=> 31 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 187): ET (0.90 #3449, 0.90 #2975, 0.90 #2974), SUD (0.57 #469, 0.30 #627, 0.29 #1410), EAT (0.43 #283, 0.19 #754, 0.17 #910), IL (0.33 #45, 0.30 #627, 0.29 #1410), RCB (0.30 #627, 0.29 #1410, 0.29 #244), EAK (0.30 #627, 0.29 #1410, 0.29 #238), EAU (0.30 #627, 0.29 #1410, 0.29 #266), DZ (0.30 #627, 0.29 #1410, 0.26 #2663), CAM (0.30 #627, 0.29 #1410, 0.26 #2663), ZRE (0.30 #627, 0.29 #1410, 0.26 #2663)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3449 for best value:
>> intensional similarity = 8
>> extensional distance = 138
>> proper extension:  R; TR; USA; PY; A; WEST; MACX; WSA; ES; L; ...
>> query: (?x186, ?x629) <- ?x186[ a Country; has neighbor ?x229[ is locatedIn of ?x53;]; has neighbor ?x629[ has ethnicGroup ?x996;]; has religion ?x116; is locatedIn of ?x531; is neighbor of ?x169;]
ranks of expected_values: 1
EVAL	SUD	neighbor!	ET	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	31.000	31.000	187.000	0.905	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: ET
=> 75 concepts (75 used for prediction)
PRED predicted values (max 10 best out of 187): ET (0.90 #9330, 0.90 #9328, 0.90 #8364), SUD (0.57 #802, 0.43 #4802, 0.42 #4801), DJI (0.57 #802, 0.42 #4801, 0.40 #1915), IL (0.57 #802, 0.42 #4801, 0.40 #801), JOR (0.57 #802, 0.40 #801, 0.38 #800), SA (0.57 #802, 0.40 #801, 0.17 #1919), YE (0.57 #802, 0.38 #800, 0.22 #1918), EAT (0.50 #1886, 0.46 #3481, 0.43 #3641), RCB (0.50 #1047, 0.33 #1847, 0.33 #729), DZ (0.44 #2655, 0.38 #800, 0.35 #4479)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #9330 for best value:
>> intensional similarity = 12
>> extensional distance = 121
>> proper extension:  Q;
>> query: (?x186, ?x629) <- ?x186[ a Country; has ethnicGroup ?x244; has neighbor ?x629[ has encompassed ?x213; has ethnicGroup ?x996; has neighbor ?x94;]; has neighbor ?x736[ a Country; has ethnicGroup ?x992; is locatedIn of ?x388;]; has religion ?x116; is locatedIn of ?x531;]
ranks of expected_values: 1
EVAL	SUD	neighbor!	ET	CNN-1.+1._MA	1.000	1.000	1.000	1.000	75.000	75.000	187.000	0.903	http://www.semwebtech.org/mondial/10/meta#neighbor

#706-J
PRED entity: J

PRED relation: locatedIn!
PRED expected values: Asahi-Dake Hotaka-Dake
=> 39 concepts (37 used for prediction)
PRED predicted values (max 10 best out of 1370): Asahi-Dake (0.77 #4245), SouthChinaSea (0.46 #5799, 0.40 #2969, 0.37 #10042), AtlanticOcean (0.45 #15603, 0.44 #17018, 0.41 #21261), NorthSea (0.38 #4267, 0.31 #7096, 0.25 #1437), CaribbeanSea (0.33 #12835, 0.23 #15665, 0.22 #29812), BeringSea (0.33 #382, 0.19 #5660, 0.16 #16976), Amur (0.33 #889, 0.18 #32537, 0.12 #2304), ArcticOcean (0.33 #74, 0.14 #12805, 0.10 #2904), Irtysch (0.33 #988, 0.12 #2403, 0.10 #12305), Argun (0.33 #833, 0.12 #2248, 0.10 #12150)
>> best conf = 0.77
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4245 for best value:
>> intensional similarity = 5
>> extensional distance = 8
>> proper extension:  RI; MAL; RP; BRU;
>> query: (?x117, ?x1456) <- ?x117[ has encompassed ?x175; has religion ?x462<Buddhist>; is locatedIn of ?x451[ is locatedOnIsland of ?x1456;];]
ranks of expected_values: 1
EVAL	J	locatedIn!	Hotaka-Dake	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	39.000	37.000	1370.000	0.769	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	J	locatedIn!	Asahi-Dake	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	39.000	37.000	1370.000	0.769	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Asahi-Dake Hotaka-Dake
=> 102 concepts (84 used for prediction)
PRED predicted values (max 10 best out of 1382): SouthChinaSea (0.85 #34133, 0.71 #36970, 0.44 #53981), AtlanticOcean (0.52 #87893, 0.50 #22705, 0.50 #15623), CaribbeanSea (0.48 #59611, 0.39 #46857, 0.35 #49693), IndianOcean (0.44 #53845, 0.37 #4249, 0.31 #8498), NorthSea (0.40 #22685, 0.38 #35435, 0.33 #11356), YellowSea (0.40 #8578, 0.37 #4249, 0.33 #80), MediterraneanSea (0.38 #15663, 0.34 #76595, 0.33 #17080), BeringSea (0.37 #4249, 0.33 #3215, 0.31 #8498), SulawesiSea (0.37 #4249, 0.31 #8498, 0.29 #35409), BandaSea (0.37 #4249, 0.31 #8498, 0.29 #35409)
>> best conf = 0.85
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #34133 for best value:
>> intensional similarity = 13
>> extensional distance = 11
>> proper extension:  THA; BRU; VN; K; SGP;
>> query: (?x117, SouthChinaSea) <- ?x117[ has encompassed ?x175; has ethnicGroup ?x2391; has government ?x2476; is locatedIn of ?x282[ is locatedInWater of ?x833[ is locatedOnIsland of ?x1741;]; is mergesWith of ?x60;]; is locatedIn of ?x620[ has locatedIn ?x232<CN>; has locatedIn ?x1568<RC>; has mergesWith ?x270;];]

*> Best rule #35410 for first EXPECTED value:
*> intensional similarity = 13
*> extensional distance = 11
*> proper extension:  THA; BRU; VN; K; SGP;
*> query: (?x117, ?x1741) <- ?x117[ has encompassed ?x175; has ethnicGroup ?x2391; has government ?x2476; is locatedIn of ?x282[ is locatedInWater of ?x833[ is locatedOnIsland of ?x1741;]; is mergesWith of ?x60;]; is locatedIn of ?x620[ has locatedIn ?x232<CN>; has locatedIn ?x1568<RC>; has mergesWith ?x270;];]
*> conf = 0.15
ranks of expected_values: 364
EVAL	J	locatedIn!	Hotaka-Dake	CNN-1.+1._MA	0.000	0.000	0.000	0.000	102.000	84.000	1382.000	0.846	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	J	locatedIn!	Asahi-Dake	CNN-1.+1._MA	0.000	0.000	0.000	0.003	102.000	84.000	1382.000	0.846	http://www.semwebtech.org/mondial/10/meta#locatedIn

#705-Viet-Kinh
PRED entity: Viet-Kinh

PRED relation: ethnicGroup!
PRED expected values: VN
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 96): EAU (0.03 #132), ETH (0.03 #100), UA (0.03 #57), EAK (0.03 #99), CZ (0.02 #98), SN (0.02 #83), MYA (0.02 #70), IR (0.02 #58), SK (0.02 #25), RCA (0.02 #139)
>> best conf = 0.03
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #132 for best value:
>> intensional similarity = 1
>> extensional distance = 279
>> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
>> query: (?x2178, EAU) <- ?x2178[ a EthnicGroup;]

*> Best rule #120 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 279
*> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
*> query: (?x2178, VN) <- ?x2178[ a EthnicGroup;]
*> conf = 0.02
ranks of expected_values: 26
EVAL	Viet-Kinh	ethnicGroup!	VN	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.038	2.000	2.000	96.000	0.032	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup!
PRED expected values: VN
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 96): EAU (0.03 #132), ETH (0.03 #100), UA (0.03 #57), EAK (0.03 #99), CZ (0.02 #98), SN (0.02 #83), MYA (0.02 #70), IR (0.02 #58), SK (0.02 #25), RCA (0.02 #139)
>> best conf = 0.03
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #132 for best value:
>> intensional similarity = 1
>> extensional distance = 279
>> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
>> query: (?x2178, EAU) <- ?x2178[ a EthnicGroup;]

*> Best rule #120 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 279
*> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
*> query: (?x2178, VN) <- ?x2178[ a EthnicGroup;]
*> conf = 0.02
ranks of expected_values: 26
EVAL	Viet-Kinh	ethnicGroup!	VN	CNN-1.+1._MA	0.000	0.000	0.000	0.038	2.000	2.000	96.000	0.032	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#704-RG
PRED entity: RG

PRED relation: locatedIn!
PRED expected values: Gambia
=> 42 concepts (41 used for prediction)
PRED predicted values (max 10 best out of 1400): Senegal (0.58 #28365, 0.25 #1024, 0.08 #6696), Niger (0.58 #28365, 0.04 #7030, 0.03 #8448), CaribbeanSea (0.33 #2941, 0.29 #18540, 0.28 #19959), PacificOcean (0.26 #44052, 0.22 #49727, 0.21 #31287), Bani (0.25 #180, 0.20 #1598, 0.18 #32621), Tanezrouft (0.25 #1353, 0.20 #2771, 0.08 #48224), ErgChech (0.25 #1058, 0.20 #2476, 0.08 #48224), Talak (0.25 #911, 0.20 #2329, 0.08 #48224), Bani (0.25 #534, 0.20 #1952, 0.08 #48224), Gambia (0.25 #477, 0.18 #32621, 0.11 #55319)
>> best conf = 0.58
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #28365 for best value:
>> intensional similarity = 6
>> extensional distance = 84
>> proper extension:  BIH; ET; LS; THA; MNE; RL; D; TAD; KGZ; WAN; ...
>> query: (?x651, ?x1801) <- ?x651[ a Country; has ethnicGroup ?x1685; has government ?x435; has religion ?x116; is locatedIn of ?x838[ has hasEstuary ?x1801;];]

*> Best rule #477 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 2
*> proper extension:  SN; RMM;
*> query: (?x651, Gambia) <- ?x651[ a Country; has ethnicGroup ?x1685; has government ?x435; has religion ?x116; is locatedIn of ?x838<Senegal>;]
*> conf = 0.25
ranks of expected_values: 10
EVAL	RG	locatedIn!	Gambia	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.100	42.000	41.000	1400.000	0.576	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Gambia
=> 103 concepts (103 used for prediction)
PRED predicted values (max 10 best out of 1430): CaribbeanSea (0.71 #39851, 0.38 #45536, 0.38 #103774), Senegal (0.63 #49697, 0.59 #78114, 0.58 #52537), Niger (0.63 #49697, 0.59 #78114, 0.58 #52537), Bani (0.58 #52536, 0.52 #62490, 0.51 #62491), Benue (0.58 #52536, 0.52 #62490, 0.51 #62491), LakeKainji (0.58 #52536, 0.52 #62490, 0.51 #62491), PacificOcean (0.46 #42673, 0.36 #125069, 0.30 #127913), Volta (0.40 #13046, 0.19 #28390, 0.18 #115034), Gambia (0.33 #4736, 0.31 #15612, 0.25 #21291), Ferlo (0.33 #5565, 0.25 #6983, 0.19 #17033)
>> best conf = 0.71
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #39851 for best value:
>> intensional similarity = 13
>> extensional distance = 22
>> proper extension:  SBAR;
>> query: (?x651, CaribbeanSea) <- ?x651[ a Country; has encompassed ?x213; has government ?x435; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x580[ is flowsInto of ?x456[ a River; has hasEstuary ?x1032; has hasSource ?x350; has locatedIn ?x839;]; is flowsInto of ?x579[ a Lake;];];]

*> Best rule #4736 for first EXPECTED value:
*> intensional similarity = 16
*> extensional distance = 1
*> proper extension:  SN;
*> query: (?x651, Gambia) <- ?x651[ has encompassed ?x213; has ethnicGroup ?x2201[ a EthnicGroup;]; has wasDependentOf ?x78; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x838<Senegal>; is neighbor of ?x621[ a Country; has ethnicGroup ?x162; has language ?x247; has religion ?x187<Muslim>;]; is neighbor of ?x1072[ has wasDependentOf ?x81;];]
*> conf = 0.33
ranks of expected_values: 9
EVAL	RG	locatedIn!	Gambia	CNN-1.+1._MA	0.000	0.000	1.000	0.111	103.000	103.000	1430.000	0.708	http://www.semwebtech.org/mondial/10/meta#locatedIn

#703-XMAS
PRED entity: XMAS

PRED relation: religion
PRED expected values: Buddhist
=> 43 concepts (43 used for prediction)
PRED predicted values (max 10 best out of 33): RomanCatholic (0.69 #933, 0.68 #893, 0.67 #326), Protestant (0.53 #929, 0.51 #1009, 0.45 #565), Hindu (0.38 #248, 0.33 #368, 0.30 #450), Buddhist (0.33 #290, 0.28 #370, 0.27 #442), Anglican (0.27 #442, 0.27 #441, 0.27 #605), ChristianOrthodox (0.27 #442, 0.27 #441, 0.27 #605), Seventh-DayAdventist (0.24 #1529, 0.22 #1248, 0.21 #695), Baptist (0.24 #1529, 0.22 #1248, 0.20 #98), United (0.24 #1529, 0.22 #1248, 0.20 #119), Jewish (0.24 #1529, 0.22 #1248, 0.19 #563)
>> best conf = 0.69
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #933 for best value:
>> intensional similarity = 5
>> extensional distance = 34
>> proper extension:  USA; ES;
>> query: (?x1731, RomanCatholic) <- ?x1731[ a Country; has ethnicGroup ?x197<European>; has religion ?x116; is locatedIn of ?x60;]

*> Best rule #290 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 10
*> proper extension:  RI; SP; EAK; CL; SY; MS; EAT; IND;
*> query: (?x1731, Buddhist) <- ?x1731[ a Country; has ethnicGroup ?x197; has government ?x907; has religion ?x116; is locatedIn of ?x60<IndianOcean>;]
*> conf = 0.33
ranks of expected_values: 4
EVAL	XMAS	religion	Buddhist	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.250	43.000	43.000	33.000	0.694	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion
PRED expected values: Buddhist
=> 77 concepts (77 used for prediction)
PRED predicted values (max 10 best out of 36): RomanCatholic (0.85 #572, 0.65 #977, 0.59 #1140), Protestant (0.71 #730, 0.69 #610, 0.54 #607), Buddhist (0.50 #212, 0.45 #1021, 0.42 #243), Hindu (0.42 #243, 0.39 #898, 0.38 #452), Jewish (0.42 #243, 0.37 #566, 0.36 #771), Anglican (0.42 #243, 0.37 #566, 0.35 #608), ChristianOrthodox (0.42 #243, 0.37 #566, 0.35 #608), JehovasWitnesses (0.42 #243, 0.37 #566, 0.29 #1867), Mormon (0.42 #243, 0.37 #566, 0.29 #1867), CopticChristian (0.42 #243, 0.37 #566, 0.29 #1867)
>> best conf = 0.85
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #572 for best value:
>> intensional similarity = 18
>> extensional distance = 11
>> proper extension:  CUR;
>> query: (?x1731, RomanCatholic) <- ?x1731[ has dependentOf ?x196[ has ethnicGroup ?x197; has religion ?x95<Protestant>; is locatedIn of ?x282;]; has religion ?x116[ is religion of ?x108[ has government ?x435;]; is religion of ?x651[ a Country; has ethnicGroup ?x1685;];]; has religion ?x187[ is religion of ?x120<D>; is religion of ?x460<RP>;]; is locatedIn of ?x60[ is locatedInWater of ?x226;];]

*> Best rule #212 for first EXPECTED value:
*> intensional similarity = 12
*> extensional distance = 2
*> proper extension:  RI;
*> query: (?x1731, Buddhist) <- ?x1731[ a Country; has encompassed ?x211<Australia-Oceania>; has ethnicGroup ?x197[ is ethnicGroup of ?x215[ a Country; has neighbor ?x296; is locatedIn of ?x214;];]; has government ?x907; has religion ?x116; is locatedIn of ?x60<IndianOcean>;]
*> conf = 0.50
ranks of expected_values: 3
EVAL	XMAS	religion	Buddhist	CNN-1.+1._MA	0.000	1.000	1.000	0.333	77.000	77.000	36.000	0.846	http://www.semwebtech.org/mondial/10/meta#religion

#702-GulfofOman
PRED entity: GulfofOman

PRED relation: locatedInWater!
PRED expected values: Gheschm
=> 30 concepts (23 used for prediction)
PRED predicted values (max 10 best out of 295): Khark (0.33 #162, 0.26 #3816, 0.25 #708), Bahrain (0.33 #235, 0.26 #3816, 0.25 #781), Gheschm (0.33 #196, 0.26 #3816, 0.25 #742), Sumatra (0.25 #334, 0.21 #1425, 0.18 #1969), Tasmania (0.25 #298, 0.08 #1389, 0.08 #1661), Lombok (0.25 #473, 0.08 #1564, 0.07 #2108), Sumbawa (0.25 #390, 0.08 #1481, 0.07 #2025), Bali (0.25 #375, 0.08 #1466, 0.07 #2010), Krakatau (0.25 #289, 0.08 #1380, 0.07 #1924), Java (0.25 #285, 0.08 #1376, 0.07 #1920)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #162 for best value:
>> intensional similarity = 8
>> extensional distance = 1
>> proper extension:  PersianGulf;
>> query: (?x926, Khark) <- ?x926[ a Sea; has locatedIn ?x83[ has language ?x559;]; has locatedIn ?x107<UAE>; has locatedIn ?x639<OM>; is locatedInWater of ?x2355;]

*> Best rule #196 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 1
*> proper extension:  PersianGulf;
*> query: (?x926, Gheschm) <- ?x926[ a Sea; has locatedIn ?x83[ has language ?x559;]; has locatedIn ?x107<UAE>; has locatedIn ?x639<OM>; is locatedInWater of ?x2355;]
*> conf = 0.33
ranks of expected_values: 3
EVAL	GulfofOman	locatedInWater!	Gheschm	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.333	30.000	23.000	295.000	0.333	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater!
PRED expected values: Gheschm
=> 91 concepts (89 used for prediction)
PRED predicted values (max 10 best out of 333): Khark (0.77 #4374, 0.33 #162, 0.25 #708), Bahrain (0.77 #4374, 0.33 #235, 0.25 #781), Gheschm (0.77 #4374, 0.33 #196, 0.25 #742), Taiwan (0.50 #878, 0.17 #4433, 0.14 #5254), Sokotra (0.33 #1899, 0.33 #531, 0.29 #2445), Sumatra (0.33 #334, 0.25 #881, 0.24 #5257), Hokkaido (0.33 #2488, 0.20 #3583, 0.19 #3856), SriLanka (0.33 #288, 0.17 #1656, 0.14 #2202), Tasmania (0.33 #298, 0.17 #1666, 0.14 #2212), Lombok (0.33 #473, 0.17 #1841, 0.14 #2387)
>> best conf = 0.77
=> the first rule below is the first best rule for 3 predicted values
>> Best rule #4374 for best value:
>> intensional similarity = 11
>> extensional distance = 16
>> proper extension:  Araguaia;
>> query: (?x926, ?x1443) <- ?x926[ is locatedInWater of ?x2355[ a Island; has locatedIn ?x304[ has ethnicGroup ?x244; has government ?x2318; has language ?x511; has neighbor ?x83; has religion ?x187; is neighbor of ?x331;]; has locatedInWater ?x918[ is locatedInWater of ?x1443;];];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 3
EVAL	GulfofOman	locatedInWater!	Gheschm	CNN-1.+1._MA	0.000	1.000	1.000	0.333	91.000	89.000	333.000	0.765	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#701-RN
PRED entity: RN

PRED relation: religion
PRED expected values: Muslim
=> 38 concepts (38 used for prediction)
PRED predicted values (max 10 best out of 34): Muslim (0.72 #468, 0.71 #426, 0.69 #510), Christian (0.69 #299, 0.51 #1139, 0.51 #1054), RomanCatholic (0.60 #641, 0.59 #387, 0.57 #683), Protestant (0.52 #381, 0.50 #592, 0.47 #635), Jewish (0.51 #1139, 0.51 #1054, 0.48 #1224), ChristianOrthodox (0.21 #928, 0.20 #970, 0.20 #1012), Buddhist (0.13 #645, 0.12 #687, 0.11 #771), Anglican (0.13 #735, 0.12 #861, 0.07 #777), Hindu (0.12 #347, 0.11 #727, 0.11 #643), JehovasWitnesses (0.11 #274, 0.10 #611, 0.09 #654)
>> best conf = 0.72
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #468 for best value:
>> intensional similarity = 8
>> extensional distance = 27
>> proper extension:  UAE;
>> query: (?x426, Muslim) <- ?x426[ has ethnicGroup ?x1109; has government ?x435; is locatedIn of ?x930[ a Desert;]; is neighbor of ?x810[ has encompassed ?x213;]; is neighbor of ?x811[ has ethnicGroup ?x2156;];]
ranks of expected_values: 1
EVAL	RN	religion	Muslim	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	38.000	38.000	34.000	0.724	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion
PRED expected values: Muslim
=> 97 concepts (97 used for prediction)
PRED predicted values (max 10 best out of 38): Muslim (0.88 #2832, 0.77 #2706, 0.76 #1068), Christian (0.88 #2832, 0.69 #1069, 0.68 #852), RomanCatholic (0.67 #1291, 0.62 #1893, 0.61 #1249), Jewish (0.64 #553, 0.62 #981, 0.57 #597), Protestant (0.57 #1243, 0.55 #685, 0.55 #642), ChristianOrthodox (0.36 #726, 0.33 #1157, 0.32 #1842), CopticChristian (0.35 #2402, 0.33 #3439, 0.32 #2014), Catholic (0.20 #463, 0.20 #379, 0.18 #2445), NewApostolic (0.20 #448, 0.18 #2445, 0.15 #2271), Adventist (0.20 #432, 0.18 #2445, 0.15 #2271)
>> best conf = 0.88
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #2832 for best value:
>> intensional similarity = 9
>> extensional distance = 67
>> proper extension:  MH; PITC; WS; NIUE; WAFU; TUV; KIR; MS; AMSA; COOK; ...
>> query: (?x426, ?x116) <- ?x426[ a Country; has encompassed ?x213; has ethnicGroup ?x1109; has government ?x435; is locatedIn of ?x2238[ has locatedIn ?x536[ a Country; has religion ?x116;]; has type ?x762;];]
ranks of expected_values: 1
EVAL	RN	religion	Muslim	CNN-1.+1._MA	1.000	1.000	1.000	1.000	97.000	97.000	38.000	0.881	http://www.semwebtech.org/mondial/10/meta#religion

#700-Malay
PRED entity: Malay

PRED relation: ethnicGroup!
PRED expected values: RI
=> 28 concepts (16 used for prediction)
PRED predicted values (max 10 best out of 220): MYA (0.50 #259, 0.41 #638, 0.40 #449), GB (0.35 #574, 0.25 #379, 0.25 #195), THA (0.33 #8, 0.25 #197, 0.22 #1332), K (0.33 #152, 0.25 #341, 0.22 #1332), CR (0.33 #60, 0.25 #249, 0.22 #1332), NAU (0.33 #179, 0.25 #368, 0.22 #1332), SLB (0.33 #73, 0.25 #262, 0.22 #1332), HONX (0.33 #140, 0.25 #329, 0.22 #1332), SME (0.33 #30, 0.25 #219, 0.22 #1332), MACX (0.33 #121, 0.25 #310, 0.22 #1332)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #259 for best value:
>> intensional similarity = 12
>> extensional distance = 2
>> proper extension:  Indian;
>> query: (?x1487, MYA) <- ?x1487[ a EthnicGroup; is ethnicGroup of ?x376<MAL>; is ethnicGroup of ?x538[ has encompassed ?x175<Asia>; has government ?x1565; has religion ?x187<Muslim>; has wasDependentOf ?x81;]; is ethnicGroup of ?x1404<SGP>;]

*> Best rule #1908 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 103
*> proper extension:  Luhya; Mestizo; Mulatto; Kimbundu; Moldovan; Belorussian; Kisii; Walloon; Meru; Fleming; ...
*> query: (?x1487, ?x91) <- ?x1487[ a EthnicGroup; is ethnicGroup of ?x376[ has government ?x92; has neighbor ?x91; has religion ?x116; has wasDependentOf ?x81; is locatedIn of ?x385[ is mergesWith of ?x339;];];]
*> conf = 0.21
ranks of expected_values: 19
EVAL	Malay	ethnicGroup!	RI	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.053	28.000	16.000	220.000	0.500	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup!
PRED expected values: RI
=> 81 concepts (79 used for prediction)
PRED predicted values (max 10 best out of 232): MYA (0.50 #1219, 0.50 #259, 0.41 #2177), UA (0.43 #2743, 0.32 #4669, 0.23 #6596), EAU (0.41 #3204, 0.26 #5322, 0.17 #8025), RI (0.38 #804, 0.28 #3265, 0.24 #10994), THA (0.33 #8, 0.29 #577, 0.28 #3265), CR (0.33 #60, 0.29 #1400, 0.25 #249), SLB (0.33 #73, 0.25 #262, 0.22 #2297), SME (0.33 #30, 0.25 #219, 0.22 #2297), K (0.33 #152, 0.25 #341, 0.22 #2297), NAU (0.33 #179, 0.25 #368, 0.22 #2297)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1219 for best value:
>> intensional similarity = 14
>> extensional distance = 12
>> proper extension:  Kazak;
>> query: (?x1487, MYA) <- ?x1487[ a EthnicGroup; is ethnicGroup of ?x376[ has neighbor ?x91; is locatedIn of ?x178;]; is ethnicGroup of ?x538[ a Country; has government ?x1565; has religion ?x116<Christian>; has religion ?x187<Muslim>; has religion ?x462<Buddhist>; is locatedIn of ?x375;];]
>> Best rule #259 for best value:
>> intensional similarity = 16
>> extensional distance = 2
>> proper extension:  Indian;
>> query: (?x1487, MYA) <- ?x1487[ is ethnicGroup of ?x376<MAL>; is ethnicGroup of ?x538[ a Country; has encompassed ?x175<Asia>; has government ?x1565; has religion ?x187<Muslim>; has religion ?x462<Buddhist>; has wasDependentOf ?x81<GB>; is locatedIn of ?x375;]; is ethnicGroup of ?x1404<SGP>;]

*> Best rule #804 for first EXPECTED value:
*> intensional similarity = 16
*> extensional distance = 6
*> proper extension:  Sundanese; Javanese; Madurese;
*> query: (?x1487, RI) <- ?x1487[ a EthnicGroup; is ethnicGroup of ?x460[ is locatedIn of ?x624[ a Island;]; is locatedIn of ?x625<SulawesiSea>;]; is ethnicGroup of ?x538[ a Country; has encompassed ?x175[ a Continent;]; has government ?x1565; has religion ?x116; has wasDependentOf ?x81[ has religion ?x95; is locatedIn of ?x121;]; is locatedIn of ?x375;];]
*> conf = 0.38
ranks of expected_values: 4
EVAL	Malay	ethnicGroup!	RI	CNN-1.+1._MA	0.000	0.000	1.000	0.250	81.000	79.000	232.000	0.500	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#699-Oahu
PRED entity: Oahu

PRED relation: type
PRED expected values: "volcanic"
=> 35 concepts (35 used for prediction)
PRED predicted values (max 10 best out of 8): "volcanic" (0.75 #2, 0.47 #66, 0.44 #50), "volcano" (0.10 #486, 0.09 #404, 0.09 #437), "dam" (0.10 #486, 0.09 #404, 0.09 #437), "salt" (0.10 #486, 0.09 #404, 0.09 #437), "atoll" (0.09 #40, 0.08 #56, 0.08 #24), "coral" (0.04 #25, 0.04 #41, 0.03 #57), "lime" (0.03 #69, 0.02 #133, 0.02 #197), "sand" (0.02 #212, 0.02 #228, 0.02 #260)
>> best conf = 0.75
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2 for best value:
>> intensional similarity = 5
>> extensional distance = 6
>> proper extension:  Niihau; Hawaii; Maui; Kauai; Lanai; Molokai;
>> query: (?x714, "volcanic") <- ?x714[ a Island; has belongsToIslands ?x1345<HawaiiIslands>; has locatedIn ?x315; has locatedInWater ?x282;]
ranks of expected_values: 1
EVAL	Oahu	type	"volcanic"	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	35.000	35.000	8.000	0.750	http://www.semwebtech.org/mondial/10/meta#type

PRED relation: type
PRED expected values: "volcanic"
=> 119 concepts (119 used for prediction)
PRED predicted values (max 10 best out of 8): "volcanic" (0.81 #114, 0.75 #2, 0.72 #34), "volcano" (0.19 #179, 0.18 #278, 0.12 #245), "atoll" (0.17 #105, 0.09 #154, 0.09 #187), "dam" (0.12 #245, 0.12 #492, 0.11 #311), "salt" (0.12 #245, 0.12 #492, 0.11 #311), "lime" (0.05 #216, 0.04 #413, 0.04 #283), "coral" (0.04 #188, 0.02 #368, 0.02 #597), "sand" (0.03 #609, 0.02 #1060, 0.02 #1092)
>> best conf = 0.81
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #114 for best value:
>> intensional similarity = 7
>> extensional distance = 28
>> proper extension:  Fakaofo;
>> query: (?x714, ?x150) <- ?x714[ a Island; has belongsToIslands ?x1345[ a Islands; is belongsToIslands of ?x723[ has type ?x150;];]; has locatedInWater ?x282<PacificOcean>;]
ranks of expected_values: 1
EVAL	Oahu	type	"volcanic"	CNN-1.+1._MA	1.000	1.000	1.000	1.000	119.000	119.000	8.000	0.812	http://www.semwebtech.org/mondial/10/meta#type

#698-Svalbard
PRED entity: Svalbard

PRED relation: locatedInWater
PRED expected values: GreenlandSea
=> 44 concepts (42 used for prediction)
PRED predicted values (max 10 best out of 29): AtlanticOcean (0.93 #503, 0.46 #285, 0.46 #251), PacificOcean (0.87 #429, 0.28 #594, 0.28 #552), GreenlandSea (0.43 #413, 0.41 #370, 0.40 #495), Svalbard (0.37 #328, 0.16 #496, 0.16 #414), KaraSea (0.33 #41, 0.33 #27, 0.08 #122), NorthSea (0.33 #44, 0.25 #125, 0.10 #582), HudsonBay (0.33 #41, 0.08 #122, 0.07 #825), EastSibirianSea (0.33 #41, 0.08 #122, 0.07 #825), CaribbeanSea (0.27 #303, 0.22 #513, 0.19 #388), MediterraneanSea (0.12 #674, 0.12 #593, 0.12 #551)
>> best conf = 0.93
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #503 for best value:
>> intensional similarity = 5
>> extensional distance = 66
>> proper extension:  SaintPierre; SaintHelena; Ascension; Greenland; Miquelon-Langlade; Faial; Tortola; MarthasVineyard; Nantucket; TristanDaCunha; ...
>> query: (?x1065, AtlanticOcean) <- ?x1065[ has locatedIn ?x973; has locatedInWater ?x373[ has mergesWith ?x1419; is locatedInWater of ?x2103<Streymoy>;];]

*> Best rule #413 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 45
*> proper extension:  PitondesNeiges; PitondelaFournaise;
*> query: (?x1065, ?x1419) <- ?x1065[ has locatedIn ?x973[ has dependentOf ?x170; has encompassed ?x195; is locatedIn of ?x1419[ has mergesWith ?x263;];];]
*> conf = 0.43
ranks of expected_values: 3
EVAL	Svalbard	locatedInWater	GreenlandSea	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.333	44.000	42.000	29.000	0.926	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater
PRED expected values: GreenlandSea
=> 75 concepts (75 used for prediction)
PRED predicted values (max 10 best out of 334): AtlanticOcean (0.93 #816, 0.52 #982, 0.51 #1024), PacificOcean (0.89 #614, 0.87 #529, 0.82 #742), LabradorSea (0.67 #140, 0.40 #266, 0.36 #352), GreenlandSea (0.47 #1488, 0.45 #1146, 0.44 #1360), NorthSea (0.29 #645, 0.25 #172, 0.25 #171), MediterraneanSea (0.26 #699, 0.20 #656, 0.20 #865), CaribbeanSea (0.23 #826, 0.17 #487, 0.15 #572), HudsonBay (0.21 #642, 0.18 #1703, 0.18 #351), KaraSea (0.18 #1703, 0.18 #1403, 0.18 #1402), Svalbard (0.16 #599, 0.16 #514, 0.15 #470)
>> best conf = 0.93
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #816 for best value:
>> intensional similarity = 9
>> extensional distance = 73
>> proper extension:  SaintPierre; SaintVincent; Ireland; Pico; Flores; GrandBermuda; Fogo; Arran; Tobago; GreatBritain; ...
>> query: (?x1065, AtlanticOcean) <- ?x1065[ has locatedInWater ?x263[ a Sea; has locatedIn ?x73[ has ethnicGroup ?x58; is neighbor of ?x194;]; has mergesWith ?x248;]; has locatedInWater ?x373[ has locatedIn ?x455<IS>;];]

*> Best rule #1488 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 230
*> proper extension:  Reunion; Sicilia; Lolland;
*> query: (?x1065, ?x182) <- ?x1065[ a Island; has locatedIn ?x973[ has government ?x1319; is locatedIn of ?x182[ is flowsInto of ?x137; is locatedInWater of ?x112;];];]
*> conf = 0.47
ranks of expected_values: 4
EVAL	Svalbard	locatedInWater	GreenlandSea	CNN-1.+1._MA	0.000	0.000	1.000	0.250	75.000	75.000	334.000	0.933	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#697-KWT
PRED entity: KWT

PRED relation: neighbor!
PRED expected values: IRQ
=> 37 concepts (37 used for prediction)
PRED predicted values (max 10 best out of 193): IRQ (0.91 #645, 0.90 #2756, 0.89 #2428), KWT (0.33 #160, 0.29 #2757, 0.28 #2921), IR (0.33 #54, 0.29 #2757, 0.28 #2921), SYR (0.33 #82, 0.29 #2757, 0.28 #2921), TR (0.33 #31, 0.29 #2757, 0.28 #2921), JOR (0.33 #126, 0.29 #2757, 0.28 #2921), UAE (0.29 #2757, 0.28 #2921, 0.27 #3411), OM (0.29 #2757, 0.28 #2921, 0.27 #3411), YE (0.29 #2757, 0.28 #2921, 0.27 #3411), Q (0.29 #2757, 0.28 #2921, 0.27 #3411)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #645 for best value:
>> intensional similarity = 8
>> extensional distance = 17
>> proper extension:  ET; RL; KGZ; Q; TR; UZB; TM; GE; MYA; MAL; ...
>> query: (?x1963, ?x302) <- ?x1963[ a Country; has encompassed ?x175<Asia>; has ethnicGroup ?x2169; has neighbor ?x302; has religion ?x187<Muslim>; has wasDependentOf ?x81;]
ranks of expected_values: 1
EVAL	KWT	neighbor!	IRQ	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	37.000	37.000	193.000	0.906	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: IRQ
=> 74 concepts (74 used for prediction)
PRED predicted values (max 10 best out of 206): IRQ (0.91 #6277, 0.91 #9293, 0.91 #8286), IL (0.60 #2347, 0.60 #1197, 0.46 #2509), MOC (0.57 #1509, 0.18 #4317, 0.17 #3319), IR (0.56 #2298, 0.56 #2189, 0.43 #1861), SYR (0.45 #4784, 0.44 #5614, 0.43 #1889), JOR (0.45 #4784, 0.44 #5614, 0.40 #1438), YE (0.45 #4784, 0.44 #5614, 0.38 #4448), OM (0.45 #4784, 0.44 #5614, 0.33 #107), UAE (0.45 #4784, 0.44 #5614, 0.31 #1475), TR (0.43 #1838, 0.41 #2985, 0.40 #1343)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #6277 for best value:
>> intensional similarity = 15
>> extensional distance = 52
>> proper extension:  NEP; I;
>> query: (?x1963, ?x302) <- ?x1963[ has neighbor ?x302[ a Country; has ethnicGroup ?x557; has religion ?x116; is locatedIn of ?x255;]; has neighbor ?x751[ has encompassed ?x175; has ethnicGroup ?x244[ a EthnicGroup;]; has language ?x1848; is neighbor of ?x668[ a Country; is locatedIn of ?x60;];]; has religion ?x187<Muslim>;]
ranks of expected_values: 1
EVAL	KWT	neighbor!	IRQ	CNN-1.+1._MA	1.000	1.000	1.000	1.000	74.000	74.000	206.000	0.914	http://www.semwebtech.org/mondial/10/meta#neighbor

#696-NordfriesischeInseln
PRED entity: NordfriesischeInseln

PRED relation: belongsToIslands!
PRED expected values: Fohr
=> 20 concepts (20 used for prediction)
PRED predicted values (max 10 best out of 265): Juist (0.33 #163, 0.25 #359, 0.22 #394), Borkum (0.33 #137, 0.25 #333, 0.22 #394), Norderney (0.33 #114, 0.25 #310, 0.22 #394), Langeoog (0.33 #87, 0.25 #283, 0.22 #394), Spiekeroog (0.33 #147, 0.25 #343, 0.17 #2764), Lipari (0.25 #378, 0.11 #971, 0.09 #1366), Alicudi (0.25 #348, 0.11 #941, 0.09 #1336), Filicudi (0.25 #339, 0.11 #932, 0.09 #1327), Vulcano (0.25 #332, 0.11 #925, 0.09 #1320), Panarea (0.25 #302, 0.11 #895, 0.09 #1290)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #163 for best value:
>> intensional similarity = 10
>> extensional distance = 1
>> proper extension:  OstfriesischeInseln;
>> query: (?x1590, Juist) <- ?x1590[ a Islands; is belongsToIslands of ?x848[ a Island; has locatedInWater ?x121<NorthSea>;]; is belongsToIslands of ?x1270[ a Island; has locatedIn ?x120<D>; has locatedInWater ?x121<NorthSea>;];]

*> Best rule #394 for first EXPECTED value:
*> intensional similarity = 12
*> extensional distance = 2
*> proper extension:  LipariIslands;
*> query: (?x1590, ?x495) <- ?x1590[ is belongsToIslands of ?x848[ a Island;]; is belongsToIslands of ?x1270[ has locatedIn ?x120[ is neighbor of ?x234<CH>;]; has locatedInWater ?x121[ has locatedIn ?x78; has mergesWith ?x182<AtlanticOcean>; is flowsInto of ?x829; is locatedInWater of ?x495;];];]
*> conf = 0.22
ranks of expected_values: 15
EVAL	NordfriesischeInseln	belongsToIslands!	Fohr	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.067	20.000	20.000	265.000	0.333	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

PRED relation: belongsToIslands!
PRED expected values: Fohr
=> 35 concepts (35 used for prediction)
PRED predicted values (max 10 best out of 265): Juist (0.33 #163, 0.25 #753, 0.25 #556), Borkum (0.33 #137, 0.25 #727, 0.25 #530), Norderney (0.33 #114, 0.25 #704, 0.25 #507), Langeoog (0.33 #87, 0.25 #677, 0.25 #480), Spiekeroog (0.33 #147, 0.25 #737, 0.25 #540), Vlieland (0.25 #566, 0.20 #1354, 0.17 #1551), Terschelling (0.25 #551, 0.20 #1339, 0.17 #1536), Schiermonnikoog (0.25 #482, 0.20 #1270, 0.17 #1467), Texel (0.25 #446, 0.20 #1234, 0.17 #1431), Ameland (0.25 #443, 0.20 #1231, 0.17 #1428)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #163 for best value:
>> intensional similarity = 7
>> extensional distance = 1
>> proper extension:  OstfriesischeInseln;
>> query: (?x1590, Juist) <- ?x1590[ a Islands; is belongsToIslands of ?x848[ a Island; has locatedIn ?x120<D>; has locatedInWater ?x121<NorthSea>;];]

*> Best rule #6501 for first EXPECTED value:
*> intensional similarity = 10
*> extensional distance = 52
*> proper extension:  Carolines; Maldives;
*> query: (?x1590, ?x70) <- ?x1590[ a Islands; is belongsToIslands of ?x848[ a Island; has locatedIn ?x120[ a Country; has encompassed ?x195; has government ?x140; is locatedIn of ?x70; is locatedIn of ?x121;]; has locatedInWater ?x121;];]
*> conf = 0.17
ranks of expected_values: 33
EVAL	NordfriesischeInseln	belongsToIslands!	Fohr	CNN-1.+1._MA	0.000	0.000	0.000	0.030	35.000	35.000	265.000	0.333	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

#695-Bioko
PRED entity: Bioko

PRED relation: type
PRED expected values: "volcanic"
=> 35 concepts (35 used for prediction)
PRED predicted values (max 10 best out of 6): "volcanic" (0.83 #338, 0.80 #274, 0.79 #370), "atoll" (0.02 #536), "volcano" (0.02 #550), "coral" (0.02 #537), "lime" (0.02 #533), "salt" (0.01 #551)
>> best conf = 0.83
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #338 for best value:
>> intensional similarity = 11
>> extensional distance = 10
>> proper extension:  Basse-Terre;
>> query: (?x772, "volcanic") <- ?x772[ a Island; is locatedOnIsland of ?x771[ a Mountain; a Volcano; has locatedIn ?x1408[ a Country; has encompassed ?x213; has government ?x435; has religion ?x352; is locatedIn of ?x182<AtlanticOcean>;];];]
ranks of expected_values: 1
EVAL	Bioko	type	"volcanic"	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	35.000	35.000	6.000	0.833	http://www.semwebtech.org/mondial/10/meta#type

PRED relation: type
PRED expected values: "volcanic"
=> 35 concepts (35 used for prediction)
PRED predicted values (max 10 best out of 6): "volcanic" (0.83 #338, 0.80 #274, 0.79 #370), "atoll" (0.02 #536), "volcano" (0.02 #550), "coral" (0.02 #537), "lime" (0.02 #533), "salt" (0.01 #551)
>> best conf = 0.83
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #338 for best value:
>> intensional similarity = 11
>> extensional distance = 10
>> proper extension:  Basse-Terre;
>> query: (?x772, "volcanic") <- ?x772[ a Island; is locatedOnIsland of ?x771[ a Mountain; a Volcano; has locatedIn ?x1408[ a Country; has encompassed ?x213; has government ?x435; has religion ?x352; is locatedIn of ?x182<AtlanticOcean>;];];]
ranks of expected_values: 1
EVAL	Bioko	type	"volcanic"	CNN-1.+1._MA	1.000	1.000	1.000	1.000	35.000	35.000	6.000	0.833	http://www.semwebtech.org/mondial/10/meta#type

#694-Irtysch
PRED entity: Irtysch

PRED relation: hasSource
PRED expected values: Irtysch
=> 45 concepts (44 used for prediction)
PRED predicted values (max 10 best out of 307): Ili (0.33 #279, 0.09 #1650, 0.06 #8001), Syrdarja (0.33 #184, 0.01 #3154, 0.01 #3839), Katun (0.25 #978, 0.24 #4799, 0.18 #7543), Argun (0.25 #1259, 0.09 #1717, 0.06 #8001), Mekong (0.09 #1827, 0.06 #8001, 0.02 #7086), Tarim-Yarkend (0.09 #1761, 0.06 #8001, 0.02 #7086), Jangtse (0.09 #1683, 0.06 #8001, 0.02 #7086), Amur (0.09 #1781, 0.06 #8001, 0.01 #6857), Angara (0.06 #8001, 0.03 #2165, 0.02 #2393), Ischim (0.06 #8001, 0.02 #7086, 0.01 #6857)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #279 for best value:
>> intensional similarity = 5
>> extensional distance = 1
>> proper extension:  Ili;
>> query: (?x1748, Ili) <- ?x1748[ has flowsInto ?x1845; has locatedIn ?x232<CN>; has locatedIn ?x403<KAZ>;]

*> Best rule #8001 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 270
*> proper extension:  GreatSlaveLake;
*> query: (?x1748, ?x874) <- ?x1748[ has flowsInto ?x1845; has locatedIn ?x232[ has religion ?x116; is locatedIn of ?x874[ a Source;];];]
*> conf = 0.06
ranks of expected_values: 11
EVAL	Irtysch	hasSource	Irtysch	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.091	45.000	44.000	307.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasSource

PRED relation: hasSource
PRED expected values: Irtysch
=> 110 concepts (110 used for prediction)
PRED predicted values (max 10 best out of 332): Katun (0.38 #1379, 0.26 #8269, 0.21 #14011), Ili (0.25 #741, 0.17 #1200, 0.12 #1892), Ischim (0.25 #1076, 0.17 #1306, 0.12 #1998), Syrdarja (0.17 #1333, 0.12 #2025, 0.12 #1794), Amudarja (0.12 #2055, 0.11 #2284, 0.04 #3891), Naryn (0.12 #1776, 0.04 #3384, 0.04 #3614), Kura (0.11 #2092, 0.03 #4387), Amur (0.09 #2481, 0.08 #2940, 0.08 #2710), Tarim-Yarkend (0.09 #2461, 0.08 #2690, 0.05 #15165), Argun (0.09 #2417, 0.08 #2646, 0.05 #15165)
>> best conf = 0.38
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1379 for best value:
>> intensional similarity = 11
>> extensional distance = 4
>> proper extension:  Ili; Syrdarja;
>> query: (?x1748, ?x1038) <- ?x1748[ a River; has flowsInto ?x1845[ is flowsInto of ?x2143[ has hasEstuary ?x2144; has hasSource ?x1038;];]; has hasEstuary ?x1213[ a Estuary;]; has locatedIn ?x73[ has ethnicGroup ?x58;]; has locatedIn ?x403<KAZ>;]

*> Best rule #15165 for first EXPECTED value:
*> intensional similarity = 12
*> extensional distance = 233
*> proper extension:  Kemijoki; Inari; Ounasjoki; Kallavesi; Oulujaervi; Kokemaeenjoki; Paeijaenne; Oulujoki; Kymijoki;
*> query: (?x1748, ?x1256) <- ?x1748[ has flowsInto ?x1845; has locatedIn ?x73[ has encompassed ?x195;]; has locatedIn ?x232[ has neighbor ?x334; is locatedIn of ?x1256[ a Source;]; is neighbor of ?x83;]; has locatedIn ?x403[ has ethnicGroup ?x58; is locatedIn of ?x1337[ is flowsInto of ?x445;];];]
*> conf = 0.05
ranks of expected_values: 17
EVAL	Irtysch	hasSource	Irtysch	CNN-1.+1._MA	0.000	0.000	0.000	0.059	110.000	110.000	332.000	0.375	http://www.semwebtech.org/mondial/10/meta#hasSource

#693-EmiKussi
PRED entity: EmiKussi

PRED relation: type
PRED expected values: "volcano"
=> 45 concepts (45 used for prediction)
PRED predicted values (max 10 best out of 10): "volcano" (0.60 #135, 0.56 #151, 0.56 #103), "volcanic" (0.50 #18, 0.39 #67, 0.38 #83), "sand" (0.45 #227, 0.24 #372, 0.19 #453), "salt" (0.45 #227, 0.24 #372, 0.19 #453), "dam" (0.06 #33, 0.03 #373, 0.03 #437), "caldera" (0.06 #35, 0.01 #439), "granite" (0.03 #62, 0.02 #289, 0.01 #353), "monolith" (0.03 #59), "atoll" (0.02 #444, 0.01 #428, 0.01 #509), "lime" (0.01 #506, 0.01 #441)
>> best conf = 0.60
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #135 for best value:
>> intensional similarity = 6
>> extensional distance = 51
>> proper extension:  MtHood;
>> query: (?x168, "volcano") <- ?x168[ a Mountain; a Volcano; has locatedIn ?x169[ has neighbor ?x139[ has encompassed ?x213;]; has wasDependentOf ?x78;];]
ranks of expected_values: 1
EVAL	EmiKussi	type	"volcano"	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	45.000	45.000	10.000	0.604	http://www.semwebtech.org/mondial/10/meta#type

PRED relation: type
PRED expected values: "volcano"
=> 72 concepts (72 used for prediction)
PRED predicted values (max 10 best out of 9): "salt" (0.71 #98, 0.50 #49, 0.15 #627), "sand" (0.71 #98, 0.50 #49, 0.15 #627), "volcano" (0.62 #184, 0.60 #328, 0.59 #232), "volcanic" (0.57 #83, 0.56 #116, 0.38 #244), "dam" (0.06 #131, 0.06 #163, 0.03 #435), "caldera" (0.06 #133, 0.03 #165), "granite" (0.03 #224, 0.02 #480, 0.02 #304), "monolith" (0.03 #221, 0.01 #445, 0.01 #477), "impact" (0.01 #444)
>> best conf = 0.71
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #98 for best value:
>> intensional similarity = 8
>> extensional distance = 5
>> proper extension:  Andringitra; Tsiafajavona; Tsaratanana;
>> query: (?x168, ?x578) <- ?x168[ a Mountain; a Volcano; has locatedIn ?x169[ has government ?x435; has wasDependentOf ?x78<F>; is locatedIn of ?x930[ has type ?x578;];];]

*> Best rule #184 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 32
*> proper extension:  QueenMarysPeak; SoufriereHills;
*> query: (?x168, "volcano") <- ?x168[ a Mountain; a Volcano; has locatedIn ?x169[ a Country; has encompassed ?x213; is locatedIn of ?x695[ has locatedIn ?x536<CAM>;];];]
*> conf = 0.62
ranks of expected_values: 3
EVAL	EmiKussi	type	"volcano"	CNN-1.+1._MA	0.000	1.000	1.000	0.333	72.000	72.000	9.000	0.714	http://www.semwebtech.org/mondial/10/meta#type

#692-Samar
PRED entity: Samar

PRED relation: locatedInWater
PRED expected values: PacificOcean
=> 39 concepts (38 used for prediction)
PRED predicted values (max 10 best out of 42): PacificOcean (0.73 #272, 0.64 #690, 0.63 #315), SouthChinaSea (0.64 #690, 0.41 #602, 0.25 #733), IndianOcean (0.39 #212, 0.26 #300, 0.09 #692), AtlanticOcean (0.29 #697, 0.28 #480, 0.28 #437), JavaSea (0.25 #733, 0.19 #822, 0.16 #219), BandaSea (0.25 #733, 0.19 #822, 0.11 #154), NorthSea (0.16 #345, 0.09 #476, 0.09 #433), MediterraneanSea (0.11 #706, 0.11 #838, 0.11 #749), CaribbeanSea (0.10 #666, 0.10 #841, 0.09 #492), EastChinaSea (0.10 #237, 0.09 #776, 0.04 #282)
>> best conf = 0.73
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #272 for best value:
>> intensional similarity = 5
>> extensional distance = 65
>> proper extension:  Tasmania; Niue; Taiwan; Hainan; NewGuinea; Singapore; VancouverIsland; EasterIsland; Banaba; Phuket; ...
>> query: (?x880, PacificOcean) <- ?x880[ a Island; has locatedIn ?x460; has locatedInWater ?x625[ has mergesWith ?x677<SuluSea>;];]
ranks of expected_values: 1
EVAL	Samar	locatedInWater	PacificOcean	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	39.000	38.000	42.000	0.731	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater
PRED expected values: PacificOcean
=> 94 concepts (94 used for prediction)
PRED predicted values (max 10 best out of 48): PacificOcean (0.86 #536, 0.86 #624, 0.68 #2055), SouthChinaSea (0.68 #2055, 0.67 #1836, 0.64 #2187), AtlanticOcean (0.43 #747, 0.42 #790, 0.41 #1226), IndianOcean (0.25 #915, 0.17 #654, 0.16 #341), BandaSea (0.21 #282, 0.18 #2544, 0.18 #2543), NorthSea (0.20 #1089, 0.19 #699, 0.18 #829), CaribbeanSea (0.19 #975, 0.18 #759, 0.18 #802), JavaSea (0.19 #439, 0.18 #2544, 0.18 #2543), SuluSea (0.16 #341, 0.13 #343, 0.13 #342), EastChinaSea (0.16 #341, 0.09 #2405, 0.09 #2451)
>> best conf = 0.86
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #536 for best value:
>> intensional similarity = 12
>> extensional distance = 55
>> proper extension:  Tasmania; Niue; Taiwan; NewGuinea; VancouverIsland; EasterIsland; Banaba; Nauru;
>> query: (?x880, PacificOcean) <- ?x880[ a Island; has locatedIn ?x460; has locatedInWater ?x625[ is locatedInWater of ?x375[ a Island; is locatedOnIsland of ?x1526;]; is locatedInWater of ?x765<Leyte>; is locatedInWater of ?x1158<Mindanao>; is mergesWith of ?x677<SuluSea>;];]
ranks of expected_values: 1
EVAL	Samar	locatedInWater	PacificOcean	CNN-1.+1._MA	1.000	1.000	1.000	1.000	94.000	94.000	48.000	0.860	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#691-H
PRED entity: H

PRED relation: neighbor!
PRED expected values: SRB
=> 45 concepts (45 used for prediction)
PRED predicted values (max 10 best out of 200): SRB (0.91 #3125, 0.91 #5641, 0.90 #2813), H (0.57 #825, 0.50 #355, 0.40 #512), BG (0.40 #494, 0.33 #181, 0.29 #4383), PL (0.33 #32, 0.29 #4383, 0.29 #814), CZ (0.33 #77, 0.29 #4383, 0.27 #5168), BIH (0.29 #4383, 0.29 #783, 0.27 #5168), MNE (0.29 #4383, 0.29 #792, 0.27 #5168), R (0.29 #4383, 0.27 #5168, 0.27 #4068), MD (0.29 #4383, 0.27 #5168, 0.27 #4068), I (0.29 #4383, 0.27 #4068, 0.26 #5484)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3125 for best value:
>> intensional similarity = 6
>> extensional distance = 73
>> proper extension:  PK;
>> query: (?x236, ?x904) <- ?x236[ a Country; has government ?x254; has language ?x684; has neighbor ?x904; is neighbor of ?x303[ is locatedIn of ?x97;];]
ranks of expected_values: 1
EVAL	H	neighbor!	SRB	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	45.000	45.000	200.000	0.907	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: SRB
=> 91 concepts (91 used for prediction)
PRED predicted values (max 10 best out of 216): SRB (0.93 #10123, 0.93 #4142, 0.93 #8181), D (0.60 #1602, 0.57 #2082, 0.50 #1743), H (0.50 #1743, 0.50 #1472, 0.50 #991), PL (0.50 #1301, 0.50 #1142, 0.45 #2580), BG (0.50 #1743, 0.50 #1454, 0.40 #1903), MD (0.50 #1743, 0.40 #1903, 0.38 #9474), I (0.50 #1743, 0.40 #1903, 0.37 #9475), R (0.43 #4471, 0.39 #4140, 0.39 #3986), CZ (0.40 #1664, 0.40 #632, 0.38 #9474), MNE (0.40 #632, 0.38 #9474, 0.37 #9475)
>> best conf = 0.93
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #10123 for best value:
>> intensional similarity = 13
>> extensional distance = 74
>> proper extension:  SD; ARM; BHT; BZ;
>> query: (?x236, ?x904) <- ?x236[ a Country; has ethnicGroup ?x164; has government ?x254; has neighbor ?x446[ has language ?x738; is locatedIn of ?x155;]; has neighbor ?x904[ has ethnicGroup ?x2213; has neighbor ?x55; has religion ?x56; has wasDependentOf ?x1197; is locatedIn of ?x2175[ a Estuary;];];]
ranks of expected_values: 1
EVAL	H	neighbor!	SRB	CNN-1.+1._MA	1.000	1.000	1.000	1.000	91.000	91.000	216.000	0.933	http://www.semwebtech.org/mondial/10/meta#neighbor

#690-BEN
PRED entity: BEN

PRED relation: neighbor!
PRED expected values: RT
=> 40 concepts (40 used for prediction)
PRED predicted values (max 10 best out of 209): RT (0.91 #2076, 0.90 #4171, 0.89 #4334), CAM (0.43 #160, 0.40 #480, 0.40 #410), BEN (0.43 #160, 0.33 #126, 0.28 #4010), GH (0.43 #160, 0.28 #2558, 0.26 #4333), CI (0.43 #160, 0.28 #2558, 0.26 #4333), ZRE (0.43 #160, 0.27 #380, 0.12 #861), RCB (0.43 #160, 0.27 #409, 0.12 #890), RG (0.43 #160, 0.24 #909, 0.20 #589), SN (0.43 #160, 0.20 #555, 0.19 #714), RIM (0.43 #160, 0.20 #567, 0.19 #726)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2076 for best value:
>> intensional similarity = 6
>> extensional distance = 41
>> proper extension:  MNE; N; SME; GCA; SUD; MOC; RCH; CO; RI; PE; ...
>> query: (?x810, ?x139) <- ?x810[ has ethnicGroup ?x162; has government ?x435; has neighbor ?x139; has wasDependentOf ?x78[ is locatedIn of ?x121; is neighbor of ?x120;];]
ranks of expected_values: 1
EVAL	BEN	neighbor!	RT	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	40.000	40.000	209.000	0.909	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: RT
=> 100 concepts (100 used for prediction)
PRED predicted values (max 10 best out of 225): RT (0.92 #11176, 0.91 #14327, 0.91 #4412), TCH (0.71 #1162, 0.40 #1135, 0.40 #998), RCA (0.60 #1095, 0.33 #607, 0.33 #444), RMM (0.50 #1433, 0.40 #2280, 0.35 #3065), CI (0.50 #799, 0.33 #148, 0.32 #1462), SN (0.50 #725, 0.25 #1377, 0.23 #3754), CAM (0.40 #2280, 0.36 #2119, 0.35 #3260), RG (0.40 #2280, 0.29 #3043, 0.28 #3369), SSD (0.40 #1018, 0.18 #3141, 0.15 #6601), BEN (0.36 #2119, 0.35 #3260, 0.33 #290)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #11176 for best value:
>> intensional similarity = 12
>> extensional distance = 83
>> proper extension:  Q;
>> query: (?x810, ?x426) <- ?x810[ a Country; has encompassed ?x213; has neighbor ?x426[ has ethnicGroup ?x1109; has neighbor ?x581; is locatedIn of ?x535;]; has neighbor ?x811[ a Country; has ethnicGroup ?x2156;]; is locatedIn of ?x182[ a Sea; is locatedInWater of ?x112;];]
ranks of expected_values: 1
EVAL	BEN	neighbor!	RT	CNN-1.+1._MA	1.000	1.000	1.000	1.000	100.000	100.000	225.000	0.915	http://www.semwebtech.org/mondial/10/meta#neighbor

#689-MA
PRED entity: MA

PRED relation: neighbor!
PRED expected values: CEU
=> 37 concepts (37 used for prediction)
PRED predicted values (max 10 best out of 208): CEU (0.90 #3039, 0.90 #3038, 0.89 #640), AND (0.50 #282, 0.17 #441, 0.12 #1241), SN (0.33 #555, 0.33 #75, 0.29 #715), RN (0.33 #558, 0.32 #1515, 0.29 #718), RMM (0.33 #131, 0.29 #771, 0.28 #480), BF (0.29 #767, 0.21 #1564, 0.17 #607), TCH (0.29 #663, 0.17 #503, 0.13 #981), RIM (0.28 #480, 0.27 #3841, 0.22 #1599), LAR (0.28 #480, 0.27 #3841, 0.22 #1599), TN (0.28 #480, 0.27 #3841, 0.22 #1599)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3039 for best value:
>> intensional similarity = 6
>> extensional distance = 100
>> proper extension:  NAM; G; BI; RCB; ROK; OM; MW;
>> query: (?x851, ?x646) <- ?x851[ has government ?x92; has neighbor ?x646[ a Country;]; has religion ?x109; has wasDependentOf ?x78; is locatedIn of ?x182;]
>> Best rule #3038 for best value:
>> intensional similarity = 7
>> extensional distance = 100
>> proper extension:  NAM; G; BI; RCB; ROK; OM; MW;
>> query: (?x851, ?x581) <- ?x851[ has government ?x92; has neighbor ?x581; has neighbor ?x646[ a Country;]; has religion ?x109; has wasDependentOf ?x78; is locatedIn of ?x182;]
ranks of expected_values: 1
EVAL	MA	neighbor!	CEU	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	37.000	37.000	208.000	0.898	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: CEU
=> 99 concepts (99 used for prediction)
PRED predicted values (max 10 best out of 226): CEU (0.92 #5385, 0.90 #6852, 0.90 #11261), RMM (0.67 #485, 0.50 #2079, 0.44 #2242), LAR (0.67 #485, 0.35 #1457, 0.33 #1607), RIM (0.67 #485, 0.35 #1457, 0.33 #88), TN (0.67 #485, 0.35 #1457, 0.33 #12), RN (0.67 #485, 0.35 #1457, 0.33 #78), MA (0.67 #485, 0.35 #1457, 0.33 #132), ET (0.67 #485, 0.33 #490, 0.25 #973), SYR (0.67 #485, 0.33 #1541, 0.25 #1053), JOR (0.67 #485, 0.25 #1096, 0.25 #934)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5385 for best value:
>> intensional similarity = 11
>> extensional distance = 20
>> proper extension:  OM;
>> query: (?x851, ?x581) <- ?x851[ a Country; has government ?x92; has neighbor ?x581; has religion ?x109[ a Religion;]; is locatedIn of ?x275[ has locatedIn ?x207[ has language ?x51; is wasDependentOf of ?x1165;];]; is locatedIn of ?x1681[ a Desert;];]
ranks of expected_values: 1
EVAL	MA	neighbor!	CEU	CNN-1.+1._MA	1.000	1.000	1.000	1.000	99.000	99.000	226.000	0.924	http://www.semwebtech.org/mondial/10/meta#neighbor

#688-BOL
PRED entity: BOL

PRED relation: ethnicGroup
PRED expected values: Mestizo
=> 43 concepts (43 used for prediction)
PRED predicted values (max 10 best out of 240): African (0.50 #261, 0.42 #771, 0.40 #6), Amerindian (0.40 #2, 0.33 #257, 0.29 #767), Mestizo (0.40 #35, 0.33 #290, 0.29 #800), Mulatto (0.33 #313, 0.20 #58, 0.17 #7652), Russian (0.21 #2877, 0.20 #3897, 0.19 #3642), German (0.15 #1539, 0.14 #2304, 0.12 #3834), Ukrainian (0.15 #3826, 0.14 #3571, 0.13 #1531), Arab (0.14 #520, 0.08 #1030, 0.08 #1795), Chinese (0.12 #10216, 0.11 #9961, 0.10 #5115), Polish (0.10 #3774, 0.10 #3009, 0.10 #1734)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #261 for best value:
>> intensional similarity = 5
>> extensional distance = 4
>> proper extension:  BR;
>> query: (?x690, African) <- ?x690[ has ethnicGroup ?x197; is locatedIn of ?x1626[ has type ?x706;]; is neighbor of ?x296<PE>;]

*> Best rule #35 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 3
*> proper extension:  RCH; CO; EC;
*> query: (?x690, Mestizo) <- ?x690[ has neighbor ?x296<PE>; has wasDependentOf ?x149; is locatedIn of ?x689[ a Mountain;];]
*> conf = 0.40
ranks of expected_values: 3
EVAL	BOL	ethnicGroup	Mestizo	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.333	43.000	43.000	240.000	0.500	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: Mestizo
=> 143 concepts (143 used for prediction)
PRED predicted values (max 10 best out of 252): Amerindian (0.67 #1787, 0.60 #1277, 0.54 #5360), Mestizo (0.56 #7946, 0.56 #3606, 0.53 #9479), African (0.50 #14299, 0.50 #1536, 0.48 #7144), Mulatto (0.48 #7144, 0.48 #8167, 0.44 #7145), Chinese (0.48 #8167, 0.36 #24264, 0.27 #16348), Creole (0.48 #8167, 0.18 #22207, 0.15 #29617), Europeans (0.48 #8167, 0.18 #22207, 0.15 #29617), Hindustani (0.48 #8167, 0.18 #22207, 0.15 #29617), Russian (0.35 #7727, 0.33 #9005, 0.29 #12578), German (0.30 #12005, 0.28 #13026, 0.28 #12771)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1787 for best value:
>> intensional similarity = 14
>> extensional distance = 4
>> proper extension:  NIC;
>> query: (?x690, Amerindian) <- ?x690[ has ethnicGroup ?x197<European>; has language ?x702; has religion ?x352<RomanCatholic>; has wasDependentOf ?x149; is locatedIn of ?x1578[ has flowsInto ?x214; has hasEstuary ?x1579;]; is locatedIn of ?x1626[ a Volcano;]; is locatedIn of ?x2164[ a Source;]; is neighbor of ?x202;]

*> Best rule #7946 for first EXPECTED value:
*> intensional similarity = 13
*> extensional distance = 16
*> proper extension:  ES;
*> query: (?x690, Mestizo) <- ?x690[ a Country; has language ?x702; has religion ?x352; is locatedIn of ?x274; is neighbor of ?x404[ a Country;]; is neighbor of ?x542[ has language ?x539; has neighbor ?x215[ has ethnicGroup ?x79<Amerindian>; is locatedIn of ?x282;]; is locatedIn of ?x48;];]
*> conf = 0.56
ranks of expected_values: 2
EVAL	BOL	ethnicGroup	Mestizo	CNN-1.+1._MA	0.000	1.000	1.000	0.500	143.000	143.000	252.000	0.667	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#687-Mindanao
PRED entity: Mindanao

PRED relation: locatedOnIsland!
PRED expected values: MountApo
=> 47 concepts (47 used for prediction)
PRED predicted values (max 10 best out of 30): Mantalingajan (0.14 #80, 0.12 #144, 0.11 #208), Kanlaon (0.14 #85, 0.12 #149, 0.11 #213), Rantekombola (0.11 #226, 0.01 #482), Kinabalu (0.11 #233), Pinatubo (0.06 #306, 0.03 #2001, 0.02 #2002), Pulog (0.06 #262, 0.03 #2001, 0.02 #2002), MountApo (0.03 #2001, 0.02 #2002, 0.02 #1738), Haleakala (0.02 #373, 0.02 #437, 0.01 #501), Ruapehu (0.02 #372, 0.02 #436, 0.01 #500), MaunaKea (0.02 #331, 0.02 #395, 0.01 #459)
>> best conf = 0.14
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #80 for best value:
>> intensional similarity = 6
>> extensional distance = 5
>> proper extension:  Panay; Samar; Negros; Palawan; Bohol;
>> query: (?x1158, Mantalingajan) <- ?x1158[ a Island; has belongsToIslands ?x370<Philipines>; has locatedIn ?x460; has locatedInWater ?x625<SulawesiSea>;]

*> Best rule #2001 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 441
*> proper extension:  Tahat; Pibor; Ene; Poopo; LakeNyos; Colorado; Apurimac; Orinoco; Tambo; RioMamore; ...
*> query: (?x1158, ?x1670) <- ?x1158[ has locatedIn ?x460[ has government ?x435; has religion ?x95; is locatedIn of ?x1670[ a Mountain; has type ?x706;];];]
*> conf = 0.03
ranks of expected_values: 7
EVAL	Mindanao	locatedOnIsland!	MountApo	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.143	47.000	47.000	30.000	0.143	http://www.semwebtech.org/mondial/10/meta#locatedOnIsland

PRED relation: locatedOnIsland!
PRED expected values: MountApo
=> 67 concepts (67 used for prediction)
PRED predicted values (max 10 best out of 29): Mt.Wilhelm (0.25 #126, 0.02 #2011, 0.02 #382), PuncakJaya (0.25 #120, 0.02 #2011, 0.02 #376), Mt.Giluwe (0.25 #115, 0.02 #2011, 0.02 #371), YuShan (0.25 #157, 0.02 #2011, 0.02 #349), Mantalingajan (0.06 #208, 0.04 #1428, 0.03 #1231), Pulog (0.06 #198, 0.04 #1428, 0.03 #1231), Pinatubo (0.06 #242, 0.04 #1428, 0.03 #1231), Kanlaon (0.06 #213, 0.04 #1428, 0.02 #2011), MountApo (0.04 #1428, 0.03 #1231, 0.02 #774), Asahi-Dake (0.02 #2011, 0.02 #294, 0.02 #358)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #126 for best value:
>> intensional similarity = 11
>> extensional distance = 2
>> proper extension:  NewGuinea;
>> query: (?x1158, Mt.Wilhelm) <- ?x1158[ a Island; has locatedIn ?x460; has locatedInWater ?x282<PacificOcean>; has locatedInWater ?x625[ has mergesWith ?x241; is locatedInWater of ?x624[ a Island; has belongsToIslands ?x370;]; is locatedInWater of ?x1005<Sulawesi>;];]

*> Best rule #1428 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 149
*> proper extension:  Principe; Gotland; Kreta; SaoTome;
*> query: (?x1158, ?x752) <- ?x1158[ a Island; has locatedIn ?x460[ has ethnicGroup ?x298; has religion ?x95; is locatedIn of ?x625[ a Sea;]; is locatedIn of ?x752[ a Mountain;];];]
*> conf = 0.04
ranks of expected_values: 9
EVAL	Mindanao	locatedOnIsland!	MountApo	CNN-1.+1._MA	0.000	0.000	1.000	0.111	67.000	67.000	29.000	0.250	http://www.semwebtech.org/mondial/10/meta#locatedOnIsland

#686-Tupungato
PRED entity: Tupungato

PRED relation: inMountains
PRED expected values: Andes
=> 55 concepts (55 used for prediction)
PRED predicted values (max 10 best out of 52): Andes (0.67 #272, 0.60 #185, 0.50 #98), RockyMountains (0.15 #616, 0.06 #1921, 0.06 #1660), CordilleraVolcanica (0.08 #761, 0.07 #848, 0.07 #587), EastAfricanRift (0.07 #811, 0.06 #724, 0.05 #985), Alps (0.06 #2005, 0.06 #1657, 0.05 #1831), CanaryIslands (0.05 #578, 0.05 #665, 0.05 #752), Hawaii (0.05 #590, 0.03 #677, 0.03 #1286), EliasRange (0.05 #624, 0.02 #1929, 0.02 #1668), Himalaya (0.04 #2007, 0.04 #1833, 0.04 #2355), CascadeRange (0.03 #563, 0.03 #650, 0.03 #737)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #272 for best value:
>> intensional similarity = 3
>> extensional distance = 4
>> proper extension:  Aconcagua;
>> query: (?x2452, Andes) <- ?x2452[ a Mountain; has locatedIn ?x379<RA>;]
ranks of expected_values: 1
EVAL	Tupungato	inMountains	Andes	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	55.000	55.000	52.000	0.667	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains
PRED expected values: Andes
=> 157 concepts (157 used for prediction)
PRED predicted values (max 10 best out of 72): Andes (0.67 #446, 0.60 #359, 0.57 #533), CordilleraVolcanica (0.24 #2241, 0.18 #2502, 0.14 #674), CanaryIslands (0.23 #1013, 0.19 #1448, 0.19 #1361), CordilleraReal (0.22 #757, 0.05 #3717, 0.05 #2237), Cevennes (0.18 #947, 0.18 #860, 0.13 #1208), BrazilianHighlands (0.14 #609, 0.04 #2437, 0.03 #2698), Hawaii (0.13 #1286, 0.12 #1373, 0.10 #2070), CascadeRange (0.13 #1259, 0.12 #1346, 0.10 #2043), Alps (0.12 #6274, 0.11 #7057, 0.11 #5053), Himalaya (0.12 #3923, 0.05 #8625, 0.04 #6014)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #446 for best value:
>> intensional similarity = 3
>> extensional distance = 4
>> proper extension:  Aconcagua;
>> query: (?x2452, Andes) <- ?x2452[ a Mountain; has locatedIn ?x379<RA>;]
ranks of expected_values: 1
EVAL	Tupungato	inMountains	Andes	CNN-1.+1._MA	1.000	1.000	1.000	1.000	157.000	157.000	72.000	0.667	http://www.semwebtech.org/mondial/10/meta#inMountains

#685-Neckar
PRED entity: Neckar

PRED relation: flowsInto
PRED expected values: Rhein
=> 29 concepts (25 used for prediction)
PRED predicted values (max 10 best out of 116): Donau (0.32 #8, 0.29 #173, 0.08 #503), Weser (0.14 #303, 0.11 #138, 0.04 #468), Inn (0.11 #80, 0.10 #245, 0.03 #2487), Rhein (0.11 #19, 0.05 #184, 0.04 #349), AtlanticOcean (0.09 #1339, 0.09 #2333, 0.09 #2167), BalticSea (0.05 #10, 0.05 #175, 0.05 #1833), Isar (0.05 #105, 0.05 #270, 0.03 #2487), Mosel (0.05 #78, 0.05 #243, 0.03 #2487), NorthSea (0.05 #6, 0.05 #171, 0.02 #3986), BlackSea (0.05 #168, 0.03 #832, 0.02 #998)
>> best conf = 0.32
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #8 for best value:
>> intensional similarity = 6
>> extensional distance = 17
>> proper extension:  Main;
>> query: (?x157, Donau) <- ?x157[ a River; has hasEstuary ?x909[ a Estuary; has locatedIn ?x120<D>;]; has locatedIn ?x120<D>;]

*> Best rule #19 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 17
*> proper extension:  Main;
*> query: (?x157, Rhein) <- ?x157[ a River; has hasEstuary ?x909[ a Estuary; has locatedIn ?x120<D>;]; has locatedIn ?x120<D>;]
*> conf = 0.11
ranks of expected_values: 4
EVAL	Neckar	flowsInto	Rhein	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.250	29.000	25.000	116.000	0.316	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto
PRED expected values: Rhein
=> 103 concepts (102 used for prediction)
PRED predicted values (max 10 best out of 190): Donau (0.33 #1163, 0.33 #668, 0.32 #833), Weser (0.30 #303, 0.25 #633, 0.22 #138), AtlanticOcean (0.14 #2512, 0.11 #6550, 0.10 #8062), MediterraneanSea (0.12 #2188, 0.11 #2356, 0.08 #2523), BalticSea (0.11 #8217, 0.10 #7545, 0.10 #6706), NorthSea (0.11 #8217, 0.10 #7545, 0.10 #6706), Inn (0.11 #740, 0.11 #80, 0.11 #905), Isar (0.11 #105, 0.10 #435, 0.10 #270), Rhein (0.11 #844, 0.10 #6363, 0.09 #2332), Ammer (0.10 #6363, 0.09 #2332, 0.09 #1492)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1163 for best value:
>> intensional similarity = 13
>> extensional distance = 22
>> proper extension:  Waag;
>> query: (?x157, Donau) <- ?x157[ a River; has locatedIn ?x120[ has government ?x140; has religion ?x95<Protestant>; is locatedIn of ?x443[ a Island;]; is locatedIn of ?x558[ is flowsInto of ?x394;]; is locatedIn of ?x1139[ a Mountain;]; is neighbor of ?x471<CZ>;];]
>> Best rule #668 for best value:
>> intensional similarity = 9
>> extensional distance = 16
>> proper extension:  Saar; Iller; Mosel; Inn; Lech; Oder; Isar; Salzach; Elbe;
>> query: (?x157, Donau) <- ?x157[ a River; has hasEstuary ?x909[ a Estuary; has locatedIn ?x120<D>;]; has hasSource ?x382[ a Source;]; has locatedIn ?x120<D>;]

*> Best rule #844 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 17
*> proper extension:  Main;
*> query: (?x157, Rhein) <- ?x157[ a River; has hasEstuary ?x909[ a Estuary; has locatedIn ?x120<D>;]; has locatedIn ?x120<D>;]
*> conf = 0.11
ranks of expected_values: 9
EVAL	Neckar	flowsInto	Rhein	CNN-1.+1._MA	0.000	0.000	1.000	0.111	103.000	102.000	190.000	0.333	http://www.semwebtech.org/mondial/10/meta#flowsInto

#684-Buna
PRED entity: Buna

PRED relation: flowsInto!
PRED expected values: LakeSkutari
=> 35 concepts (25 used for prediction)
PRED predicted values (max 10 best out of 101): Drin (0.25 #95, 0.06 #397, 0.05 #700), Moraca (0.25 #297, 0.06 #599, 0.05 #902), Nile (0.25 #289, 0.06 #591, 0.05 #894), Arno (0.25 #259, 0.06 #561, 0.05 #864), Ebro (0.25 #247, 0.06 #549, 0.05 #852), Rhone (0.25 #178, 0.06 #480, 0.05 #783), Tiber (0.25 #136, 0.06 #438, 0.05 #741), Etsch (0.25 #112, 0.06 #414, 0.05 #717), Po (0.25 #96, 0.06 #398, 0.05 #701), LakeOhrid (0.06 #503, 0.05 #908, 0.05 #806)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #95 for best value:
>> intensional similarity = 4
>> extensional distance = 2
>> proper extension:  LakeSkutari; MediterraneanSea;
>> query: (?x203, Drin) <- ?x203[ has locatedIn ?x106<MNE>; has locatedIn ?x204<AL>;]

*> Best rule #1818 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 73
*> proper extension:  Guernsey;
*> query: (?x203, ?x105) <- ?x203[ has locatedIn ?x106[ is locatedIn of ?x105;]; has locatedIn ?x204[ has encompassed ?x195; has government ?x254<"parliamentary democracy">;];]
*> conf = 0.02
ranks of expected_values: 14
EVAL	Buna	flowsInto!	LakeSkutari	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.071	35.000	25.000	101.000	0.250	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto!
PRED expected values: LakeSkutari
=> 109 concepts (109 used for prediction)
PRED predicted values (max 10 best out of 261): LakeOhrid (0.25 #807, 0.25 #504, 0.12 #2019), Drin (0.25 #1004, 0.16 #3032, 0.11 #3640), BlackDrin (0.25 #693, 0.16 #3032, 0.10 #2208), WhiteDrin (0.25 #726, 0.16 #3032, 0.10 #2241), Moraca (0.25 #1206, 0.11 #3640, 0.10 #4554), Nile (0.25 #1198, 0.11 #3640, 0.09 #2713), Arno (0.25 #1168, 0.11 #3640, 0.09 #2683), Ebro (0.25 #1156, 0.11 #3640, 0.09 #2671), Rhone (0.25 #1087, 0.11 #3640, 0.09 #2602), Tiber (0.25 #1045, 0.11 #3640, 0.09 #2560)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #807 for best value:
>> intensional similarity = 12
>> extensional distance = 2
>> proper extension:  Drin;
>> query: (?x203, LakeOhrid) <- ?x203[ a River; has hasEstuary ?x1934; has locatedIn ?x106[ a Country; has ethnicGroup ?x1472<Albanian>; is locatedIn of ?x104<LakeSkutari>; is neighbor of ?x692<KOS>;]; has locatedIn ?x204<AL>;]
>> Best rule #504 for best value:
>> intensional similarity = 12
>> extensional distance = 2
>> proper extension:  BlackDrin; WhiteDrin;
>> query: (?x203, LakeOhrid) <- ?x203[ a River; has hasSource ?x183[ a Source;]; has locatedIn ?x106[ has encompassed ?x195; has ethnicGroup ?x775; has language ?x1251; has neighbor ?x692<KOS>; has religion ?x56;]; has locatedIn ?x204<AL>;]

*> Best rule #909 for first EXPECTED value:
*> intensional similarity = 13
*> extensional distance = 2
*> proper extension:  Drin;
*> query: (?x203, ?x105) <- ?x203[ a River; has hasEstuary ?x1934; has locatedIn ?x106[ a Country; has ethnicGroup ?x1472<Albanian>; is locatedIn of ?x104<LakeSkutari>; is locatedIn of ?x105; is neighbor of ?x692<KOS>;]; has locatedIn ?x204<AL>;]
*> conf = 0.09
ranks of expected_values: 23
EVAL	Buna	flowsInto!	LakeSkutari	CNN-1.+1._MA	0.000	0.000	0.000	0.043	109.000	109.000	261.000	0.250	http://www.semwebtech.org/mondial/10/meta#flowsInto

#683-Thames
PRED entity: Thames

PRED relation: locatedIn
PRED expected values: GB
=> 31 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 131): GB (0.91 #4519, 0.91 #3095, 0.91 #5234), D (0.47 #716, 0.47 #497, 0.41 #237), B (0.41 #237, 0.35 #7144, 0.35 #7143), NL (0.41 #237, 0.35 #7144, 0.35 #7143), N (0.41 #237, 0.33 #239, 0.30 #238), USA (0.37 #311, 0.15 #1504, 0.14 #1979), CZ (0.36 #715, 0.35 #7144, 0.35 #7143), F (0.35 #7144, 0.35 #7143, 0.33 #239), DK (0.33 #239, 0.20 #1431, 0.18 #6666), R (0.30 #5, 0.27 #721, 0.26 #959)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4519 for best value:
>> intensional similarity = 7
>> extensional distance = 179
>> proper extension:  Buna; Niger; SchattalArab; Okavango; Karun; Saluen;
>> query: (?x1381, ?x81) <- ?x1381[ a River; has hasEstuary ?x1499[ a Estuary; has locatedIn ?x81[ has neighbor ?x154; has religion ?x95;];]; has hasSource ?x1734;]
ranks of expected_values: 1
EVAL	Thames	locatedIn	GB	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	31.000	31.000	131.000	0.913	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: GB
=> 127 concepts (127 used for prediction)
PRED predicted values (max 10 best out of 157): GB (0.93 #27069, 0.93 #21524, 0.92 #20571), USA (0.88 #6040, 0.88 #5802, 0.87 #5321), R (0.87 #8833, 0.86 #8356, 0.85 #7881), CDN (0.82 #4122, 0.42 #7223, 0.42 #4837), I (0.75 #2674, 0.71 #2434, 0.32 #6969), CN (0.67 #3160, 0.50 #1727, 0.45 #3637), E (0.60 #981, 0.27 #3847, 0.27 #3608), D (0.56 #6226, 0.47 #16731, 0.47 #16512), BR (0.50 #2035, 0.33 #3468, 0.31 #5136), AUS (0.50 #2193, 0.17 #2147, 0.16 #7205)
>> best conf = 0.93
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #27069 for best value:
>> intensional similarity = 12
>> extensional distance = 143
>> proper extension:  Jordan;
>> query: (?x1381, ?x81) <- ?x1381[ a River; has flowsInto ?x121[ has locatedIn ?x120; is flowsInto of ?x829[ a River; has locatedIn ?x78;];]; has hasEstuary ?x1499[ has locatedIn ?x81[ has encompassed ?x195; has ethnicGroup ?x1196; has neighbor ?x154; has religion ?x95;];];]
ranks of expected_values: 1
EVAL	Thames	locatedIn	GB	CNN-1.+1._MA	1.000	1.000	1.000	1.000	127.000	127.000	157.000	0.933	http://www.semwebtech.org/mondial/10/meta#locatedIn

#682-KAZ
PRED entity: KAZ

PRED relation: neighbor
PRED expected values: R TM
=> 33 concepts (33 used for prediction)
PRED predicted values (max 10 best out of 170): R (0.90 #1750, 0.90 #3827, 0.90 #4147), TM (0.90 #1750, 0.90 #3827, 0.90 #4147), PL (0.33 #33, 0.25 #4309, 0.25 #4633), KAZ (0.33 #68, 0.25 #4309, 0.25 #4633), BY (0.33 #40, 0.25 #4309, 0.25 #4633), LV (0.33 #77, 0.25 #4309, 0.25 #4633), UA (0.33 #49, 0.25 #4309, 0.25 #4633), LT (0.33 #139, 0.25 #4309, 0.25 #4633), EW (0.33 #98, 0.25 #4309, 0.25 #4633), GE (0.33 #59, 0.25 #4309, 0.25 #4633)
>> best conf = 0.90
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #1750 for best value:
>> intensional similarity = 5
>> extensional distance = 49
>> proper extension:  DJI;
>> query: (?x403, ?x73) <- ?x403[ has ethnicGroup ?x1948[ a EthnicGroup;]; is locatedIn of ?x1971[ has type ?x762;]; is neighbor of ?x73;]
ranks of expected_values: 1, 2
EVAL	KAZ	neighbor	TM	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	33.000	33.000	170.000	0.904	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	KAZ	neighbor	R	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	33.000	33.000	170.000	0.904	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: R TM
=> 89 concepts (89 used for prediction)
PRED predicted values (max 10 best out of 225): TM (0.92 #6747, 0.92 #3946, 0.91 #4116), R (0.92 #6747, 0.92 #3946, 0.91 #4116), H (0.60 #1359, 0.33 #535, 0.27 #3614), KAZ (0.50 #1054, 0.50 #721, 0.33 #399), PL (0.50 #2988, 0.40 #1349, 0.38 #2330), TR (0.50 #1181, 0.27 #3614, 0.25 #848), UA (0.40 #1477, 0.40 #1365, 0.30 #1147), AFG (0.40 #1544, 0.33 #396, 0.31 #11210), PK (0.40 #1484, 0.33 #5, 0.30 #1147), MD (0.40 #1449, 0.33 #625, 0.27 #3614)
>> best conf = 0.92
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #6747 for best value:
>> intensional similarity = 13
>> extensional distance = 32
>> proper extension:  IL;
>> query: (?x403, ?x73) <- ?x403[ a Country; has ethnicGroup ?x58; has ethnicGroup ?x1193[ a EthnicGroup;]; has neighbor ?x232[ has neighbor ?x641[ has religion ?x352;]; is locatedIn of ?x231;]; has religion ?x56[ is religion of ?x886<MD>;]; is locatedIn of ?x127; is neighbor of ?x73;]
ranks of expected_values: 1, 2
EVAL	KAZ	neighbor	TM	CNN-1.+1._MA	1.000	1.000	1.000	1.000	89.000	89.000	225.000	0.917	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	KAZ	neighbor	R	CNN-1.+1._MA	1.000	1.000	1.000	1.000	89.000	89.000	225.000	0.917	http://www.semwebtech.org/mondial/10/meta#neighbor

#681-RioNegro
PRED entity: RioNegro

PRED relation: flowsInto
PRED expected values: Amazonas
=> 35 concepts (21 used for prediction)
PRED predicted values (max 10 best out of 93): AtlanticOcean (0.38 #511, 0.25 #177, 0.21 #1177), Donau (0.18 #1676, 0.13 #1842, 0.10 #1173), CaribbeanSea (0.17 #499, 0.17 #364, 0.09 #1164), Amazonas (0.17 #677, 0.12 #512, 0.06 #498), PacificOcean (0.17 #357, 0.09 #1164, 0.06 #498), Ucayali (0.17 #760, 0.03 #1764, 0.02 #1930), MediterraneanSea (0.14 #1188, 0.04 #1857, 0.04 #2022), Parana (0.12 #550, 0.05 #1048), Tambo (0.08 #774, 0.01 #1778, 0.01 #1944), BlackSea (0.07 #1168, 0.05 #1671, 0.04 #1837)
>> best conf = 0.38
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #511 for best value:
>> intensional similarity = 6
>> extensional distance = 6
>> proper extension:  RioSaoFrancisco;
>> query: (?x1186, AtlanticOcean) <- ?x1186[ a River; has hasEstuary ?x2231; has hasSource ?x729[ a Source;]; has locatedIn ?x542<BR>;]

*> Best rule #677 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 10
*> proper extension:  Urubamba;
*> query: (?x1186, Amazonas) <- ?x1186[ has hasSource ?x729[ has inMountains ?x431;]; has locatedIn ?x215[ has ethnicGroup ?x676<Mestizo>;];]
*> conf = 0.17
ranks of expected_values: 4
EVAL	RioNegro	flowsInto	Amazonas	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.250	35.000	21.000	93.000	0.375	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto
PRED expected values: Amazonas
=> 126 concepts (126 used for prediction)
PRED predicted values (max 10 best out of 157): AtlanticOcean (0.56 #1179, 0.37 #2848, 0.35 #2681), CaribbeanSea (0.32 #2835, 0.31 #4344, 0.25 #3002), MurrayRiver (0.29 #960, 0.03 #5640, 0.02 #8149), PacificOcean (0.25 #1334, 0.24 #2167, 0.20 #498), Donau (0.22 #4853, 0.20 #5522, 0.19 #5688), Amazonas (0.18 #1348, 0.17 #1513, 0.14 #1845), Ucayali (0.18 #1431, 0.17 #1596, 0.14 #1928), Parana (0.17 #550, 0.14 #719, 0.06 #6347), IndianOcean (0.14 #835, 0.12 #1002, 0.08 #1333), Tambo (0.14 #1942, 0.09 #1445, 0.08 #1610)
>> best conf = 0.56
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1179 for best value:
>> intensional similarity = 10
>> extensional distance = 7
>> proper extension:  Guadalquivir;
>> query: (?x1186, AtlanticOcean) <- ?x1186[ a River; has hasEstuary ?x2231; has locatedIn ?x215[ has ethnicGroup ?x79; has language ?x796<Spanish>; is locatedIn of ?x282[ is locatedInWater of ?x205; is mergesWith of ?x60;]; is wasDependentOf of ?x783;];]

*> Best rule #1348 for first EXPECTED value:
*> intensional similarity = 11
*> extensional distance = 9
*> proper extension:  Maranon; Ucayali; Ene; Tambo; Perene; Urubamba; Apurimac;
*> query: (?x1186, Amazonas) <- ?x1186[ a River; has hasSource ?x729[ a Source; has inMountains ?x431<Andes>;]; has locatedIn ?x215[ has ethnicGroup ?x79; has language ?x796; has neighbor ?x902<EC>; has religion ?x352;];]
*> conf = 0.18
ranks of expected_values: 6
EVAL	RioNegro	flowsInto	Amazonas	CNN-1.+1._MA	0.000	0.000	1.000	0.167	126.000	126.000	157.000	0.556	http://www.semwebtech.org/mondial/10/meta#flowsInto

#680-GE
PRED entity: GE

PRED relation: neighbor
PRED expected values: ARM
=> 43 concepts (43 used for prediction)
PRED predicted values (max 10 best out of 209): ARM (0.90 #1427, 0.90 #2544, 0.89 #4626), IRQ (0.50 #49, 0.25 #5583, 0.25 #5903), CN (0.37 #199, 0.28 #834, 0.26 #676), UA (0.25 #5583, 0.25 #5903, 0.25 #4467), GE (0.25 #5583, 0.25 #5903, 0.25 #4467), BG (0.25 #5583, 0.25 #5903, 0.25 #4467), KAZ (0.25 #5583, 0.25 #5903, 0.25 #4467), IR (0.25 #5583, 0.25 #5903, 0.25 #4467), PL (0.25 #5583, 0.25 #5903, 0.25 #4467), GR (0.25 #5583, 0.25 #5903, 0.25 #4467)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1427 for best value:
>> intensional similarity = 6
>> extensional distance = 46
>> proper extension:  SSD;
>> query: (?x353, ?x331) <- ?x353[ a Country; has government ?x435<"republic">; has neighbor ?x73; is locatedIn of ?x98; is neighbor of ?x331;]
ranks of expected_values: 1
EVAL	GE	neighbor	ARM	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	43.000	43.000	209.000	0.904	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: ARM
=> 114 concepts (114 used for prediction)
PRED predicted values (max 10 best out of 230): ARM (0.94 #1928, 0.93 #9106, 0.92 #4691), IL (0.50 #1491, 0.38 #4409, 0.28 #11555), KAZ (0.50 #1193, 0.33 #390, 0.33 #162), AFG (0.50 #1190, 0.33 #227, 0.30 #2807), IRQ (0.40 #1335, 0.33 #1496, 0.33 #211), CN (0.36 #4083, 0.33 #5227, 0.33 #363), IR (0.33 #1660, 0.33 #855, 0.33 #162), GE (0.33 #863, 0.33 #381, 0.33 #162), SYR (0.33 #2741, 0.33 #1526, 0.33 #162), BY (0.33 #361, 0.33 #162, 0.33 #160)
>> best conf = 0.94
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1928 for best value:
>> intensional similarity = 15
>> extensional distance = 5
>> proper extension:  RO; UA;
>> query: (?x353, ?x73) <- ?x353[ has ethnicGroup ?x908; has language ?x555; is locatedIn of ?x98<BlackSea>; is neighbor of ?x73; is neighbor of ?x185[ a Country; has neighbor ?x304[ has ethnicGroup ?x244; has language ?x511; is locatedIn of ?x1337<CaspianSea>;]; has religion ?x187; has wasDependentOf ?x1656; is locatedIn of ?x184;];]
ranks of expected_values: 1
EVAL	GE	neighbor	ARM	CNN-1.+1._MA	1.000	1.000	1.000	1.000	114.000	114.000	230.000	0.943	http://www.semwebtech.org/mondial/10/meta#neighbor

#679-GunungBinaiya
PRED entity: GunungBinaiya

PRED relation: locatedIn
PRED expected values: RI
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 33): USA (0.10 #72, 0.05 #308), CN (0.06 #56, 0.02 #292), I (0.04 #48, 0.02 #284), RI (0.04 #52, 0.02 #288), R (0.04 #241, 0.03 #5), CDN (0.04 #63, 0.03 #299), E (0.04 #27, 0.01 #263), D (0.03 #256, 0.02 #20), NEP (0.03 #17), ZRE (0.03 #315)
>> best conf = 0.10
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #72 for best value:
>> intensional similarity = 1
>> extensional distance = 250
>> proper extension:  JabalKatrina; Tahat; Annapurna; Bjelucha; Schchara; GranSasso; EmiKussi; Zugspitze; Tahan; Demirkazik; ...
>> query: (?x2530, USA) <- ?x2530[ a Mountain;]

*> Best rule #52 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 250
*> proper extension:  JabalKatrina; Tahat; Annapurna; Bjelucha; Schchara; GranSasso; EmiKussi; Zugspitze; Tahan; Demirkazik; ...
*> query: (?x2530, RI) <- ?x2530[ a Mountain;]
*> conf = 0.04
ranks of expected_values: 4
EVAL	GunungBinaiya	locatedIn	RI	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.250	2.000	2.000	33.000	0.099	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: RI
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 33): USA (0.10 #72, 0.05 #308), CN (0.06 #56, 0.02 #292), I (0.04 #48, 0.02 #284), RI (0.04 #52, 0.02 #288), R (0.04 #241, 0.03 #5), CDN (0.04 #63, 0.03 #299), E (0.04 #27, 0.01 #263), D (0.03 #256, 0.02 #20), NEP (0.03 #17), ZRE (0.03 #315)
>> best conf = 0.10
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #72 for best value:
>> intensional similarity = 1
>> extensional distance = 250
>> proper extension:  JabalKatrina; Tahat; Annapurna; Bjelucha; Schchara; GranSasso; EmiKussi; Zugspitze; Tahan; Demirkazik; ...
>> query: (?x2530, USA) <- ?x2530[ a Mountain;]

*> Best rule #52 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 250
*> proper extension:  JabalKatrina; Tahat; Annapurna; Bjelucha; Schchara; GranSasso; EmiKussi; Zugspitze; Tahan; Demirkazik; ...
*> query: (?x2530, RI) <- ?x2530[ a Mountain;]
*> conf = 0.04
ranks of expected_values: 4
EVAL	GunungBinaiya	locatedIn	RI	CNN-1.+1._MA	0.000	0.000	1.000	0.250	2.000	2.000	33.000	0.099	http://www.semwebtech.org/mondial/10/meta#locatedIn

#678-Luapula
PRED entity: Luapula

PRED relation: inMountains
PRED expected values: EastAfricanRift
=> 43 concepts (43 used for prediction)
PRED predicted values (max 10 best out of 22): EastAfricanRift (0.22 #463, 0.22 #376, 0.09 #1072), Drakensberge (0.20 #166, 0.11 #253, 0.08 #688), Andes (0.15 #881, 0.13 #968, 0.07 #1142), SnowyMountains (0.13 #543, 0.04 #891, 0.02 #1761), Alps (0.09 #1657, 0.08 #1570, 0.08 #1135), Balkan (0.08 #977, 0.06 #890, 0.04 #1151), Karpaten (0.06 #1009, 0.04 #1183, 0.04 #1270), Pamir (0.04 #539, 0.02 #1496, 0.01 #887), GreatDividingRange (0.04 #531, 0.01 #879), SudetyMountains (0.04 #1015, 0.02 #1189, 0.02 #1276)
>> best conf = 0.22
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #463 for best value:
>> intensional similarity = 6
>> extensional distance = 16
>> proper extension:  Busira;
>> query: (?x709, EastAfricanRift) <- ?x709[ a Source; is hasSource of ?x2185[ has hasEstuary ?x1541[ a Estuary; has locatedIn ?x348<ZRE>;];];]
>> Best rule #376 for best value:
>> intensional similarity = 8
>> extensional distance = 16
>> proper extension:  Fimi; Tshuapa; Lulua; Lomami; Semliki; Aruwimi; Lualaba; Lukuga; Luvua; Ruki; ...
>> query: (?x709, EastAfricanRift) <- ?x709[ has locatedIn ?x525[ has neighbor ?x934<ANG>; is locatedIn of ?x284<LakeTanganjika>; is locatedIn of ?x1977[ has flowsInto ?x60;];]; is hasSource of ?x2185;]
ranks of expected_values: 1
EVAL	Luapula	inMountains	EastAfricanRift	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	43.000	43.000	22.000	0.222	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains
PRED expected values: EastAfricanRift
=> 90 concepts (90 used for prediction)
PRED predicted values (max 10 best out of 41): EastAfricanRift (0.33 #811, 0.27 #1072, 0.22 #550), Balkan (0.17 #1412, 0.08 #2195, 0.07 #1847), Andes (0.16 #1925, 0.14 #2099, 0.13 #2186), Alps (0.15 #1831, 0.10 #3745, 0.10 #2614), Drakensberge (0.11 #166, 0.04 #1036, 0.03 #3211), SnowyMountains (0.11 #1239, 0.04 #3066, 0.04 #2979), Karpaten (0.07 #1705, 0.06 #2227, 0.04 #2836), Vogesen (0.05 #1701, 0.03 #2571, 0.02 #3789), RockyMountains (0.05 #4705, 0.04 #5227, 0.03 #5662), JabalLubnan (0.05 #953, 0.03 #1475, 0.02 #1736)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #811 for best value:
>> intensional similarity = 10
>> extensional distance = 19
>> proper extension:  Uelle;
>> query: (?x709, EastAfricanRift) <- ?x709[ a Source; has locatedIn ?x525[ has ethnicGroup ?x162; has neighbor ?x192[ has ethnicGroup ?x1196;]; has neighbor ?x820<EAT>; has religion ?x116; has wasDependentOf ?x81[ is locatedIn of ?x121;];];]
ranks of expected_values: 1
EVAL	Luapula	inMountains	EastAfricanRift	CNN-1.+1._MA	1.000	1.000	1.000	1.000	90.000	90.000	41.000	0.333	http://www.semwebtech.org/mondial/10/meta#inMountains

#677-HudsonBay
PRED entity: HudsonBay

PRED relation: locatedIn
PRED expected values: CDN
=> 33 concepts (33 used for prediction)
PRED predicted values (max 10 best out of 220): CDN (0.91 #4504, 0.91 #5215, 0.91 #4266), USA (0.80 #2677, 0.79 #1492, 0.67 #1729), R (0.41 #5220, 0.40 #478, 0.33 #5), GROX (0.40 #876, 0.33 #404, 0.33 #167), SVAX (0.40 #665, 0.33 #429, 0.14 #2086), N (0.40 #507, 0.14 #2165, 0.12 #2877), RCH (0.33 #283, 0.25 #992, 0.22 #1229), GB (0.33 #246, 0.20 #3089, 0.20 #482), IS (0.33 #345, 0.20 #581, 0.12 #1054), FARX (0.33 #319, 0.20 #555, 0.12 #1028)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4504 for best value:
>> intensional similarity = 6
>> extensional distance = 34
>> proper extension:  Waag; LakeToba;
>> query: (?x248, ?x272) <- ?x248[ is locatedInWater of ?x869[ has locatedIn ?x272[ a Country; has ethnicGroup ?x197; has religion ?x95; has wasDependentOf ?x81;];];]
ranks of expected_values: 1
EVAL	HudsonBay	locatedIn	CDN	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	33.000	33.000	220.000	0.907	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: CDN
=> 92 concepts (92 used for prediction)
PRED predicted values (max 10 best out of 226): CDN (0.95 #713, 0.91 #9509, 0.88 #7843), USA (0.95 #713, 0.83 #1901, 0.80 #13386), GROX (0.95 #713, 0.83 #1901, 0.74 #2139), R (0.95 #713, 0.83 #1901, 0.74 #2139), SVAX (0.95 #713, 0.83 #1901, 0.50 #668), ZRE (0.95 #713, 0.83 #1901, 0.48 #10778), GB (0.95 #713, 0.83 #1901, 0.40 #722), C (0.95 #713, 0.83 #1901, 0.40 #977), IS (0.95 #713, 0.83 #1901, 0.40 #821), FARX (0.95 #713, 0.83 #1901, 0.40 #795)
>> best conf = 0.95
=> the first rule below is the first best rule for 69 predicted values
>> Best rule #713 for best value:
>> intensional similarity = 12
>> extensional distance = 2
>> proper extension:  BarentsSea;
>> query: (?x248, ?x50) <- ?x248[ a Sea; is flowsInto of ?x1025; is locatedInWater of ?x1891[ has belongsToIslands ?x479;]; is mergesWith of ?x249[ has mergesWith ?x182[ has locatedIn ?x50; has mergesWith ?x373; is flowsInto of ?x137; is locatedInWater of ?x112;]; is locatedInWater of ?x1949<DevonIsland>;];]
ranks of expected_values: 1
EVAL	HudsonBay	locatedIn	CDN	CNN-1.+1._MA	1.000	1.000	1.000	1.000	92.000	92.000	226.000	0.946	http://www.semwebtech.org/mondial/10/meta#locatedIn

#676-NMIS
PRED entity: NMIS

PRED relation: locatedIn!
PRED expected values: PacificOcean
=> 45 concepts (43 used for prediction)
PRED predicted values (max 10 best out of 1270): PacificOcean (0.89 #44061, 0.89 #45483, 0.86 #41218), AtlanticOcean (0.57 #11411, 0.56 #12832, 0.53 #15677), CaribbeanSea (0.30 #4369, 0.29 #10054, 0.25 #22847), Ruapehu (0.25 #969, 0.17 #2391, 0.12 #3812), Mt.Cook (0.25 #892, 0.17 #2314, 0.12 #3735), TeIka-a-Maui-NorthIsland- (0.25 #447, 0.17 #1869, 0.12 #3290), TeWaka-a-Maui-SouthIsland- (0.25 #255, 0.17 #1677, 0.12 #3098), Nauru (0.25 #1225, 0.06 #18282, 0.05 #21125), IndianOcean (0.18 #14215, 0.17 #7109, 0.17 #1425), Uluru (0.17 #2784, 0.12 #4205, 0.08 #48326)
>> best conf = 0.89
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #44061 for best value:
>> intensional similarity = 7
>> extensional distance = 72
>> proper extension:  THA; D; CN; JA; MAL; CV; RP; BRU; TT; GBG; ...
>> query: (?x322, ?x282) <- ?x322[ has encompassed ?x211[ is encompassed of ?x1731[ has religion ?x116;];]; has ethnicGroup ?x982[ a EthnicGroup;]; is locatedIn of ?x1470[ has locatedInWater ?x282;];]
ranks of expected_values: 1
EVAL	NMIS	locatedIn!	PacificOcean	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	45.000	43.000	1270.000	0.894	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: PacificOcean
=> 85 concepts (85 used for prediction)
PRED predicted values (max 10 best out of 1304): PacificOcean (0.90 #39867, 0.89 #46984, 0.86 #56956), Guam (0.75 #48406, 0.67 #34174, 0.08 #102510), AtlanticOcean (0.67 #38483, 0.65 #45601, 0.60 #55573), CaribbeanSea (0.62 #20042, 0.44 #41395, 0.40 #38547), Tutuila (0.36 #9971, 0.33 #254, 0.25 #8803), TeIka-a-Maui-NorthIsland- (0.36 #9971, 0.33 #1871, 0.25 #11840), TeWaka-a-Maui-SouthIsland- (0.36 #9971, 0.33 #1679, 0.25 #11648), Ruapehu (0.36 #9971, 0.33 #2393, 0.25 #12362), Mt.Cook (0.36 #9971, 0.33 #2316, 0.25 #12285), IrishSea (0.36 #9971, 0.29 #14237, 0.25 #8171)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #39867 for best value:
>> intensional similarity = 15
>> extensional distance = 13
>> proper extension:  M;
>> query: (?x322, ?x282) <- ?x322[ has government ?x2110; has language ?x247<English>; has language ?x1155[ a Language; is language of ?x1154[ has ethnicGroup ?x1064;];]; is locatedIn of ?x1470[ a Island; has belongsToIslands ?x66[ a Islands;]; has locatedInWater ?x282[ has locatedIn ?x73; is flowsInto of ?x602; is mergesWith of ?x60;];];]
ranks of expected_values: 1
EVAL	NMIS	locatedIn!	PacificOcean	CNN-1.+1._MA	1.000	1.000	1.000	1.000	85.000	85.000	1304.000	0.905	http://www.semwebtech.org/mondial/10/meta#locatedIn

#675-GQ
PRED entity: GQ

PRED relation: neighbor
PRED expected values: CAM
=> 42 concepts (42 used for prediction)
PRED predicted values (max 10 best out of 213): CAM (0.89 #5007, 0.89 #3382, 0.89 #3221), RCA (0.33 #119, 0.29 #485, 0.29 #441), ZRE (0.33 #59, 0.29 #381, 0.09 #705), ANG (0.33 #139, 0.20 #300, 0.14 #461), RCB (0.29 #485, 0.29 #412, 0.27 #3545), TCH (0.29 #485, 0.29 #345, 0.25 #5332), GQ (0.29 #485, 0.27 #3545, 0.25 #5332), WAN (0.29 #485, 0.25 #5332, 0.25 #5658), RN (0.29 #399, 0.10 #1526, 0.07 #1365), SUD (0.29 #352, 0.04 #2416, 0.04 #676)
>> best conf = 0.89
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5007 for best value:
>> intensional similarity = 6
>> extensional distance = 120
>> proper extension:  BHT; V;
>> query: (?x1408, ?x536) <- ?x1408[ has religion ?x352; is neighbor of ?x536[ a Country; has religion ?x116; is locatedIn of ?x1899[ is hasSource of ?x1525;];];]
ranks of expected_values: 1
EVAL	GQ	neighbor	CAM	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	42.000	42.000	213.000	0.894	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: CAM
=> 87 concepts (87 used for prediction)
PRED predicted values (max 10 best out of 241): CAM (0.91 #8689, 0.91 #9193, 0.91 #6674), CN (0.57 #4382, 0.20 #2865, 0.11 #11775), ZRE (0.53 #4567, 0.50 #1331, 0.46 #333), RCA (0.50 #1331, 0.50 #1116, 0.46 #333), RCB (0.50 #995, 0.50 #920, 0.46 #333), ANG (0.50 #1331, 0.46 #333, 0.46 #331), RSA (0.50 #1212, 0.46 #333, 0.46 #331), Z (0.50 #1255, 0.46 #333, 0.46 #331), TCH (0.46 #333, 0.46 #331, 0.43 #1999), RN (0.46 #333, 0.46 #331, 0.43 #4077)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #8689 for best value:
>> intensional similarity = 15
>> extensional distance = 44
>> proper extension:  F; SLO; B; NL; L; FGU;
>> query: (?x1408, ?x536) <- ?x1408[ has language ?x796; has religion ?x352<RomanCatholic>; is neighbor of ?x536[ has ethnicGroup ?x122; has neighbor ?x169[ has religion ?x116; has wasDependentOf ?x78;]; has neighbor ?x528[ has government ?x435; is locatedIn of ?x265;]; has neighbor ?x736[ has ethnicGroup ?x992;]; has wasDependentOf ?x485; is locatedIn of ?x182;];]
ranks of expected_values: 1
EVAL	GQ	neighbor	CAM	CNN-1.+1._MA	1.000	1.000	1.000	1.000	87.000	87.000	241.000	0.913	http://www.semwebtech.org/mondial/10/meta#neighbor

#674-SeaofOkhotsk
PRED entity: SeaofOkhotsk

PRED relation: mergesWith
PRED expected values: SeaofJapan
=> 37 concepts (32 used for prediction)
PRED predicted values (max 10 best out of 137): SeaofJapan (0.84 #80, 0.83 #396, 0.81 #356), SeaofOkhotsk (0.46 #557, 0.25 #21, 0.17 #397), ArcticOcean (0.38 #51, 0.22 #367, 0.19 #131), AtlanticOcean (0.32 #203, 0.30 #164, 0.26 #282), BeringSea (0.25 #67, 0.25 #27, 0.17 #397), IndianOcean (0.25 #1, 0.19 #317, 0.19 #357), BandaSea (0.25 #25, 0.17 #397, 0.13 #341), SuluSea (0.25 #24, 0.17 #397, 0.12 #64), EastChinaSea (0.25 #22, 0.17 #397, 0.12 #62), SulawesiSea (0.25 #23, 0.17 #397, 0.12 #63)
>> best conf = 0.84
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #80 for best value:
>> intensional similarity = 5
>> extensional distance = 6
>> proper extension:  SeaofAzov; BlackSea;
>> query: (?x507, ?x271) <- ?x507[ a Sea; has locatedIn ?x73<R>; is flowsInto of ?x1585; is mergesWith of ?x271;]
ranks of expected_values: 1
EVAL	SeaofOkhotsk	mergesWith	SeaofJapan	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	37.000	32.000	137.000	0.842	http://www.semwebtech.org/mondial/10/meta#mergesWith

PRED relation: mergesWith
PRED expected values: SeaofJapan
=> 111 concepts (111 used for prediction)
PRED predicted values (max 10 best out of 370): SeaofJapan (0.87 #657, 0.83 #1275, 0.83 #614), SeaofOkhotsk (0.53 #452, 0.53 #451, 0.53 #163), YellowSea (0.50 #123, 0.50 #94, 0.33 #12), EastChinaSea (0.50 #123, 0.25 #104, 0.18 #268), ArcticOcean (0.43 #134, 0.33 #52, 0.29 #627), AtlanticOcean (0.35 #579, 0.33 #665, 0.28 #538), SouthChinaSea (0.33 #18, 0.25 #100, 0.14 #1066), EastSibirianSea (0.33 #61, 0.15 #1363, 0.15 #1859), BeringSea (0.29 #150, 0.16 #1277, 0.16 #1110), BandaSea (0.25 #436, 0.25 #189, 0.22 #230)
>> best conf = 0.87
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #657 for best value:
>> intensional similarity = 8
>> extensional distance = 19
>> proper extension:  SeaofAzov; BlackSea;
>> query: (?x507, ?x282) <- ?x507[ is mergesWith of ?x282[ a Sea; has locatedIn ?x73<R>; has locatedIn ?x196[ has religion ?x56;]; has locatedIn ?x902[ has government ?x435;];];]
ranks of expected_values: 1
EVAL	SeaofOkhotsk	mergesWith	SeaofJapan	CNN-1.+1._MA	1.000	1.000	1.000	1.000	111.000	111.000	370.000	0.870	http://www.semwebtech.org/mondial/10/meta#mergesWith

#673-Enns
PRED entity: Enns

PRED relation: flowsInto
PRED expected values: Donau
=> 29 concepts (22 used for prediction)
PRED predicted values (max 10 best out of 83): Donau (0.50 #8, 0.46 #173, 0.22 #503), AtlanticOcean (0.10 #1507, 0.09 #1175, 0.09 #1341), Inn (0.08 #80, 0.08 #245, 0.07 #662), Drau (0.08 #67, 0.08 #232, 0.07 #662), BlackSea (0.08 #3, 0.08 #168, 0.04 #666), Rhein (0.07 #662, 0.04 #349, 0.03 #994), Isar (0.07 #662, 0.02 #2993, 0.02 #3328), BalticSea (0.06 #1173, 0.05 #1505, 0.05 #1339), MediterraneanSea (0.04 #686, 0.04 #1518, 0.04 #2016), Weser (0.03 #966, 0.02 #1134, 0.02 #801)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #8 for best value:
>> intensional similarity = 6
>> extensional distance = 10
>> proper extension:  Donau; Mur; Rhein; Iller; Drau; Inn; Lech; March; Isar; Salzach;
>> query: (?x490, Donau) <- ?x490[ a River; has hasEstuary ?x491; has hasSource ?x1466[ has inMountains ?x261;]; has locatedIn ?x424<A>;]
ranks of expected_values: 1
EVAL	Enns	flowsInto	Donau	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	29.000	22.000	83.000	0.500	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto
PRED expected values: Donau
=> 91 concepts (91 used for prediction)
PRED predicted values (max 10 best out of 127): Donau (0.50 #1997, 0.50 #669, 0.50 #503), Inn (0.33 #80, 0.25 #245, 0.14 #410), Drau (0.19 #4485, 0.14 #397, 0.12 #562), Mur (0.19 #4485, 0.04 #4484, 0.04 #4486), Weser (0.17 #2624, 0.12 #2293, 0.11 #2959), LakeKeban (0.15 #1276, 0.06 #2272, 0.03 #4435), Rhone (0.14 #1769, 0.09 #939, 0.07 #1936), Aare (0.14 #1583, 0.07 #4076, 0.04 #6572), MediterraneanSea (0.13 #1846, 0.12 #2343, 0.11 #4341), Po (0.13 #1898, 0.12 #2395, 0.07 #1565)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1997 for best value:
>> intensional similarity = 10
>> extensional distance = 14
>> proper extension:  WesternBug; Prypjat;
>> query: (?x490, Donau) <- ?x490[ a River; has hasSource ?x1466[ a Source; has locatedIn ?x424[ a Country; has ethnicGroup ?x160; has language ?x511; has neighbor ?x236<H>; has religion ?x95;];];]
>> Best rule #669 for best value:
>> intensional similarity = 10
>> extensional distance = 8
>> proper extension:  Rhein; Drau;
>> query: (?x490, Donau) <- ?x490[ a River; has hasEstuary ?x491[ a Estuary;]; has hasSource ?x1466[ a Source; has inMountains ?x261<Alps>;]; has locatedIn ?x424<A>;]
>> Best rule #503 for best value:
>> intensional similarity = 11
>> extensional distance = 6
>> proper extension:  Save;
>> query: (?x490, Donau) <- ?x490[ a River; has hasSource ?x1466[ a Source; has inMountains ?x261<Alps>; has locatedIn ?x424[ has ethnicGroup ?x160; has language ?x511; has neighbor ?x120; is locatedIn of ?x155<Mur>;];];]
ranks of expected_values: 1
EVAL	Enns	flowsInto	Donau	CNN-1.+1._MA	1.000	1.000	1.000	1.000	91.000	91.000	127.000	0.500	http://www.semwebtech.org/mondial/10/meta#flowsInto

#672-P
PRED entity: P

PRED relation: wasDependentOf!
PRED expected values: BR STP
=> 42 concepts (42 used for prediction)
PRED predicted values (max 10 best out of 205): RG (0.29 #914, 0.25 #97, 0.06 #553), RCB (0.29 #914, 0.25 #85, 0.06 #541), SN (0.29 #914, 0.25 #67, 0.06 #523), RSA (0.29 #914, 0.17 #190, 0.07 #342), MW (0.29 #914, 0.17 #269, 0.07 #421), ZW (0.29 #914, 0.17 #300, 0.07 #452), EAT (0.29 #914, 0.17 #270, 0.07 #422), Z (0.29 #914, 0.17 #236, 0.07 #388), SD (0.29 #914, 0.17 #180, 0.07 #332), NAM (0.29 #914, 0.06 #925, 0.04 #1838)
>> best conf = 0.29
=> the first rule below is the first best rule for 12 predicted values
>> Best rule #914 for best value:
>> intensional similarity = 6
>> extensional distance = 16
>> proper extension:  B;
>> query: (?x1027, ?x138) <- ?x1027[ has neighbor ?x149; is locatedIn of ?x182; is wasDependentOf of ?x639[ is locatedIn of ?x637;]; is wasDependentOf of ?x934[ has neighbor ?x138;];]

*> Best rule #4430 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 82
*> proper extension:  DJI; JOR;
*> query: (?x1027, ?x50) <- ?x1027[ is locatedIn of ?x182[ has locatedIn ?x50; has mergesWith ?x60;]; is locatedIn of ?x827[ has type ?x150;];]
*> conf = 0.01
ranks of expected_values: 163, 173
EVAL	P	wasDependentOf!	STP	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.006	42.000	42.000	205.000	0.287	http://www.semwebtech.org/mondial/10/meta#wasDependentOf
EVAL	P	wasDependentOf!	BR	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.006	42.000	42.000	205.000	0.287	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

PRED relation: wasDependentOf!
PRED expected values: BR STP
=> 120 concepts (120 used for prediction)
PRED predicted values (max 10 best out of 230): Z (0.53 #2919, 0.52 #306, 0.40 #2920), NAM (0.53 #2919, 0.52 #306, 0.40 #2920), RSA (0.53 #2919, 0.52 #306, 0.40 #2920), EAT (0.53 #2919, 0.52 #306, 0.40 #2920), RG (0.53 #2919, 0.52 #306, 0.40 #2920), RCB (0.53 #2919, 0.52 #306, 0.40 #2920), SN (0.53 #2919, 0.40 #2920, 0.40 #2918), SD (0.52 #306, 0.40 #2920, 0.40 #2918), MW (0.52 #306, 0.40 #2920, 0.40 #2918), ZRE (0.52 #306, 0.40 #2918, 0.37 #4307)
>> best conf = 0.53
=> the first rule below is the first best rule for 7 predicted values
>> Best rule #2919 for best value:
>> intensional similarity = 12
>> extensional distance = 5
>> proper extension:  S;
>> query: (?x1027, ?x416) <- ?x1027[ is locatedIn of ?x199[ a Island;]; is locatedIn of ?x1149[ has type ?x150;]; is wasDependentOf of ?x450[ has ethnicGroup ?x162;]; is wasDependentOf of ?x1755[ has neighbor ?x416[ has government ?x435<"republic">; has wasDependentOf ?x78; is locatedIn of ?x838;];];]

*> Best rule #9403 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 21
*> proper extension:  HolyRomanEmpire;
*> query: (?x1027, ?x63) <- ?x1027[ is wasDependentOf of ?x934[ a Country; has ethnicGroup ?x197[ is ethnicGroup of ?x63;]; is locatedIn of ?x182[ is flowsInto of ?x137;]; is neighbor of ?x138;];]
*> conf = 0.17
ranks of expected_values: 108, 111
EVAL	P	wasDependentOf!	STP	CNN-1.+1._MA	0.000	0.000	0.000	0.009	120.000	120.000	230.000	0.529	http://www.semwebtech.org/mondial/10/meta#wasDependentOf
EVAL	P	wasDependentOf!	BR	CNN-1.+1._MA	0.000	0.000	0.000	0.009	120.000	120.000	230.000	0.529	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

#671-RCH
PRED entity: RCH

PRED relation: ethnicGroup
PRED expected values: Amerindian
=> 24 concepts (24 used for prediction)
PRED predicted values (max 10 best out of 186): Mestizo (0.44 #1835, 0.40 #1320, 0.38 #1577), African (0.33 #1806, 0.33 #2321, 0.29 #2064), Amerindian (0.33 #1802, 0.27 #2058, 0.25 #1544), Quechua (0.27 #2058, 0.25 #732, 0.20 #1800), Aymara (0.27 #2058, 0.25 #681, 0.20 #1800), Mulatto (0.25 #1600, 0.25 #315, 0.22 #1858), Asian (0.25 #1046, 0.08 #2830, 0.05 #3105), French (0.25 #1152, 0.08 #2830, 0.03 #3211), BritishIsles (0.25 #1218, 0.08 #2830, 0.01 #2248), Inuit (0.25 #1041, 0.08 #2830, 0.01 #2071)
>> best conf = 0.44
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1835 for best value:
>> intensional similarity = 5
>> extensional distance = 7
>> proper extension:  EC;
>> query: (?x202, Mestizo) <- ?x202[ has neighbor ?x690[ has ethnicGroup ?x197; is locatedIn of ?x480<LakeTiticaca>;]; is locatedIn of ?x201;]

*> Best rule #1802 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 7
*> proper extension:  EC;
*> query: (?x202, Amerindian) <- ?x202[ has neighbor ?x690[ has ethnicGroup ?x197; is locatedIn of ?x480<LakeTiticaca>;]; is locatedIn of ?x201;]
*> conf = 0.33
ranks of expected_values: 3
EVAL	RCH	ethnicGroup	Amerindian	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.333	24.000	24.000	186.000	0.444	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: Amerindian
=> 101 concepts (101 used for prediction)
PRED predicted values (max 10 best out of 251): Quechua (0.63 #1287, 0.60 #6691, 0.45 #3344), Aymara (0.63 #1287, 0.60 #6691, 0.45 #3344), Mestizo (0.60 #6691, 0.57 #4923, 0.53 #6468), Amerindian (0.60 #6691, 0.53 #6435, 0.50 #4890), African (0.50 #5666, 0.50 #1035, 0.41 #6439), Mulatto (0.33 #1087, 0.23 #3660, 0.21 #5718), Russian (0.28 #7020, 0.25 #9085, 0.23 #17600), Belorussian (0.28 #7033, 0.21 #9098, 0.19 #9356), Asian (0.23 #10044, 0.17 #12885, 0.17 #8238), PacificIslander (0.23 #10044, 0.10 #2390, 0.07 #25773)
>> best conf = 0.63
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #1287 for best value:
>> intensional similarity = 15
>> extensional distance = 4
>> proper extension:  CO; EC;
>> query: (?x202, ?x2045) <- ?x202[ a Country; has encompassed ?x521; has neighbor ?x296<PE>; has neighbor ?x690[ has ethnicGroup ?x197<European>; has ethnicGroup ?x2045; has government ?x2135; has language ?x796<Spanish>; is locatedIn of ?x689[ a Mountain;];]; has religion ?x95; is locatedIn of ?x182;]

*> Best rule #6691 for first EXPECTED value:
*> intensional similarity = 12
*> extensional distance = 15
*> proper extension:  ROU;
*> query: (?x202, ?x197) <- ?x202[ a Country; has language ?x796<Spanish>; has religion ?x95; is locatedIn of ?x282[ has mergesWith ?x60; is flowsInto of ?x602; is locatedInWater of ?x205;]; is neighbor of ?x690[ has ethnicGroup ?x197; has government ?x2135; is locatedIn of ?x274;];]
*> conf = 0.60
ranks of expected_values: 4
EVAL	RCH	ethnicGroup	Amerindian	CNN-1.+1._MA	0.000	0.000	1.000	0.250	101.000	101.000	251.000	0.632	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#670-Reuss
PRED entity: Reuss

PRED relation: hasEstuary!
PRED expected values: Reuss
=> 41 concepts (30 used for prediction)
PRED predicted values (max 10 best out of 39): Aare (0.25 #104, 0.10 #330, 0.03 #557), Limmat (0.25 #94, 0.10 #320, 0.03 #547), Ticino (0.10 #361, 0.03 #2272, 0.03 #1816), Adda (0.10 #337, 0.01 #1017), Mincio (0.10 #334, 0.01 #1014), Tiber (0.10 #331, 0.01 #1011), Etsch (0.10 #312, 0.01 #992), Po (0.10 #298, 0.01 #978), Rhone (0.03 #2272, 0.03 #1816, 0.02 #453), Reuss (0.03 #2272, 0.03 #1816, 0.02 #453)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #104 for best value:
>> intensional similarity = 3
>> extensional distance = 2
>> proper extension:  Aare; Limmat;
>> query: (?x1018, Aare) <- ?x1018[ a Estuary; has locatedIn ?x234<CH>;]

*> Best rule #2272 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 162
*> proper extension:  Umeaelv; Euphrat; Bahrel-Ghasal; Goetaaelv; Vaesterdalaelv; Sanaga; Tigris; SchattalArab; Klaraelv; Bahrel-Djebel-Albert-Nil; ...
*> query: (?x1018, ?x233) <- ?x1018[ a Estuary; has locatedIn ?x234[ has government ?x2472; is locatedIn of ?x233; is neighbor of ?x424[ has encompassed ?x195;];];]
*> conf = 0.03
ranks of expected_values: 10
EVAL	Reuss	hasEstuary!	Reuss	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.100	41.000	30.000	39.000	0.250	http://www.semwebtech.org/mondial/10/meta#hasEstuary

PRED relation: hasEstuary!
PRED expected values: Reuss
=> 108 concepts (108 used for prediction)
PRED predicted values (max 10 best out of 182): Limmat (0.25 #94, 0.10 #6156, 0.10 #3652), Aare (0.25 #104, 0.10 #6156, 0.10 #3652), Ticino (0.10 #6156, 0.10 #3652, 0.10 #455), Rhone (0.10 #6156, 0.10 #3652, 0.09 #594), Doubs (0.10 #6156, 0.10 #3652, 0.09 #581), Reuss (0.10 #6156, 0.10 #3652, 0.07 #684), Inn (0.10 #6156, 0.10 #3652, 0.07 #684), Po (0.10 #298, 0.05 #683, 0.03 #6841), Adda (0.10 #337, 0.01 #3079, 0.01 #3764), Mincio (0.10 #334, 0.01 #3076, 0.01 #3761)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #94 for best value:
>> intensional similarity = 3
>> extensional distance = 2
>> proper extension:  Aare; Limmat;
>> query: (?x1018, Limmat) <- ?x1018[ a Estuary; has locatedIn ?x234<CH>;]

*> Best rule #6156 for first EXPECTED value:
*> intensional similarity = 13
*> extensional distance = 114
*> proper extension:  Thjorsa; JoekulsaaFjoellum;
*> query: (?x1018, ?x1178) <- ?x1018[ a Estuary; has locatedIn ?x234[ has ethnicGroup ?x237; has government ?x2472; has religion ?x187[ is religion of ?x81<GB>; is religion of ?x156<HR>;]; is locatedIn of ?x847[ a Mountain;]; is locatedIn of ?x1178[ a River;];];]
*> conf = 0.10
ranks of expected_values: 6
EVAL	Reuss	hasEstuary!	Reuss	CNN-1.+1._MA	0.000	0.000	1.000	0.167	108.000	108.000	182.000	0.250	http://www.semwebtech.org/mondial/10/meta#hasEstuary

#669-TN
PRED entity: TN

PRED relation: religion
PRED expected values: Muslim
=> 39 concepts (39 used for prediction)
PRED predicted values (max 10 best out of 31): RomanCatholic (0.69 #406, 0.68 #446, 0.64 #526), Muslim (0.64 #483, 0.62 #363, 0.57 #805), Protestant (0.56 #162, 0.51 #442, 0.51 #522), ChristianOrthodox (0.30 #201, 0.25 #241, 0.25 #121), Buddhist (0.25 #1447, 0.25 #761, 0.24 #923), Hindu (0.25 #1447, 0.25 #761, 0.24 #923), Anglican (0.25 #1447, 0.25 #761, 0.24 #923), Mormon (0.25 #1447, 0.25 #761, 0.24 #923), JehovasWitnesses (0.25 #1447, 0.24 #923, 0.24 #1245), UkrainianGreekCatholic (0.25 #1447, 0.24 #923, 0.24 #1245)
>> best conf = 0.69
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #406 for best value:
>> intensional similarity = 6
>> extensional distance = 30
>> proper extension:  C; AUS; CDN; SLB; CV; NZ; BDS; NAU;
>> query: (?x108, RomanCatholic) <- ?x108[ a Country; has encompassed ?x213; has ethnicGroup ?x197<European>; has religion ?x109; has wasDependentOf ?x78;]

*> Best rule #483 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 42
*> proper extension:  CEU;
*> query: (?x108, Muslim) <- ?x108[ a Country; has encompassed ?x213<Africa>; is locatedIn of ?x275; is neighbor of ?x581[ is neighbor of ?x426;];]
*> conf = 0.64
ranks of expected_values: 2
EVAL	TN	religion	Muslim	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	39.000	39.000	31.000	0.688	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion
PRED expected values: Muslim
=> 95 concepts (95 used for prediction)
PRED predicted values (max 10 best out of 35): Muslim (0.88 #1984, 0.88 #1946, 0.84 #2731), RomanCatholic (0.83 #3104, 0.69 #1665, 0.69 #1461), Protestant (0.68 #3100, 0.53 #1661, 0.51 #1373), ChristianOrthodox (0.51 #1373, 0.50 #41, 0.33 #1658), CopticChristian (0.50 #41, 0.49 #525, 0.44 #1536), Druze (0.50 #41, 0.44 #1414, 0.25 #1863), Buddhist (0.50 #41, 0.34 #1009, 0.33 #1658), Hindu (0.50 #41, 0.33 #1658, 0.27 #2270), HoaHao (0.34 #1009, 0.31 #2728, 0.29 #1008), CaoDai (0.34 #1009, 0.31 #2728, 0.29 #1008)
>> best conf = 0.88
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1984 for best value:
>> intensional similarity = 17
>> extensional distance = 41
>> proper extension:  WSA;
>> query: (?x108, ?x187) <- ?x108[ has encompassed ?x213; has religion ?x116[ is religion of ?x416<SN>; is religion of ?x568<WEST>; is religion of ?x820<EAT>; is religion of ?x1755<GNB>;]; is locatedIn of ?x275[ is locatedInWater of ?x68;]; is neighbor of ?x1184[ has religion ?x187<Muslim>; has wasDependentOf ?x207[ is locatedIn of ?x166;];];]
>> Best rule #1946 for best value:
>> intensional similarity = 17
>> extensional distance = 41
>> proper extension:  WSA;
>> query: (?x108, Muslim) <- ?x108[ has encompassed ?x213; has religion ?x116[ is religion of ?x416<SN>; is religion of ?x568<WEST>; is religion of ?x820<EAT>; is religion of ?x1755<GNB>;]; is locatedIn of ?x275[ is locatedInWater of ?x68;]; is neighbor of ?x1184[ has religion ?x187<Muslim>; has wasDependentOf ?x207[ is locatedIn of ?x166;];];]
ranks of expected_values: 1
EVAL	TN	religion	Muslim	CNN-1.+1._MA	1.000	1.000	1.000	1.000	95.000	95.000	35.000	0.884	http://www.semwebtech.org/mondial/10/meta#religion

#668-Ameland
PRED entity: Ameland

PRED relation: locatedInWater
PRED expected values: NorthSea
=> 52 concepts (50 used for prediction)
PRED predicted values (max 10 best out of 50): NorthSea (0.64 #1449, 0.64 #1362, 0.50 #3), AtlanticOcean (0.53 #268, 0.50 #136, 0.42 #399), PacificOcean (0.37 #190, 0.30 #365, 0.29 #891), JavaSea (0.28 #95, 0.21 #313, 0.12 #532), IndianOcean (0.24 #569, 0.21 #88, 0.15 #306), MediterraneanSea (0.24 #671, 0.22 #451, 0.20 #758), SouthChinaSea (0.17 #108, 0.13 #326, 0.09 #545), BandaSea (0.17 #115, 0.13 #333, 0.08 #552), BalticSea (0.14 #222, 0.12 #397, 0.08 #484), ArcticOcean (0.10 #581, 0.10 #449, 0.08 #888)
>> best conf = 0.64
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1449 for best value:
>> intensional similarity = 6
>> extensional distance = 196
>> proper extension:  Fakaofo; Guadalcanal; Bougainville; Streymoy;
>> query: (?x731, ?x121) <- ?x731[ a Island; has belongsToIslands ?x795[ a Islands; is belongsToIslands of ?x1121[ a Island; has locatedInWater ?x121;];];]
ranks of expected_values: 1
EVAL	Ameland	locatedInWater	NorthSea	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	52.000	50.000	50.000	0.641	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater
PRED expected values: NorthSea
=> 149 concepts (149 used for prediction)
PRED predicted values (max 10 best out of 187): NorthSea (0.70 #402, 0.67 #625, 0.67 #584), MediterraneanSea (0.59 #913, 0.43 #504, 0.42 #1232), AtlanticOcean (0.55 #722, 0.50 #542, 0.49 #1359), BalticSea (0.43 #270, 0.22 #493, 0.16 #1402), PacificOcean (0.42 #1010, 0.41 #867, 0.34 #1956), Maas (0.40 #1396, 0.39 #2027, 0.13 #850), SulawesiSea (0.26 #832, 0.10 #2374, 0.09 #2604), Donau (0.25 #180, 0.07 #1401, 0.07 #1585), SouthChinaSea (0.23 #826, 0.10 #2368, 0.10 #1106), JavaSea (0.20 #1093, 0.19 #1137, 0.16 #1317)
>> best conf = 0.70
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #402 for best value:
>> intensional similarity = 12
>> extensional distance = 18
>> proper extension:  Rugen; Helgoland; Fohr; Baltrum; Fehmarn; Wangerooge;
>> query: (?x731, NorthSea) <- ?x731[ a Island; has locatedIn ?x575[ a Country; has ethnicGroup ?x734; is locatedIn of ?x257[ a Estuary;]; is neighbor of ?x120[ has encompassed ?x195; is locatedIn of ?x737<Oder>;]; is neighbor of ?x543<B>;];]
ranks of expected_values: 1
EVAL	Ameland	locatedInWater	NorthSea	CNN-1.+1._MA	1.000	1.000	1.000	1.000	149.000	149.000	187.000	0.700	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#667-EAU
PRED entity: EAU

PRED relation: locatedIn!
PRED expected values: Akagera Bahrel-Djebel-Albert-Nil
=> 36 concepts (32 used for prediction)
PRED predicted values (max 10 best out of 1386): AtlanticOcean (0.88 #12742, 0.33 #42, 0.28 #18387), Semliki (0.54 #15523, 0.33 #857, 0.08 #39519), Akagera (0.54 #15523, 0.08 #39519, 0.08 #21168), PacificOcean (0.33 #8552, 0.27 #4319, 0.25 #15609), LakeTanganjika (0.33 #88, 0.08 #39519, 0.08 #21168), Ruzizi (0.33 #552, 0.08 #39519, 0.08 #21168), Ruzizi (0.33 #791, 0.08 #39519, 0.08 #21168), Karisimbi (0.33 #238, 0.08 #39519, 0.08 #21168), Luapula (0.33 #1238, 0.08 #39519, 0.05 #2649), Luapula (0.33 #863, 0.08 #39519, 0.05 #2274)
>> best conf = 0.88
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #12742 for best value:
>> intensional similarity = 5
>> extensional distance = 58
>> proper extension:  NLSM; BS; VIRG;
>> query: (?x688, AtlanticOcean) <- ?x688[ has government ?x435; has religion ?x95; is locatedIn of ?x600[ has locatedIn ?x348<ZRE>;];]

*> Best rule #39519 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 145
*> proper extension:  YE;
*> query: (?x688, ?x113) <- ?x688[ a Country; has neighbor ?x348[ is locatedIn of ?x113;]; has religion ?x95; is locatedIn of ?x600;]
*> conf = 0.08
ranks of expected_values: 92
EVAL	EAU	locatedIn!	Bahrel-Djebel-Albert-Nil	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.011	36.000	32.000	1386.000	0.883	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	EAU	locatedIn!	Akagera	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	36.000	32.000	1386.000	0.883	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Akagera Bahrel-Djebel-Albert-Nil
=> 71 concepts (69 used for prediction)
PRED predicted values (max 10 best out of 1411): AtlanticOcean (0.88 #52331, 0.80 #24064, 0.79 #32540), Akagera (0.78 #12716, 0.65 #35326), Bahrel-Djebel-Albert-Nil (0.71 #35325, 0.67 #12715, 0.33 #971), IndianOcean (0.67 #25438, 0.61 #18371, 0.55 #21198), PacificOcean (0.67 #7154, 0.50 #19872, 0.50 #11389), ChewBahir (0.61 #18371, 0.55 #21198, 0.35 #1417), MountKenia (0.61 #18371, 0.55 #21198, 0.35 #1417), LakeTurkana (0.61 #18371, 0.55 #21198, 0.35 #1417), Ruzizi (0.61 #18371, 0.35 #1417, 0.33 #9033), Karisimbi (0.61 #18371, 0.35 #1417, 0.33 #8719)
>> best conf = 0.88
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #52331 for best value:
>> intensional similarity = 12
>> extensional distance = 50
>> proper extension:  FALK;
>> query: (?x688, AtlanticOcean) <- ?x688[ a Country; has ethnicGroup ?x529[ a EthnicGroup;]; has government ?x435; is locatedIn of ?x730[ has locatedIn ?x474[ has ethnicGroup ?x244; has neighbor ?x220; has religion ?x95;];]; is locatedIn of ?x1770[ has locatedIn ?x348<ZRE>;];]

*> Best rule #12716 for first EXPECTED value:
*> intensional similarity = 20
*> extensional distance = 4
*> proper extension:  AUS; CDN;
*> query: (?x688, ?x647) <- ?x688[ a Country; has encompassed ?x213; has ethnicGroup ?x529; has ethnicGroup ?x861[ a EthnicGroup;]; has religion ?x95<Protestant>; has religion ?x187<Muslim>; has religion ?x352<RomanCatholic>; is locatedIn of ?x730[ has type ?x150;]; is locatedIn of ?x769[ a River;]; is locatedIn of ?x1194[ has hasEstuary ?x647;]; is locatedIn of ?x1770[ a Lake; has flowsInto ?x1727;];]
*> conf = 0.78
ranks of expected_values: 2, 3
EVAL	EAU	locatedIn!	Bahrel-Djebel-Albert-Nil	CNN-1.+1._MA	0.000	1.000	1.000	0.500	71.000	69.000	1411.000	0.885	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	EAU	locatedIn!	Akagera	CNN-1.+1._MA	0.000	1.000	1.000	0.500	71.000	69.000	1411.000	0.885	http://www.semwebtech.org/mondial/10/meta#locatedIn

#666-SA
PRED entity: SA

PRED relation: neighbor
PRED expected values: Q YE JOR
=> 36 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 171): JOR (0.89 #1108, 0.89 #1267, 0.89 #2228), Q (0.89 #1108, 0.89 #1267, 0.89 #2228), YE (0.89 #1108, 0.89 #1267, 0.89 #2228), SA (0.50 #277, 0.25 #4153, 0.23 #474), TR (0.40 #29, 0.33 #345, 0.17 #188), SYR (0.40 #80, 0.25 #4153, 0.23 #474), IL (0.40 #45, 0.25 #4153, 0.23 #474), WEST (0.25 #4153, 0.20 #96, 0.04 #570), IR (0.23 #474, 0.20 #51, 0.17 #367), SUD (0.23 #474, 0.16 #632, 0.13 #1266)
>> best conf = 0.89
=> the first rule below is the first best rule for 3 predicted values
>> Best rule #1108 for best value:
>> intensional similarity = 7
>> extensional distance = 61
>> proper extension:  ET; THA; MNE; UAE; RL; D; TAD; KGZ; WAN; HR; ...
>> query: (?x751, ?x107) <- ?x751[ a Country; has ethnicGroup ?x244; has government ?x640; has religion ?x187<Muslim>; is locatedIn of ?x637; is neighbor of ?x107;]
ranks of expected_values: 1, 2, 3
EVAL	SA	neighbor	JOR	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	36.000	36.000	171.000	0.893	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	SA	neighbor	YE	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	36.000	36.000	171.000	0.893	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	SA	neighbor	Q	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	36.000	36.000	171.000	0.893	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: Q YE JOR
=> 95 concepts (95 used for prediction)
PRED predicted values (max 10 best out of 205): JOR (0.92 #13085, 0.91 #7160, 0.91 #7159), Q (0.91 #7160, 0.91 #7159, 0.91 #5856), YE (0.91 #5856, 0.91 #7158, 0.90 #14907), IR (0.60 #2004, 0.46 #3903, 0.35 #9136), CN (0.55 #3786, 0.40 #3460, 0.33 #5415), MOC (0.50 #2633, 0.36 #3613, 0.33 #2799), TR (0.50 #1494, 0.35 #9136, 0.33 #680), PK (0.46 #3903, 0.33 #656, 0.30 #3423), SYR (0.44 #4884, 0.35 #9136, 0.34 #2764), SA (0.44 #4884, 0.35 #9136, 0.34 #2764)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #13085 for best value:
>> intensional similarity = 18
>> extensional distance = 75
>> proper extension:  BHT;
>> query: (?x751, ?x803) <- ?x751[ has ethnicGroup ?x244[ a EthnicGroup; is ethnicGroup of ?x115[ a Country; has wasDependentOf ?x485;];]; has government ?x640; is neighbor of ?x803[ a Country; has ethnicGroup ?x1235; has religion ?x187<Muslim>; is neighbor of ?x302[ a Country; is locatedIn of ?x255;]; is neighbor of ?x568[ has ethnicGroup ?x852; has language ?x1398; is locatedIn of ?x419;];];]
ranks of expected_values: 1, 2, 3
EVAL	SA	neighbor	JOR	CNN-1.+1._MA	1.000	1.000	1.000	1.000	95.000	95.000	205.000	0.923	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	SA	neighbor	YE	CNN-1.+1._MA	1.000	1.000	1.000	1.000	95.000	95.000	205.000	0.923	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	SA	neighbor	Q	CNN-1.+1._MA	1.000	1.000	1.000	1.000	95.000	95.000	205.000	0.923	http://www.semwebtech.org/mondial/10/meta#neighbor

#665-VictoriaNile
PRED entity: VictoriaNile

PRED relation: flowsInto
PRED expected values: LakeSeseSeko-Albertsee
=> 48 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 117): LakeSeseSeko-Albertsee (0.33 #319, 0.25 #484, 0.20 #649), Bahrel-Djebel-Albert-Nil (0.33 #150, 0.20 #647, 0.07 #982), Zaire (0.26 #1255, 0.23 #1421, 0.04 #2254), LakeVictoria (0.25 #440, 0.07 #940, 0.04 #1272), VictoriaNile (0.20 #579, 0.14 #914, 0.04 #1578), Donau (0.14 #2340, 0.07 #4173, 0.07 #4339), Lualaba (0.10 #1051, 0.09 #718, 0.09 #1218), Po (0.10 #1072, 0.04 #1737, 0.03 #2071), AtlanticOcean (0.09 #4343, 0.09 #4510, 0.09 #676), PacificOcean (0.09 #689, 0.08 #1687, 0.07 #2021)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #319 for best value:
>> intensional similarity = 6
>> extensional distance = 1
>> proper extension:  Semliki;
>> query: (?x769, LakeSeseSeko-Albertsee) <- ?x769[ has hasEstuary ?x1223; has locatedIn ?x688<EAU>; is flowsInto of ?x1195[ a Lake; has locatedIn ?x474;];]
ranks of expected_values: 1
EVAL	VictoriaNile	flowsInto	LakeSeseSeko-Albertsee	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	48.000	36.000	117.000	0.333	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto
PRED expected values: LakeSeseSeko-Albertsee
=> 112 concepts (111 used for prediction)
PRED predicted values (max 10 best out of 186): LakeSeseSeko-Albertsee (0.33 #152, 0.25 #317, 0.17 #651), Zaire (0.27 #1093, 0.26 #1760, 0.19 #2263), LakeVictoria (0.25 #273, 0.14 #775, 0.07 #1278), Lualaba (0.18 #1056, 0.10 #1556, 0.09 #886), VictoriaNile (0.17 #581, 0.14 #1252, 0.04 #3429), MediterraneanSea (0.17 #353, 0.12 #1859, 0.11 #2696), Nile (0.17 #492, 0.11 #2334, 0.09 #994), Bahrel-Djebel-Albert-Nil (0.17 #649, 0.09 #982, 0.07 #1320), WhiteNile (0.17 #596, 0.09 #929, 0.05 #1599), IndianOcean (0.17 #331, 0.05 #1503, 0.04 #2005)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #152 for best value:
>> intensional similarity = 9
>> extensional distance = 1
>> proper extension:  Semliki;
>> query: (?x769, LakeSeseSeko-Albertsee) <- ?x769[ has hasEstuary ?x1223[ a Estuary;]; has hasSource ?x2205[ a Source; has inMountains ?x1066<EastAfricanRift>;]; has locatedIn ?x688<EAU>; is flowsInto of ?x768;]
ranks of expected_values: 1
EVAL	VictoriaNile	flowsInto	LakeSeseSeko-Albertsee	CNN-1.+1._MA	1.000	1.000	1.000	1.000	112.000	111.000	186.000	0.333	http://www.semwebtech.org/mondial/10/meta#flowsInto

#664-Sobat
PRED entity: Sobat

PRED relation: locatedIn
PRED expected values: SSD
=> 31 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 216): SSD (0.92 #2854, 0.91 #6191, 0.90 #5715), ETH (0.89 #4762, 0.72 #4046, 0.71 #4045), ZRE (0.74 #1030, 0.18 #3805, 0.18 #4523), SUD (0.50 #280, 0.33 #755, 0.25 #42), ET (0.25 #242, 0.25 #4, 0.02 #3572), SP (0.25 #53, 0.11 #766, 0.03 #4099), EAK (0.18 #3805, 0.18 #4523, 0.18 #4283), DJI (0.18 #3805, 0.18 #4523, 0.18 #4283), RCA (0.18 #3805, 0.18 #4523, 0.17 #4285), EAU (0.18 #3805, 0.18 #4523, 0.17 #4285)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2854 for best value:
>> intensional similarity = 7
>> extensional distance = 66
>> proper extension:  Mantaro;
>> query: (?x252, ?x229) <- ?x252[ a River; has hasEstuary ?x2339[ a Estuary; has locatedIn ?x229[ has government ?x435; is neighbor of ?x186;];]; is flowsInto of ?x747;]
ranks of expected_values: 1
EVAL	Sobat	locatedIn	SSD	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	31.000	31.000	216.000	0.919	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: SSD
=> 76 concepts (76 used for prediction)
PRED predicted values (max 10 best out of 217): SSD (0.92 #10306, 0.92 #13664, 0.92 #12707), ETH (0.85 #13426, 0.82 #9588, 0.74 #11507), ZRE (0.74 #6075, 0.71 #4875, 0.62 #7035), D (0.54 #4576, 0.28 #7217, 0.22 #7457), SUD (0.50 #718, 0.50 #521, 0.50 #280), SP (0.40 #1487, 0.33 #1727, 0.29 #2687), ET (0.33 #1678, 0.33 #4, 0.25 #723), SRB (0.33 #4262, 0.17 #5221, 0.15 #6664), F (0.32 #6245, 0.20 #5283, 0.17 #1681), A (0.31 #4655, 0.17 #7296, 0.16 #7536)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #10306 for best value:
>> intensional similarity = 9
>> extensional distance = 45
>> proper extension:  Mississippi; Drin; Irtysch;
>> query: (?x252, ?x229) <- ?x252[ has hasEstuary ?x2339[ a Estuary; has locatedIn ?x229[ is neighbor of ?x186;];]; is flowsInto of ?x747[ a River; has hasSource ?x964[ a Source; has locatedIn ?x476;];];]
ranks of expected_values: 1
EVAL	Sobat	locatedIn	SSD	CNN-1.+1._MA	1.000	1.000	1.000	1.000	76.000	76.000	217.000	0.925	http://www.semwebtech.org/mondial/10/meta#locatedIn

#663-WG
PRED entity: WG

PRED relation: locatedIn!
PRED expected values: AtlanticOcean
=> 41 concepts (35 used for prediction)
PRED predicted values (max 10 best out of 1309): AtlanticOcean (0.93 #24184, 0.91 #5691, 0.91 #22761), PacificOcean (0.68 #25693, 0.53 #22847, 0.45 #15735), RioSanJuan (0.17 #102, 0.14 #1524, 0.09 #5793), RioSanJuan (0.17 #246, 0.14 #1668, 0.09 #5937), LakeNicaragua (0.17 #101, 0.14 #1523, 0.09 #5792), Hispaniola (0.17 #1267, 0.12 #4111, 0.10 #5534), MediterraneanSea (0.14 #45609, 0.13 #41341, 0.12 #42764), RioNegro (0.14 #2063, 0.08 #641, 0.07 #9176), Amazonas (0.14 #1473, 0.08 #51, 0.07 #8586), ChadLake (0.14 #2704, 0.07 #9817, 0.04 #38271)
>> best conf = 0.93
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #24184 for best value:
>> intensional similarity = 8
>> extensional distance = 41
>> proper extension:  TO;
>> query: (?x1073, ?x182) <- ?x1073[ a Country; has encompassed ?x521; is locatedIn of ?x317[ has locatedIn ?x408<NIC>;]; is locatedIn of ?x1219[ a Island; has locatedInWater ?x182;];]
ranks of expected_values: 1
EVAL	WG	locatedIn!	AtlanticOcean	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	41.000	35.000	1309.000	0.928	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: AtlanticOcean
=> 84 concepts (80 used for prediction)
PRED predicted values (max 10 best out of 1236): AtlanticOcean (0.91 #56964, 0.91 #35601, 0.90 #31322), PacificOcean (0.67 #57052, 0.61 #74171, 0.47 #32836), Hispaniola (0.40 #5537, 0.25 #15658, 0.25 #15500), MediterraneanSea (0.37 #64180, 0.29 #71313, 0.20 #35600), RioSanJuan (0.29 #12913, 0.20 #5795, 0.17 #27153), RioSanJuan (0.29 #13057, 0.20 #5939, 0.17 #27297), LakeNicaragua (0.29 #12912, 0.20 #5794, 0.17 #27152), SaintLucia (0.26 #24203, 0.25 #1142, 0.20 #6835), Nevis (0.26 #24203, 0.25 #997, 0.20 #6690), SaintVincent (0.26 #24203, 0.20 #2870, 0.14 #11411)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #56964 for best value:
>> intensional similarity = 13
>> extensional distance = 42
>> proper extension:  GBG;
>> query: (?x1073, ?x182) <- ?x1073[ a Country; has ethnicGroup ?x162[ is ethnicGroup of ?x215[ has encompassed ?x521; is locatedIn of ?x214;]; is ethnicGroup of ?x450[ has government ?x435; is locatedIn of ?x449;];]; is locatedIn of ?x317[ has mergesWith ?x1371;]; is locatedIn of ?x1219[ has belongsToIslands ?x877; has locatedInWater ?x182;];]
ranks of expected_values: 1
EVAL	WG	locatedIn!	AtlanticOcean	CNN-1.+1._MA	1.000	1.000	1.000	1.000	84.000	80.000	1236.000	0.908	http://www.semwebtech.org/mondial/10/meta#locatedIn

#662-PA
PRED entity: PA

PRED relation: ethnicGroup
PRED expected values: Amerindian
=> 45 concepts (45 used for prediction)
PRED predicted values (max 10 best out of 232): African (0.58 #262, 0.53 #1286, 0.50 #518), Amerindian (0.58 #2, 0.43 #2818, 0.42 #514), Mulatto (0.33 #313, 0.25 #569, 0.24 #1593), Chinese (0.20 #2318, 0.17 #8961, 0.16 #2062), Russian (0.20 #6471, 0.17 #4679, 0.15 #7239), German (0.19 #4617, 0.14 #3849, 0.13 #6409), White (0.18 #1601, 0.16 #2113, 0.15 #2369), Black (0.16 #2103, 0.16 #1847, 0.15 #2359), Ukrainian (0.15 #6401, 0.15 #4609, 0.11 #3841), Polynesian (0.15 #4439, 0.13 #5207, 0.07 #5975)
>> best conf = 0.58
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #262 for best value:
>> intensional similarity = 6
>> extensional distance = 10
>> proper extension:  AG;
>> query: (?x783, African) <- ?x783[ a Country; has government ?x180; has language ?x247; has wasDependentOf ?x215; is locatedIn of ?x317<CaribbeanSea>;]

*> Best rule #2 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 10
*> proper extension:  BZ;
*> query: (?x783, Amerindian) <- ?x783[ has encompassed ?x521; has ethnicGroup ?x676<Mestizo>; has neighbor ?x318[ is locatedIn of ?x282;]; has religion ?x95;]
*> conf = 0.58
ranks of expected_values: 2
EVAL	PA	ethnicGroup	Amerindian	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	45.000	45.000	232.000	0.583	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: Amerindian
=> 84 concepts (84 used for prediction)
PRED predicted values (max 10 best out of 255): African (0.65 #3614, 0.65 #3613, 0.62 #1032), Chinese (0.65 #3614, 0.65 #3613, 0.62 #1032), Amerindian (0.65 #3614, 0.65 #3613, 0.62 #1032), Mulatto (0.65 #3614, 0.65 #3613, 0.58 #2069), Russian (0.29 #6520, 0.22 #15545, 0.20 #14000), Polynesian (0.26 #6021, 0.22 #7571, 0.21 #8857), Mongol (0.25 #357, 0.12 #1133, 0.05 #3715), Indo-Aryan (0.25 #496, 0.12 #1272, 0.05 #3854), HanChinese (0.25 #721, 0.12 #1239, 0.03 #7174), Ukrainian (0.21 #6450, 0.17 #13930, 0.16 #12642)
>> best conf = 0.65
=> the first rule below is the first best rule for 4 predicted values
>> Best rule #3614 for best value:
>> intensional similarity = 13
>> extensional distance = 19
>> proper extension:  GCA; RCH; CO; PE; USA; YV; ROU; RA; PY; MEX; ...
>> query: (?x783, ?x79) <- ?x783[ has encompassed ?x521<America>; has religion ?x95; is neighbor of ?x318[ a Country; has ethnicGroup ?x79; has ethnicGroup ?x197<European>; has government ?x711; has language ?x796<Spanish>; is locatedIn of ?x310[ is locatedInWater of ?x1426;];];]
>> Best rule #3613 for best value:
>> intensional similarity = 13
>> extensional distance = 19
>> proper extension:  GCA; RCH; CO; PE; USA; YV; ROU; RA; PY; MEX; ...
>> query: (?x783, ?x298) <- ?x783[ has encompassed ?x521<America>; has religion ?x95; is neighbor of ?x318[ a Country; has ethnicGroup ?x197<European>; has ethnicGroup ?x298; has government ?x711; has language ?x796<Spanish>; is locatedIn of ?x310[ is locatedInWater of ?x1426;];];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 3
EVAL	PA	ethnicGroup	Amerindian	CNN-1.+1._MA	0.000	1.000	1.000	0.333	84.000	84.000	255.000	0.651	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#661-SoufriereHills
PRED entity: SoufriereHills

PRED relation: locatedOnIsland
PRED expected values: Montserrat
=> 50 concepts (41 used for prediction)
PRED predicted values (max 10 best out of 53): Martinique (0.20 #97, 0.03 #360, 0.03 #413), SaintVincent (0.20 #53, 0.03 #316, 0.03 #369), Basse-Terre (0.20 #102, 0.03 #365, 0.03 #418), Montserrat (0.15 #105, 0.09 #901, 0.04 #846), SoufriereHills (0.15 #105, 0.02 #902, 0.02 #900), CaribbeanSea (0.15 #105, 0.02 #902, 0.02 #900), AtlanticOcean (0.15 #105, 0.02 #902, 0.02 #900), Iceland (0.12 #279, 0.09 #385, 0.09 #438), TristanDaCunha (0.07 #243, 0.04 #296, 0.03 #349), Madagaskar (0.05 #885, 0.04 #1100, 0.03 #1208)
>> best conf = 0.20
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #97 for best value:
>> intensional similarity = 7
>> extensional distance = 3
>> proper extension:  LaSoufriere; Soufriere; Pelee;
>> query: (?x2234, Martinique) <- ?x2234[ a Mountain; a Volcano; has locatedIn ?x1444[ is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x317<CaribbeanSea>;];]

*> Best rule #105 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 3
*> proper extension:  LaSoufriere; Soufriere; Pelee;
*> query: (?x2234, ?x817) <- ?x2234[ a Mountain; a Volcano; has locatedIn ?x1444[ is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x317<CaribbeanSea>; is locatedIn of ?x817;];]
*> conf = 0.15
ranks of expected_values: 4
EVAL	SoufriereHills	locatedOnIsland	Montserrat	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.250	50.000	41.000	53.000	0.200	http://www.semwebtech.org/mondial/10/meta#locatedOnIsland

PRED relation: locatedOnIsland
PRED expected values: Montserrat
=> 121 concepts (121 used for prediction)
PRED predicted values (max 10 best out of 52): Montserrat (0.78 #369, 0.60 #158, 0.20 #370), SoufriereHills (0.20 #370, 0.19 #105, 0.16 #159), CaribbeanSea (0.20 #370, 0.19 #105, 0.16 #159), AtlanticOcean (0.20 #370, 0.19 #105, 0.16 #159), Martinique (0.20 #150, 0.11 #415, 0.11 #361), SaintVincent (0.20 #106, 0.11 #371, 0.11 #317), IsladaOmetepe (0.20 #141, 0.11 #352, 0.08 #512), TristanDaCunha (0.20 #86, 0.08 #618, 0.07 #778), TeIka-a-Maui-NorthIsland- (0.20 #73, 0.04 #1299, 0.03 #1566), NewGuinea (0.20 #82, 0.04 #1308, 0.03 #1575)
>> best conf = 0.78
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #369 for best value:
>> intensional similarity = 10
>> extensional distance = 7
>> proper extension:  BlueMountainPeak;
>> query: (?x2234, ?x817) <- ?x2234[ a Mountain; has locatedIn ?x1444[ a Country; has encompassed ?x521<America>; has government ?x562; is locatedIn of ?x317<CaribbeanSea>; is locatedIn of ?x817[ a Island;];];]
ranks of expected_values: 1
EVAL	SoufriereHills	locatedOnIsland	Montserrat	CNN-1.+1._MA	1.000	1.000	1.000	1.000	121.000	121.000	52.000	0.778	http://www.semwebtech.org/mondial/10/meta#locatedOnIsland

#660-GUAD
PRED entity: GUAD

PRED relation: locatedIn!
PRED expected values: Grande-Terre Basse-Terre
=> 28 concepts (24 used for prediction)
PRED predicted values (max 10 best out of 1398): PacificOcean (0.50 #5770, 0.33 #12875, 0.32 #10033), Martinique (0.33 #1053, 0.25 #2474, 0.17 #3895), Pelee (0.33 #1052, 0.25 #2473, 0.17 #3894), St.Martin (0.25 #5024, 0.25 #2182, 0.06 #9288), RioSanJuan (0.20 #5786, 0.20 #24169, 0.19 #24170), RioSanJuan (0.20 #5930, 0.09 #10193, 0.06 #13035), LakeNicaragua (0.20 #5785, 0.09 #10048, 0.06 #12890), Orinoco (0.20 #24169, 0.19 #24170, 0.10 #5807), Amazonas (0.20 #24169, 0.19 #24170, 0.10 #5735), SaintLawrenceRiver (0.20 #24169, 0.19 #24170, 0.07 #12094)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5770 for best value:
>> intensional similarity = 8
>> extensional distance = 8
>> proper extension:  WV; MNTS;
>> query: (?x633, PacificOcean) <- ?x633[ has encompassed ?x521<America>; has government ?x828; is locatedIn of ?x317<CaribbeanSea>; is locatedIn of ?x1435[ a Mountain; a Volcano;];]

*> Best rule #32699 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 177
*> proper extension:  PK; IL; IRQ; RM; VU; B; EAT; GBM; GUAM;
*> query: (?x633, ?x123) <- ?x633[ a Country; is locatedIn of ?x317[ a Sea; has locatedIn ?x345[ has government ?x140;]; has locatedIn ?x899[ has religion ?x95;]; is locatedInWater of ?x123;];]
*> conf = 0.03
ranks of expected_values: 622, 628
EVAL	GUAD	locatedIn!	Basse-Terre	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.002	28.000	24.000	1398.000	0.500	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	GUAD	locatedIn!	Grande-Terre	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.002	28.000	24.000	1398.000	0.500	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Grande-Terre Basse-Terre
=> 82 concepts (78 used for prediction)
PRED predicted values (max 10 best out of 1400): Basse-Terre (0.50 #5688, 0.33 #1423, 0.05 #9953), PacificOcean (0.48 #44193, 0.44 #47043, 0.36 #49891), NorwegianSea (0.33 #134, 0.17 #20040, 0.17 #8664), NorthSea (0.33 #22, 0.17 #8552, 0.16 #69751), TheChannel (0.33 #656, 0.17 #9186, 0.16 #69751), IrishSea (0.33 #1045, 0.17 #9575, 0.16 #69751), Trinidad (0.33 #2016, 0.17 #9123, 0.13 #34137), Tobago (0.33 #1615, 0.17 #8722, 0.13 #34137), Ireland (0.33 #34, 0.17 #8564, 0.12 #14255), GreatBritain (0.33 #205, 0.17 #8735, 0.12 #14426)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5688 for best value:
>> intensional similarity = 15
>> extensional distance = 2
>> proper extension:  WV;
>> query: (?x633, ?x2152) <- ?x633[ a Country; has encompassed ?x521<America>; has government ?x828; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x317<CaribbeanSea>; is locatedIn of ?x1435[ a Mountain; a Volcano; has locatedOnIsland ?x2152[ a Island; has belongsToIslands ?x877;]; has type ?x706;];]
ranks of expected_values: 1, 516
EVAL	GUAD	locatedIn!	Basse-Terre	CNN-1.+1._MA	1.000	1.000	1.000	1.000	82.000	78.000	1400.000	0.500	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	GUAD	locatedIn!	Grande-Terre	CNN-1.+1._MA	0.000	0.000	0.000	0.002	82.000	78.000	1400.000	0.500	http://www.semwebtech.org/mondial/10/meta#locatedIn

#659-PicoBasile
PRED entity: PicoBasile

PRED relation: type
PRED expected values: "volcano"
=> 45 concepts (45 used for prediction)
PRED predicted values (max 10 best out of 9): "volcano" (0.73 #54, 0.65 #38, 0.63 #102), "volcanic" (0.60 #18, 0.35 #258, 0.33 #306), "salt" (0.06 #87, 0.04 #439, 0.03 #535), "sand" (0.06 #84, 0.02 #436, 0.02 #500), "dam" (0.03 #81, 0.02 #513, 0.02 #433), "granite" (0.02 #254, 0.01 #382, 0.01 #286), "lime" (0.02 #501, 0.01 #549), "crater" (0.01 #301), "caldera" (0.01 #531)
>> best conf = 0.73
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #54 for best value:
>> intensional similarity = 6
>> extensional distance = 20
>> proper extension:  Etna; Leuser; MaunaKea; Tambora; Kerinci; PicodelosNieves; Pico; PicoRuivo; PicodeTeide; Rantekombola; ...
>> query: (?x771, "volcano") <- ?x771[ a Mountain; a Volcano; has locatedIn ?x1408[ has neighbor ?x172;]; has locatedOnIsland ?x772[ a Island;];]
ranks of expected_values: 1
EVAL	PicoBasile	type	"volcano"	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	45.000	45.000	9.000	0.727	http://www.semwebtech.org/mondial/10/meta#type

PRED relation: type
PRED expected values: "volcano"
=> 108 concepts (108 used for prediction)
PRED predicted values (max 10 best out of 11): "volcano" (0.75 #294, 0.70 #310, 0.67 #182), "volcanic" (0.50 #66, 0.40 #226, 0.34 #546), "monolith" (0.25 #43, 0.07 #235, 0.02 #571), "salt" (0.06 #1191, 0.05 #855, 0.05 #279), "sand" (0.05 #276, 0.03 #788, 0.03 #452), "dam" (0.04 #593, 0.03 #849, 0.02 #977), "granite" (0.03 #382, 0.02 #574, 0.02 #686), "impact" (0.02 #602), "crater" (0.02 #653, 0.02 #669, 0.01 #765), "lime" (0.01 #1029)
>> best conf = 0.75
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #294 for best value:
>> intensional similarity = 10
>> extensional distance = 18
>> proper extension:  Etna; Leuser; Tambora; Kerinci; Rantekombola; Semeru; Rinjani; GunungAgung; Krakatau; Concepcion; ...
>> query: (?x771, "volcano") <- ?x771[ a Mountain; a Volcano; has locatedIn ?x1408[ a Country; has neighbor ?x172; is locatedIn of ?x182[ is mergesWith of ?x60;]; is neighbor of ?x536;]; has locatedOnIsland ?x772[ a Island;];]
ranks of expected_values: 1
EVAL	PicoBasile	type	"volcano"	CNN-1.+1._MA	1.000	1.000	1.000	1.000	108.000	108.000	11.000	0.750	http://www.semwebtech.org/mondial/10/meta#type

#658-H
PRED entity: H

PRED relation: locatedIn!
PRED expected values: Raab
=> 33 concepts (18 used for prediction)
PRED predicted values (max 10 best out of 1247): MediterraneanSea (0.88 #5741, 0.40 #1496, 0.16 #12812), Theiss (0.52 #11317, 0.20 #412, 0.09 #3243), Mur (0.52 #11317, 0.20 #2394, 0.09 #3810), Drau (0.52 #11317, 0.07 #16976, 0.05 #21224), Donau (0.52 #11317), BlackSea (0.40 #13, 0.27 #2844, 0.21 #4258), AtlanticOcean (0.28 #14186, 0.28 #15601, 0.27 #21264), Inn (0.23 #5660, 0.21 #4596, 0.20 #21223), Save (0.23 #5660, 0.20 #21223, 0.20 #1447), March (0.23 #5660, 0.20 #21223, 0.20 #1990)
>> best conf = 0.88
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5741 for best value:
>> intensional similarity = 6
>> extensional distance = 23
>> proper extension:  GBZ;
>> query: (?x236, MediterraneanSea) <- ?x236[ has religion ?x95; is locatedIn of ?x614[ has locatedIn ?x424[ has ethnicGroup ?x160;]; has locatedIn ?x446<SLO>;];]

*> Best rule #2459 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 3
*> proper extension:  SLO;
*> query: (?x236, Raab) <- ?x236[ has ethnicGroup ?x164; has neighbor ?x156; is locatedIn of ?x133[ is flowsInto of ?x132; is locatedInWater of ?x151;]; is locatedIn of ?x155<Mur>;]
*> conf = 0.20
ranks of expected_values: 63
EVAL	H	locatedIn!	Raab	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.016	33.000	18.000	1247.000	0.880	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Raab
=> 88 concepts (85 used for prediction)
PRED predicted values (max 10 best out of 1409): Raab (0.92 #36841, 0.82 #15583, 0.40 #1416), MediterraneanSea (0.82 #17079, 0.43 #21330, 0.40 #8580), Donau (0.65 #36842, 0.62 #59506, 0.57 #2832), Theiss (0.65 #36842, 0.62 #59506, 0.57 #2832), Drau (0.65 #36842, 0.62 #59506, 0.57 #2832), Theiss (0.61 #59508, 0.61 #36844, 0.60 #59505), Mur (0.61 #59508, 0.61 #36844, 0.60 #59505), Drau (0.61 #59508, 0.61 #36844, 0.60 #59505), Donau (0.61 #59508, 0.61 #36844, 0.60 #59505), AtlanticOcean (0.58 #97809, 0.54 #72305, 0.42 #43967)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #36841 for best value:
>> intensional similarity = 14
>> extensional distance = 23
>> proper extension:  CDN;
>> query: (?x236, ?x1838) <- ?x236[ a Country; has encompassed ?x195; has ethnicGroup ?x164; has language ?x684; has religion ?x95<Protestant>; is locatedIn of ?x614[ a River; has hasEstuary ?x1993; has hasSource ?x1267; has locatedIn ?x207[ is neighbor of ?x78;];]; is locatedIn of ?x1265[ is hasEstuary of ?x1838;];]
ranks of expected_values: 1
EVAL	H	locatedIn!	Raab	CNN-1.+1._MA	1.000	1.000	1.000	1.000	88.000	85.000	1409.000	0.917	http://www.semwebtech.org/mondial/10/meta#locatedIn

#657-MOC
PRED entity: MOC

PRED relation: neighbor!
PRED expected values: ZW
=> 45 concepts (45 used for prediction)
PRED predicted values (max 10 best out of 219): ZW (0.94 #635, 0.92 #3318, 0.92 #3320), MOC (0.43 #33, 0.42 #988, 0.31 #159), EAU (0.33 #1066, 0.33 #907, 0.31 #159), RWA (0.33 #1048, 0.33 #889, 0.31 #159), SSD (0.33 #838, 0.25 #997, 0.15 #1942), BI (0.31 #159, 0.29 #1114, 0.29 #62), RB (0.31 #159, 0.29 #1114, 0.26 #5228), ZRE (0.31 #159, 0.29 #1114, 0.26 #5228), NAM (0.31 #159, 0.29 #1114, 0.26 #5228), LS (0.31 #159, 0.29 #1114, 0.26 #5228)
>> best conf = 0.94
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #635 for best value:
>> intensional similarity = 5
>> extensional distance = 7
>> proper extension:  THA;
>> query: (?x192, ?x193) <- ?x192[ has ethnicGroup ?x197; has neighbor ?x193; is locatedIn of ?x60[ is mergesWith of ?x262<GulfofBengal>;];]
ranks of expected_values: 1
EVAL	MOC	neighbor!	ZW	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	45.000	45.000	219.000	0.938	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: ZW
=> 108 concepts (108 used for prediction)
PRED predicted values (max 10 best out of 236): ZW (0.93 #6590, 0.91 #11587, 0.90 #9000), TCH (0.50 #2433, 0.40 #1308, 0.29 #2753), RCA (0.43 #3331, 0.33 #2528, 0.20 #5259), RG (0.40 #1234, 0.33 #107, 0.17 #5570), ZRE (0.40 #1822, 0.32 #4819, 0.31 #6430), RWA (0.40 #2017, 0.32 #4819, 0.31 #6430), SSD (0.40 #1966, 0.20 #4652, 0.17 #4537), RIM (0.40 #1213, 0.17 #5549, 0.17 #4423), MOC (0.33 #196, 0.32 #4819, 0.31 #6430), CI (0.33 #146, 0.20 #1431, 0.20 #1273)
>> best conf = 0.93
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #6590 for best value:
>> intensional similarity = 15
>> extensional distance = 21
>> proper extension:  MEL;
>> query: (?x192, ?x193) <- ?x192[ has encompassed ?x213<Africa>; has government ?x435; has neighbor ?x193; is locatedIn of ?x60[ a Sea; is flowsInto of ?x750[ a River;]; is locatedInWater of ?x1611[ has locatedIn ?x797;]; is locatedInWater of ?x1666[ is locatedOnIsland of ?x1247;]; is locatedInWater of ?x2233[ has belongsToIslands ?x2536;]; is mergesWith of ?x182;];]
ranks of expected_values: 1
EVAL	MOC	neighbor!	ZW	CNN-1.+1._MA	1.000	1.000	1.000	1.000	108.000	108.000	236.000	0.934	http://www.semwebtech.org/mondial/10/meta#neighbor

#656-SeaofJapan
PRED entity: SeaofJapan

PRED relation: mergesWith!
PRED expected values: SeaofOkhotsk
=> 25 concepts (25 used for prediction)
PRED predicted values (max 10 best out of 137): SeaofOkhotsk (0.85 #191, 0.83 #425, 0.83 #694), SeaofJapan (0.50 #51, 0.45 #735, 0.45 #734), BeringSea (0.30 #386, 0.30 #179, 0.25 #64), ArcticOcean (0.30 #386, 0.21 #385, 0.20 #165), EastSibirianSea (0.30 #386, 0.21 #385, 0.20 #173), BarentsSea (0.30 #386, 0.21 #385, 0.10 #163), BlackSea (0.30 #386, 0.21 #385, 0.10 #156), SeaofAzov (0.30 #386, 0.21 #385, 0.10 #155), AtlanticOcean (0.29 #471, 0.26 #313, 0.25 #509), IndianOcean (0.25 #39, 0.21 #385, 0.16 #657)
>> best conf = 0.85
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #191 for best value:
>> intensional similarity = 6
>> extensional distance = 8
>> proper extension:  SeaofAzov; BlackSea; BarentsSea; ArcticOcean; EastSibirianSea; BeringSea;
>> query: (?x271, ?x270) <- ?x271[ has locatedIn ?x73<R>; has locatedIn ?x117[ has ethnicGroup ?x2391; has religion ?x462;]; has mergesWith ?x270;]
ranks of expected_values: 1
EVAL	SeaofJapan	mergesWith!	SeaofOkhotsk	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	25.000	25.000	137.000	0.846	http://www.semwebtech.org/mondial/10/meta#mergesWith

PRED relation: mergesWith!
PRED expected values: SeaofOkhotsk
=> 98 concepts (98 used for prediction)
PRED predicted values (max 10 best out of 593): SeaofOkhotsk (0.88 #400, 0.87 #401, 0.84 #891), SeaofJapan (0.52 #765, 0.50 #524, 0.50 #93), JavaSea (0.44 #329, 0.25 #612, 0.25 #493), AtlanticOcean (0.40 #733, 0.38 #899, 0.31 #1276), BeringSea (0.38 #266, 0.33 #388, 0.33 #307), EastSibirianSea (0.33 #220, 0.33 #1145, 0.31 #810), SulawesiSea (0.33 #22, 0.25 #508, 0.25 #102), IndianOcean (0.33 #1, 0.25 #487, 0.25 #81), BandaSea (0.33 #24, 0.25 #510, 0.25 #104), SuluSea (0.33 #23, 0.25 #103, 0.24 #442)
>> best conf = 0.88
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #400 for best value:
>> intensional similarity = 11
>> extensional distance = 7
>> proper extension:  SeaofAzov; BlackSea;
>> query: (?x271, ?x270) <- ?x271[ a Sea; has locatedIn ?x73<R>; has locatedIn ?x117[ a Country; has encompassed ?x175; has language ?x118;]; has mergesWith ?x270; has mergesWith ?x282[ has locatedIn ?x158; is flowsInto of ?x602;];]
ranks of expected_values: 1
EVAL	SeaofJapan	mergesWith!	SeaofOkhotsk	CNN-1.+1._MA	1.000	1.000	1.000	1.000	98.000	98.000	593.000	0.875	http://www.semwebtech.org/mondial/10/meta#mergesWith

#655-DjebelAures
PRED entity: DjebelAures

PRED relation: inMountains
PRED expected values: Rif
=> 40 concepts (40 used for prediction)
PRED predicted values (max 10 best out of 63): Atlas (0.33 #47, 0.17 #134, 0.05 #221), RockyMountains (0.14 #529, 0.09 #703, 0.08 #877), CanaryIslands (0.14 #230, 0.04 #578, 0.02 #1013), Karpaten (0.11 #313), Alps (0.10 #613, 0.07 #1483, 0.06 #1396), Pyrenees (0.09 #236, 0.03 #584, 0.01 #1019), EastAfricanRift (0.09 #376, 0.07 #463, 0.05 #637), Andes (0.08 #707, 0.08 #794, 0.08 #533), Kaukasus (0.07 #367, 0.05 #454, 0.04 #628), Himalaya (0.07 #615, 0.04 #1398, 0.04 #1224)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #47 for best value:
>> intensional similarity = 3
>> extensional distance = 1
>> proper extension:  Tubkhal;
>> query: (?x2109, Atlas) <- ?x2109[ a Mountain; has locatedIn ?x851<MA>;]

No rule for expected values
ranks of expected_values: 
EVAL	DjebelAures	inMountains	Rif	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	40.000	40.000	63.000	0.333	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains
PRED expected values: Rif
=> 97 concepts (97 used for prediction)
PRED predicted values (max 10 best out of 77): Andes (0.36 #620, 0.27 #1490, 0.14 #2534), Atlas (0.33 #47, 0.25 #134, 0.21 #3308), RockyMountains (0.30 #1138, 0.22 #1834, 0.18 #2269), Azbine (0.25 #133), EliasRange (0.24 #885, 0.10 #1146, 0.10 #1581), Himalaya (0.16 #1398, 0.15 #1311, 0.12 #963), CordilleraVolcanica (0.14 #1544, 0.11 #1457, 0.07 #2588), EastAfricanRift (0.12 #985, 0.12 #898, 0.09 #2203), Alps (0.11 #3224, 0.10 #3660, 0.09 #2440), Balkan (0.09 #629, 0.07 #3152, 0.06 #2630)
>> best conf = 0.36
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #620 for best value:
>> intensional similarity = 10
>> extensional distance = 9
>> proper extension:  Tahat; OjosdelSalado; MontePissis; Aconcagua; Llullaillaco; RomanKosch; Musala; Tupungato;
>> query: (?x2109, Andes) <- ?x2109[ a Mountain; has locatedIn ?x851[ has government ?x92; has religion ?x109<Jewish>; has wasDependentOf ?x78; is neighbor of ?x581[ has ethnicGroup ?x197; has government ?x435; is locatedIn of ?x84;];];]

No rule for expected values
ranks of expected_values: 
EVAL	DjebelAures	inMountains	Rif	CNN-1.+1._MA	0.000	0.000	0.000	0.000	97.000	97.000	77.000	0.364	http://www.semwebtech.org/mondial/10/meta#inMountains

#654-AFG
PRED entity: AFG

PRED relation: language
PRED expected values: Turkic
=> 36 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 95): Spanish (0.33 #406, 0.27 #1174, 0.25 #598), Russian (0.33 #299, 0.25 #587, 0.25 #203), Balochi (0.33 #170, 0.12 #842, 0.08 #554), Hindko (0.33 #184, 0.08 #568, 0.06 #856), Sindhi (0.33 #180, 0.08 #564, 0.06 #852), Brahui (0.33 #148, 0.08 #532, 0.06 #820), Urdu (0.33 #143, 0.08 #527, 0.06 #815), Siraiki (0.33 #112, 0.08 #496, 0.06 #784), Punjabi (0.33 #108, 0.08 #492, 0.06 #780), Armenian (0.33 #314, 0.02 #1370, 0.02 #1658)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #406 for best value:
>> intensional similarity = 6
>> extensional distance = 4
>> proper extension:  BZ;
>> query: (?x381, Spanish) <- ?x381[ has encompassed ?x175; has language ?x1033; has neighbor ?x83; has religion ?x187; has wasDependentOf ?x81<GB>;]

*> Best rule #763 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 12
*> proper extension:  IR; KAZ;
*> query: (?x381, Turkic) <- ?x381[ a Country; has language ?x1033; is locatedIn of ?x82[ a Desert;]; is locatedIn of ?x682[ is flowsInto of ?x683;]; is neighbor of ?x83;]
*> conf = 0.07
ranks of expected_values: 39
EVAL	AFG	language	Turkic	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.026	36.000	36.000	95.000	0.333	http://www.semwebtech.org/mondial/10/meta#language

PRED relation: language
PRED expected values: Turkic
=> 97 concepts (97 used for prediction)
PRED predicted values (max 10 best out of 96): Russian (0.60 #2244, 0.54 #2050, 0.50 #2437), Balochi (0.50 #777, 0.35 #3007, 0.33 #3298), Hindko (0.50 #777, 0.35 #3007, 0.33 #3298), Sindhi (0.50 #777, 0.35 #3007, 0.33 #3298), Brahui (0.50 #777, 0.35 #3007, 0.33 #3298), Urdu (0.50 #777, 0.35 #3007, 0.33 #3298), Siraiki (0.50 #777, 0.35 #3007, 0.33 #3298), Punjabi (0.50 #777, 0.35 #3007, 0.33 #3298), Hindi (0.50 #769, 0.17 #1254, 0.17 #7860), Uzbek (0.47 #2233, 0.45 #2330, 0.41 #2136)
>> best conf = 0.60
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2244 for best value:
>> intensional similarity = 17
>> extensional distance = 13
>> proper extension:  LV;
>> query: (?x381, Russian) <- ?x381[ has encompassed ?x175; has ethnicGroup ?x2116[ a EthnicGroup;]; has government ?x2442; has language ?x1033; is locatedIn of ?x82; is neighbor of ?x277[ a Country; has ethnicGroup ?x1326<Tatar>; has language ?x278; is locatedIn of ?x968[ a Estuary;];]; is neighbor of ?x290[ a Country; has government ?x2518; has religion ?x56;];]

*> Best rule #4462 for first EXPECTED value:
*> intensional similarity = 16
*> extensional distance = 45
*> proper extension:  IRL; GCA; GR; PA; EC; HCA;
*> query: (?x381, ?x278) <- ?x381[ has encompassed ?x175; has ethnicGroup ?x2116; has government ?x2442; has language ?x1033; is locatedIn of ?x682[ is flowsInto of ?x683;]; is neighbor of ?x277[ a Country; has ethnicGroup ?x1193; has language ?x278; is locatedIn of ?x968[ a Estuary;];]; is neighbor of ?x290[ a Country; has government ?x2518; has religion ?x56;];]
*> conf = 0.27
ranks of expected_values: 21
EVAL	AFG	language	Turkic	CNN-1.+1._MA	0.000	0.000	0.000	0.048	97.000	97.000	96.000	0.600	http://www.semwebtech.org/mondial/10/meta#language

#653-GH
PRED entity: GH

PRED relation: neighbor
PRED expected values: BF
=> 41 concepts (41 used for prediction)
PRED predicted values (max 10 best out of 219): BF (0.89 #5313, 0.89 #5312, 0.88 #2894), SN (0.50 #235, 0.40 #559, 0.33 #75), RMM (0.40 #616, 0.33 #132, 0.28 #4020), RN (0.40 #562, 0.25 #5151, 0.25 #238), LB (0.33 #103, 0.28 #4020, 0.27 #805), WAL (0.33 #147, 0.25 #307, 0.21 #482), GNB (0.33 #156, 0.25 #316, 0.21 #482), RG (0.28 #4020, 0.27 #805, 0.27 #3216), GH (0.28 #4020, 0.27 #805, 0.27 #3216), BEN (0.27 #805, 0.27 #3216, 0.27 #3538)
>> best conf = 0.89
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5313 for best value:
>> intensional similarity = 4
>> extensional distance = 137
>> proper extension:  SSD;
>> query: (?x483, ?x1206) <- ?x483[ is locatedIn of ?x182[ is flowsInto of ?x137;]; is neighbor of ?x1206[ is locatedIn of ?x350;];]
ranks of expected_values: 1
EVAL	GH	neighbor	BF	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	41.000	41.000	219.000	0.891	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: BF
=> 94 concepts (94 used for prediction)
PRED predicted values (max 10 best out of 229): BF (0.93 #5957, 0.92 #5956, 0.92 #4961), TCH (0.40 #683, 0.25 #1507, 0.22 #1836), RMM (0.33 #296, 0.33 #132, 0.31 #6619), BEN (0.33 #291, 0.33 #127, 0.31 #6619), RN (0.33 #409, 0.33 #242, 0.31 #11094), WAN (0.33 #349, 0.33 #18, 0.22 #6617), PE (0.33 #1042, 0.33 #874, 0.15 #8108), RG (0.33 #605, 0.31 #6619, 0.31 #5623), GH (0.33 #251, 0.31 #6619, 0.31 #5623), DZ (0.33 #100, 0.29 #1418, 0.22 #6617)
>> best conf = 0.93
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5957 for best value:
>> intensional similarity = 11
>> extensional distance = 35
>> proper extension:  DJI; MAL; EAK;
>> query: (?x483, ?x1206) <- ?x483[ a Country; has ethnicGroup ?x162[ is ethnicGroup of ?x450[ has encompassed ?x213; is locatedIn of ?x449;];]; has religion ?x116; has wasDependentOf ?x81; is locatedIn of ?x135[ has type ?x136;]; is neighbor of ?x1206[ is locatedIn of ?x350;];]
ranks of expected_values: 1
EVAL	GH	neighbor	BF	CNN-1.+1._MA	1.000	1.000	1.000	1.000	94.000	94.000	229.000	0.934	http://www.semwebtech.org/mondial/10/meta#neighbor

#652-ArabianSea
PRED entity: ArabianSea

PRED relation: mergesWith
PRED expected values: IndianOcean GulfofAden
=> 38 concepts (29 used for prediction)
PRED predicted values (max 10 best out of 55): GulfofAden (0.87 #158, 0.85 #358, 0.84 #198), IndianOcean (0.87 #158, 0.85 #358, 0.84 #198), AtlanticOcean (0.33 #5, 0.27 #123, 0.25 #204), PacificOcean (0.33 #15, 0.25 #214, 0.25 #173), ArabianSea (0.33 #31, 0.25 #70, 0.17 #357), GulfofBengal (0.33 #10, 0.25 #49, 0.14 #88), BandaSea (0.33 #26, 0.25 #65, 0.12 #225), ArcticOcean (0.23 #129, 0.22 #169, 0.16 #210), PersianGulf (0.17 #357, 0.03 #228, 0.03 #187), NorwegianSea (0.15 #136, 0.12 #217, 0.12 #176)
>> best conf = 0.87
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #158 for best value:
>> intensional similarity = 5
>> extensional distance = 24
>> proper extension:  SeaofAzov; BlackSea; Skagerrak;
>> query: (?x1333, ?x60) <- ?x1333[ is flowsInto of ?x411[ has hasEstuary ?x383; has locatedIn ?x924[ has religion ?x116;];]; is mergesWith of ?x60;]
ranks of expected_values: 1, 2
EVAL	ArabianSea	mergesWith	GulfofAden	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	38.000	29.000	55.000	0.868	http://www.semwebtech.org/mondial/10/meta#mergesWith
EVAL	ArabianSea	mergesWith	IndianOcean	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	38.000	29.000	55.000	0.868	http://www.semwebtech.org/mondial/10/meta#mergesWith

PRED relation: mergesWith
PRED expected values: IndianOcean GulfofAden
=> 99 concepts (99 used for prediction)
PRED predicted values (max 10 best out of 157): IndianOcean (0.86 #943, 0.86 #942, 0.85 #986), GulfofAden (0.86 #943, 0.86 #942, 0.85 #986), GulfofBengal (0.40 #217, 0.33 #301, 0.33 #53), ArabianSea (0.38 #1236, 0.33 #74, 0.27 #124), AtlanticOcean (0.37 #703, 0.35 #621, 0.33 #48), PacificOcean (0.36 #468, 0.33 #959, 0.33 #58), BandaSea (0.33 #69, 0.25 #109, 0.20 #233), ArcticOcean (0.27 #464, 0.26 #913, 0.21 #709), AndamanSea (0.27 #124, 0.20 #224, 0.17 #1112), PersianGulf (0.25 #289, 0.25 #154, 0.24 #902)
>> best conf = 0.86
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #943 for best value:
>> intensional similarity = 11
>> extensional distance = 21
>> proper extension:  HudsonBay; KaraSea;
>> query: (?x1333, ?x2407) <- ?x1333[ is flowsInto of ?x411; is locatedInWater of ?x1476[ a Island;]; is mergesWith of ?x2407[ a Sea; has locatedIn ?x668[ has neighbor ?x639; has religion ?x187<Muslim>; has wasDependentOf ?x2153;]; is mergesWith of ?x1552;];]
ranks of expected_values: 1, 2
EVAL	ArabianSea	mergesWith	GulfofAden	CNN-1.+1._MA	1.000	1.000	1.000	1.000	99.000	99.000	157.000	0.860	http://www.semwebtech.org/mondial/10/meta#mergesWith
EVAL	ArabianSea	mergesWith	IndianOcean	CNN-1.+1._MA	1.000	1.000	1.000	1.000	99.000	99.000	157.000	0.860	http://www.semwebtech.org/mondial/10/meta#mergesWith

#651-CaribbeanSea
PRED entity: CaribbeanSea

PRED relation: locatedInWater!
PRED expected values: Nevis SaintKitts Aruba SaintLucia
=> 39 concepts (32 used for prediction)
PRED predicted values (max 10 best out of 299): Taiwan (0.33 #300, 0.25 #1045, 0.10 #2039), Hokkaido (0.33 #276, 0.25 #1021, 0.10 #2015), Kyushu (0.33 #362, 0.25 #1107, 0.10 #2101), Tasmania (0.33 #273, 0.25 #1018, 0.10 #1764), Paramuschir (0.33 #387, 0.25 #1132, 0.08 #1628), Okinawa (0.33 #294, 0.25 #1039, 0.08 #1535), Unalaska (0.33 #420, 0.25 #1165, 0.08 #1661), Mindanao (0.33 #354, 0.25 #1099, 0.08 #1595), NewGuinea (0.33 #345, 0.25 #1090, 0.08 #1586), Leyte (0.33 #305, 0.25 #1050, 0.08 #1546)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #300 for best value:
>> intensional similarity = 9
>> extensional distance = 1
>> proper extension:  PacificOcean;
>> query: (?x317, Taiwan) <- ?x317[ has locatedIn ?x318<CR>; has locatedIn ?x745[ has encompassed ?x521;]; has locatedIn ?x783<PA>; is locatedInWater of ?x123; is locatedInWater of ?x1093[ a Island;];]

*> Best rule #206 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 1
*> proper extension:  AtlanticOcean;
*> query: (?x317, SaintLucia) <- ?x317[ has locatedIn ?x697<RH>; is locatedInWater of ?x1397<SaintThomas>; is locatedInWater of ?x1928<Cuba>;]
*> conf = 0.33
ranks of expected_values: 58, 59, 74
EVAL	CaribbeanSea	locatedInWater!	SaintLucia	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.017	39.000	32.000	299.000	0.333	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	CaribbeanSea	locatedInWater!	Aruba	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	39.000	32.000	299.000	0.333	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	CaribbeanSea	locatedInWater!	SaintKitts	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.014	39.000	32.000	299.000	0.333	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	CaribbeanSea	locatedInWater!	Nevis	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.017	39.000	32.000	299.000	0.333	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater!
PRED expected values: Nevis SaintKitts Aruba SaintLucia
=> 75 concepts (75 used for prediction)
PRED predicted values (max 10 best out of 519): SaintLucia (0.49 #5994, 0.44 #9753, 0.33 #705), Nevis (0.49 #5994, 0.44 #9753, 0.33 #678), IsladaOmetepe (0.49 #5994, 0.44 #9753, 0.16 #2251), GreatBritain (0.36 #3531, 0.33 #530, 0.27 #5245), Greenland (0.36 #3597, 0.33 #596, 0.27 #5245), Hokkaido (0.33 #27, 0.27 #3777, 0.17 #2279), Kyushu (0.33 #113, 0.27 #3863, 0.17 #2365), Streymoy (0.33 #717, 0.27 #5245, 0.25 #1715), Ireland (0.33 #505, 0.27 #5245, 0.25 #1503), LongIsland (0.33 #684, 0.27 #5245, 0.25 #1682)
>> best conf = 0.49
=> the first rule below is the first best rule for 3 predicted values
>> Best rule #5994 for best value:
>> intensional similarity = 10
>> extensional distance = 18
>> proper extension:  Guernsey;
>> query: (?x317, ?x1117) <- ?x317[ has locatedIn ?x407[ has dependentOf ?x81<GB>; has encompassed ?x521;]; has locatedIn ?x667[ is locatedIn of ?x1117[ a Island;];]; has locatedIn ?x697[ has ethnicGroup ?x162; has government ?x435;];]
ranks of expected_values: 1, 2, 36
EVAL	CaribbeanSea	locatedInWater!	SaintLucia	CNN-1.+1._MA	1.000	1.000	1.000	1.000	75.000	75.000	519.000	0.487	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	CaribbeanSea	locatedInWater!	Aruba	CNN-1.+1._MA	0.000	0.000	0.000	0.000	75.000	75.000	519.000	0.487	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	CaribbeanSea	locatedInWater!	SaintKitts	CNN-1.+1._MA	0.000	0.000	0.000	0.029	75.000	75.000	519.000	0.487	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	CaribbeanSea	locatedInWater!	Nevis	CNN-1.+1._MA	1.000	1.000	1.000	1.000	75.000	75.000	519.000	0.487	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#650-Iranian
PRED entity: Iranian

PRED relation: ethnicGroup!
PRED expected values: BRN KWT
=> 30 concepts (18 used for prediction)
PRED predicted values (max 10 best out of 204): IR (0.64 #445, 0.56 #388, 0.47 #639), EAK (0.56 #388, 0.33 #1265, 0.27 #1558), CL (0.56 #388, 0.27 #1558, 0.27 #684), SA (0.56 #388, 0.27 #1558, 0.25 #140), JOR (0.56 #388, 0.27 #1558, 0.25 #147), RL (0.56 #388, 0.27 #1558, 0.25 #13), SUD (0.56 #388, 0.27 #1558, 0.25 #32), GB (0.56 #388, 0.27 #1558, 0.25 #6), IL (0.56 #388, 0.27 #1558, 0.25 #48), SYR (0.56 #388, 0.27 #1558, 0.25 #96)
>> best conf = 0.64
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #445 for best value:
>> intensional similarity = 8
>> extensional distance = 9
>> proper extension:  Kurd; Baloch; Lur; Azerbaijani; Afro-Asian; GilakiMazandarani; Turkmen;
>> query: (?x826, IR) <- ?x826[ a EthnicGroup; is ethnicGroup of ?x174[ has ethnicGroup ?x244<Arab>; has religion ?x187<Muslim>; is locatedIn of ?x918<PersianGulf>;];]

*> Best rule #382 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 7
*> proper extension:  Assyrian;
*> query: (?x826, BRN) <- ?x826[ a EthnicGroup; is ethnicGroup of ?x174[ has religion ?x187; has wasDependentOf ?x81; is locatedIn of ?x918<PersianGulf>;];]
*> conf = 0.22
ranks of expected_values: 11, 17
EVAL	Iranian	ethnicGroup!	KWT	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.062	30.000	18.000	204.000	0.636	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	Iranian	ethnicGroup!	BRN	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.091	30.000	18.000	204.000	0.636	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup!
PRED expected values: BRN KWT
=> 56 concepts (48 used for prediction)
PRED predicted values (max 10 best out of 212): GB (0.67 #1774, 0.57 #1768, 0.57 #1767), IR (0.64 #2022, 0.57 #1768, 0.57 #1767), CL (0.57 #1768, 0.57 #1767, 0.57 #1086), JOR (0.57 #1768, 0.57 #1767, 0.54 #1374), SA (0.57 #1768, 0.57 #1767, 0.54 #1374), RL (0.57 #1768, 0.57 #1767, 0.54 #1374), SYR (0.57 #1768, 0.57 #1767, 0.54 #1374), EAK (0.57 #1768, 0.57 #1767, 0.54 #1374), SUD (0.57 #1768, 0.57 #1767, 0.54 #1374), IL (0.57 #1768, 0.57 #1767, 0.54 #1374)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1774 for best value:
>> intensional similarity = 16
>> extensional distance = 7
>> proper extension:  Scottish; Welsh; English; NorthernIrish;
>> query: (?x826, GB) <- ?x826[ a EthnicGroup; is ethnicGroup of ?x174[ a Country; has encompassed ?x175[ a Continent; is encompassed of ?x185<TR>; is encompassed of ?x403<KAZ>;]; has ethnicGroup ?x1686<Pakistani>; has religion ?x187<Muslim>; is locatedIn of ?x918[ a Sea; is locatedInWater of ?x1443;];];]

*> Best rule #1568 for first EXPECTED value:
*> intensional similarity = 15
*> extensional distance = 6
*> proper extension:  Uzbek; Turkmen;
*> query: (?x826, ?x107) <- ?x826[ a EthnicGroup; is ethnicGroup of ?x174[ a Country; has encompassed ?x175<Asia>; has neighbor ?x751[ has ethnicGroup ?x1563; has government ?x640; has religion ?x187; is locatedIn of ?x637;]; has wasDependentOf ?x81; is locatedIn of ?x918[ has locatedIn ?x107; has locatedIn ?x304<IR>;];];]
*> conf = 0.29
ranks of expected_values: 15, 17
EVAL	Iranian	ethnicGroup!	KWT	CNN-1.+1._MA	0.000	0.000	0.000	0.062	56.000	48.000	212.000	0.667	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	Iranian	ethnicGroup!	BRN	CNN-1.+1._MA	0.000	0.000	0.000	0.067	56.000	48.000	212.000	0.667	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#649-USA
PRED entity: USA

PRED relation: wasDependentOf!
PRED expected values: RP
=> 31 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 160): SF (0.33 #93, 0.09 #1039, 0.06 #1354), TO (0.14 #785, 0.09 #943, 0.08 #946), WL (0.14 #781, 0.09 #939, 0.08 #946), AG (0.14 #773, 0.09 #931, 0.08 #946), BDS (0.14 #769, 0.09 #927, 0.08 #946), WAL (0.14 #768, 0.09 #926, 0.08 #946), KIR (0.14 #741, 0.09 #899, 0.08 #946), TUV (0.14 #734, 0.09 #892, 0.08 #946), GH (0.14 #712, 0.09 #870, 0.08 #946), NZ (0.14 #707, 0.09 #865, 0.08 #946)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #93 for best value:
>> intensional similarity = 7
>> extensional distance = 1
>> proper extension:  R;
>> query: (?x315, SF) <- ?x315[ has religion ?x95; is locatedIn of ?x809<BeringSea>; is locatedIn of ?x832[ a Source;]; is locatedIn of ?x1764[ a Estuary;];]

No rule for expected values
ranks of expected_values: 
EVAL	USA	wasDependentOf!	RP	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	31.000	31.000	160.000	0.333	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

PRED relation: wasDependentOf!
PRED expected values: RP
=> 113 concepts (113 used for prediction)
PRED predicted values (max 10 best out of 226): SF (0.33 #566, 0.20 #1043, 0.08 #3575), RH (0.20 #1376, 0.20 #1217, 0.17 #1691), MA (0.20 #1392, 0.20 #1233, 0.17 #1707), CI (0.20 #1410, 0.20 #1251, 0.17 #1725), BEN (0.20 #1386, 0.20 #1227, 0.17 #1701), RG (0.20 #1368, 0.20 #1209, 0.17 #1683), RIM (0.20 #1352, 0.20 #1193, 0.17 #1667), SN (0.20 #1336, 0.20 #1177, 0.17 #1651), RCB (0.20 #1355, 0.20 #1196, 0.17 #1670), G (0.20 #1288, 0.20 #1129, 0.17 #1603)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #566 for best value:
>> intensional similarity = 10
>> extensional distance = 1
>> proper extension:  R;
>> query: (?x315, SF) <- ?x315[ has wasDependentOf ?x81; is locatedIn of ?x263<ArcticOcean>; is locatedIn of ?x1221[ has type ?x136;]; is locatedIn of ?x1366[ has flowsThrough ?x1989;]; is locatedIn of ?x2128[ a Mountain;]; is neighbor of ?x482;]

*> Best rule #2534 for first EXPECTED value:
*> intensional similarity = 10
*> extensional distance = 6
*> proper extension:  NEP;
*> query: (?x315, ?x50) <- ?x315[ has encompassed ?x521; has religion ?x352[ is religion of ?x50; is religion of ?x81<GB>;]; has religion ?x462<Buddhist>; is locatedIn of ?x1942[ a Mountain; has inMountains ?x337;];]
*> conf = 0.04
ranks of expected_values: 159
EVAL	USA	wasDependentOf!	RP	CNN-1.+1._MA	0.000	0.000	0.000	0.006	113.000	113.000	226.000	0.333	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

#648-Buna
PRED entity: Buna

PRED relation: flowsInto
PRED expected values: MediterraneanSea
=> 35 concepts (29 used for prediction)
PRED predicted values (max 10 best out of 99): Drina (0.33 #87, 0.04 #751, 0.03 #1084), Donau (0.14 #1005, 0.09 #1338, 0.09 #2002), Drin (0.12 #405, 0.10 #572, 0.08 #738), AtlanticOcean (0.09 #2173, 0.09 #2671, 0.09 #3167), MediterraneanSea (0.09 #1020, 0.06 #354, 0.06 #166), BalticSea (0.08 #1007, 0.07 #1340, 0.06 #1505), BlackSea (0.06 #1000, 0.05 #1333, 0.03 #1664), BlackDrin (0.06 #401, 0.06 #166, 0.05 #568), Buna (0.06 #166, 0.05 #2160, 0.03 #1827), Piva (0.06 #166, 0.05 #2160, 0.03 #1827)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #87 for best value:
>> intensional similarity = 7
>> extensional distance = 1
>> proper extension:  Piva;
>> query: (?x203, Drina) <- ?x203[ a River; has locatedIn ?x106<MNE>; has locatedIn ?x204[ has encompassed ?x195<Europe>; is neighbor of ?x399;];]

*> Best rule #1020 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 64
*> proper extension:  DarlingRiver; MurrumbidgeeRiver; EucumbeneRiver;
*> query: (?x203, MediterraneanSea) <- ?x203[ a River; has locatedIn ?x106[ has encompassed ?x195; has government ?x435; has religion ?x56<ChristianOrthodox>;];]
*> conf = 0.09
ranks of expected_values: 5
EVAL	Buna	flowsInto	MediterraneanSea	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.200	35.000	29.000	99.000	0.333	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto
PRED expected values: MediterraneanSea
=> 114 concepts (114 used for prediction)
PRED predicted values (max 10 best out of 164): Drina (0.33 #87, 0.25 #254, 0.18 #587), Donau (0.25 #3189, 0.25 #842, 0.21 #2518), Drin (0.25 #241, 0.22 #1168, 0.17 #908), MediterraneanSea (0.23 #2176, 0.23 #2031, 0.22 #1168), BlackDrin (0.22 #1168, 0.15 #2175, 0.15 #1839), LakeSkutari (0.22 #1168, 0.15 #2175, 0.15 #1839), Morava (0.18 #507, 0.17 #841, 0.06 #2517), AtlanticOcean (0.16 #1853, 0.16 #5711, 0.16 #1350), Buna (0.14 #166, 0.12 #334, 0.06 #167), Piva (0.14 #166, 0.12 #334, 0.06 #167)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #87 for best value:
>> intensional similarity = 11
>> extensional distance = 1
>> proper extension:  Piva;
>> query: (?x203, Drina) <- ?x203[ has hasEstuary ?x1934; has hasSource ?x183[ a Source;]; has locatedIn ?x106<MNE>; has locatedIn ?x204[ a Country; has government ?x254; has language ?x1251; has neighbor ?x399; has religion ?x56;];]

*> Best rule #2176 for first EXPECTED value:
*> intensional similarity = 14
*> extensional distance = 29
*> proper extension:  Nile;
*> query: (?x203, ?x275) <- ?x203[ has hasEstuary ?x1934; has hasSource ?x183[ a Source;]; has locatedIn ?x106[ a Country; has neighbor ?x692[ has language ?x1251;]; has religion ?x56; is locatedIn of ?x104[ is flowsInto of ?x2296;]; is locatedIn of ?x275<MediterraneanSea>; is locatedIn of ?x814[ has inMountains ?x785;];];]
*> conf = 0.23
ranks of expected_values: 4
EVAL	Buna	flowsInto	MediterraneanSea	CNN-1.+1._MA	0.000	0.000	1.000	0.250	114.000	114.000	164.000	0.333	http://www.semwebtech.org/mondial/10/meta#flowsInto

#647-SLB
PRED entity: SLB

PRED relation: ethnicGroup
PRED expected values: Polynesian
=> 43 concepts (43 used for prediction)
PRED predicted values (max 10 best out of 209): Asian (0.40 #525, 0.18 #1541, 0.17 #2557), African (0.30 #1784, 0.20 #260, 0.18 #5848), Amerindian (0.30 #1780, 0.18 #3304, 0.18 #3812), Mestizo (0.30 #1812, 0.14 #3590, 0.14 #3336), White (0.25 #64, 0.18 #1588, 0.17 #2604), French (0.25 #122, 0.14 #9401, 0.06 #3170), Black (0.25 #55, 0.12 #1579, 0.08 #2595), Mixed (0.25 #125, 0.12 #1649, 0.08 #2665), Malay (0.24 #2382, 0.17 #858, 0.14 #9401), PacificIslander (0.20 #581, 0.14 #9401, 0.08 #2613)
>> best conf = 0.40
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #525 for best value:
>> intensional similarity = 8
>> extensional distance = 3
>> proper extension:  AUS; NZ; NCA;
>> query: (?x390, Asian) <- ?x390[ has encompassed ?x211<Australia-Oceania>; has ethnicGroup ?x197<European>; has religion ?x95<Protestant>; has religion ?x352<RomanCatholic>;]

*> Best rule #9401 for first EXPECTED value:
*> intensional similarity = 9
*> extensional distance = 185
*> proper extension:  ER; GBJ; GBG;
*> query: (?x390, ?x1487) <- ?x390[ a Country; has ethnicGroup ?x298[ is ethnicGroup of ?x538[ has encompassed ?x175; has ethnicGroup ?x1487;]; is ethnicGroup of ?x667[ is locatedIn of ?x182;]; is ethnicGroup of ?x745[ has government ?x828;];];]
*> conf = 0.14
ranks of expected_values: 23
EVAL	SLB	ethnicGroup	Polynesian	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.043	43.000	43.000	209.000	0.400	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: Polynesian
=> 91 concepts (91 used for prediction)
PRED predicted values (max 10 best out of 243): Asian (0.60 #3326, 0.50 #2054, 0.43 #4600), African (0.56 #10974, 0.43 #255, 0.40 #14295), Polynesian (0.43 #255, 0.40 #2037, 0.38 #6203), White (0.43 #255, 0.40 #2037, 0.33 #510), Malay (0.43 #255, 0.40 #2037, 0.33 #5444), Indonesian (0.43 #255, 0.40 #2037, 0.33 #1058), Vietnamese (0.43 #255, 0.40 #2037, 0.33 #1097), Wallisian (0.43 #255, 0.40 #2037, 0.33 #1243), PacificIslander (0.43 #255, 0.40 #3382, 0.33 #510), Portuguese (0.43 #255, 0.40 #2037, 0.33 #510)
>> best conf = 0.60
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3326 for best value:
>> intensional similarity = 15
>> extensional distance = 3
>> proper extension:  NMIS;
>> query: (?x390, Asian) <- ?x390[ has encompassed ?x211<Australia-Oceania>; has ethnicGroup ?x298[ a EthnicGroup; is ethnicGroup of ?x212[ a Country; has encompassed ?x213; is locatedIn of ?x182;]; is ethnicGroup of ?x366[ has religion ?x116; is locatedIn of ?x262;];]; has government ?x1947; has language ?x247<English>; is locatedIn of ?x1083;]

*> Best rule #255 for first EXPECTED value:
*> intensional similarity = 15
*> extensional distance = 1
*> proper extension:  BERM;
*> query: (?x390, ?x79) <- ?x390[ a Country; has encompassed ?x211[ a Continent; is encompassed of ?x217[ has neighbor ?x735; is locatedIn of ?x60;];]; has ethnicGroup ?x298[ is ethnicGroup of ?x179[ has ethnicGroup ?x79;];]; has religion ?x95<Protestant>; has religion ?x429<Seventh-DayAdventist>; has religion ?x713<Anglican>;]
*> conf = 0.43
ranks of expected_values: 3
EVAL	SLB	ethnicGroup	Polynesian	CNN-1.+1._MA	0.000	1.000	1.000	0.333	91.000	91.000	243.000	0.600	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#646-Raab
PRED entity: Raab

PRED relation: locatedIn
PRED expected values: H
=> 38 concepts (33 used for prediction)
PRED predicted values (max 10 best out of 102): H (0.92 #3310, 0.90 #2129, 0.33 #236), D (0.81 #1439, 0.59 #1676, 0.36 #256), HR (0.33 #29, 0.22 #500, 0.18 #976), SLO (0.33 #103, 0.17 #1183, 0.15 #2365), TR (0.29 #1224, 0.15 #1697, 0.08 #2601), BIH (0.21 #949, 0.06 #473, 0.06 #5905), USA (0.18 #4324, 0.18 #4560, 0.16 #4796), R (0.17 #2134, 0.16 #2370, 0.15 #4257), SK (0.17 #1183, 0.15 #2365, 0.14 #947), CH (0.17 #1183, 0.15 #2365, 0.14 #947)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3310 for best value:
>> intensional similarity = 5
>> extensional distance = 198
>> proper extension:  Araguaia; Leine; Morava; Donau; Oranje; Neckar; Buna; Amazonas; WesternBug; Limpopo; ...
>> query: (?x1838, ?x236) <- ?x1838[ a River; has hasEstuary ?x1265[ a Estuary; has locatedIn ?x236[ is neighbor of ?x156;];];]
ranks of expected_values: 1
EVAL	Raab	locatedIn	H	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	38.000	33.000	102.000	0.916	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: H
=> 100 concepts (96 used for prediction)
PRED predicted values (max 10 best out of 169): H (0.90 #8804, 0.89 #6419, 0.89 #7135), D (0.81 #7155, 0.78 #2638, 0.59 #8087), SK (0.59 #8087, 0.50 #710, 0.40 #506), SLO (0.50 #710, 0.50 #473, 0.50 #339), HR (0.50 #710, 0.50 #265, 0.40 #503), SRB (0.50 #710, 0.35 #4941, 0.33 #472), UA (0.50 #710, 0.33 #472, 0.29 #6489), RO (0.50 #710, 0.33 #472, 0.29 #13327), I (0.40 #2427, 0.29 #13327, 0.26 #9566), CH (0.39 #5764, 0.37 #2197, 0.33 #1959)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #8804 for best value:
>> intensional similarity = 8
>> extensional distance = 151
>> proper extension:  Sobat;
>> query: (?x1838, ?x236) <- ?x1838[ a River; has hasEstuary ?x1265[ a Estuary; has locatedIn ?x236[ a Country; has government ?x254; has neighbor ?x156; is neighbor of ?x156;];];]
ranks of expected_values: 1
EVAL	Raab	locatedIn	H	CNN-1.+1._MA	1.000	1.000	1.000	1.000	100.000	96.000	169.000	0.898	http://www.semwebtech.org/mondial/10/meta#locatedIn

#645-BF
PRED entity: BF

PRED relation: locatedIn!
PRED expected values: Volta
=> 34 concepts (32 used for prediction)
PRED predicted values (max 10 best out of 947): AtlanticOcean (0.96 #19969, 0.96 #17122, 0.50 #1466), ChadLake (0.40 #4130, 0.33 #1283, 0.25 #2707), Niger (0.33 #4523, 0.33 #253, 0.29 #5947), Benue (0.33 #1061, 0.25 #2485, 0.20 #3908), Niger (0.33 #1363, 0.25 #2787, 0.20 #4210), Benue (0.33 #1362, 0.25 #2786, 0.20 #4209), AsoRock (0.33 #378, 0.25 #1802, 0.20 #3225), LakeKainji (0.33 #252, 0.25 #1676, 0.20 #3099), Senegal (0.33 #4678, 0.17 #7525, 0.16 #8948), MediterraneanSea (0.29 #5777, 0.17 #7200, 0.16 #24283)
>> best conf = 0.96
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #19969 for best value:
>> intensional similarity = 7
>> extensional distance = 50
>> proper extension:  FALK;
>> query: (?x811, AtlanticOcean) <- ?x811[ a Country; has ethnicGroup ?x2156[ a EthnicGroup;]; is locatedIn of ?x610[ has locatedIn ?x483<GH>; is flowsInto of ?x135;];]

No rule for expected values
ranks of expected_values: 
EVAL	BF	locatedIn!	Volta	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	34.000	32.000	947.000	0.962	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Volta
=> 75 concepts (75 used for prediction)
PRED predicted values (max 10 best out of 1384): AtlanticOcean (0.96 #58498, 0.92 #65582, 0.92 #64199), Niger (0.50 #7381, 0.40 #10233, 0.33 #11660), Senegal (0.50 #7536, 0.40 #10388, 0.33 #11815), ChadLake (0.50 #9836, 0.33 #4136, 0.29 #14116), PacificOcean (0.38 #72795, 0.28 #48562, 0.26 #54266), MediterraneanSea (0.36 #15769, 0.33 #1509, 0.27 #18618), Tanezrouft (0.33 #2784, 0.29 #14191, 0.25 #8486), ErgChech (0.33 #2489, 0.29 #13896, 0.25 #8191), Talak (0.33 #3769, 0.25 #9469, 0.25 #8044), Bani (0.33 #4461, 0.25 #7309, 0.23 #11404)
>> best conf = 0.96
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #58498 for best value:
>> intensional similarity = 8
>> extensional distance = 50
>> proper extension:  FALK;
>> query: (?x811, AtlanticOcean) <- ?x811[ a Country; has ethnicGroup ?x2156[ a EthnicGroup;]; is locatedIn of ?x610[ has locatedIn ?x483<GH>; is flowsInto of ?x135[ has locatedIn ?x483<GH>;];];]

No rule for expected values
ranks of expected_values: 
EVAL	BF	locatedIn!	Volta	CNN-1.+1._MA	0.000	0.000	0.000	0.000	75.000	75.000	1384.000	0.962	http://www.semwebtech.org/mondial/10/meta#locatedIn

#644-ArcticOcean
PRED entity: ArcticOcean

PRED relation: flowsInto!
PRED expected values: MackenzieRiver
=> 24 concepts (21 used for prediction)
PRED predicted values (max 10 best out of 342): SaintLawrenceRiver (0.17 #495, 0.17 #192, 0.04 #797), MerrimackRiver (0.17 #568, 0.17 #265, 0.04 #870), HudsonRiver (0.17 #501, 0.17 #198, 0.04 #803), ConnecticutRiver (0.17 #429, 0.17 #126, 0.04 #731), RioSaoFrancisco (0.17 #549, 0.17 #246, 0.04 #851), Sanaga (0.17 #530, 0.17 #227, 0.04 #832), Douro (0.17 #529, 0.17 #226, 0.04 #831), Tajo (0.17 #524, 0.17 #221, 0.04 #826), Loire (0.17 #486, 0.17 #183, 0.04 #788), Guadiana (0.17 #476, 0.17 #173, 0.04 #778)
>> best conf = 0.17
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #495 for best value:
>> intensional similarity = 6
>> extensional distance = 4
>> proper extension:  HudsonBay;
>> query: (?x263, SaintLawrenceRiver) <- ?x263[ has mergesWith ?x1419[ has locatedIn ?x455;]; is locatedInWater of ?x2220[ a Island; has locatedIn ?x272<CDN>;];]
>> Best rule #192 for best value:
>> intensional similarity = 5
>> extensional distance = 4
>> proper extension:  Greenland;
>> query: (?x263, SaintLawrenceRiver) <- ?x263[ has locatedIn ?x272[ has religion ?x95<Protestant>;]; has locatedIn ?x792<GROX>;]

*> Best rule #303 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 4
*> proper extension:  Greenland;
*> query: (?x263, ?x182) <- ?x263[ has locatedIn ?x272[ has religion ?x95<Protestant>; is locatedIn of ?x182;]; has locatedIn ?x792<GROX>;]
*> conf = 0.04
ranks of expected_values: 73
EVAL	ArcticOcean	flowsInto!	MackenzieRiver	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.014	24.000	21.000	342.000	0.167	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto!
PRED expected values: MackenzieRiver
=> 80 concepts (80 used for prediction)
PRED predicted values (max 10 best out of 343): SaintLawrenceRiver (0.33 #192, 0.25 #798, 0.20 #1404), MerrimackRiver (0.33 #265, 0.25 #871, 0.20 #1477), HudsonRiver (0.33 #198, 0.25 #804, 0.20 #1410), ConnecticutRiver (0.33 #126, 0.25 #732, 0.20 #1338), RioSaoFrancisco (0.33 #246, 0.25 #852, 0.20 #1458), Sanaga (0.33 #227, 0.25 #833, 0.20 #1439), Douro (0.33 #226, 0.25 #832, 0.20 #1438), Tajo (0.33 #221, 0.25 #827, 0.20 #1433), Loire (0.33 #183, 0.25 #789, 0.20 #1395), Guadiana (0.33 #173, 0.25 #779, 0.20 #1385)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #192 for best value:
>> intensional similarity = 10
>> extensional distance = 1
>> proper extension:  AtlanticOcean;
>> query: (?x263, SaintLawrenceRiver) <- ?x263[ a Sea; has locatedIn ?x73; is locatedInWater of ?x931[ a Island;]; is locatedInWater of ?x1075<Greenland>; is mergesWith of ?x249<LabradorSea>; is mergesWith of ?x452[ is flowsInto of ?x919;];]

*> Best rule #2430 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 17
*> proper extension:  RedSea;
*> query: (?x263, ?x514) <- ?x263[ has locatedIn ?x272[ has government ?x2416; has language ?x51; has religion ?x95; has wasDependentOf ?x81; is locatedIn of ?x514[ a River;];]; is mergesWith of ?x248;]
*> conf = 0.16
ranks of expected_values: 39
EVAL	ArcticOcean	flowsInto!	MackenzieRiver	CNN-1.+1._MA	0.000	0.000	0.000	0.026	80.000	80.000	343.000	0.333	http://www.semwebtech.org/mondial/10/meta#flowsInto

#643-Kazak
PRED entity: Kazak

PRED relation: ethnicGroup!
PRED expected values: UZB
=> 28 concepts (19 used for prediction)
PRED predicted values (max 10 best out of 205): IND (0.50 #353, 0.33 #160, 0.21 #581), MYA (0.41 #457, 0.35 #651, 0.30 #846), GB (0.30 #587, 0.15 #977, 0.07 #970), MAL (0.24 #458, 0.17 #847, 0.10 #1042), CL (0.24 #490, 0.17 #879, 0.10 #1074), R (0.18 #2138, 0.13 #3702, 0.13 #3505), SGP (0.18 #568, 0.13 #957, 0.10 #762), UA (0.18 #775, 0.17 #1222, 0.12 #1806), LV (0.18 #775, 0.11 #1255, 0.08 #1839), CN (0.18 #775, 0.11 #2140, 0.02 #2725)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #353 for best value:
>> intensional similarity = 9
>> extensional distance = 2
>> proper extension:  Indo-Aryan;
>> query: (?x1823, IND) <- ?x1823[ a EthnicGroup; is ethnicGroup of ?x1010[ has ethnicGroup ?x1553<Mongol>; has government ?x2058; has language ?x335; has religion ?x116; is locatedIn of ?x72; is neighbor of ?x73;];]

*> Best rule #1217 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 52
*> proper extension:  Chamorro;
*> query: (?x1823, UZB) <- ?x1823[ a EthnicGroup; is ethnicGroup of ?x1010[ a Country; has government ?x2058; has language ?x335; is locatedIn of ?x72[ has locatedIn ?x73<R>;];];]
*> conf = 0.06
ranks of expected_values: 88
EVAL	Kazak	ethnicGroup!	UZB	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.011	28.000	19.000	205.000	0.500	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup!
PRED expected values: UZB
=> 70 concepts (70 used for prediction)
PRED predicted values (max 10 best out of 208): UA (0.64 #2031, 0.56 #2231, 0.47 #2830), MYA (0.64 #1053, 0.58 #1651, 0.54 #1847), KAZ (0.55 #1257, 0.38 #2453, 0.32 #2851), GB (0.55 #1386, 0.25 #4360, 0.25 #4170), IND (0.50 #353, 0.33 #160, 0.31 #2772), RG (0.50 #711, 0.15 #3300, 0.14 #3696), R (0.48 #5560, 0.31 #8156, 0.31 #8357), N (0.40 #414, 0.27 #5559, 0.21 #3373), KGZ (0.36 #1196, 0.25 #2392, 0.21 #2790), FJI (0.33 #804, 0.09 #1403, 0.09 #1006)
>> best conf = 0.64
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2031 for best value:
>> intensional similarity = 21
>> extensional distance = 12
>> proper extension:  Ukrainian; Hungarian; Jewish; Moldovan; Russian; Belorussian; Bulgarian; CrimeanTatar; Polish;
>> query: (?x1823, UA) <- ?x1823[ a EthnicGroup; is ethnicGroup of ?x1010[ a Country; has government ?x2058; has language ?x335; has religion ?x187[ is religion of ?x179<SME>; is religion of ?x217<RI>; is religion of ?x353<GE>; is religion of ?x819<MW>;]; has wasDependentOf ?x232; is locatedIn of ?x72[ a River;]; is locatedIn of ?x956[ a Source;]; is neighbor of ?x73<R>;];]

*> Best rule #1231 for first EXPECTED value:
*> intensional similarity = 24
*> extensional distance = 9
*> proper extension:  German; Tatar; Uzbek; Uighur;
*> query: (?x1823, UZB) <- ?x1823[ a EthnicGroup; is ethnicGroup of ?x1010[ a Country; has government ?x2058; has language ?x335; has religion ?x116[ is religion of ?x416<SN>; is religion of ?x483<GH>; is religion of ?x536<CAM>; is religion of ?x568<WEST>; is religion of ?x651<RG>; is religion of ?x803<JOR>;]; is locatedIn of ?x72[ a River;]; is locatedIn of ?x956[ a Source;]; is neighbor of ?x73<R>;];]
*> conf = 0.27
ranks of expected_values: 22
EVAL	Kazak	ethnicGroup!	UZB	CNN-1.+1._MA	0.000	0.000	0.000	0.045	70.000	70.000	208.000	0.643	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#642-SaoTome
PRED entity: SaoTome

PRED relation: locatedInWater
PRED expected values: AtlanticOcean
=> 58 concepts (57 used for prediction)
PRED predicted values (max 10 best out of 40): AtlanticOcean (0.63 #1093, 0.63 #1056, 0.42 #2075), PacificOcean (0.44 #800, 0.43 #623, 0.38 #667), IndianOcean (0.36 #262, 0.24 #348, 0.24 #434), JavaSea (0.32 #269, 0.21 #355, 0.21 #226), MediterraneanSea (0.17 #843, 0.11 #1110, 0.10 #1955), CaribbeanSea (0.15 #582, 0.15 #538, 0.14 #322), SulawesiSea (0.14 #288, 0.13 #855, 0.12 #899), SouthChinaSea (0.14 #282, 0.11 #849, 0.10 #893), ArcticOcean (0.13 #620, 0.12 #664, 0.11 #708), BandaSea (0.09 #289, 0.08 #856, 0.06 #375)
>> best conf = 0.63
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1093 for best value:
>> intensional similarity = 6
>> extensional distance = 76
>> proper extension:  St.Martin; SaintThomas; PortoSanto; NewProvidence;
>> query: (?x1790, ?x182) <- ?x1790[ a Island; has locatedIn ?x994[ has government ?x435; has religion ?x316; is locatedIn of ?x182<AtlanticOcean>;];]
>> Best rule #1056 for best value:
>> intensional similarity = 6
>> extensional distance = 76
>> proper extension:  St.Martin; SaintThomas; PortoSanto; NewProvidence;
>> query: (?x1790, AtlanticOcean) <- ?x1790[ a Island; has locatedIn ?x994[ has government ?x435; has religion ?x316; is locatedIn of ?x182<AtlanticOcean>;];]
ranks of expected_values: 1
EVAL	SaoTome	locatedInWater	AtlanticOcean	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	58.000	57.000	40.000	0.628	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater
PRED expected values: AtlanticOcean
=> 117 concepts (117 used for prediction)
PRED predicted values (max 10 best out of 41): AtlanticOcean (0.82 #1231, 0.82 #1194, 0.75 #262), JavaSea (0.39 #671, 0.33 #757, 0.29 #977), IndianOcean (0.39 #664, 0.33 #750, 0.26 #1057), PacificOcean (0.32 #2365, 0.30 #2188, 0.29 #1874), CaribbeanSea (0.27 #416, 0.25 #548, 0.21 #326), MediterraneanSea (0.21 #1650, 0.21 #1159, 0.20 #1248), SulawesiSea (0.17 #690, 0.14 #1662, 0.14 #776), SouthChinaSea (0.17 #684, 0.14 #770, 0.12 #1656), ArcticOcean (0.14 #1603, 0.13 #411, 0.12 #543), LabradorSea (0.13 #408, 0.12 #540, 0.08 #1600)
>> best conf = 0.82
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1231 for best value:
>> intensional similarity = 8
>> extensional distance = 32
>> proper extension:  Barbuda;
>> query: (?x1790, ?x182) <- ?x1790[ a Island; has locatedIn ?x994[ has encompassed ?x213[ a Continent;]; has government ?x435; is locatedIn of ?x182<AtlanticOcean>;]; has type ?x150;]
>> Best rule #1194 for best value:
>> intensional similarity = 8
>> extensional distance = 32
>> proper extension:  Barbuda;
>> query: (?x1790, AtlanticOcean) <- ?x1790[ a Island; has locatedIn ?x994[ has encompassed ?x213[ a Continent;]; has government ?x435; is locatedIn of ?x182<AtlanticOcean>;]; has type ?x150;]
ranks of expected_values: 1
EVAL	SaoTome	locatedInWater	AtlanticOcean	CNN-1.+1._MA	1.000	1.000	1.000	1.000	117.000	117.000	41.000	0.824	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#641-Euphrat
PRED entity: Euphrat

PRED relation: hasSource
PRED expected values: Euphrat
=> 45 concepts (37 used for prediction)
PRED predicted values (max 10 best out of 138): SchattalArab (0.25 #255, 0.04 #457, 0.02 #1399), Tigris (0.25 #352, 0.04 #457, 0.02 #6403), Angara (0.04 #1251, 0.02 #1480, 0.02 #1708), Dnepr (0.04 #1314, 0.02 #1543, 0.02 #2687), Newa (0.04 #1343, 0.02 #1572, 0.02 #2716), Volga (0.04 #1166, 0.02 #1395, 0.02 #2539), Jenissej (0.04 #1337, 0.02 #1566, 0.01 #2939), Karasu (0.04 #457, 0.02 #6403, 0.01 #7547), Kura (0.04 #457, 0.02 #6403, 0.01 #7547), Ararat (0.04 #457, 0.02 #6403, 0.01 #7547)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #255 for best value:
>> intensional similarity = 5
>> extensional distance = 2
>> proper extension:  Tigris;
>> query: (?x1644, SchattalArab) <- ?x1644[ a River; has locatedIn ?x302<IRQ>; has locatedIn ?x466[ has neighbor ?x115;];]

*> Best rule #457 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 2
*> proper extension:  Tigris;
*> query: (?x1644, ?x275) <- ?x1644[ a River; has locatedIn ?x302<IRQ>; has locatedIn ?x466[ has neighbor ?x115; is locatedIn of ?x275;];]
*> conf = 0.04
ranks of expected_values: 22
EVAL	Euphrat	hasSource	Euphrat	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.045	45.000	37.000	138.000	0.250	http://www.semwebtech.org/mondial/10/meta#hasSource

PRED relation: hasSource
PRED expected values: Euphrat
=> 99 concepts (99 used for prediction)
PRED predicted values (max 10 best out of 213): Tigris (0.33 #124, 0.26 #6435, 0.25 #810), SchattalArab (0.25 #713, 0.22 #915, 0.20 #1406), Kura (0.22 #915, 0.05 #916, 0.05 #3696), Karasu (0.22 #915, 0.05 #916, 0.05 #3885), Murat (0.22 #915, 0.05 #916, 0.05 #3788), Euphrat (0.22 #915, 0.05 #916, 0.03 #1377), Karun (0.20 #1477, 0.04 #4691, 0.04 #4922), Newa (0.09 #3185, 0.05 #3645, 0.05 #4104), Angara (0.09 #3093, 0.05 #3553, 0.05 #4012), Volga (0.09 #3008, 0.05 #3468, 0.05 #3927)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #124 for best value:
>> intensional similarity = 14
>> extensional distance = 1
>> proper extension:  Tigris;
>> query: (?x1644, Tigris) <- ?x1644[ a River; has flowsInto ?x1422; has locatedIn ?x302<IRQ>; has locatedIn ?x466[ has ethnicGroup ?x244; has neighbor ?x115[ has ethnicGroup ?x1420; has religion ?x116; is locatedIn of ?x1564;]; has neighbor ?x803<JOR>; is locatedIn of ?x275[ is locatedInWater of ?x68;];];]

*> Best rule #915 for first EXPECTED value:
*> intensional similarity = 15
*> extensional distance = 2
*> proper extension:  SchattalArab;
*> query: (?x1644, ?x469) <- ?x1644[ has hasEstuary ?x255; has locatedIn ?x185[ has ethnicGroup ?x638; has neighbor ?x177; is locatedIn of ?x469[ a Source;]; is locatedIn of ?x666<Tigris>;]; has locatedIn ?x302<IRQ>; has locatedIn ?x466[ a Country; has ethnicGroup ?x244; has government ?x2550; has neighbor ?x115;];]
*> conf = 0.22
ranks of expected_values: 6
EVAL	Euphrat	hasSource	Euphrat	CNN-1.+1._MA	0.000	0.000	1.000	0.167	99.000	99.000	213.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasSource

#640-LagoJunin
PRED entity: LagoJunin

PRED relation: locatedIn
PRED expected values: PE
=> 41 concepts (41 used for prediction)
PRED predicted values (max 10 best out of 92): PE (0.73 #1188, 0.71 #1901, 0.70 #4038), USA (0.43 #311, 0.27 #4823, 0.24 #2686), BR (0.25 #125, 0.12 #5939, 0.11 #1425), YV (0.25 #78, 0.05 #1979, 0.04 #3403), ZRE (0.23 #4355, 0.21 #2456, 0.18 #3880), CH (0.20 #1721, 0.18 #1245, 0.15 #1483), CDN (0.18 #4814, 0.12 #2677, 0.12 #5288), MEX (0.14 #355, 0.09 #592, 0.05 #2017), AUS (0.14 #284, 0.05 #4559, 0.04 #4796), D (0.14 #733, 0.11 #8096, 0.11 #5722)
>> best conf = 0.73
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1188 for best value:
>> intensional similarity = 7
>> extensional distance = 25
>> proper extension:  Drina;
>> query: (?x1331, ?x296) <- ?x1331[ has flowsInto ?x1332[ a River; has flowsInto ?x1207[ has hasSource ?x430;]; has locatedIn ?x296[ has language ?x702; has wasDependentOf ?x149;];];]
ranks of expected_values: 1
EVAL	LagoJunin	locatedIn	PE	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	41.000	41.000	92.000	0.727	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: PE
=> 87 concepts (87 used for prediction)
PRED predicted values (max 10 best out of 98): PE (0.80 #1908, 0.78 #5722, 0.78 #9783), USA (0.54 #2693, 0.50 #1504, 0.48 #9377), CH (0.50 #1012, 0.29 #8170, 0.21 #6257), CDN (0.46 #2684, 0.25 #1495, 0.24 #13667), ZRE (0.39 #10101, 0.25 #11289, 0.23 #2938), BOL (0.33 #153, 0.21 #3249, 0.16 #7634), YV (0.33 #318, 0.06 #4128, 0.06 #5320), R (0.28 #5727, 0.23 #16237, 0.18 #11695), SF (0.25 #3942, 0.24 #4659, 0.22 #5374), D (0.23 #14577, 0.19 #4310, 0.18 #12664)
>> best conf = 0.80
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1908 for best value:
>> intensional similarity = 14
>> extensional distance = 8
>> proper extension:  SaintMarysRiver; StraitsofMackinac;
>> query: (?x1331, ?x296) <- ?x1331[ has flowsInto ?x1332[ has flowsInto ?x1207[ a River; has flowsInto ?x987; has hasSource ?x430;]; has locatedIn ?x296[ a Country; has encompassed ?x521; has ethnicGroup ?x79; has language ?x796<Spanish>; has neighbor ?x202; is locatedIn of ?x282<PacificOcean>;];];]
ranks of expected_values: 1
EVAL	LagoJunin	locatedIn	PE	CNN-1.+1._MA	1.000	1.000	1.000	1.000	87.000	87.000	98.000	0.800	http://www.semwebtech.org/mondial/10/meta#locatedIn

#639-Lhotse
PRED entity: Lhotse

PRED relation: inMountains
PRED expected values: Himalaya
=> 24 concepts (24 used for prediction)
PRED predicted values (max 10 best out of 30): Himalaya (0.75 #180, 0.67 #93, 0.60 #6), RockyMountains (0.20 #268, 0.05 #965, 0.04 #791), Alps (0.13 #265, 0.05 #962, 0.05 #788), EliasRange (0.07 #276, 0.02 #973, 0.01 #799), TianShan (0.06 #1568, 0.03 #466, 0.03 #523), Karakorum (0.06 #1568, 0.03 #443, 0.03 #523), Kunlun (0.06 #1568, 0.03 #445, 0.03 #523), Pamir (0.06 #1568, 0.03 #523, 0.02 #975), Transhimalaya (0.06 #1568, 0.03 #523, 0.02 #459), Andes (0.05 #969, 0.03 #1056, 0.03 #1143)
>> best conf = 0.75
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #180 for best value:
>> intensional similarity = 7
>> extensional distance = 6
>> proper extension:  Annapurna; Dhaulagiri;
>> query: (?x328, Himalaya) <- ?x328[ a Mountain; has locatedIn ?x111<NEP>; has locatedIn ?x232[ has neighbor ?x403[ has ethnicGroup ?x58; is locatedIn of ?x127;];];]
ranks of expected_values: 1
EVAL	Lhotse	inMountains	Himalaya	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	24.000	24.000	30.000	0.750	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains
PRED expected values: Himalaya
=> 80 concepts (80 used for prediction)
PRED predicted values (max 10 best out of 42): Himalaya (0.78 #438, 0.78 #356, 0.75 #268), Kunlun (0.26 #350, 0.12 #622, 0.07 #2970), Pamir (0.26 #350, 0.07 #2970, 0.06 #4626), TianShan (0.26 #350, 0.07 #2970, 0.06 #4626), Karakorum (0.26 #350, 0.07 #2970, 0.06 #4626), Transhimalaya (0.26 #350, 0.07 #2970, 0.06 #4626), Alps (0.24 #968, 0.17 #1229, 0.14 #1404), RockyMountains (0.13 #1494, 0.09 #2279, 0.09 #2540), Apennin (0.08 #967, 0.05 #1577, 0.04 #1664), Kaukasus (0.08 #1244, 0.06 #1419, 0.03 #2552)
>> best conf = 0.78
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #438 for best value:
>> intensional similarity = 16
>> extensional distance = 7
>> proper extension:  NandaDevi;
>> query: (?x328, ?x309) <- ?x328[ a Mountain; has locatedIn ?x111[ a Country; has encompassed ?x175; has government ?x1779; has language ?x2295; has religion ?x410<Hindu>; has religion ?x462<Buddhist>; is locatedIn of ?x110[ a Mountain; has inMountains ?x309<Himalaya>;]; is locatedIn of ?x489<Kangchendzonga>;];]
>> Best rule #356 for best value:
>> intensional similarity = 16
>> extensional distance = 7
>> proper extension:  NandaDevi;
>> query: (?x328, Himalaya) <- ?x328[ a Mountain; has locatedIn ?x111[ a Country; has encompassed ?x175; has government ?x1779; has language ?x2295; has religion ?x410<Hindu>; has religion ?x462<Buddhist>; is locatedIn of ?x110[ a Mountain; has inMountains ?x309<Himalaya>;]; is locatedIn of ?x489<Kangchendzonga>;];]
ranks of expected_values: 1
EVAL	Lhotse	inMountains	Himalaya	CNN-1.+1._MA	1.000	1.000	1.000	1.000	80.000	80.000	42.000	0.778	http://www.semwebtech.org/mondial/10/meta#inMountains

#638-LabradorSea
PRED entity: LabradorSea

PRED relation: locatedIn
PRED expected values: CDN
=> 25 concepts (23 used for prediction)
PRED predicted values (max 10 best out of 215): CDN (0.94 #3148, 0.91 #2609, 0.89 #1418), USA (0.46 #3392, 0.33 #3157, 0.33 #72), R (0.38 #3325, 0.24 #2138, 0.22 #2614), GB (0.36 #479, 0.30 #244, 0.21 #719), RI (0.33 #762, 0.31 #998, 0.27 #1234), SVAX (0.33 #191, 0.21 #661, 0.20 #1182), IS (0.33 #108, 0.21 #578, 0.20 #1182), F (0.29 #706, 0.29 #477, 0.20 #1182), C (0.21 #496, 0.20 #1182, 0.20 #946), N (0.21 #504, 0.11 #1929, 0.11 #2167)
>> best conf = 0.94
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3148 for best value:
>> intensional similarity = 8
>> extensional distance = 67
>> proper extension:  DetroitRiver; ReneLevasseurIsland; MtSt.Elias; VictoriaIsland; SaintMarysRiver; SaintMarysRiver; MtColumbia; MtBona; SaintLawrenceRiver; DetroitRiver; ...
>> query: (?x249, CDN) <- ?x249[ has locatedIn ?x792[ a Country; has government ?x2552; has religion ?x95; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x263<ArcticOcean>;];]
ranks of expected_values: 1
EVAL	LabradorSea	locatedIn	CDN	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	25.000	23.000	215.000	0.942	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: CDN
=> 65 concepts (63 used for prediction)
PRED predicted values (max 10 best out of 224): CDN (0.91 #1652, 0.91 #6168, 0.91 #1180), SVAX (0.56 #1371, 0.33 #900, 0.33 #428), USA (0.38 #7896, 0.37 #8370, 0.33 #781), GB (0.36 #1661, 0.33 #1425, 0.29 #3787), IS (0.33 #1288, 0.33 #817, 0.33 #345), R (0.33 #236, 0.33 #5, 0.25 #2368), RI (0.33 #2890, 0.30 #3126, 0.29 #4079), F (0.29 #3787, 0.27 #1659, 0.25 #951), E (0.29 #3787, 0.23 #6170, 0.23 #6169), RH (0.29 #3787, 0.23 #6170, 0.23 #6169)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1652 for best value:
>> intensional similarity = 8
>> extensional distance = 7
>> proper extension:  IrishSea;
>> query: (?x249, ?x272) <- ?x249[ has locatedIn ?x792[ a Country;]; is locatedInWater of ?x1238[ a Island; has belongsToIslands ?x479; has locatedIn ?x272;]; is mergesWith of ?x182<AtlanticOcean>;]
ranks of expected_values: 1
EVAL	LabradorSea	locatedIn	CDN	CNN-1.+1._MA	1.000	1.000	1.000	1.000	65.000	63.000	224.000	0.914	http://www.semwebtech.org/mondial/10/meta#locatedIn

#637-Baltrum
PRED entity: Baltrum

PRED relation: belongsToIslands
PRED expected values: OstfriesischeInseln
=> 38 concepts (38 used for prediction)
PRED predicted values (max 10 best out of 37): OstfriesischeInseln (0.40 #1225, 0.33 #314, 0.33 #42), NordfriesischeInseln (0.40 #1225, 0.25 #36, 0.21 #104), OrkneyIslands (0.19 #425, 0.03 #561, 0.02 #833), WestfriesischeInseln (0.14 #421, 0.03 #625, 0.03 #693), SundaIslands (0.09 #898, 0.09 #626, 0.08 #1034), LesserAntilles (0.08 #1444, 0.08 #1512, 0.08 #1580), HawaiiIslands (0.07 #573, 0.07 #641, 0.06 #709), Azores (0.06 #548, 0.06 #616, 0.05 #684), LipariIslands (0.06 #546, 0.06 #614, 0.05 #682), Canares (0.06 #567, 0.06 #635, 0.05 #703)
>> best conf = 0.40
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #1225 for best value:
>> intensional similarity = 6
>> extensional distance = 192
>> proper extension:  SaintVincent; Mayotte; Barbuda; Gozo; Montserrat; Malta; Male; Koror; IsleofMan; Antigua;
>> query: (?x1637, ?x1590) <- ?x1637[ a Island; has locatedIn ?x120[ is locatedIn of ?x1589[ a Island; has belongsToIslands ?x1590;];]; has locatedInWater ?x121;]
ranks of expected_values: 1
EVAL	Baltrum	belongsToIslands	OstfriesischeInseln	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	38.000	38.000	37.000	0.400	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

PRED relation: belongsToIslands
PRED expected values: OstfriesischeInseln
=> 116 concepts (116 used for prediction)
PRED predicted values (max 10 best out of 43): OstfriesischeInseln (0.40 #3824, 0.40 #206, 0.37 #3483), NordfriesischeInseln (0.40 #3824, 0.40 #206, 0.37 #3483), SundaIslands (0.20 #697, 0.15 #765, 0.15 #834), WestfriesischeInseln (0.20 #150, 0.14 #423, 0.14 #615), OrkneyIslands (0.19 #427, 0.16 #563, 0.14 #615), LesserAntilles (0.15 #2335, 0.12 #3085, 0.08 #4111), InnerHebrides (0.15 #679, 0.06 #953, 0.04 #1498), BritishIsles (0.14 #615, 0.11 #137, 0.09 #634), ShetlandIslands (0.14 #615, 0.11 #137, 0.08 #3140), HawaiiIslands (0.12 #780, 0.11 #849, 0.09 #1054)
>> best conf = 0.40
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #3824 for best value:
>> intensional similarity = 6
>> extensional distance = 192
>> proper extension:  SaintVincent; Tinian; Mayotte; Barbuda; Gozo; Montserrat; Malta; EastFalkland; Svalbard; Male; ...
>> query: (?x1637, ?x1856) <- ?x1637[ a Island; has locatedIn ?x120[ a Country; is locatedIn of ?x1930[ has belongsToIslands ?x1856;];]; has locatedInWater ?x121;]
>> Best rule #206 for best value:
>> intensional similarity = 12
>> extensional distance = 13
>> proper extension:  Schiermonnikoog; Terschelling; Vlieland;
>> query: (?x1637, ?x1590) <- ?x1637[ a Island; has locatedIn ?x120[ has neighbor ?x424[ is locatedIn of ?x155;]; has religion ?x352<RomanCatholic>; is locatedIn of ?x256[ a River;]; is locatedIn of ?x848[ has belongsToIslands ?x1590;];]; has locatedInWater ?x121<NorthSea>;]
ranks of expected_values: 1
EVAL	Baltrum	belongsToIslands	OstfriesischeInseln	CNN-1.+1._MA	1.000	1.000	1.000	1.000	116.000	116.000	43.000	0.400	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

#636-Garifuna
PRED entity: Garifuna

PRED relation: ethnicGroup!
PRED expected values: BZ
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 96): EAU (0.03 #132), ETH (0.03 #100), UA (0.03 #57), EAK (0.03 #99), CZ (0.02 #98), SN (0.02 #83), MYA (0.02 #70), IR (0.02 #58), SK (0.02 #25), RCA (0.02 #139)
>> best conf = 0.03
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #132 for best value:
>> intensional similarity = 1
>> extensional distance = 279
>> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
>> query: (?x2242, EAU) <- ?x2242[ a EthnicGroup;]

*> Best rule #129 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 279
*> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
*> query: (?x2242, BZ) <- ?x2242[ a EthnicGroup;]
*> conf = 0.01
ranks of expected_values: 69
EVAL	Garifuna	ethnicGroup!	BZ	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.014	2.000	2.000	96.000	0.032	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup!
PRED expected values: BZ
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 96): EAU (0.03 #132), ETH (0.03 #100), UA (0.03 #57), EAK (0.03 #99), CZ (0.02 #98), SN (0.02 #83), MYA (0.02 #70), IR (0.02 #58), SK (0.02 #25), RCA (0.02 #139)
>> best conf = 0.03
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #132 for best value:
>> intensional similarity = 1
>> extensional distance = 279
>> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
>> query: (?x2242, EAU) <- ?x2242[ a EthnicGroup;]

*> Best rule #129 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 279
*> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
*> query: (?x2242, BZ) <- ?x2242[ a EthnicGroup;]
*> conf = 0.01
ranks of expected_values: 69
EVAL	Garifuna	ethnicGroup!	BZ	CNN-1.+1._MA	0.000	0.000	0.000	0.014	2.000	2.000	96.000	0.032	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#635-GrandBallon
PRED entity: GrandBallon

PRED relation: inMountains
PRED expected values: Vosges
=> 42 concepts (42 used for prediction)
PRED predicted values (max 10 best out of 56): Alps (0.53 #91, 0.16 #178, 0.16 #874), Cevennes (0.29 #77, 0.13 #164, 0.05 #251), Pyrenees (0.14 #62, 0.07 #149, 0.05 #236), Corse (0.14 #74, 0.07 #161, 0.03 #248), RockyMountains (0.13 #616, 0.06 #1486, 0.05 #1573), CanaryIslands (0.08 #230, 0.05 #491, 0.05 #578), Vogesen (0.08 #309, 0.07 #396, 0.03 #1005), SudetyMountains (0.07 #145), Andes (0.07 #1055, 0.06 #1229, 0.05 #1403), Kaukasus (0.06 #454, 0.06 #541, 0.05 #715)
>> best conf = 0.53
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #91 for best value:
>> intensional similarity = 6
>> extensional distance = 13
>> proper extension:  Hochgolling; GrandCombin; Finsteraarhorn; Schneekoppe; CrapSognGion; Grossglockner;
>> query: (?x1932, Alps) <- ?x1932[ a Mountain; has locatedIn ?x78[ a Country; has religion ?x95; is neighbor of ?x120<D>;];]

No rule for expected values
ranks of expected_values: 
EVAL	GrandBallon	inMountains	Vosges	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	42.000	42.000	56.000	0.533	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains
PRED expected values: Vosges
=> 106 concepts (106 used for prediction)
PRED predicted values (max 10 best out of 70): Alps (0.50 #91, 0.41 #613, 0.38 #352), Cevennes (0.29 #77, 0.21 #3577, 0.20 #2092), RockyMountains (0.24 #877, 0.23 #1052, 0.20 #1226), EliasRange (0.24 #450, 0.13 #798, 0.08 #885), Pyrenees (0.21 #3577, 0.20 #2092, 0.20 #3402), Corse (0.21 #3577, 0.20 #2092, 0.20 #3402), Vogesen (0.21 #3577, 0.20 #2092, 0.20 #3402), Jura (0.21 #3577, 0.20 #2092, 0.20 #3402), CanaryIslands (0.19 #317, 0.10 #839, 0.10 #752), Andes (0.14 #1230, 0.10 #1404, 0.10 #1579)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #91 for best value:
>> intensional similarity = 12
>> extensional distance = 10
>> proper extension:  GrandCombin; Finsteraarhorn; CrapSognGion;
>> query: (?x1932, Alps) <- ?x1932[ a Mountain; has locatedIn ?x78[ is locatedIn of ?x182[ is flowsInto of ?x137;]; is neighbor of ?x120<D>; is neighbor of ?x207<I>; is neighbor of ?x234[ has language ?x51; has religion ?x56; is locatedIn of ?x233;];];]

No rule for expected values
ranks of expected_values: 
EVAL	GrandBallon	inMountains	Vosges	CNN-1.+1._MA	0.000	0.000	0.000	0.000	106.000	106.000	70.000	0.500	http://www.semwebtech.org/mondial/10/meta#inMountains

#634-BOL
PRED entity: BOL

PRED relation: religion
PRED expected values: Protestant
=> 43 concepts (43 used for prediction)
PRED predicted values (max 10 best out of 38): Protestant (0.73 #248, 0.71 #2, 0.69 #166), Muslim (0.65 #333, 0.56 #1440, 0.56 #1482), JehovasWitnesses (0.51 #1477, 0.29 #20, 0.27 #61), Jewish (0.51 #1477, 0.19 #85, 0.15 #126), ChristianOrthodox (0.38 #124, 0.36 #575, 0.32 #698), Christian (0.35 #619, 0.31 #1357, 0.30 #1234), Buddhist (0.16 #216, 0.14 #790, 0.13 #626), Anglican (0.16 #263, 0.15 #181, 0.14 #304), Hindu (0.14 #788, 0.13 #173, 0.11 #255), Mormon (0.07 #25, 0.07 #66, 0.05 #189)
>> best conf = 0.73
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #248 for best value:
>> intensional similarity = 5
>> extensional distance = 42
>> proper extension:  ARU; VIRG;
>> query: (?x690, Protestant) <- ?x690[ a Country; has encompassed ?x521<America>; has religion ?x352; is locatedIn of ?x274;]
ranks of expected_values: 1
EVAL	BOL	religion	Protestant	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	43.000	43.000	38.000	0.727	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion
PRED expected values: Protestant
=> 134 concepts (134 used for prediction)
PRED predicted values (max 10 best out of 41): Protestant (0.74 #1576, 0.73 #624, 0.71 #3278), Muslim (0.65 #2904, 0.62 #3238, 0.61 #4029), JehovasWitnesses (0.62 #1657, 0.61 #1205, 0.59 #291), Jewish (0.62 #1657, 0.61 #1205, 0.59 #291), ChristianOrthodox (0.43 #3360, 0.43 #2942, 0.42 #2195), Mormon (0.42 #912, 0.39 #3066, 0.36 #2567), Buddhist (0.42 #912, 0.39 #3066, 0.36 #2567), Anglican (0.39 #3066, 0.36 #2567, 0.35 #3233), ChurchofGod (0.39 #3066, 0.36 #2567, 0.35 #3233), UnitedChurch (0.39 #3066, 0.36 #2567, 0.35 #3233)
>> best conf = 0.74
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1576 for best value:
>> intensional similarity = 11
>> extensional distance = 17
>> proper extension:  WL;
>> query: (?x690, Protestant) <- ?x690[ a Country; has ethnicGroup ?x197[ is ethnicGroup of ?x379[ is locatedIn of ?x512<Paraguay>;];]; has ethnicGroup ?x2045[ a EthnicGroup;]; has religion ?x352<RomanCatholic>; is locatedIn of ?x1834[ has type ?x762;];]
ranks of expected_values: 1
EVAL	BOL	religion	Protestant	CNN-1.+1._MA	1.000	1.000	1.000	1.000	134.000	134.000	41.000	0.737	http://www.semwebtech.org/mondial/10/meta#religion

#633-BR
PRED entity: BR

PRED relation: locatedIn!
PRED expected values: Araguaia Parana Parana RioSaoFrancisco
=> 43 concepts (41 used for prediction)
PRED predicted values (max 10 best out of 1394): Araguaia (0.92 #36487, 0.20 #43505, 0.13 #36488), Uruguay (0.72 #12628, 0.33 #2655, 0.25 #5461), Paraguay (0.72 #12628, 0.25 #5132, 0.17 #9341), CaribbeanSea (0.60 #7118, 0.50 #2909, 0.27 #22554), Hispaniola (0.50 #4057, 0.20 #8266, 0.18 #11072), Llullaillaco (0.50 #5131, 0.18 #10743, 0.17 #9340), OjosdelSalado (0.50 #4716, 0.18 #10328, 0.17 #8925), PacificOcean (0.44 #19728, 0.41 #11307, 0.39 #21131), LicancaburCraterLake (0.33 #9695, 0.33 #1277, 0.25 #5486), Licancabur (0.33 #9326, 0.33 #908, 0.25 #5117)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #36487 for best value:
>> intensional similarity = 6
>> extensional distance = 90
>> proper extension:  WV; MV; MAYO; M; AG; MNTS; PAL;
>> query: (?x542, ?x47) <- ?x542[ a Country; has encompassed ?x521; is locatedIn of ?x214[ is flowsInto of ?x949;]; is locatedIn of ?x1304[ has locatedInWater ?x47;];]
ranks of expected_values: 1, 24
EVAL	BR	locatedIn!	RioSaoFrancisco	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	43.000	41.000	1394.000	0.918	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	BR	locatedIn!	Parana	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	43.000	41.000	1394.000	0.918	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	BR	locatedIn!	Parana	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.043	43.000	41.000	1394.000	0.918	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	BR	locatedIn!	Araguaia	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	43.000	41.000	1394.000	0.918	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Araguaia Parana Parana RioSaoFrancisco
=> 102 concepts (102 used for prediction)
PRED predicted values (max 10 best out of 1418): Araguaia (0.93 #33718, 0.92 #47775, 0.92 #120880), CaribbeanSea (0.82 #59129, 0.81 #47881, 0.73 #19774), RioNegro (0.77 #33719, 0.71 #54808, 0.70 #4217), Amazonas (0.77 #33719, 0.71 #54808, 0.70 #4217), RioMadeira (0.77 #33719, 0.71 #54808, 0.70 #4217), RioSaoFrancisco (0.77 #33719, 0.71 #54808, 0.70 #4217), PacificOcean (0.67 #85807, 0.61 #63325, 0.60 #38019), Paraguay (0.62 #123694, 0.62 #92755, 0.33 #2332), Uruguay (0.62 #123694, 0.62 #92755, 0.33 #2661), Ucayali (0.51 #73079, 0.46 #108225, 0.43 #82912)
>> best conf = 0.93
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #33718 for best value:
>> intensional similarity = 17
>> extensional distance = 14
>> proper extension:  F; I;
>> query: (?x542, ?x47) <- ?x542[ has encompassed ?x521; has language ?x539; has neighbor ?x363[ a Country; has language ?x796;]; is locatedIn of ?x48[ is hasSource of ?x47;]; is locatedIn of ?x1186[ a River; has hasSource ?x729;]; is locatedIn of ?x1305[ a Estuary;]; is locatedIn of ?x2165[ has type ?x136;]; is locatedIn of ?x2500[ a Mountain;]; is neighbor of ?x351;]
ranks of expected_values: 1, 6, 17
EVAL	BR	locatedIn!	RioSaoFrancisco	CNN-1.+1._MA	0.000	0.000	1.000	0.200	102.000	102.000	1418.000	0.927	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	BR	locatedIn!	Parana	CNN-1.+1._MA	0.000	0.000	0.000	0.000	102.000	102.000	1418.000	0.927	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	BR	locatedIn!	Parana	CNN-1.+1._MA	0.000	0.000	0.000	0.067	102.000	102.000	1418.000	0.927	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	BR	locatedIn!	Araguaia	CNN-1.+1._MA	1.000	1.000	1.000	1.000	102.000	102.000	1418.000	0.927	http://www.semwebtech.org/mondial/10/meta#locatedIn

#632-Ural
PRED entity: Ural

PRED relation: hasSource
PRED expected values: Ural
=> 35 concepts (32 used for prediction)
PRED predicted values (max 10 best out of 125): Oka (0.07 #4116, 0.06 #3202, 0.05 #900), Swir (0.07 #4116, 0.06 #3202, 0.05 #894), Newa (0.07 #4116, 0.06 #3202, 0.05 #885), Kama (0.07 #4116, 0.06 #3202, 0.05 #882), Jenissej (0.07 #4116, 0.06 #3202, 0.05 #879), Amur (0.07 #4116, 0.06 #3202, 0.05 #867), Kolyma (0.07 #4116, 0.06 #3202, 0.05 #858), Dnepr (0.07 #4116, 0.06 #3202, 0.05 #856), Don (0.07 #4116, 0.06 #3202, 0.05 #853), Angara (0.07 #4116, 0.06 #3202, 0.05 #793)
>> best conf = 0.07
=> the first rule below is the first best rule for 25 predicted values
>> Best rule #4116 for best value:
>> intensional similarity = 5
>> extensional distance = 218
>> proper extension:  Pjandsh; Amudarja; RioMamore; RioDesaguadero; Bartang; Semliki; Murgab; Baro; VictoriaNile; Limmat; ...
>> query: (?x890, ?x418) <- ?x890[ a River; has locatedIn ?x73[ has neighbor ?x170; is locatedIn of ?x418[ a Source;];];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 21
EVAL	Ural	hasSource	Ural	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.048	35.000	32.000	125.000	0.074	http://www.semwebtech.org/mondial/10/meta#hasSource

PRED relation: hasSource
PRED expected values: Ural
=> 94 concepts (94 used for prediction)
PRED predicted values (max 10 best out of 243): Kura (0.21 #11692, 0.10 #938, 0.04 #2542), Volga (0.21 #11692, 0.06 #12152, 0.04 #2543), Ili (0.20 #510, 0.10 #967, 0.08 #1425), Syrdarja (0.20 #643, 0.10 #1100, 0.08 #1558), Ischim (0.20 #616, 0.10 #1073, 0.06 #12152), Amur (0.10 #1327, 0.06 #12152, 0.05 #2014), Argun (0.10 #1263, 0.05 #1950, 0.04 #2409), Amudarja (0.10 #1130, 0.03 #3420, 0.02 #3878), Karun (0.10 #1014, 0.01 #4908), SchattalArab (0.10 #943, 0.01 #4837)
>> best conf = 0.21
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #11692 for best value:
>> intensional similarity = 9
>> extensional distance = 191
>> proper extension:  Ob;
>> query: (?x890, ?x469) <- ?x890[ a River; has flowsInto ?x1337[ is flowsInto of ?x468[ a River; has hasEstuary ?x2351; has hasSource ?x469; has locatedIn ?x185;];]; has hasEstuary ?x891[ a Estuary;];]

*> Best rule #12152 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 231
*> proper extension:  Kwa; MaleboPool; LakeTanganjika; LakeMweru; Kasai; Chiemsee; Rutanzige-Eduardsee; KoliSarez; LakeKioga; Ammersee; ...
*> query: (?x890, ?x1512) <- ?x890[ has flowsInto ?x1337[ has locatedIn ?x290;]; has locatedIn ?x403[ has ethnicGroup ?x58; has neighbor ?x130; has religion ?x56; is locatedIn of ?x1512[ a Source;];];]
*> conf = 0.06
ranks of expected_values: 43
EVAL	Ural	hasSource	Ural	CNN-1.+1._MA	0.000	0.000	0.000	0.023	94.000	94.000	243.000	0.205	http://www.semwebtech.org/mondial/10/meta#hasSource

#631-RN
PRED entity: RN

PRED relation: neighbor
PRED expected values: TCH
=> 30 concepts (30 used for prediction)
PRED predicted values (max 10 best out of 164): TCH (0.90 #2037, 0.89 #2195, 0.89 #2036), RN (0.50 #234, 0.35 #781, 0.32 #157), RG (0.43 #421, 0.32 #157, 0.30 #2352), CAM (0.35 #781, 0.32 #157, 0.30 #2352), CI (0.32 #157, 0.30 #2352, 0.29 #469), SN (0.32 #157, 0.30 #2352, 0.29 #387), RIM (0.32 #157, 0.30 #2352, 0.28 #313), RT (0.32 #157, 0.30 #2352, 0.27 #470), SUD (0.32 #157, 0.30 #2352, 0.27 #3763), TN (0.32 #157, 0.30 #2352, 0.27 #3763)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2037 for best value:
>> intensional similarity = 6
>> extensional distance = 99
>> proper extension:  ROU;
>> query: (?x426, ?x839) <- ?x426[ has ethnicGroup ?x1109; has government ?x435; is locatedIn of ?x535; is neighbor of ?x839[ is locatedIn of ?x456[ has hasSource ?x350;];];]
ranks of expected_values: 1
EVAL	RN	neighbor	TCH	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	30.000	30.000	164.000	0.899	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: TCH
=> 85 concepts (85 used for prediction)
PRED predicted values (max 10 best out of 220): TCH (0.93 #4794, 0.93 #2711, 0.92 #8170), RN (0.60 #1278, 0.55 #2714, 0.45 #1756), CAM (0.60 #1278, 0.55 #2714, 0.45 #1756), RG (0.57 #2185, 0.45 #1756, 0.40 #2981), IL (0.55 #3079, 0.44 #2759, 0.43 #2282), ET (0.50 #644, 0.44 #482, 0.33 #1438), ZRE (0.50 #1179, 0.30 #4372, 0.29 #4798), MW (0.50 #1246, 0.25 #2681, 0.25 #161), MOC (0.50 #1151, 0.25 #2586, 0.23 #4669), RT (0.45 #1756, 0.40 #481, 0.35 #2712)
>> best conf = 0.93
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4794 for best value:
>> intensional similarity = 16
>> extensional distance = 20
>> proper extension:  SD; LB;
>> query: (?x426, ?x139) <- ?x426[ has neighbor ?x1184; is neighbor of ?x139; is neighbor of ?x169[ has encompassed ?x213; has government ?x435<"republic">; has neighbor ?x186[ has neighbor ?x63; is locatedIn of ?x531;]; is locatedIn of ?x168;]; is neighbor of ?x839[ has ethnicGroup ?x1537; has language ?x1228; has religion ?x116<Christian>; is locatedIn of ?x456;];]
ranks of expected_values: 1
EVAL	RN	neighbor	TCH	CNN-1.+1._MA	1.000	1.000	1.000	1.000	85.000	85.000	220.000	0.933	http://www.semwebtech.org/mondial/10/meta#neighbor

#630-Mississippi
PRED entity: Mississippi

PRED relation: hasSource
PRED expected values: Mississippi
=> 56 concepts (46 used for prediction)
PRED predicted values (max 10 best out of 253): RioGrande (0.33 #111, 0.02 #10288, 0.02 #1936), SaintLawrenceRiver (0.10 #487, 0.09 #715, 0.07 #943), Amazonas (0.10 #654, 0.09 #882, 0.04 #1338), Zaire (0.10 #676, 0.09 #904, 0.04 #1360), Oranje (0.10 #637, 0.04 #1321, 0.02 #1778), Parana (0.10 #575, 0.04 #1259, 0.02 #1716), Tocantins (0.10 #526, 0.04 #1210, 0.02 #1667), Po (0.10 #280, 0.02 #1649, 0.02 #1877), Glomma (0.10 #332, 0.02 #1701, 0.02 #2157), MurrayRiver (0.10 #373, 0.02 #1742, 0.02 #2198)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #111 for best value:
>> intensional similarity = 5
>> extensional distance = 1
>> proper extension:  RioGrande;
>> query: (?x361, RioGrande) <- ?x361[ a River; has flowsInto ?x1371<GulfofMexico>; has locatedIn ?x315<USA>;]

*> Best rule #10288 for first EXPECTED value:
*> intensional similarity = 4
*> extensional distance = 1028
*> proper extension:  VelikiRatnoOstrvo; Euphrat; Morava; Bani; WesternMorava; SchattalArab; Senegal; JoekulsaaFjoellum; Hvannadalshnukur; Iceland; ...
*> query: (?x361, ?x494) <- ?x361[ has locatedIn ?x315[ has ethnicGroup ?x79; is locatedIn of ?x494[ a Source;];];]
*> conf = 0.02
ranks of expected_values: 71
EVAL	Mississippi	hasSource	Mississippi	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.014	56.000	46.000	253.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasSource

PRED relation: hasSource
PRED expected values: Mississippi
=> 147 concepts (147 used for prediction)
PRED predicted values (max 10 best out of 294): SaintLawrenceRiver (0.33 #31, 0.20 #716, 0.20 #488), RioGrande (0.33 #340, 0.20 #1253, 0.19 #21296), OhioRiver (0.20 #950, 0.10 #2777, 0.08 #3006), RioSanJuan (0.20 #1186, 0.08 #3242, 0.06 #4612), DetroitRiver (0.20 #662, 0.04 #9372, 0.04 #9371), Jenissej (0.14 #1792, 0.12 #2249, 0.08 #3163), MerrimackRiver (0.14 #1883, 0.08 #3254, 0.06 #3938), Colorado (0.14 #1921, 0.06 #3976, 0.06 #4205), ColumbiaRiver (0.14 #1874, 0.06 #3929, 0.06 #4158), YukonRiver (0.14 #1902, 0.06 #3957, 0.06 #4186)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #31 for best value:
>> intensional similarity = 9
>> extensional distance = 1
>> proper extension:  SaintLawrenceRiver;
>> query: (?x361, SaintLawrenceRiver) <- ?x361[ a River; has flowsInto ?x1371; has locatedIn ?x315<USA>; is flowsInto of ?x1366[ is flowsInto of ?x1113;]; is flowsInto of ?x1527[ has hasEstuary ?x2453; has hasSource ?x1528;];]

*> Best rule #8913 for first EXPECTED value:
*> intensional similarity = 11
*> extensional distance = 46
*> proper extension:  Suchona;
*> query: (?x361, ?x494) <- ?x361[ a River; has locatedIn ?x315[ is locatedIn of ?x282<PacificOcean>; is locatedIn of ?x494[ a Source;]; is locatedIn of ?x1294[ has belongsToIslands ?x2237;]; is locatedIn of ?x1371[ a Sea;]; is neighbor of ?x482;];]
*> conf = 0.04
ranks of expected_values: 70
EVAL	Mississippi	hasSource	Mississippi	CNN-1.+1._MA	0.000	0.000	0.000	0.014	147.000	147.000	294.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasSource

#629-Vuoksi
PRED entity: Vuoksi

PRED relation: hasEstuary!
PRED expected values: Vuoksi
=> 30 concepts (26 used for prediction)
PRED predicted values (max 10 best out of 154): Amur (0.04 #2500, 0.04 #181, 0.04 #407), Paatsjoki (0.04 #2500, 0.04 #64, 0.04 #290), Swir (0.04 #2500, 0.04 #17, 0.04 #243), Irtysch (0.04 #2500, 0.04 #194, 0.04 #420), Narva (0.04 #2500, 0.04 #82, 0.04 #308), Jenissej (0.04 #2500, 0.04 #81, 0.04 #307), Newa (0.04 #2500, 0.04 #57, 0.04 #283), Dnepr (0.04 #2500, 0.03 #2499, 0.02 #3183), Vuoksi (0.04 #2500, 0.03 #2499, 0.02 #3183), OzeroLadoga (0.04 #2500, 0.03 #2499, 0.02 #3183)
>> best conf = 0.04
=> the first rule below is the first best rule for 22 predicted values
>> Best rule #2500 for best value:
>> intensional similarity = 6
>> extensional distance = 210
>> proper extension:  EucumbeneRiver; SaintLawrenceRiver; Manicouagan; Thjorsa; DarlingRiver; JoekulsaaFjoellum; MurrayRiver; MackenzieRiver; RiviereRichelieu; SnowyRiver; ...
>> query: (?x1192, ?x1396) <- ?x1192[ a Estuary; has locatedIn ?x73[ has ethnicGroup ?x58; has religion ?x56; is locatedIn of ?x1396[ is flowsInto of ?x1395;];];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 9
EVAL	Vuoksi	hasEstuary!	Vuoksi	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.111	30.000	26.000	154.000	0.044	http://www.semwebtech.org/mondial/10/meta#hasEstuary

PRED relation: hasEstuary!
PRED expected values: Vuoksi
=> 90 concepts (90 used for prediction)
PRED predicted values (max 10 best out of 173): Irtysch (0.08 #5949, 0.08 #6867, 0.08 #7559), Amur (0.08 #5949, 0.08 #6867, 0.08 #7559), Paatsjoki (0.08 #5949, 0.08 #6867, 0.08 #7559), Swir (0.08 #5949, 0.08 #6867, 0.08 #7559), Narva (0.08 #5949, 0.08 #6867, 0.08 #7559), Jenissej (0.08 #5949, 0.08 #6867, 0.08 #7559), Newa (0.08 #5949, 0.08 #6867, 0.08 #7559), Kolyma (0.08 #5949, 0.08 #6867, 0.08 #7559), Don (0.08 #5949, 0.08 #6867, 0.08 #7559), Lena (0.08 #5949, 0.08 #6867, 0.08 #7559)
>> best conf = 0.08
=> the first rule below is the first best rule for 27 predicted values
>> Best rule #5949 for best value:
>> intensional similarity = 9
>> extensional distance = 145
>> proper extension:  Rhein; Jordan; Mur; Maas; Bani; Raab; Irawaddy; Limpopo; Chire; Zambezi; ...
>> query: (?x1192, ?x2089) <- ?x1192[ a Estuary; has locatedIn ?x73[ has language ?x555; has neighbor ?x353[ has encompassed ?x175;]; has neighbor ?x565[ is locatedIn of ?x660;]; is locatedIn of ?x2089[ a River;];];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 18
EVAL	Vuoksi	hasEstuary!	Vuoksi	CNN-1.+1._MA	0.000	0.000	0.000	0.056	90.000	90.000	173.000	0.083	http://www.semwebtech.org/mondial/10/meta#hasEstuary

#628-black-Amerindian
PRED entity: black-Amerindian

PRED relation: ethnicGroup!
PRED expected values: CO
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 96): EAU (0.03 #132), ETH (0.03 #100), UA (0.03 #57), EAK (0.03 #99), CZ (0.02 #98), SN (0.02 #83), MYA (0.02 #70), IR (0.02 #58), SK (0.02 #25), RCA (0.02 #139)
>> best conf = 0.03
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #132 for best value:
>> intensional similarity = 1
>> extensional distance = 279
>> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
>> query: (?x2474, EAU) <- ?x2474[ a EthnicGroup;]

*> Best rule #41 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 279
*> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
*> query: (?x2474, CO) <- ?x2474[ a EthnicGroup;]
*> conf = 0.02
ranks of expected_values: 31
EVAL	black-Amerindian	ethnicGroup!	CO	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.032	2.000	2.000	96.000	0.032	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup!
PRED expected values: CO
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 96): EAU (0.03 #132), ETH (0.03 #100), UA (0.03 #57), EAK (0.03 #99), CZ (0.02 #98), SN (0.02 #83), MYA (0.02 #70), IR (0.02 #58), SK (0.02 #25), RCA (0.02 #139)
>> best conf = 0.03
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #132 for best value:
>> intensional similarity = 1
>> extensional distance = 279
>> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
>> query: (?x2474, EAU) <- ?x2474[ a EthnicGroup;]

*> Best rule #41 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 279
*> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
*> query: (?x2474, CO) <- ?x2474[ a EthnicGroup;]
*> conf = 0.02
ranks of expected_values: 31
EVAL	black-Amerindian	ethnicGroup!	CO	CNN-1.+1._MA	0.000	0.000	0.000	0.032	2.000	2.000	96.000	0.032	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#627-Narva
PRED entity: Narva

PRED relation: flowsInto
PRED expected values: BalticSea
=> 32 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 139): Narva (0.33 #85, 0.01 #2824, 0.01 #3155), AtlanticOcean (0.10 #1010, 0.10 #2175, 0.10 #2505), OzeroLadoga (0.09 #226, 0.07 #393, 0.02 #561), BlackSea (0.07 #498, 0.07 #335, 0.04 #168), Donau (0.07 #1006, 0.07 #2171, 0.07 #2501), BalticSea (0.06 #1008, 0.06 #675, 0.05 #2173), MediterraneanSea (0.06 #1021, 0.04 #2186, 0.04 #2351), BarentsSea (0.04 #182, 0.04 #349, 0.03 #517), Ob (0.04 #318, 0.04 #485, 0.03 #653), CaspianSea (0.04 #289, 0.04 #456, 0.02 #624)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #85 for best value:
>> intensional similarity = 4
>> extensional distance = 1
>> proper extension:  OzeroPskovskoje;
>> query: (?x802, Narva) <- ?x802[ has locatedIn ?x73<R>; has locatedIn ?x591<EW>;]

*> Best rule #1008 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 124
*> proper extension:  Selenge; Morava; Donau; Oranje; Save; Mur; Buna; Amazonas; WesternBug; Limpopo; ...
*> query: (?x802, BalticSea) <- ?x802[ a River; has locatedIn ?x591[ has government ?x1174; has language ?x555; has neighbor ?x448;];]
*> conf = 0.06
ranks of expected_values: 6
EVAL	Narva	flowsInto	BalticSea	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.167	32.000	31.000	139.000	0.333	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto
PRED expected values: BalticSea
=> 76 concepts (76 used for prediction)
PRED predicted values (max 10 best out of 162): BlackSea (0.33 #3, 0.14 #502, 0.14 #335), Narva (0.33 #252, 0.03 #166, 0.02 #11942), Ob (0.22 #817, 0.14 #652, 0.09 #1482), KaraSea (0.22 #1080, 0.10 #1746, 0.10 #1246), BalticSea (0.21 #2171, 0.14 #509, 0.11 #1006), OzeroLadoga (0.18 #1556, 0.14 #393, 0.11 #1057), BarentsSea (0.14 #516, 0.14 #349, 0.12 #2012), CaspianSea (0.14 #623, 0.11 #1120, 0.11 #788), SeaofOkhotsk (0.14 #381, 0.11 #1045, 0.11 #879), Amur (0.14 #472, 0.11 #970, 0.08 #2135)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3 for best value:
>> intensional similarity = 10
>> extensional distance = 1
>> proper extension:  Dnepr;
>> query: (?x802, BlackSea) <- ?x802[ has hasSource ?x1794; has locatedIn ?x73<R>; has locatedIn ?x591[ has encompassed ?x195; has ethnicGroup ?x1322<Belorussian>; has government ?x1174; has language ?x555;]; is flowsInto of ?x2054;]

*> Best rule #2171 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 27
*> proper extension:  Vaesterdalaelv;
*> query: (?x802, BalticSea) <- ?x802[ a River; has hasEstuary ?x2196[ a Estuary;]; has hasSource ?x1794[ a Source; has locatedIn ?x73[ is neighbor of ?x170<N>;];];]
*> conf = 0.21
ranks of expected_values: 5
EVAL	Narva	flowsInto	BalticSea	CNN-1.+1._MA	0.000	0.000	1.000	0.200	76.000	76.000	162.000	0.333	http://www.semwebtech.org/mondial/10/meta#flowsInto

#626-PacificOcean
PRED entity: PacificOcean

PRED relation: locatedIn
PRED expected values: NMIS SLB MH RP PNG AMSA
=> 28 concepts (28 used for prediction)
PRED predicted values (max 10 best out of 188): PNG (0.92 #2591, 0.90 #2991, 0.90 #2990), RP (0.92 #2591, 0.90 #2991, 0.90 #2990), AMSA (0.92 #2591, 0.90 #2991, 0.90 #2990), MH (0.92 #2591, 0.90 #2991, 0.90 #2990), NMIS (0.92 #2591, 0.90 #2991, 0.90 #2990), GB (0.38 #1395, 0.36 #1195, 0.31 #1003), F (0.38 #1395, 0.36 #1195, 0.23 #1001), CN (0.33 #445, 0.31 #2192, 0.08 #1641), YV (0.31 #2192, 0.25 #263, 0.15 #1059), MAL (0.31 #2192, 0.17 #470, 0.11 #1865)
>> best conf = 0.92
=> the first rule below is the first best rule for 5 predicted values
>> Best rule #2591 for best value:
>> intensional similarity = 5
>> extensional distance = 31
>> proper extension:  KaraSea;
>> query: (?x282, ?x315) <- ?x282[ is locatedInWater of ?x587[ a Island;]; is locatedInWater of ?x714[ has locatedIn ?x315;]; is mergesWith of ?x60;]
ranks of expected_values: 1, 2, 3, 4, 5
EVAL	PacificOcean	locatedIn	AMSA	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	28.000	28.000	188.000	0.917	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	PacificOcean	locatedIn	PNG	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	28.000	28.000	188.000	0.917	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	PacificOcean	locatedIn	RP	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	28.000	28.000	188.000	0.917	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	PacificOcean	locatedIn	MH	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	28.000	28.000	188.000	0.917	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	PacificOcean	locatedIn	SLB	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	28.000	28.000	188.000	0.917	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	PacificOcean	locatedIn	NMIS	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	28.000	28.000	188.000	0.917	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: NMIS SLB MH RP PNG AMSA
=> 82 concepts (82 used for prediction)
PRED predicted values (max 10 best out of 209): PNG (0.93 #4997, 0.93 #4996, 0.93 #4995), RP (0.93 #4997, 0.93 #4996, 0.93 #4995), NMIS (0.93 #4997, 0.93 #4996, 0.93 #4995), AMSA (0.93 #4997, 0.93 #4996, 0.93 #4995), MH (0.93 #4997, 0.93 #4996, 0.93 #4995), YV (0.64 #3460, 0.33 #264, 0.25 #1062), TL (0.52 #998, 0.51 #400, 0.44 #1198), BR (0.52 #998, 0.51 #400, 0.44 #1198), MAL (0.52 #998, 0.51 #400, 0.44 #1198), RA (0.52 #998, 0.51 #400, 0.44 #1198)
>> best conf = 0.93
=> the first rule below is the first best rule for 5 predicted values
>> Best rule #4997 for best value:
>> intensional similarity = 8
>> extensional distance = 19
>> proper extension:  KaraSea;
>> query: (?x282, ?x728) <- ?x282[ is flowsInto of ?x602[ has hasSource ?x1346;]; is locatedInWater of ?x1684[ a Island; has locatedIn ?x728;]; is mergesWith of ?x60; is mergesWith of ?x770[ is locatedInWater of ?x216;];]
>> Best rule #4996 for best value:
>> intensional similarity = 8
>> extensional distance = 19
>> proper extension:  KaraSea;
>> query: (?x282, ?x550) <- ?x282[ is flowsInto of ?x602[ has hasSource ?x1346;]; is locatedInWater of ?x583[ a Island; has locatedIn ?x550;]; is mergesWith of ?x60; is mergesWith of ?x770[ is locatedInWater of ?x216;];]
>> Best rule #4995 for best value:
>> intensional similarity = 10
>> extensional distance = 19
>> proper extension:  KaraSea;
>> query: (?x282, ?x564) <- ?x282[ is flowsInto of ?x602[ has hasSource ?x1346;]; is locatedInWater of ?x458[ has locatedIn ?x564;]; is locatedInWater of ?x583[ a Island; has locatedIn ?x550;]; is mergesWith of ?x60; is mergesWith of ?x770[ is locatedInWater of ?x216;];]
ranks of expected_values: 1, 2, 3, 4, 5, 146
EVAL	PacificOcean	locatedIn	AMSA	CNN-1.+1._MA	1.000	1.000	1.000	1.000	82.000	82.000	209.000	0.931	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	PacificOcean	locatedIn	PNG	CNN-1.+1._MA	1.000	1.000	1.000	1.000	82.000	82.000	209.000	0.931	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	PacificOcean	locatedIn	RP	CNN-1.+1._MA	1.000	1.000	1.000	1.000	82.000	82.000	209.000	0.931	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	PacificOcean	locatedIn	MH	CNN-1.+1._MA	1.000	1.000	1.000	1.000	82.000	82.000	209.000	0.931	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	PacificOcean	locatedIn	SLB	CNN-1.+1._MA	0.000	0.000	0.000	0.007	82.000	82.000	209.000	0.931	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	PacificOcean	locatedIn	NMIS	CNN-1.+1._MA	1.000	1.000	1.000	1.000	82.000	82.000	209.000	0.931	http://www.semwebtech.org/mondial/10/meta#locatedIn

#625-F
PRED entity: F

PRED relation: dependentOf!
PRED expected values: SBAR
=> 36 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 110): CEU (0.25 #90, 0.14 #182, 0.10 #212), HONX (0.25 #74, 0.09 #226, 0.08 #258), MACX (0.25 #71, 0.09 #223, 0.08 #255), NLSM (0.14 #153, 0.09 #213, 0.06 #368), ARU (0.14 #175, 0.09 #235, 0.06 #390), CUR (0.14 #157, 0.09 #217, 0.06 #372), GROX (0.14 #167, 0.09 #227, 0.06 #413), FARX (0.14 #159, 0.09 #219, 0.06 #405), SVAX (0.14 #171, 0.09 #231, 0.05 #244), VIRG (0.14 #176, 0.09 #236, 0.05 #244)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #90 for best value:
>> intensional similarity = 6
>> extensional distance = 2
>> proper extension:  E; CN;
>> query: (?x78, CEU) <- ?x78[ has government ?x435; is dependentOf of ?x61; is locatedIn of ?x1225[ has hasSource ?x1707;]; is neighbor of ?x120; is wasDependentOf of ?x94;]

*> Best rule #244 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 9
*> proper extension:  AUS; NZ;
*> query: (?x78, ?x234) <- ?x78[ has encompassed ?x195; is dependentOf of ?x61; is locatedIn of ?x275[ is locatedInWater of ?x68;]; is locatedIn of ?x1225[ has locatedIn ?x234;];]
*> conf = 0.05
ranks of expected_values: 42
EVAL	F	dependentOf!	SBAR	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.024	36.000	36.000	110.000	0.250	http://www.semwebtech.org/mondial/10/meta#dependentOf

PRED relation: dependentOf!
PRED expected values: SBAR
=> 96 concepts (96 used for prediction)
PRED predicted values (max 10 best out of 105): CEU (0.33 #92, 0.25 #307, 0.25 #183), HONX (0.33 #14, 0.17 #480, 0.17 #447), MACX (0.33 #11, 0.17 #477, 0.17 #444), PR (0.33 #49, 0.11 #703, 0.11 #430), VIRG (0.33 #56, 0.11 #710, 0.11 #430), AMSA (0.33 #57, 0.11 #711, 0.10 #773), NMIS (0.33 #38, 0.11 #692, 0.10 #754), FALK (0.25 #299, 0.25 #238, 0.20 #421), GBJ (0.25 #289, 0.25 #228, 0.20 #411), TUCA (0.25 #287, 0.25 #226, 0.20 #409)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #92 for best value:
>> intensional similarity = 13
>> extensional distance = 1
>> proper extension:  E;
>> query: (?x78, CEU) <- ?x78[ has language ?x51; is dependentOf of ?x297[ a Country;]; is dependentOf of ?x1002[ is locatedIn of ?x282;]; is locatedIn of ?x182<AtlanticOcean>; is neighbor of ?x543[ has language ?x544;]; is wasDependentOf of ?x515[ is locatedIn of ?x572;]; is wasDependentOf of ?x871[ is neighbor of ?x91;];]

*> Best rule #430 for first EXPECTED value:
*> intensional similarity = 11
*> extensional distance = 3
*> proper extension:  GBJ; GBG;
*> query: (?x78, ?x543) <- ?x78[ is locatedIn of ?x182[ a Sea; has locatedIn ?x520[ has language ?x796;]; is flowsInto of ?x137; is locatedInWater of ?x112; is mergesWith of ?x60;]; is locatedIn of ?x829[ has locatedIn ?x543;]; is locatedIn of ?x1211<TheChannel>;]
*> conf = 0.11
ranks of expected_values: 96
EVAL	F	dependentOf!	SBAR	CNN-1.+1._MA	0.000	0.000	0.000	0.010	96.000	96.000	105.000	0.333	http://www.semwebtech.org/mondial/10/meta#dependentOf

#624-Murat
PRED entity: Murat

PRED relation: inMountains
PRED expected values: Kurdistan
=> 36 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 35): Kurdistan (0.33 #122, 0.33 #35, 0.15 #209), Antitaurus (0.17 #159, 0.08 #610, 0.08 #2090), EastAfricanRift (0.14 #289, 0.04 #1334, 0.04 #1508), Alps (0.09 #1658, 0.08 #1049, 0.08 #1571), Taurus (0.08 #610, 0.05 #200, 0.04 #374), Balkan (0.07 #630, 0.06 #717, 0.05 #281), WaldaiHills (0.07 #486, 0.01 #1009, 0.01 #922), Ural (0.07 #505, 0.01 #1376), Andes (0.06 #1317, 0.06 #1491, 0.05 #1578), SnowyMountains (0.05 #282, 0.02 #979, 0.02 #1153)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #122 for best value:
>> intensional similarity = 3
>> extensional distance = 4
>> proper extension:  Euphrat;
>> query: (?x1540, Kurdistan) <- ?x1540[ a Source; has locatedIn ?x185<TR>;]
>> Best rule #35 for best value:
>> intensional similarity = 6
>> extensional distance = 1
>> proper extension:  Karasu;
>> query: (?x1540, Kurdistan) <- ?x1540[ a Source; has locatedIn ?x185; is hasSource of ?x1271[ has flowsInto ?x1272<LakeKeban>; has hasEstuary ?x2419;];]
ranks of expected_values: 1
EVAL	Murat	inMountains	Kurdistan	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	36.000	36.000	35.000	0.333	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains
PRED expected values: Kurdistan
=> 126 concepts (126 used for prediction)
PRED predicted values (max 10 best out of 48): Kurdistan (0.50 #122, 0.43 #958, 0.40 #296), Antitaurus (0.43 #958, 0.27 #5489, 0.21 #2700), Taurus (0.43 #958, 0.21 #2700, 0.10 #2177), Balkan (0.36 #890, 0.33 #716, 0.27 #1152), Alps (0.30 #2181, 0.27 #2529, 0.24 #1745), Pamir (0.25 #539, 0.03 #3763, 0.02 #3937), CordilleraIberica (0.17 #838, 0.17 #751, 0.10 #2580), Zagros (0.17 #361, 0.01 #3846, 0.01 #4020), EastAfricanRift (0.15 #1595, 0.12 #1247, 0.12 #1421), Apennin (0.14 #1048, 0.10 #1744, 0.07 #2528)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #122 for best value:
>> intensional similarity = 11
>> extensional distance = 2
>> proper extension:  Kura;
>> query: (?x1540, Kurdistan) <- ?x1540[ a Source; has locatedIn ?x185<TR>; is hasSource of ?x1271[ a River; has flowsInto ?x1272[ a Lake;]; has hasEstuary ?x2419[ a Estuary;]; has locatedIn ?x185<TR>;];]
ranks of expected_values: 1
EVAL	Murat	inMountains	Kurdistan	CNN-1.+1._MA	1.000	1.000	1.000	1.000	126.000	126.000	48.000	0.500	http://www.semwebtech.org/mondial/10/meta#inMountains

#623-LAO
PRED entity: LAO

PRED relation: neighbor!
PRED expected values: CN
=> 30 concepts (30 used for prediction)
PRED predicted values (max 10 best out of 159): CN (0.91 #1592, 0.90 #2708, 0.90 #1591), UZB (0.40 #364, 0.33 #524, 0.16 #685), LAO (0.40 #238, 0.27 #2549, 0.26 #1909), KGZ (0.27 #2549, 0.26 #1909, 0.26 #2547), TAD (0.27 #2549, 0.26 #1909, 0.26 #2547), PK (0.27 #2549, 0.26 #1909, 0.26 #2547), IND (0.27 #2549, 0.26 #1909, 0.26 #2547), R (0.27 #2549, 0.26 #1909, 0.26 #2547), AFG (0.27 #2549, 0.26 #1909, 0.26 #2547), KAZ (0.27 #2549, 0.26 #1909, 0.26 #2547)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1592 for best value:
>> intensional similarity = 8
>> extensional distance = 110
>> proper extension:  I; YV; OM; DK; P;
>> query: (?x463, ?x617) <- ?x463[ a Country; has encompassed ?x175; has government ?x831; has neighbor ?x617[ has ethnicGroup ?x872;]; has religion ?x462; is locatedIn of ?x1152; is neighbor of ?x871;]
ranks of expected_values: 1
EVAL	LAO	neighbor!	CN	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	30.000	30.000	159.000	0.907	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: CN
=> 81 concepts (81 used for prediction)
PRED predicted values (max 10 best out of 209): CN (0.91 #10793, 0.89 #10792, 0.89 #9654), LAO (0.50 #716, 0.50 #556, 0.46 #7556), R (0.48 #3525, 0.33 #5614, 0.32 #7553), MOC (0.40 #2273, 0.16 #3841, 0.12 #8039), IND (0.37 #4811, 0.37 #4971, 0.37 #7063), BD (0.37 #4811, 0.37 #4971, 0.37 #7063), MAL (0.33 #380, 0.33 #5614, 0.32 #7553), RI (0.33 #1484, 0.33 #5614, 0.20 #1161), UZB (0.33 #2769, 0.29 #3086, 0.22 #3570), KAZ (0.33 #5614, 0.32 #7553, 0.30 #9333)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #10793 for best value:
>> intensional similarity = 15
>> extensional distance = 120
>> proper extension:  Q;
>> query: (?x463, ?x91) <- ?x463[ a Country; has ethnicGroup ?x1647; has ethnicGroup ?x1696[ a EthnicGroup;]; has neighbor ?x91[ has ethnicGroup ?x298; has neighbor ?x376[ a Country; has government ?x92; is locatedIn of ?x178;]; has religion ?x116; is locatedIn of ?x384[ is locatedInWater of ?x518;];]; has religion ?x462; is locatedIn of ?x1152;]
ranks of expected_values: 1
EVAL	LAO	neighbor!	CN	CNN-1.+1._MA	1.000	1.000	1.000	1.000	81.000	81.000	209.000	0.908	http://www.semwebtech.org/mondial/10/meta#neighbor

#622-WhiteDrin
PRED entity: WhiteDrin

PRED relation: hasEstuary!
PRED expected values: WhiteDrin
=> 25 concepts (21 used for prediction)
PRED predicted values (max 10 best out of 31): Buna (0.20 #8, 0.06 #234, 0.04 #679), Drin (0.20 #71, 0.06 #297, 0.04 #523), BlackDrin (0.20 #65, 0.06 #291, 0.04 #517), Moraca (0.04 #674), Tara (0.04 #495), Piva (0.04 #472), WhiteDrin (0.02 #3643, 0.02 #1136, 0.02 #1135), Drin (0.02 #1136, 0.02 #1135, 0.02 #1365), Drin (0.02 #1136, 0.02 #1135, 0.02 #1365), Buna (0.02 #1136, 0.02 #1135, 0.02 #1365)
>> best conf = 0.20
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #8 for best value:
>> intensional similarity = 3
>> extensional distance = 3
>> proper extension:  BlackDrin; Buna; Drin;
>> query: (?x1723, Buna) <- ?x1723[ a Estuary; has locatedIn ?x204<AL>;]

*> Best rule #3643 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 853
*> proper extension:  Araguaia; Bahrel-Ghasal; Menorca; Breg; Stromboli; Leine; StarnbergerSee; Bjelucha; Dagoe; Neckar; ...
*> query: (?x1723, ?x887) <- ?x1723[ has locatedIn ?x204[ a Country; has government ?x254; is locatedIn of ?x887[ a River;]; is neighbor of ?x399;];]
*> conf = 0.02
ranks of expected_values: 7
EVAL	WhiteDrin	hasEstuary!	WhiteDrin	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.143	25.000	21.000	31.000	0.200	http://www.semwebtech.org/mondial/10/meta#hasEstuary

PRED relation: hasEstuary!
PRED expected values: WhiteDrin
=> 109 concepts (109 used for prediction)
PRED predicted values (max 10 best out of 222): BlackDrin (0.20 #65, 0.12 #9585, 0.08 #519), Buna (0.20 #8, 0.12 #9585, 0.08 #462), Drin (0.20 #71, 0.08 #525, 0.08 #297), WhiteDrin (0.12 #9585, 0.07 #453, 0.06 #10042), Moraca (0.08 #676, 0.08 #448, 0.08 #906), Piva (0.08 #474, 0.08 #246, 0.08 #704), Tara (0.08 #497, 0.08 #269, 0.08 #727), Drina (0.08 #538, 0.08 #310, 0.08 #768), Drau (0.08 #289, 0.08 #747, 0.04 #1659), Mur (0.08 #232, 0.08 #690, 0.04 #1602)
>> best conf = 0.20
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #65 for best value:
>> intensional similarity = 3
>> extensional distance = 3
>> proper extension:  BlackDrin; Buna; Drin;
>> query: (?x1723, BlackDrin) <- ?x1723[ a Estuary; has locatedIn ?x204<AL>;]

*> Best rule #9585 for first EXPECTED value:
*> intensional similarity = 14
*> extensional distance = 139
*> proper extension:  Bahrel-Ghasal; Bahrel-Djebel-Albert-Nil; Sobat;
*> query: (?x1723, ?x887) <- ?x1723[ a Estuary; has locatedIn ?x204[ a Country; has government ?x254; is locatedIn of ?x104[ is flowsInto of ?x2296;]; is locatedIn of ?x887[ has hasSource ?x784;]; is neighbor of ?x692[ has government ?x435<"republic">;]; is neighbor of ?x701[ a Country; is neighbor of ?x177;];];]
*> conf = 0.12
ranks of expected_values: 4
EVAL	WhiteDrin	hasEstuary!	WhiteDrin	CNN-1.+1._MA	0.000	0.000	1.000	0.250	109.000	109.000	222.000	0.200	http://www.semwebtech.org/mondial/10/meta#hasEstuary

#621-I
PRED entity: I

PRED relation: locatedIn!
PRED expected values: Vulcano Arno LagodiBracciano
=> 45 concepts (44 used for prediction)
PRED predicted values (max 10 best out of 1278): AtlanticOcean (0.40 #12433, 0.36 #48240, 0.36 #56509), IndianOcean (0.38 #11019, 0.29 #9642, 0.26 #30301), AndamanSea (0.38 #11129, 0.29 #9752, 0.18 #13883), Rhone (0.33 #4774, 0.33 #2020, 0.25 #7528), LacLeman (0.33 #4838, 0.33 #2084, 0.25 #7592), NorthSea (0.33 #4152, 0.29 #19300, 0.25 #15168), Donau (0.33 #2779, 0.27 #27569, 0.12 #39963), Bodensee (0.33 #2253, 0.25 #7761, 0.12 #24788), Inn (0.33 #1720, 0.25 #7228, 0.12 #24788), CrapSognGion (0.33 #2670, 0.25 #8178, 0.12 #24788)
>> best conf = 0.40
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #12433 for best value:
>> intensional similarity = 6
>> extensional distance = 8
>> proper extension:  J;
>> query: (?x207, AtlanticOcean) <- ?x207[ has government ?x435; has language ?x51; is locatedIn of ?x86; is locatedIn of ?x323[ a Mountain;]; is wasDependentOf of ?x1165;]

*> Best rule #49578 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 103
*> proper extension:  GBM;
*> query: (?x207, ?x68) <- ?x207[ a Country; is locatedIn of ?x275[ is locatedInWater of ?x68;]; is locatedIn of ?x2190[ a Island;];]
*> conf = 0.04
ranks of expected_values: 812
EVAL	I	locatedIn!	LagodiBracciano	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	45.000	44.000	1278.000	0.400	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	I	locatedIn!	Arno	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	45.000	44.000	1278.000	0.400	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	I	locatedIn!	Vulcano	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.001	45.000	44.000	1278.000	0.400	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Vulcano Arno LagodiBracciano
=> 117 concepts (117 used for prediction)
PRED predicted values (max 10 best out of 1394): AtlanticOcean (0.85 #88265, 0.79 #55179, 0.75 #51004), Ticino (0.75 #51004, 0.72 #35845, 0.64 #13776), CrapSognGion (0.75 #51004, 0.64 #13776, 0.35 #102010), Finsteraarhorn (0.75 #51004, 0.64 #13776, 0.35 #102010), Rhein (0.75 #51004, 0.64 #13776, 0.35 #102010), GrandCombin (0.75 #51004, 0.64 #13776, 0.35 #102010), Inn (0.75 #51004, 0.64 #13776, 0.35 #102010), Isere (0.75 #51004, 0.64 #13776, 0.35 #102010), Rhone (0.75 #51004, 0.49 #35844, 0.33 #6152), NorthSea (0.75 #51004, 0.41 #158537, 0.33 #4153)
>> best conf = 0.85
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #88265 for best value:
>> intensional similarity = 13
>> extensional distance = 25
>> proper extension:  SVAX;
>> query: (?x207, AtlanticOcean) <- ?x207[ a Country; has encompassed ?x195; has government ?x435; is locatedIn of ?x275[ has locatedIn ?x851<MA>; is flowsInto of ?x698; is locatedInWater of ?x68; is mergesWith of ?x182;]; is locatedIn of ?x1255[ a Island;]; is locatedIn of ?x2190[ has belongsToIslands ?x87;];]

*> Best rule #86845 for first EXPECTED value:
*> intensional similarity = 18
*> extensional distance = 24
*> proper extension:  E; N; CO; RI; USA; YV; S; NIC; MEX; BR; ...
*> query: (?x207, ?x1993) <- ?x207[ has religion ?x56[ is religion of ?x163<SK>; is religion of ?x222<BY>;]; has religion ?x352<RomanCatholic>; is locatedIn of ?x437[ a Source;]; is locatedIn of ?x614[ has hasEstuary ?x1993;]; is locatedIn of ?x1996[ a Estuary;]; is neighbor of ?x446[ has ethnicGroup ?x160; has neighbor ?x236[ has encompassed ?x195; has ethnicGroup ?x164;];];]
*> conf = 0.70
ranks of expected_values: 61, 1149
EVAL	I	locatedIn!	LagodiBracciano	CNN-1.+1._MA	0.000	0.000	0.000	0.000	117.000	117.000	1394.000	0.852	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	I	locatedIn!	Arno	CNN-1.+1._MA	0.000	0.000	0.000	0.016	117.000	117.000	1394.000	0.852	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	I	locatedIn!	Vulcano	CNN-1.+1._MA	0.000	0.000	0.000	0.001	117.000	117.000	1394.000	0.852	http://www.semwebtech.org/mondial/10/meta#locatedIn

#620-PY
PRED entity: PY

PRED relation: government
PRED expected values: "constitutional republic"
=> 49 concepts (49 used for prediction)
PRED predicted values (max 10 best out of 63): "republic" (0.41 #1159, 0.38 #943, 0.37 #1881), "constitutional republic" (0.33 #9, 0.25 #81, 0.17 #297), "parliamentary monarchy" (0.32 #1226, 0.05 #676, 0.05 #749), "federal republic" (0.25 #147, 0.18 #1803, 0.17 #291), "republic; Social Unitarian State" (0.20 #1948, 0.18 #1803, 0.17 #334), "republic; executive branch dominates government structure" (0.17 #310, 0.14 #382, 0.09 #454), "parliamentary democracy" (0.16 #1663, 0.15 #1808, 0.14 #1953), "constitutional democracy" (0.14 #364, 0.09 #436, 0.07 #869), "parliamentary democracy and a Commonwealth realm" (0.12 #1045, 0.10 #1117, 0.10 #1334), "British Overseas Territories" (0.12 #1016, 0.10 #1449, 0.08 #1305)
>> best conf = 0.41
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1159 for best value:
>> intensional similarity = 6
>> extensional distance = 39
>> proper extension:  RM;
>> query: (?x404, "republic") <- ?x404[ has religion ?x95; has wasDependentOf ?x149[ has government ?x1657; is locatedIn of ?x275<MediterraneanSea>;]; is locatedIn of ?x512;]

*> Best rule #9 for first EXPECTED value:
*> intensional similarity = 9
*> extensional distance = 1
*> proper extension:  ROU;
*> query: (?x404, "constitutional republic") <- ?x404[ has language ?x796; has religion ?x95<Protestant>; has wasDependentOf ?x149; is locatedIn of ?x512; is neighbor of ?x379<RA>; is neighbor of ?x542<BR>;]
*> conf = 0.33
ranks of expected_values: 2
EVAL	PY	government	"constitutional republic"	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	49.000	49.000	63.000	0.415	http://www.semwebtech.org/mondial/10/meta#government

PRED relation: government
PRED expected values: "constitutional republic"
=> 106 concepts (106 used for prediction)
PRED predicted values (max 10 best out of 68): "republic" (0.46 #2902, 0.40 #368, 0.37 #6449), "parliamentary democracy" (0.26 #1307, 0.26 #1598, 0.22 #3334), "republic; executive branch dominates government structure" (0.26 #1593, 0.25 #240, 0.19 #7385), "federal republic" (0.26 #1593, 0.20 #437, 0.19 #7385), "republic; Social Unitarian State" (0.25 #336, 0.25 #264, 0.20 #3838), "constitutional republic" (0.25 #299, 0.25 #154, 0.20 #443), "constitutional democracy" (0.25 #149, 0.14 #5067, 0.12 #4344), "Communist state" (0.20 #375, 0.13 #1738, 0.12 #4344), "constitutional monarchy" (0.20 #724, 0.12 #1159, 0.12 #1087), "parliamentary" (0.14 #618, 0.12 #4344, 0.12 #4345)
>> best conf = 0.46
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2902 for best value:
>> intensional similarity = 13
>> extensional distance = 33
>> proper extension:  SSD;
>> query: (?x404, "republic") <- ?x404[ a Country; is locatedIn of ?x512; is neighbor of ?x379[ a Country; has government ?x435; has religion ?x109[ is religion of ?x239<IL>; is religion of ?x581<DZ>;]; is locatedIn of ?x2452[ a Volcano; has type ?x706;];];]

*> Best rule #299 for first EXPECTED value:
*> intensional similarity = 17
*> extensional distance = 2
*> proper extension:  ROU;
*> query: (?x404, "constitutional republic") <- ?x404[ a Country; has encompassed ?x521<America>; has ethnicGroup ?x676; has language ?x796<Spanish>; has language ?x2456[ a Language;]; has religion ?x352<RomanCatholic>; has wasDependentOf ?x149; is locatedIn of ?x513[ has hasEstuary ?x1150;]; is neighbor of ?x379<RA>; is neighbor of ?x690[ a Country;];]
*> conf = 0.25
ranks of expected_values: 6
EVAL	PY	government	"constitutional republic"	CNN-1.+1._MA	0.000	0.000	1.000	0.167	106.000	106.000	68.000	0.457	http://www.semwebtech.org/mondial/10/meta#government

#619-Reuss
PRED entity: Reuss

PRED relation: hasEstuary
PRED expected values: Reuss
=> 47 concepts (42 used for prediction)
PRED predicted values (max 10 best out of 122): Limmat (0.20 #54, 0.08 #280, 0.07 #733), Ticino (0.08 #354, 0.08 #581, 0.07 #1033), Aare (0.08 #235, 0.07 #914, 0.07 #688), Inn (0.08 #319, 0.07 #772, 0.03 #1677), Lagen (0.08 #679, 0.07 #1131, 0.04 #1358), Ammer (0.08 #648, 0.07 #1100, 0.04 #1327), Angara (0.08 #635, 0.07 #1087, 0.04 #1314), MurrumbidgeeRiver (0.08 #611, 0.07 #1063, 0.04 #1290), Adda (0.08 #568, 0.07 #1020, 0.04 #1247), Alz (0.08 #532, 0.07 #984, 0.04 #1211)
>> best conf = 0.20
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #54 for best value:
>> intensional similarity = 4
>> extensional distance = 3
>> proper extension:  Brienzersee; Thunersee;
>> query: (?x1178, Limmat) <- ?x1178[ has flowsInto ?x958<Aare>; has locatedIn ?x234<CH>;]

*> Best rule #5885 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 204
*> proper extension:  Schari; Garonne; Hwangho; Loire; Urubamba; Naryn;
*> query: (?x1178, ?x233) <- ?x1178[ has hasSource ?x493[ a Source;]; has locatedIn ?x234[ has neighbor ?x78; is locatedIn of ?x233;];]
*> conf = 0.02
ranks of expected_values: 75
EVAL	Reuss	hasEstuary	Reuss	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.013	47.000	42.000	122.000	0.200	http://www.semwebtech.org/mondial/10/meta#hasEstuary

PRED relation: hasEstuary
PRED expected values: Reuss
=> 143 concepts (143 used for prediction)
PRED predicted values (max 10 best out of 236): Aare (0.33 #9, 0.25 #461, 0.20 #688), Limmat (0.33 #280, 0.24 #6123, 0.20 #1186), Ticino (0.25 #580, 0.20 #1034, 0.20 #807), Rhone (0.20 #779, 0.17 #1684, 0.12 #2137), Rhein (0.17 #1595, 0.12 #2048, 0.12 #1821), BlueNile (0.17 #1397, 0.07 #5254, 0.06 #6162), Chire (0.17 #1481, 0.06 #6019, 0.05 #7835), Inn (0.12 #2810, 0.10 #3490, 0.08 #3945), Doubs (0.12 #2402, 0.07 #21117, 0.07 #5127), Dnepr (0.12 #1871, 0.06 #5503, 0.05 #7546)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #9 for best value:
>> intensional similarity = 10
>> extensional distance = 1
>> proper extension:  Aare;
>> query: (?x1178, Aare) <- ?x1178[ a River; has flowsInto ?x958[ has flowsThrough ?x1413; has hasSource ?x1641;]; has flowsThrough ?x1177[ a Lake; has locatedIn ?x234<CH>;]; has hasSource ?x493[ a Source;];]

*> Best rule #20889 for first EXPECTED value:
*> intensional similarity = 9
*> extensional distance = 180
*> proper extension:  Garonne; Loire;
*> query: (?x1178, ?x1018) <- ?x1178[ a River; has flowsInto ?x958; has hasSource ?x493[ a Source;]; has locatedIn ?x234[ has religion ?x56; is locatedIn of ?x1018[ a Estuary;]; is neighbor of ?x78;];]
*> conf = 0.07
ranks of expected_values: 31
EVAL	Reuss	hasEstuary	Reuss	CNN-1.+1._MA	0.000	0.000	0.000	0.032	143.000	143.000	236.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasEstuary

#618-Garonne
PRED entity: Garonne

PRED relation: locatedIn
PRED expected values: E
=> 46 concepts (42 used for prediction)
PRED predicted values (max 10 best out of 174): D (0.63 #1430, 0.44 #6602, 0.27 #19), CH (0.58 #1232, 0.44 #2650, 0.44 #6602), USA (0.49 #2428, 0.44 #2901, 0.33 #7619), ZRE (0.48 #2117, 0.48 #1959, 0.32 #5661), A (0.44 #6602, 0.29 #1509, 0.09 #98), FL (0.44 #6602, 0.09 #97, 0.05 #2830), E (0.32 #5661, 0.30 #3800, 0.19 #7076), CDN (0.32 #5661, 0.20 #7139, 0.17 #7846), BR (0.32 #5661, 0.19 #7076, 0.16 #595), SN (0.32 #5661, 0.19 #7076, 0.13 #7077)
>> best conf = 0.63
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1430 for best value:
>> intensional similarity = 5
>> extensional distance = 33
>> proper extension:  StarnbergerSee; Chiemsee; Ammersee; Bodensee;
>> query: (?x862, D) <- ?x862[ has flowsInto ?x182; has locatedIn ?x78[ a Country; is locatedIn of ?x256<Rhein>;];]

*> Best rule #5661 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 209
*> proper extension:  Tobol;
*> query: (?x862, ?x149) <- ?x862[ a River; has flowsInto ?x182[ is flowsInto of ?x1198[ has locatedIn ?x149;];]; has locatedIn ?x78;]
*> conf = 0.32
ranks of expected_values: 7
EVAL	Garonne	locatedIn	E	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.143	46.000	42.000	174.000	0.629	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: E
=> 114 concepts (114 used for prediction)
PRED predicted values (max 10 best out of 197): USA (0.76 #10502, 0.74 #10742, 0.65 #13597), D (0.74 #3090, 0.69 #9025, 0.69 #3804), E (0.50 #261, 0.49 #7130, 0.36 #18319), CN (0.45 #9301, 0.36 #10011, 0.34 #11441), R (0.39 #6632, 0.32 #19052, 0.27 #5448), BR (0.36 #18319, 0.36 #18318, 0.36 #18317), CDN (0.36 #18319, 0.36 #18318, 0.36 #18317), CO (0.36 #18319, 0.36 #18318, 0.36 #18317), GH (0.36 #18319, 0.36 #18318, 0.36 #18317), RA (0.36 #18319, 0.36 #18318, 0.36 #18317)
>> best conf = 0.76
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #10502 for best value:
>> intensional similarity = 10
>> extensional distance = 157
>> proper extension:  ArcticOcean; PacificOcean; MtAdams; MtElbert; Tennessee; KingsPeak; Mississippi; MtSt.Elias; SaintMarysRiver; Niihau; ...
>> query: (?x862, USA) <- ?x862[ has locatedIn ?x78[ has neighbor ?x207[ is locatedIn of ?x86;]; is dependentOf of ?x564[ is locatedIn of ?x282;]; is dependentOf of ?x1002[ has ethnicGroup ?x197; has government ?x916;]; is locatedIn of ?x440[ a Volcano;];];]

*> Best rule #261 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 4
*> proper extension:  Guadalquivir;
*> query: (?x862, E) <- ?x862[ a River; has flowsInto ?x182<AtlanticOcean>; has hasEstuary ?x2362; has locatedIn ?x78[ has neighbor ?x789<AND>; is dependentOf of ?x61;];]
*> conf = 0.50
ranks of expected_values: 3
EVAL	Garonne	locatedIn	E	CNN-1.+1._MA	0.000	1.000	1.000	0.333	114.000	114.000	197.000	0.761	http://www.semwebtech.org/mondial/10/meta#locatedIn

#617-ZRE
PRED entity: ZRE

PRED relation: government
PRED expected values: "republic"
=> 28 concepts (28 used for prediction)
PRED predicted values (max 10 best out of 60): "republic" (0.60 #78, 0.46 #438, 0.36 #582), "republic; multiparty presidential regime" (0.33 #29, 0.16 #1370, 0.15 #1297), "federal republic" (0.15 #291, 0.12 #219, 0.12 #363), "parliamentary democracy" (0.12 #941, 0.11 #581, 0.11 #653), "British Overseas Territories" (0.09 #727, 0.04 #1593, 0.03 #1737), "constitutional monarchy" (0.08 #794, 0.08 #650, 0.08 #866), "parliamentary democracy and a Commonwealth realm" (0.06 #756, 0.04 #1622, 0.04 #1766), "constitutional democracy" (0.05 #652, 0.04 #724, 0.04 #436), "overseas department of France" (0.04 #732, 0.02 #1598, 0.02 #1670), "constitutional republic" (0.04 #369, 0.03 #729, 0.03 #225)
>> best conf = 0.60
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #78 for best value:
>> intensional similarity = 5
>> extensional distance = 3
>> proper extension:  BI; Z; EAT;
>> query: (?x348, "republic") <- ?x348[ has religion ?x187<Muslim>; is locatedIn of ?x284<LakeTanganjika>; is neighbor of ?x229;]
ranks of expected_values: 1
EVAL	ZRE	government	"republic"	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	28.000	28.000	60.000	0.600	http://www.semwebtech.org/mondial/10/meta#government

PRED relation: government
PRED expected values: "republic"
=> 86 concepts (86 used for prediction)
PRED predicted values (max 10 best out of 68): "republic" (0.50 #870, 0.41 #1806, 0.41 #1518), "republic; multiparty presidential regime" (0.50 #533, 0.33 #389, 0.25 #677), "republic; presidential, multiparty system" (0.33 #121, 0.25 #625, 0.21 #4686), "federal republic" (0.22 #795, 0.21 #1731, 0.20 #1443), "constitutional monarchy" (0.18 #938, 0.15 #1226, 0.09 #3747), "parliamentary democracy" (0.16 #5192, 0.14 #2813, 0.14 #1661), "constitutional monarchy and Commonwealth realm" (0.11 #826, 0.09 #970, 0.08 #6134), "constitutional democracy" (0.11 #724, 0.08 #2236, 0.08 #6134), "parliamentary monarchy" (0.11 #820, 0.08 #6134, 0.08 #1036), "military junta" (0.11 #773, 0.08 #6134, 0.08 #5989)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #870 for best value:
>> intensional similarity = 11
>> extensional distance = 8
>> proper extension:  IRQ;
>> query: (?x348, "republic") <- ?x348[ a Country; has religion ?x95; is locatedIn of ?x358[ a Estuary;]; is locatedIn of ?x549[ a Source;]; is locatedIn of ?x2185[ has hasSource ?x709;]; is neighbor of ?x546[ has wasDependentOf ?x485<UnitedNations>;];]
ranks of expected_values: 1
EVAL	ZRE	government	"republic"	CNN-1.+1._MA	1.000	1.000	1.000	1.000	86.000	86.000	68.000	0.500	http://www.semwebtech.org/mondial/10/meta#government

#616-White
PRED entity: White

PRED relation: ethnicGroup!
PRED expected values: PR
=> 24 concepts (20 used for prediction)
PRED predicted values (max 10 best out of 211): PR (0.40 #343, 0.33 #153, 0.23 #761), CR (0.33 #438, 0.23 #761, 0.22 #819), GB (0.30 #1146, 0.23 #1336, 0.18 #1908), CO (0.23 #761, 0.22 #419, 0.20 #229), EC (0.23 #761, 0.22 #534, 0.20 #344), SLB (0.23 #761, 0.22 #452, 0.20 #1213), NIC (0.23 #761, 0.22 #458, 0.20 #268), USA (0.23 #761, 0.22 #437, 0.20 #247), FPOL (0.23 #761, 0.22 #433, 0.17 #2282), NAU (0.23 #761, 0.22 #558, 0.17 #2282)
>> best conf = 0.40
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #343 for best value:
>> intensional similarity = 13
>> extensional distance = 3
>> proper extension:  Amerindian; Mixed;
>> query: (?x1147, PR) <- ?x1147[ is ethnicGroup of ?x80<BVIR>; is ethnicGroup of ?x212[ has language ?x247; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x1390[ a Island;];]; is ethnicGroup of ?x407[ has dependentOf ?x81<GB>; has religion ?x352<RomanCatholic>;];]
ranks of expected_values: 1
EVAL	White	ethnicGroup!	PR	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	24.000	20.000	211.000	0.400	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup!
PRED expected values: PR
=> 57 concepts (49 used for prediction)
PRED predicted values (max 10 best out of 223): CR (0.45 #816, 0.33 #1387, 0.31 #1197), CO (0.45 #797, 0.33 #1368, 0.25 #1557), PR (0.40 #722, 0.33 #153, 0.15 #1292), EC (0.36 #912, 0.27 #1483, 0.25 #534), C (0.36 #776, 0.27 #1347, 0.25 #398), SME (0.31 #1547, 0.20 #1358, 0.20 #598), NIC (0.27 #836, 0.25 #458, 0.23 #1217), DOM (0.27 #860, 0.25 #482, 0.20 #1431), BR (0.27 #864, 0.25 #486, 0.20 #1435), HCA (0.27 #934, 0.25 #556, 0.20 #1505)
>> best conf = 0.45
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #816 for best value:
>> intensional similarity = 14
>> extensional distance = 9
>> proper extension:  European; Chinese; Asian; Mestizo; Mulatto; MediterraneanNordic;
>> query: (?x1147, CR) <- ?x1147[ a EthnicGroup; is ethnicGroup of ?x407[ has religion ?x95; is locatedIn of ?x182<AtlanticOcean>;]; is ethnicGroup of ?x865[ a Country; has language ?x796<Spanish>; has religion ?x1667; is locatedIn of ?x687;]; is ethnicGroup of ?x1008[ has encompassed ?x521; has government ?x2483;];]

*> Best rule #722 for first EXPECTED value:
*> intensional similarity = 15
*> extensional distance = 3
*> proper extension:  Amerindian;
*> query: (?x1147, PR) <- ?x1147[ a EthnicGroup; is ethnicGroup of ?x80<BVIR>; is ethnicGroup of ?x407[ has religion ?x352<RomanCatholic>; is locatedIn of ?x182<AtlanticOcean>;]; is ethnicGroup of ?x865[ has government ?x254; has religion ?x1667; is locatedIn of ?x687;]; is ethnicGroup of ?x1008[ has encompassed ?x521; has government ?x2483;];]
*> conf = 0.40
ranks of expected_values: 3
EVAL	White	ethnicGroup!	PR	CNN-1.+1._MA	0.000	1.000	1.000	0.333	57.000	49.000	223.000	0.455	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#615-R
PRED entity: R

PRED relation: encompassed
PRED expected values: Asia
=> 28 concepts (28 used for prediction)
PRED predicted values (max 10 best out of 4): Asia (0.84 #17, 0.59 #25, 0.26 #53), America (0.33 #4, 0.31 #32, 0.27 #72), Africa (0.31 #79, 0.30 #75, 0.30 #59), Australia-Oceania (0.11 #102, 0.11 #94, 0.11 #26)
>> best conf = 0.84
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #17 for best value:
>> intensional similarity = 3
>> extensional distance = 23
>> proper extension:  SGP;
>> query: (?x73, Asia) <- ?x73[ is locatedIn of ?x1748[ has locatedIn ?x232<CN>;];]
ranks of expected_values: 1
EVAL	R	encompassed	Asia	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	28.000	28.000	4.000	0.840	http://www.semwebtech.org/mondial/10/meta#encompassed

PRED relation: encompassed
PRED expected values: Asia
=> 94 concepts (94 used for prediction)
PRED predicted values (max 10 best out of 4): Asia (0.86 #227, 0.84 #277, 0.84 #252), America (0.85 #114, 0.62 #74, 0.50 #45), Australia-Oceania (0.64 #164, 0.53 #188, 0.47 #184), Africa (0.54 #117, 0.51 #275, 0.50 #141)
>> best conf = 0.86
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #227 for best value:
>> intensional similarity = 12
>> extensional distance = 46
>> proper extension:  LS; D; E; PL; CH; S; A; BR;
>> query: (?x73, ?x175) <- ?x73[ has ethnicGroup ?x58; is locatedIn of ?x465[ has hasEstuary ?x2211;]; is locatedIn of ?x800[ has flowsInto ?x801;]; is locatedIn of ?x2326[ a Source;]; is neighbor of ?x232[ has encompassed ?x175; is locatedIn of ?x231; is locatedIn of ?x497[ a River;];];]
ranks of expected_values: 1
EVAL	R	encompassed	Asia	CNN-1.+1._MA	1.000	1.000	1.000	1.000	94.000	94.000	4.000	0.863	http://www.semwebtech.org/mondial/10/meta#encompassed

#614-GUAM
PRED entity: GUAM

PRED relation: religion
PRED expected values: RomanCatholic
=> 34 concepts (34 used for prediction)
PRED predicted values (max 10 best out of 38): RomanCatholic (0.73 #218, 0.64 #596, 0.63 #428), Protestant (0.59 #212, 0.51 #464, 0.48 #506), Muslim (0.43 #1183, 0.43 #1140, 0.41 #1225), Christian (0.33 #46, 0.24 #1182, 0.24 #1139), ChristianOrthodox (0.27 #631, 0.27 #673, 0.26 #799), JehovasWitnesses (0.23 #231, 0.17 #1178, 0.16 #1347), Buddhist (0.20 #1051, 0.18 #222, 0.18 #138), Anglican (0.20 #1051, 0.18 #354, 0.17 #1178), Jewish (0.20 #1051, 0.17 #1178, 0.16 #1347), Hindu (0.20 #1051, 0.17 #1178, 0.16 #1347)
>> best conf = 0.73
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #218 for best value:
>> intensional similarity = 6
>> extensional distance = 20
>> proper extension:  R; USA; ES;
>> query: (?x1154, RomanCatholic) <- ?x1154[ a Country; has ethnicGroup ?x1064; has language ?x1155[ a Language;]; is locatedIn of ?x282<PacificOcean>;]
ranks of expected_values: 1
EVAL	GUAM	religion	RomanCatholic	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	34.000	34.000	38.000	0.727	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion
PRED expected values: RomanCatholic
=> 89 concepts (89 used for prediction)
PRED predicted values (max 10 best out of 41): RomanCatholic (0.85 #723, 0.81 #937, 0.77 #1363), Protestant (0.69 #931, 0.62 #717, 0.58 #1654), Christian (0.55 #2075, 0.47 #886, 0.41 #1014), Muslim (0.44 #3398, 0.43 #3569, 0.43 #2037), ChristianOrthodox (0.31 #2033, 0.30 #2926, 0.28 #2969), JehovasWitnesses (0.31 #736, 0.28 #2118, 0.25 #992), Buddhist (0.30 #516, 0.28 #1452, 0.25 #898), Mormon (0.28 #2118, 0.25 #278, 0.19 #3054), Anglican (0.27 #1160, 0.26 #1244, 0.23 #1923), EkalesiaNiue (0.25 #265, 0.19 #3054, 0.19 #3181)
>> best conf = 0.85
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #723 for best value:
>> intensional similarity = 15
>> extensional distance = 11
>> proper extension:  GCA; AUS; RCH; CO; CDN; NIC; NZ; MEX;
>> query: (?x1154, RomanCatholic) <- ?x1154[ a Country; has ethnicGroup ?x1064[ a EthnicGroup;]; has ethnicGroup ?x2149[ is ethnicGroup of ?x773[ has language ?x247; has religion ?x116<Christian>;];]; has government ?x2344; has language ?x1155[ a Language;]; is locatedIn of ?x282<PacificOcean>; is locatedIn of ?x1401[ has type ?x1402;];]
ranks of expected_values: 1
EVAL	GUAM	religion	RomanCatholic	CNN-1.+1._MA	1.000	1.000	1.000	1.000	89.000	89.000	41.000	0.846	http://www.semwebtech.org/mondial/10/meta#religion

#613-Sicilia
PRED entity: Sicilia

PRED relation: locatedInWater
PRED expected values: MediterraneanSea
=> 46 concepts (46 used for prediction)
PRED predicted values (max 10 best out of 89): MediterraneanSea (0.82 #61, 0.69 #104, 0.31 #236), AtlanticOcean (0.38 #7, 0.30 #756, 0.28 #183), JavaSea (0.32 #141, 0.21 #273, 0.19 #316), PacificOcean (0.28 #766, 0.24 #193, 0.21 #544), IndianOcean (0.27 #134, 0.24 #266, 0.22 #309), ArcticOcean (0.19 #234, 0.08 #190, 0.06 #763), Donau (0.15 #92, 0.05 #665, 0.04 #487), CaribbeanSea (0.15 #414, 0.13 #370, 0.09 #283), NorthSea (0.14 #530, 0.13 #931, 0.12 #708), BalticSea (0.11 #666, 0.08 #843, 0.08 #799)
>> best conf = 0.82
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #61 for best value:
>> intensional similarity = 3
>> extensional distance = 9
>> proper extension:  Stromboli; Salina; Sardegna; Panarea; Filicudi; Lampedusa; Alicudi; Linosa; Lipari;
>> query: (?x1894, MediterraneanSea) <- ?x1894[ a Island; has locatedIn ?x207<I>;]
ranks of expected_values: 1
EVAL	Sicilia	locatedInWater	MediterraneanSea	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	46.000	46.000	89.000	0.818	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater
PRED expected values: MediterraneanSea
=> 150 concepts (150 used for prediction)
PRED predicted values (max 10 best out of 146): MediterraneanSea (0.82 #774, 0.52 #2512, 0.50 #133), JavaSea (0.64 #722, 0.54 #810, 0.39 #991), PacificOcean (0.60 #686, 0.40 #642, 0.40 #286), AtlanticOcean (0.57 #498, 0.50 #408, 0.41 #1125), IndianOcean (0.55 #715, 0.46 #803, 0.42 #1030), NorthSea (0.40 #44, 0.33 #3, 0.25 #137), TheChannel (0.40 #44, 0.33 #37, 0.25 #171), ArcticOcean (0.40 #327, 0.29 #460, 0.22 #1221), LabradorSea (0.40 #324, 0.29 #457, 0.22 #548), LagoMaggiore (0.40 #44, 0.29 #401, 0.20 #223)
>> best conf = 0.82
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #774 for best value:
>> intensional similarity = 3
>> extensional distance = 9
>> proper extension:  Stromboli; Salina; Sardegna; Panarea; Filicudi; Lampedusa; Alicudi; Linosa; Lipari;
>> query: (?x1894, MediterraneanSea) <- ?x1894[ a Island; has locatedIn ?x207<I>;]
ranks of expected_values: 1
EVAL	Sicilia	locatedInWater	MediterraneanSea	CNN-1.+1._MA	1.000	1.000	1.000	1.000	150.000	150.000	146.000	0.818	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#612-VIRG
PRED entity: VIRG

PRED relation: locatedIn!
PRED expected values: CaribbeanSea
=> 33 concepts (29 used for prediction)
PRED predicted values (max 10 best out of 1292): CaribbeanSea (0.89 #21337, 0.88 #18491, 0.53 #11485), PacificOcean (0.33 #85, 0.33 #17153, 0.29 #18576), Guam (0.33 #777), NorthCaicos (0.25 #2620, 0.07 #4042, 0.06 #5464), Providenciales (0.25 #2570, 0.07 #3992, 0.06 #5414), NewProvidence (0.25 #2743, 0.04 #8433, 0.03 #12700), IndianOcean (0.17 #5692, 0.14 #19917, 0.13 #18494), MediterraneanSea (0.15 #25685, 0.14 #28534, 0.14 #29958), NorwegianSea (0.11 #31300, 0.11 #4402, 0.11 #29876), GreenlandSea (0.11 #31300, 0.11 #5053, 0.11 #29876)
>> best conf = 0.89
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #21337 for best value:
>> intensional similarity = 4
>> extensional distance = 86
>> proper extension:  S; RM; EAT;
>> query: (?x1230, ?x317) <- ?x1230[ has religion ?x352; is locatedIn of ?x1397[ a Island; has locatedInWater ?x317;];]
ranks of expected_values: 1
EVAL	VIRG	locatedIn!	CaribbeanSea	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	33.000	29.000	1292.000	0.893	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: CaribbeanSea
=> 85 concepts (79 used for prediction)
PRED predicted values (max 10 best out of 1278): CaribbeanSea (0.92 #66979, 0.91 #72675, 0.90 #65555), PacificOcean (0.48 #48548, 0.33 #25740, 0.33 #12821), Anguilla (0.39 #24229, 0.38 #52734, 0.33 #1423), NorthCaicos (0.39 #24229, 0.38 #52734, 0.28 #57010), Providenciales (0.39 #24229, 0.38 #52734, 0.28 #57010), Tortola (0.39 #24229, 0.28 #57010, 0.11 #19169), PuertoRico (0.33 #2304, 0.25 #5152, 0.20 #8000), CerrodePunta (0.33 #1727, 0.25 #4575, 0.20 #7423), NewProvidence (0.33 #1320, 0.25 #4165, 0.20 #7013), Tobago (0.33 #1423, 0.17 #13018, 0.12 #15869)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #66979 for best value:
>> intensional similarity = 21
>> extensional distance = 49
>> proper extension:  TL; WD;
>> query: (?x1230, ?x317) <- ?x1230[ has encompassed ?x521; has government ?x2344; has religion ?x352[ is religion of ?x179<SME>; is religion of ?x272<CDN>; is religion of ?x745[ has ethnicGroup ?x298;]; is religion of ?x793<DK>; is religion of ?x853<PNG>;]; has religion ?x1082[ a Religion; is religion of ?x279<BS>;]; is locatedIn of ?x182[ is locatedInWater of ?x112;]; is locatedIn of ?x1397[ has locatedInWater ?x317;];]
ranks of expected_values: 1
EVAL	VIRG	locatedIn!	CaribbeanSea	CNN-1.+1._MA	1.000	1.000	1.000	1.000	85.000	79.000	1278.000	0.920	http://www.semwebtech.org/mondial/10/meta#locatedIn

#611-PersianGulf
PRED entity: PersianGulf

PRED relation: flowsInto!
PRED expected values: SchattalArab
=> 33 concepts (27 used for prediction)
PRED predicted values (max 10 best out of 139): Euphrat (0.10 #1450, 0.08 #1753, 0.03 #3632), Tigris (0.10 #1299, 0.08 #1602, 0.03 #3419), LakeKeban (0.10 #1395, 0.08 #1698, 0.03 #3515), Nile (0.08 #1801, 0.05 #2104, 0.04 #2407), Arno (0.08 #1771, 0.05 #2074, 0.04 #2377), Ebro (0.08 #1759, 0.05 #2062, 0.04 #2365), Rhone (0.08 #1690, 0.05 #1993, 0.04 #2296), Tiber (0.08 #1648, 0.05 #1951, 0.04 #2254), Etsch (0.08 #1624, 0.05 #1927, 0.04 #2230), Po (0.08 #1608, 0.05 #1911, 0.04 #2214)
>> best conf = 0.10
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1450 for best value:
>> intensional similarity = 9
>> extensional distance = 8
>> proper extension:  Euphrat; SchattalArab; Tigris; SyrianDesert; SchattalArab; Euphrat; Tigris; SchattalArab;
>> query: (?x918, Euphrat) <- ?x918[ has locatedIn ?x302<IRQ>; has locatedIn ?x639[ a Country; has religion ?x187<Muslim>; is neighbor of ?x668;]; has locatedIn ?x1963[ has wasDependentOf ?x81;];]

*> Best rule #1815 for first EXPECTED value:
*> intensional similarity = 9
*> extensional distance = 10
*> proper extension:  Jordan;
*> query: (?x918, ?x637) <- ?x918[ has locatedIn ?x302[ has neighbor ?x803<JOR>; has religion ?x116;]; has locatedIn ?x639[ a Country; has encompassed ?x175; is locatedIn of ?x637; is neighbor of ?x668;];]
*> conf = 0.03
ranks of expected_values: 82
EVAL	PersianGulf	flowsInto!	SchattalArab	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.012	33.000	27.000	139.000	0.100	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto!
PRED expected values: SchattalArab
=> 91 concepts (91 used for prediction)
PRED predicted values (max 10 best out of 212): Indus (0.33 #646, 0.06 #3076, 0.04 #3987), Nile (0.17 #2408, 0.17 #1801, 0.06 #3323), Arno (0.17 #2378, 0.17 #1771, 0.06 #3293), Ebro (0.17 #2366, 0.17 #1759, 0.06 #3281), Rhone (0.17 #2297, 0.17 #1690, 0.06 #3212), Tiber (0.17 #2255, 0.17 #1648, 0.06 #3170), Etsch (0.17 #2231, 0.17 #1624, 0.06 #3146), Po (0.17 #2215, 0.17 #1608, 0.06 #3130), Drin (0.17 #2214, 0.17 #1607, 0.06 #3129), Dnister (0.17 #2341, 0.17 #1734, 0.04 #5381)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #646 for best value:
>> intensional similarity = 14
>> extensional distance = 1
>> proper extension:  ArabianSea;
>> query: (?x918, Indus) <- ?x918[ a Sea; has locatedIn ?x174[ a Country;]; has locatedIn ?x302[ has religion ?x116; is neighbor of ?x185;]; has locatedIn ?x639[ has government ?x640; has religion ?x187<Muslim>;]; is locatedInWater of ?x2062[ a Island;]; is mergesWith of ?x926<GulfofOman>;]

*> Best rule #4853 for first EXPECTED value:
*> intensional similarity = 14
*> extensional distance = 23
*> proper extension:  BalticSea;
*> query: (?x918, ?x666) <- ?x918[ a Sea; has locatedIn ?x174[ has ethnicGroup ?x244; has wasDependentOf ?x81;]; has locatedIn ?x302[ a Country; has ethnicGroup ?x557; has neighbor ?x185; has religion ?x116; is locatedIn of ?x666[ a River;];]; has mergesWith ?x926; is locatedInWater of ?x1736[ a Island;];]
*> conf = 0.09
ranks of expected_values: 17
EVAL	PersianGulf	flowsInto!	SchattalArab	CNN-1.+1._MA	0.000	0.000	0.000	0.059	91.000	91.000	212.000	0.333	http://www.semwebtech.org/mondial/10/meta#flowsInto

#610-TCH
PRED entity: TCH

PRED relation: neighbor!
PRED expected values: SUD RN
=> 36 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 185): SUD (0.90 #3665, 0.90 #3505, 0.90 #3345), RN (0.90 #3665, 0.90 #3505, 0.90 #3345), TCH (0.50 #499, 0.33 #341, 0.33 #181), BF (0.43 #924, 0.43 #764, 0.40 #1084), CI (0.43 #785, 0.30 #1105, 0.29 #945), SSD (0.40 #1161, 0.36 #1319, 0.31 #3347), EAT (0.40 #1248, 0.29 #1406, 0.21 #1880), BEN (0.33 #284, 0.31 #3347, 0.30 #3668), DZ (0.33 #257, 0.31 #3347, 0.30 #3668), G (0.33 #343, 0.31 #3347, 0.28 #3507)
>> best conf = 0.90
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #3665 for best value:
>> intensional similarity = 7
>> extensional distance = 99
>> proper extension:  B;
>> query: (?x169, ?x186) <- ?x169[ has neighbor ?x139[ has encompassed ?x213; has government ?x140; is locatedIn of ?x182;]; has neighbor ?x186; is locatedIn of ?x695[ a River;];]
ranks of expected_values: 1, 2
EVAL	TCH	neighbor!	RN	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	36.000	36.000	185.000	0.899	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	TCH	neighbor!	SUD	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	36.000	36.000	185.000	0.899	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: SUD RN
=> 103 concepts (103 used for prediction)
PRED predicted values (max 10 best out of 222): SUD (0.91 #5196, 0.91 #14250, 0.90 #13255), RN (0.91 #5196, 0.91 #14250, 0.90 #13255), RIM (0.50 #1383, 0.40 #652, 0.33 #2194), EAK (0.50 #3164, 0.25 #487, 0.23 #3407), TCH (0.44 #3593, 0.40 #1966, 0.40 #652), BEN (0.40 #1906, 0.40 #654, 0.40 #652), BF (0.40 #654, 0.40 #652, 0.34 #8146), DZ (0.40 #654, 0.40 #652, 0.34 #8146), RMM (0.40 #654, 0.40 #652, 0.34 #8146), SSD (0.40 #1986, 0.38 #3244, 0.38 #3123)
>> best conf = 0.91
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #5196 for best value:
>> intensional similarity = 17
>> extensional distance = 12
>> proper extension:  AZ;
>> query: (?x169, ?x186) <- ?x169[ has government ?x435<"republic">; has neighbor ?x186; has religion ?x116[ a Religion; is religion of ?x232<CN>; is religion of ?x416<SN>; is religion of ?x924<IND>;]; has wasDependentOf ?x78; is locatedIn of ?x2238[ has type ?x762;]; is neighbor of ?x736[ is neighbor of ?x229[ is locatedIn of ?x53;];];]
ranks of expected_values: 1, 2
EVAL	TCH	neighbor!	RN	CNN-1.+1._MA	1.000	1.000	1.000	1.000	103.000	103.000	222.000	0.911	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	TCH	neighbor!	SUD	CNN-1.+1._MA	1.000	1.000	1.000	1.000	103.000	103.000	222.000	0.911	http://www.semwebtech.org/mondial/10/meta#neighbor

#609-ER
PRED entity: ER

PRED relation: ethnicGroup
PRED expected values: Tigrinya
=> 30 concepts (30 used for prediction)
PRED predicted values (max 10 best out of 226): European (0.36 #2038, 0.27 #4069, 0.25 #2799), Arab (0.33 #11, 0.29 #772, 0.29 #519), Kikuyu (0.33 #227, 0.14 #735, 0.08 #1495), Kamba (0.33 #160, 0.14 #668, 0.08 #1428), Luo (0.33 #147, 0.14 #655, 0.08 #1415), Kalenjin (0.33 #137, 0.14 #645, 0.08 #1405), Meru (0.33 #119, 0.14 #627, 0.08 #1387), Kisii (0.33 #94, 0.14 #602, 0.08 #1362), Luhya (0.33 #13, 0.14 #521, 0.08 #1281), Somali (0.29 #3045, 0.26 #3299, 0.20 #3808)
>> best conf = 0.36
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2038 for best value:
>> intensional similarity = 8
>> extensional distance = 23
>> proper extension:  HELX; CV; SY; MS; STP;
>> query: (?x629, European) <- ?x629[ a Country; has encompassed ?x213<Africa>; has ethnicGroup ?x1853[ a EthnicGroup;]; has government ?x1090; is locatedIn of ?x1552[ a Sea;];]

No rule for expected values
ranks of expected_values: 
EVAL	ER	ethnicGroup	Tigrinya	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	30.000	30.000	226.000	0.360	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: Tigrinya
=> 86 concepts (86 used for prediction)
PRED predicted values (max 10 best out of 242): European (0.61 #7140, 0.59 #7903, 0.47 #11721), Arab (0.56 #4597, 0.50 #2813, 0.42 #5869), Somali (0.50 #2292, 0.50 #2146, 0.40 #510), Mestizo (0.50 #7168, 0.45 #7931, 0.24 #11241), African (0.44 #4338, 0.36 #6371, 0.32 #12735), Afar (0.40 #510, 0.38 #2294, 0.33 #509), Gurage (0.40 #510, 0.38 #2294, 0.33 #504), Amhara (0.40 #510, 0.38 #2294, 0.33 #495), Oromo (0.40 #510, 0.38 #2294, 0.33 #458), Sidama (0.40 #510, 0.38 #2294, 0.33 #395)
>> best conf = 0.61
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #7140 for best value:
>> intensional similarity = 21
>> extensional distance = 16
>> proper extension:  C;
>> query: (?x629, European) <- ?x629[ a Country; has ethnicGroup ?x2131[ a EthnicGroup;]; has wasDependentOf ?x476[ a Country; has encompassed ?x213; has ethnicGroup ?x1179; has ethnicGroup ?x1798[ a EthnicGroup;]; has religion ?x187[ is religion of ?x120<D>; is religion of ?x272<CDN>;]; is locatedIn of ?x655[ a Mountain;]; is locatedIn of ?x1783[ a Lake;]; is locatedIn of ?x1875[ a Source; has inMountains ?x2477;];];]

No rule for expected values
ranks of expected_values: 
EVAL	ER	ethnicGroup	Tigrinya	CNN-1.+1._MA	0.000	0.000	0.000	0.000	86.000	86.000	242.000	0.611	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#608-ZRE
PRED entity: ZRE

PRED relation: neighbor
PRED expected values: BI RWA EAU
=> 28 concepts (28 used for prediction)
PRED predicted values (max 10 best out of 183): EAU (0.91 #622, 0.89 #3591, 0.89 #3434), RWA (0.91 #622, 0.89 #3591, 0.89 #3434), BI (0.91 #622, 0.89 #3591, 0.89 #3434), ZRE (0.50 #525, 0.33 #370, 0.33 #215), MOC (0.33 #343, 0.28 #2185, 0.26 #3120), MW (0.33 #437, 0.28 #2185, 0.26 #3120), NAM (0.33 #328, 0.28 #2185, 0.26 #3120), G (0.33 #181, 0.28 #2185, 0.26 #3120), ZW (0.33 #461, 0.28 #2185, 0.26 #3120), EAK (0.33 #549, 0.28 #2185, 0.26 #3120)
>> best conf = 0.91
=> the first rule below is the first best rule for 3 predicted values
>> Best rule #622 for best value:
>> intensional similarity = 5
>> extensional distance = 4
>> proper extension:  EAK;
>> query: (?x348, ?x359) <- ?x348[ has neighbor ?x229; is locatedIn of ?x1189[ has inMountains ?x1066<EastAfricanRift>;]; is neighbor of ?x359;]
ranks of expected_values: 1, 2, 3
EVAL	ZRE	neighbor	EAU	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	28.000	28.000	183.000	0.909	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	ZRE	neighbor	RWA	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	28.000	28.000	183.000	0.909	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	ZRE	neighbor	BI	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	28.000	28.000	183.000	0.909	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: BI RWA EAU
=> 81 concepts (81 used for prediction)
PRED predicted values (max 10 best out of 224): EAU (0.94 #4136, 0.93 #4137, 0.92 #2693), RWA (0.94 #4136, 0.93 #4137, 0.92 #2693), BI (0.94 #4136, 0.93 #4137, 0.92 #2693), ZRE (0.50 #2593, 0.50 #1960, 0.45 #1898), BR (0.50 #1832, 0.21 #3976, 0.21 #4933), TCH (0.45 #1898, 0.38 #3653, 0.37 #2057), CAM (0.45 #1898, 0.38 #3653, 0.37 #2057), SUD (0.45 #1898, 0.38 #3653, 0.37 #2057), G (0.45 #1898, 0.38 #3653, 0.37 #4135), LAR (0.40 #1725, 0.38 #3478, 0.35 #2531)
>> best conf = 0.94
=> the first rule below is the first best rule for 3 predicted values
>> Best rule #4136 for best value:
>> intensional similarity = 14
>> extensional distance = 20
>> proper extension:  RL; SK; RO; TR; UA; AFG; SLO; CZ;
>> query: (?x348, ?x546) <- ?x348[ has neighbor ?x736[ has religion ?x352; has wasDependentOf ?x78; is locatedIn of ?x695;]; has religion ?x95; has wasDependentOf ?x543; is locatedIn of ?x1244[ is flowsInto of ?x1604;]; is locatedIn of ?x2181[ a Source; has inMountains ?x1066;]; is neighbor of ?x359[ has encompassed ?x213;]; is neighbor of ?x546;]
ranks of expected_values: 1, 2, 3
EVAL	ZRE	neighbor	EAU	CNN-1.+1._MA	1.000	1.000	1.000	1.000	81.000	81.000	224.000	0.937	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	ZRE	neighbor	RWA	CNN-1.+1._MA	1.000	1.000	1.000	1.000	81.000	81.000	224.000	0.937	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	ZRE	neighbor	BI	CNN-1.+1._MA	1.000	1.000	1.000	1.000	81.000	81.000	224.000	0.937	http://www.semwebtech.org/mondial/10/meta#neighbor

#607-HR
PRED entity: HR

PRED relation: neighbor!
PRED expected values: SRB
=> 39 concepts (35 used for prediction)
PRED predicted values (max 10 best out of 207): RO (0.56 #341, 0.50 #3177, 0.50 #3178), A (0.50 #3177, 0.50 #74, 0.50 #3178), UA (0.50 #3177, 0.50 #3178, 0.50 #2860), HR (0.50 #3177, 0.50 #3178, 0.50 #2860), SK (0.50 #3177, 0.50 #3178, 0.50 #2860), D (0.50 #3177, 0.50 #3178, 0.50 #2860), SRB (0.50 #3177, 0.50 #3178, 0.50 #2860), BG (0.50 #3177, 0.50 #3178, 0.50 #2860), MD (0.50 #3177, 0.50 #3178, 0.50 #2860), I (0.50 #3178, 0.50 #2860, 0.50 #2859)
>> best conf = 0.56
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #341 for best value:
>> intensional similarity = 6
>> extensional distance = 7
>> proper extension:  SK; RO; BG; UA; MD; SRB;
>> query: (?x156, RO) <- ?x156[ a Country; has neighbor ?x55; has religion ?x56; has wasDependentOf ?x1197; is locatedIn of ?x133<Donau>;]

*> Best rule #3177 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 94
*> proper extension:  F;
*> query: (?x156, ?x446) <- ?x156[ has religion ?x56; is locatedIn of ?x614[ has hasSource ?x1267; has locatedIn ?x446[ a Country; has ethnicGroup ?x160;];]; is neighbor of ?x55;]
*> conf = 0.50
ranks of expected_values: 7
EVAL	HR	neighbor!	SRB	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.143	39.000	35.000	207.000	0.556	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: SRB
=> 104 concepts (104 used for prediction)
PRED predicted values (max 10 best out of 226): D (0.59 #3514, 0.56 #4317, 0.54 #2890), A (0.59 #3514, 0.56 #4317, 0.47 #2071), HR (0.59 #3514, 0.56 #4317, 0.47 #2071), SRB (0.59 #3514, 0.56 #4317, 0.47 #2071), SK (0.59 #3514, 0.56 #4317, 0.47 #2071), RO (0.59 #3514, 0.56 #4317, 0.47 #2071), UA (0.59 #3514, 0.56 #4317, 0.47 #2071), BG (0.59 #3514, 0.56 #4317, 0.47 #2071), MD (0.59 #3514, 0.56 #4317, 0.47 #2071), I (0.59 #3514, 0.56 #4317, 0.47 #2071)
>> best conf = 0.59
=> the first rule below is the first best rule for 10 predicted values
>> Best rule #3514 for best value:
>> intensional similarity = 15
>> extensional distance = 12
>> proper extension:  AUS; CDN;
>> query: (?x156, ?x446) <- ?x156[ a Country; has ethnicGroup ?x160; has government ?x254; has language ?x878[ a Language;]; has religion ?x187<Muslim>; is locatedIn of ?x152[ a River; is flowsInto of ?x813;]; is locatedIn of ?x155[ a River; has locatedIn ?x446;]; is locatedIn of ?x830[ a Estuary;];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 4
EVAL	HR	neighbor!	SRB	CNN-1.+1._MA	0.000	0.000	1.000	0.250	104.000	104.000	226.000	0.587	http://www.semwebtech.org/mondial/10/meta#neighbor

#606-SouthAsian
PRED entity: SouthAsian

PRED relation: ethnicGroup!
PRED expected values: KWT
=> 34 concepts (20 used for prediction)
PRED predicted values (max 10 best out of 175): IR (0.58 #444, 0.50 #640, 0.47 #833), SA (0.33 #333, 0.21 #3706, 0.20 #3313), OM (0.21 #3706, 0.21 #1166, 0.20 #3313), Q (0.21 #1166, 0.20 #802, 0.17 #219), JOR (0.21 #1166, 0.19 #1119, 0.17 #340), KWT (0.21 #1166, 0.15 #3705, 0.14 #1362), YE (0.21 #1166, 0.15 #3705, 0.14 #1362), TM (0.19 #1024, 0.08 #439, 0.07 #635), AZ (0.19 #1035, 0.04 #1231, 0.04 #1619), ARM (0.19 #1034, 0.04 #1230, 0.04 #1618)
>> best conf = 0.58
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #444 for best value:
>> intensional similarity = 9
>> extensional distance = 10
>> proper extension:  Kurd; Baloch; Lur; Azerbaijani; GilakiMazandarani; Turkmen;
>> query: (?x1792, IR) <- ?x1792[ a EthnicGroup; is ethnicGroup of ?x107[ a Country; has encompassed ?x175; has government ?x1136; has neighbor ?x639; has religion ?x187; is locatedIn of ?x918<PersianGulf>;];]

*> Best rule #1166 for first EXPECTED value:
*> intensional similarity = 9
*> extensional distance = 14
*> proper extension:  Yezidi;
*> query: (?x1792, ?x174) <- ?x1792[ a EthnicGroup; is ethnicGroup of ?x107[ a Country; has government ?x1136; has neighbor ?x751[ has language ?x1848; is locatedIn of ?x918<PersianGulf>; is neighbor of ?x174;];];]
*> conf = 0.21
ranks of expected_values: 6
EVAL	SouthAsian	ethnicGroup!	KWT	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.167	34.000	20.000	175.000	0.583	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup!
PRED expected values: KWT
=> 57 concepts (49 used for prediction)
PRED predicted values (max 10 best out of 197): IR (0.64 #1041, 0.50 #1771, 0.50 #1633), SA (0.53 #2361, 0.33 #333, 0.31 #7313), OM (0.53 #2361, 0.29 #387, 0.25 #389), YE (0.53 #2361, 0.25 #389, 0.21 #1574), JOR (0.43 #736, 0.25 #389, 0.21 #1525), Q (0.38 #813, 0.25 #389, 0.23 #784), IRQ (0.33 #587, 0.33 #446, 0.29 #387), KAZ (0.33 #2241, 0.26 #2638, 0.13 #3429), KWT (0.29 #387, 0.25 #389, 0.23 #784), RL (0.29 #601, 0.17 #205, 0.14 #1390)
>> best conf = 0.64
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1041 for best value:
>> intensional similarity = 16
>> extensional distance = 9
>> proper extension:  Arab; Kurd; Baloch; Lur; Azerbaijani; GilakiMazandarani; Turkmen;
>> query: (?x1792, IR) <- ?x1792[ a EthnicGroup; is ethnicGroup of ?x107[ a Country; has encompassed ?x175<Asia>; has government ?x1136; has neighbor ?x639[ has wasDependentOf ?x1027;]; has neighbor ?x751[ has language ?x1848; has neighbor ?x302; is locatedIn of ?x953;]; has religion ?x187<Muslim>; is locatedIn of ?x926<GulfofOman>;];]

*> Best rule #387 for first EXPECTED value:
*> intensional similarity = 19
*> extensional distance = 4
*> proper extension:  Afro-Asian;
*> query: (?x1792, ?x1963) <- ?x1792[ a EthnicGroup; is ethnicGroup of ?x107[ a Country; has government ?x1136; has neighbor ?x751[ a Country; has neighbor ?x302<IRQ>; has neighbor ?x1963; is locatedIn of ?x953;]; has religion ?x187<Muslim>; is locatedIn of ?x637<RubAlChali>; is locatedIn of ?x918<PersianGulf>; is locatedIn of ?x926[ has mergesWith ?x1333; is locatedInWater of ?x2355;];];]
*> conf = 0.29
ranks of expected_values: 9
EVAL	SouthAsian	ethnicGroup!	KWT	CNN-1.+1._MA	0.000	0.000	1.000	0.111	57.000	49.000	197.000	0.636	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#605-E
PRED entity: E

PRED relation: wasDependentOf!
PRED expected values: CR GQ
=> 44 concepts (44 used for prediction)
PRED predicted values (max 10 best out of 199): ANG (0.33 #118, 0.07 #546, 0.06 #687), CV (0.33 #65, 0.07 #493, 0.06 #634), GNB (0.33 #138, 0.07 #566, 0.06 #707), OM (0.33 #88, 0.07 #516, 0.06 #657), MOC (0.33 #26, 0.07 #454, 0.06 #595), F (0.12 #995, 0.11 #1137, 0.06 #853), AND (0.12 #995, 0.11 #1137, 0.05 #3410), V (0.11 #266, 0.08 #410, 0.07 #553), LAR (0.11 #267, 0.08 #411, 0.07 #554), ZRE (0.11 #188, 0.07 #475, 0.05 #1042)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #118 for best value:
>> intensional similarity = 7
>> extensional distance = 1
>> proper extension:  P;
>> query: (?x149, ANG) <- ?x149[ is locatedIn of ?x1198<Guadiana>; is locatedIn of ?x1739<Guadiana>; is neighbor of ?x78; is wasDependentOf of ?x482[ is locatedIn of ?x282;];]

*> Best rule #1421 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 18
*> proper extension:  MergerofNorth-SouthYemen;
*> query: (?x149, ?x50) <- ?x149[ is wasDependentOf of ?x1364[ has encompassed ?x521; has religion ?x352[ is religion of ?x50; is religion of ?x745<MART>;];];]
*> conf = 0.03
ranks of expected_values: 145, 162
EVAL	E	wasDependentOf!	GQ	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.007	44.000	44.000	199.000	0.333	http://www.semwebtech.org/mondial/10/meta#wasDependentOf
EVAL	E	wasDependentOf!	CR	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.006	44.000	44.000	199.000	0.333	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

PRED relation: wasDependentOf!
PRED expected values: CR GQ
=> 148 concepts (148 used for prediction)
PRED predicted values (max 10 best out of 219): USA (0.38 #427, 0.30 #8580, 0.29 #10447), BZ (0.38 #427, 0.30 #8580, 0.29 #10447), B (0.38 #427, 0.30 #8580, 0.29 #10447), PA (0.38 #427, 0.30 #8580, 0.29 #10447), BR (0.38 #427, 0.30 #8580, 0.29 #10447), CR (0.38 #427, 0.30 #8580, 0.29 #10447), D (0.38 #427, 0.30 #8580, 0.29 #10447), ROU (0.38 #427, 0.30 #8580, 0.29 #10447), RG (0.33 #374, 0.25 #803, 0.25 #659), RH (0.33 #380, 0.25 #809, 0.25 #665)
>> best conf = 0.38
=> the first rule below is the first best rule for 8 predicted values
>> Best rule #427 for best value:
>> intensional similarity = 12
>> extensional distance = 1
>> proper extension:  F;
>> query: (?x149, ?x363) <- ?x149[ is locatedIn of ?x2193[ a Source;]; is wasDependentOf of ?x379[ a Country; has ethnicGroup ?x197; has neighbor ?x363; has religion ?x95; is locatedIn of ?x512;]; is wasDependentOf of ?x575[ has language ?x544; is locatedIn of ?x121; is wasDependentOf of ?x179;];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 6, 133
EVAL	E	wasDependentOf!	GQ	CNN-1.+1._MA	0.000	0.000	0.000	0.008	148.000	148.000	219.000	0.385	http://www.semwebtech.org/mondial/10/meta#wasDependentOf
EVAL	E	wasDependentOf!	CR	CNN-1.+1._MA	0.000	0.000	1.000	0.167	148.000	148.000	219.000	0.385	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

#604-NAM
PRED entity: NAM

PRED relation: neighbor
PRED expected values: RB
=> 39 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 232): RB (0.91 #2386, 0.88 #2864, 0.88 #1908), F (0.56 #163, 0.12 #3825, 0.12 #3025), D (0.44 #173, 0.14 #967, 0.10 #2241), RCB (0.25 #5108, 0.25 #89, 0.12 #3825), NAM (0.25 #5108, 0.25 #17, 0.12 #3825), EAT (0.25 #5108, 0.25 #129, 0.11 #1240), MOC (0.25 #5108, 0.25 #32, 0.08 #350), ZRE (0.25 #5108, 0.12 #3825, 0.12 #3025), ZW (0.25 #5108, 0.12 #3825, 0.12 #3025), LS (0.25 #5108, 0.12 #3825, 0.12 #3025)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2386 for best value:
>> intensional similarity = 6
>> extensional distance = 75
>> proper extension:  SSD; ER;
>> query: (?x138, ?x1239) <- ?x138[ has neighbor ?x934[ has ethnicGroup ?x197; has religion ?x95<Protestant>;]; is locatedIn of ?x137; is neighbor of ?x1239;]
ranks of expected_values: 1
EVAL	NAM	neighbor	RB	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	39.000	36.000	232.000	0.912	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: RB
=> 97 concepts (97 used for prediction)
PRED predicted values (max 10 best out of 244): RB (0.92 #10643, 0.90 #10642, 0.90 #11467), RCB (0.60 #2037, 0.50 #1387, 0.48 #1944), RCA (0.60 #1576, 0.50 #2392, 0.29 #807), EAT (0.53 #5538, 0.53 #5502, 0.50 #3903), NAM (0.48 #1944, 0.46 #2270, 0.33 #2438), ZW (0.46 #2270, 0.33 #2438, 0.33 #800), LS (0.46 #2270, 0.33 #653, 0.33 #4716), ZRE (0.44 #2437, 0.35 #5921, 0.33 #2438), MW (0.40 #1912, 0.33 #2438, 0.33 #290), G (0.40 #1483, 0.33 #2299, 0.33 #509)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #10643 for best value:
>> intensional similarity = 14
>> extensional distance = 69
>> proper extension:  CH; KAZ; WEST;
>> query: (?x138, ?x525) <- ?x138[ has language ?x247; is locatedIn of ?x182[ is flowsInto of ?x1519[ a River;];]; is neighbor of ?x525[ has encompassed ?x213; has government ?x435; has religion ?x116; is locatedIn of ?x284; is neighbor of ?x819[ has religion ?x95;];]; is neighbor of ?x1239[ a Country; is locatedIn of ?x242;];]
ranks of expected_values: 1
EVAL	NAM	neighbor	RB	CNN-1.+1._MA	1.000	1.000	1.000	1.000	97.000	97.000	244.000	0.917	http://www.semwebtech.org/mondial/10/meta#neighbor

#603-Wangerooge
PRED entity: Wangerooge

PRED relation: belongsToIslands
PRED expected values: OstfriesischeInseln
=> 35 concepts (35 used for prediction)
PRED predicted values (max 10 best out of 31): OstfriesischeInseln (0.33 #178, 0.33 #42, 0.27 #110), NordfriesischeInseln (0.25 #36, 0.20 #172, 0.20 #104), WestfriesischeInseln (0.20 #81, 0.14 #285, 0.06 #1293), OrkneyIslands (0.19 #289, 0.18 #221, 0.06 #1293), SundaIslands (0.09 #694, 0.07 #830, 0.06 #898), LesserAntilles (0.08 #1512, 0.07 #1716, 0.07 #1103), Azores (0.06 #480, 0.05 #820, 0.03 #1501), BritishIsles (0.06 #223, 0.06 #1293, 0.05 #291), ShetlandIslands (0.06 #231, 0.06 #1293, 0.05 #299), HawaiiIslands (0.06 #709, 0.05 #845, 0.04 #913)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #178 for best value:
>> intensional similarity = 3
>> extensional distance = 13
>> proper extension:  Spiekeroog;
>> query: (?x2348, OstfriesischeInseln) <- ?x2348[ a Island; has locatedIn ?x120<D>;]
>> Best rule #42 for best value:
>> intensional similarity = 5
>> extensional distance = 10
>> proper extension:  Pellworm; Langeoog; Amrum; Norderney; Helgoland; Fohr; Sylt; Baltrum; Borkum; Juist;
>> query: (?x2348, OstfriesischeInseln) <- ?x2348[ a Island; has locatedIn ?x120<D>; has locatedInWater ?x121<NorthSea>;]
ranks of expected_values: 1
EVAL	Wangerooge	belongsToIslands	OstfriesischeInseln	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	35.000	35.000	31.000	0.333	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

PRED relation: belongsToIslands
PRED expected values: OstfriesischeInseln
=> 99 concepts (99 used for prediction)
PRED predicted values (max 10 best out of 45): OstfriesischeInseln (0.42 #3618, 0.40 #4030, 0.39 #3276), NordfriesischeInseln (0.42 #3618, 0.40 #4030, 0.39 #3276), LipariIslands (0.23 #616, 0.12 #890, 0.09 #958), CanadianArcticIslands (0.22 #690, 0.07 #1508, 0.07 #1644), WestfriesischeInseln (0.22 #559, 0.18 #764, 0.14 #491), LesserAntilles (0.20 #1787, 0.20 #1856, 0.09 #3564), OrkneyIslands (0.19 #495, 0.11 #138, 0.11 #137), SundaIslands (0.18 #902, 0.14 #970, 0.14 #1038), InnerHebrides (0.15 #883, 0.06 #1020, 0.06 #1156), BritishIsles (0.11 #138, 0.11 #137, 0.09 #838)
>> best conf = 0.42
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #3618 for best value:
>> intensional similarity = 9
>> extensional distance = 172
>> proper extension:  EastFalkland; IsleofMan;
>> query: (?x2348, ?x1590) <- ?x2348[ a Island; has locatedIn ?x120[ a Country; has government ?x140; is locatedIn of ?x848[ a Island; has belongsToIslands ?x1590;];]; has locatedInWater ?x121[ has locatedIn ?x78;];]
ranks of expected_values: 1
EVAL	Wangerooge	belongsToIslands	OstfriesischeInseln	CNN-1.+1._MA	1.000	1.000	1.000	1.000	99.000	99.000	45.000	0.417	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

#602-Baro
PRED entity: Baro

PRED relation: hasEstuary
PRED expected values: Baro
=> 34 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 119): Pibor (0.20 #6, 0.17 #232, 0.14 #458), Atbara (0.17 #377, 0.14 #603, 0.11 #829), BlueNile (0.17 #265, 0.14 #491, 0.11 #717), Shabelle (0.17 #429, 0.14 #655, 0.11 #881), Jubba (0.14 #485, 0.05 #938, 0.03 #1165), WhiteNile (0.11 #833, 0.03 #1287, 0.01 #1514), Bahrel-Djebel-Albert-Nil (0.11 #856, 0.02 #905, 0.01 #3625), Bahrel-Ghasal (0.11 #695, 0.02 #905, 0.01 #3625), Nile (0.03 #1237), Baro (0.02 #905, 0.02 #1812, 0.01 #3398)
>> best conf = 0.20
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #6 for best value:
>> intensional similarity = 4
>> extensional distance = 3
>> proper extension:  Pibor; Baro;
>> query: (?x747, Pibor) <- ?x747[ has locatedIn ?x229<SSD>; has locatedIn ?x476<ETH>;]

*> Best rule #905 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 7
*> proper extension:  Bahrel-Ghasal; Bahrel-Djebel-Albert-Nil;
*> query: (?x747, ?x228) <- ?x747[ a River; has hasSource ?x964; has locatedIn ?x476[ is locatedIn of ?x228; is locatedIn of ?x1895<Pibor>;];]
*> conf = 0.02
ranks of expected_values: 10
EVAL	Baro	hasEstuary	Baro	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.100	34.000	31.000	119.000	0.200	http://www.semwebtech.org/mondial/10/meta#hasEstuary

PRED relation: hasEstuary
PRED expected values: Baro
=> 118 concepts (118 used for prediction)
PRED predicted values (max 10 best out of 208): Pibor (0.50 #11821, 0.33 #6, 0.26 #7952), Atbara (0.20 #377, 0.18 #907, 0.17 #1059), BlueNile (0.20 #265, 0.18 #907, 0.17 #947), Shabelle (0.18 #907, 0.17 #1111, 0.14 #1339), Bahrel-Djebel-Albert-Nil (0.18 #907, 0.17 #858, 0.14 #1314), WhiteNile (0.18 #907, 0.17 #835, 0.11 #1746), Bahrel-Ghasal (0.18 #907, 0.17 #697, 0.11 #1608), Jubba (0.18 #907, 0.12 #1396, 0.09 #1852), VictoriaNile (0.14 #1223, 0.02 #4405, 0.02 #5086), Sobat (0.11 #1793, 0.09 #12959, 0.04 #908)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #11821 for best value:
>> intensional similarity = 10
>> extensional distance = 166
>> proper extension:  RioLerma; Ob;
>> query: (?x747, ?x228) <- ?x747[ a River; has flowsInto ?x252[ is flowsInto of ?x1895[ a River; has hasEstuary ?x228; has hasSource ?x2183; has locatedIn ?x476[ is locatedIn of ?x964[ a Source;];];];]; has hasSource ?x964;]

*> Best rule #12959 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 205
*> proper extension:  JoekulsaaFjoellum; GreatSlaveLake; NelsonRiver; AtlinLake; Manicouagan; SaskatchewanRiver; RiviereRichelieu;
*> query: (?x747, ?x228) <- ?x747[ has flowsInto ?x252; has locatedIn ?x229[ has government ?x435; is locatedIn of ?x228[ a Estuary;];]; has locatedIn ?x476[ has ethnicGroup ?x1179; has religion ?x56;];]
*> conf = 0.09
ranks of expected_values: 12
EVAL	Baro	hasEstuary	Baro	CNN-1.+1._MA	0.000	0.000	0.000	0.083	118.000	118.000	208.000	0.503	http://www.semwebtech.org/mondial/10/meta#hasEstuary

#601-Hungarian
PRED entity: Hungarian

PRED relation: ethnicGroup!
PRED expected values: SK
=> 23 concepts (20 used for prediction)
PRED predicted values (max 10 best out of 213): MD (0.56 #911, 0.44 #379, 0.43 #380), SK (0.50 #213, 0.44 #379, 0.43 #380), MNE (0.50 #390, 0.44 #379, 0.43 #380), KAZ (0.50 #263, 0.33 #834, 0.16 #3233), BG (0.44 #379, 0.43 #380, 0.38 #949), PL (0.44 #379, 0.43 #380, 0.38 #189), R (0.44 #379, 0.43 #380, 0.38 #189), SLO (0.44 #379, 0.43 #380, 0.38 #189), A (0.44 #379, 0.43 #380, 0.38 #189), BIH (0.44 #379, 0.43 #380, 0.38 #189)
>> best conf = 0.56
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #911 for best value:
>> intensional similarity = 10
>> extensional distance = 7
>> proper extension:  Moldavian-Romanian; Bulgarian; Gagauz;
>> query: (?x517, MD) <- ?x517[ a EthnicGroup; is ethnicGroup of ?x176[ has government ?x435; has religion ?x56; has wasDependentOf ?x1656; is locatedIn of ?x133<Donau>; is locatedIn of ?x2078<Pruth>; is neighbor of ?x177;];]

*> Best rule #213 for first EXPECTED value:
*> intensional similarity = 10
*> extensional distance = 4
*> proper extension:  Ukrainian; German; Russian;
*> query: (?x517, SK) <- ?x517[ a EthnicGroup; is ethnicGroup of ?x176<RO>; is ethnicGroup of ?x904[ has language ?x684; has neighbor ?x55[ has ethnicGroup ?x160; is locatedIn of ?x275;]; has wasDependentOf ?x1197; is locatedIn of ?x132;];]
*> conf = 0.50
ranks of expected_values: 2
EVAL	Hungarian	ethnicGroup!	SK	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	23.000	20.000	213.000	0.556	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup!
PRED expected values: SK
=> 79 concepts (79 used for prediction)
PRED predicted values (max 10 best out of 226): SK (0.67 #1528, 0.67 #1362, 0.62 #1911), BY (0.67 #1189, 0.57 #1571, 0.50 #759), LV (0.67 #1233, 0.57 #1615, 0.50 #656), PL (0.67 #987, 0.52 #1338, 0.52 #1337), SLO (0.52 #1338, 0.52 #1337, 0.50 #759), BIH (0.52 #1338, 0.52 #1337, 0.50 #759), MNE (0.52 #1338, 0.52 #1337, 0.50 #759), A (0.52 #1338, 0.52 #1337, 0.46 #3923), D (0.52 #1338, 0.52 #1337, 0.46 #2686), MD (0.50 #759, 0.47 #952, 0.47 #951)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1528 for best value:
>> intensional similarity = 18
>> extensional distance = 4
>> proper extension:  Polish;
>> query: (?x517, ?x163) <- ?x517[ a EthnicGroup; is ethnicGroup of ?x236[ a Country; has encompassed ?x195; has government ?x254; has religion ?x95; is locatedIn of ?x133<Donau>; is neighbor of ?x163<SK>; is neighbor of ?x446[ has ethnicGroup ?x160; has government ?x1174; has language ?x738; has religion ?x187; is locatedIn of ?x275;];]; is ethnicGroup of ?x471<CZ>;]
>> Best rule #1362 for best value:
>> intensional similarity = 18
>> extensional distance = 4
>> proper extension:  Polish;
>> query: (?x517, SK) <- ?x517[ a EthnicGroup; is ethnicGroup of ?x236[ a Country; has encompassed ?x195; has government ?x254; has religion ?x95; is locatedIn of ?x133<Donau>; is neighbor of ?x163<SK>; is neighbor of ?x446[ has ethnicGroup ?x160; has government ?x1174; has language ?x738; has religion ?x187; is locatedIn of ?x275;];]; is ethnicGroup of ?x471<CZ>;]
ranks of expected_values: 1
EVAL	Hungarian	ethnicGroup!	SK	CNN-1.+1._MA	1.000	1.000	1.000	1.000	79.000	79.000	226.000	0.667	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#600-RN
PRED entity: RN

PRED relation: neighbor!
PRED expected values: LAR
=> 34 concepts (34 used for prediction)
PRED predicted values (max 10 best out of 161): LAR (0.91 #2679, 0.90 #2677, 0.90 #945), CAM (0.57 #405, 0.33 #247, 0.33 #90), RN (0.36 #706, 0.33 #864, 0.33 #76), SSD (0.33 #987, 0.26 #1142, 0.15 #1616), RCA (0.33 #275, 0.14 #433, 0.11 #1219), RIM (0.29 #3784, 0.27 #716, 0.27 #1574), CI (0.29 #3784, 0.27 #775, 0.27 #1574), SN (0.29 #3784, 0.27 #703, 0.27 #1574), RG (0.29 #3784, 0.27 #737, 0.27 #1574), TN (0.29 #3784, 0.27 #1574, 0.27 #1572)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2679 for best value:
>> intensional similarity = 8
>> extensional distance = 107
>> proper extension:  FL; HONX;
>> query: (?x426, ?x839) <- ?x426[ has ethnicGroup ?x1109; has government ?x435; has neighbor ?x810[ has neighbor ?x1307;]; has neighbor ?x839[ has ethnicGroup ?x1537; is locatedIn of ?x456;]; is locatedIn of ?x535;]
ranks of expected_values: 1
EVAL	RN	neighbor!	LAR	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	34.000	34.000	161.000	0.910	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: LAR
=> 112 concepts (112 used for prediction)
PRED predicted values (max 10 best out of 220): LAR (0.91 #7920, 0.91 #15856, 0.90 #13634), CAM (0.67 #3087, 0.57 #2453, 0.50 #1665), RG (0.45 #3740, 0.44 #9667, 0.42 #1890), GH (0.44 #9667, 0.42 #1890, 0.40 #156), RN (0.42 #1890, 0.40 #3472, 0.40 #156), LB (0.42 #1890, 0.40 #156, 0.33 #890), RT (0.42 #1890, 0.40 #156, 0.33 #6801), GNB (0.42 #1890, 0.40 #156, 0.30 #11566), WAG (0.42 #1890, 0.40 #156, 0.30 #11566), WAL (0.42 #1890, 0.40 #156, 0.30 #11566)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #7920 for best value:
>> intensional similarity = 14
>> extensional distance = 28
>> proper extension:  TL;
>> query: (?x426, ?x1184) <- ?x426[ has government ?x435<"republic">; has neighbor ?x1184; is locatedIn of ?x535; is neighbor of ?x139[ a Country; has religion ?x116[ is religion of ?x232<CN>;]; has wasDependentOf ?x81; is locatedIn of ?x182; is locatedIn of ?x2387[ a Estuary;]; is neighbor of ?x536;];]
ranks of expected_values: 1
EVAL	RN	neighbor!	LAR	CNN-1.+1._MA	1.000	1.000	1.000	1.000	112.000	112.000	220.000	0.911	http://www.semwebtech.org/mondial/10/meta#neighbor

#599-L
PRED entity: L

PRED relation: government
PRED expected values: "constitutional monarchy"
=> 45 concepts (45 used for prediction)
PRED predicted values (max 10 best out of 53): "republic" (0.35 #2168, 0.34 #2240, 0.34 #2673), "hereditary constitutional monarchy" (0.33 #37, 0.25 #109, 0.20 #397), "parliamentary democracy and a Commonwealth realm" (0.27 #1116, 0.25 #684, 0.25 #324), "formally a confederation but similar in structure to a federal republic" (0.25 #274, 0.25 #202, 0.20 #418), "federal republic" (0.25 #147, 0.14 #579, 0.12 #795), "a parliamentary democracy, a federation, and a constitutional monarchy" (0.20 #487, 0.17 #559, 0.12 #919), "constitutional monarchy" (0.20 #362, 0.14 #578, 0.13 #1010), "parliamentary" (0.20 #472, 0.12 #688, 0.03 #1192), "British Overseas Territories" (0.19 #1159, 0.06 #2096, 0.05 #2023), "parliamentary republic" (0.17 #523, 0.07 #1027, 0.04 #1747)
>> best conf = 0.35
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2168 for best value:
>> intensional similarity = 5
>> extensional distance = 117
>> proper extension:  YE;
>> query: (?x718, "republic") <- ?x718[ has neighbor ?x120[ a Country; is locatedIn of ?x146[ is locatedInWater of ?x145;];]; has wasDependentOf ?x575;]

*> Best rule #362 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 3
*> proper extension:  CH; MC;
*> query: (?x718, "constitutional monarchy") <- ?x718[ a Country; has ethnicGroup ?x2314<Italian>; has language ?x51; has neighbor ?x78; has religion ?x95; is locatedIn of ?x742;]
*> conf = 0.20
ranks of expected_values: 7
EVAL	L	government	"constitutional monarchy"	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.143	45.000	45.000	53.000	0.353	http://www.semwebtech.org/mondial/10/meta#government

PRED relation: government
PRED expected values: "constitutional monarchy"
=> 111 concepts (111 used for prediction)
PRED predicted values (max 10 best out of 65): "republic" (0.43 #2973, 0.43 #2828, 0.43 #1020), "constitutional monarchy" (0.33 #74, 0.29 #1087, 0.25 #1162), "federal parliamentary democracy under a constitutional monarchy" (0.33 #164, 0.25 #453, 0.25 #380), "federal republic" (0.33 #3, 0.21 #3476, 0.20 #1088), "parliamentary" (0.33 #256, 0.07 #1922, 0.06 #2210), "parliamentary democracy" (0.31 #1598, 0.27 #1454, 0.25 #511), "parliamentary democracy and a Commonwealth realm" (0.29 #2278, 0.25 #1124, 0.23 #1701), "constitutional republic" (0.25 #442, 0.17 #806, 0.10 #2397), "republic; parliamentary democracy" (0.25 #358, 0.11 #7966, 0.07 #1879), "monarchical sacerdotal state" (0.25 #391, 0.05 #2564, 0.04 #3143)
>> best conf = 0.43
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2973 for best value:
>> intensional similarity = 15
>> extensional distance = 21
>> proper extension:  RCB; RCA;
>> query: (?x718, "republic") <- ?x718[ a Country; has neighbor ?x78[ is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x323[ a Mountain; has inMountains ?x261;]; is locatedIn of ?x1211[ a Sea;];]; has religion ?x95; has wasDependentOf ?x575[ a Country; has government ?x92; has religion ?x187; is locatedIn of ?x257;];]
>> Best rule #2828 for best value:
>> intensional similarity = 9
>> extensional distance = 21
>> proper extension:  VU;
>> query: (?x718, "republic") <- ?x718[ a Country; has ethnicGroup ?x237; has religion ?x95[ is religion of ?x735[ is locatedIn of ?x60;];]; has wasDependentOf ?x575[ a Country; is locatedIn of ?x829<Maas>;];]

*> Best rule #74 for first EXPECTED value:
*> intensional similarity = 12
*> extensional distance = 1
*> proper extension:  NL;
*> query: (?x718, "constitutional monarchy") <- ?x718[ has ethnicGroup ?x237; has language ?x247[ a Language; is language of ?x138[ is neighbor of ?x243;];]; has neighbor ?x120<D>; has neighbor ?x543<B>; has religion ?x95<Protestant>; has wasDependentOf ?x575;]
*> conf = 0.33
ranks of expected_values: 2
EVAL	L	government	"constitutional monarchy"	CNN-1.+1._MA	0.000	1.000	1.000	0.500	111.000	111.000	65.000	0.435	http://www.semwebtech.org/mondial/10/meta#government

#598-CAYM
PRED entity: CAYM

PRED relation: locatedIn!
PRED expected values: CaymanBrac
=> 45 concepts (39 used for prediction)
PRED predicted values (max 10 best out of 1285): AtlanticOcean (0.82 #29886, 0.75 #18516, 0.71 #9990), PacificOcean (0.59 #31352, 0.39 #28509, 0.31 #14297), Tortola (0.33 #638, 0.25 #6323, 0.25 #3481), Donau (0.30 #12816, 0.18 #19922, 0.10 #32713), Anguilla (0.25 #6023, 0.20 #7444, 0.17 #8865), IrishSea (0.25 #2466, 0.20 #12414, 0.15 #15256), TheChannel (0.25 #2076, 0.12 #20551, 0.10 #12024), NorthCaicos (0.25 #5460, 0.10 #12565, 0.06 #38374), Providenciales (0.25 #5410, 0.10 #12515, 0.06 #38374), GrandBermuda (0.25 #2996, 0.07 #15785, 0.04 #24312)
>> best conf = 0.82
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #29886 for best value:
>> intensional similarity = 7
>> extensional distance = 54
>> proper extension:  ZRE; WAG; RT;
>> query: (?x865, AtlanticOcean) <- ?x865[ has ethnicGroup ?x1147; has religion ?x352; is locatedIn of ?x317[ has locatedIn ?x697<RH>; is locatedInWater of ?x2161<St.Barthelemy>;];]

*> Best rule #2843 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 2
*> proper extension:  GBM;
*> query: (?x865, ?x123) <- ?x865[ has government ?x254<"parliamentary democracy">; has language ?x247<English>; is locatedIn of ?x317[ is locatedInWater of ?x123;];]
*> conf = 0.11
ranks of expected_values: 40
EVAL	CAYM	locatedIn!	CaymanBrac	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.025	45.000	39.000	1285.000	0.821	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: CaymanBrac
=> 92 concepts (92 used for prediction)
PRED predicted values (max 10 best out of 1376): AtlanticOcean (0.85 #31360, 0.80 #61230, 0.79 #35630), PacificOcean (0.72 #48473, 0.60 #65544, 0.55 #38518), Tortola (0.33 #11382, 0.33 #3489, 0.23 #54079), IrishSea (0.33 #12428, 0.29 #18127, 0.25 #20977), Ireland (0.33 #11417, 0.29 #17116, 0.25 #19966), Anguilla (0.33 #11382, 0.25 #7455, 0.25 #6031), NorthCaicos (0.33 #1196, 0.25 #6889, 0.17 #15428), Providenciales (0.33 #1146, 0.25 #6839, 0.17 #15378), GrandBermuda (0.33 #4425, 0.23 #54079, 0.19 #9960), TristanDaCunha (0.33 #11382, 0.23 #54079, 0.19 #9960)
>> best conf = 0.85
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #31360 for best value:
>> intensional similarity = 20
>> extensional distance = 11
>> proper extension:  SVAX;
>> query: (?x865, AtlanticOcean) <- ?x865[ a Country; has dependentOf ?x81[ has language ?x247; has religion ?x95; is locatedIn of ?x121;]; has encompassed ?x521; has ethnicGroup ?x1147; is locatedIn of ?x317[ has locatedIn ?x697<RH>; has locatedIn ?x899<PR>; has locatedIn ?x1209<AG>; is flowsInto of ?x311; is locatedInWater of ?x609<Dominica>; is locatedInWater of ?x1380<St.Martin>; is mergesWith of ?x182;];]

*> Best rule #35588 for first EXPECTED value:
*> intensional similarity = 12
*> extensional distance = 11
*> proper extension:  WV;
*> query: (?x865, ?x599) <- ?x865[ a Country; has encompassed ?x521<America>; has government ?x254; is locatedIn of ?x317<CaribbeanSea>; is locatedIn of ?x1093[ a Island; has belongsToIslands ?x1357[ a Islands; is belongsToIslands of ?x599;]; has locatedInWater ?x317<CaribbeanSea>;];]
*> conf = 0.07
ranks of expected_values: 522
EVAL	CAYM	locatedIn!	CaymanBrac	CNN-1.+1._MA	0.000	0.000	0.000	0.002	92.000	92.000	1376.000	0.846	http://www.semwebtech.org/mondial/10/meta#locatedIn

#597-ZRE
PRED entity: ZRE

PRED relation: neighbor!
PRED expected values: ANG
=> 29 concepts (29 used for prediction)
PRED predicted values (max 10 best out of 185): ANG (0.89 #4002, 0.89 #3074, 0.89 #4001), EAK (0.33 #389, 0.33 #82, 0.29 #542), TCH (0.33 #177, 0.29 #483, 0.29 #154), ETH (0.33 #83, 0.29 #543, 0.29 #154), ZRE (0.33 #60, 0.29 #154, 0.26 #3381), G (0.33 #179, 0.29 #154, 0.26 #3381), SUD (0.33 #32, 0.29 #154, 0.26 #3381), WAN (0.33 #173, 0.15 #2148, 0.13 #1994), CAM (0.29 #154, 0.26 #3381, 0.25 #4003), NAM (0.29 #154, 0.26 #3381, 0.25 #4003)
>> best conf = 0.89
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4002 for best value:
>> intensional similarity = 5
>> extensional distance = 156
>> proper extension:  SD; ARM; BHT; LB; BZ; AND; RSM; V;
>> query: (?x348, ?x229) <- ?x348[ has neighbor ?x229[ a Country; has neighbor ?x186; is locatedIn of ?x53;]; is neighbor of ?x359;]
ranks of expected_values: 1
EVAL	ZRE	neighbor!	ANG	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	29.000	29.000	185.000	0.893	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: ANG
=> 87 concepts (87 used for prediction)
PRED predicted values (max 10 best out of 223): ANG (0.90 #11307, 0.90 #8147, 0.90 #7671), ZRE (0.57 #156, 0.44 #5161, 0.44 #5160), CAM (0.57 #156, 0.43 #1805, 0.40 #1648), NAM (0.57 #156, 0.40 #1243, 0.36 #5005), G (0.57 #156, 0.40 #1243, 0.36 #5005), WAN (0.57 #156, 0.40 #1243, 0.29 #1736), GH (0.57 #156, 0.40 #1243, 0.25 #2114), RSA (0.57 #156, 0.40 #1243, 0.25 #980), GQ (0.57 #156, 0.40 #1243, 0.20 #1707), BR (0.57 #156, 0.40 #1243, 0.19 #2658)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #11307 for best value:
>> intensional similarity = 10
>> extensional distance = 121
>> proper extension:  WSA; FGU; MEL;
>> query: (?x348, ?x934) <- ?x348[ has neighbor ?x528[ has government ?x435; is locatedIn of ?x2087;]; has neighbor ?x934[ has ethnicGroup ?x197; has wasDependentOf ?x1027; is locatedIn of ?x933;]; is locatedIn of ?x509[ is flowsInto of ?x1057;]; is neighbor of ?x546;]
ranks of expected_values: 1
EVAL	ZRE	neighbor!	ANG	CNN-1.+1._MA	1.000	1.000	1.000	1.000	87.000	87.000	223.000	0.903	http://www.semwebtech.org/mondial/10/meta#neighbor

#596-RioLerma
PRED entity: RioLerma

PRED relation: locatedIn
PRED expected values: MEX
=> 36 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 192): MEX (0.90 #7138, 0.90 #6665, 0.90 #6427), USA (0.54 #950, 0.52 #2142, 0.52 #1977), CDN (0.50 #952, 0.35 #8331, 0.35 #8330), PE (0.31 #1259, 0.25 #67, 0.15 #7613), CN (0.29 #530, 0.08 #2200, 0.07 #2437), AUS (0.28 #4527, 0.25 #1191, 0.25 #997), NZ (0.28 #4527, 0.25 #110, 0.15 #7613), GCA (0.26 #1429, 0.25 #40, 0.16 #5715), BZ (0.26 #1429, 0.16 #5715, 0.16 #5953), CO (0.25 #51, 0.23 #1243, 0.15 #7613)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #7138 for best value:
>> intensional similarity = 4
>> extensional distance = 211
>> proper extension:  Raab;
>> query: (?x602, ?x482) <- ?x602[ a River; has hasEstuary ?x603[ a Estuary; has locatedIn ?x482;];]
ranks of expected_values: 1
EVAL	RioLerma	locatedIn	MEX	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	36.000	36.000	192.000	0.905	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: MEX
=> 129 concepts (129 used for prediction)
PRED predicted values (max 10 best out of 226): MEX (0.94 #16991, 0.94 #15559, 0.94 #20346), USA (0.88 #7007, 0.87 #6531, 0.86 #9649), R (0.62 #5261, 0.62 #5028, 0.56 #24672), CDN (0.50 #3579, 0.46 #10358, 0.45 #9640), PE (0.45 #4060, 0.45 #3885, 0.33 #4373), IND (0.43 #3051, 0.09 #18855, 0.09 #20535), GCA (0.39 #3339, 0.33 #237, 0.30 #13162), BZ (0.39 #3339, 0.33 #237, 0.30 #13162), D (0.39 #7431, 0.23 #5284, 0.21 #22291), NIC (0.38 #3198, 0.25 #4402, 0.25 #1524)
>> best conf = 0.94
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #16991 for best value:
>> intensional similarity = 12
>> extensional distance = 44
>> proper extension:  Morava; Donau; Swir; Pjandsh; Amudarja; Newa; Drau; Paatsjoki; BlackDrin; Kemijoki; ...
>> query: (?x602, ?x482) <- ?x602[ a River; has flowsInto ?x282; has hasEstuary ?x603[ has locatedIn ?x482[ a Country; has ethnicGroup ?x79; has language ?x796; has religion ?x95; is neighbor of ?x181;];]; has hasSource ?x1346[ a Source;]; is flowsInto of ?x900;]
ranks of expected_values: 1
EVAL	RioLerma	locatedIn	MEX	CNN-1.+1._MA	1.000	1.000	1.000	1.000	129.000	129.000	226.000	0.940	http://www.semwebtech.org/mondial/10/meta#locatedIn

#595-MNG
PRED entity: MNG

PRED relation: encompassed
PRED expected values: Asia
=> 33 concepts (33 used for prediction)
PRED predicted values (max 10 best out of 5): Europe (0.52 #22, 0.44 #37, 0.38 #47), Asia (0.50 #11, 0.40 #16, 0.38 #36), Africa (0.40 #19, 0.39 #29, 0.34 #54), Australia-Oceania (0.31 #43, 0.31 #33, 0.20 #158), America (0.28 #50, 0.28 #35, 0.27 #75)
>> best conf = 0.52
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #22 for best value:
>> intensional similarity = 9
>> extensional distance = 19
>> proper extension:  CDN;
>> query: (?x1010, Europe) <- ?x1010[ has language ?x335; has religion ?x187<Muslim>; has religion ?x462[ is religion of ?x315<USA>; is religion of ?x461<NZ>;]; is locatedIn of ?x72;]

*> Best rule #11 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 4
*> proper extension:  AUS; CL;
*> query: (?x1010, Asia) <- ?x1010[ has ethnicGroup ?x1553; has government ?x2058; has language ?x335; has religion ?x187<Muslim>; has religion ?x462<Buddhist>; has wasDependentOf ?x232;]
*> conf = 0.50
ranks of expected_values: 2
EVAL	MNG	encompassed	Asia	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	33.000	33.000	5.000	0.524	http://www.semwebtech.org/mondial/10/meta#encompassed

PRED relation: encompassed
PRED expected values: Asia
=> 85 concepts (85 used for prediction)
PRED predicted values (max 10 best out of 5): Europe (0.78 #91, 0.78 #87, 0.64 #221), Asia (0.73 #70, 0.64 #65, 0.60 #33), Africa (0.58 #121, 0.57 #126, 0.53 #153), America (0.53 #144, 0.53 #164, 0.52 #117), Australia-Oceania (0.33 #25, 0.31 #184, 0.27 #194)
>> best conf = 0.78
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #91 for best value:
>> intensional similarity = 16
>> extensional distance = 16
>> proper extension:  R;
>> query: (?x1010, ?x195) <- ?x1010[ has ethnicGroup ?x1553; has language ?x335; has religion ?x116; is neighbor of ?x73[ has encompassed ?x195; has ethnicGroup ?x58; is locatedIn of ?x97<SeaofAzov>; is locatedIn of ?x282[ is flowsInto of ?x602; is locatedInWater of ?x205;]; is locatedIn of ?x919[ a River;]; is locatedIn of ?x1337[ a Lake;]; is neighbor of ?x170;];]
>> Best rule #87 for best value:
>> intensional similarity = 16
>> extensional distance = 16
>> proper extension:  R;
>> query: (?x1010, Europe) <- ?x1010[ has ethnicGroup ?x1553; has language ?x335; has religion ?x116; is neighbor of ?x73[ has encompassed ?x195; has ethnicGroup ?x58; is locatedIn of ?x97<SeaofAzov>; is locatedIn of ?x282[ is flowsInto of ?x602; is locatedInWater of ?x205;]; is locatedIn of ?x919[ a River;]; is locatedIn of ?x1337[ a Lake;]; is neighbor of ?x170;];]

*> Best rule #70 for first EXPECTED value:
*> intensional similarity = 16
*> extensional distance = 9
*> proper extension:  BHT; LAO; K;
*> query: (?x1010, Asia) <- ?x1010[ has ethnicGroup ?x1553; has government ?x2058; has religion ?x187[ is religion of ?x129[ has encompassed ?x175; is locatedIn of ?x276;]; is religion of ?x399[ has wasDependentOf ?x1656; is locatedIn of ?x275;]; is religion of ?x460<RP>; is religion of ?x617<VN>;]; has religion ?x462<Buddhist>; is neighbor of ?x73;]
*> conf = 0.73
ranks of expected_values: 2
EVAL	MNG	encompassed	Asia	CNN-1.+1._MA	0.000	1.000	1.000	0.500	85.000	85.000	5.000	0.778	http://www.semwebtech.org/mondial/10/meta#encompassed

#594-BR
PRED entity: BR

PRED relation: neighbor
PRED expected values: GUY RA FGU
=> 36 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 192): RA (0.89 #4497, 0.89 #4496, 0.88 #4028), GUY (0.89 #4497, 0.89 #4496, 0.88 #4028), BR (0.33 #242, 0.11 #397, 0.10 #4341), E (0.22 #173, 0.11 #328, 0.10 #4341), AND (0.22 #271, 0.11 #426, 0.09 #580), RMM (0.15 #1051, 0.13 #1514, 0.11 #1205), SN (0.15 #995, 0.11 #1149, 0.11 #1458), RG (0.15 #1030, 0.11 #1184, 0.11 #1493), F (0.13 #467, 0.11 #158, 0.10 #4341), RCB (0.11 #1165, 0.11 #1011, 0.11 #1474)
>> best conf = 0.89
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #4497 for best value:
>> intensional similarity = 5
>> extensional distance = 153
>> proper extension:  ARM;
>> query: (?x542, ?x379) <- ?x542[ has neighbor ?x345[ has encompassed ?x521;]; is neighbor of ?x379[ has religion ?x95; is locatedIn of ?x182;];]
ranks of expected_values: 1, 2, 49
EVAL	BR	neighbor	FGU	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.021	36.000	36.000	192.000	0.894	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	BR	neighbor	RA	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	36.000	36.000	192.000	0.894	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	BR	neighbor	GUY	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	36.000	36.000	192.000	0.894	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: GUY RA FGU
=> 106 concepts (106 used for prediction)
PRED predicted values (max 10 best out of 231): RA (0.94 #11318, 0.94 #4226, 0.93 #12586), GUY (0.94 #11318, 0.93 #4545, 0.93 #11005), BR (0.50 #2430, 0.33 #3690, 0.33 #403), AND (0.40 #1211, 0.25 #1677, 0.20 #1055), RCH (0.33 #349, 0.30 #2376, 0.28 #12742), PA (0.28 #12742, 0.28 #5330, 0.27 #15749), EC (0.28 #12742, 0.28 #5330, 0.27 #15749), FGU (0.28 #12742, 0.28 #5330, 0.27 #15749), E (0.25 #1579, 0.20 #1113, 0.20 #957), RH (0.25 #579, 0.20 #1514, 0.20 #1358)
>> best conf = 0.94
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #11318 for best value:
>> intensional similarity = 11
>> extensional distance = 43
>> proper extension:  ET; LS; WAN; TCH; SUD; RI; CN; MAL; EAK; ETH; ...
>> query: (?x542, ?x379) <- ?x542[ a Country; has neighbor ?x179; is locatedIn of ?x2500[ a Mountain;]; is neighbor of ?x379[ has encompassed ?x521; has ethnicGroup ?x197; has wasDependentOf ?x149[ has encompassed ?x195; has language ?x790;]; is locatedIn of ?x513;];]
ranks of expected_values: 1, 2, 8
EVAL	BR	neighbor	FGU	CNN-1.+1._MA	0.000	0.000	1.000	0.167	106.000	106.000	231.000	0.941	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	BR	neighbor	RA	CNN-1.+1._MA	1.000	1.000	1.000	1.000	106.000	106.000	231.000	0.941	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	BR	neighbor	GUY	CNN-1.+1._MA	1.000	1.000	1.000	1.000	106.000	106.000	231.000	0.941	http://www.semwebtech.org/mondial/10/meta#neighbor

#593-Tara
PRED entity: Tara

PRED relation: locatedIn
PRED expected values: BIH
=> 38 concepts (30 used for prediction)
PRED predicted values (max 10 best out of 94): BIH (0.66 #3302, 0.65 #1415, 0.65 #2358), GR (0.49 #560, 0.09 #2684, 0.09 #2919), AL (0.27 #282, 0.23 #707, 0.23 #517), I (0.26 #2642, 0.26 #2877, 0.09 #4764), USA (0.23 #4788, 0.19 #5025, 0.18 #5262), F (0.21 #2602, 0.21 #2837, 0.09 #243), MK (0.20 #626, 0.02 #2750, 0.02 #2985), E (0.19 #2621, 0.18 #2856, 0.09 #262), R (0.16 #948, 0.15 #5196, 0.14 #5433), D (0.16 #727, 0.12 #1434, 0.12 #1669)
>> best conf = 0.66
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3302 for best value:
>> intensional similarity = 6
>> extensional distance = 185
>> proper extension:  EucumbeneRiver; SaintLawrenceRiver; Manicouagan; Thjorsa; DarlingRiver; JoekulsaaFjoellum; MackenzieRiver; RiviereRichelieu; SaskatchewanRiver; NelsonRiver;
>> query: (?x2462, ?x55) <- ?x2462[ a Estuary; has locatedIn ?x106; is hasEstuary of ?x473[ a River; has hasSource ?x814; has locatedIn ?x55;];]
ranks of expected_values: 1
EVAL	Tara	locatedIn	BIH	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	38.000	30.000	94.000	0.657	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: BIH
=> 112 concepts (108 used for prediction)
PRED predicted values (max 10 best out of 225): BIH (0.71 #10683, 0.68 #4031, 0.66 #5927), AL (0.67 #2178, 0.40 #1941, 0.39 #1893), E (0.65 #3109, 0.42 #5241, 0.20 #2132), SRB (0.60 #1132, 0.59 #2554, 0.56 #894), I (0.51 #5974, 0.26 #9537, 0.20 #2132), USA (0.47 #10989, 0.44 #11228, 0.35 #11946), F (0.41 #5934, 0.21 #9497, 0.20 #2132), R (0.41 #3563, 0.34 #11402, 0.29 #11880), MK (0.39 #1812, 0.35 #2050, 0.18 #3556), GR (0.36 #2935, 0.22 #5066, 0.20 #2132)
>> best conf = 0.71
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #10683 for best value:
>> intensional similarity = 13
>> extensional distance = 225
>> proper extension:  Schari; Sanga; Sanga;
>> query: (?x2462, ?x55) <- ?x2462[ has locatedIn ?x106[ has ethnicGroup ?x775; has government ?x435<"republic">; has neighbor ?x692[ a Country; has ethnicGroup ?x223;]; has religion ?x56; is locatedIn of ?x104[ is flowsInto of ?x2296;]; is locatedIn of ?x2319[ a Estuary; has locatedIn ?x55;];];]
ranks of expected_values: 1
EVAL	Tara	locatedIn	BIH	CNN-1.+1._MA	1.000	1.000	1.000	1.000	112.000	108.000	225.000	0.706	http://www.semwebtech.org/mondial/10/meta#locatedIn

#592-Kreta
PRED entity: Kreta

PRED relation: locatedInWater
PRED expected values: MediterraneanSea
=> 42 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 93): MediterraneanSea (0.77 #16, 0.56 #59, 0.55 #102), JavaSea (0.28 #139, 0.17 #272, 0.11 #405), AtlanticOcean (0.26 #491, 0.24 #137, 0.23 #580), IndianOcean (0.24 #132, 0.15 #265, 0.11 #398), PacificOcean (0.24 #413, 0.22 #501, 0.18 #590), CaribbeanSea (0.15 #282, 0.08 #592, 0.04 #149), NorthSea (0.11 #443, 0.11 #531, 0.07 #576), SulawesiSea (0.10 #291, 0.08 #158, 0.03 #601), SouthChinaSea (0.10 #285, 0.07 #418, 0.06 #462), BandaSea (0.08 #159, 0.07 #425, 0.05 #469)
>> best conf = 0.77
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #16 for best value:
>> intensional similarity = 3
>> extensional distance = 11
>> proper extension:  Lefkas; Rhodos; Zakynthos; Ikaria; Syros; Korfu; Samos; Mykonos; Lesbos; Kos; ...
>> query: (?x1452, MediterraneanSea) <- ?x1452[ a Island; has locatedIn ?x399<GR>;]
ranks of expected_values: 1
EVAL	Kreta	locatedInWater	MediterraneanSea	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	42.000	31.000	93.000	0.769	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater
PRED expected values: MediterraneanSea
=> 105 concepts (105 used for prediction)
PRED predicted values (max 10 best out of 93): MediterraneanSea (0.77 #192, 0.60 #307, 0.60 #278), AtlanticOcean (0.36 #1371, 0.33 #95, 0.30 #1047), PacificOcean (0.33 #61, 0.31 #1011, 0.24 #1838), OzeroBaikal (0.33 #25), LakePrespa (0.31 #352, 0.13 #88, 0.07 #579), NorthSea (0.27 #811, 0.26 #950, 0.20 #1322), JavaSea (0.20 #679, 0.19 #544, 0.11 #1142), IndianOcean (0.20 #672, 0.19 #1229, 0.17 #1181), CaribbeanSea (0.17 #554, 0.17 #151, 0.17 #63), BandaSea (0.17 #73, 0.12 #699, 0.08 #117)
>> best conf = 0.77
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #192 for best value:
>> intensional similarity = 3
>> extensional distance = 11
>> proper extension:  Lefkas; Rhodos; Zakynthos; Ikaria; Syros; Korfu; Samos; Mykonos; Lesbos; Kos; ...
>> query: (?x1452, MediterraneanSea) <- ?x1452[ a Island; has locatedIn ?x399<GR>;]
ranks of expected_values: 1
EVAL	Kreta	locatedInWater	MediterraneanSea	CNN-1.+1._MA	1.000	1.000	1.000	1.000	105.000	105.000	93.000	0.769	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#591-Würm
PRED entity: Würm

PRED relation: locatedIn
PRED expected values: D
=> 50 concepts (50 used for prediction)
PRED predicted values (max 10 best out of 175): D (0.90 #10187, 0.90 #10661, 0.90 #10899), A (0.40 #335, 0.31 #809, 0.27 #3177), F (0.36 #1901, 0.23 #717, 0.21 #954), ZRE (0.33 #1499, 0.27 #1262, 0.22 #4578), SF (0.22 #1789, 0.08 #3921, 0.07 #5341), SUD (0.21 #989, 0.18 #515, 0.17 #1462), CH (0.21 #1004, 0.18 #530, 0.16 #8526), USA (0.20 #5045, 0.16 #2203, 0.15 #2678), R (0.18 #478, 0.15 #2373, 0.15 #8294), N (0.18 #1928, 0.08 #744, 0.04 #2165)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #10187 for best value:
>> intensional similarity = 5
>> extensional distance = 186
>> proper extension:  Selenge; NorthernDwina; Tajo;
>> query: (?x394, ?x120) <- ?x394[ a River; has flowsInto ?x558; has hasSource ?x395[ a Source; has locatedIn ?x120;];]
ranks of expected_values: 1
EVAL	Würm	locatedIn	D	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	50.000	50.000	175.000	0.898	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: D
=> 150 concepts (150 used for prediction)
PRED predicted values (max 10 best out of 218): D (0.91 #32859, 0.90 #34049, 0.90 #30237), CH (0.60 #5516, 0.50 #7423, 0.46 #9089), R (0.53 #19281, 0.40 #11656, 0.24 #32625), A (0.52 #18322, 0.50 #7126, 0.41 #12604), F (0.44 #13805, 0.33 #11418, 0.33 #244), UA (0.43 #3152, 0.17 #7197, 0.16 #14583), ZRE (0.35 #12923, 0.33 #12446, 0.33 #7684), L (0.33 #394, 0.24 #28568, 0.23 #28569), CZ (0.31 #8906, 0.24 #28568, 0.23 #28569), I (0.29 #3607, 0.25 #4082, 0.23 #17178)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #32859 for best value:
>> intensional similarity = 11
>> extensional distance = 88
>> proper extension:  Suchona; NorthernDwina;
>> query: (?x394, ?x120) <- ?x394[ a River; has hasSource ?x395[ a Source; has locatedIn ?x120[ a Country; has encompassed ?x195<Europe>; has ethnicGroup ?x237; has neighbor ?x78; has religion ?x95; is neighbor of ?x194;];];]
ranks of expected_values: 1
EVAL	Würm	locatedIn	D	CNN-1.+1._MA	1.000	1.000	1.000	1.000	150.000	150.000	218.000	0.911	http://www.semwebtech.org/mondial/10/meta#locatedIn

#590-Z
PRED entity: Z

PRED relation: locatedIn!
PRED expected values: LakeMweru LakeBangweulu
=> 28 concepts (17 used for prediction)
PRED predicted values (max 10 best out of 768): AtlanticOcean (0.90 #12791, 0.67 #7124, 0.64 #8540), Akagera (0.40 #4894, 0.33 #6310, 0.33 #2060), CaribbeanSea (0.39 #8603, 0.38 #7187, 0.26 #12854), LakeVictoria (0.33 #2061, 0.33 #645, 0.22 #6311), IndianOcean (0.33 #3, 0.30 #17000, 0.20 #2835), LakeSeseSeko-Albertsee (0.33 #2421, 0.20 #5255, 0.11 #12749), Ruwenzori (0.33 #2283, 0.20 #5117, 0.11 #12749), Semliki (0.33 #2057, 0.20 #4891, 0.11 #12749), Rutanzige-Eduardsee (0.33 #1680, 0.20 #4514, 0.11 #12749), LakeMalawi (0.33 #930, 0.20 #3762, 0.11 #6596)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #12791 for best value:
>> intensional similarity = 4
>> extensional distance = 67
>> proper extension:  NLSM;
>> query: (?x525, AtlanticOcean) <- ?x525[ has government ?x435; is locatedIn of ?x1541[ has locatedIn ?x348<ZRE>;];]

*> Best rule #12749 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 47
*> proper extension:  GB; LS; D; WAN; E; SME; MOC; RCH; CO; CH; ...
*> query: (?x525, ?x113) <- ?x525[ a Country; has ethnicGroup ?x162; has neighbor ?x348[ is locatedIn of ?x113; is locatedIn of ?x182<AtlanticOcean>;]; is locatedIn of ?x284;]
*> conf = 0.11
ranks of expected_values: 101
EVAL	Z	locatedIn!	LakeBangweulu	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	28.000	17.000	768.000	0.899	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	Z	locatedIn!	LakeMweru	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.010	28.000	17.000	768.000	0.899	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: LakeMweru LakeBangweulu
=> 82 concepts (82 used for prediction)
PRED predicted values (max 10 best out of 1404): AtlanticOcean (0.95 #59639, 0.90 #34093, 0.88 #26999), CaribbeanSea (0.85 #34156, 0.55 #39833, 0.44 #48348), IndianOcean (0.75 #32633, 0.34 #100748, 0.34 #95072), LakeSeseSeko-Albertsee (0.50 #8103, 0.31 #11354, 0.28 #17026), Semliki (0.50 #7739, 0.31 #11354, 0.28 #17026), Ruwenzori (0.50 #7965, 0.31 #11354, 0.28 #17026), Rutanzige-Eduardsee (0.50 #7362, 0.31 #11354, 0.28 #17026), PacificOcean (0.46 #44072, 0.33 #51166, 0.30 #54006), RubAlChali (0.43 #14477, 0.05 #32917, 0.03 #76915), Akagera (0.40 #11998, 0.38 #19093, 0.33 #6324)
>> best conf = 0.95
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #59639 for best value:
>> intensional similarity = 17
>> extensional distance = 54
>> proper extension:  VIRG;
>> query: (?x525, AtlanticOcean) <- ?x525[ a Country; has encompassed ?x213; has religion ?x116; is locatedIn of ?x933[ has locatedIn ?x138<NAM>; has locatedIn ?x243<RSA>; has locatedIn ?x1239[ has ethnicGroup ?x2322; is neighbor of ?x1576;];]; is locatedIn of ?x1977[ is flowsInto of ?x387[ has locatedIn ?x192;]; is flowsInto of ?x2061[ has hasSource ?x2154; is flowsInto of ?x1650;];];]

*> Best rule #11354 for first EXPECTED value:
*> intensional similarity = 17
*> extensional distance = 3
*> proper extension:  ANG;
*> query: (?x525, ?x113) <- ?x525[ a Country; has government ?x435; has neighbor ?x138<NAM>; has neighbor ?x348[ has neighbor ?x229; has religion ?x95<Protestant>; is locatedIn of ?x113; is locatedIn of ?x358[ a Estuary;]; is locatedIn of ?x509[ a River;];]; has neighbor ?x820[ a Country;]; is locatedIn of ?x933<Kalahari>;]
*> conf = 0.31
ranks of expected_values: 86
EVAL	Z	locatedIn!	LakeBangweulu	CNN-1.+1._MA	0.000	0.000	0.000	0.000	82.000	82.000	1404.000	0.946	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	Z	locatedIn!	LakeMweru	CNN-1.+1._MA	0.000	0.000	0.000	0.012	82.000	82.000	1404.000	0.946	http://www.semwebtech.org/mondial/10/meta#locatedIn

#589-RT
PRED entity: RT

PRED relation: government
PRED expected values: "republic under transition to multiparty democratic rule"
=> 36 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 54): "republic" (0.53 #150, 0.50 #510, 0.47 #438), "constitutional democracy" (0.17 #4, 0.15 #76, 0.14 #220), "republic; multiparty presidential regime" (0.17 #29, 0.15 #101, 0.10 #245), "parliamentary democracy and a Commonwealth realm" (0.14 #324, 0.05 #612, 0.05 #1332), "federal republic" (0.12 #147, 0.08 #363, 0.08 #3), "parliamentary democracy" (0.12 #1157, 0.11 #1445, 0.11 #1589), "constitutional monarchy" (0.10 #938, 0.10 #794, 0.08 #2), "constitutional republic" (0.05 #585, 0.05 #657, 0.04 #873), "republic; multiparty presidential regime established 1960" (0.05 #281, 0.05 #2234, 0.03 #569), "military junta" (0.05 #269, 0.05 #2234, 0.03 #557)
>> best conf = 0.53
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #150 for best value:
>> intensional similarity = 8
>> extensional distance = 15
>> proper extension:  LB;
>> query: (?x1307, "republic") <- ?x1307[ a Country; has encompassed ?x213<Africa>; has neighbor ?x483; has religion ?x116<Christian>; has religion ?x187<Muslim>;]

No rule for expected values
ranks of expected_values: 
EVAL	RT	government	"republic under transition to multiparty democratic rule"	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	36.000	36.000	54.000	0.529	http://www.semwebtech.org/mondial/10/meta#government

PRED relation: government
PRED expected values: "republic under transition to multiparty democratic rule"
=> 82 concepts (82 used for prediction)
PRED predicted values (max 10 best out of 70): "republic" (0.67 #657, 0.56 #2770, 0.56 #1094), "federal republic" (0.39 #1453, 0.38 #797, 0.25 #1380), "republic; multiparty presidential regime established 1960" (0.38 #797, 0.33 #65, 0.25 #1380), "constitutional democracy" (0.29 #509, 0.26 #724, 0.24 #1233), "parliamentary democracy and a Commonwealth realm" (0.19 #1050, 0.13 #2581, 0.11 #3016), "republic; multiparty presidential regime" (0.14 #971, 0.14 #534, 0.12 #607), "military junta" (0.14 #486, 0.08 #2182, 0.07 #922), "constitutional monarchy" (0.12 #2038, 0.12 #580, 0.11 #653), "parliamentary democracy" (0.12 #4583, 0.12 #5377, 0.12 #4655), "democratic republic" (0.10 #1535, 0.09 #2764, 0.09 #4504)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #657 for best value:
>> intensional similarity = 16
>> extensional distance = 7
>> proper extension:  DJI; MA;
>> query: (?x1307, "republic") <- ?x1307[ has ethnicGroup ?x162[ is ethnicGroup of ?x561[ has language ?x247; has religion ?x280;]; is ethnicGroup of ?x1364[ has encompassed ?x521;];]; has religion ?x116<Christian>; has wasDependentOf ?x78<F>; is neighbor of ?x483[ is locatedIn of ?x135; is neighbor of ?x811;]; is neighbor of ?x810[ has encompassed ?x213; has government ?x435;];]

No rule for expected values
ranks of expected_values: 
EVAL	RT	government	"republic under transition to multiparty democratic rule"	CNN-1.+1._MA	0.000	0.000	0.000	0.000	82.000	82.000	70.000	0.667	http://www.semwebtech.org/mondial/10/meta#government

#588-ARU
PRED entity: ARU

PRED relation: ethnicGroup
PRED expected values: European-CaribbeanAmerindian
=> 39 concepts (39 used for prediction)
PRED predicted values (max 10 best out of 172): Black (0.67 #1089, 0.30 #1605, 0.27 #2121), White (0.50 #1099, 0.20 #3421, 0.20 #1615), European (0.47 #4913, 0.47 #4394, 0.46 #3620), Norman-French (0.40 #853, 0.09 #3175, 0.08 #3433), African (0.39 #4911, 0.38 #3618, 0.38 #2586), Roma (0.36 #1813, 0.08 #5170, 0.08 #5686), Amerindian (0.33 #4388, 0.33 #1034, 0.31 #3614), Chinese (0.33 #531, 0.22 #4659, 0.17 #1305), EastIndian (0.33 #654, 0.20 #1686, 0.18 #2202), Mixed (0.33 #1160, 0.20 #902, 0.14 #2966)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1089 for best value:
>> intensional similarity = 11
>> extensional distance = 4
>> proper extension:  BVIR; AXA; PR; BERM;
>> query: (?x1171, Black) <- ?x1171[ a Country; has dependentOf ?x575; has encompassed ?x521<America>; has government ?x254; has religion ?x95<Protestant>; has religion ?x352<RomanCatholic>; is locatedIn of ?x1865[ a Island;];]

No rule for expected values
ranks of expected_values: 
EVAL	ARU	ethnicGroup	European-CaribbeanAmerindian	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	39.000	39.000	172.000	0.667	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: European-CaribbeanAmerindian
=> 83 concepts (83 used for prediction)
PRED predicted values (max 10 best out of 194): White (0.60 #2657, 0.45 #6547, 0.43 #3953), Black (0.60 #2647, 0.40 #5761, 0.38 #4721), European (0.47 #10891, 0.46 #10375, 0.44 #10633), Roma (0.40 #1818, 0.38 #4153, 0.33 #7266), German (0.40 #1821, 0.38 #4156, 0.25 #7269), Slovak (0.40 #2023, 0.38 #4358, 0.25 #7471), Mixed (0.40 #2718, 0.33 #128, 0.29 #4014), Norman-French (0.40 #2928, 0.33 #3447, 0.20 #6041), African (0.38 #10373, 0.38 #8818, 0.37 #10631), Polish (0.38 #4352, 0.29 #7985, 0.27 #8243)
>> best conf = 0.60
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2657 for best value:
>> intensional similarity = 24
>> extensional distance = 3
>> proper extension:  BVIR;
>> query: (?x1171, White) <- ?x1171[ a Country; has dependentOf ?x575[ a Country; has encompassed ?x195; has ethnicGroup ?x734; has government ?x92; has language ?x544; is locatedIn of ?x121<NorthSea>; is locatedIn of ?x257[ a Estuary;]; is locatedIn of ?x731[ a Island;]; is wasDependentOf of ?x179[ has government ?x180;]; is wasDependentOf of ?x217[ is locatedIn of ?x60;];]; has encompassed ?x521<America>; has government ?x254; has religion ?x95<Protestant>; is locatedIn of ?x1865[ a Island;];]

No rule for expected values
ranks of expected_values: 
EVAL	ARU	ethnicGroup	European-CaribbeanAmerindian	CNN-1.+1._MA	0.000	0.000	0.000	0.000	83.000	83.000	194.000	0.600	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#587-Saimaa
PRED entity: Saimaa

PRED relation: locatedIn
PRED expected values: SF
=> 40 concepts (40 used for prediction)
PRED predicted values (max 10 best out of 95): SF (0.88 #3332, 0.77 #7385, 0.68 #5003), R (0.68 #5003, 0.67 #4765, 0.64 #5242), D (0.38 #3591, 0.25 #5976, 0.12 #4071), USA (0.32 #6268, 0.26 #6506, 0.21 #4362), UA (0.21 #544, 0.19 #2688, 0.19 #1020), CH (0.18 #1484, 0.17 #1723, 0.14 #2914), A (0.17 #3670, 0.12 #4150, 0.11 #6055), CDN (0.16 #6259, 0.12 #4353, 0.12 #4591), ZRE (0.14 #7464, 0.14 #7700, 0.13 #7939), CN (0.10 #3867, 0.10 #6910, 0.10 #7858)
>> best conf = 0.88
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3332 for best value:
>> intensional similarity = 7
>> extensional distance = 40
>> proper extension:  StarnbergerSee; BarragedeMbakaou; LagodeChapala; LakeJindabyne;
>> query: (?x1573, ?x565) <- ?x1573[ a Lake; has flowsInto ?x1396[ a River; has flowsInto ?x589; has flowsThrough ?x1395[ a Lake; has locatedIn ?x565;];];]
ranks of expected_values: 1
EVAL	Saimaa	locatedIn	SF	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	40.000	40.000	95.000	0.875	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: SF
=> 88 concepts (88 used for prediction)
PRED predicted values (max 10 best out of 101): SF (0.93 #14851, 0.89 #18683, 0.88 #18920), R (0.72 #9815, 0.71 #9335, 0.71 #6463), UA (0.44 #5099, 0.43 #1501, 0.41 #5576), USA (0.41 #7493, 0.38 #7254, 0.34 #8689), D (0.39 #11992, 0.19 #5288, 0.17 #8876), S (0.33 #3440, 0.24 #1908, 0.20 #5744), CH (0.29 #6042, 0.26 #6281, 0.25 #7717), UZB (0.25 #300, 0.08 #3892, 0.07 #11796), KGZ (0.25 #259, 0.08 #3851, 0.03 #8879), N (0.24 #1908, 0.22 #2182, 0.20 #5744)
>> best conf = 0.93
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #14851 for best value:
>> intensional similarity = 11
>> extensional distance = 52
>> proper extension:  LakeHume;
>> query: (?x1573, ?x565) <- ?x1573[ a Lake; has flowsInto ?x1396[ a River; has flowsInto ?x589[ has locatedIn ?x73;]; has hasSource ?x2282[ a Source; has locatedIn ?x565[ is locatedIn of ?x1395[ a Lake;];];]; is flowsInto of ?x1395;];]
ranks of expected_values: 1
EVAL	Saimaa	locatedIn	SF	CNN-1.+1._MA	1.000	1.000	1.000	1.000	88.000	88.000	101.000	0.930	http://www.semwebtech.org/mondial/10/meta#locatedIn

#586-Arno
PRED entity: Arno

PRED relation: locatedIn
PRED expected values: I
=> 33 concepts (33 used for prediction)
PRED predicted values (max 10 best out of 87): I (0.68 #4523, 0.67 #1904, 0.67 #1715), SUD (0.27 #1947, 0.03 #4326, 0.03 #4565), D (0.26 #2879, 0.26 #2640, 0.19 #4543), UA (0.21 #2214, 0.06 #3168, 0.04 #4593), R (0.21 #4289, 0.12 #3103, 0.12 #4053), TR (0.14 #2383, 0.14 #2185, 0.09 #4761), F (0.14 #244, 0.12 #719, 0.12 #481), E (0.14 #264, 0.12 #739, 0.12 #501), SRB (0.13 #3044, 0.13 #2805, 0.11 #1135), USA (0.13 #3408, 0.12 #3170, 0.12 #3882)
>> best conf = 0.68
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4523 for best value:
>> intensional similarity = 7
>> extensional distance = 70
>> proper extension:  Dalaelv;
>> query: (?x1813, ?x207) <- ?x1813[ a Estuary; is hasEstuary of ?x1812[ a River; has hasSource ?x1849; has locatedIn ?x207[ is neighbor of ?x78; is wasDependentOf of ?x1184;];];]
ranks of expected_values: 1
EVAL	Arno	locatedIn	I	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	33.000	33.000	87.000	0.675	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: I
=> 97 concepts (97 used for prediction)
PRED predicted values (max 10 best out of 97): I (0.81 #15317, 0.79 #16037, 0.79 #8378), D (0.61 #15817, 0.59 #15098, 0.57 #8159), USA (0.54 #14431, 0.54 #14670, 0.50 #7971), R (0.54 #15562, 0.50 #16282, 0.50 #8384), F (0.38 #3596, 0.36 #5993, 0.35 #10538), SRB (0.33 #9042, 0.17 #16222, 0.17 #16701), CDN (0.32 #13468, 0.24 #12750, 0.20 #5090), E (0.27 #5530, 0.20 #21064, 0.20 #21063), SUD (0.27 #6267, 0.12 #10094, 0.09 #13207), ZRE (0.26 #21143, 0.26 #20903, 0.19 #9415)
>> best conf = 0.81
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #15317 for best value:
>> intensional similarity = 15
>> extensional distance = 25
>> proper extension:  Main; Alz; Werra; Fulda; Ammer; Aller;
>> query: (?x1813, ?x207) <- ?x1813[ a Estuary; is hasEstuary of ?x1812[ a River; has flowsInto ?x275[ has locatedIn ?x55;]; has locatedIn ?x207[ a Country; has neighbor ?x234<CH>; has religion ?x95<Protestant>; has religion ?x187<Muslim>; has religion ?x352<RomanCatholic>;];];]
ranks of expected_values: 1
EVAL	Arno	locatedIn	I	CNN-1.+1._MA	1.000	1.000	1.000	1.000	97.000	97.000	97.000	0.815	http://www.semwebtech.org/mondial/10/meta#locatedIn

#585-Indus
PRED entity: Indus

PRED relation: hasSource!
PRED expected values: Indus
=> 32 concepts (24 used for prediction)
PRED predicted values (max 10 best out of 79): Argun (0.09 #42, 0.07 #1832, 0.03 #457), Mekong (0.09 #128, 0.07 #1832, 0.03 #356), Saluen (0.09 #228, 0.07 #1832, 0.03 #456), Jangtse (0.09 #76, 0.07 #1832, 0.03 #304), Tarim-Yarkend (0.09 #47, 0.07 #1832, 0.03 #275), Ili (0.09 #28, 0.07 #1832, 0.03 #256), Ganges (0.09 #143, 0.03 #371, 0.03 #601), Amur (0.07 #1832, 0.03 #412, 0.03 #642), Brahmaputra (0.07 #1832, 0.02 #2062, 0.02 #1833), Irtysch (0.07 #1832, 0.02 #2062, 0.02 #1833)
>> best conf = 0.09
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #42 for best value:
>> intensional similarity = 3
>> extensional distance = 9
>> proper extension:  Ili; Jangtse; Ganges; Argun; Irawaddy; Saluen; Tarim-Yarkend; Brahmaputra; Mekong;
>> query: (?x2208, Argun) <- ?x2208[ a Source; has locatedIn ?x232<CN>;]

*> Best rule #1832 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 213
*> proper extension:  Araguaia; Breg; Buna; Moraca; Ene; Apurimac; Piva; Bani; Waag; Neckar; ...
*> query: (?x2208, ?x338) <- ?x2208[ a Source; has locatedIn ?x232[ has religion ?x116; is locatedIn of ?x338[ a River;]; is neighbor of ?x73;];]
*> conf = 0.07
ranks of expected_values: 12
EVAL	Indus	hasSource!	Indus	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.083	32.000	24.000	79.000	0.091	http://www.semwebtech.org/mondial/10/meta#hasSource

PRED relation: hasSource!
PRED expected values: Indus
=> 76 concepts (76 used for prediction)
PRED predicted values (max 10 best out of 138): Ili (0.10 #687, 0.09 #1148, 0.09 #28), Mekong (0.10 #687, 0.09 #1148, 0.09 #128), Jangtse (0.10 #687, 0.09 #1148, 0.09 #76), Tarim-Yarkend (0.10 #687, 0.09 #1148, 0.09 #47), Argun (0.10 #687, 0.09 #1148, 0.09 #42), Saluen (0.10 #687, 0.09 #1148, 0.09 #228), Amur (0.10 #687, 0.09 #1148, 0.09 #1380), Hwangho (0.10 #687, 0.09 #1148, 0.09 #1380), Brahmaputra (0.09 #1148, 0.09 #1380, 0.09 #1379), Irtysch (0.09 #1148, 0.09 #1380, 0.09 #1379)
>> best conf = 0.10
=> the first rule below is the first best rule for 8 predicted values
>> Best rule #687 for best value:
>> intensional similarity = 13
>> extensional distance = 15
>> proper extension:  Bartang; Pjandsh; Irtysch; Ischim; Syrdarja; Amudarja;
>> query: (?x2208, ?x1022) <- ?x2208[ a Source; has locatedIn ?x232[ has ethnicGroup ?x2285; has neighbor ?x334; has religion ?x116; is locatedIn of ?x1022[ a River; has hasSource ?x2525;]; is neighbor of ?x130<KGZ>; is neighbor of ?x641[ has encompassed ?x175; has religion ?x95;];];]

*> Best rule #1148 for first EXPECTED value:
*> intensional similarity = 13
*> extensional distance = 20
*> proper extension:  Oranje;
*> query: (?x2208, ?x411) <- ?x2208[ a Source; has locatedIn ?x232[ a Country; has ethnicGroup ?x2285; has neighbor ?x334; has religion ?x116<Christian>; is locatedIn of ?x411[ has hasEstuary ?x383;]; is neighbor of ?x130[ has ethnicGroup ?x58;]; is neighbor of ?x641[ has religion ?x95;];];]
*> conf = 0.09
ranks of expected_values: 11
EVAL	Indus	hasSource!	Indus	CNN-1.+1._MA	0.000	0.000	0.000	0.091	76.000	76.000	138.000	0.104	http://www.semwebtech.org/mondial/10/meta#hasSource

#584-TCH
PRED entity: TCH

PRED relation: locatedIn!
PRED expected values: ErdiEnnedi
=> 36 concepts (30 used for prediction)
PRED predicted values (max 10 best out of 1146): AtlanticOcean (0.95 #19903, 0.93 #18485, 0.62 #7136), Benue (0.56 #24117, 0.33 #2477, 0.33 #1058), Niger (0.56 #24117, 0.33 #252, 0.30 #2838), Niger (0.56 #24117, 0.33 #1358, 0.30 #2838), Benue (0.56 #24117, 0.33 #1357, 0.30 #2838), AsoRock (0.56 #24117, 0.33 #377, 0.30 #2838), LakeKainji (0.56 #24117, 0.33 #251, 0.30 #2838), Sanga (0.56 #24117, 0.33 #2615, 0.18 #1419), Benue (0.56 #24117, 0.33 #2806, 0.18 #1419), Sanaga (0.56 #24117, 0.33 #2505, 0.18 #1419)
>> best conf = 0.95
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #19903 for best value:
>> intensional similarity = 7
>> extensional distance = 63
>> proper extension:  FALK;
>> query: (?x169, AtlanticOcean) <- ?x169[ a Country; has government ?x435; is locatedIn of ?x2238[ has locatedIn ?x139<WAN>; has locatedIn ?x536<CAM>;];]

No rule for expected values
ranks of expected_values: 
EVAL	TCH	locatedIn!	ErdiEnnedi	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	36.000	30.000	1146.000	0.954	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: ErdiEnnedi
=> 95 concepts (92 used for prediction)
PRED predicted values (max 10 best out of 1421): AtlanticOcean (0.95 #80989, 0.92 #54006, 0.91 #39809), MediterraneanSea (0.68 #55467, 0.51 #99424, 0.50 #8603), Schari (0.66 #42606, 0.59 #48283, 0.57 #8521), Sanga (0.58 #102263, 0.33 #6878, 0.33 #1419), Benue (0.58 #102263, 0.33 #6740, 0.33 #5319), Niger (0.58 #102263, 0.33 #4513, 0.28 #7101), LakeKainji (0.58 #102263, 0.33 #4512, 0.28 #7101), AsoRock (0.58 #102263, 0.33 #4638, 0.28 #7101), Niger (0.58 #102263, 0.33 #5619, 0.28 #7101), Benue (0.58 #102263, 0.33 #5618, 0.28 #7101)
>> best conf = 0.95
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #80989 for best value:
>> intensional similarity = 15
>> extensional distance = 53
>> proper extension:  NLSM; SPMI; BVIR; FARX; AXA; TUCA; GROX; PR; BERM; VIRG;
>> query: (?x169, AtlanticOcean) <- ?x169[ a Country; has government ?x435; has religion ?x116[ is religion of ?x91[ is neighbor of ?x463;]; is religion of ?x508[ is locatedIn of ?x60<IndianOcean>;]; is religion of ?x924[ has language ?x2392<Hindi>; is locatedIn of ?x339;];]; is locatedIn of ?x695[ has locatedIn ?x536<CAM>;];]

No rule for expected values
ranks of expected_values: 
EVAL	TCH	locatedIn!	ErdiEnnedi	CNN-1.+1._MA	0.000	0.000	0.000	0.000	95.000	92.000	1421.000	0.945	http://www.semwebtech.org/mondial/10/meta#locatedIn

#583-BR
PRED entity: BR

PRED relation: wasDependentOf
PRED expected values: P
=> 33 concepts (33 used for prediction)
PRED predicted values (max 10 best out of 34): E (0.50 #69, 0.50 #7, 0.44 #286), GB (0.31 #743, 0.29 #314, 0.23 #498), F (0.20 #33, 0.17 #438, 0.15 #497), RH (0.20 #49, 0.07 #142, 0.04 #236), SovietUnion (0.19 #391, 0.18 #486, 0.15 #575), BR (0.17 #78, 0.07 #139, 0.04 #233), CO (0.12 #61, 0.12 #60, 0.11 #62), PE (0.12 #61, 0.12 #60, 0.11 #62), BOL (0.12 #61, 0.12 #60, 0.11 #62), PY (0.12 #61, 0.12 #60, 0.11 #62)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #69 for best value:
>> intensional similarity = 6
>> extensional distance = 4
>> proper extension:  ROU; PY;
>> query: (?x542, E) <- ?x542[ has ethnicGroup ?x162; is locatedIn of ?x48; is neighbor of ?x296[ has ethnicGroup ?x79;]; is neighbor of ?x379<RA>;]
>> Best rule #7 for best value:
>> intensional similarity = 7
>> extensional distance = 2
>> proper extension:  RCH; BOL;
>> query: (?x542, E) <- ?x542[ has ethnicGroup ?x1052[ a EthnicGroup;]; is locatedIn of ?x48; is neighbor of ?x296<PE>; is neighbor of ?x379<RA>;]

*> Best rule #207 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 21
*> proper extension:  SD;
*> query: (?x542, P) <- ?x542[ has ethnicGroup ?x197<European>; is neighbor of ?x215[ is locatedIn of ?x214;]; is neighbor of ?x690[ has language ?x702;];]
*> conf = 0.09
ranks of expected_values: 17
EVAL	BR	wasDependentOf	P	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.059	33.000	33.000	34.000	0.500	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

PRED relation: wasDependentOf
PRED expected values: P
=> 108 concepts (108 used for prediction)
PRED predicted values (max 10 best out of 67): E (0.71 #850, 0.67 #232, 0.67 #166), GB (0.46 #517, 0.45 #687, 0.40 #523), F (0.28 #1838, 0.23 #815, 0.21 #1140), SovietUnion (0.23 #2058, 0.20 #2025, 0.20 #1552), RH (0.20 #148, 0.14 #309, 0.11 #1599), P (0.15 #835, 0.11 #1599, 0.10 #1258), Yugoslavia (0.14 #1692, 0.12 #378, 0.11 #1625), CO (0.13 #583, 0.11 #1599, 0.10 #652), PE (0.13 #583, 0.10 #652, 0.10 #651), BOL (0.13 #583, 0.10 #652, 0.10 #651)
>> best conf = 0.71
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #850 for best value:
>> intensional similarity = 11
>> extensional distance = 12
>> proper extension:  GCA; ES; PA;
>> query: (?x542, E) <- ?x542[ has ethnicGroup ?x162; has religion ?x95; is locatedIn of ?x48; is neighbor of ?x296[ a Country; has ethnicGroup ?x676<Mestizo>; has language ?x702; is locatedIn of ?x264; is locatedIn of ?x705[ a Mountain;];];]

*> Best rule #835 for first EXPECTED value:
*> intensional similarity = 11
*> extensional distance = 11
*> proper extension:  WAN; ZRE; GH; ANG;
*> query: (?x542, P) <- ?x542[ a Country; has encompassed ?x521; has ethnicGroup ?x162; is locatedIn of ?x182<AtlanticOcean>; is neighbor of ?x296[ a Country; has ethnicGroup ?x79; has wasDependentOf ?x149; is locatedIn of ?x295[ a Source;];];]
*> conf = 0.15
ranks of expected_values: 6
EVAL	BR	wasDependentOf	P	CNN-1.+1._MA	0.000	0.000	1.000	0.167	108.000	108.000	67.000	0.714	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

#582-LakeMweru
PRED entity: LakeMweru

PRED relation: flowsInto!
PRED expected values: Luapula
=> 36 concepts (32 used for prediction)
PRED predicted values (max 10 best out of 107): Ruzizi (0.04 #455, 0.04 #151, 0.02 #758), Tshuapa (0.04 #555, 0.04 #251, 0.02 #858), Lukenie (0.04 #547, 0.04 #243, 0.02 #850), LakeMaiNdombe (0.04 #542, 0.04 #238, 0.02 #845), Lulua (0.04 #454, 0.04 #150, 0.02 #757), Cuilo (0.04 #432, 0.04 #128, 0.02 #735), Cuango (0.04 #350, 0.04 #46, 0.02 #653), LakeMweru (0.04 #338, 0.04 #34, 0.02 #641), LakeTanganjika (0.04 #324, 0.04 #20, 0.02 #627), Fimi (0.04 #485, 0.04 #181, 0.02 #788)
>> best conf = 0.04
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #455 for best value:
>> intensional similarity = 4
>> extensional distance = 22
>> proper extension:  Zaire;
>> query: (?x364, Ruzizi) <- ?x364[ has flowsInto ?x365[ has locatedIn ?x348<ZRE>;]; has locatedIn ?x348<ZRE>;]
>> Best rule #151 for best value:
>> intensional similarity = 5
>> extensional distance = 22
>> proper extension:  Kwa; MaleboPool; Ruki; Ubangi; Cuango; Kasai; Lualaba; Cuilo; Fimi; Aruwimi; ...
>> query: (?x364, Ruzizi) <- ?x364[ has flowsInto ?x365[ a River; has flowsInto ?x527;]; has locatedIn ?x348<ZRE>;]

No rule for expected values
ranks of expected_values: 
EVAL	LakeMweru	flowsInto!	Luapula	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	36.000	32.000	107.000	0.042	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto!
PRED expected values: Luapula
=> 95 concepts (95 used for prediction)
PRED predicted values (max 10 best out of 124): Ruzizi (0.33 #151, 0.25 #455, 0.20 #759), LakeKivu (0.20 #862, 0.04 #3608, 0.03 #6088), Semliki (0.17 #1298, 0.11 #1602, 0.09 #1907), Akagera (0.11 #1694, 0.02 #4436, 0.02 #5043), LakeTanganjika (0.09 #1848, 0.07 #2153, 0.06 #2764), Tshuapa (0.09 #2079, 0.07 #2384, 0.06 #2995), LakeMweru (0.09 #1862, 0.07 #2167, 0.06 #2778), Lukenie (0.07 #2376, 0.06 #2987, 0.04 #3597), Cuilo (0.07 #2261, 0.06 #2872, 0.04 #3482), Cuango (0.07 #2179, 0.06 #2790, 0.04 #3400)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #151 for best value:
>> intensional similarity = 10
>> extensional distance = 1
>> proper extension:  LakeTanganjika;
>> query: (?x364, Ruzizi) <- ?x364[ a Lake; has flowsInto ?x365[ a River; has flowsInto ?x527<Lualaba>; has hasEstuary ?x2120[ a Estuary;]; has hasSource ?x2239;]; has locatedIn ?x348<ZRE>;]

*> Best rule #1827 for first EXPECTED value:
*> intensional similarity = 13
*> extensional distance = 7
*> proper extension:  LakeKioga; LakeVictoria;
*> query: (?x364, ?x388) <- ?x364[ a Lake; has locatedIn ?x348[ has encompassed ?x213; has neighbor ?x229; has religion ?x95; is locatedIn of ?x388[ a River;]; is locatedIn of ?x600<Rutanzige-Eduardsee>; is locatedIn of ?x1538<Ruwenzori>; is locatedIn of ?x2181[ has inMountains ?x1066;];];]
*> conf = 0.03
ranks of expected_values: 39
EVAL	LakeMweru	flowsInto!	Luapula	CNN-1.+1._MA	0.000	0.000	0.000	0.026	95.000	95.000	124.000	0.333	http://www.semwebtech.org/mondial/10/meta#flowsInto

#581-RubAlChali
PRED entity: RubAlChali

PRED relation: type
PRED expected values: "sand"
=> 31 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 6): "sand" (0.19 #100, 0.17 #52, 0.16 #148), "salt" (0.16 #71, 0.03 #327, 0.03 #343), "volcanic" (0.11 #178, 0.09 #370, 0.07 #434), "volcano" (0.10 #182, 0.05 #70, 0.05 #230), "lime" (0.02 #197, 0.02 #85, 0.02 #101), "dam" (0.02 #209, 0.02 #257, 0.02 #241)
>> best conf = 0.19
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #100 for best value:
>> intensional similarity = 5
>> extensional distance = 52
>> proper extension:  Namib; Djourab;
>> query: (?x637, "sand") <- ?x637[ a Desert; has locatedIn ?x107[ has encompassed ?x175; has government ?x1136; has religion ?x187;];]
ranks of expected_values: 1
EVAL	RubAlChali	type	"sand"	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	31.000	31.000	6.000	0.185	http://www.semwebtech.org/mondial/10/meta#type

PRED relation: type
PRED expected values: "sand"
=> 90 concepts (90 used for prediction)
PRED predicted values (max 10 best out of 9): "sand" (0.27 #132, 0.22 #212, 0.21 #276), "volcanic" (0.12 #754, 0.11 #802, 0.11 #546), "salt" (0.12 #231, 0.07 #263, 0.07 #407), "volcano" (0.09 #550, 0.08 #582, 0.08 #518), "lime" (0.07 #133, 0.06 #213, 0.03 #277), "atoll" (0.06 #168, 0.02 #584, 0.02 #760), "dam" (0.06 #385, 0.06 #193, 0.04 #241), "caldera" (0.02 #515, 0.02 #547, 0.02 #563), "granite" (0.02 #446, 0.02 #462, 0.01 #478)
>> best conf = 0.27
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #132 for best value:
>> intensional similarity = 15
>> extensional distance = 13
>> proper extension:  Mujunkum;
>> query: (?x637, "sand") <- ?x637[ a Desert; has locatedIn ?x639[ a Country;]; has locatedIn ?x668[ has encompassed ?x175; has government ?x435; has religion ?x187<Muslim>; has religion ?x410[ is religion of ?x351<GUY>; is religion of ?x633<GUAD>; is religion of ?x667<TT>;];];]
ranks of expected_values: 1
EVAL	RubAlChali	type	"sand"	CNN-1.+1._MA	1.000	1.000	1.000	1.000	90.000	90.000	9.000	0.267	http://www.semwebtech.org/mondial/10/meta#type

#580-BalticSea
PRED entity: BalticSea

PRED relation: locatedInWater!
PRED expected values: Gotland Lolland
=> 35 concepts (34 used for prediction)
PRED predicted values (max 10 best out of 336): GreatBritain (0.17 #1076, 0.14 #554, 0.13 #1337), Cuba (0.17 #1256, 0.13 #1517, 0.12 #1778), Sumatra (0.15 #1626, 0.15 #1887, 0.13 #1365), VelikiRatnoOstrvo (0.14 #528, 0.08 #789, 0.08 #1572), Wangerooge (0.14 #779, 0.08 #1040, 0.07 #2873), Juist (0.14 #735, 0.08 #996, 0.07 #2873), Borkum (0.14 #702, 0.08 #963, 0.07 #2873), Baltrum (0.14 #699, 0.08 #960, 0.07 #2873), Sylt (0.14 #695, 0.08 #956, 0.07 #2873), Fohr (0.14 #689, 0.08 #950, 0.07 #2873)
>> best conf = 0.17
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1076 for best value:
>> intensional similarity = 5
>> extensional distance = 16
>> proper extension:  BlackSea; Skagerrak;
>> query: (?x146, GreatBritain) <- ?x146[ has locatedIn ?x120; has mergesWith ?x1663; is flowsInto of ?x1725[ has locatedIn ?x402; is flowsInto of ?x2013;];]

*> Best rule #2873 for first EXPECTED value:
*> intensional similarity = 4
*> extensional distance = 38
*> proper extension:  JavaSea; LabradorSea; ArcticOcean; SeaofJapan; LakeNicaragua; NorwegianSea; SulawesiSea; BandaSea; LakeManicouagan; Waag; ...
*> query: (?x146, ?x70) <- ?x146[ has locatedIn ?x120[ is locatedIn of ?x70;]; is locatedInWater of ?x804[ has locatedIn ?x565;];]
*> conf = 0.07
ranks of expected_values: 57
EVAL	BalticSea	locatedInWater!	Lolland	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.018	35.000	34.000	336.000	0.167	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	BalticSea	locatedInWater!	Gotland	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	35.000	34.000	336.000	0.167	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater!
PRED expected values: Gotland Lolland
=> 111 concepts (111 used for prediction)
PRED predicted values (max 10 best out of 592): Juist (0.35 #8382, 0.31 #4447, 0.28 #7593), Borkum (0.35 #8382, 0.31 #4447, 0.28 #7593), Sylt (0.35 #8382, 0.31 #4447, 0.28 #7593), Norderney (0.35 #8382, 0.31 #4447, 0.28 #7593), Amrum (0.35 #8382, 0.31 #4447, 0.28 #7593), Langeoog (0.35 #8382, 0.31 #4447, 0.28 #7593), Pellworm (0.35 #8382, 0.31 #4447, 0.28 #7593), Spiekeroog (0.35 #8382, 0.31 #4447, 0.28 #7593), Wangerooge (0.35 #8382, 0.28 #7593, 0.12 #6806), Baltrum (0.35 #8382, 0.28 #7593, 0.12 #6806)
>> best conf = 0.35
=> the first rule below is the first best rule for 12 predicted values
>> Best rule #8382 for best value:
>> intensional similarity = 11
>> extensional distance = 30
>> proper extension:  SuluSea;
>> query: (?x146, ?x443) <- ?x146[ a Sea; has locatedIn ?x120[ has encompassed ?x195; has neighbor ?x78[ is locatedIn of ?x165; is wasDependentOf of ?x94;]; has neighbor ?x424[ has religion ?x95; is locatedIn of ?x155;]; is locatedIn of ?x443[ a Island;];];]

*> Best rule #3922 for first EXPECTED value:
*> intensional similarity = 9
*> extensional distance = 16
*> proper extension:  BlackSea; Skagerrak;
*> query: (?x146, ?x70) <- ?x146[ a Sea; has locatedIn ?x120[ is locatedIn of ?x70;]; has mergesWith ?x1663; is flowsInto of ?x1725[ a River; has hasEstuary ?x191; has locatedIn ?x402; is flowsInto of ?x2013;];]
*> conf = 0.08
ranks of expected_values: 143, 565
EVAL	BalticSea	locatedInWater!	Lolland	CNN-1.+1._MA	0.000	0.000	0.000	0.007	111.000	111.000	592.000	0.349	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	BalticSea	locatedInWater!	Gotland	CNN-1.+1._MA	0.000	0.000	0.000	0.002	111.000	111.000	592.000	0.349	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#579-SK
PRED entity: SK

PRED relation: ethnicGroup
PRED expected values: Hungarian
=> 47 concepts (47 used for prediction)
PRED predicted values (max 10 best out of 239): Hungarian (0.56 #523, 0.33 #21, 0.23 #774), Russian (0.33 #571, 0.21 #3583, 0.21 #4587), Serb (0.33 #539, 0.18 #8033, 0.17 #8787), Moravian (0.33 #247, 0.18 #8033, 0.17 #8787), European (0.31 #2516, 0.30 #2265, 0.28 #4022), Italian (0.31 #961, 0.05 #4726, 0.04 #3973), Turkish (0.25 #433, 0.18 #8033, 0.17 #8787), Croat (0.25 #255, 0.18 #8033, 0.17 #8787), Slovene (0.25 #260, 0.18 #8033, 0.17 #8787), Byelorussian (0.25 #456, 0.18 #8033, 0.17 #8787)
>> best conf = 0.56
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #523 for best value:
>> intensional similarity = 7
>> extensional distance = 7
>> proper extension:  D;
>> query: (?x163, Hungarian) <- ?x163[ has government ?x254; has religion ?x56; is locatedIn of ?x133<Donau>; is locatedIn of ?x1097[ a Estuary;]; is neighbor of ?x194;]
ranks of expected_values: 1
EVAL	SK	ethnicGroup	Hungarian	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	47.000	47.000	239.000	0.556	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: Hungarian
=> 101 concepts (101 used for prediction)
PRED predicted values (max 10 best out of 251): Russian (0.83 #4593, 0.75 #2581, 0.67 #2330), Hungarian (0.56 #2784, 0.50 #2030, 0.43 #4796), European (0.50 #6540, 0.47 #7548, 0.43 #6038), Belorussian (0.50 #1587, 0.40 #1838, 0.38 #2593), Bulgarian (0.50 #2396, 0.37 #7542, 0.33 #2898), Serb (0.37 #7542, 0.33 #2800, 0.33 #2046), Jewish (0.37 #7542, 0.33 #41, 0.33 #7290), CrimeanTatar (0.37 #7542, 0.33 #143, 0.33 #7290), Moldovan (0.37 #7542, 0.33 #65, 0.33 #7290), Romanian (0.37 #7542, 0.33 #346, 0.33 #7290)
>> best conf = 0.83
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4593 for best value:
>> intensional similarity = 15
>> extensional distance = 10
>> proper extension:  GE; LT;
>> query: (?x163, Russian) <- ?x163[ has ethnicGroup ?x58[ is ethnicGroup of ?x73<R>; is ethnicGroup of ?x176<RO>; is ethnicGroup of ?x222<BY>; is ethnicGroup of ?x448<LV>;]; has government ?x254; has language ?x684; has neighbor ?x194; has wasDependentOf ?x1339; is locatedIn of ?x133[ is flowsInto of ?x132;];]

*> Best rule #2784 for first EXPECTED value:
*> intensional similarity = 11
*> extensional distance = 7
*> proper extension:  HR; SRB;
*> query: (?x163, Hungarian) <- ?x163[ has ethnicGroup ?x58[ a EthnicGroup; is ethnicGroup of ?x176<RO>;]; has language ?x684; has neighbor ?x303[ has ethnicGroup ?x517; is locatedIn of ?x97;]; has wasDependentOf ?x1339; is locatedIn of ?x133<Donau>;]
*> conf = 0.56
ranks of expected_values: 2
EVAL	SK	ethnicGroup	Hungarian	CNN-1.+1._MA	0.000	1.000	1.000	0.500	101.000	101.000	251.000	0.833	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#578-VitiLevu
PRED entity: VitiLevu

PRED relation: locatedInWater
PRED expected values: PacificOcean
=> 81 concepts (81 used for prediction)
PRED predicted values (max 10 best out of 60): PacificOcean (0.73 #1516, 0.73 #1489, 0.70 #1690), MediterraneanSea (0.54 #274, 0.35 #532, 0.22 #965), AtlanticOcean (0.45 #1042, 0.44 #394, 0.42 #480), JavaSea (0.37 #439, 0.33 #612, 0.30 #656), IndianOcean (0.32 #432, 0.30 #649, 0.29 #605), CaribbeanSea (0.21 #492, 0.17 #882, 0.17 #406), NorthSea (0.18 #1822, 0.15 #1038, 0.15 #2299), SulawesiSea (0.16 #458, 0.15 #762, 0.14 #631), SouthChinaSea (0.16 #452, 0.14 #625, 0.13 #669), IrishSea (0.14 #214, 0.09 #1077, 0.04 #776)
>> best conf = 0.73
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1516 for best value:
>> intensional similarity = 7
>> extensional distance = 60
>> proper extension:  Cebu; Ternate;
>> query: (?x532, ?x282) <- ?x532[ a Island; has belongsToIslands ?x2001[ a Islands; is belongsToIslands of ?x1778[ a Island; has locatedInWater ?x282<PacificOcean>;];];]
>> Best rule #1489 for best value:
>> intensional similarity = 7
>> extensional distance = 60
>> proper extension:  Cebu; Ternate;
>> query: (?x532, PacificOcean) <- ?x532[ a Island; has belongsToIslands ?x2001[ a Islands; is belongsToIslands of ?x1778[ a Island; has locatedInWater ?x282<PacificOcean>;];];]
ranks of expected_values: 1
EVAL	VitiLevu	locatedInWater	PacificOcean	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	81.000	81.000	60.000	0.726	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater
PRED expected values: PacificOcean
=> 169 concepts (169 used for prediction)
PRED predicted values (max 10 best out of 65): PacificOcean (0.90 #3037, 0.90 #3010, 0.88 #2770), MediterraneanSea (0.64 #1815, 0.30 #2566, 0.20 #627), AtlanticOcean (0.57 #925, 0.55 #2469, 0.54 #1588), JavaSea (0.57 #1016, 0.54 #1633, 0.50 #1191), IndianOcean (0.54 #1626, 0.44 #1979, 0.43 #1009), Mt.Victoria (0.38 #438, 0.37 #1756, 0.35 #875), VitiLevu (0.38 #438, 0.37 #1756, 0.35 #875), CaribbeanSea (0.33 #151, 0.29 #937, 0.23 #1731), SulawesiSea (0.33 #72, 0.28 #2977, 0.25 #2005), SouthChinaSea (0.33 #765, 0.25 #1559, 0.25 #1247)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3037 for best value:
>> intensional similarity = 9
>> extensional distance = 27
>> proper extension:  Fakaofo;
>> query: (?x532, ?x282) <- ?x532[ a Island; has belongsToIslands ?x2001[ a Islands; is belongsToIslands of ?x1778[ a Island; has locatedInWater ?x282<PacificOcean>; has type ?x150;];]; has type ?x150;]
>> Best rule #3010 for best value:
>> intensional similarity = 9
>> extensional distance = 27
>> proper extension:  Fakaofo;
>> query: (?x532, PacificOcean) <- ?x532[ a Island; has belongsToIslands ?x2001[ a Islands; is belongsToIslands of ?x1778[ a Island; has locatedInWater ?x282<PacificOcean>; has type ?x150;];]; has type ?x150;]
ranks of expected_values: 1
EVAL	VitiLevu	locatedInWater	PacificOcean	CNN-1.+1._MA	1.000	1.000	1.000	1.000	169.000	169.000	65.000	0.897	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#577-CR
PRED entity: CR

PRED relation: wasDependentOf
PRED expected values: E
=> 33 concepts (33 used for prediction)
PRED predicted values (max 10 best out of 26): E (0.75 #128, 0.67 #97, 0.36 #66), GB (0.30 #33, 0.27 #368, 0.23 #308), UnitedNations (0.20 #15, 0.12 #319, 0.09 #301), SovietUnion (0.14 #479, 0.12 #510, 0.08 #665), F (0.14 #523, 0.13 #647, 0.13 #584), P (0.13 #176, 0.09 #387, 0.09 #82), NL (0.10 #47, 0.09 #301, 0.06 #445), MAL (0.09 #301, 0.05 #254, 0.04 #283), RH (0.09 #78, 0.07 #172, 0.06 #862), CO (0.08 #130, 0.08 #99, 0.06 #862)
>> best conf = 0.75
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #128 for best value:
>> intensional similarity = 6
>> extensional distance = 10
>> proper extension:  PE; PY;
>> query: (?x318, E) <- ?x318[ has ethnicGroup ?x298[ is ethnicGroup of ?x366[ has religion ?x116;];]; has ethnicGroup ?x676<Mestizo>; is locatedIn of ?x282;]
ranks of expected_values: 1
EVAL	CR	wasDependentOf	E	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	33.000	33.000	26.000	0.750	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

PRED relation: wasDependentOf
PRED expected values: E
=> 95 concepts (95 used for prediction)
PRED predicted values (max 10 best out of 160): E (0.67 #704, 0.62 #671, 0.60 #1035), CO (0.44 #663, 0.21 #1397, 0.18 #2882), GB (0.38 #896, 0.38 #1538, 0.37 #1264), UnitedNations (0.33 #15, 0.18 #2882, 0.14 #1484), NL (0.25 #142, 0.18 #2882, 0.12 #1634), F (0.18 #2882, 0.15 #2481, 0.14 #2550), RH (0.18 #2882, 0.12 #650, 0.12 #1634), BR (0.18 #2882, 0.12 #647, 0.09 #2986), P (0.18 #2882, 0.11 #1760, 0.08 #881), MAL (0.18 #2882, 0.01 #728, 0.01 #1257)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #704 for best value:
>> intensional similarity = 15
>> extensional distance = 7
>> proper extension:  PE;
>> query: (?x318, E) <- ?x318[ has encompassed ?x521; has ethnicGroup ?x676<Mestizo>; has neighbor ?x408; has religion ?x95<Protestant>; has religion ?x352[ is religion of ?x697<RH>; is religion of ?x819<MW>;]; has religion ?x1151[ is religion of ?x671<BZ>;]; is locatedIn of ?x282;]
ranks of expected_values: 1
EVAL	CR	wasDependentOf	E	CNN-1.+1._MA	1.000	1.000	1.000	1.000	95.000	95.000	160.000	0.667	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

#576-EAK
PRED entity: EAK

PRED relation: religion
PRED expected values: RomanCatholic
=> 31 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 32): Muslim (0.75 #168, 0.73 #332, 0.69 #373), RomanCatholic (0.60 #458, 0.52 #499, 0.51 #904), Christian (0.51 #904, 0.50 #85, 0.50 #946), ChristianOrthodox (0.50 #946, 0.26 #534, 0.20 #617), Catholic (0.50 #946, 0.04 #488, 0.04 #529), Buddhist (0.36 #339, 0.33 #11, 0.27 #421), Hindu (0.33 #9, 0.27 #337, 0.27 #419), Bahai (0.26 #575, 0.12 #195, 0.11 #318), Jewish (0.26 #575, 0.11 #289, 0.11 #207), Druze (0.26 #575, 0.11 #321, 0.11 #239)
>> best conf = 0.75
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #168 for best value:
>> intensional similarity = 6
>> extensional distance = 6
>> proper extension:  IR; SA;
>> query: (?x474, Muslim) <- ?x474[ has encompassed ?x213; has ethnicGroup ?x244<Arab>; has religion ?x95; is locatedIn of ?x60; is neighbor of ?x220;]

*> Best rule #458 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 45
*> proper extension:  MNTS;
*> query: (?x474, RomanCatholic) <- ?x474[ a Country; has government ?x435; is locatedIn of ?x730[ a Mountain; a Volcano;];]
*> conf = 0.60
ranks of expected_values: 2
EVAL	EAK	religion	RomanCatholic	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	31.000	31.000	32.000	0.750	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion
PRED expected values: RomanCatholic
=> 94 concepts (94 used for prediction)
PRED predicted values (max 10 best out of 39): RomanCatholic (0.82 #2218, 0.65 #2468, 0.62 #1844), Muslim (0.76 #877, 0.75 #835, 0.75 #543), Christian (0.58 #1792, 0.57 #2798, 0.57 #873), ChristianOrthodox (0.58 #1792, 0.57 #2798, 0.57 #873), Catholic (0.58 #1792, 0.57 #2798, 0.57 #873), Buddhist (0.43 #1834, 0.37 #2840, 0.36 #759), Hindu (0.43 #1834, 0.37 #2840, 0.36 #2377), Kimbanguist (0.42 #998, 0.41 #2756, 0.38 #3092), Jewish (0.36 #1122, 0.36 #2587, 0.33 #206), Sikh (0.36 #1122, 0.33 #206, 0.33 #115)
>> best conf = 0.82
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2218 for best value:
>> intensional similarity = 12
>> extensional distance = 58
>> proper extension:  CUR;
>> query: (?x474, RomanCatholic) <- ?x474[ a Country; has religion ?x95<Protestant>; is locatedIn of ?x60[ has mergesWith ?x182; is locatedInWater of ?x226;]; is locatedIn of ?x1195[ has locatedIn ?x688[ a Country; has encompassed ?x213; has ethnicGroup ?x529; has neighbor ?x229;];];]
ranks of expected_values: 1
EVAL	EAK	religion	RomanCatholic	CNN-1.+1._MA	1.000	1.000	1.000	1.000	94.000	94.000	39.000	0.817	http://www.semwebtech.org/mondial/10/meta#religion

#575-DZ
PRED entity: DZ

PRED relation: religion
PRED expected values: Muslim
=> 42 concepts (42 used for prediction)
PRED predicted values (max 10 best out of 36): Muslim (0.78 #203, 0.71 #243, 0.70 #364), RomanCatholic (0.60 #607, 0.51 #727, 0.51 #1409), Protestant (0.47 #603, 0.42 #723, 0.41 #1364), ChristianOrthodox (0.36 #241, 0.25 #281, 0.24 #842), CopticChristian (0.17 #149, 0.07 #269, 0.06 #309), Anglican (0.15 #617, 0.09 #1419, 0.09 #1338), Buddhist (0.12 #691, 0.11 #1332, 0.10 #731), Hindu (0.11 #609, 0.11 #689, 0.11 #1411), JehovasWitnesses (0.07 #500, 0.07 #540, 0.06 #660), Catholic (0.06 #357, 0.06 #477, 0.03 #437)
>> best conf = 0.78
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #203 for best value:
>> intensional similarity = 6
>> extensional distance = 7
>> proper extension:  RG; WAL; CI; GNB;
>> query: (?x581, Muslim) <- ?x581[ has government ?x435; has wasDependentOf ?x78; is locatedIn of ?x84; is neighbor of ?x515[ has neighbor ?x416<SN>;];]
ranks of expected_values: 1
EVAL	DZ	religion	Muslim	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	42.000	42.000	36.000	0.778	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion
PRED expected values: Muslim
=> 68 concepts (68 used for prediction)
PRED predicted values (max 10 best out of 38): Muslim (0.81 #1306, 0.81 #1267, 0.78 #535), RomanCatholic (0.65 #1188, 0.60 #1682, 0.52 #1764), Protestant (0.52 #1184, 0.45 #1966, 0.43 #1553), CopticChristian (0.40 #123, 0.28 #1964, 0.22 #2584), ChristianOrthodox (0.33 #819, 0.33 #533, 0.31 #1595), JehovasWitnesses (0.29 #469, 0.28 #1964, 0.22 #1201), Buddhist (0.28 #1964, 0.21 #2005, 0.20 #2460), Hindu (0.28 #1964, 0.21 #2005, 0.20 #2460), Anglican (0.28 #1964, 0.17 #1594, 0.12 #1692), Catholic (0.28 #1964, 0.17 #1594, 0.06 #935)
>> best conf = 0.81
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1306 for best value:
>> intensional similarity = 15
>> extensional distance = 24
>> proper extension:  UZB;
>> query: (?x581, ?x187) <- ?x581[ has ethnicGroup ?x197; has neighbor ?x515[ a Country; has religion ?x187<Muslim>; is locatedIn of ?x182[ is flowsInto of ?x137;];]; has neighbor ?x1184[ has ethnicGroup ?x1215; has government ?x1522; has neighbor ?x63[ is locatedIn of ?x62;];]; is locatedIn of ?x84; is locatedIn of ?x1298[ a Desert;];]
>> Best rule #1267 for best value:
>> intensional similarity = 15
>> extensional distance = 24
>> proper extension:  UZB;
>> query: (?x581, Muslim) <- ?x581[ has ethnicGroup ?x197; has neighbor ?x515[ a Country; has religion ?x187<Muslim>; is locatedIn of ?x182[ is flowsInto of ?x137;];]; has neighbor ?x1184[ has ethnicGroup ?x1215; has government ?x1522; has neighbor ?x63[ is locatedIn of ?x62;];]; is locatedIn of ?x84; is locatedIn of ?x1298[ a Desert;];]
ranks of expected_values: 1
EVAL	DZ	religion	Muslim	CNN-1.+1._MA	1.000	1.000	1.000	1.000	68.000	68.000	38.000	0.808	http://www.semwebtech.org/mondial/10/meta#religion

#574-Selenge
PRED entity: Selenge

PRED relation: hasEstuary
PRED expected values: Selenge
=> 32 concepts (30 used for prediction)
PRED predicted values (max 10 best out of 107): Kama (0.17 #38, 0.10 #264, 0.05 #490), Swir (0.17 #25, 0.10 #251, 0.05 #477), Volga (0.17 #187, 0.04 #865, 0.04 #1091), Schilka (0.10 #451, 0.05 #677, 0.04 #903), Angara (0.10 #408, 0.05 #634, 0.04 #860), Oka (0.10 #398, 0.05 #624, 0.04 #850), Argun (0.10 #338, 0.05 #564, 0.04 #790), Katun (0.10 #405, 0.04 #857, 0.04 #1083), Ischim (0.05 #641), Petschora (0.04 #879, 0.04 #1105, 0.04 #1331)
>> best conf = 0.17
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #38 for best value:
>> intensional similarity = 5
>> extensional distance = 4
>> proper extension:  Swir; Volga; Vuoksi; Kama;
>> query: (?x72, Kama) <- ?x72[ has flowsInto ?x464[ a Lake;]; has hasSource ?x956; has locatedIn ?x73<R>;]

No rule for expected values
ranks of expected_values: 
EVAL	Selenge	hasEstuary	Selenge	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	32.000	30.000	107.000	0.167	http://www.semwebtech.org/mondial/10/meta#hasEstuary

PRED relation: hasEstuary
PRED expected values: Selenge
=> 131 concepts (131 used for prediction)
PRED predicted values (max 10 best out of 291): Paatsjoki (0.25 #721, 0.17 #1176, 0.11 #2307), WesternDwina (0.17 #1186, 0.11 #2317, 0.07 #2770), Dnepr (0.17 #1196, 0.11 #2327, 0.07 #2780), Amur (0.14 #1763, 0.14 #1536, 0.06 #3347), Irtysch (0.14 #1446, 0.11 #2351, 0.06 #3031), Argun (0.14 #1473, 0.06 #3058, 0.06 #3284), RioLerma (0.14 #1625, 0.05 #3889, 0.03 #7068), ColumbiaRiver (0.14 #1733, 0.04 #4449, 0.02 #9222), Colorado (0.14 #1602, 0.04 #4318, 0.02 #9318), SnowyRiver (0.14 #1796, 0.01 #11781)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #721 for best value:
>> intensional similarity = 12
>> extensional distance = 2
>> proper extension:  Paatsjoki; Vuoksi;
>> query: (?x72, Paatsjoki) <- ?x72[ a River; has flowsInto ?x464; has hasSource ?x956[ a Source;]; has locatedIn ?x73<R>; has locatedIn ?x1010[ has government ?x2058; has religion ?x116; has wasDependentOf ?x232[ is locatedIn of ?x472<Argun>;];];]

*> Best rule #19091 for first EXPECTED value:
*> intensional similarity = 13
*> extensional distance = 251
*> proper extension:  LakeVolta; LakeKainji;
*> query: (?x72, ?x198) <- ?x72[ has flowsInto ?x464; has locatedIn ?x73[ has neighbor ?x170[ is locatedIn of ?x121;]; has neighbor ?x962[ has government ?x254;]; is locatedIn of ?x198[ a Estuary;]; is locatedIn of ?x293[ a River;];]; has locatedIn ?x1010[ a Country; has ethnicGroup ?x1553;];]
*> conf = 0.05
ranks of expected_values: 33
EVAL	Selenge	hasEstuary	Selenge	CNN-1.+1._MA	0.000	0.000	0.000	0.030	131.000	131.000	291.000	0.250	http://www.semwebtech.org/mondial/10/meta#hasEstuary

#573-Lanzarote
PRED entity: Lanzarote

PRED relation: type
PRED expected values: "volcanic"
=> 46 concepts (46 used for prediction)
PRED predicted values (max 10 best out of 9): "volcanic" (0.75 #18, 0.71 #2, 0.50 #34), "volcano" (0.20 #209, 0.10 #692, 0.09 #498), "lime" (0.06 #133, 0.05 #85, 0.04 #101), "atoll" (0.04 #281, 0.03 #313, 0.03 #329), "salt" (0.03 #617, 0.02 #699, 0.02 #682), "dam" (0.02 #450, 0.02 #466, 0.02 #482), "coral" (0.02 #105, 0.02 #137, 0.02 #314), "sand" (0.02 #453, 0.02 #469), "caldera" (0.01 #404)
>> best conf = 0.75
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #18 for best value:
>> intensional similarity = 5
>> extensional distance = 6
>> proper extension:  LaPalma;
>> query: (?x1505, "volcanic") <- ?x1505[ a Island; has locatedIn ?x149<E>; has locatedInWater ?x182<AtlanticOcean>;]
ranks of expected_values: 1
EVAL	Lanzarote	type	"volcanic"	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	46.000	46.000	9.000	0.750	http://www.semwebtech.org/mondial/10/meta#type

PRED relation: type
PRED expected values: "volcanic"
=> 128 concepts (128 used for prediction)
PRED predicted values (max 10 best out of 10): "volcanic" (0.78 #51, 0.75 #18, 0.71 #2), "volcano" (0.23 #49, 0.22 #552, 0.18 #309), "lime" (0.16 #1086, 0.09 #118, 0.08 #167), "coral" (0.16 #1086, 0.02 #268, 0.02 #805), "atoll" (0.06 #641, 0.05 #836, 0.04 #722), "salt" (0.04 #1368, 0.03 #1887, 0.03 #1903), "dam" (0.03 #1120, 0.02 #989, 0.02 #1037), "sand" (0.02 #1123, 0.01 #1852), "caldera" (0.02 #766, 0.01 #847, 0.01 #911), "impact" (0.01 #708)
>> best conf = 0.78
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #51 for best value:
>> intensional similarity = 9
>> extensional distance = 16
>> proper extension:  Faial; PortoSanto; Hispaniola;
>> query: (?x1505, "volcanic") <- ?x1505[ a Island; has locatedIn ?x149[ has neighbor ?x1826; is neighbor of ?x78[ is locatedIn of ?x121; is neighbor of ?x120;]; is wasDependentOf of ?x148;]; has locatedInWater ?x182<AtlanticOcean>;]
ranks of expected_values: 1
EVAL	Lanzarote	type	"volcanic"	CNN-1.+1._MA	1.000	1.000	1.000	1.000	128.000	128.000	10.000	0.778	http://www.semwebtech.org/mondial/10/meta#type

#572-ROU
PRED entity: ROU

PRED relation: neighbor
PRED expected values: RA BR
=> 51 concepts (51 used for prediction)
PRED predicted values (max 10 best out of 216): RA (0.91 #5846, 0.90 #5030, 0.90 #6336), BR (0.91 #5846, 0.90 #5030, 0.90 #6336), BOL (0.50 #604, 0.50 #114, 0.30 #767), PE (0.50 #540, 0.40 #377, 0.28 #5847), PY (0.50 #72, 0.33 #562, 0.28 #5847), ROU (0.50 #64, 0.25 #7647, 0.24 #5357), RCH (0.40 #362, 0.28 #5847, 0.25 #7647), EC (0.40 #464, 0.08 #4052, 0.07 #1922), CO (0.28 #5847, 0.25 #7647, 0.25 #38), YV (0.28 #5847, 0.25 #7647, 0.25 #59)
>> best conf = 0.91
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #5846 for best value:
>> intensional similarity = 7
>> extensional distance = 94
>> proper extension:  RN;
>> query: (?x363, ?x379) <- ?x363[ a Country; has government ?x700; is locatedIn of ?x182; is neighbor of ?x379[ has language ?x796; is locatedIn of ?x512; is neighbor of ?x202;];]
ranks of expected_values: 1, 2
EVAL	ROU	neighbor	BR	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	51.000	51.000	216.000	0.907	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	ROU	neighbor	RA	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	51.000	51.000	216.000	0.907	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: RA BR
=> 97 concepts (97 used for prediction)
PRED predicted values (max 10 best out of 235): RA (0.92 #13706, 0.91 #12859, 0.91 #9166), BR (0.92 #13706, 0.91 #12859, 0.91 #9166), PE (0.60 #872, 0.27 #12860, 0.27 #9167), BOL (0.50 #442, 0.40 #1103, 0.40 #936), PY (0.50 #400, 0.40 #894, 0.35 #1153), ROU (0.50 #392, 0.35 #1153, 0.33 #64), RCH (0.35 #1153, 0.33 #35, 0.27 #12860), LAR (0.33 #1302, 0.20 #1805, 0.20 #1636), CO (0.27 #12860, 0.27 #9167, 0.26 #6994), GUY (0.27 #12860, 0.27 #9167, 0.26 #9168)
>> best conf = 0.92
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #13706 for best value:
>> intensional similarity = 9
>> extensional distance = 106
>> proper extension:  ARM;
>> query: (?x363, ?x542) <- ?x363[ has ethnicGroup ?x197[ is ethnicGroup of ?x1731[ has encompassed ?x211;];]; has government ?x700; has religion ?x95; is neighbor of ?x542[ is locatedIn of ?x182[ is locatedInWater of ?x112; is mergesWith of ?x60;];];]
ranks of expected_values: 1, 2
EVAL	ROU	neighbor	BR	CNN-1.+1._MA	1.000	1.000	1.000	1.000	97.000	97.000	235.000	0.915	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	ROU	neighbor	RA	CNN-1.+1._MA	1.000	1.000	1.000	1.000	97.000	97.000	235.000	0.915	http://www.semwebtech.org/mondial/10/meta#neighbor

#571-WestFalkland
PRED entity: WestFalkland

PRED relation: locatedInWater
PRED expected values: AtlanticOcean
=> 40 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 36): AtlanticOcean (0.69 #528, 0.69 #491, 0.65 #663), PacificOcean (0.44 #103, 0.39 #191, 0.31 #324), WestFalkland (0.34 #394, 0.31 #218, 0.25 #130), EastFalkland (0.34 #394, 0.31 #218, 0.25 #130), CaribbeanSea (0.24 #237, 0.21 #369, 0.21 #326), MediterraneanSea (0.18 #769, 0.10 #902, 0.10 #724), NorthSea (0.11 #667, 0.10 #844, 0.08 #889), ArcticOcean (0.11 #543, 0.10 #498, 0.09 #633), IndianOcean (0.08 #1067, 0.07 #309, 0.07 #843), BalticSea (0.06 #758, 0.04 #1070, 0.03 #846)
>> best conf = 0.69
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #528 for best value:
>> intensional similarity = 6
>> extensional distance = 60
>> proper extension:  SaintVincent; Pico; Corvo; Terceira; Graciosa; SantaMaria; Madeira; SaoJorge; SaoMiguel; NewProvidence;
>> query: (?x2397, ?x182) <- ?x2397[ has belongsToIslands ?x2389; has locatedIn ?x1087[ a Country; has government ?x562; is locatedIn of ?x182<AtlanticOcean>;];]
>> Best rule #491 for best value:
>> intensional similarity = 6
>> extensional distance = 60
>> proper extension:  SaintVincent; Pico; Corvo; Terceira; Graciosa; SantaMaria; Madeira; SaoJorge; SaoMiguel; NewProvidence;
>> query: (?x2397, AtlanticOcean) <- ?x2397[ has belongsToIslands ?x2389; has locatedIn ?x1087[ a Country; has government ?x562; is locatedIn of ?x182<AtlanticOcean>;];]
ranks of expected_values: 1
EVAL	WestFalkland	locatedInWater	AtlanticOcean	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	40.000	36.000	36.000	0.694	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater
PRED expected values: AtlanticOcean
=> 112 concepts (112 used for prediction)
PRED predicted values (max 10 best out of 64): AtlanticOcean (0.83 #1769, 0.83 #1732, 0.82 #1681), CaribbeanSea (0.42 #281, 0.38 #414, 0.29 #105), PacificOcean (0.39 #722, 0.31 #457, 0.30 #678), WestFalkland (0.39 #131, 0.38 #794, 0.38 #484), EastFalkland (0.39 #131, 0.38 #794, 0.38 #484), MediterraneanSea (0.20 #2096, 0.15 #2006, 0.15 #2141), NorthSea (0.18 #2260, 0.15 #1376, 0.13 #978), ArcticOcean (0.13 #1121, 0.13 #989, 0.13 #1254), TheChannel (0.09 #256, 0.08 #2124, 0.07 #3107), IndianOcean (0.09 #3597, 0.08 #3958, 0.08 #2752)
>> best conf = 0.83
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1769 for best value:
>> intensional similarity = 7
>> extensional distance = 61
>> proper extension:  Streymoy;
>> query: (?x2397, ?x182) <- ?x2397[ a Island; has belongsToIslands ?x2389[ a Islands; is belongsToIslands of ?x867[ a Island; has locatedInWater ?x182<AtlanticOcean>;];];]
>> Best rule #1732 for best value:
>> intensional similarity = 7
>> extensional distance = 61
>> proper extension:  Streymoy;
>> query: (?x2397, AtlanticOcean) <- ?x2397[ a Island; has belongsToIslands ?x2389[ a Islands; is belongsToIslands of ?x867[ a Island; has locatedInWater ?x182<AtlanticOcean>;];];]
ranks of expected_values: 1
EVAL	WestFalkland	locatedInWater	AtlanticOcean	CNN-1.+1._MA	1.000	1.000	1.000	1.000	112.000	112.000	64.000	0.825	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#570-EastChinaSea
PRED entity: EastChinaSea

PRED relation: mergesWith!
PRED expected values: SouthChinaSea
=> 29 concepts (26 used for prediction)
PRED predicted values (max 10 best out of 100): SouthChinaSea (0.84 #498, 0.83 #499, 0.82 #420), EastChinaSea (0.45 #577, 0.45 #578, 0.45 #539), JavaSea (0.44 #82, 0.40 #158, 0.40 #120), AtlanticOcean (0.32 #235, 0.25 #310, 0.24 #349), IndianOcean (0.29 #191, 0.20 #114, 0.17 #268), SulawesiSea (0.24 #212, 0.20 #173, 0.20 #135), BandaSea (0.20 #137, 0.18 #214, 0.16 #422), SuluSea (0.20 #136, 0.16 #422, 0.12 #213), AndamanSea (0.18 #205, 0.11 #90, 0.10 #282), MalakkaStrait (0.17 #56, 0.16 #422, 0.12 #208)
>> best conf = 0.84
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #498 for best value:
>> intensional similarity = 5
>> extensional distance = 38
>> proper extension:  SeaofAzov; BlackSea; RedSea;
>> query: (?x620, ?x270) <- ?x620[ a Sea; has mergesWith ?x270; has mergesWith ?x282[ has locatedIn ?x217[ has wasDependentOf ?x575;];];]
ranks of expected_values: 1
EVAL	EastChinaSea	mergesWith!	SouthChinaSea	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	29.000	26.000	100.000	0.840	http://www.semwebtech.org/mondial/10/meta#mergesWith

PRED relation: mergesWith!
PRED expected values: SouthChinaSea
=> 84 concepts (84 used for prediction)
PRED predicted values (max 10 best out of 236): SouthChinaSea (0.82 #1051, 0.82 #154, 0.80 #76), EastChinaSea (0.51 #1130, 0.50 #1090, 0.50 #504), JavaSea (0.43 #355, 0.38 #155, 0.33 #512), SulawesiSea (0.38 #153, 0.38 #155, 0.33 #527), IndianOcean (0.38 #153, 0.38 #155, 0.33 #39), BandaSea (0.38 #153, 0.38 #155, 0.33 #62), BeringSea (0.38 #153, 0.38 #155, 0.33 #64), SuluSea (0.38 #153, 0.38 #155, 0.33 #61), SeaofOkhotsk (0.38 #153, 0.38 #155, 0.33 #58), MalakkaStrait (0.38 #155, 0.33 #18, 0.21 #821)
>> best conf = 0.82
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1051 for best value:
>> intensional similarity = 10
>> extensional distance = 20
>> proper extension:  KaraSea;
>> query: (?x620, ?x384) <- ?x620[ has mergesWith ?x384[ has locatedIn ?x91; has mergesWith ?x241; is locatedInWater of ?x518;]; is flowsInto of ?x725; is locatedInWater of ?x1224[ a Island; has locatedIn ?x117;]; is mergesWith of ?x270[ has locatedIn ?x232;];]
ranks of expected_values: 1
EVAL	EastChinaSea	mergesWith!	SouthChinaSea	CNN-1.+1._MA	1.000	1.000	1.000	1.000	84.000	84.000	236.000	0.825	http://www.semwebtech.org/mondial/10/meta#mergesWith

#569-Latvian
PRED entity: Latvian

PRED relation: language!
PRED expected values: LV
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 60): PK (0.08 #8), SF (0.07 #79), IR (0.07 #45), BZ (0.06 #88), AND (0.05 #97), L (0.05 #93), MK (0.05 #92), NZ (0.05 #70), I (0.05 #30), NLSM (0.05 #1)
>> best conf = 0.08
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #8 for best value:
>> intensional similarity = 1
>> extensional distance = 102
>> proper extension:  French; Japanese; Samoan; English; Tajik; KhalkhaMongol; Italian; Turkish; Portuguese; Dutch; ...
>> query: (?x915, PK) <- ?x915[ a Language;]

No rule for expected values
ranks of expected_values: 
EVAL	Latvian	language!	LV	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	2.000	2.000	60.000	0.077	http://www.semwebtech.org/mondial/10/meta#language

PRED relation: language!
PRED expected values: LV
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 60): PK (0.08 #8), SF (0.07 #79), IR (0.07 #45), BZ (0.06 #88), AND (0.05 #97), L (0.05 #93), MK (0.05 #92), NZ (0.05 #70), I (0.05 #30), NLSM (0.05 #1)
>> best conf = 0.08
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #8 for best value:
>> intensional similarity = 1
>> extensional distance = 102
>> proper extension:  French; Japanese; Samoan; English; Tajik; KhalkhaMongol; Italian; Turkish; Portuguese; Dutch; ...
>> query: (?x915, PK) <- ?x915[ a Language;]

No rule for expected values
ranks of expected_values: 
EVAL	Latvian	language!	LV	CNN-1.+1._MA	0.000	0.000	0.000	0.000	2.000	2.000	60.000	0.077	http://www.semwebtech.org/mondial/10/meta#language

#568-Oranje
PRED entity: Oranje

PRED relation: locatedIn
PRED expected values: RSA
=> 48 concepts (48 used for prediction)
PRED predicted values (max 10 best out of 215): RSA (0.90 #7765, 0.68 #5647, 0.54 #471), ZRE (0.59 #4548, 0.40 #1017, 0.37 #8713), RB (0.54 #471, 0.50 #679, 0.30 #1148), ANG (0.54 #471, 0.40 #1127, 0.25 #658), Z (0.54 #471, 0.25 #590, 0.25 #355), BR (0.37 #8713, 0.37 #8712, 0.30 #1298), USA (0.37 #8713, 0.37 #8712, 0.28 #6824), E (0.37 #8713, 0.37 #8712, 0.28 #6824), CDN (0.37 #8713, 0.37 #8712, 0.28 #6824), RA (0.37 #8713, 0.37 #8712, 0.28 #6824)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #7765 for best value:
>> intensional similarity = 4
>> extensional distance = 211
>> proper extension:  Leine;
>> query: (?x137, ?x138) <- ?x137[ a River; has hasEstuary ?x1624[ a Estuary; has locatedIn ?x138;];]
ranks of expected_values: 1
EVAL	Oranje	locatedIn	RSA	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	48.000	48.000	215.000	0.905	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: RSA
=> 140 concepts (139 used for prediction)
PRED predicted values (max 10 best out of 232): RSA (0.94 #16108, 0.93 #16109, 0.93 #18723), ZRE (0.88 #12619, 0.73 #14280, 0.72 #7648), RB (0.60 #678, 0.56 #7098, 0.51 #12781), Z (0.56 #7098, 0.51 #12781, 0.40 #589), ZW (0.56 #7098, 0.51 #12781, 0.30 #21331), MOC (0.53 #4053, 0.36 #5713, 0.30 #21331), USA (0.49 #15872, 0.48 #21165, 0.39 #22840), CDN (0.49 #15872, 0.45 #6924, 0.36 #15871), BR (0.49 #15872, 0.36 #15871, 0.36 #15870), RA (0.49 #15872, 0.36 #15871, 0.36 #15870)
>> best conf = 0.94
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #16108 for best value:
>> intensional similarity = 9
>> extensional distance = 117
>> proper extension:  Irtysch;
>> query: (?x137, ?x243) <- ?x137[ a River; has flowsInto ?x182[ is flowsInto of ?x929[ is flowsInto of ?x113;];]; has hasEstuary ?x1624[ has locatedIn ?x243[ a Country; has encompassed ?x213; is neighbor of ?x89;];];]
ranks of expected_values: 1
EVAL	Oranje	locatedIn	RSA	CNN-1.+1._MA	1.000	1.000	1.000	1.000	140.000	139.000	232.000	0.941	http://www.semwebtech.org/mondial/10/meta#locatedIn

#567-Baro
PRED entity: Baro

PRED relation: hasEstuary!
PRED expected values: Baro
=> 32 concepts (23 used for prediction)
PRED predicted values (max 10 best out of 47): Pibor (0.33 #205, 0.20 #431, 0.17 #1134), Atbara (0.17 #906, 0.17 #882, 0.10 #2275), BlueNile (0.17 #862, 0.10 #2275, 0.09 #2730), Sobat (0.17 #919, 0.07 #1147, 0.06 #2729), Bahrel-Djebel-Albert-Nil (0.17 #1100, 0.07 #1328, 0.05 #1362), Bahrel-Ghasal (0.17 #1037, 0.07 #1265, 0.05 #1362), WhiteNile (0.17 #789, 0.05 #1362, 0.04 #1135), Jubba (0.10 #2275, 0.09 #2730, 0.07 #907), Shabelle (0.10 #2275, 0.09 #2730, 0.07 #907), LakeTana (0.10 #2275, 0.09 #2730, 0.07 #907)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #205 for best value:
>> intensional similarity = 5
>> extensional distance = 1
>> proper extension:  Pibor;
>> query: (?x2436, Pibor) <- ?x2436[ a Estuary; has locatedIn ?x229<SSD>; has locatedIn ?x476<ETH>;]

*> Best rule #2275 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 26
*> proper extension:  Sanga;
*> query: (?x2436, ?x228) <- ?x2436[ a Estuary; has locatedIn ?x476[ has encompassed ?x213<Africa>; has government ?x140; has neighbor ?x94; is locatedIn of ?x228;];]
*> conf = 0.10
ranks of expected_values: 11
EVAL	Baro	hasEstuary!	Baro	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.091	32.000	23.000	47.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasEstuary

PRED relation: hasEstuary!
PRED expected values: Baro
=> 69 concepts (66 used for prediction)
PRED predicted values (max 10 best out of 176): Pibor (0.33 #205, 0.23 #455, 0.21 #1139), WhiteNile (0.23 #455, 0.21 #1139, 0.17 #1138), Bahrel-Djebel-Albert-Nil (0.23 #455, 0.21 #1139, 0.17 #1333), Bahrel-Ghasal (0.23 #455, 0.21 #1139, 0.17 #1270), Baro (0.23 #455, 0.21 #1139, 0.12 #7812), Semliki (0.20 #285, 0.04 #2123, 0.02 #3962), VictoriaNile (0.20 #306, 0.02 #3983, 0.02 #4215), BlueNile (0.17 #1092, 0.14 #1367, 0.12 #7812), Atbara (0.17 #1112, 0.14 #1367, 0.12 #7812), Sobat (0.17 #1152, 0.07 #1381, 0.05 #1844)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #205 for best value:
>> intensional similarity = 5
>> extensional distance = 1
>> proper extension:  Pibor;
>> query: (?x2436, Pibor) <- ?x2436[ a Estuary; has locatedIn ?x229<SSD>; has locatedIn ?x476<ETH>;]

*> Best rule #455 for first EXPECTED value:
*> intensional similarity = 14
*> extensional distance = 3
*> proper extension:  VictoriaNile;
*> query: (?x2436, ?x747) <- ?x2436[ a Estuary; has locatedIn ?x229[ is locatedIn of ?x747[ a River;]; is neighbor of ?x474<EAK>; is neighbor of ?x688[ is locatedIn of ?x600; is neighbor of ?x546<RWA>;];]; has locatedIn ?x476[ has ethnicGroup ?x1179; has religion ?x95<Protestant>;];]
*> conf = 0.23
ranks of expected_values: 5
EVAL	Baro	hasEstuary!	Baro	CNN-1.+1._MA	0.000	0.000	1.000	0.200	69.000	66.000	176.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasEstuary

#566-F
PRED entity: F

PRED relation: locatedIn!
PRED expected values: Doubs Loire Saone BarredesEcrins
=> 33 concepts (30 used for prediction)
PRED predicted values (max 10 best out of 1346): Mosel (0.63 #20802, 0.57 #23575, 0.33 #496), Saar (0.63 #20802, 0.57 #23575, 0.33 #98), Maas (0.63 #20802, 0.57 #23575, 0.20 #2773), Rhein (0.63 #20802, 0.57 #23575, 0.20 #2773), Bodensee (0.48 #8319, 0.33 #880, 0.29 #11093), Main (0.48 #8319, 0.33 #267, 0.29 #11093), Doubs (0.48 #8319, 0.29 #11093, 0.25 #9706), Aare (0.48 #8319, 0.29 #11093, 0.25 #9706), NorwegianSea (0.44 #5674, 0.17 #2902, 0.12 #4288), PacificOcean (0.36 #27815, 0.25 #7013, 0.21 #38910)
>> best conf = 0.63
=> the first rule below is the first best rule for 4 predicted values
>> Best rule #20802 for best value:
>> intensional similarity = 5
>> extensional distance = 32
>> proper extension:  MNE; RL; TAD; KGZ; NAM; TCH; RO; MOC; PL; SSD; ...
>> query: (?x78, ?x313) <- ?x78[ has government ?x435<"republic">; has neighbor ?x149; is locatedIn of ?x312[ has hasEstuary ?x313;];]

*> Best rule #8319 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 10
*> proper extension:  RI;
*> query: (?x78, ?x613) <- ?x78[ is locatedIn of ?x256[ a River; is flowsInto of ?x613;]; is neighbor of ?x120; is wasDependentOf of ?x94;]
*> conf = 0.48
ranks of expected_values: 7
EVAL	F	locatedIn!	BarredesEcrins	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	33.000	30.000	1346.000	0.627	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	F	locatedIn!	Saone	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	33.000	30.000	1346.000	0.627	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	F	locatedIn!	Loire	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	33.000	30.000	1346.000	0.627	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	F	locatedIn!	Doubs	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.143	33.000	30.000	1346.000	0.627	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Doubs Loire Saone BarredesEcrins
=> 105 concepts (105 used for prediction)
PRED predicted values (max 10 best out of 1381): Doubs (0.88 #20826, 0.57 #1387, 0.36 #77740), Tajo (0.81 #73577, 0.76 #47204, 0.57 #1387), Guadiana (0.81 #73577, 0.76 #47204, 0.57 #1387), Ebro (0.81 #73577, 0.76 #47204, 0.57 #1387), Douro (0.81 #73577, 0.76 #47204, 0.57 #1387), Guadalquivir (0.81 #73577, 0.76 #47204, 0.57 #1387), Po (0.81 #73577, 0.76 #47204, 0.57 #1387), Arno (0.81 #73577, 0.76 #47204, 0.57 #1387), Tiber (0.81 #73577, 0.76 #47204, 0.57 #1387), Etsch (0.81 #73577, 0.76 #47204, 0.57 #1387)
>> best conf = 0.88
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #20826 for best value:
>> intensional similarity = 15
>> extensional distance = 6
>> proper extension:  CDN;
>> query: (?x78, ?x1114) <- ?x78[ has religion ?x95<Protestant>; has religion ?x109[ is religion of ?x363<ROU>; is religion of ?x379<RA>;]; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x1115[ is hasSource of ?x1114;]; is locatedIn of ?x1211[ is locatedInWater of ?x495;]; is locatedIn of ?x2440[ has inMountains ?x1864;];]
ranks of expected_values: 1, 1289
EVAL	F	locatedIn!	BarredesEcrins	CNN-1.+1._MA	0.000	0.000	0.000	0.000	105.000	105.000	1381.000	0.875	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	F	locatedIn!	Saone	CNN-1.+1._MA	0.000	0.000	0.000	0.000	105.000	105.000	1381.000	0.875	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	F	locatedIn!	Loire	CNN-1.+1._MA	0.000	0.000	0.000	0.001	105.000	105.000	1381.000	0.875	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	F	locatedIn!	Doubs	CNN-1.+1._MA	1.000	1.000	1.000	1.000	105.000	105.000	1381.000	0.875	http://www.semwebtech.org/mondial/10/meta#locatedIn

#565-RM
PRED entity: RM

PRED relation: encompassed
PRED expected values: Africa
=> 39 concepts (39 used for prediction)
PRED predicted values (max 10 best out of 5): Africa (0.73 #24, 0.50 #44, 0.45 #64), Australia-Oceania (0.41 #48, 0.37 #191, 0.30 #103), Asia (0.37 #191, 0.30 #26, 0.29 #116), America (0.29 #60, 0.29 #50, 0.29 #70), Europe (0.25 #27, 0.24 #77, 0.22 #167)
>> best conf = 0.73
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #24 for best value:
>> intensional similarity = 6
>> extensional distance = 13
>> proper extension:  RIM; VN; RCA; CI;
>> query: (?x434, Africa) <- ?x434[ a Country; has religion ?x187<Muslim>; has wasDependentOf ?x78<F>; is locatedIn of ?x60;]
ranks of expected_values: 1
EVAL	RM	encompassed	Africa	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	39.000	39.000	5.000	0.733	http://www.semwebtech.org/mondial/10/meta#encompassed

PRED relation: encompassed
PRED expected values: Africa
=> 87 concepts (87 used for prediction)
PRED predicted values (max 10 best out of 5): Africa (0.67 #121, 0.60 #50, 0.51 #236), Australia-Oceania (0.46 #349, 0.43 #52, 0.41 #219), Asia (0.46 #349, 0.43 #52, 0.41 #149), America (0.43 #163, 0.43 #158, 0.38 #132), Europe (0.25 #144, 0.24 #453, 0.22 #340)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #121 for best value:
>> intensional similarity = 14
>> extensional distance = 10
>> proper extension:  RT;
>> query: (?x434, Africa) <- ?x434[ a Country; has religion ?x187<Muslim>; has wasDependentOf ?x78<F>; is locatedIn of ?x60[ has locatedIn ?x508[ has ethnicGroup ?x244; has language ?x1978; has religion ?x410;]; is flowsInto of ?x242; is locatedInWater of ?x226; is mergesWith of ?x770[ is locatedInWater of ?x216;];];]
ranks of expected_values: 1
EVAL	RM	encompassed	Africa	CNN-1.+1._MA	1.000	1.000	1.000	1.000	87.000	87.000	5.000	0.667	http://www.semwebtech.org/mondial/10/meta#encompassed

#564-Nevis
PRED entity: Nevis

PRED relation: locatedInWater
PRED expected values: CaribbeanSea
=> 59 concepts (58 used for prediction)
PRED predicted values (max 10 best out of 57): CaribbeanSea (0.91 #809, 0.74 #360, 0.71 #231), PacificOcean (0.44 #1424, 0.42 #528, 0.37 #867), Nevis (0.33 #85, 0.06 #1020, 0.05 #724), MediterraneanSea (0.17 #527, 0.16 #611, 0.13 #1254), IndianOcean (0.16 #1283, 0.13 #895, 0.13 #1368), ArcticOcean (0.14 #567, 0.10 #864, 0.10 #822), NorthSea (0.11 #1496, 0.11 #1669, 0.09 #1582), JavaSea (0.08 #689, 0.08 #1289, 0.08 #732), LabradorSea (0.07 #564, 0.05 #861, 0.05 #819), SouthChinaSea (0.07 #1687, 0.06 #1514, 0.06 #1302)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #809 for best value:
>> intensional similarity = 6
>> extensional distance = 51
>> proper extension:  GrandTurk; Streymoy; Mull;
>> query: (?x1753, ?x317) <- ?x1753[ a Island; has belongsToIslands ?x877[ is belongsToIslands of ?x2161[ has locatedInWater ?x317;];]; has locatedInWater ?x182<AtlanticOcean>;]
ranks of expected_values: 1
EVAL	Nevis	locatedInWater	CaribbeanSea	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	59.000	58.000	57.000	0.911	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater
PRED expected values: CaribbeanSea
=> 128 concepts (128 used for prediction)
PRED predicted values (max 10 best out of 63): CaribbeanSea (0.92 #1584, 0.91 #1712, 0.91 #2055), PacificOcean (0.73 #569, 0.70 #526, 0.70 #441), Nevis (0.49 #897, 0.46 #340, 0.10 #682), MediterraneanSea (0.24 #3300, 0.23 #1514, 0.17 #2812), GreenlandSea (0.18 #4602, 0.14 #425, 0.09 #592), NorwegianSea (0.18 #4602, 0.14 #425, 0.09 #573), IrishSea (0.18 #4602, 0.14 #425, 0.07 #2141), LabradorSea (0.18 #4602, 0.14 #425, 0.07 #2141), GulfofMexico (0.18 #4602, 0.14 #425, 0.07 #2141), TheChannel (0.18 #4602, 0.14 #425, 0.07 #2141)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1584 for best value:
>> intensional similarity = 10
>> extensional distance = 34
>> proper extension:  Arran; Lanzarote; Jura; Skye;
>> query: (?x1753, ?x317) <- ?x1753[ a Island; has belongsToIslands ?x877[ is belongsToIslands of ?x703[ a Island; has type ?x704;]; is belongsToIslands of ?x1397[ has locatedInWater ?x317;];]; has locatedIn ?x161; has locatedInWater ?x182<AtlanticOcean>;]
ranks of expected_values: 1
EVAL	Nevis	locatedInWater	CaribbeanSea	CNN-1.+1._MA	1.000	1.000	1.000	1.000	128.000	128.000	63.000	0.920	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#563-HR
PRED entity: HR

PRED relation: neighbor
PRED expected values: SRB
=> 41 concepts (41 used for prediction)
PRED predicted values (max 10 best out of 216): HR (0.51 #3790, 0.51 #3791, 0.43 #648), SK (0.51 #3790, 0.51 #3791, 0.40 #491), SRB (0.51 #3790, 0.51 #3791, 0.33 #134), RO (0.51 #3790, 0.51 #3791, 0.33 #181), BG (0.51 #3790, 0.51 #3791, 0.33 #182), D (0.51 #3790, 0.51 #3791, 0.33 #326), A (0.51 #3790, 0.51 #3791, 0.31 #3789), UA (0.51 #3790, 0.51 #3791, 0.31 #3789), MD (0.51 #3790, 0.51 #3791, 0.31 #3789), I (0.51 #3790, 0.51 #3791, 0.27 #5697)
>> best conf = 0.51
=> the first rule below is the first best rule for 10 predicted values
>> Best rule #3790 for best value:
>> intensional similarity = 5
>> extensional distance = 101
>> proper extension:  SSD;
>> query: (?x156, ?x424) <- ?x156[ has neighbor ?x55; is locatedIn of ?x614[ a River; has locatedIn ?x424[ is neighbor of ?x120;];];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 3
EVAL	HR	neighbor	SRB	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.333	41.000	41.000	216.000	0.510	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: SRB
=> 87 concepts (87 used for prediction)
PRED predicted values (max 10 best out of 227): UA (0.56 #794, 0.56 #793, 0.53 #8039), SRB (0.56 #794, 0.56 #793, 0.53 #8039), RO (0.56 #794, 0.56 #793, 0.53 #8039), SK (0.56 #794, 0.56 #793, 0.53 #8039), HR (0.56 #794, 0.56 #793, 0.53 #8039), A (0.56 #794, 0.56 #793, 0.53 #8039), MD (0.56 #794, 0.56 #793, 0.53 #8039), I (0.56 #794, 0.56 #793, 0.53 #8039), D (0.56 #794, 0.53 #8039, 0.51 #8526), BG (0.56 #794, 0.53 #8039, 0.51 #8526)
>> best conf = 0.56
=> the first rule below is the first best rule for 10 predicted values
>> Best rule #794 for best value:
>> intensional similarity = 14
>> extensional distance = 2
>> proper extension:  MK;
>> query: (?x156, ?x120) <- ?x156[ a Country; has ethnicGroup ?x160; has neighbor ?x55; has religion ?x187<Muslim>; has wasDependentOf ?x1197<Yugoslavia>; is locatedIn of ?x133[ has flowsInto ?x98; has locatedIn ?x120; has locatedIn ?x424[ has encompassed ?x195; has language ?x511;]; is flowsInto of ?x132;];]
>> Best rule #793 for best value:
>> intensional similarity = 13
>> extensional distance = 2
>> proper extension:  MK;
>> query: (?x156, ?x424) <- ?x156[ a Country; has ethnicGroup ?x160; has neighbor ?x55; has religion ?x187<Muslim>; has wasDependentOf ?x1197<Yugoslavia>; is locatedIn of ?x133[ has flowsInto ?x98; has locatedIn ?x424[ has encompassed ?x195; has language ?x511;]; is flowsInto of ?x132;];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 2
EVAL	HR	neighbor	SRB	CNN-1.+1._MA	0.000	1.000	1.000	0.500	87.000	87.000	227.000	0.556	http://www.semwebtech.org/mondial/10/meta#neighbor

#562-Amazonas
PRED entity: Amazonas

PRED relation: locatedIn
PRED expected values: PE
=> 44 concepts (40 used for prediction)
PRED predicted values (max 10 best out of 221): PE (0.90 #5640, 0.90 #5405, 0.89 #2584), BOL (0.74 #3760, 0.74 #3759, 0.68 #3524), ZRE (0.48 #1645, 0.48 #1488, 0.22 #1959), R (0.33 #1179, 0.15 #3057, 0.15 #4000), USA (0.30 #6656, 0.24 #1952, 0.19 #6890), RA (0.25 #321, 0.21 #4230, 0.19 #6584), YV (0.21 #4230, 0.19 #6584, 0.19 #6582), GUY (0.21 #4230, 0.19 #6584, 0.19 #6582), PY (0.21 #4230, 0.16 #5169, 0.15 #5170), PA (0.21 #4230, 0.16 #5169, 0.15 #5170)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5640 for best value:
>> intensional similarity = 5
>> extensional distance = 180
>> proper extension:  Araguaia; Leine; Vaesterdalaelv; Thames;
>> query: (?x214, ?x296) <- ?x214[ a River; has hasEstuary ?x2499[ has locatedIn ?x542;]; has hasSource ?x2254[ has locatedIn ?x296;];]
ranks of expected_values: 1
EVAL	Amazonas	locatedIn	PE	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	44.000	40.000	221.000	0.905	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: PE
=> 139 concepts (139 used for prediction)
PRED predicted values (max 10 best out of 235): PE (0.86 #11355, 0.78 #7330, 0.75 #16575), BOL (0.78 #7330, 0.75 #16575, 0.75 #16574), USA (0.62 #22105, 0.60 #22342, 0.56 #23291), MEX (0.50 #1054, 0.43 #10527, 0.37 #11470), PA (0.50 #1102, 0.40 #236, 0.40 #235), NIC (0.50 #1034, 0.28 #8139, 0.21 #10507), CR (0.50 #1011, 0.25 #8116, 0.16 #11427), GCA (0.50 #979, 0.15 #3573, 0.10 #4517), HCA (0.50 #1152, 0.15 #3746, 0.10 #4690), CDN (0.49 #18706, 0.43 #7157, 0.36 #18708)
>> best conf = 0.86
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #11355 for best value:
>> intensional similarity = 8
>> extensional distance = 44
>> proper extension:  Sobat;
>> query: (?x214, ?x296) <- ?x214[ a River; has hasEstuary ?x2499; has hasSource ?x2254[ has locatedIn ?x296;]; is flowsInto of ?x987[ a River; has hasEstuary ?x2048; has hasSource ?x1691;];]
ranks of expected_values: 1
EVAL	Amazonas	locatedIn	PE	CNN-1.+1._MA	1.000	1.000	1.000	1.000	139.000	139.000	235.000	0.860	http://www.semwebtech.org/mondial/10/meta#locatedIn

#561-Dalaelv
PRED entity: Dalaelv

PRED relation: flowsInto
PRED expected values: BalticSea
=> 55 concepts (44 used for prediction)
PRED predicted values (max 10 best out of 108): Dalaelv (0.29 #620, 0.17 #288, 0.04 #1290), Goetaaelv (0.25 #102, 0.14 #434, 0.11 #766), BalticSea (0.17 #177, 0.14 #509, 0.07 #3354), Kattegat (0.17 #312, 0.14 #644, 0.04 #1314), Vaenern (0.17 #255, 0.14 #587, 0.04 #1257), Skagerrak (0.14 #478, 0.11 #831, 0.11 #810), AtlanticOcean (0.11 #1847, 0.11 #2014, 0.10 #2181), Donau (0.11 #840, 0.09 #1843, 0.09 #1008), MediterraneanSea (0.09 #1357, 0.08 #2359, 0.07 #1858), Zaire (0.07 #1926, 0.06 #2093, 0.06 #2260)
>> best conf = 0.29
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #620 for best value:
>> intensional similarity = 6
>> extensional distance = 5
>> proper extension:  Vaesterdalaelv;
>> query: (?x1328, Dalaelv) <- ?x1328[ a River; has hasEstuary ?x2292[ a Estuary; has locatedIn ?x402<S>;]; has hasSource ?x401;]

*> Best rule #177 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 4
*> proper extension:  Klaraelv;
*> query: (?x1328, BalticSea) <- ?x1328[ a River; has hasEstuary ?x2292[ a Estuary;]; has hasSource ?x401; has locatedIn ?x402<S>;]
*> conf = 0.17
ranks of expected_values: 3
EVAL	Dalaelv	flowsInto	BalticSea	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.333	55.000	44.000	108.000	0.286	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto
PRED expected values: BalticSea
=> 143 concepts (143 used for prediction)
PRED predicted values (max 10 best out of 131): BalticSea (0.40 #2020, 0.25 #2356, 0.25 #680), Kattegat (0.33 #481, 0.25 #815, 0.20 #1149), Skagerrak (0.33 #146, 0.17 #6218, 0.17 #1315), KaraSea (0.29 #1756, 0.14 #1924, 0.10 #2935), Goetaaelv (0.25 #605, 0.14 #11446, 0.13 #11615), Dalaelv (0.20 #1125, 0.20 #958, 0.17 #1460), Donau (0.19 #4042, 0.15 #10286, 0.11 #2689), Vaenern (0.17 #1427, 0.14 #11446, 0.13 #11615), AtlanticOcean (0.16 #3879, 0.15 #5220, 0.15 #6060), MediterraneanSea (0.16 #3890, 0.15 #5735, 0.08 #5566)
>> best conf = 0.40
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2020 for best value:
>> intensional similarity = 12
>> extensional distance = 8
>> proper extension:  Vuoksi; Kymijoki;
>> query: (?x1328, BalticSea) <- ?x1328[ a River; has hasSource ?x401; has locatedIn ?x402[ has religion ?x95<Protestant>; is locatedIn of ?x1043[ a Mountain;]; is locatedIn of ?x1119[ a Estuary;]; is neighbor of ?x170<N>;]; is flowsInto of ?x1118;]
ranks of expected_values: 1
EVAL	Dalaelv	flowsInto	BalticSea	CNN-1.+1._MA	1.000	1.000	1.000	1.000	143.000	143.000	131.000	0.400	http://www.semwebtech.org/mondial/10/meta#flowsInto

#560-GreenlandSea
PRED entity: GreenlandSea

PRED relation: locatedInWater!
PRED expected values: Svalbard
=> 30 concepts (22 used for prediction)
PRED predicted values (max 10 best out of 286): Streymoy (0.50 #238, 0.19 #3536, 0.19 #3535), BaffinIsland (0.33 #350, 0.19 #3536, 0.19 #3535), DevonIsland (0.33 #492, 0.19 #3536, 0.19 #3535), EllesmereIsland (0.33 #399, 0.19 #3536, 0.19 #3535), GreatBritain (0.25 #33, 0.19 #3536, 0.19 #3535), Cuba (0.25 #216, 0.19 #3536, 0.19 #3535), Svalbard (0.25 #103, 0.19 #3536, 0.19 #3535), Hispaniola (0.25 #253, 0.19 #3536, 0.19 #3535), St.Barthelemy (0.25 #247, 0.19 #3536, 0.19 #3535), Martinique (0.25 #208, 0.19 #3536, 0.19 #3535)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #238 for best value:
>> intensional similarity = 5
>> extensional distance = 2
>> proper extension:  AtlanticOcean; NorwegianSea;
>> query: (?x1419, Streymoy) <- ?x1419[ has locatedIn ?x455<IS>; is mergesWith of ?x373[ is locatedInWater of ?x634; is mergesWith of ?x121;];]

*> Best rule #103 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 2
*> proper extension:  AtlanticOcean; NorwegianSea;
*> query: (?x1419, Svalbard) <- ?x1419[ has locatedIn ?x455<IS>; is mergesWith of ?x373[ is locatedInWater of ?x634; is mergesWith of ?x121;];]
*> conf = 0.25
ranks of expected_values: 7
EVAL	GreenlandSea	locatedInWater!	Svalbard	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.143	30.000	22.000	286.000	0.500	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater!
PRED expected values: Svalbard
=> 149 concepts (149 used for prediction)
PRED predicted values (max 10 best out of 775): Svalbard (0.37 #6546, 0.34 #1906, 0.33 #1192), Hekla (0.34 #2179, 0.20 #6273, 0.14 #544), Hvannadalshnukur (0.34 #2179, 0.20 #6273, 0.14 #544), Streymoy (0.34 #1906, 0.33 #1327, 0.33 #1054), GreatBritain (0.34 #1906, 0.33 #1940, 0.33 #33), Cuba (0.34 #1906, 0.33 #216, 0.25 #760), Martinique (0.34 #1906, 0.33 #208, 0.25 #752), Montserrat (0.34 #1906, 0.33 #68, 0.25 #612), Barbuda (0.34 #1906, 0.33 #54, 0.25 #598), SaintVincent (0.34 #1906, 0.33 #4, 0.25 #548)
>> best conf = 0.37
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #6546 for best value:
>> intensional similarity = 11
>> extensional distance = 22
>> proper extension:  Donau; LakeHuron;
>> query: (?x1419, ?x1065) <- ?x1419[ has locatedIn ?x973[ a Country; has encompassed ?x195; has ethnicGroup ?x798; is locatedIn of ?x251[ is mergesWith of ?x263;]; is locatedIn of ?x1065[ a Island;];]; is flowsInto of ?x534; is locatedInWater of ?x807[ a Island;];]
ranks of expected_values: 1
EVAL	GreenlandSea	locatedInWater!	Svalbard	CNN-1.+1._MA	1.000	1.000	1.000	1.000	149.000	149.000	775.000	0.368	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#559-DetroitRiver
PRED entity: DetroitRiver

PRED relation: hasEstuary
PRED expected values: DetroitRiver
=> 35 concepts (33 used for prediction)
PRED predicted values (max 10 best out of 256): SaintLawrenceRiver (0.12 #76, 0.11 #302, 0.10 #528), SaintMarysRiver (0.11 #380, 0.10 #606, 0.08 #832), YukonRiver (0.11 #276, 0.10 #502, 0.08 #728), ColumbiaRiver (0.11 #371, 0.10 #597, 0.08 #823), RiviereRichelieu (0.10 #658, 0.07 #1110, 0.07 #1337), Manicouagan (0.10 #547, 0.04 #4529, 0.02 #2357), NelsonRiver (0.10 #676, 0.04 #4529, 0.01 #4983), SaskatchewanRiver (0.10 #666, 0.04 #4529, 0.01 #4983), Missouri (0.08 #769, 0.05 #1449, 0.04 #4529), StraitsofMackinac (0.08 #864, 0.05 #1544, 0.02 #2222)
>> best conf = 0.12
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #76 for best value:
>> intensional similarity = 6
>> extensional distance = 6
>> proper extension:  AtlanticOcean; PacificOcean;
>> query: (?x219, SaintLawrenceRiver) <- ?x219[ has locatedIn ?x272<CDN>; has locatedIn ?x315<USA>; is flowsInto of ?x218[ is flowsInto of ?x514;];]

*> Best rule #4529 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 147
*> proper extension:  LakeTanganjika; MalakkaStrait; LopNor; Jubba; LakeVictoria; LakeSeseSeko-Albertsee; ChadLake;
*> query: (?x219, ?x2411) <- ?x219[ has locatedIn ?x272[ has ethnicGroup ?x197; is locatedIn of ?x2411[ a Estuary;];]; is flowsInto of ?x218;]
*> conf = 0.04
ranks of expected_values: 24
EVAL	DetroitRiver	hasEstuary	DetroitRiver	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.042	35.000	33.000	256.000	0.125	http://www.semwebtech.org/mondial/10/meta#hasEstuary

PRED relation: hasEstuary
PRED expected values: DetroitRiver
=> 156 concepts (156 used for prediction)
PRED predicted values (max 10 best out of 297): SaintLawrenceRiver (0.33 #756, 0.25 #1663, 0.20 #2797), Angara (0.33 #182, 0.25 #1542, 0.20 #2676), NiagaraRiver (0.33 #674, 0.20 #2941, 0.17 #3168), Donau (0.33 #348, 0.14 #3749, 0.05 #6927), RioSanJuan (0.25 #1395, 0.20 #2529, 0.14 #3663), OhioRiver (0.25 #1716, 0.08 #16586, 0.07 #6027), Asahan (0.20 #2659, 0.14 #3793, 0.04 #7198), Mississippi (0.20 #2742, 0.08 #16586, 0.07 #5919), SaintMarysRiver (0.17 #3556, 0.17 #3330, 0.14 #4009), YukonRiver (0.17 #3226, 0.14 #3905, 0.12 #4358)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #756 for best value:
>> intensional similarity = 10
>> extensional distance = 1
>> proper extension:  SaintLawrenceRiver;
>> query: (?x219, SaintLawrenceRiver) <- ?x219[ a River; has flowsInto ?x2166; has hasSource ?x2299[ a Source;]; has locatedIn ?x272<CDN>; has locatedIn ?x315<USA>; is flowsInto of ?x218[ is flowsInto of ?x514;];]

*> Best rule #17724 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 165
*> proper extension:  Neckar; Buna; Hwangho; Uruguay; RioNegro; Perene; Karun; Apurimac; Thjorsa; Luapula; ...
*> query: (?x219, ?x2514) <- ?x219[ has hasSource ?x2299; has locatedIn ?x272[ has ethnicGroup ?x197; has religion ?x95; is locatedIn of ?x263[ has mergesWith ?x248;]; is locatedIn of ?x2514[ a Estuary;];];]
*> conf = 0.07
ranks of expected_values: 37
EVAL	DetroitRiver	hasEstuary	DetroitRiver	CNN-1.+1._MA	0.000	0.000	0.000	0.027	156.000	156.000	297.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasEstuary

#558-KaraSea
PRED entity: KaraSea

PRED relation: mergesWith
PRED expected values: BarentsSea
=> 28 concepts (26 used for prediction)
PRED predicted values (max 10 best out of 131): BarentsSea (0.50 #471, 0.46 #634, 0.37 #511), KaraSea (0.50 #471, 0.46 #634, 0.37 #511), PacificOcean (0.39 #210, 0.25 #406, 0.24 #526), AtlanticOcean (0.38 #123, 0.35 #201, 0.29 #241), NorwegianSea (0.25 #17, 0.17 #213, 0.17 #253), GreenlandSea (0.25 #33, 0.17 #553, 0.17 #594), EastSibirianSea (0.25 #20, 0.17 #553, 0.17 #594), HudsonBay (0.25 #7, 0.17 #553, 0.17 #594), IndianOcean (0.19 #513, 0.19 #393, 0.18 #432), BeringSea (0.17 #553, 0.14 #66, 0.11 #236)
>> best conf = 0.50
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #471 for best value:
>> intensional similarity = 5
>> extensional distance = 31
>> proper extension:  IrishSea;
>> query: (?x801, ?x251) <- ?x801[ a Sea; is locatedInWater of ?x931[ a Island; has locatedInWater ?x251;]; is mergesWith of ?x263;]
ranks of expected_values: 1
EVAL	KaraSea	mergesWith	BarentsSea	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	28.000	26.000	131.000	0.500	http://www.semwebtech.org/mondial/10/meta#mergesWith

PRED relation: mergesWith
PRED expected values: BarentsSea
=> 72 concepts (72 used for prediction)
PRED predicted values (max 10 best out of 247): AtlanticOcean (0.55 #613, 0.50 #739, 0.38 #862), BarentsSea (0.47 #197, 0.31 #283, 0.25 #402), KaraSea (0.47 #197, 0.31 #283, 0.25 #402), NorwegianSea (0.43 #214, 0.35 #238, 0.33 #174), PacificOcean (0.40 #539, 0.35 #238, 0.33 #1353), LabradorSea (0.35 #238, 0.29 #205, 0.25 #402), BeringSea (0.35 #238, 0.25 #389, 0.22 #471), HudsonBay (0.35 #238, 0.25 #402, 0.22 #566), EastSibirianSea (0.35 #238, 0.22 #566, 0.20 #504), IndianOcean (0.27 #778, 0.22 #1833, 0.20 #1917)
>> best conf = 0.55
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #613 for best value:
>> intensional similarity = 16
>> extensional distance = 9
>> proper extension:  IndianOcean; MediterraneanSea; CaribbeanSea; TheChannel; GulfofMexico;
>> query: (?x801, AtlanticOcean) <- ?x801[ a Sea; is flowsInto of ?x800; is mergesWith of ?x263[ a Sea; has locatedIn ?x315<USA>; is locatedInWater of ?x1075<Greenland>; is locatedInWater of ?x1949[ a Island;]; is mergesWith of ?x249<LabradorSea>; is mergesWith of ?x452[ is flowsInto of ?x919;]; is mergesWith of ?x1419<GreenlandSea>;];]

*> Best rule #197 for first EXPECTED value:
*> intensional similarity = 13
*> extensional distance = 4
*> proper extension:  HudsonBay; EastSibirianSea; GreenlandSea;
*> query: (?x801, ?x251) <- ?x801[ a Sea; is flowsInto of ?x800[ a River; has hasEstuary ?x1301[ a Estuary;]; has hasSource ?x2232; has locatedIn ?x73;]; is locatedInWater of ?x931[ a Island; has locatedInWater ?x251[ a Sea;];]; is mergesWith of ?x263<ArcticOcean>;]
*> conf = 0.47
ranks of expected_values: 2
EVAL	KaraSea	mergesWith	BarentsSea	CNN-1.+1._MA	0.000	1.000	1.000	0.500	72.000	72.000	247.000	0.545	http://www.semwebtech.org/mondial/10/meta#mergesWith

#557-SF
PRED entity: SF

PRED relation: locatedIn!
PRED expected values: BalticSea Saimaa
=> 42 concepts (39 used for prediction)
PRED predicted values (max 10 best out of 1370): BalticSea (0.91 #11195, 0.89 #5597, 0.71 #9795), BarentsSea (0.71 #9795, 0.70 #19590, 0.68 #30786), OzeroLadoga (0.71 #9795, 0.70 #19590, 0.68 #30786), PacificOcean (0.68 #12680, 0.33 #9881, 0.31 #7082), AtlanticOcean (0.48 #5639, 0.48 #1441, 0.33 #39223), IndianOcean (0.32 #4200, 0.16 #9798, 0.10 #43382), CaribbeanSea (0.31 #5703, 0.24 #1505, 0.22 #19696), Skagerrak (0.25 #928, 0.09 #22389, 0.09 #3726), Klaraelv (0.25 #1342, 0.09 #22389, 0.09 #4140), Sulitjelma (0.25 #430, 0.09 #22389, 0.09 #3228)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #11195 for best value:
>> intensional similarity = 5
>> extensional distance = 41
>> proper extension:  SGP;
>> query: (?x565, ?x146) <- ?x565[ a Country; has ethnicGroup ?x1193; has wasDependentOf ?x73; is locatedIn of ?x804[ has locatedInWater ?x146;];]
ranks of expected_values: 1, 41
EVAL	SF	locatedIn!	Saimaa	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.025	42.000	39.000	1370.000	0.910	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	SF	locatedIn!	BalticSea	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	42.000	39.000	1370.000	0.910	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: BalticSea Saimaa
=> 118 concepts (118 used for prediction)
PRED predicted values (max 10 best out of 1407): BalticSea (0.92 #71471, 0.92 #49050, 0.90 #47647), PacificOcean (0.83 #47735, 0.81 #54741, 0.50 #12700), Saimaa (0.80 #40637, 0.71 #19617, 0.69 #58859), OzeroLadoga (0.76 #61666, 0.75 #86886, 0.74 #84083), BarentsSea (0.76 #61666, 0.75 #86886, 0.74 #84083), AtlanticOcean (0.62 #44888, 0.51 #86929, 0.50 #56097), Paatsjoki (0.60 #89689, 0.59 #140131, 0.59 #120511), NorwegianSea (0.50 #57457, 0.37 #61668, 0.29 #149946), CaribbeanSea (0.45 #30934, 0.42 #68776, 0.41 #115015), BlackSea (0.43 #19633, 0.36 #40652, 0.33 #7023)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #71471 for best value:
>> intensional similarity = 13
>> extensional distance = 34
>> proper extension:  COM;
>> query: (?x565, ?x146) <- ?x565[ a Country; has encompassed ?x195; has government ?x435; has religion ?x95[ is religion of ?x575<NL>;]; is locatedIn of ?x804[ a Island; has locatedInWater ?x146;]; is locatedIn of ?x1396[ is flowsInto of ?x1573;]; is locatedIn of ?x2030[ a Mountain;];]
>> Best rule #49050 for best value:
>> intensional similarity = 13
>> extensional distance = 16
>> proper extension:  PAL;
>> query: (?x565, ?x146) <- ?x565[ a Country; has government ?x435; is locatedIn of ?x631[ has locatedIn ?x73<R>;]; is locatedIn of ?x804[ has belongsToIslands ?x944[ a Islands;]; has locatedInWater ?x146[ has locatedIn ?x120; is flowsInto of ?x590;];]; is locatedIn of ?x1396[ is flowsInto of ?x1573;];]
ranks of expected_values: 1, 3
EVAL	SF	locatedIn!	Saimaa	CNN-1.+1._MA	0.000	1.000	1.000	0.500	118.000	118.000	1407.000	0.917	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	SF	locatedIn!	BalticSea	CNN-1.+1._MA	1.000	1.000	1.000	1.000	118.000	118.000	1407.000	0.917	http://www.semwebtech.org/mondial/10/meta#locatedIn

#556-RI
PRED entity: RI

PRED relation: locatedIn!
PRED expected values: Borneo Ternate GunungBinaiya
=> 27 concepts (17 used for prediction)
PRED predicted values (max 10 best out of 1339): CaribbeanSea (0.47 #5642, 0.19 #9799, 0.17 #19500), AtlanticOcean (0.33 #19440, 0.32 #16670, 0.26 #20825), Borneo (0.33 #130, 0.25 #2900, 0.25 #1515), Labuan (0.33 #716, 0.25 #3486, 0.25 #2101), Kinabalu (0.33 #832, 0.25 #3602, 0.25 #2217), SuluSea (0.33 #296, 0.25 #3066, 0.25 #1681), Tahan (0.33 #40, 0.25 #2810, 0.25 #1425), Phuket (0.25 #2383, 0.08 #5541, 0.06 #23556), KoSamui (0.25 #1998, 0.08 #5541, 0.06 #23556), Singapore (0.25 #3456, 0.08 #5541, 0.06 #23556)
>> best conf = 0.47
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5642 for best value:
>> intensional similarity = 6
>> extensional distance = 13
>> proper extension:  R; GCA; RCH; CO; PE; USA; CR; NIC; MEX; ES; ...
>> query: (?x217, CaribbeanSea) <- ?x217[ has religion ?x95; is locatedIn of ?x282<PacificOcean>; is locatedIn of ?x385[ is locatedInWater of ?x1299;]; is neighbor of ?x376;]

*> Best rule #130 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 1
*> proper extension:  MAL;
*> query: (?x217, Borneo) <- ?x217[ has ethnicGroup ?x425; has religion ?x95; is locatedIn of ?x385<MalakkaStrait>; is locatedIn of ?x1098[ has belongsToIslands ?x1099;];]
*> conf = 0.33
ranks of expected_values: 3, 240
EVAL	RI	locatedIn!	GunungBinaiya	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	27.000	17.000	1339.000	0.467	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	RI	locatedIn!	Ternate	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.004	27.000	17.000	1339.000	0.467	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	RI	locatedIn!	Borneo	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.333	27.000	17.000	1339.000	0.467	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Borneo Ternate GunungBinaiya
=> 89 concepts (88 used for prediction)
PRED predicted values (max 10 best out of 1409): Mt.Wilhelm (0.81 #61101, 0.33 #5280, 0.33 #1391), Mt.Giluwe (0.81 #61101, 0.33 #5102, 0.33 #1391), CaribbeanSea (0.54 #69540, 0.50 #58427, 0.45 #34812), AtlanticOcean (0.54 #107005, 0.52 #108398, 0.50 #61105), MediterraneanSea (0.50 #19527, 0.47 #63963, 0.47 #62574), NorthSea (0.50 #18082, 0.33 #41673, 0.33 #20859), SeaofOkhotsk (0.40 #14097, 0.35 #8331, 0.33 #16878), SeaofJapan (0.40 #13965, 0.35 #8331, 0.33 #16746), LakeVictoria (0.38 #27011, 0.25 #38110, 0.25 #10346), Akagera (0.38 #27010, 0.21 #46439, 0.20 #27775)
>> best conf = 0.81
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #61101 for best value:
>> intensional similarity = 11
>> extensional distance = 18
>> proper extension:  WV;
>> query: (?x217, ?x1697) <- ?x217[ has encompassed ?x211; has government ?x435; is locatedIn of ?x60[ is flowsInto of ?x242; is locatedInWater of ?x594[ a Island;]; is mergesWith of ?x182<AtlanticOcean>;]; is locatedIn of ?x1074[ a Island; is locatedOnIsland of ?x1697;];]

*> Best rule #130 for first EXPECTED value:
*> intensional similarity = 13
*> extensional distance = 1
*> proper extension:  MAL;
*> query: (?x217, Borneo) <- ?x217[ has government ?x435; is locatedIn of ?x282[ has mergesWith ?x271; is locatedInWater of ?x458[ a Island;]; is locatedInWater of ?x1747[ has belongsToIslands ?x1919; has type ?x150;];]; is locatedIn of ?x385<MalakkaStrait>; is locatedIn of ?x1074[ is locatedOnIsland of ?x1697;]; is neighbor of ?x376;]
*> conf = 0.33
ranks of expected_values: 19, 837
EVAL	RI	locatedIn!	GunungBinaiya	CNN-1.+1._MA	0.000	0.000	0.000	0.000	89.000	88.000	1409.000	0.808	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	RI	locatedIn!	Ternate	CNN-1.+1._MA	0.000	0.000	0.000	0.001	89.000	88.000	1409.000	0.808	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	RI	locatedIn!	Borneo	CNN-1.+1._MA	0.000	0.000	0.000	0.053	89.000	88.000	1409.000	0.808	http://www.semwebtech.org/mondial/10/meta#locatedIn

#555-LagunaMarChiquita
PRED entity: LagunaMarChiquita

PRED relation: locatedIn
PRED expected values: RA
=> 5 concepts (5 used for prediction)
PRED predicted values (max 10 best out of 66): USA (0.11 #544, 0.10 #308, 0.08 #780), EAT (0.10 #175, 0.05 #411, 0.04 #647), CN (0.10 #56, 0.05 #292, 0.03 #528), IR (0.10 #71, 0.05 #307, 0.03 #543), ETH (0.10 #115, 0.05 #351, 0.03 #587), KAZ (0.10 #93, 0.05 #329, 0.02 #565), CDN (0.08 #535, 0.03 #1007, 0.02 #299), BOL (0.07 #153, 0.05 #389, 0.03 #625), EAK (0.07 #114, 0.03 #350, 0.02 #586), AUS (0.07 #281, 0.03 #517, 0.03 #753)
>> best conf = 0.11
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #544 for best value:
>> intensional similarity = 1
>> extensional distance = 142
>> proper extension:  LakeSkutari; StarnbergerSee; LakeHuron; MaleboPool; ChickamaugaLake; LakeTanganjika; LakeNicaragua; LakeMweru; KuybyshevReservoir; LagunadeBay; ...
>> query: (?x75, USA) <- ?x75[ a Lake;]

*> Best rule #795 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 261
*> proper extension:  Saipan; Tahat; Stromboli; SaintVincent; Pico; Cayambe; Elbrus; QueenMarysPeak; MtAdams; Flores; ...
*> query: (?x75, RA) <- ?x75[ has type ?x762;]
*> conf = 0.01
ranks of expected_values: 64
EVAL	LagunaMarChiquita	locatedIn	RA	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.016	5.000	5.000	66.000	0.111	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: RA
=> 5 concepts (5 used for prediction)
PRED predicted values (max 10 best out of 66): USA (0.11 #544, 0.10 #308, 0.08 #780), EAT (0.10 #175, 0.05 #411, 0.04 #647), CN (0.10 #56, 0.05 #292, 0.03 #528), IR (0.10 #71, 0.05 #307, 0.03 #543), ETH (0.10 #115, 0.05 #351, 0.03 #587), KAZ (0.10 #93, 0.05 #329, 0.02 #565), CDN (0.08 #535, 0.03 #1007, 0.02 #299), BOL (0.07 #153, 0.05 #389, 0.03 #625), EAK (0.07 #114, 0.03 #350, 0.02 #586), AUS (0.07 #281, 0.03 #517, 0.03 #753)
>> best conf = 0.11
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #544 for best value:
>> intensional similarity = 1
>> extensional distance = 142
>> proper extension:  LakeSkutari; StarnbergerSee; LakeHuron; MaleboPool; ChickamaugaLake; LakeTanganjika; LakeNicaragua; LakeMweru; KuybyshevReservoir; LagunadeBay; ...
>> query: (?x75, USA) <- ?x75[ a Lake;]

*> Best rule #795 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 261
*> proper extension:  Saipan; Tahat; Stromboli; SaintVincent; Pico; Cayambe; Elbrus; QueenMarysPeak; MtAdams; Flores; ...
*> query: (?x75, RA) <- ?x75[ has type ?x762;]
*> conf = 0.01
ranks of expected_values: 64
EVAL	LagunaMarChiquita	locatedIn	RA	CNN-1.+1._MA	0.000	0.000	0.000	0.016	5.000	5.000	66.000	0.111	http://www.semwebtech.org/mondial/10/meta#locatedIn

#554-WD
PRED entity: WD

PRED relation: religion
PRED expected values: RomanCatholic
=> 38 concepts (38 used for prediction)
PRED predicted values (max 10 best out of 36): RomanCatholic (0.87 #294, 0.86 #253, 0.82 #212), Muslim (0.54 #619, 0.51 #1320, 0.50 #1032), Anglican (0.50 #17, 0.33 #345, 0.27 #181), Christian (0.36 #618, 0.31 #866, 0.30 #1031), JehovasWitnesses (0.33 #143, 0.21 #821, 0.18 #863), ChristianOrthodox (0.32 #411, 0.26 #370, 0.21 #739), Hindu (0.25 #9, 0.21 #821, 0.21 #624), ChurchofGod (0.25 #27, 0.21 #821, 0.18 #863), UnitedChurch (0.25 #36, 0.21 #821, 0.16 #1522), Buddhist (0.23 #585, 0.21 #626, 0.18 #863)
>> best conf = 0.87
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #294 for best value:
>> intensional similarity = 8
>> extensional distance = 13
>> proper extension:  CUR;
>> query: (?x922, RomanCatholic) <- ?x922[ a Country; has encompassed ?x521; has religion ?x95; is locatedIn of ?x317<CaribbeanSea>; is locatedIn of ?x609[ a Island; has locatedInWater ?x182;];]
ranks of expected_values: 1
EVAL	WD	religion	RomanCatholic	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	38.000	38.000	36.000	0.867	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion
PRED expected values: RomanCatholic
=> 67 concepts (67 used for prediction)
PRED predicted values (max 10 best out of 37): RomanCatholic (0.88 #1383, 0.86 #1297, 0.85 #1967), Muslim (0.60 #2049, 0.56 #832, 0.55 #1038), Anglican (0.51 #1418, 0.50 #387, 0.50 #263), Hindu (0.51 #1418, 0.49 #1333, 0.49 #1332), Jewish (0.51 #1418, 0.49 #1333, 0.49 #1332), Buddhist (0.51 #1418, 0.49 #1333, 0.49 #1332), Sikh (0.51 #1418, 0.49 #1333, 0.49 #1332), ChristianOrthodox (0.50 #1078, 0.43 #1164, 0.33 #829), JehovasWitnesses (0.37 #870, 0.36 #1501, 0.33 #890), Christian (0.36 #1037, 0.33 #831, 0.33 #788)
>> best conf = 0.88
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1383 for best value:
>> intensional similarity = 17
>> extensional distance = 23
>> proper extension:  ROU; DOM; PA;
>> query: (?x922, RomanCatholic) <- ?x922[ a Country; has encompassed ?x521<America>; has government ?x254; has religion ?x95; has wasDependentOf ?x81[ has religion ?x109; is locatedIn of ?x373[ is mergesWith of ?x251;]; is wasDependentOf of ?x272[ has ethnicGroup ?x197; is locatedIn of ?x218;]; is wasDependentOf of ?x366[ a Country; is locatedIn of ?x262; is neighbor of ?x91;];]; is locatedIn of ?x317;]
ranks of expected_values: 1
EVAL	WD	religion	RomanCatholic	CNN-1.+1._MA	1.000	1.000	1.000	1.000	67.000	67.000	37.000	0.880	http://www.semwebtech.org/mondial/10/meta#religion

#553-Galician
PRED entity: Galician

PRED relation: language!
PRED expected values: E
=> 27 concepts (27 used for prediction)
PRED predicted values (max 10 best out of 173): SF (0.44 #692, 0.23 #1179, 0.19 #1544), E (0.40 #978, 0.40 #244, 0.38 #612), F (0.40 #244, 0.37 #365, 0.37 #490), BZ (0.38 #578, 0.35 #944, 0.32 #1067), P (0.37 #365, 0.36 #977, 0.35 #611), GBZ (0.37 #365, 0.36 #977, 0.35 #611), L (0.33 #213, 0.33 #93, 0.31 #706), NLSM (0.33 #121, 0.33 #1, 0.26 #980), BOL (0.33 #209, 0.30 #333, 0.25 #456), PE (0.33 #162, 0.30 #286, 0.25 #409)
>> best conf = 0.44
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #692 for best value:
>> intensional similarity = 8
>> extensional distance = 14
>> proper extension:  Maltese;
>> query: (?x1653, SF) <- ?x1653[ a Language; is language of ?x789[ a Country; has encompassed ?x195<Europe>; has language ?x539[ is language of ?x643<GBJ>;];];]

*> Best rule #978 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 15
*> proper extension:  Basque;
*> query: (?x1653, ?x149) <- ?x1653[ a Language; is language of ?x789[ has encompassed ?x195; has ethnicGroup ?x746; is neighbor of ?x149[ has language ?x796<Spanish>;];];]
*> conf = 0.40
ranks of expected_values: 2
EVAL	Galician	language!	E	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	27.000	27.000	173.000	0.438	http://www.semwebtech.org/mondial/10/meta#language

PRED relation: language!
PRED expected values: E
=> 41 concepts (41 used for prediction)
PRED predicted values (max 10 best out of 177): L (0.56 #720, 0.50 #1391, 0.50 #1357), NLSM (0.56 #755, 0.43 #247, 0.40 #370), NZ (0.50 #1079, 0.36 #3301, 0.36 #1767), SF (0.47 #1849, 0.32 #2739, 0.28 #2995), CUR (0.44 #792, 0.40 #370, 0.36 #3301), CH (0.44 #535, 0.40 #1425, 0.38 #498), E (0.43 #389, 0.40 #370, 0.38 #2148), BZ (0.40 #370, 0.40 #1987, 0.38 #2114), BOL (0.40 #370, 0.36 #3301, 0.34 #624), PE (0.40 #370, 0.36 #3301, 0.34 #624)
>> best conf = 0.56
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #720 for best value:
>> intensional similarity = 24
>> extensional distance = 7
>> proper extension:  Luxembourgish;
>> query: (?x1653, L) <- ?x1653[ a Language; is language of ?x789[ a Country; has encompassed ?x195<Europe>; has ethnicGroup ?x1672[ a EthnicGroup; is ethnicGroup of ?x234<CH>; is ethnicGroup of ?x1577<MC>;]; has language ?x51<French>; has language ?x539<Portuguese>; has language ?x796[ a Language; is language of ?x50<NLSM>; is language of ?x783<PA>;]; has religion ?x352; is neighbor of ?x78<F>;];]

*> Best rule #389 for first EXPECTED value:
*> intensional similarity = 18
*> extensional distance = 5
*> proper extension:  Basque;
*> query: (?x1653, E) <- ?x1653[ a Language; is language of ?x789[ a Country; has ethnicGroup ?x746; has language ?x51[ a Language; is language of ?x50<NLSM>;]; has language ?x796<Spanish>; is neighbor of ?x149[ a Country; has government ?x1657; has language ?x1112; has religion ?x352; is locatedIn of ?x275<MediterraneanSea>; is wasDependentOf of ?x148;];];]
*> conf = 0.43
ranks of expected_values: 7
EVAL	Galician	language!	E	CNN-1.+1._MA	0.000	0.000	1.000	0.143	41.000	41.000	177.000	0.556	http://www.semwebtech.org/mondial/10/meta#language

#552-KOS
PRED entity: KOS

PRED relation: neighbor
PRED expected values: SRB
=> 40 concepts (35 used for prediction)
PRED predicted values (max 10 best out of 177): SRB (0.91 #2884, 0.90 #960, 0.90 #4483), KOS (0.51 #2882, 0.27 #4966, 0.25 #2723), GR (0.27 #4966, 0.25 #2723, 0.13 #67), HR (0.27 #4966, 0.25 #2723, 0.13 #20), BG (0.27 #4966, 0.25 #2723, 0.09 #5447), RO (0.27 #4966, 0.25 #2723, 0.09 #5447), BIH (0.27 #4966, 0.25 #2723, 0.09 #5447), H (0.27 #4966, 0.14 #523, 0.10 #1323), BR (0.19 #252, 0.17 #412, 0.13 #732), IL (0.17 #44, 0.07 #2767, 0.06 #2445)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2884 for best value:
>> intensional similarity = 6
>> extensional distance = 67
>> proper extension:  F; I;
>> query: (?x692, ?x701) <- ?x692[ has encompassed ?x195; is locatedIn of ?x887[ has flowsInto ?x698;]; is neighbor of ?x701[ has ethnicGroup ?x354; has language ?x511;];]
ranks of expected_values: 1
EVAL	KOS	neighbor	SRB	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	40.000	35.000	177.000	0.914	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: SRB
=> 93 concepts (93 used for prediction)
PRED predicted values (max 10 best out of 216): SRB (0.91 #9862, 0.90 #3058, 0.90 #7762), KOS (0.50 #916, 0.49 #8414, 0.38 #6626), DZ (0.50 #2513, 0.40 #2352, 0.21 #6076), BIH (0.50 #483, 0.38 #6626, 0.33 #642), TR (0.47 #3579, 0.44 #4065, 0.43 #1152), HR (0.44 #1627, 0.40 #2112, 0.38 #6626), GR (0.41 #10025, 0.38 #6626, 0.34 #2736), H (0.40 #1813, 0.38 #6626, 0.34 #2736), SLO (0.38 #6626, 0.34 #2736, 0.31 #1769), RO (0.38 #6626, 0.33 #988, 0.31 #318)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #9862 for best value:
>> intensional similarity = 16
>> extensional distance = 56
>> proper extension:  ROK;
>> query: (?x692, ?x904) <- ?x692[ a Country; has neighbor ?x106[ a Country; is locatedIn of ?x104;]; has neighbor ?x204[ has encompassed ?x195; has language ?x1251; has neighbor ?x399; has wasDependentOf ?x1656; is locatedIn of ?x1516[ a Mountain;];]; is locatedIn of ?x784; is neighbor of ?x904[ is locatedIn of ?x132; is neighbor of ?x236[ has ethnicGroup ?x164;];];]
ranks of expected_values: 1
EVAL	KOS	neighbor	SRB	CNN-1.+1._MA	1.000	1.000	1.000	1.000	93.000	93.000	216.000	0.908	http://www.semwebtech.org/mondial/10/meta#neighbor

#551-AFG
PRED entity: AFG

PRED relation: ethnicGroup
PRED expected values: Tajik Uzbek Hazara
=> 33 concepts (33 used for prediction)
PRED predicted values (max 10 best out of 223): Russian (0.67 #1101, 0.33 #844, 0.33 #330), Uzbek (0.56 #1184, 0.33 #927, 0.33 #413), Tajik (0.33 #889, 0.33 #375, 0.29 #3088), Tatar (0.33 #1115, 0.33 #344, 0.29 #3088), Arab (0.33 #525, 0.29 #3088, 0.26 #2573), Turkmen (0.33 #717, 0.29 #3088, 0.26 #2573), Karakalpak (0.33 #370, 0.29 #3088, 0.26 #2573), GilakiMazandarani (0.33 #684, 0.29 #3088, 0.26 #2573), Azerbaijani (0.33 #602, 0.29 #3088, 0.26 #2573), Lur (0.33 #599, 0.29 #3088, 0.26 #2573)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1101 for best value:
>> intensional similarity = 7
>> extensional distance = 7
>> proper extension:  CN;
>> query: (?x381, Russian) <- ?x381[ has neighbor ?x277[ has encompassed ?x175; has language ?x278; has religion ?x56; is locatedIn of ?x1835<UstUrt>;]; is locatedIn of ?x82;]

*> Best rule #1184 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 7
*> proper extension:  CN;
*> query: (?x381, Uzbek) <- ?x381[ has neighbor ?x277[ has encompassed ?x175; has language ?x278; has religion ?x56; is locatedIn of ?x1835<UstUrt>;]; is locatedIn of ?x82;]
*> conf = 0.56
ranks of expected_values: 2, 3
EVAL	AFG	ethnicGroup	Hazara	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	33.000	33.000	223.000	0.667	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	AFG	ethnicGroup	Uzbek	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	33.000	33.000	223.000	0.667	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	AFG	ethnicGroup	Tajik	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	33.000	33.000	223.000	0.667	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: Tajik Uzbek Hazara
=> 105 concepts (105 used for prediction)
PRED predicted values (max 10 best out of 251): Russian (0.60 #1362, 0.56 #6776, 0.50 #3423), Uzbek (0.50 #3506, 0.43 #2733, 0.43 #2217), Ukrainian (0.43 #2319, 0.42 #5672, 0.40 #1290), Tatar (0.43 #259, 0.40 #1376, 0.33 #4211), Tajik (0.43 #259, 0.33 #118, 0.32 #11345), Turkmen (0.43 #259, 0.32 #11345, 0.32 #14953), Karakalpak (0.43 #259, 0.32 #14953, 0.31 #2835), German (0.42 #5681, 0.28 #7744, 0.27 #8518), Arab (0.36 #4649, 0.32 #11345, 0.32 #14953), European (0.34 #10320, 0.33 #14961, 0.33 #15735)
>> best conf = 0.60
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1362 for best value:
>> intensional similarity = 16
>> extensional distance = 3
>> proper extension:  R;
>> query: (?x381, Russian) <- ?x381[ a Country; has ethnicGroup ?x2116[ a EthnicGroup;]; has neighbor ?x83[ has language ?x559;]; has neighbor ?x232<CN>; has neighbor ?x277[ is locatedIn of ?x289[ a Desert;]; is locatedIn of ?x1835<UstUrt>;]; is locatedIn of ?x276[ a Estuary;]; is locatedIn of ?x300[ a River;];]

*> Best rule #3506 for first EXPECTED value:
*> intensional similarity = 16
*> extensional distance = 6
*> proper extension:  TM;
*> query: (?x381, Uzbek) <- ?x381[ a Country; has encompassed ?x175; has ethnicGroup ?x2116; has government ?x2442; has language ?x1033; has neighbor ?x232[ has neighbor ?x334; is locatedIn of ?x328[ a Mountain;]; is locatedIn of ?x576[ has inMountains ?x309;]; is locatedIn of ?x1748<Irtysch>; is neighbor of ?x130<KGZ>;]; is locatedIn of ?x82;]
*> conf = 0.50
ranks of expected_values: 2, 5
EVAL	AFG	ethnicGroup	Hazara	CNN-1.+1._MA	0.000	0.000	0.000	0.000	105.000	105.000	251.000	0.600	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	AFG	ethnicGroup	Uzbek	CNN-1.+1._MA	0.000	1.000	1.000	0.500	105.000	105.000	251.000	0.600	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	AFG	ethnicGroup	Tajik	CNN-1.+1._MA	0.000	0.000	1.000	0.250	105.000	105.000	251.000	0.600	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#550-SVAX
PRED entity: SVAX

PRED relation: locatedIn!
PRED expected values: ArcticOcean
=> 28 concepts (22 used for prediction)
PRED predicted values (max 10 best out of 1232): CaribbeanSea (0.39 #4363, 0.36 #2944, 0.34 #14302), MediterraneanSea (0.38 #5760, 0.28 #8599, 0.25 #1421), NorthSea (0.33 #22, 0.18 #5701, 0.13 #8540), Aust-Vagoey (0.33 #1107, 0.08 #7099, 0.05 #17039), Skagerrak (0.33 #939, 0.06 #6618, 0.04 #9457), Paatsjoki (0.33 #282, 0.05 #7381, 0.04 #26983), Klaraelv (0.33 #1361, 0.03 #31245, 0.03 #7040), Sulitjelma (0.33 #432, 0.03 #31245, 0.03 #6111), Jostedalsbre (0.33 #1379, 0.03 #31245, 0.03 #7058), Joekul (0.33 #1347, 0.03 #31245, 0.03 #7026)
>> best conf = 0.39
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4363 for best value:
>> intensional similarity = 7
>> extensional distance = 16
>> proper extension:  NLSM;
>> query: (?x973, CaribbeanSea) <- ?x973[ a Country; has dependentOf ?x170; has government ?x1319; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x1419[ is locatedInWater of ?x807;];]

*> Best rule #1493 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 2
*> proper extension:  IS;
*> query: (?x973, ArcticOcean) <- ?x973[ a Country; has ethnicGroup ?x798[ a EthnicGroup;]; has government ?x1319; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x1419<GreenlandSea>;]
*> conf = 0.25
ranks of expected_values: 23
EVAL	SVAX	locatedIn!	ArcticOcean	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.043	28.000	22.000	1232.000	0.389	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: ArcticOcean
=> 69 concepts (65 used for prediction)
PRED predicted values (max 10 best out of 1371): PacificOcean (0.89 #28555, 0.40 #32827, 0.38 #65541), ArcticOcean (0.89 #24203, 0.50 #4269, 0.40 #5690), CaribbeanSea (0.50 #15760, 0.50 #10065, 0.41 #55605), LabradorSea (0.43 #29893, 0.38 #4267, 0.33 #4334), BeringSea (0.43 #29893, 0.38 #4267, 0.11 #71146), EastSibirianSea (0.43 #29893, 0.38 #4267, 0.11 #71146), KaraSea (0.43 #29893, 0.38 #4267, 0.11 #71146), HudsonBay (0.43 #29893, 0.38 #4267, 0.11 #71146), NorthSea (0.40 #5690, 0.38 #4267, 0.33 #22), Aust-Vagoey (0.33 #1107, 0.31 #25627, 0.25 #5692)
>> best conf = 0.89
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #28555 for best value:
>> intensional similarity = 11
>> extensional distance = 34
>> proper extension:  ES; TOK;
>> query: (?x973, PacificOcean) <- ?x973[ a Country; has encompassed ?x195; has ethnicGroup ?x979[ a EthnicGroup;]; is locatedIn of ?x1419[ is flowsInto of ?x534; is locatedInWater of ?x807; is mergesWith of ?x263[ is locatedInWater of ?x478; is mergesWith of ?x452<EastSibirianSea>;];];]

*> Best rule #24203 for first EXPECTED value:
*> intensional similarity = 15
*> extensional distance = 18
*> proper extension:  R;
*> query: (?x973, ?x263) <- ?x973[ has ethnicGroup ?x979[ a EthnicGroup; is ethnicGroup of ?x170[ a Country; is locatedIn of ?x121;];]; is locatedIn of ?x1065[ has belongsToIslands ?x143; has locatedInWater ?x263;]; is locatedIn of ?x1419[ has locatedIn ?x455[ has encompassed ?x195;]; is locatedInWater of ?x807[ is locatedOnIsland of ?x806;]; is locatedInWater of ?x1075<Greenland>;];]
*> conf = 0.89
ranks of expected_values: 2
EVAL	SVAX	locatedIn!	ArcticOcean	CNN-1.+1._MA	0.000	1.000	1.000	0.500	69.000	65.000	1371.000	0.889	http://www.semwebtech.org/mondial/10/meta#locatedIn

#549-VU
PRED entity: VU

PRED relation: encompassed
PRED expected values: Australia-Oceania
=> 38 concepts (38 used for prediction)
PRED predicted values (max 10 best out of 5): Australia-Oceania (0.67 #33, 0.65 #28, 0.62 #23), America (0.50 #45, 0.45 #40, 0.44 #50), Europe (0.43 #17, 0.37 #161, 0.32 #97), Asia (0.37 #161, 0.20 #156, 0.20 #167), Africa (0.26 #114, 0.25 #89, 0.25 #104)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #33 for best value:
>> intensional similarity = 6
>> extensional distance = 16
>> proper extension:  GUAM;
>> query: (?x439, Australia-Oceania) <- ?x439[ has ethnicGroup ?x1672; has government ?x1174; is locatedIn of ?x282<PacificOcean>; is locatedIn of ?x1488[ has belongsToIslands ?x2250;];]
ranks of expected_values: 1
EVAL	VU	encompassed	Australia-Oceania	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	38.000	38.000	5.000	0.667	http://www.semwebtech.org/mondial/10/meta#encompassed

PRED relation: encompassed
PRED expected values: Australia-Oceania
=> 73 concepts (73 used for prediction)
PRED predicted values (max 10 best out of 5): Africa (0.65 #89, 0.64 #30, 0.62 #68), America (0.57 #186, 0.52 #159, 0.48 #79), Australia-Oceania (0.50 #110, 0.48 #82, 0.44 #24), Asia (0.50 #12, 0.41 #192, 0.36 #357), Europe (0.50 #18, 0.36 #357, 0.35 #389)
>> best conf = 0.65
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #89 for best value:
>> intensional similarity = 13
>> extensional distance = 21
>> proper extension:  BF;
>> query: (?x439, Africa) <- ?x439[ has religion ?x429[ a Religion; is religion of ?x853[ has encompassed ?x211; has government ?x854; is locatedIn of ?x1074; is neighbor of ?x217<RI>;]; is religion of ?x1008[ has ethnicGroup ?x1009;];]; has wasDependentOf ?x78<F>; is locatedIn of ?x282;]

*> Best rule #110 for first EXPECTED value:
*> intensional similarity = 9
*> extensional distance = 28
*> proper extension:  PITC; COOK; RC;
*> query: (?x439, Australia-Oceania) <- ?x439[ a Country; has ethnicGroup ?x1672; has government ?x1174; has religion ?x713[ is religion of ?x196[ has language ?x247; is locatedIn of ?x60;];]; is locatedIn of ?x282<PacificOcean>;]
*> conf = 0.50
ranks of expected_values: 3
EVAL	VU	encompassed	Australia-Oceania	CNN-1.+1._MA	0.000	1.000	1.000	0.333	73.000	73.000	5.000	0.652	http://www.semwebtech.org/mondial/10/meta#encompassed

#548-STP
PRED entity: STP

PRED relation: religion
PRED expected values: Protestant
=> 58 concepts (58 used for prediction)
PRED predicted values (max 10 best out of 35): RomanCatholic (0.71 #46, 0.68 #787, 0.66 #631), Muslim (0.67 #239, 0.63 #824, 0.63 #668), Protestant (0.57 #80, 0.56 #782, 0.52 #1133), Christian (0.53 #667, 0.49 #823, 0.46 #511), Jewish (0.29 #42, 0.18 #1366, 0.16 #2187), ChristianOrthodox (0.21 #1093, 0.19 #1327, 0.19 #1367), Hindu (0.20 #9, 0.18 #126, 0.18 #1366), Buddhist (0.18 #128, 0.18 #1366, 0.17 #752), Anglican (0.18 #1366, 0.16 #914, 0.16 #875), Kimbanguist (0.18 #1366, 0.16 #2187, 0.15 #1523)
>> best conf = 0.71
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #46 for best value:
>> intensional similarity = 8
>> extensional distance = 5
>> proper extension:  RA; GQ;
>> query: (?x994, RomanCatholic) <- ?x994[ a Country; has government ?x435<"republic">; has religion ?x316; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x438[ a Volcano;];]

*> Best rule #80 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 5
*> proper extension:  F; NAM; RH;
*> query: (?x994, Protestant) <- ?x994[ a Country; has government ?x435<"republic">; has religion ?x316; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x1790[ a Island;];]
*> conf = 0.57
ranks of expected_values: 3
EVAL	STP	religion	Protestant	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.333	58.000	58.000	35.000	0.714	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion
PRED expected values: Protestant
=> 94 concepts (94 used for prediction)
PRED predicted values (max 10 best out of 39): Protestant (0.90 #1143, 0.63 #1025, 0.62 #431), RomanCatholic (0.87 #712, 0.86 #1462, 0.83 #514), Muslim (0.78 #989, 0.71 #671, 0.64 #2604), Christian (0.67 #949, 0.58 #590, 0.56 #472), Buddhist (0.37 #903, 0.33 #11, 0.29 #3039), Anglican (0.37 #903, 0.30 #1158, 0.29 #3039), ChristianOrthodox (0.37 #903, 0.28 #666, 0.26 #1771), Jewish (0.33 #42, 0.33 #3, 0.29 #3039), Mormon (0.33 #24, 0.28 #1063, 0.28 #3513), Hindu (0.29 #3039, 0.24 #3276, 0.23 #635)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1143 for best value:
>> intensional similarity = 10
>> extensional distance = 18
>> proper extension:  NLSM;
>> query: (?x994, Protestant) <- ?x994[ a Country; has government ?x435; has religion ?x2256[ a Religion; is religion of ?x476<ETH>;]; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x993[ a Island;];]
ranks of expected_values: 1
EVAL	STP	religion	Protestant	CNN-1.+1._MA	1.000	1.000	1.000	1.000	94.000	94.000	39.000	0.900	http://www.semwebtech.org/mondial/10/meta#religion

#547-Drin
PRED entity: Drin

PRED relation: hasSource!
PRED expected values: Drin
=> 39 concepts (37 used for prediction)
PRED predicted values (max 10 best out of 116): Buna (0.33 #240, 0.20 #468, 0.14 #697), BlackDrin (0.20 #523, 0.12 #981, 0.11 #1210), SouthernMorava (0.20 #631), WhiteDrin (0.14 #778, 0.12 #1007, 0.09 #685), Drina (0.14 #771, 0.12 #1000, 0.07 #3890), WesternMorava (0.14 #716), Morava (0.14 #691), Moraca (0.12 #1138, 0.07 #3890, 0.03 #1824), Tara (0.12 #957, 0.07 #3890, 0.03 #1643), Piva (0.12 #939, 0.07 #3890, 0.03 #1625)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #240 for best value:
>> intensional similarity = 3
>> extensional distance = 1
>> proper extension:  Buna;
>> query: (?x2445, Buna) <- ?x2445[ a Source; has locatedIn ?x204<AL>;]

*> Best rule #685 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 3
*> proper extension:  SouthernMorava;
*> query: (?x2445, ?x104) <- ?x2445[ a Source; has locatedIn ?x204[ has neighbor ?x106; is locatedIn of ?x104; is locatedIn of ?x1516<Korab>;];]
*> conf = 0.09
ranks of expected_values: 14
EVAL	Drin	hasSource!	Drin	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.071	39.000	37.000	116.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasSource

PRED relation: hasSource!
PRED expected values: Drin
=> 120 concepts (120 used for prediction)
PRED predicted values (max 10 best out of 232): BlackDrin (0.33 #67, 0.31 #1145, 0.25 #1604), Buna (0.33 #469, 0.25 #1604, 0.20 #697), WhiteDrin (0.31 #1145, 0.25 #1604, 0.16 #9674), Moraca (0.31 #1145, 0.15 #6438, 0.14 #1599), Piva (0.31 #1145, 0.15 #6438, 0.14 #1400), Tara (0.31 #1145, 0.15 #6438, 0.14 #1418), Drina (0.31 #1145, 0.15 #6438, 0.14 #5977), Drin (0.25 #1604, 0.16 #9674, 0.15 #8289), SouthernMorava (0.20 #860, 0.04 #3393, 0.03 #5463), Karasu (0.17 #1077, 0.03 #5680, 0.02 #6139)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #67 for best value:
>> intensional similarity = 12
>> extensional distance = 1
>> proper extension:  BlackDrin;
>> query: (?x2445, BlackDrin) <- ?x2445[ a Source; has inMountains ?x785<Balkan>; has locatedIn ?x204[ has ethnicGroup ?x1472<Albanian>; has government ?x254; has language ?x1251<Albanian>; is locatedIn of ?x1004[ a Mountain;]; is neighbor of ?x106;];]

*> Best rule #1604 for first EXPECTED value:
*> intensional similarity = 12
*> extensional distance = 5
*> proper extension:  Moraca; Piva; WhiteDrin; Tara;
*> query: (?x2445, ?x698) <- ?x2445[ a Source; has inMountains ?x785<Balkan>; has locatedIn ?x204[ has ethnicGroup ?x1472<Albanian>; has government ?x254; has language ?x1251<Albanian>; is locatedIn of ?x698[ a River;]; is neighbor of ?x106;];]
*> conf = 0.25
ranks of expected_values: 8
EVAL	Drin	hasSource!	Drin	CNN-1.+1._MA	0.000	0.000	1.000	0.125	120.000	120.000	232.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasSource

#546-EW
PRED entity: EW

PRED relation: locatedIn!
PRED expected values: BalticSea Oesel
=> 32 concepts (26 used for prediction)
PRED predicted values (max 10 best out of 1266): AtlanticOcean (0.41 #7147, 0.32 #14254, 0.26 #17097), WesternBug (0.30 #1475, 0.25 #2896, 0.23 #4317), PacificOcean (0.30 #24167, 0.25 #14298, 0.22 #12877), BarentsSea (0.30 #24167, 0.10 #1487, 0.08 #2908), OzeroBaikal (0.30 #24167, 0.08 #19899, 0.08 #32699), ArcticOcean (0.30 #24167, 0.08 #32699, 0.08 #27013), BeringSea (0.30 #24167, 0.08 #32699, 0.08 #27013), SeaofJapan (0.30 #24167, 0.08 #32699, 0.08 #27013), SeaofOkhotsk (0.30 #24167, 0.08 #32699, 0.08 #27013), EastSibirianSea (0.30 #24167, 0.08 #32699, 0.08 #27013)
>> best conf = 0.41
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #7147 for best value:
>> intensional similarity = 4
>> extensional distance = 57
>> proper extension:  CUR;
>> query: (?x591, AtlanticOcean) <- ?x591[ a Country; has language ?x555; is locatedIn of ?x145[ a Island;];]

*> Best rule #1451 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 8
*> proper extension:  N; PL; BY; UA; KAZ; LV; SF; LT;
*> query: (?x591, BalticSea) <- ?x591[ has encompassed ?x195<Europe>; has ethnicGroup ?x58; is locatedIn of ?x145; is neighbor of ?x73<R>;]
*> conf = 0.20
ranks of expected_values: 16
EVAL	EW	locatedIn!	Oesel	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	32.000	26.000	1266.000	0.407	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	EW	locatedIn!	BalticSea	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.062	32.000	26.000	1266.000	0.407	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: BalticSea Oesel
=> 81 concepts (81 used for prediction)
PRED predicted values (max 10 best out of 1359): BalticSea (0.91 #41267, 0.88 #36995, 0.62 #1422), Narva (0.57 #55502, 0.40 #4267, 0.33 #1255), Narva (0.56 #19913, 0.44 #11376, 0.33 #1022), AtlanticOcean (0.53 #88280, 0.53 #48422, 0.40 #45579), Donau (0.44 #19939, 0.35 #34175, 0.33 #2871), WesternDwina (0.40 #10768, 0.40 #9347, 0.38 #16461), Dnepr (0.40 #10261, 0.38 #15954, 0.33 #3153), WesternBug (0.40 #10007, 0.38 #15700, 0.33 #2899), Prypjat (0.40 #10251, 0.33 #3143, 0.25 #18789), BlackSea (0.33 #2858, 0.33 #13, 0.25 #38421)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #41267 for best value:
>> intensional similarity = 15
>> extensional distance = 26
>> proper extension:  ROK;
>> query: (?x591, ?x146) <- ?x591[ a Country; has encompassed ?x195; is locatedIn of ?x145[ a Island; has locatedInWater ?x146;]; is neighbor of ?x448[ a Country; has government ?x254; has language ?x555; is locatedIn of ?x885; is neighbor of ?x222[ has ethnicGroup ?x58;]; is neighbor of ?x962[ a Country; has religion ?x56;];];]
ranks of expected_values: 1
EVAL	EW	locatedIn!	Oesel	CNN-1.+1._MA	0.000	0.000	0.000	0.000	81.000	81.000	1359.000	0.907	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	EW	locatedIn!	BalticSea	CNN-1.+1._MA	1.000	1.000	1.000	1.000	81.000	81.000	1359.000	0.907	http://www.semwebtech.org/mondial/10/meta#locatedIn

#545-GulfofAden
PRED entity: GulfofAden

PRED relation: mergesWith!
PRED expected values: ArabianSea
=> 35 concepts (22 used for prediction)
PRED predicted values (max 10 best out of 142): ArabianSea (0.84 #401, 0.83 #402, 0.83 #360), GulfofAden (0.50 #77, 0.38 #117, 0.25 #38), AtlanticOcean (0.25 #163, 0.21 #326, 0.20 #367), PacificOcean (0.22 #213, 0.22 #173, 0.22 #295), BandaSea (0.17 #65, 0.17 #198, 0.16 #197), GulfofBengal (0.17 #49, 0.17 #198, 0.16 #197), JavaSea (0.17 #45, 0.12 #85, 0.11 #204), AndamanSea (0.17 #56, 0.12 #96, 0.08 #215), GulfofOman (0.17 #69, 0.12 #109, 0.06 #228), ArcticOcean (0.12 #373, 0.08 #169, 0.08 #250)
>> best conf = 0.84
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #401 for best value:
>> intensional similarity = 7
>> extensional distance = 38
>> proper extension:  HudsonBay; KaraSea;
>> query: (?x2407, ?x60) <- ?x2407[ a Sea; has mergesWith ?x60; has mergesWith ?x1552[ has locatedIn ?x751[ a Country; has ethnicGroup ?x244; has religion ?x187;];];]
ranks of expected_values: 1
EVAL	GulfofAden	mergesWith!	ArabianSea	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	35.000	22.000	142.000	0.841	http://www.semwebtech.org/mondial/10/meta#mergesWith

PRED relation: mergesWith!
PRED expected values: ArabianSea
=> 83 concepts (83 used for prediction)
PRED predicted values (max 10 best out of 190): ArabianSea (0.86 #648, 0.82 #604, 0.81 #605), GulfofAden (0.50 #156, 0.33 #400, 0.33 #38), PacificOcean (0.44 #497, 0.26 #622, 0.24 #664), GulfofOman (0.33 #30, 0.25 #308, 0.22 #359), GreenlandSea (0.33 #73, 0.22 #359, 0.14 #273), LabradorSea (0.33 #47, 0.22 #359, 0.14 #247), GulfofMexico (0.33 #72, 0.22 #359, 0.14 #272), CaribbeanSea (0.33 #55, 0.22 #359, 0.14 #255), TheChannel (0.33 #70, 0.22 #359, 0.14 #270), MediterraneanSea (0.33 #53, 0.22 #359, 0.14 #253)
>> best conf = 0.86
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #648 for best value:
>> intensional similarity = 15
>> extensional distance = 29
>> proper extension:  HudsonBay; KaraSea;
>> query: (?x2407, ?x1333) <- ?x2407[ has mergesWith ?x1333[ has mergesWith ?x926;]; is mergesWith of ?x60[ has locatedIn ?x924[ has encompassed ?x175; has language ?x2392; has religion ?x462<Buddhist>; is neighbor of ?x83;]; is locatedInWater of ?x433[ has locatedIn ?x196;]; is locatedInWater of ?x740[ has belongsToIslands ?x875;]; is locatedInWater of ?x1157[ a Island;];];]
ranks of expected_values: 1
EVAL	GulfofAden	mergesWith!	ArabianSea	CNN-1.+1._MA	1.000	1.000	1.000	1.000	83.000	83.000	190.000	0.864	http://www.semwebtech.org/mondial/10/meta#mergesWith

#544-MtRedoubt
PRED entity: MtRedoubt

PRED relation: locatedIn
PRED expected values: USA
=> 13 concepts (13 used for prediction)
PRED predicted values (max 10 best out of 71): USA (0.17 #72, 0.15 #1960, 0.13 #308), RI (0.12 #996, 0.09 #1704, 0.09 #1232), MEX (0.10 #116, 0.09 #352, 0.09 #588), E (0.10 #27, 0.07 #263, 0.06 #499), RCH (0.07 #46, 0.06 #754, 0.04 #282), PE (0.07 #67, 0.05 #2427, 0.04 #303), EC (0.07 #184, 0.04 #420, 0.04 #656), R (0.07 #241, 0.06 #477, 0.06 #713), CN (0.06 #2180, 0.06 #1944, 0.04 #2416), RA (0.06 #795, 0.04 #323, 0.04 #559)
>> best conf = 0.17
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #72 for best value:
>> intensional similarity = 6
>> extensional distance = 27
>> proper extension:  Cayambe; MtAdams; Fogo; Irazu; Karisimbi; MaunaLoa; Ampato; Coropuna; MaunaKea; MtRainier; ...
>> query: (?x719, USA) <- ?x719[ a Mountain; a Volcano; has inMountains ?x1387[ a Mountains;]; has type ?x706<"volcano">;]
ranks of expected_values: 1
EVAL	MtRedoubt	locatedIn	USA	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	13.000	13.000	71.000	0.172	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: USA
=> 13 concepts (13 used for prediction)
PRED predicted values (max 10 best out of 71): USA (0.17 #72, 0.15 #1960, 0.13 #308), RI (0.12 #996, 0.09 #1704, 0.09 #1232), MEX (0.10 #116, 0.09 #352, 0.09 #588), E (0.10 #27, 0.07 #263, 0.06 #499), RCH (0.07 #46, 0.06 #754, 0.04 #282), PE (0.07 #67, 0.05 #2427, 0.04 #303), EC (0.07 #184, 0.04 #420, 0.04 #656), R (0.07 #241, 0.06 #477, 0.06 #713), CN (0.06 #2180, 0.06 #1944, 0.04 #2416), RA (0.06 #795, 0.04 #323, 0.04 #559)
>> best conf = 0.17
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #72 for best value:
>> intensional similarity = 6
>> extensional distance = 27
>> proper extension:  Cayambe; MtAdams; Fogo; Irazu; Karisimbi; MaunaLoa; Ampato; Coropuna; MaunaKea; MtRainier; ...
>> query: (?x719, USA) <- ?x719[ a Mountain; a Volcano; has inMountains ?x1387[ a Mountains;]; has type ?x706<"volcano">;]
ranks of expected_values: 1
EVAL	MtRedoubt	locatedIn	USA	CNN-1.+1._MA	1.000	1.000	1.000	1.000	13.000	13.000	71.000	0.172	http://www.semwebtech.org/mondial/10/meta#locatedIn

#543-Q
PRED entity: Q

PRED relation: neighbor!
PRED expected values: SA
=> 36 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 194): SA (0.90 #4433, 0.90 #2123, 0.90 #3109), IL (0.43 #537, 0.36 #1025, 0.35 #1190), SUD (0.36 #1010, 0.12 #2452, 0.09 #4597), IR (0.29 #1197, 0.25 #978, 0.24 #1632), IRQ (0.29 #542, 0.27 #2616, 0.26 #3110), SYR (0.29 #572, 0.25 #245, 0.24 #1225), UAE (0.28 #2124, 0.27 #2616, 0.26 #3110), OM (0.28 #2124, 0.27 #2616, 0.26 #3110), KWT (0.28 #2124, 0.27 #2616, 0.26 #3110), YE (0.28 #2124, 0.25 #765, 0.22 #926)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4433 for best value:
>> intensional similarity = 7
>> extensional distance = 144
>> proper extension:  SSD;
>> query: (?x174, ?x751) <- ?x174[ a Country; has neighbor ?x751[ has ethnicGroup ?x244; has neighbor ?x107; has religion ?x187; is locatedIn of ?x637;]; is locatedIn of ?x918;]
ranks of expected_values: 1
EVAL	Q	neighbor!	SA	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	36.000	36.000	194.000	0.903	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: SA
=> 69 concepts (69 used for prediction)
PRED predicted values (max 10 best out of 199): SA (0.91 #7217, 0.90 #6878, 0.89 #4854), CN (0.50 #2041, 0.40 #1203, 0.22 #4903), IL (0.50 #869, 0.33 #1712, 0.27 #3172), OM (0.43 #5026, 0.33 #268, 0.32 #2497), IRQ (0.43 #5026, 0.33 #986, 0.32 #2497), JOR (0.43 #5026, 0.33 #2293, 0.28 #2496), UAE (0.43 #5026, 0.32 #2497, 0.29 #2499), SYR (0.43 #5026, 0.28 #2496, 0.27 #3172), YE (0.43 #5026, 0.28 #2496, 0.26 #4013), IR (0.40 #2386, 0.38 #2495, 0.32 #2497)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #7217 for best value:
>> intensional similarity = 17
>> extensional distance = 89
>> proper extension:  F;
>> query: (?x174, ?x751) <- ?x174[ has neighbor ?x751[ a Country; has encompassed ?x175; has ethnicGroup ?x244; has language ?x1848; has neighbor ?x107; has religion ?x187[ is religion of ?x81<GB>; is religion of ?x156<HR>; is religion of ?x207<I>;]; is locatedIn of ?x918[ is locatedInWater of ?x1443;]; is locatedIn of ?x1552[ a Sea;];];]
ranks of expected_values: 1
EVAL	Q	neighbor!	SA	CNN-1.+1._MA	1.000	1.000	1.000	1.000	69.000	69.000	199.000	0.907	http://www.semwebtech.org/mondial/10/meta#neighbor

#542-EC
PRED entity: EC

PRED relation: locatedIn!
PRED expected values: Cayambe
=> 39 concepts (34 used for prediction)
PRED predicted values (max 10 best out of 1350): AtlanticOcean (0.67 #24187, 0.64 #36975, 0.60 #8562), CaribbeanSea (0.67 #1525, 0.60 #24250, 0.58 #5785), RioSanJuan (0.25 #246, 0.18 #4506, 0.17 #1666), RioSanJuan (0.25 #101, 0.18 #4361, 0.17 #1521), LakeNicaragua (0.25 #100, 0.18 #4360, 0.17 #1520), RioNegro (0.25 #640, 0.18 #4900, 0.17 #2060), Amazonas (0.25 #51, 0.18 #4311, 0.17 #1471), Orinoco (0.25 #123, 0.17 #1543, 0.12 #12782), NevadodelRuiz (0.25 #972, 0.17 #2392, 0.12 #12782), NevadodelHuila (0.25 #302, 0.17 #1722, 0.12 #12782)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #24187 for best value:
>> intensional similarity = 5
>> extensional distance = 46
>> proper extension:  MNTS; SBAR;
>> query: (?x902, AtlanticOcean) <- ?x902[ has encompassed ?x521<America>; is locatedIn of ?x282[ is locatedInWater of ?x205; is mergesWith of ?x60;];]

No rule for expected values
ranks of expected_values: 
EVAL	EC	locatedIn!	Cayambe	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	39.000	34.000	1350.000	0.667	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Cayambe
=> 86 concepts (86 used for prediction)
PRED predicted values (max 10 best out of 1383): AtlanticOcean (0.86 #41285, 0.76 #61196, 0.74 #64046), CaribbeanSea (0.76 #37077, 0.59 #61259, 0.58 #28543), Amazonas (0.40 #9996, 0.34 #25592, 0.33 #14263), RioNegro (0.40 #10585, 0.33 #14852, 0.33 #6325), Uruguay (0.40 #10497, 0.33 #6237, 0.26 #44089), Tambo (0.34 #25592, 0.32 #27015, 0.32 #22745), Ucayali (0.34 #25592, 0.32 #27015, 0.32 #22745), Maranon (0.34 #25592, 0.32 #27015, 0.32 #22745), RioDesaguadero (0.34 #25592, 0.32 #27015, 0.32 #22745), RioMadeira (0.33 #15102, 0.33 #6575, 0.26 #44089)
>> best conf = 0.86
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #41285 for best value:
>> intensional similarity = 13
>> extensional distance = 19
>> proper extension:  VIRG;
>> query: (?x902, AtlanticOcean) <- ?x902[ has encompassed ?x521<America>; has government ?x435; has religion ?x352; is locatedIn of ?x282[ has locatedIn ?x315<USA>; is flowsInto of ?x602; is locatedInWater of ?x205; is mergesWith of ?x271[ is mergesWith of ?x270;];]; is locatedIn of ?x901[ a Island;];]

No rule for expected values
ranks of expected_values: 
EVAL	EC	locatedIn!	Cayambe	CNN-1.+1._MA	0.000	0.000	0.000	0.000	86.000	86.000	1383.000	0.857	http://www.semwebtech.org/mondial/10/meta#locatedIn

#541-Würm
PRED entity: Würm

PRED relation: locatedIn
PRED expected values: D
=> 44 concepts (44 used for prediction)
PRED predicted values (max 10 best out of 65): D (0.82 #6162, 0.81 #5925, 0.75 #1896), ZRE (0.31 #1975, 0.18 #6952, 0.17 #4582), SUD (0.25 #1464, 0.19 #1938, 0.06 #4071), NL (0.20 #371, 0.05 #2978, 0.03 #3926), CH (0.17 #1479, 0.12 #1953, 0.11 #2190), A (0.17 #810, 0.12 #1284, 0.08 #4365), USA (0.16 #5049, 0.12 #5523, 0.12 #3627), I (0.15 #2655, 0.14 #3129, 0.12 #3603), RO (0.12 #1222, 0.05 #4303, 0.03 #6199), UA (0.12 #1255, 0.03 #3862, 0.03 #5758)
>> best conf = 0.82
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #6162 for best value:
>> intensional similarity = 7
>> extensional distance = 64
>> proper extension:  Mississippi; Jubba; Drin;
>> query: (?x2279, ?x120) <- ?x2279[ a Estuary; is hasEstuary of ?x394[ a River; has flowsInto ?x558[ has locatedIn ?x120[ is locatedIn of ?x119;];]; is flowsInto of ?x119;];]
ranks of expected_values: 1
EVAL	Würm	locatedIn	D	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	44.000	44.000	65.000	0.825	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: D
=> 84 concepts (84 used for prediction)
PRED predicted values (max 10 best out of 65): D (0.82 #16353, 0.81 #15642, 0.80 #6636), ZRE (0.50 #6478, 0.50 #2923, 0.32 #10033), SUD (0.40 #1464, 0.38 #4782, 0.27 #7152), CH (0.33 #294, 0.33 #57, 0.25 #1242), NIC (0.33 #807, 0.17 #3414, 0.10 #6732), CR (0.33 #784, 0.17 #3391, 0.10 #6709), I (0.25 #4077, 0.23 #8106, 0.22 #5499), CDN (0.25 #5040, 0.15 #7884, 0.14 #10965), R (0.20 #1901, 0.12 #4982, 0.12 #4745), F (0.20 #1903, 0.12 #4984, 0.12 #4747)
>> best conf = 0.82
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #16353 for best value:
>> intensional similarity = 7
>> extensional distance = 64
>> proper extension:  Mississippi; Jubba; Drin;
>> query: (?x2279, ?x120) <- ?x2279[ a Estuary; is hasEstuary of ?x394[ a River; has flowsInto ?x558[ has locatedIn ?x120[ is locatedIn of ?x119;];]; is flowsInto of ?x119;];]
ranks of expected_values: 1
EVAL	Würm	locatedIn	D	CNN-1.+1._MA	1.000	1.000	1.000	1.000	84.000	84.000	65.000	0.825	http://www.semwebtech.org/mondial/10/meta#locatedIn

#540-NorthSea
PRED entity: NorthSea

PRED relation: flowsInto!
PRED expected values: Rhein Weser
=> 34 concepts (30 used for prediction)
PRED predicted values (max 10 best out of 587): Loire (0.33 #481, 0.25 #780, 0.20 #1079), Garonne (0.33 #415, 0.25 #714, 0.20 #1013), Douro (0.33 #523, 0.25 #822, 0.20 #1121), Tajo (0.33 #518, 0.25 #817, 0.20 #1116), Guadiana (0.33 #471, 0.25 #770, 0.20 #1069), Guadalquivir (0.33 #462, 0.25 #761, 0.20 #1060), MerrimackRiver (0.33 #561, 0.25 #860, 0.20 #1159), RioSaoFrancisco (0.33 #542, 0.25 #841, 0.20 #1140), Sanaga (0.33 #524, 0.25 #823, 0.20 #1122), HudsonRiver (0.33 #496, 0.25 #795, 0.20 #1094)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #481 for best value:
>> intensional similarity = 7
>> extensional distance = 1
>> proper extension:  AtlanticOcean;
>> query: (?x121, Loire) <- ?x121[ has locatedIn ?x78; has mergesWith ?x373<NorwegianSea>; is locatedInWater of ?x495<GreatBritain>; is locatedInWater of ?x1515[ a Island;];]

*> Best rule #1496 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 8
*> proper extension:  Rhein; Mosel; Rhone; Seine; Saone;
*> query: (?x121, ?x146) <- ?x121[ has locatedIn ?x78<F>; has locatedIn ?x793[ a Country; has religion ?x95; is locatedIn of ?x146;]; is flowsInto of ?x829;]
*> conf = 0.03
ranks of expected_values: 105, 140
EVAL	NorthSea	flowsInto!	Weser	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.007	34.000	30.000	587.000	0.333	http://www.semwebtech.org/mondial/10/meta#flowsInto
EVAL	NorthSea	flowsInto!	Rhein	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.010	34.000	30.000	587.000	0.333	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto!
PRED expected values: Rhein Weser
=> 108 concepts (108 used for prediction)
PRED predicted values (max 10 best out of 423): Oder (0.25 #456, 0.11 #6010, 0.09 #15962), Maelaren (0.25 #596, 0.11 #6010, 0.08 #2699), Kymijoki (0.25 #579, 0.11 #6010, 0.08 #2682), Umeaelv (0.25 #546, 0.11 #6010, 0.08 #2649), Oulujoki (0.25 #538, 0.11 #6010, 0.08 #2641), Kokemaeenjoki (0.25 #516, 0.11 #6010, 0.08 #2619), WesternDwina (0.25 #514, 0.11 #6010, 0.08 #2617), Weichsel (0.25 #511, 0.11 #6010, 0.08 #2614), Kemijoki (0.25 #390, 0.11 #6010, 0.08 #2493), Newa (0.25 #377, 0.11 #6010, 0.08 #2480)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #456 for best value:
>> intensional similarity = 8
>> extensional distance = 2
>> proper extension:  BalticSea; Kattegat;
>> query: (?x121, Oder) <- ?x121[ has locatedIn ?x78[ is neighbor of ?x149; is wasDependentOf of ?x94;]; has locatedIn ?x793<DK>; has mergesWith ?x182; is locatedInWater of ?x1100[ a Island;];]

*> Best rule #8421 for first EXPECTED value:
*> intensional similarity = 12
*> extensional distance = 27
*> proper extension:  LakeWinnipeg; MackenzieRiver; RiviereRichelieu;
*> query: (?x121, ?x256) <- ?x121[ has locatedIn ?x78[ has government ?x435; has religion ?x95; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x256[ has flowsThrough ?x1602;]; is locatedIn of ?x1460[ a Estuary;];]; has locatedIn ?x543[ has language ?x51;]; is flowsInto of ?x829;]
*> conf = 0.12
ranks of expected_values: 48, 109
EVAL	NorthSea	flowsInto!	Weser	CNN-1.+1._MA	0.000	0.000	0.000	0.009	108.000	108.000	423.000	0.250	http://www.semwebtech.org/mondial/10/meta#flowsInto
EVAL	NorthSea	flowsInto!	Rhein	CNN-1.+1._MA	0.000	0.000	0.000	0.021	108.000	108.000	423.000	0.250	http://www.semwebtech.org/mondial/10/meta#flowsInto

#539-SP
PRED entity: SP

PRED relation: wasDependentOf
PRED expected values: GB
=> 33 concepts (33 used for prediction)
PRED predicted values (max 10 best out of 45): GB (0.43 #95, 0.40 #220, 0.38 #157), F (0.33 #248, 0.29 #783, 0.16 #340), ET (0.20 #30, 0.12 #123, 0.07 #185), P (0.17 #84, 0.14 #114, 0.12 #176), NL (0.17 #79, 0.14 #109, 0.12 #171), MergerofNorth-SouthYemen (0.17 #88, 0.05 #243, 0.02 #395), E (0.15 #468, 0.15 #375, 0.14 #437), UnitedNations (0.13 #199, 0.12 #414, 0.08 #260), RI (0.12 #163, 0.07 #194, 0.05 #226), SovietUnion (0.09 #642, 0.09 #674, 0.08 #772)
>> best conf = 0.43
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #95 for best value:
>> intensional similarity = 7
>> extensional distance = 5
>> proper extension:  EAK; EAT;
>> query: (?x220, GB) <- ?x220[ has ethnicGroup ?x1593; has government ?x1766; is locatedIn of ?x60<IndianOcean>; is locatedIn of ?x1333[ is locatedInWater of ?x1476;]; is neighbor of ?x94;]
ranks of expected_values: 1
EVAL	SP	wasDependentOf	GB	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	33.000	33.000	45.000	0.429	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

PRED relation: wasDependentOf
PRED expected values: GB
=> 72 concepts (72 used for prediction)
PRED predicted values (max 10 best out of 67): GB (0.50 #447, 0.50 #305, 0.50 #278), E (0.44 #653, 0.30 #826, 0.20 #1369), F (0.35 #1245, 0.30 #788, 0.30 #928), P (0.27 #403, 0.25 #124, 0.20 #190), NL (0.27 #403, 0.20 #185, 0.19 #852), MergerofNorth-SouthYemen (0.25 #93, 0.14 #856, 0.12 #1655), UnitedNations (0.17 #528, 0.11 #1228, 0.11 #1121), RI (0.16 #2028, 0.14 #856, 0.14 #249), EAK (0.12 #781, 0.07 #780, 0.06 #270), PK (0.11 #312, 0.03 #964, 0.03 #1000)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #447 for best value:
>> intensional similarity = 14
>> extensional distance = 8
>> proper extension:  RWA;
>> query: (?x220, GB) <- ?x220[ a Country; has ethnicGroup ?x1593; has government ?x1766; is locatedIn of ?x60[ has locatedIn ?x508[ a Country; has encompassed ?x175; has ethnicGroup ?x244;]; has locatedIn ?x820<EAT>; has locatedIn ?x1248[ has encompassed ?x213; has religion ?x187;];]; is neighbor of ?x94;]
>> Best rule #305 for best value:
>> intensional similarity = 17
>> extensional distance = 6
>> proper extension:  EAU;
>> query: (?x220, ?x81) <- ?x220[ a Country; has ethnicGroup ?x1593; has government ?x1766; is locatedIn of ?x60[ has locatedIn ?x196[ a Country; has encompassed ?x211;]; has locatedIn ?x508[ has ethnicGroup ?x244<Arab>; has government ?x435; has religion ?x116; has wasDependentOf ?x81;]; has locatedIn ?x820<EAT>; is flowsInto of ?x242;]; is neighbor of ?x94;]
>> Best rule #278 for best value:
>> intensional similarity = 17
>> extensional distance = 6
>> proper extension:  EAU;
>> query: (?x220, GB) <- ?x220[ a Country; has ethnicGroup ?x1593; has government ?x1766; is locatedIn of ?x60[ has locatedIn ?x196[ a Country; has encompassed ?x211;]; has locatedIn ?x508[ has ethnicGroup ?x244<Arab>; has government ?x435; has religion ?x116; has wasDependentOf ?x81;]; has locatedIn ?x820<EAT>; is flowsInto of ?x242;]; is neighbor of ?x94;]
>> Best rule #138 for best value:
>> intensional similarity = 16
>> extensional distance = 2
>> proper extension:  EAK;
>> query: (?x220, GB) <- ?x220[ a Country; has ethnicGroup ?x1593; has government ?x1766; has religion ?x187; is locatedIn of ?x60<IndianOcean>; is locatedIn of ?x1333[ is locatedInWater of ?x1476;]; is neighbor of ?x94[ has government ?x435; is locatedIn of ?x415;]; is neighbor of ?x474[ has ethnicGroup ?x244; has religion ?x95; is locatedIn of ?x1468<ChewBahir>;];]
ranks of expected_values: 1
EVAL	SP	wasDependentOf	GB	CNN-1.+1._MA	1.000	1.000	1.000	1.000	72.000	72.000	67.000	0.500	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

#538-DJI
PRED entity: DJI

PRED relation: locatedIn!
PRED expected values: LakeAbbe
=> 31 concepts (24 used for prediction)
PRED predicted values (max 10 best out of 1114): IndianOcean (0.85 #5689, 0.77 #4267, 0.33 #1424), Atbara (0.50 #3917, 0.14 #4264, 0.08 #25596), BlueNile (0.50 #3744, 0.14 #4264, 0.08 #25596), AtlanticOcean (0.48 #8572, 0.35 #27060, 0.34 #22794), ChewBahir (0.33 #824, 0.25 #3666, 0.14 #4264), LakeVictoria (0.33 #647, 0.15 #6333, 0.15 #4911), ArabianSea (0.33 #2152, 0.14 #4264, 0.13 #19910), RubAlChali (0.33 #1708, 0.08 #5973, 0.08 #4551), JabalShuayb (0.33 #2842, 0.08 #7107, 0.08 #5685), Sokotra (0.33 #2694, 0.08 #6959, 0.08 #5537)
>> best conf = 0.85
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5689 for best value:
>> intensional similarity = 6
>> extensional distance = 11
>> proper extension:  MV;
>> query: (?x94, IndianOcean) <- ?x94[ has government ?x435<"republic">; is locatedIn of ?x2407[ a Sea; has mergesWith ?x1333<ArabianSea>;];]

*> Best rule #4097 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 2
*> proper extension:  SUD; ETH;
*> query: (?x94, LakeAbbe) <- ?x94[ a Country; has government ?x435; is locatedIn of ?x415[ a Lake;]; is neighbor of ?x220[ is locatedIn of ?x60;]; is neighbor of ?x629<ER>;]
*> conf = 0.25
ranks of expected_values: 23
EVAL	DJI	locatedIn!	LakeAbbe	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.043	31.000	24.000	1114.000	0.846	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: LakeAbbe
=> 99 concepts (98 used for prediction)
PRED predicted values (max 10 best out of 1411): IndianOcean (0.90 #46963, 0.89 #39844, 0.50 #19922), MediterraneanSea (0.88 #57004, 0.50 #29960, 0.33 #14309), AtlanticOcean (0.61 #118166, 0.58 #105355, 0.56 #37033), PacificOcean (0.45 #22850, 0.44 #41263, 0.39 #41266), ArabianSea (0.44 #41263, 0.40 #120975, 0.33 #5000), GulfofBengal (0.44 #41263, 0.39 #41266, 0.20 #19992), BandaSea (0.44 #41263, 0.39 #41266, 0.19 #42693), Donau (0.42 #28479, 0.36 #25635, 0.16 #85408), Jordan (0.40 #12963, 0.33 #14386, 0.29 #18657), DeadSea (0.40 #13047, 0.33 #14470, 0.29 #18741)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #46963 for best value:
>> intensional similarity = 15
>> extensional distance = 19
>> proper extension:  MV; MAYO;
>> query: (?x94, IndianOcean) <- ?x94[ a Country; has government ?x435; is locatedIn of ?x1552[ a Sea; has locatedIn ?x186[ has encompassed ?x213; has ethnicGroup ?x244; is neighbor of ?x169;];]; is locatedIn of ?x2407[ a Sea; has locatedIn ?x668<YE>; has mergesWith ?x1333<ArabianSea>; is mergesWith of ?x60;];]

*> Best rule #6946 for first EXPECTED value:
*> intensional similarity = 15
*> extensional distance = 1
*> proper extension:  ETH;
*> query: (?x94, LakeAbbe) <- ?x94[ a Country; has ethnicGroup ?x1593<Somali>; has government ?x435; has neighbor ?x629[ has encompassed ?x213;]; is locatedIn of ?x415[ a Lake;]; is locatedIn of ?x1552[ has locatedIn ?x63[ has religion ?x1929; has wasDependentOf ?x81;]; has locatedIn ?x751[ has ethnicGroup ?x244; is neighbor of ?x107;];];]
*> conf = 0.33
ranks of expected_values: 33
EVAL	DJI	locatedIn!	LakeAbbe	CNN-1.+1._MA	0.000	0.000	0.000	0.030	99.000	98.000	1411.000	0.905	http://www.semwebtech.org/mondial/10/meta#locatedIn

#537-Tara
PRED entity: Tara

PRED relation: locatedIn
PRED expected values: MNE
=> 49 concepts (27 used for prediction)
PRED predicted values (max 10 best out of 174): MNE (0.90 #3303, 0.90 #4011, 0.90 #3067), SRB (0.73 #1360, 0.62 #2830, 0.51 #1885), HR (0.50 #734, 0.22 #499, 0.20 #1647), I (0.39 #2404, 0.16 #5003, 0.14 #753), R (0.36 #1890, 0.34 #4724, 0.33 #4960), F (0.31 #2363, 0.11 #477, 0.08 #1653), H (0.29 #764, 0.09 #1234, 0.06 #1469), A (0.21 #804, 0.11 #2220, 0.11 #1745), SLO (0.21 #809, 0.11 #574, 0.06 #1044), D (0.20 #2141, 0.12 #3322, 0.11 #1666)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3303 for best value:
>> intensional similarity = 6
>> extensional distance = 163
>> proper extension:  MurrayRiver;
>> query: (?x473, ?x106) <- ?x473[ has flowsInto ?x813; has hasEstuary ?x2462; has hasSource ?x814[ a Source; has locatedIn ?x106[ has encompassed ?x195;];];]
ranks of expected_values: 1
EVAL	Tara	locatedIn	MNE	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	49.000	27.000	174.000	0.901	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: MNE
=> 166 concepts (161 used for prediction)
PRED predicted values (max 10 best out of 204): MNE (0.96 #24729, 0.95 #25686, 0.95 #22103), SRB (0.76 #4976, 0.74 #11157, 0.73 #10443), HR (0.50 #1679, 0.39 #1888, 0.35 #1889), USA (0.49 #6951, 0.44 #26948, 0.29 #28370), AL (0.46 #6450, 0.44 #6688, 0.33 #10014), R (0.44 #12586, 0.39 #10447, 0.24 #16400), H (0.38 #1887, 0.29 #1709, 0.20 #1473), SLO (0.38 #1887, 0.21 #1754, 0.21 #2705), UA (0.38 #3146, 0.21 #7425, 0.21 #21217), I (0.34 #23590, 0.17 #3550, 0.17 #4311)
>> best conf = 0.96
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #24729 for best value:
>> intensional similarity = 13
>> extensional distance = 144
>> proper extension:  Enns; Narva; Perene; Apurimac; Luapula;
>> query: (?x473, ?x106) <- ?x473[ a River; has hasEstuary ?x2462[ a Estuary;]; has hasSource ?x814[ has locatedIn ?x106;]; has locatedIn ?x55[ has religion ?x352[ is religion of ?x202<RCH>; is religion of ?x234<CH>; is religion of ?x654<ES>;];];]
ranks of expected_values: 1
EVAL	Tara	locatedIn	MNE	CNN-1.+1._MA	1.000	1.000	1.000	1.000	166.000	161.000	204.000	0.960	http://www.semwebtech.org/mondial/10/meta#locatedIn

#536-LakeHume
PRED entity: LakeHume

PRED relation: flowsThrough!
PRED expected values: MurrayRiver
=> 56 concepts (48 used for prediction)
PRED predicted values (max 10 best out of 68): MurrayRiver (0.73 #106, 0.67 #145, 0.57 #292), Aare (0.18 #87, 0.17 #126, 0.07 #273), EucumbeneRiver (0.17 #61, 0.06 #173, 0.05 #210), MurrumbidgeeRiver (0.17 #53, 0.06 #165, 0.05 #202), SnowyRiver (0.17 #55, 0.06 #167, 0.05 #204), Zambezi (0.12 #589, 0.09 #103, 0.06 #180), Jubba (0.12 #589, 0.01 #927), Limpopo (0.12 #589, 0.01 #927), Dnepr (0.11 #269, 0.05 #414, 0.05 #452), Rhone (0.09 #95, 0.08 #134, 0.04 #281)
>> best conf = 0.73
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #106 for best value:
>> intensional similarity = 6
>> extensional distance = 9
>> proper extension:  MaleboPool; LakeCabora-Bassa;
>> query: (?x1997, ?x1356) <- ?x1997[ a Lake; has flowsInto ?x1356[ has flowsInto ?x60; is flowsInto of ?x413[ has hasSource ?x371;];]; has locatedIn ?x196;]
ranks of expected_values: 1
EVAL	LakeHume	flowsThrough!	MurrayRiver	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	56.000	48.000	68.000	0.727	http://www.semwebtech.org/mondial/10/meta#flowsThrough

PRED relation: flowsThrough!
PRED expected values: MurrayRiver
=> 141 concepts (141 used for prediction)
PRED predicted values (max 10 best out of 68): MurrayRiver (0.71 #293, 0.67 #220, 0.60 #329), SnowyRiver (0.33 #20, 0.25 #57, 0.20 #167), EucumbeneRiver (0.25 #63, 0.20 #173, 0.17 #291), MurrumbidgeeRiver (0.20 #165, 0.17 #291, 0.17 #238), Aare (0.20 #310, 0.09 #500, 0.08 #646), Lagen (0.17 #191, 0.14 #263, 0.05 #417), DarlingRiver (0.17 #291, 0.05 #1671, 0.04 #1108), Rhone (0.14 #280, 0.10 #318, 0.05 #434), Zambezi (0.14 #1445, 0.13 #1521, 0.12 #1332), Jubba (0.14 #1445, 0.13 #1521, 0.12 #1332)
>> best conf = 0.71
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #293 for best value:
>> intensional similarity = 12
>> extensional distance = 5
>> proper extension:  LacLeman;
>> query: (?x1997, ?x1356) <- ?x1997[ a Lake; has flowsInto ?x1356; has locatedIn ?x196[ has government ?x1903; has religion ?x462[ is religion of ?x315<USA>;]; is dependentOf of ?x210; is locatedIn of ?x969[ has hasEstuary ?x1943;]; is locatedIn of ?x2381[ a Estuary;];];]
ranks of expected_values: 1
EVAL	LakeHume	flowsThrough!	MurrayRiver	CNN-1.+1._MA	1.000	1.000	1.000	1.000	141.000	141.000	68.000	0.714	http://www.semwebtech.org/mondial/10/meta#flowsThrough

#535-SUD
PRED entity: SUD

PRED relation: neighbor
PRED expected values: TCH ETH
=> 30 concepts (28 used for prediction)
PRED predicted values (max 10 best out of 201): ETH (0.90 #1728, 0.90 #1571, 0.90 #2199), TCH (0.90 #1728, 0.90 #1571, 0.90 #2199), SUD (0.33 #342, 0.33 #186, 0.33 #30), EAK (0.33 #394, 0.33 #82, 0.28 #3621), ZRE (0.33 #58, 0.32 #782, 0.29 #683), CAM (0.33 #246, 0.32 #782, 0.28 #3621), DJI (0.33 #319, 0.32 #782, 0.28 #3621), RN (0.33 #232, 0.32 #782, 0.26 #2830), WAN (0.33 #173, 0.29 #642, 0.26 #2830), EAU (0.33 #110, 0.28 #3621, 0.27 #3622)
>> best conf = 0.90
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #1728 for best value:
>> intensional similarity = 6
>> extensional distance = 91
>> proper extension:  BIH; ET; R; LS; THA; MNE; RL; D; TAD; KGZ; ...
>> query: (?x186, ?x169) <- ?x186[ a Country; has ethnicGroup ?x244; has neighbor ?x63; is locatedIn of ?x1879[ a River;]; is neighbor of ?x169;]
ranks of expected_values: 1, 2
EVAL	SUD	neighbor	ETH	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	30.000	28.000	201.000	0.900	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	SUD	neighbor	TCH	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	30.000	28.000	201.000	0.900	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: TCH ETH
=> 76 concepts (76 used for prediction)
PRED predicted values (max 10 best out of 209): ETH (0.91 #7383, 0.90 #10286, 0.89 #1911), TCH (0.91 #7383, 0.90 #10286, 0.89 #1911), ZRE (0.57 #2773, 0.43 #1433, 0.38 #957), SUD (0.55 #3681, 0.51 #8020, 0.50 #4970), RMM (0.46 #2520, 0.44 #3004, 0.33 #1720), GAZA (0.43 #1433, 0.38 #957, 0.36 #478), IL (0.43 #1433, 0.38 #957, 0.36 #478), DZ (0.43 #1433, 0.38 #957, 0.36 #6412), CAM (0.43 #1433, 0.38 #957, 0.36 #6412), TN (0.43 #1433, 0.38 #957, 0.36 #6412)
>> best conf = 0.91
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #7383 for best value:
>> intensional similarity = 16
>> extensional distance = 73
>> proper extension:  UAE;
>> query: (?x186, ?x476) <- ?x186[ has ethnicGroup ?x244; has government ?x140; has neighbor ?x63[ has religion ?x187;]; has neighbor ?x736[ has encompassed ?x213; has ethnicGroup ?x992; has neighbor ?x169; is locatedIn of ?x388;]; has neighbor ?x1184[ a Country; has wasDependentOf ?x207;]; is locatedIn of ?x1552[ a Sea; has mergesWith ?x2407;]; is neighbor of ?x476;]
ranks of expected_values: 1, 2
EVAL	SUD	neighbor	ETH	CNN-1.+1._MA	1.000	1.000	1.000	1.000	76.000	76.000	209.000	0.912	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	SUD	neighbor	TCH	CNN-1.+1._MA	1.000	1.000	1.000	1.000	76.000	76.000	209.000	0.912	http://www.semwebtech.org/mondial/10/meta#neighbor

#534-AZ
PRED entity: AZ

PRED relation: encompassed
PRED expected values: Asia
=> 44 concepts (44 used for prediction)
PRED predicted values (max 10 best out of 5): Asia (0.87 #71, 0.81 #82, 0.81 #189), Europe (0.81 #82, 0.81 #189, 0.80 #178), America (0.35 #97, 0.33 #102, 0.32 #45), Africa (0.30 #128, 0.30 #149, 0.30 #111), Australia-Oceania (0.15 #43, 0.13 #95, 0.12 #217)
>> best conf = 0.87
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #71 for best value:
>> intensional similarity = 7
>> extensional distance = 64
>> proper extension:  TCH; G; SME; SUD; RI; YV; ZRE; GUY; BI; MAL; ...
>> query: (?x332, ?x175) <- ?x332[ has neighbor ?x353[ has encompassed ?x175; has religion ?x352<RomanCatholic>; is locatedIn of ?x98;]; has wasDependentOf ?x903; is locatedIn of ?x468;]
ranks of expected_values: 1
EVAL	AZ	encompassed	Asia	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	44.000	44.000	5.000	0.866	http://www.semwebtech.org/mondial/10/meta#encompassed

PRED relation: encompassed
PRED expected values: Asia
=> 96 concepts (96 used for prediction)
PRED predicted values (max 10 best out of 5): Asia (0.83 #235, 0.79 #466, 0.79 #297), Europe (0.79 #466, 0.79 #297, 0.77 #219), America (0.46 #192, 0.42 #203, 0.39 #229), Africa (0.41 #233, 0.41 #207, 0.36 #176), Australia-Oceania (0.22 #331, 0.20 #120, 0.20 #227)
>> best conf = 0.83
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #235 for best value:
>> intensional similarity = 16
>> extensional distance = 44
>> proper extension:  SSD;
>> query: (?x332, ?x175) <- ?x332[ has government ?x435<"republic">; has neighbor ?x185[ has wasDependentOf ?x1656;]; has neighbor ?x304[ a Country; has encompassed ?x175; has ethnicGroup ?x244; has ethnicGroup ?x1062[ a EthnicGroup;]; has government ?x2318; has neighbor ?x290[ is locatedIn of ?x289;]; has religion ?x187; is locatedIn of ?x573;]; is locatedIn of ?x468;]
ranks of expected_values: 1
EVAL	AZ	encompassed	Asia	CNN-1.+1._MA	1.000	1.000	1.000	1.000	96.000	96.000	5.000	0.830	http://www.semwebtech.org/mondial/10/meta#encompassed

#533-RT
PRED entity: RT

PRED relation: neighbor!
PRED expected values: BF
=> 39 concepts (30 used for prediction)
PRED predicted values (max 10 best out of 218): BF (0.89 #4190, 0.88 #3221, 0.88 #4351), RN (0.50 #78, 0.40 #238, 0.28 #1608), TCH (0.40 #183, 0.25 #23, 0.12 #1311), RT (0.28 #1608, 0.27 #2256, 0.26 #4191), CI (0.28 #1608, 0.27 #2256, 0.26 #4191), WAN (0.28 #1608, 0.27 #2256, 0.20 #179), CAM (0.25 #91, 0.20 #251, 0.12 #3222), DZ (0.20 #259, 0.14 #1387, 0.13 #1547), RMM (0.20 #291, 0.13 #934, 0.12 #612), EAT (0.16 #1418, 0.15 #1578, 0.11 #2225)
>> best conf = 0.89
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4190 for best value:
>> intensional similarity = 5
>> extensional distance = 129
>> proper extension:  MEL;
>> query: (?x1307, ?x811) <- ?x1307[ has neighbor ?x811[ is locatedIn of ?x610;]; is neighbor of ?x483[ has religion ?x187<Muslim>;];]
ranks of expected_values: 1
EVAL	RT	neighbor!	BF	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	39.000	30.000	218.000	0.886	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: BF
=> 89 concepts (89 used for prediction)
PRED predicted values (max 10 best out of 242): BF (0.90 #8554, 0.89 #1141, 0.88 #11047), RG (0.55 #1901, 0.36 #2065, 0.36 #2392), RMM (0.50 #1109, 0.44 #1436, 0.44 #1272), WAN (0.50 #506, 0.40 #344, 0.33 #19), DZ (0.45 #1729, 0.29 #749, 0.22 #3933), MA (0.44 #1273, 0.38 #1110, 0.10 #5906), MOC (0.43 #2153, 0.38 #848, 0.20 #1955), RCB (0.40 #1558, 0.21 #2373, 0.20 #8051), RIM (0.38 #1065, 0.33 #1228, 0.22 #1392), CI (0.36 #1956, 0.36 #1941, 0.36 #1465)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #8554 for best value:
>> intensional similarity = 9
>> extensional distance = 63
>> proper extension:  GB;
>> query: (?x1307, ?x483) <- ?x1307[ a Country; has encompassed ?x213[ is encompassed of ?x1408[ is neighbor of ?x536;];]; has ethnicGroup ?x162; has neighbor ?x483; has religion ?x187<Muslim>; is locatedIn of ?x182;]
ranks of expected_values: 1
EVAL	RT	neighbor!	BF	CNN-1.+1._MA	1.000	1.000	1.000	1.000	89.000	89.000	242.000	0.897	http://www.semwebtech.org/mondial/10/meta#neighbor

#532-MurrayRiver
PRED entity: MurrayRiver

PRED relation: flowsThrough
PRED expected values: LakeHume
=> 45 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 71): LakeHume (0.60 #134, 0.60 #133, 0.57 #225), MurrumbidgeeRiver (0.60 #134, 0.60 #133, 0.45 #454), DarlingRiver (0.60 #134, 0.60 #133, 0.45 #454), LakeKariba (0.20 #77, 0.11 #167, 0.08 #212), LakeCabora-Bassa (0.20 #49, 0.11 #139, 0.08 #184), LakeJindabyne (0.20 #118, 0.02 #301, 0.02 #439), LakeEucumbene (0.20 #117, 0.02 #300, 0.02 #438), LakeBurleyGriffin (0.20 #109, 0.02 #292, 0.02 #430), KuybyshevReservoir (0.08 #186, 0.02 #233, 0.02 #278), LakeNasser (0.08 #190, 0.02 #237, 0.02 #329)
>> best conf = 0.60
=> the first rule below is the first best rule for 3 predicted values
>> Best rule #134 for best value:
>> intensional similarity = 6
>> extensional distance = 3
>> proper extension:  MurrumbidgeeRiver; SnowyRiver; EucumbeneRiver;
>> query: (?x1356, ?x413) <- ?x1356[ a River; has hasEstuary ?x2049; has hasSource ?x1820; is flowsInto of ?x413[ has locatedIn ?x196<AUS>;];]
>> Best rule #133 for best value:
>> intensional similarity = 7
>> extensional distance = 3
>> proper extension:  MurrumbidgeeRiver; SnowyRiver; EucumbeneRiver;
>> query: (?x1356, ?x969) <- ?x1356[ a River; has hasEstuary ?x2049; has hasSource ?x1820; is flowsInto of ?x413[ has locatedIn ?x196<AUS>;]; is flowsInto of ?x969;]
ranks of expected_values: 1
EVAL	MurrayRiver	flowsThrough	LakeHume	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	45.000	31.000	71.000	0.600	http://www.semwebtech.org/mondial/10/meta#flowsThrough

PRED relation: flowsThrough
PRED expected values: LakeHume
=> 153 concepts (151 used for prediction)
PRED predicted values (max 10 best out of 91): LakeHume (0.73 #1531, 0.69 #1951, 0.67 #1300), MurrumbidgeeRiver (0.67 #649, 0.61 #1952, 0.60 #791), DarlingRiver (0.67 #649, 0.61 #1952, 0.60 #791), LakeCabora-Bassa (0.40 #839, 0.40 #838, 0.33 #790), LakeKariba (0.40 #839, 0.33 #126, 0.21 #5892), Chire (0.40 #838, 0.33 #790, 0.09 #5891), Shabelle (0.40 #838, 0.33 #790, 0.06 #2187), LakeJindabyne (0.33 #169, 0.25 #634, 0.25 #402), Ammersee (0.33 #64, 0.25 #576, 0.14 #1178), LakeEucumbene (0.25 #353, 0.20 #821, 0.20 #773)
>> best conf = 0.73
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1531 for best value:
>> intensional similarity = 14
>> extensional distance = 8
>> proper extension:  Niger;
>> query: (?x1356, ?x1997) <- ?x1356[ a River; has hasEstuary ?x2049; has hasSource ?x1820[ a Source;]; is flowsInto of ?x413[ a River;]; is flowsInto of ?x969[ has hasSource ?x1679;]; is flowsInto of ?x1997[ a Lake; has locatedIn ?x196[ has ethnicGroup ?x197; has religion ?x187<Muslim>;];];]
ranks of expected_values: 1
EVAL	MurrayRiver	flowsThrough	LakeHume	CNN-1.+1._MA	1.000	1.000	1.000	1.000	153.000	151.000	91.000	0.727	http://www.semwebtech.org/mondial/10/meta#flowsThrough

#531-CAM
PRED entity: CAM

PRED relation: neighbor!
PRED expected values: RCB GQ
=> 36 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 209): RCB (0.89 #4762, 0.89 #3332, 0.89 #4128), GQ (0.89 #4762, 0.89 #3332, 0.89 #4128), CAM (0.33 #247, 0.33 #89, 0.28 #633), RN (0.33 #75, 0.28 #633, 0.28 #3013), BEN (0.33 #124, 0.28 #3013, 0.25 #4604), LAR (0.33 #304, 0.28 #3013, 0.25 #4604), ZRE (0.28 #3013, 0.25 #4604, 0.25 #4446), SSD (0.28 #3013, 0.25 #4604, 0.25 #4446), SUD (0.28 #3013, 0.25 #4604, 0.25 #4446), RA (0.16 #698, 0.12 #380, 0.11 #3970)
>> best conf = 0.89
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #4762 for best value:
>> intensional similarity = 5
>> extensional distance = 154
>> proper extension:  RSM; V;
>> query: (?x536, ?x528) <- ?x536[ has neighbor ?x528; is neighbor of ?x172[ a Country; has religion ?x116; is locatedIn of ?x182;];]
ranks of expected_values: 1, 2
EVAL	CAM	neighbor!	GQ	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	36.000	36.000	209.000	0.891	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	CAM	neighbor!	RCB	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	36.000	36.000	209.000	0.891	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: RCB GQ
=> 89 concepts (89 used for prediction)
PRED predicted values (max 10 best out of 232): GQ (0.94 #7436, 0.92 #4680, 0.91 #7598), RCB (0.94 #7436, 0.91 #8410, 0.91 #7598), SSD (0.50 #1333, 0.41 #1775, 0.33 #1454), CAM (0.43 #159, 0.43 #158, 0.41 #2094), ZRE (0.43 #159, 0.43 #158, 0.41 #1775), RN (0.43 #159, 0.43 #158, 0.41 #1775), BEN (0.43 #159, 0.43 #158, 0.41 #1775), ANG (0.43 #159, 0.43 #158, 0.33 #1454), WSA (0.43 #159, 0.43 #158, 0.29 #1561), RT (0.43 #159, 0.43 #158, 0.24 #10669)
>> best conf = 0.94
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #7436 for best value:
>> intensional similarity = 15
>> extensional distance = 44
>> proper extension:  NOK; TL; PNG;
>> query: (?x536, ?x169) <- ?x536[ a Country; has government ?x1721; has neighbor ?x169[ has government ?x435<"republic">; is locatedIn of ?x168;]; has neighbor ?x172[ a Country;]; has neighbor ?x528[ has encompassed ?x213; has neighbor ?x348; has religion ?x116; is locatedIn of ?x265;]; is locatedIn of ?x1745[ a Mountain;];]
ranks of expected_values: 1, 2
EVAL	CAM	neighbor!	GQ	CNN-1.+1._MA	1.000	1.000	1.000	1.000	89.000	89.000	232.000	0.936	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	CAM	neighbor!	RCB	CNN-1.+1._MA	1.000	1.000	1.000	1.000	89.000	89.000	232.000	0.936	http://www.semwebtech.org/mondial/10/meta#neighbor

#530-Ebro
PRED entity: Ebro

PRED relation: inMountains
PRED expected values: CordilleraCantabrica
=> 51 concepts (51 used for prediction)
PRED predicted values (max 10 best out of 41): CordilleraIberica (0.60 #142, 0.50 #229, 0.50 #55), CordilleraBetica (0.17 #190, 0.09 #277, 0.06 #3047), Andes (0.11 #620, 0.07 #794, 0.07 #881), Alps (0.10 #700, 0.08 #526, 0.08 #1048), CanaryIslands (0.09 #404, 0.06 #3047, 0.06 #3483), Balkan (0.07 #629, 0.05 #803, 0.05 #890), Pyrenees (0.06 #3047, 0.06 #3483, 0.06 #410), CordilleraCentral (0.06 #3047, 0.06 #3483, 0.05 #4096), CordilleraCantabrica (0.06 #3047, 0.06 #3483, 0.05 #4096), RockyMountains (0.05 #1138, 0.03 #1660, 0.03 #1747)
>> best conf = 0.60
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #142 for best value:
>> intensional similarity = 6
>> extensional distance = 3
>> proper extension:  Tajo;
>> query: (?x1737, CordilleraIberica) <- ?x1737[ a Source; has locatedIn ?x149<E>; is hasSource of ?x1709[ has flowsInto ?x275[ has locatedIn ?x55;];];]

*> Best rule #3047 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 654
*> proper extension:  LakeSkutari; Annapurna; Mur; Demirkazik; Atacama; Buna; LakeHuron; DetroitRiver; WesternBug; Aare; ...
*> query: (?x1737, ?x1701) <- ?x1737[ has locatedIn ?x149[ has language ?x790; has neighbor ?x78; is locatedIn of ?x1007[ a Mountain;]; is locatedIn of ?x1726[ has inMountains ?x1701;];];]
*> conf = 0.06
ranks of expected_values: 9
EVAL	Ebro	inMountains	CordilleraCantabrica	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.111	51.000	51.000	41.000	0.600	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains
PRED expected values: CordilleraCantabrica
=> 129 concepts (129 used for prediction)
PRED predicted values (max 10 best out of 49): CordilleraIberica (0.60 #142, 0.50 #229, 0.50 #55), Alps (0.31 #352, 0.24 #526, 0.13 #265), Andes (0.29 #881, 0.12 #446, 0.11 #4364), Vogesen (0.20 #309, 0.18 #483, 0.12 #396), CordilleraBetica (0.17 #190, 0.11 #2699, 0.07 #1741), Apennin (0.12 #351, 0.10 #525, 0.05 #960), CanaryIslands (0.11 #2699, 0.09 #839, 0.07 #1741), Pyrenees (0.11 #2699, 0.07 #1741, 0.07 #323), CordilleraCentral (0.11 #2699, 0.07 #1741, 0.07 #8804), CordilleraCantabrica (0.11 #2699, 0.07 #1741, 0.07 #8804)
>> best conf = 0.60
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #142 for best value:
>> intensional similarity = 10
>> extensional distance = 3
>> proper extension:  Tajo;
>> query: (?x1737, CordilleraIberica) <- ?x1737[ a Source; is hasSource of ?x1709[ a River; has flowsInto ?x275[ has locatedIn ?x851<MA>; is locatedInWater of ?x68; is mergesWith of ?x182;]; has locatedIn ?x149<E>;];]

*> Best rule #2699 for first EXPECTED value:
*> intensional similarity = 11
*> extensional distance = 106
*> proper extension:  DarlingRiver; ColumbiaRiver; YukonRiver; EucumbeneRiver; RiviereRichelieu; MurrumbidgeeRiver; SaskatchewanRiver; MurrayRiver; MackenzieRiver; Manicouagan; ...
*> query: (?x1737, ?x1712) <- ?x1737[ a Source; has locatedIn ?x149[ has encompassed ?x195; has language ?x790; is locatedIn of ?x1249[ a Estuary;]; is locatedIn of ?x1762[ a Mountain; has inMountains ?x1712;];]; is hasSource of ?x1709[ has hasEstuary ?x1536;];]
*> conf = 0.11
ranks of expected_values: 10
EVAL	Ebro	inMountains	CordilleraCantabrica	CNN-1.+1._MA	0.000	0.000	1.000	0.100	129.000	129.000	49.000	0.600	http://www.semwebtech.org/mondial/10/meta#inMountains

#529-EuropeanSyrian-Lebanese
PRED entity: EuropeanSyrian-Lebanese

PRED relation: ethnicGroup!
PRED expected values: RT
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 96): EAU (0.03 #132), ETH (0.03 #100), UA (0.03 #57), EAK (0.03 #99), CZ (0.02 #98), SN (0.02 #83), MYA (0.02 #70), IR (0.02 #58), SK (0.02 #25), RCA (0.02 #139)
>> best conf = 0.03
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #132 for best value:
>> intensional similarity = 1
>> extensional distance = 279
>> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
>> query: (?x2544, EAU) <- ?x2544[ a EthnicGroup;]

No rule for expected values
ranks of expected_values: 
EVAL	EuropeanSyrian-Lebanese	ethnicGroup!	RT	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	2.000	2.000	96.000	0.032	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup!
PRED expected values: RT
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 96): EAU (0.03 #132), ETH (0.03 #100), UA (0.03 #57), EAK (0.03 #99), CZ (0.02 #98), SN (0.02 #83), MYA (0.02 #70), IR (0.02 #58), SK (0.02 #25), RCA (0.02 #139)
>> best conf = 0.03
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #132 for best value:
>> intensional similarity = 1
>> extensional distance = 279
>> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
>> query: (?x2544, EAU) <- ?x2544[ a EthnicGroup;]

No rule for expected values
ranks of expected_values: 
EVAL	EuropeanSyrian-Lebanese	ethnicGroup!	RT	CNN-1.+1._MA	0.000	0.000	0.000	0.000	2.000	2.000	96.000	0.032	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#528-Uelle
PRED entity: Uelle

PRED relation: hasSource
PRED expected values: Uelle
=> 37 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 181): Bomu (0.33 #62, 0.06 #290, 0.04 #518), Ruki (0.06 #440, 0.04 #668, 0.03 #896), Lualaba (0.06 #403, 0.04 #631, 0.03 #859), Aruwimi (0.06 #391, 0.04 #619, 0.03 #847), Lomami (0.06 #311, 0.04 #539, 0.03 #767), Ubangi (0.06 #254, 0.04 #482, 0.03 #710), Lukenie (0.06 #450, 0.03 #906, 0.02 #1135), Luvua (0.06 #423, 0.03 #879, 0.02 #1108), Lukuga (0.06 #414, 0.03 #870, 0.02 #1099), Tshuapa (0.06 #263, 0.03 #719, 0.02 #948)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #62 for best value:
>> intensional similarity = 5
>> extensional distance = 1
>> proper extension:  Bomu;
>> query: (?x343, Bomu) <- ?x343[ has flowsInto ?x388<Ubangi>; has hasEstuary ?x1901; has locatedIn ?x348<ZRE>;]

*> Best rule #5024 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 203
*> proper extension:  Neckar; Buna; Rhein; Enns; Niger; OhioRiver; VictoriaNile; Narva; Hwangho; Uruguay; ...
*> query: (?x343, ?x113) <- ?x343[ a River; has hasEstuary ?x1901; has locatedIn ?x348[ has neighbor ?x229; is locatedIn of ?x113;];]
*> conf = 0.01
ranks of expected_values: 60
EVAL	Uelle	hasSource	Uelle	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.017	37.000	31.000	181.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasSource

PRED relation: hasSource
PRED expected values: Uelle
=> 97 concepts (97 used for prediction)
PRED predicted values (max 10 best out of 201): Bomu (0.33 #62, 0.21 #12165, 0.14 #915), Luvua (0.12 #423, 0.08 #1110, 0.08 #1340), Lukuga (0.12 #414, 0.08 #1101, 0.08 #1331), Lukenie (0.12 #450, 0.08 #1137, 0.08 #1367), Busira (0.12 #453, 0.08 #1140, 0.08 #1370), Tshuapa (0.12 #263, 0.08 #950, 0.08 #1180), Ruki (0.11 #669, 0.10 #898, 0.07 #12395), Lualaba (0.11 #632, 0.10 #861, 0.07 #12395), Aruwimi (0.11 #620, 0.10 #849, 0.07 #12395), Lomami (0.11 #540, 0.10 #769, 0.07 #12395)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #62 for best value:
>> intensional similarity = 8
>> extensional distance = 1
>> proper extension:  Bomu;
>> query: (?x343, Bomu) <- ?x343[ a River; has flowsInto ?x388<Ubangi>; has hasEstuary ?x1901[ a Estuary; has locatedIn ?x348<ZRE>;]; has locatedIn ?x348<ZRE>;]

*> Best rule #8721 for first EXPECTED value:
*> intensional similarity = 10
*> extensional distance = 126
*> proper extension:  LagodiComo;
*> query: (?x343, ?x732) <- ?x343[ has flowsInto ?x388[ has flowsInto ?x929; has hasSource ?x549;]; has locatedIn ?x348[ has neighbor ?x229; is locatedIn of ?x545[ a Mountain;]; is locatedIn of ?x732[ a Source;]; is neighbor of ?x359;];]
*> conf = 0.04
ranks of expected_values: 47
EVAL	Uelle	hasSource	Uelle	CNN-1.+1._MA	0.000	0.000	0.000	0.021	97.000	97.000	201.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasSource

#527-AMSA
PRED entity: AMSA

PRED relation: locatedIn!
PRED expected values: PacificOcean
=> 39 concepts (39 used for prediction)
PRED predicted values (max 10 best out of 1325): PacificOcean (0.89 #18512, 0.89 #15663, 0.83 #11391), Upolu (0.50 #2847, 0.33 #968, 0.06 #31320), Savaii (0.50 #2847, 0.33 #653, 0.06 #31320), AtlanticOcean (0.48 #10009, 0.43 #5736, 0.41 #17129), Rota (0.33 #2249, 0.09 #3673, 0.07 #7944), Tinian (0.33 #1638, 0.09 #3062, 0.07 #7333), Saipan (0.33 #1428, 0.09 #2852, 0.07 #7123), CaribbeanSea (0.26 #10073, 0.25 #14345, 0.25 #8647), ArcticOcean (0.15 #9966, 0.09 #4344, 0.05 #22855), LakeHuron (0.15 #9966, 0.09 #4323, 0.03 #12869)
>> best conf = 0.89
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #18512 for best value:
>> intensional similarity = 7
>> extensional distance = 39
>> proper extension:  F; NAM; I; DK;
>> query: (?x1276, ?x282) <- ?x1276[ a Country; has government ?x2533; has language ?x189; has religion ?x95; is locatedIn of ?x585[ has locatedInWater ?x282[ is flowsInto of ?x602;];];]
ranks of expected_values: 1
EVAL	AMSA	locatedIn!	PacificOcean	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	39.000	39.000	1325.000	0.887	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: PacificOcean
=> 121 concepts (118 used for prediction)
PRED predicted values (max 10 best out of 1258): PacificOcean (0.95 #112595, 0.94 #104044, 0.93 #59865), Upolu (0.82 #22809, 0.78 #24233, 0.75 #25659), Savaii (0.82 #22809, 0.78 #24233, 0.75 #25659), AtlanticOcean (0.67 #28553, 0.67 #20002, 0.64 #41385), CaribbeanSea (0.60 #15783, 0.60 #12926, 0.44 #31468), Rota (0.33 #7951, 0.31 #51315, 0.25 #9375), Tinian (0.33 #7340, 0.31 #51315, 0.25 #8764), Saipan (0.33 #7130, 0.31 #51315, 0.25 #8554), TeIka-a-Maui-NorthIsland- (0.33 #6148, 0.25 #10421, 0.17 #23257), TeWaka-a-Maui-SouthIsland- (0.33 #5956, 0.25 #10229, 0.17 #23065)
>> best conf = 0.95
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #112595 for best value:
>> intensional similarity = 12
>> extensional distance = 51
>> proper extension:  THA;
>> query: (?x1276, ?x282) <- ?x1276[ has encompassed ?x211; has ethnicGroup ?x982[ a EthnicGroup;]; has government ?x2533; has religion ?x1277[ a Religion;]; is locatedIn of ?x585[ a Island; has locatedInWater ?x282[ has locatedIn ?x73; is flowsInto of ?x602; is mergesWith of ?x60;];];]
ranks of expected_values: 1
EVAL	AMSA	locatedIn!	PacificOcean	CNN-1.+1._MA	1.000	1.000	1.000	1.000	121.000	118.000	1258.000	0.950	http://www.semwebtech.org/mondial/10/meta#locatedIn

#526-Malta
PRED entity: Malta

PRED relation: belongsToIslands!
PRED expected values: Malta
=> 15 concepts (15 used for prediction)
PRED predicted values (max 10 best out of 208): Lipari (0.33 #184, 0.23 #795, 0.17 #1989), Alicudi (0.33 #154, 0.23 #795, 0.17 #1989), Filicudi (0.33 #145, 0.23 #795, 0.17 #1989), Vulcano (0.33 #138, 0.23 #795, 0.17 #1989), Panarea (0.33 #107, 0.23 #795, 0.17 #1989), Salina (0.33 #13, 0.23 #795, 0.17 #1989), Stromboli (0.33 #2, 0.23 #795, 0.17 #1989), Malta (0.26 #1193, 0.23 #795, 0.20 #1591), MediterraneanSea (0.26 #1193, 0.20 #1591, 0.19 #397), Chios (0.23 #795, 0.20 #370, 0.17 #1989)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #184 for best value:
>> intensional similarity = 10
>> extensional distance = 1
>> proper extension:  LipariIslands;
>> query: (?x1302, Lipari) <- ?x1302[ a Islands; is belongsToIslands of ?x777[ a Island; has locatedIn ?x850[ has encompassed ?x195; has government ?x435<"republic">;]; has locatedInWater ?x275<MediterraneanSea>; has type ?x704;];]

*> Best rule #1193 for first EXPECTED value:
*> intensional similarity = 9
*> extensional distance = 7
*> proper extension:  Galapagos;
*> query: (?x1302, ?x275) <- ?x1302[ a Islands; is belongsToIslands of ?x777[ a Island; has locatedIn ?x850[ has government ?x435<"republic">; has wasDependentOf ?x81; is locatedIn of ?x275;]; has type ?x704;];]
*> conf = 0.26
ranks of expected_values: 8
EVAL	Malta	belongsToIslands!	Malta	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.125	15.000	15.000	208.000	0.333	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

PRED relation: belongsToIslands!
PRED expected values: Malta
=> 47 concepts (47 used for prediction)
PRED predicted values (max 10 best out of 208): Malta (0.83 #3389, 0.78 #1794, 0.66 #5978), MediterraneanSea (0.46 #3986, 0.43 #599, 0.32 #3189), Male (0.33 #294, 0.25 #1888, 0.25 #1688), Lipari (0.33 #1181, 0.25 #2377, 0.15 #1793), Alicudi (0.33 #1151, 0.25 #2347, 0.15 #1793), Filicudi (0.33 #1142, 0.25 #2338, 0.15 #1793), Vulcano (0.33 #1135, 0.25 #2331, 0.15 #1793), Panarea (0.33 #1104, 0.25 #2300, 0.15 #1793), Salina (0.33 #1010, 0.25 #2206, 0.15 #1793), Stromboli (0.33 #999, 0.25 #2195, 0.15 #1793)
>> best conf = 0.83
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3389 for best value:
>> intensional similarity = 18
>> extensional distance = 4
>> proper extension:  Comores;
>> query: (?x1302, ?x849) <- ?x1302[ a Islands; is belongsToIslands of ?x777[ a Island; has locatedIn ?x850[ a Country; has encompassed ?x195; has government ?x435<"republic">; has wasDependentOf ?x81; is locatedIn of ?x275[ a Sea; has mergesWith ?x182<AtlanticOcean>; is flowsInto of ?x698; is locatedInWater of ?x849; is mergesWith of ?x182<AtlanticOcean>;]; is locatedIn of ?x849;]; has locatedInWater ?x275;];]
ranks of expected_values: 1
EVAL	Malta	belongsToIslands!	Malta	CNN-1.+1._MA	1.000	1.000	1.000	1.000	47.000	47.000	208.000	0.833	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

#525-LakeSeseSeko-Albertsee
PRED entity: LakeSeseSeko-Albertsee

PRED relation: flowsInto!
PRED expected values: VictoriaNile
=> 38 concepts (35 used for prediction)
PRED predicted values (max 10 best out of 312): Ruzizi (0.07 #451, 0.07 #753, 0.02 #5442), LakeTanganjika (0.07 #321, 0.07 #623, 0.02 #5442), Lomami (0.07 #600, 0.07 #902, 0.02 #5442), Aruwimi (0.07 #557, 0.07 #859, 0.02 #5442), Fimi (0.07 #481, 0.07 #783, 0.02 #5442), Busira (0.07 #374, 0.07 #676, 0.02 #5442), Lualaba (0.07 #367, 0.07 #669, 0.02 #5442), Kasai (0.07 #362, 0.07 #664, 0.02 #5442), Ubangi (0.07 #339, 0.07 #641, 0.02 #5442), Luvua (0.07 #336, 0.07 #638, 0.02 #5442)
>> best conf = 0.07
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #451 for best value:
>> intensional similarity = 4
>> extensional distance = 12
>> proper extension:  Ruzizi;
>> query: (?x1770, Ruzizi) <- ?x1770[ has flowsInto ?x1727; has locatedIn ?x348<ZRE>; is flowsInto of ?x601;]

*> Best rule #5745 for first EXPECTED value:
*> intensional similarity = 4
*> extensional distance = 114
*> proper extension:  Ob;
*> query: (?x1770, ?x600) <- ?x1770[ has flowsInto ?x1727; is flowsInto of ?x601[ has locatedIn ?x688[ is locatedIn of ?x600;];];]
*> conf = 0.03
ranks of expected_values: 55
EVAL	LakeSeseSeko-Albertsee	flowsInto!	VictoriaNile	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.018	38.000	35.000	312.000	0.071	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto!
PRED expected values: VictoriaNile
=> 119 concepts (119 used for prediction)
PRED predicted values (max 10 best out of 366): Ruzizi (0.33 #150, 0.25 #757, 0.14 #2577), Akagera (0.25 #777, 0.25 #473, 0.20 #1993), Rutanzige-Eduardsee (0.25 #381, 0.21 #1518, 0.20 #1294), LakeSeseSeko-Albertsee (0.21 #1518, 0.20 #1771, 0.10 #3588), LakeVictoria (0.21 #1518, 0.20 #1387, 0.10 #606), LakeKioga (0.21 #1518, 0.20 #1320, 0.10 #606), LakeKivu (0.21 #1518, 0.10 #606, 0.10 #1519), MaleboPool (0.21 #1518, 0.10 #606, 0.10 #1519), LakeTanganjika (0.21 #1518, 0.10 #606, 0.10 #1519), LakeMweru (0.21 #1518, 0.10 #606, 0.10 #1519)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #150 for best value:
>> intensional similarity = 9
>> extensional distance = 1
>> proper extension:  LakeTanganjika;
>> query: (?x1770, Ruzizi) <- ?x1770[ a Lake; has flowsInto ?x1727; has locatedIn ?x348<ZRE>; has locatedIn ?x688[ has ethnicGroup ?x529; has neighbor ?x229<SSD>;]; is flowsInto of ?x601;]

*> Best rule #1519 for first EXPECTED value:
*> intensional similarity = 12
*> extensional distance = 3
*> proper extension:  VictoriaNile;
*> query: (?x1770, ?x113) <- ?x1770[ has locatedIn ?x348[ has encompassed ?x213; has neighbor ?x528[ has religion ?x116;]; is locatedIn of ?x113; is locatedIn of ?x265[ a Lake;]; is locatedIn of ?x1538<Ruwenzori>;]; has locatedIn ?x688<EAU>; is flowsInto of ?x601;]
*> conf = 0.10
ranks of expected_values: 26
EVAL	LakeSeseSeko-Albertsee	flowsInto!	VictoriaNile	CNN-1.+1._MA	0.000	0.000	0.000	0.038	119.000	119.000	366.000	0.333	http://www.semwebtech.org/mondial/10/meta#flowsInto

#524-RO
PRED entity: RO

PRED relation: neighbor!
PRED expected values: MD
=> 43 concepts (43 used for prediction)
PRED predicted values (max 10 best out of 206): MD (0.91 #2693, 0.91 #2215, 0.90 #6027), D (0.50 #648, 0.50 #330, 0.48 #948), A (0.48 #948, 0.45 #864, 0.40 #707), SK (0.48 #948, 0.40 #655, 0.36 #812), RO (0.48 #948, 0.36 #632, 0.29 #315), TR (0.48 #948, 0.36 #632, 0.29 #2532), R (0.48 #948, 0.33 #318, 0.29 #315), HR (0.48 #948, 0.29 #315, 0.29 #2532), GE (0.48 #948, 0.29 #315, 0.19 #2531), AL (0.44 #509, 0.33 #193, 0.19 #2531)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2693 for best value:
>> intensional similarity = 6
>> extensional distance = 76
>> proper extension:  RSM;
>> query: (?x176, ?x886) <- ?x176[ has encompassed ?x195; has language ?x684; has neighbor ?x886; has religion ?x56; is neighbor of ?x177[ is neighbor of ?x185;];]
ranks of expected_values: 1
EVAL	RO	neighbor!	MD	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	43.000	43.000	206.000	0.909	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: MD
=> 90 concepts (90 used for prediction)
PRED predicted values (max 10 best out of 222): MD (0.93 #3421, 0.93 #5051, 0.92 #7661), A (0.57 #558, 0.50 #885, 0.45 #1862), R (0.53 #2446, 0.43 #3101, 0.43 #2282), D (0.50 #1316, 0.45 #1640, 0.44 #1154), SK (0.40 #1323, 0.40 #344, 0.38 #1788), SLO (0.40 #398, 0.38 #1788, 0.33 #76), CZ (0.40 #402, 0.36 #1705, 0.30 #1381), TR (0.38 #1788, 0.35 #3128, 0.33 #4892), RO (0.38 #1788, 0.33 #188, 0.33 #27), HR (0.38 #1788, 0.33 #21, 0.33 #4892)
>> best conf = 0.93
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3421 for best value:
>> intensional similarity = 13
>> extensional distance = 21
>> proper extension:  DK;
>> query: (?x176, ?x177) <- ?x176[ has government ?x435; has language ?x684; has neighbor ?x177[ has ethnicGroup ?x1193[ is ethnicGroup of ?x403<KAZ>;]; has neighbor ?x185; has religion ?x109;]; has neighbor ?x303[ is locatedIn of ?x97;]; has religion ?x352<RomanCatholic>; is locatedIn of ?x98;]
ranks of expected_values: 1
EVAL	RO	neighbor!	MD	CNN-1.+1._MA	1.000	1.000	1.000	1.000	90.000	90.000	222.000	0.929	http://www.semwebtech.org/mondial/10/meta#neighbor

#523-GUAM
PRED entity: GUAM

PRED relation: dependentOf
PRED expected values: USA
=> 40 concepts (40 used for prediction)
PRED predicted values (max 10 best out of 9): USA (0.33 #7, 0.06 #132, 0.06 #38), CN (0.33 #16, 0.02 #283, 0.02 #317), GB (0.18 #105, 0.16 #127, 0.13 #180), F (0.15 #52, 0.14 #22, 0.12 #82), NZ (0.07 #29, 0.06 #79, 0.05 #59), AUS (0.03 #66, 0.02 #86, 0.02 #338), NL (0.03 #187, 0.02 #112, 0.02 #144), N (0.02 #129, 0.01 #182, 0.01 #225), DK (0.01 #332, 0.01 #231, 0.01 #343)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #7 for best value:
>> intensional similarity = 7
>> extensional distance = 1
>> proper extension:  VIRG;
>> query: (?x1154, USA) <- ?x1154[ has government ?x2344<"organized">; is locatedIn of ?x282[ has mergesWith ?x60; is locatedInWater of ?x205;]; is locatedIn of ?x1401[ a Island;];]
ranks of expected_values: 1
EVAL	GUAM	dependentOf	USA	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	40.000	40.000	9.000	0.333	http://www.semwebtech.org/mondial/10/meta#dependentOf

PRED relation: dependentOf
PRED expected values: USA
=> 68 concepts (68 used for prediction)
PRED predicted values (max 10 best out of 19): F (0.50 #22, 0.40 #54, 0.33 #32), GB (0.29 #216, 0.15 #194, 0.12 #487), CN (0.19 #122, 0.11 #588, 0.11 #652), NZ (0.10 #73, 0.08 #83, 0.07 #130), AUS (0.08 #90, 0.07 #137, 0.04 #439), CO (0.06 #525, 0.03 #423, 0.02 #621), USA (0.05 #199, 0.05 #221, 0.04 #263), NL (0.03 #451, 0.03 #523, 0.03 #553), GUAM (0.03 #21), NMIS (0.03 #21)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #22 for best value:
>> intensional similarity = 12
>> extensional distance = 2
>> proper extension:  NCA;
>> query: (?x1154, F) <- ?x1154[ a Country; has ethnicGroup ?x2149[ a EthnicGroup; is ethnicGroup of ?x773<HONX>;]; has government ?x2344; is locatedIn of ?x282<PacificOcean>; is locatedIn of ?x1401[ a Island; has belongsToIslands ?x66[ a Islands;];];]

*> Best rule #199 for first EXPECTED value:
*> intensional similarity = 12
*> extensional distance = 18
*> proper extension:  LB;
*> query: (?x1154, USA) <- ?x1154[ a Country; has ethnicGroup ?x2149[ a EthnicGroup; is ethnicGroup of ?x773[ has encompassed ?x175; has neighbor ?x232; has religion ?x116; is locatedIn of ?x384;];]; has language ?x1155[ a Language; is language of ?x322<NMIS>;];]
*> conf = 0.05
ranks of expected_values: 7
EVAL	GUAM	dependentOf	USA	CNN-1.+1._MA	0.000	0.000	1.000	0.143	68.000	68.000	19.000	0.500	http://www.semwebtech.org/mondial/10/meta#dependentOf

#522-Suchona
PRED entity: Suchona

PRED relation: hasEstuary
PRED expected values: Suchona
=> 31 concepts (28 used for prediction)
PRED predicted values (max 10 best out of 117): Schilka (0.08 #2947, 0.05 #225, 0.05 #451), Kolyma (0.08 #2947, 0.05 #190, 0.05 #416), Narva (0.08 #2947, 0.05 #181, 0.05 #407), Katun (0.08 #2947, 0.05 #179, 0.05 #405), Amur (0.08 #2947, 0.05 #175, 0.05 #401), Oka (0.08 #2947, 0.05 #172, 0.05 #398), Newa (0.08 #2947, 0.05 #125, 0.05 #351), Don (0.08 #2947, 0.05 #119, 0.05 #345), Jenissej (0.08 #2947, 0.05 #97, 0.05 #323), Chatanga (0.08 #2947, 0.05 #57, 0.05 #283)
>> best conf = 0.08
=> the first rule below is the first best rule for 22 predicted values
>> Best rule #2947 for best value:
>> intensional similarity = 5
>> extensional distance = 224
>> proper extension:  Mincio;
>> query: (?x103, ?x1213) <- ?x103[ a River; has locatedIn ?x73[ is locatedIn of ?x1213[ a Estuary;]; is neighbor of ?x170;];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 22
EVAL	Suchona	hasEstuary	Suchona	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.045	31.000	28.000	117.000	0.075	http://www.semwebtech.org/mondial/10/meta#hasEstuary

PRED relation: hasEstuary
PRED expected values: Suchona
=> 87 concepts (87 used for prediction)
PRED predicted values (max 10 best out of 265): Narva (0.06 #10010, 0.05 #181, 0.05 #407), Amur (0.06 #10010, 0.05 #175, 0.05 #401), Newa (0.06 #10010, 0.05 #125, 0.05 #351), Jenissej (0.06 #10010, 0.05 #97, 0.05 #323), Swir (0.06 #10010, 0.05 #25, 0.05 #251), Schilka (0.06 #10010, 0.05 #225, 0.05 #451), Kolyma (0.06 #10010, 0.05 #190, 0.05 #416), Katun (0.06 #10010, 0.05 #179, 0.05 #405), Oka (0.06 #10010, 0.05 #172, 0.05 #398), Don (0.06 #10010, 0.05 #119, 0.05 #345)
>> best conf = 0.06
=> the first rule below is the first best rule for 22 predicted values
>> Best rule #10010 for best value:
>> intensional similarity = 12
>> extensional distance = 199
>> proper extension:  Uelle; Jordan; Drin; Jubba; Maas; Raab; Sanga;
>> query: (?x103, ?x920) <- ?x103[ a River; has locatedIn ?x73[ a Country; is locatedIn of ?x920[ a Estuary;]; is locatedIn of ?x1915[ a Lake;]; is neighbor of ?x232[ has ethnicGroup ?x2285; is locatedIn of ?x231;]; is neighbor of ?x353[ a Country;];];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 22
EVAL	Suchona	hasEstuary	Suchona	CNN-1.+1._MA	0.000	0.000	0.000	0.045	87.000	87.000	265.000	0.065	http://www.semwebtech.org/mondial/10/meta#hasEstuary

#521-Mestizo
PRED entity: Mestizo

PRED relation: ethnicGroup!
PRED expected values: ROU NIC BOL
=> 32 concepts (24 used for prediction)
PRED predicted values (max 10 best out of 206): NIC (0.50 #809, 0.50 #441, 0.48 #917), USA (0.50 #789, 0.48 #917, 0.33 #238), BR (0.50 #468, 0.48 #917, 0.33 #285), CV (0.50 #451, 0.33 #268, 0.33 #85), DOM (0.50 #464, 0.33 #281, 0.25 #832), BDS (0.50 #528, 0.33 #345, 0.25 #896), ROU (0.50 #430, 0.33 #247, 0.25 #798), GNB (0.50 #546, 0.33 #363, 0.25 #914), GH (0.50 #461, 0.33 #278, 0.25 #829), Z (0.50 #465, 0.33 #282, 0.25 #833)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #809 for best value:
>> intensional similarity = 12
>> extensional distance = 2
>> proper extension:  Amerindian;
>> query: (?x676, NIC) <- ?x676[ a EthnicGroup; is ethnicGroup of ?x318<CR>; is ethnicGroup of ?x654<ES>; is ethnicGroup of ?x671[ has religion ?x95;]; is ethnicGroup of ?x1364[ has ethnicGroup ?x197<European>; has neighbor ?x408; is locatedIn of ?x282;];]
>> Best rule #441 for best value:
>> intensional similarity = 10
>> extensional distance = 2
>> proper extension:  African;
>> query: (?x676, NIC) <- ?x676[ is ethnicGroup of ?x296[ is locatedIn of ?x705[ a Mountain; a Volcano;];]; is ethnicGroup of ?x671[ has language ?x247; has wasDependentOf ?x81<GB>;]; is ethnicGroup of ?x1364<HCA>;]
ranks of expected_values: 1, 7, 16
EVAL	Mestizo	ethnicGroup!	BOL	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.071	32.000	24.000	206.000	0.500	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	Mestizo	ethnicGroup!	NIC	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	32.000	24.000	206.000	0.500	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	Mestizo	ethnicGroup!	ROU	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.167	32.000	24.000	206.000	0.500	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup!
PRED expected values: ROU NIC BOL
=> 53 concepts (53 used for prediction)
PRED predicted values (max 10 best out of 216): BR (0.60 #1021, 0.56 #734, 0.56 #185), DOM (0.60 #1017, 0.50 #649, 0.33 #98), RCH (0.56 #734, 0.56 #185, 0.49 #1102), BOL (0.56 #734, 0.56 #185, 0.49 #1102), RA (0.56 #734, 0.56 #185, 0.33 #67), NIC (0.56 #734, 0.50 #626, 0.40 #994), USA (0.56 #734, 0.49 #1102, 0.33 #424), YV (0.56 #734, 0.23 #5719, 0.22 #4055), CV (0.50 #636, 0.40 #1004, 0.33 #270), ROU (0.50 #615, 0.40 #983, 0.33 #64)
>> best conf = 0.60
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1021 for best value:
>> intensional similarity = 17
>> extensional distance = 3
>> proper extension:  Mulatto;
>> query: (?x676, BR) <- ?x676[ a EthnicGroup; is ethnicGroup of ?x148<C>; is ethnicGroup of ?x296[ has language ?x702; is locatedIn of ?x264[ a Source;]; is locatedIn of ?x949[ has flowsInto ?x214;]; is neighbor of ?x202;]; is ethnicGroup of ?x783[ a Country; has language ?x247[ a Language; is language of ?x50<NLSM>;]; has religion ?x95;];]

*> Best rule #734 for first EXPECTED value:
*> intensional similarity = 16
*> extensional distance = 2
*> proper extension:  African;
*> query: (?x676, ?x379) <- ?x676[ a EthnicGroup; is ethnicGroup of ?x148<C>; is ethnicGroup of ?x296[ has language ?x702; is locatedIn of ?x264;]; is ethnicGroup of ?x404[ has language ?x2456; has neighbor ?x379;]; is ethnicGroup of ?x783[ has ethnicGroup ?x197<European>; has language ?x247<English>; has religion ?x95<Protestant>;];]
*> conf = 0.56
ranks of expected_values: 4, 6, 10
EVAL	Mestizo	ethnicGroup!	BOL	CNN-1.+1._MA	0.000	0.000	1.000	0.250	53.000	53.000	216.000	0.600	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	Mestizo	ethnicGroup!	NIC	CNN-1.+1._MA	0.000	0.000	1.000	0.200	53.000	53.000	216.000	0.600	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	Mestizo	ethnicGroup!	ROU	CNN-1.+1._MA	0.000	0.000	1.000	0.125	53.000	53.000	216.000	0.600	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#520-MNE
PRED entity: MNE

PRED relation: neighbor
PRED expected values: BIH
=> 40 concepts (40 used for prediction)
PRED predicted values (max 10 best out of 176): BIH (0.88 #3155, 0.88 #2367, 0.88 #1260), MNE (0.38 #481, 0.33 #639, 0.33 #630), RO (0.33 #340, 0.27 #4261, 0.26 #3628), UA (0.33 #365, 0.20 #50, 0.10 #2257), GR (0.27 #4261, 0.26 #3628, 0.25 #5055), H (0.27 #4261, 0.26 #3628, 0.25 #5055), SLO (0.27 #4261, 0.26 #3628, 0.25 #5055), MK (0.27 #4261, 0.26 #3628, 0.25 #5055), BG (0.27 #4261, 0.26 #3628, 0.25 #5055), F (0.22 #634, 0.18 #791, 0.17 #948)
>> best conf = 0.88
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3155 for best value:
>> intensional similarity = 6
>> extensional distance = 65
>> proper extension:  SSD;
>> query: (?x106, ?x55) <- ?x106[ has government ?x435; has neighbor ?x904[ is locatedIn of ?x132;]; is locatedIn of ?x105[ is hasEstuary of ?x2296;]; is neighbor of ?x55;]
ranks of expected_values: 1
EVAL	MNE	neighbor	BIH	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	40.000	40.000	176.000	0.879	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: BIH
=> 93 concepts (93 used for prediction)
PRED predicted values (max 10 best out of 221): BIH (0.95 #3213, 0.94 #2092, 0.94 #2090), MNE (0.50 #651, 0.43 #1292, 0.40 #972), H (0.43 #2455, 0.36 #482, 0.36 #8570), GR (0.40 #1030, 0.36 #482, 0.36 #8570), MK (0.36 #482, 0.36 #8570, 0.35 #1926), SLO (0.36 #482, 0.36 #8570, 0.35 #1926), BG (0.36 #482, 0.36 #8570, 0.34 #11342), RO (0.36 #482, 0.36 #8570, 0.34 #11342), I (0.36 #2447, 0.25 #838, 0.25 #5317), SK (0.31 #2113, 0.29 #1764, 0.24 #7264)
>> best conf = 0.95
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3213 for best value:
>> intensional similarity = 16
>> extensional distance = 16
>> proper extension:  D;
>> query: (?x106, ?x692) <- ?x106[ a Country; has encompassed ?x195<Europe>; has ethnicGroup ?x775; is locatedIn of ?x306[ has flowsInto ?x813;]; is locatedIn of ?x814[ is hasSource of ?x473;]; is locatedIn of ?x2319[ a Estuary;]; is neighbor of ?x692[ has ethnicGroup ?x223[ a EthnicGroup;]; has government ?x435; is locatedIn of ?x784; is neighbor of ?x701;];]
ranks of expected_values: 1
EVAL	MNE	neighbor	BIH	CNN-1.+1._MA	1.000	1.000	1.000	1.000	93.000	93.000	221.000	0.946	http://www.semwebtech.org/mondial/10/meta#neighbor

#519-MD
PRED entity: MD

PRED relation: neighbor
PRED expected values: RO
=> 34 concepts (33 used for prediction)
PRED predicted values (max 10 best out of 176): RO (0.91 #321, 0.90 #643, 0.90 #4214), H (0.50 #205, 0.25 #45, 0.25 #1450), R (0.38 #324, 0.25 #3, 0.25 #1450), PL (0.30 #194, 0.25 #34, 0.25 #1450), SK (0.30 #182, 0.25 #22, 0.25 #1450), CZ (0.30 #242, 0.13 #3723, 0.10 #3394), SLO (0.30 #238, 0.10 #3394, 0.09 #560), MD (0.25 #135, 0.25 #1450, 0.25 #4051), SRB (0.25 #137, 0.25 #1450, 0.25 #4051), BY (0.25 #41, 0.25 #4051, 0.21 #804)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #321 for best value:
>> intensional similarity = 6
>> extensional distance = 8
>> proper extension:  D;
>> query: (?x886, ?x176) <- ?x886[ has encompassed ?x195; has ethnicGroup ?x58; has religion ?x56; is locatedIn of ?x133<Donau>; is neighbor of ?x176;]
ranks of expected_values: 1
EVAL	MD	neighbor	RO	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	34.000	33.000	176.000	0.913	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: RO
=> 102 concepts (102 used for prediction)
PRED predicted values (max 10 best out of 215): RO (0.93 #12371, 0.92 #9220, 0.92 #5079), R (0.60 #1637, 0.60 #1475, 0.50 #494), PL (0.50 #1180, 0.50 #687, 0.40 #1668), LV (0.50 #1225, 0.45 #1145, 0.40 #1713), LT (0.50 #1287, 0.45 #1145, 0.40 #818), H (0.50 #698, 0.33 #1959, 0.33 #1958), BY (0.45 #1145, 0.40 #1675, 0.40 #818), MD (0.45 #1145, 0.40 #818, 0.40 #817), SK (0.45 #1145, 0.40 #818, 0.40 #817), EW (0.45 #1145, 0.40 #818, 0.40 #817)
>> best conf = 0.93
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #12371 for best value:
>> intensional similarity = 10
>> extensional distance = 84
>> proper extension:  KWT;
>> query: (?x886, ?x176) <- ?x886[ a Country; has encompassed ?x195; has ethnicGroup ?x58; has religion ?x56; is locatedIn of ?x133; is neighbor of ?x176[ has government ?x435; has language ?x684; is locatedIn of ?x98; is neighbor of ?x236;];]
ranks of expected_values: 1
EVAL	MD	neighbor	RO	CNN-1.+1._MA	1.000	1.000	1.000	1.000	102.000	102.000	215.000	0.926	http://www.semwebtech.org/mondial/10/meta#neighbor

#518-SnowyRiver
PRED entity: SnowyRiver

PRED relation: locatedIn
PRED expected values: AUS
=> 44 concepts (44 used for prediction)
PRED predicted values (max 10 best out of 212): AUS (0.91 #8052, 0.90 #8763, 0.86 #1419), RI (0.50 #289, 0.14 #9950, 0.14 #9711), USA (0.35 #10189, 0.35 #10188, 0.25 #7103), CDN (0.35 #10189, 0.35 #10188, 0.25 #7103), MEX (0.35 #10189, 0.35 #10188, 0.25 #7103), D (0.25 #730, 0.20 #494, 0.14 #1913), R (0.25 #242, 0.18 #5925, 0.17 #2134), NIC (0.25 #333, 0.14 #9950, 0.14 #9711), PE (0.25 #304, 0.14 #9950, 0.14 #9711), CO (0.25 #288, 0.14 #9950, 0.14 #9711)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #8052 for best value:
>> intensional similarity = 5
>> extensional distance = 194
>> proper extension:  Leine; Neckar; Buna; Enns; Hwangho; Uruguay; RioNegro; Perene; Okavango; Karun; ...
>> query: (?x1041, ?x196) <- ?x1041[ a River; has hasEstuary ?x2381[ a Estuary; has locatedIn ?x196;]; has hasSource ?x1782;]
ranks of expected_values: 1
EVAL	SnowyRiver	locatedIn	AUS	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	44.000	44.000	212.000	0.906	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: AUS
=> 124 concepts (124 used for prediction)
PRED predicted values (max 10 best out of 226): AUS (0.91 #25593, 0.91 #25356, 0.90 #16482), USA (0.73 #13201, 0.54 #3097, 0.52 #20388), R (0.64 #12656, 0.59 #17929, 0.59 #17693), CDN (0.55 #9604, 0.50 #3099, 0.46 #11997), MEX (0.54 #3097, 0.50 #3099, 0.45 #24879), NZ (0.47 #20557, 0.33 #238, 0.30 #17925), PE (0.44 #8177, 0.33 #238, 0.30 #17925), ZRE (0.43 #7712, 0.14 #25435, 0.11 #3416), IND (0.40 #5671, 0.14 #9966, 0.11 #4720), I (0.38 #6963, 0.29 #1959, 0.20 #14609)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #25593 for best value:
>> intensional similarity = 11
>> extensional distance = 113
>> proper extension:  Leine; Neckar;
>> query: (?x1041, ?x196) <- ?x1041[ a River; has hasEstuary ?x2381[ a Estuary; has locatedIn ?x196[ a Country; has encompassed ?x211; has ethnicGroup ?x197; has religion ?x95<Protestant>;];]; has hasSource ?x1782[ a Source;];]
ranks of expected_values: 1
EVAL	SnowyRiver	locatedIn	AUS	CNN-1.+1._MA	1.000	1.000	1.000	1.000	124.000	124.000	226.000	0.913	http://www.semwebtech.org/mondial/10/meta#locatedIn

#517-GUAD
PRED entity: GUAD

PRED relation: religion
PRED expected values: RomanCatholic
=> 37 concepts (37 used for prediction)
PRED predicted values (max 10 best out of 27): RomanCatholic (0.88 #687, 0.87 #768, 0.85 #928), Muslim (0.76 #564, 0.60 #1045, 0.50 #1205), Christian (0.40 #603, 0.35 #563, 0.25 #1004), Anglican (0.38 #216, 0.25 #336, 0.23 #376), Buddhist (0.35 #570, 0.30 #610, 0.12 #971), JehovasWitnesses (0.27 #459, 0.20 #780, 0.17 #699), Jewish (0.18 #761, 0.10 #883, 0.08 #1083), ChristianOrthodox (0.17 #1042, 0.17 #922, 0.14 #1122), Methodist (0.12 #205, 0.09 #806, 0.08 #325), Sikh (0.12 #592, 0.02 #993, 0.01 #1033)
>> best conf = 0.88
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #687 for best value:
>> intensional similarity = 7
>> extensional distance = 22
>> proper extension:  GCA; CO; CR; JA; NIC; MEX; PA; WD; HCA;
>> query: (?x633, RomanCatholic) <- ?x633[ a Country; has religion ?x95; is locatedIn of ?x182[ is locatedInWater of ?x477<Tobago>;]; is locatedIn of ?x317<CaribbeanSea>;]
ranks of expected_values: 1
EVAL	GUAD	religion	RomanCatholic	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	37.000	37.000	27.000	0.875	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion
PRED expected values: RomanCatholic
=> 52 concepts (52 used for prediction)
PRED predicted values (max 10 best out of 33): RomanCatholic (0.97 #1442, 0.96 #1400, 0.94 #1196), Muslim (0.61 #1771, 0.60 #1813, 0.50 #1938), ChristianOrthodox (0.49 #1105, 0.29 #1600, 0.26 #1767), Christian (0.45 #1434, 0.45 #1476, 0.44 #1230), Buddhist (0.45 #1434, 0.45 #1476, 0.44 #1230), Sikh (0.44 #1230, 0.26 #1767, 0.24 #1435), Jains (0.44 #1230, 0.24 #1435, 0.16 #817), Anglican (0.43 #584, 0.40 #792, 0.38 #693), Jewish (0.40 #42, 0.38 #41, 0.36 #366), JehovasWitnesses (0.38 #693, 0.38 #692, 0.32 #1808)
>> best conf = 0.97
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1442 for best value:
>> intensional similarity = 23
>> extensional distance = 58
>> proper extension:  S;
>> query: (?x633, RomanCatholic) <- ?x633[ a Country; has government ?x828; has religion ?x95<Protestant>; has religion ?x410[ a Religion; is religion of ?x158[ has encompassed ?x211; has ethnicGroup ?x1196; is locatedIn of ?x282;]; is religion of ?x376[ a Country; is locatedIn of ?x178; is neighbor of ?x217<RI>;]; is religion of ?x667<TT>; is religion of ?x745<MART>;]; is locatedIn of ?x317[ has locatedIn ?x482[ has ethnicGroup ?x79; has neighbor ?x315;];];]
ranks of expected_values: 1
EVAL	GUAD	religion	RomanCatholic	CNN-1.+1._MA	1.000	1.000	1.000	1.000	52.000	52.000	33.000	0.967	http://www.semwebtech.org/mondial/10/meta#religion

#516-BY
PRED entity: BY

PRED relation: neighbor
PRED expected values: UA
=> 40 concepts (40 used for prediction)
PRED predicted values (max 10 best out of 201): UA (0.91 #475, 0.90 #2540, 0.90 #4289), BY (0.60 #356, 0.33 #39, 0.28 #3018), CN (0.42 #995, 0.28 #3018, 0.27 #4448), TR (0.36 #823, 0.32 #1142, 0.10 #4926), SK (0.33 #21, 0.28 #3018, 0.27 #794), MD (0.33 #133, 0.28 #3018, 0.27 #794), RO (0.33 #25, 0.28 #3018, 0.27 #794), H (0.33 #43, 0.28 #3018, 0.27 #4290), CZ (0.28 #3018, 0.27 #794, 0.27 #4448), GE (0.28 #3018, 0.27 #794, 0.27 #4448)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #475 for best value:
>> intensional similarity = 7
>> extensional distance = 3
>> proper extension:  LT;
>> query: (?x222, ?x73) <- ?x222[ has ethnicGroup ?x1193<Russian>; has religion ?x56; is locatedIn of ?x221; is neighbor of ?x73; is neighbor of ?x194<PL>;]
ranks of expected_values: 1
EVAL	BY	neighbor	UA	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	40.000	40.000	201.000	0.909	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: UA
=> 90 concepts (90 used for prediction)
PRED predicted values (max 10 best out of 223): UA (0.92 #1598, 0.92 #1597, 0.91 #12631), BY (0.50 #679, 0.50 #358, 0.49 #5470), EW (0.50 #417, 0.43 #801, 0.43 #800), SK (0.50 #661, 0.33 #21, 0.31 #4025), MD (0.43 #801, 0.43 #800, 0.43 #799), GE (0.43 #801, 0.43 #800, 0.43 #799), SF (0.43 #801, 0.43 #800, 0.43 #799), UZB (0.43 #801, 0.43 #800, 0.43 #799), KGZ (0.43 #801, 0.43 #800, 0.43 #799), TM (0.43 #801, 0.43 #800, 0.43 #799)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1598 for best value:
>> intensional similarity = 11
>> extensional distance = 8
>> proper extension:  IL;
>> query: (?x222, ?x73) <- ?x222[ has ethnicGroup ?x2273[ a EthnicGroup;]; has government ?x1621; has language ?x555; has religion ?x56; has wasDependentOf ?x903; is locatedIn of ?x679[ has flowsThrough ?x457; has hasEstuary ?x939;]; is neighbor of ?x73[ has ethnicGroup ?x1326;];]
>> Best rule #1597 for best value:
>> intensional similarity = 12
>> extensional distance = 8
>> proper extension:  IL;
>> query: (?x222, ?x194) <- ?x222[ has ethnicGroup ?x2273[ a EthnicGroup;]; has government ?x1621; has language ?x555; has religion ?x56; has wasDependentOf ?x903; is locatedIn of ?x679[ has flowsThrough ?x457; has hasEstuary ?x939;]; is neighbor of ?x73[ has ethnicGroup ?x1326;]; is neighbor of ?x194;]
ranks of expected_values: 1
EVAL	BY	neighbor	UA	CNN-1.+1._MA	1.000	1.000	1.000	1.000	90.000	90.000	223.000	0.919	http://www.semwebtech.org/mondial/10/meta#neighbor

#515-CO
PRED entity: CO

PRED relation: ethnicGroup
PRED expected values: black-Amerindian
=> 39 concepts (39 used for prediction)
PRED predicted values (max 10 best out of 225): EastIndian (0.33 #386, 0.08 #1145, 0.08 #2916), Russian (0.24 #3863, 0.18 #4369, 0.17 #5128), Ukrainian (0.20 #3796, 0.14 #4302, 0.13 #3543), Asian (0.18 #775, 0.12 #1534, 0.11 #1787), Chinese (0.17 #1024, 0.12 #8108, 0.11 #2542), German (0.16 #3802, 0.15 #3549, 0.12 #4308), Indian (0.12 #1334, 0.06 #1587, 0.05 #4623), Polish (0.11 #3996, 0.09 #4502, 0.09 #3743), Hungarian (0.11 #3816, 0.08 #4322, 0.07 #3563), Serb (0.11 #3832, 0.08 #4338, 0.07 #3579)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #386 for best value:
>> intensional similarity = 6
>> extensional distance = 1
>> proper extension:  GUY;
>> query: (?x215, EastIndian) <- ?x215[ a Country; has encompassed ?x521; has neighbor ?x345<YV>; has wasDependentOf ?x149; is locatedIn of ?x214;]

No rule for expected values
ranks of expected_values: 
EVAL	CO	ethnicGroup	black-Amerindian	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	39.000	39.000	225.000	0.333	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: black-Amerindian
=> 85 concepts (85 used for prediction)
PRED predicted values (max 10 best out of 252): German (0.31 #5577, 0.28 #7856, 0.24 #6843), Russian (0.29 #10450, 0.26 #12223, 0.26 #9943), Quechua (0.25 #1480, 0.20 #2240, 0.19 #7596), Aymara (0.25 #1429, 0.20 #2189, 0.19 #7596), Japanese (0.25 #2000, 0.13 #8356, 0.10 #1520), Madurese (0.25 #1985, 0.13 #8356, 0.10 #1520), Javanese (0.25 #1811, 0.13 #8356, 0.10 #1520), Sundanese (0.25 #1791, 0.13 #8356, 0.10 #1520), Hungarian (0.24 #7870, 0.17 #9896, 0.15 #9390), Chinese (0.21 #12926, 0.20 #2291, 0.16 #11153)
>> best conf = 0.31
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5577 for best value:
>> intensional similarity = 13
>> extensional distance = 14
>> proper extension:  F;
>> query: (?x215, German) <- ?x215[ has encompassed ?x521; has neighbor ?x345; is locatedIn of ?x1186[ a River;]; is neighbor of ?x542[ a Country; has ethnicGroup ?x162; has government ?x140<"federal republic">; is locatedIn of ?x48; is neighbor of ?x690[ has language ?x702; is locatedIn of ?x274;];];]

No rule for expected values
ranks of expected_values: 
EVAL	CO	ethnicGroup	black-Amerindian	CNN-1.+1._MA	0.000	0.000	0.000	0.000	85.000	85.000	252.000	0.312	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#514-Formentera
PRED entity: Formentera

PRED relation: locatedIn
PRED expected values: E
=> 22 concepts (22 used for prediction)
PRED predicted values (max 10 best out of 82): E (0.82 #710, 0.81 #473, 0.35 #2621), GR (0.45 #563, 0.43 #326, 0.33 #800), I (0.30 #758, 0.29 #284, 0.27 #521), GB (0.09 #1429, 0.09 #955, 0.09 #1191), USA (0.09 #1974, 0.08 #2214, 0.08 #1018), F (0.08 #2383, 0.08 #1899, 0.07 #1658), ET (0.08 #1899, 0.07 #1658, 0.03 #4529), RI (0.08 #1713, 0.07 #2436, 0.07 #2673), M (0.07 #887, 0.05 #4769, 0.05 #4768), D (0.05 #3352, 0.05 #3114, 0.05 #3589)
>> best conf = 0.82
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #710 for best value:
>> intensional similarity = 8
>> extensional distance = 20
>> proper extension:  Samos;
>> query: (?x1714, ?x149) <- ?x1714[ a Island; has belongsToIslands ?x1715[ a Islands; is belongsToIslands of ?x2304[ a Island; has locatedIn ?x149; has locatedInWater ?x275<MediterraneanSea>;];];]
ranks of expected_values: 1
EVAL	Formentera	locatedIn	E	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	22.000	22.000	82.000	0.818	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: E
=> 40 concepts (40 used for prediction)
PRED predicted values (max 10 best out of 111): E (0.82 #710, 0.81 #473, 0.35 #6063), GR (0.45 #563, 0.43 #326, 0.33 #800), I (0.30 #758, 0.29 #284, 0.27 #521), GB (0.16 #1684, 0.15 #2173, 0.15 #1928), F (0.11 #1185, 0.10 #4361, 0.09 #1675), ET (0.11 #1185, 0.08 #4117, 0.08 #4848), USA (0.10 #2967, 0.09 #3457, 0.09 #3701), D (0.10 #3382, 0.10 #3160, 0.09 #1455), M (0.09 #1675, 0.07 #3867, 0.07 #887), CY (0.09 #1675, 0.07 #3867, 0.05 #9234)
>> best conf = 0.82
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #710 for best value:
>> intensional similarity = 8
>> extensional distance = 20
>> proper extension:  Samos;
>> query: (?x1714, ?x149) <- ?x1714[ a Island; has belongsToIslands ?x1715[ a Islands; is belongsToIslands of ?x2304[ a Island; has locatedIn ?x149; has locatedInWater ?x275<MediterraneanSea>;];];]
ranks of expected_values: 1
EVAL	Formentera	locatedIn	E	CNN-1.+1._MA	1.000	1.000	1.000	1.000	40.000	40.000	111.000	0.818	http://www.semwebtech.org/mondial/10/meta#locatedIn

#513-RWA
PRED entity: RWA

PRED relation: locatedIn!
PRED expected values: LakeKivu
=> 40 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 1357): AtlanticOcean (0.88 #14237, 0.33 #42, 0.32 #49724), LakeVictoria (0.71 #12776, 0.71 #12775, 0.50 #3484), LakeTanganjika (0.71 #12776, 0.71 #12775, 0.50 #1507), Ruzizi (0.50 #1548, 0.33 #129, 0.14 #5807), LakeKivu (0.46 #4259, 0.45 #7098, 0.33 #1011), IndianOcean (0.40 #4262, 0.29 #5681, 0.25 #2842), PacificOcean (0.39 #8603, 0.33 #7184, 0.31 #15700), Semliki (0.33 #642, 0.29 #6320, 0.25 #3481), LakeSeseSeko-Albertsee (0.33 #1007, 0.29 #6685, 0.25 #3846), Ruwenzori (0.33 #866, 0.29 #6544, 0.25 #3705)
>> best conf = 0.88
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #14237 for best value:
>> intensional similarity = 5
>> extensional distance = 58
>> proper extension:  NLSM; BS; VIRG;
>> query: (?x546, AtlanticOcean) <- ?x546[ has government ?x2266; has religion ?x95; is locatedIn of ?x1060[ has locatedIn ?x348<ZRE>;];]

*> Best rule #4259 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 2
*> proper extension:  EAT;
*> query: (?x546, ?x1776) <- ?x546[ has ethnicGroup ?x1946; has religion ?x95; is locatedIn of ?x1060[ is flowsInto of ?x1776;]; is locatedIn of ?x1194<Akagera>; is neighbor of ?x359;]
*> conf = 0.46
ranks of expected_values: 5
EVAL	RWA	locatedIn!	LakeKivu	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.200	40.000	36.000	1357.000	0.883	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: LakeKivu
=> 96 concepts (96 used for prediction)
PRED predicted values (max 10 best out of 1425): AtlanticOcean (0.79 #46929, 0.72 #85303, 0.71 #28457), IndianOcean (0.71 #26997, 0.33 #3, 0.33 #127905), LakeTanganjika (0.67 #35524, 0.62 #34103, 0.58 #32681), LakeVictoria (0.67 #35524, 0.33 #3485, 0.33 #645), Ruzizi (0.62 #34103, 0.51 #35525, 0.27 #56834), LakeKivu (0.62 #34103, 0.40 #32682, 0.27 #56834), Ruwenzori (0.62 #34103, 0.33 #3706, 0.29 #15068), LakeSeseSeko-Albertsee (0.62 #34103, 0.33 #3847, 0.29 #15209), Rutanzige-Eduardsee (0.62 #34103, 0.33 #3104, 0.29 #14466), Semliki (0.62 #34103, 0.33 #3482, 0.29 #14844)
>> best conf = 0.79
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #46929 for best value:
>> intensional similarity = 15
>> extensional distance = 26
>> proper extension:  NLSM;
>> query: (?x546, AtlanticOcean) <- ?x546[ a Country; has government ?x2266; has religion ?x95<Protestant>; has religion ?x352<RomanCatholic>; is locatedIn of ?x1194[ has locatedIn ?x688[ is neighbor of ?x229;]; has locatedIn ?x820[ a Country; has government ?x435; has neighbor ?x192<MOC>; has religion ?x116;];];]

*> Best rule #34103 for first EXPECTED value:
*> intensional similarity = 15
*> extensional distance = 18
*> proper extension:  NZ;
*> query: (?x546, ?x113) <- ?x546[ a Country; has encompassed ?x213; has ethnicGroup ?x2171[ a EthnicGroup;]; has government ?x2266; has religion ?x95<Protestant>; has religion ?x352<RomanCatholic>; is locatedIn of ?x545[ has inMountains ?x1066; has locatedIn ?x348[ is locatedIn of ?x113;];]; is locatedIn of ?x1060[ is flowsInto of ?x1776;];]
*> conf = 0.62
ranks of expected_values: 6
EVAL	RWA	locatedIn!	LakeKivu	CNN-1.+1._MA	0.000	0.000	1.000	0.167	96.000	96.000	1425.000	0.786	http://www.semwebtech.org/mondial/10/meta#locatedIn

#512-GrandErgEst
PRED entity: GrandErgEst

PRED relation: locatedIn
PRED expected values: TN
=> 31 concepts (20 used for prediction)
PRED predicted values (max 10 best out of 189): RMM (0.47 #884, 0.26 #1123, 0.25 #175), SUD (0.41 #1468, 0.12 #1186, 0.11 #4269), USA (0.35 #2446, 0.22 #1425, 0.22 #1260), RIM (0.33 #589, 0.16 #1188, 0.16 #1187), WAN (0.30 #973, 0.12 #1186, 0.11 #4269), AUS (0.25 #1233, 0.13 #1947, 0.09 #2419), ET (0.25 #1430, 0.12 #1186, 0.11 #4269), MA (0.20 #412, 0.16 #1188, 0.16 #1187), TR (0.19 #2178, 0.10 #276, 0.07 #750), CDN (0.19 #2437, 0.14 #3150, 0.13 #3386)
>> best conf = 0.47
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #884 for best value:
>> intensional similarity = 8
>> extensional distance = 13
>> proper extension:  Bani; Niger; Senegal; Bani;
>> query: (?x1360, RMM) <- ?x1360[ has locatedIn ?x581[ is locatedIn of ?x2380<Tanezrouft>; is neighbor of ?x426<RN>; is neighbor of ?x1184[ has ethnicGroup ?x1215; is neighbor of ?x63;];];]

*> Best rule #1188 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 25
*> proper extension:  AtlanticOcean; LakeKainji; Volta; AsoRock; Benue; ChadLake; Benue; Niger;
*> query: (?x1360, ?x108) <- ?x1360[ has locatedIn ?x581[ has encompassed ?x213; has wasDependentOf ?x78; is neighbor of ?x108; is neighbor of ?x426<RN>; is neighbor of ?x1184[ is neighbor of ?x63;];];]
*> conf = 0.16
ranks of expected_values: 15
EVAL	GrandErgEst	locatedIn	TN	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.067	31.000	20.000	189.000	0.467	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: TN
=> 73 concepts (72 used for prediction)
PRED predicted values (max 10 best out of 222): USA (0.67 #6308, 0.60 #6544, 0.51 #7264), AL (0.59 #2435, 0.23 #4075, 0.18 #1190), ZRE (0.45 #5594, 0.17 #10139, 0.15 #3671), ET (0.42 #1436, 0.23 #4075, 0.18 #1190), RIM (0.40 #1069, 0.27 #1790, 0.23 #4075), MA (0.38 #888, 0.23 #4075, 0.20 #1369), WAN (0.35 #1937, 0.30 #2652, 0.24 #3829), IND (0.34 #3298, 0.06 #4021, 0.06 #6900), GH (0.33 #1309, 0.14 #2986, 0.13 #3466), RMM (0.32 #1846, 0.30 #2087, 0.26 #2802)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #6308 for best value:
>> intensional similarity = 14
>> extensional distance = 178
>> proper extension:  DarlingRiver; DarlingRiver; Tasmania; EucumbeneRiver; Mt.Bogong; MurrumbidgeeRiver; LakeBurleyGriffin; LakeEyre; Mt.Kosciuszko; EucumbeneRiver; ...
>> query: (?x1360, USA) <- ?x1360[ has locatedIn ?x581[ a Country; has encompassed ?x213; has ethnicGroup ?x197; has religion ?x109; is locatedIn of ?x84[ has type ?x150;]; is locatedIn of ?x275[ is flowsInto of ?x698; is locatedInWater of ?x68; is mergesWith of ?x182<AtlanticOcean>;]; is locatedIn of ?x1298[ a Desert;];];]

*> Best rule #4075 for first EXPECTED value:
*> intensional similarity = 11
*> extensional distance = 47
*> proper extension:  Majuro;
*> query: (?x1360, ?x55) <- ?x1360[ has locatedIn ?x581[ a Country; has ethnicGroup ?x197[ a EthnicGroup; is ethnicGroup of ?x390<SLB>;]; has religion ?x116<Christian>; has wasDependentOf ?x78; is locatedIn of ?x275[ has locatedIn ?x55;];];]
*> conf = 0.23
ranks of expected_values: 25
EVAL	GrandErgEst	locatedIn	TN	CNN-1.+1._MA	0.000	0.000	0.000	0.040	73.000	72.000	222.000	0.672	http://www.semwebtech.org/mondial/10/meta#locatedIn

#511-R
PRED entity: R

PRED relation: locatedIn!
PRED expected values: BalticSea KaraSea Ischim Ob Sachalin Ob Angara Volga Olkhon Petschora Dychtau OzeroChanka
=> 32 concepts (32 used for prediction)
PRED predicted values (max 10 best out of 1269): BalticSea (0.79 #6616, 0.74 #19851, 0.08 #35735), Ob (0.79 #6616, 0.74 #19851), KaraSea (0.79 #6616, 0.74 #19851), CaribbeanSea (0.49 #15972, 0.36 #5385, 0.26 #8032), Kura (0.40 #4137, 0.12 #33086, 0.08 #35735), AtlanticOcean (0.35 #35771, 0.34 #15915, 0.33 #22533), Ili (0.33 #1417, 0.33 #94, 0.12 #33086), PikChan-Tengri (0.33 #1887, 0.33 #564, 0.08 #35735), Mekong (0.33 #1893, 0.12 #33086, 0.08 #35735), Brahmaputra (0.33 #2374, 0.12 #33086, 0.08 #35735)
>> best conf = 0.79
=> the first rule below is the first best rule for 3 predicted values
>> Best rule #6616 for best value:
>> intensional similarity = 6
>> extensional distance = 9
>> proper extension:  AUS; CDN;
>> query: (?x73, ?x1845) <- ?x73[ is locatedIn of ?x103[ a River;]; is locatedIn of ?x282<PacificOcean>; is locatedIn of ?x2143[ has flowsInto ?x1845;];]
ranks of expected_values: 1, 2, 3, 18, 34, 825, 826
EVAL	R	locatedIn!	OzeroChanka	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.033	32.000	32.000	1269.000	0.786	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	R	locatedIn!	Dychtau	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	32.000	32.000	1269.000	0.786	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	R	locatedIn!	Petschora	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	32.000	32.000	1269.000	0.786	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	R	locatedIn!	Olkhon	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.001	32.000	32.000	1269.000	0.786	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	R	locatedIn!	Volga	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	32.000	32.000	1269.000	0.786	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	R	locatedIn!	Angara	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	32.000	32.000	1269.000	0.786	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	R	locatedIn!	Ob	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	32.000	32.000	1269.000	0.786	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	R	locatedIn!	Sachalin	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.001	32.000	32.000	1269.000	0.786	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	R	locatedIn!	Ob	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	32.000	32.000	1269.000	0.786	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	R	locatedIn!	Ischim	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.067	32.000	32.000	1269.000	0.786	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	R	locatedIn!	KaraSea	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	32.000	32.000	1269.000	0.786	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	R	locatedIn!	BalticSea	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	32.000	32.000	1269.000	0.786	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: BalticSea KaraSea Ischim Ob Sachalin Ob Angara Volga Olkhon Petschora Dychtau OzeroChanka
=> 98 concepts (98 used for prediction)
PRED predicted values (max 10 best out of 1333): Ischim (0.93 #41071, 0.50 #1324, 0.40 #6620), KaraSea (0.88 #39746, 0.87 #75515, 0.87 #80815), Dnepr (0.66 #92744, 0.43 #11919, 0.40 #7944), Ural (0.66 #92744, 0.43 #11919, 0.40 #7944), WesternDwina (0.66 #92744, 0.43 #11919, 0.40 #7944), Petschora (0.66 #92744, 0.43 #11919, 0.40 #7944), Volga (0.66 #92744, 0.43 #11919, 0.40 #7944), Angara (0.66 #92744, 0.43 #11919, 0.40 #7944), BalticSea (0.63 #86119, 0.42 #9268, 0.40 #94069), Ob (0.63 #86119, 0.42 #9268)
>> best conf = 0.93
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #41071 for best value:
>> intensional similarity = 12
>> extensional distance = 20
>> proper extension:  AZ;
>> query: (?x73, ?x1761) <- ?x73[ has ethnicGroup ?x58; has language ?x555; has neighbor ?x170; has religion ?x56; is locatedIn of ?x198[ a Estuary;]; is locatedIn of ?x465[ a River;]; is locatedIn of ?x876[ has type ?x150;]; is locatedIn of ?x2264[ is hasEstuary of ?x1761;];]
ranks of expected_values: 1, 2, 6, 7, 8, 9, 10, 102, 837, 838
EVAL	R	locatedIn!	OzeroChanka	CNN-1.+1._MA	0.000	0.000	0.000	0.011	98.000	98.000	1333.000	0.925	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	R	locatedIn!	Dychtau	CNN-1.+1._MA	0.000	0.000	0.000	0.000	98.000	98.000	1333.000	0.925	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	R	locatedIn!	Petschora	CNN-1.+1._MA	0.000	0.000	1.000	0.250	98.000	98.000	1333.000	0.925	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	R	locatedIn!	Olkhon	CNN-1.+1._MA	0.000	0.000	0.000	0.001	98.000	98.000	1333.000	0.925	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	R	locatedIn!	Volga	CNN-1.+1._MA	0.000	0.000	1.000	0.250	98.000	98.000	1333.000	0.925	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	R	locatedIn!	Angara	CNN-1.+1._MA	0.000	0.000	1.000	0.250	98.000	98.000	1333.000	0.925	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	R	locatedIn!	Ob	CNN-1.+1._MA	0.000	0.000	1.000	0.250	98.000	98.000	1333.000	0.925	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	R	locatedIn!	Sachalin	CNN-1.+1._MA	0.000	0.000	0.000	0.001	98.000	98.000	1333.000	0.925	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	R	locatedIn!	Ob	CNN-1.+1._MA	0.000	0.000	0.000	0.000	98.000	98.000	1333.000	0.925	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	R	locatedIn!	Ischim	CNN-1.+1._MA	1.000	1.000	1.000	1.000	98.000	98.000	1333.000	0.925	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	R	locatedIn!	KaraSea	CNN-1.+1._MA	1.000	1.000	1.000	1.000	98.000	98.000	1333.000	0.925	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	R	locatedIn!	BalticSea	CNN-1.+1._MA	0.000	0.000	1.000	0.250	98.000	98.000	1333.000	0.925	http://www.semwebtech.org/mondial/10/meta#locatedIn

#510-Kefallinia
PRED entity: Kefallinia

PRED relation: locatedIn
PRED expected values: GR
=> 15 concepts (15 used for prediction)
PRED predicted values (max 10 best out of 81): GR (0.33 #90, 0.05 #326, 0.05 #565), I (0.30 #48, 0.08 #2635, 0.05 #3119), USA (0.09 #308, 0.09 #547, 0.08 #785), E (0.08 #2635, 0.07 #27, 0.05 #3119), F (0.08 #2635, 0.05 #3119, 0.05 #3118), ET (0.08 #2635, 0.03 #1431, 0.03 #2880), RI (0.07 #765, 0.06 #2932, 0.05 #3171), GB (0.07 #964, 0.07 #2402, 0.07 #1441), M (0.07 #177, 0.05 #3119, 0.05 #3118), D (0.06 #975, 0.06 #1452, 0.06 #1214)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #90 for best value:
>> intensional similarity = 3
>> extensional distance = 28
>> proper extension:  Menorca; Stromboli; Mallorca; Salina; Lefkas; Rhodos; Gozo; Malta; Zakynthos; Cyprus; ...
>> query: (?x1956, GR) <- ?x1956[ a Island; has locatedInWater ?x275<MediterraneanSea>;]
ranks of expected_values: 1
EVAL	Kefallinia	locatedIn	GR	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	15.000	15.000	81.000	0.333	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: GR
=> 29 concepts (29 used for prediction)
PRED predicted values (max 10 best out of 81): GR (0.33 #90, 0.32 #326, 0.08 #574), I (0.30 #48, 0.29 #284, 0.11 #483), GB (0.13 #493, 0.08 #1723, 0.08 #741), E (0.11 #483, 0.08 #2203, 0.08 #731), F (0.11 #483, 0.08 #2203, 0.08 #731), ET (0.11 #483, 0.08 #2203, 0.08 #731), USA (0.10 #804, 0.09 #1048, 0.09 #1293), D (0.09 #504, 0.06 #2721, 0.06 #2224), P (0.08 #681, 0.05 #929, 0.05 #1173), M (0.07 #478, 0.07 #177, 0.06 #413)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #90 for best value:
>> intensional similarity = 3
>> extensional distance = 28
>> proper extension:  Menorca; Stromboli; Mallorca; Salina; Lefkas; Rhodos; Gozo; Malta; Zakynthos; Cyprus; ...
>> query: (?x1956, GR) <- ?x1956[ a Island; has locatedInWater ?x275<MediterraneanSea>;]
ranks of expected_values: 1
EVAL	Kefallinia	locatedIn	GR	CNN-1.+1._MA	1.000	1.000	1.000	1.000	29.000	29.000	81.000	0.333	http://www.semwebtech.org/mondial/10/meta#locatedIn

#509-Ob
PRED entity: Ob

PRED relation: locatedIn
PRED expected values: R
=> 49 concepts (49 used for prediction)
PRED predicted values (max 10 best out of 218): R (0.91 #951, 0.91 #719, 0.90 #9724), KAZ (0.82 #238, 0.80 #237, 0.74 #6403), CN (0.82 #238, 0.74 #6403, 0.74 #6402), UA (0.20 #5689, 0.16 #3795, 0.13 #546), EW (0.20 #5689, 0.16 #3795, 0.11 #136), BY (0.20 #5689, 0.16 #3795, 0.09 #768), PL (0.20 #5689, 0.16 #3795, 0.08 #9013), N (0.20 #5689, 0.16 #3795, 0.08 #9013), GE (0.20 #5689, 0.16 #3795, 0.08 #9013), AZ (0.20 #5689, 0.16 #3795, 0.08 #9013)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #951 for best value:
>> intensional similarity = 5
>> extensional distance = 20
>> proper extension:  Suchona;
>> query: (?x1845, ?x73) <- ?x1845[ a River; has hasSource ?x976[ a Source; has locatedIn ?x73<R>;];]
>> Best rule #719 for best value:
>> intensional similarity = 5
>> extensional distance = 20
>> proper extension:  Suchona;
>> query: (?x1845, R) <- ?x1845[ a River; has hasSource ?x976[ a Source; has locatedIn ?x73<R>;];]
ranks of expected_values: 1
EVAL	Ob	locatedIn	R	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	49.000	49.000	218.000	0.909	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: R
=> 129 concepts (129 used for prediction)
PRED predicted values (max 10 best out of 223): R (0.94 #14788, 0.92 #16931, 0.92 #25997), CN (0.86 #9302, 0.86 #9299, 0.82 #3347), KAZ (0.86 #9302, 0.86 #9299, 0.82 #3347), TAD (0.57 #2414, 0.22 #24326, 0.20 #24324), USA (0.50 #9134, 0.41 #14621, 0.33 #17241), AFG (0.43 #2480, 0.22 #24326, 0.20 #24324), UZB (0.33 #304, 0.29 #2456, 0.22 #24326), SF (0.29 #5149, 0.21 #11101, 0.20 #2046), UA (0.28 #11756, 0.20 #24324, 0.19 #22885), IND (0.27 #4010, 0.22 #24326, 0.20 #24324)
>> best conf = 0.94
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #14788 for best value:
>> intensional similarity = 14
>> extensional distance = 30
>> proper extension:  Arkansas; AlleghenyRiver;
>> query: (?x1845, ?x73) <- ?x1845[ a River; has flowsInto ?x801; has hasEstuary ?x1765; has hasSource ?x976[ a Source; has locatedIn ?x73[ has encompassed ?x195; has ethnicGroup ?x58; has neighbor ?x170; has wasDependentOf ?x903; is locatedIn of ?x263<ArcticOcean>; is locatedIn of ?x809<BeringSea>;];];]
ranks of expected_values: 1
EVAL	Ob	locatedIn	R	CNN-1.+1._MA	1.000	1.000	1.000	1.000	129.000	129.000	223.000	0.938	http://www.semwebtech.org/mondial/10/meta#locatedIn

#508-Moldau
PRED entity: Moldau

PRED relation: inMountains
PRED expected values: BohemianMountains
=> 46 concepts (46 used for prediction)
PRED predicted values (max 10 best out of 30): SudetyMountains (0.60 #58, 0.36 #145, 0.21 #319), Alps (0.38 #352, 0.14 #701, 0.12 #178), Karpaten (0.13 #487, 0.08 #662, 0.05 #836), BlackForest (0.12 #175, 0.02 #698, 0.02 #785), Andes (0.11 #882, 0.08 #1317, 0.08 #1056), Beskides (0.11 #291, 0.08 #378, 0.04 #553), Balkan (0.07 #891, 0.05 #1326, 0.05 #804), Rhön (0.06 #228, 0.01 #751, 0.01 #838), ThueringerWald (0.06 #219, 0.01 #742, 0.01 #829), Fichtelgebirge (0.06 #218)
>> best conf = 0.60
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #58 for best value:
>> intensional similarity = 7
>> extensional distance = 3
>> proper extension:  Oder; Elbe; March;
>> query: (?x2367, SudetyMountains) <- ?x2367[ a Source; has locatedIn ?x471<CZ>; is hasSource of ?x946[ a River; has hasEstuary ?x470[ a Estuary;];];]

No rule for expected values
ranks of expected_values: 
EVAL	Moldau	inMountains	BohemianMountains	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	46.000	46.000	30.000	0.600	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains
PRED expected values: BohemianMountains
=> 88 concepts (88 used for prediction)
PRED predicted values (max 10 best out of 36): SudetyMountains (0.60 #58, 0.36 #145, 0.23 #232), Alps (0.46 #178, 0.43 #526, 0.37 #613), Karpaten (0.14 #574, 0.12 #835, 0.11 #1009), BlackForest (0.12 #262, 0.10 #523, 0.03 #1132), Vogesen (0.11 #657, 0.05 #1179, 0.05 #570), Beskides (0.11 #378, 0.08 #204, 0.05 #552), Andes (0.08 #2882, 0.08 #2099, 0.07 #2186), Balkan (0.07 #1151, 0.06 #1499, 0.05 #1064), EastAfricanRift (0.06 #2464, 0.06 #2725, 0.05 #2203), WaldaiHills (0.06 #834, 0.05 #1008, 0.01 #3009)
>> best conf = 0.60
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #58 for best value:
>> intensional similarity = 9
>> extensional distance = 3
>> proper extension:  Oder; Elbe;
>> query: (?x2367, SudetyMountains) <- ?x2367[ a Source; has locatedIn ?x471<CZ>; is hasSource of ?x946[ a River; has flowsInto ?x1631[ has locatedIn ?x120<D>;]; has hasEstuary ?x470;];]

No rule for expected values
ranks of expected_values: 
EVAL	Moldau	inMountains	BohemianMountains	CNN-1.+1._MA	0.000	0.000	0.000	0.000	88.000	88.000	36.000	0.600	http://www.semwebtech.org/mondial/10/meta#inMountains

#507-RN
PRED entity: RN

PRED relation: ethnicGroup
PRED expected values: Fula
=> 39 concepts (39 used for prediction)
PRED predicted values (max 10 best out of 228): European (0.60 #514, 0.33 #2541, 0.32 #1781), African (0.40 #259, 0.37 #3045, 0.24 #1272), Arab-Berber (0.40 #535, 0.29 #1041, 0.25 #29), Jewish (0.29 #1057, 0.25 #45, 0.20 #551), Peuhl (0.25 #219, 0.20 #5319, 0.19 #6333), Mande (0.25 #113, 0.20 #5319, 0.19 #6333), Voltaic (0.25 #111, 0.20 #5319, 0.19 #6333), Songhai (0.25 #100, 0.20 #5319, 0.19 #6333), Arab (0.20 #1530, 0.12 #3303, 0.08 #2797), Malinke (0.20 #447, 0.07 #1460, 0.04 #3233)
>> best conf = 0.60
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #514 for best value:
>> intensional similarity = 7
>> extensional distance = 3
>> proper extension:  ET; TN; DZ;
>> query: (?x426, European) <- ?x426[ has ethnicGroup ?x1109; has government ?x435<"republic">; has neighbor ?x1184<LAR>; is locatedIn of ?x535; is neighbor of ?x139;]

No rule for expected values
ranks of expected_values: 
EVAL	RN	ethnicGroup	Fula	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	39.000	39.000	228.000	0.600	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: Fula
=> 107 concepts (107 used for prediction)
PRED predicted values (max 10 best out of 250): Peuhl (0.70 #14460, 0.68 #9638, 0.66 #16744), Mande (0.70 #14460, 0.68 #9638, 0.66 #16744), Voltaic (0.70 #14460, 0.68 #9638, 0.66 #16744), Songhai (0.70 #14460, 0.68 #9638, 0.66 #16744), European (0.67 #3306, 0.61 #7109, 0.54 #7355), African (0.66 #16744, 0.66 #6086, 0.64 #20041), Fulani (0.66 #16744, 0.66 #6086, 0.64 #20041), Kirdi (0.66 #16744, 0.66 #6086, 0.64 #20041), CameroonHighlanders (0.66 #16744, 0.66 #6086, 0.64 #20041), EasternNigritic (0.66 #16744, 0.66 #6086, 0.64 #20041)
>> best conf = 0.70
=> the first rule below is the first best rule for 4 predicted values
>> Best rule #14460 for best value:
>> intensional similarity = 13
>> extensional distance = 36
>> proper extension:  BVIR; PITC; WAFU; AMSA;
>> query: (?x426, ?x1537) <- ?x426[ a Country; has encompassed ?x213[ a Continent;]; has ethnicGroup ?x1109; has ethnicGroup ?x2068[ a EthnicGroup;]; has government ?x435; is locatedIn of ?x1618[ has locatedIn ?x839[ has ethnicGroup ?x1537; has language ?x1228; has religion ?x116;]; has type ?x578;];]

No rule for expected values
ranks of expected_values: 
EVAL	RN	ethnicGroup	Fula	CNN-1.+1._MA	0.000	0.000	0.000	0.000	107.000	107.000	250.000	0.698	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#506-MtHood
PRED entity: MtHood

PRED relation: inMountains
PRED expected values: CascadeRange
=> 44 concepts (44 used for prediction)
PRED predicted values (max 10 best out of 41): RockyMountains (0.35 #355, 0.22 #1918, 0.13 #703), Hawaii (0.29 #68, 0.25 #155, 0.22 #1918), CascadeRange (0.29 #41, 0.25 #128, 0.22 #1918), WrangellMountains (0.22 #1918, 0.14 #49, 0.12 #136), EliasRange (0.22 #1918, 0.12 #363, 0.09 #1655), AppalachianMountains (0.22 #1918, 0.09 #1655, 0.08 #1393), SierraNevadaCalifornia (0.22 #1918, 0.09 #1655, 0.08 #1393), Adirondacks (0.22 #1918, 0.09 #1655, 0.08 #1393), AlaskaRange (0.22 #1918, 0.09 #1655, 0.08 #1393), Andes (0.13 #446, 0.12 #620, 0.10 #1142)
>> best conf = 0.35
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #355 for best value:
>> intensional similarity = 3
>> extensional distance = 24
>> proper extension:  MtElbert; KingsPeak; MtSt.Elias; MtBona; MtMitchell; GannettPeak; BoundaryPeak; MtWhitney; MtMcKinley; MtMarcy; ...
>> query: (?x799, RockyMountains) <- ?x799[ a Mountain; has locatedIn ?x315<USA>;]

*> Best rule #41 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 5
*> proper extension:  MtAdams; MaunaKea; MtRainier; MtBlackburn; Haleakala;
*> query: (?x799, CascadeRange) <- ?x799[ a Mountain; has locatedIn ?x315<USA>; has type ?x706<"volcano">;]
*> conf = 0.29
ranks of expected_values: 3
EVAL	MtHood	inMountains	CascadeRange	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.333	44.000	44.000	41.000	0.346	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains
PRED expected values: CascadeRange
=> 128 concepts (128 used for prediction)
PRED predicted values (max 10 best out of 71): RockyMountains (0.35 #791, 0.34 #1315, 0.33 #965), Hawaii (0.29 #68, 0.28 #2964, 0.26 #2614), CascadeRange (0.29 #41, 0.28 #2964, 0.26 #2614), WrangellMountains (0.28 #2964, 0.26 #2614, 0.22 #1220), EliasRange (0.28 #2964, 0.26 #2614, 0.22 #1220), AppalachianMountains (0.28 #2964, 0.26 #2614, 0.22 #1220), SierraNevadaCalifornia (0.28 #2964, 0.26 #2614, 0.22 #1220), Adirondacks (0.28 #2964, 0.26 #2614, 0.22 #1220), AlaskaRange (0.28 #2964, 0.26 #2614, 0.22 #1220), CanaryIslands (0.27 #405, 0.13 #666, 0.11 #927)
>> best conf = 0.35
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #791 for best value:
>> intensional similarity = 3
>> extensional distance = 24
>> proper extension:  MtElbert; KingsPeak; MtSt.Elias; MtBona; MtMitchell; GannettPeak; BoundaryPeak; MtWhitney; MtMcKinley; MtMarcy; ...
>> query: (?x799, RockyMountains) <- ?x799[ a Mountain; has locatedIn ?x315<USA>;]

*> Best rule #41 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 5
*> proper extension:  MtAdams; MaunaKea; MtRainier; MtBlackburn; Haleakala;
*> query: (?x799, CascadeRange) <- ?x799[ a Mountain; a Volcano; has locatedIn ?x315<USA>; has type ?x706<"volcano">;]
*> conf = 0.29
ranks of expected_values: 3
EVAL	MtHood	inMountains	CascadeRange	CNN-1.+1._MA	0.000	1.000	1.000	0.333	128.000	128.000	71.000	0.346	http://www.semwebtech.org/mondial/10/meta#inMountains

#505-Fohr
PRED entity: Fohr

PRED relation: belongsToIslands
PRED expected values: NordfriesischeInseln
=> 39 concepts (39 used for prediction)
PRED predicted values (max 10 best out of 39): OstfriesischeInseln (0.33 #110, 0.33 #42, 0.24 #246), NordfriesischeInseln (0.25 #36, 0.20 #104, 0.15 #172), LesserAntilles (0.20 #628, 0.20 #696, 0.07 #1104), OrkneyIslands (0.19 #289, 0.16 #357, 0.15 #153), WestfriesischeInseln (0.15 #149, 0.14 #285, 0.14 #409), BritishIsles (0.14 #409, 0.07 #496, 0.06 #1296), ShetlandIslands (0.14 #409, 0.06 #1296, 0.06 #1227), InnerHebrides (0.11 #541, 0.06 #677, 0.05 #745), SundaIslands (0.08 #899, 0.05 #967, 0.04 #1035), Azores (0.08 #617, 0.08 #685, 0.05 #889)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #110 for best value:
>> intensional similarity = 3
>> extensional distance = 13
>> proper extension:  Spiekeroog;
>> query: (?x1515, OstfriesischeInseln) <- ?x1515[ a Island; has locatedIn ?x120<D>;]
>> Best rule #42 for best value:
>> intensional similarity = 5
>> extensional distance = 10
>> proper extension:  Pellworm; Langeoog; Amrum; Norderney; Helgoland; Sylt; Baltrum; Borkum; Juist; Wangerooge;
>> query: (?x1515, OstfriesischeInseln) <- ?x1515[ a Island; has locatedIn ?x120<D>; has locatedInWater ?x121<NorthSea>;]

*> Best rule #36 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 10
*> proper extension:  Pellworm; Langeoog; Amrum; Norderney; Helgoland; Sylt; Baltrum; Borkum; Juist; Wangerooge;
*> query: (?x1515, NordfriesischeInseln) <- ?x1515[ a Island; has locatedIn ?x120<D>; has locatedInWater ?x121<NorthSea>;]
*> conf = 0.25
ranks of expected_values: 2
EVAL	Fohr	belongsToIslands	NordfriesischeInseln	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	39.000	39.000	39.000	0.333	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

PRED relation: belongsToIslands
PRED expected values: NordfriesischeInseln
=> 97 concepts (97 used for prediction)
PRED predicted values (max 10 best out of 47): OstfriesischeInseln (0.40 #3418, 0.39 #342, 0.39 #3349), NordfriesischeInseln (0.40 #3418, 0.39 #342, 0.39 #3349), WestfriesischeInseln (0.25 #286, 0.14 #423, 0.11 #137), SundaIslands (0.22 #696, 0.16 #970, 0.11 #1584), Canares (0.21 #501, 0.15 #569, 0.12 #774), OrkneyIslands (0.19 #427, 0.11 #137, 0.07 #631), LesserAntilles (0.15 #1653, 0.12 #2609, 0.09 #3433), LipariIslands (0.15 #548, 0.12 #753, 0.12 #821), Azores (0.14 #823, 0.10 #1028, 0.09 #1096), HawaiiIslands (0.13 #916, 0.09 #1121, 0.09 #1190)
>> best conf = 0.40
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #3418 for best value:
>> intensional similarity = 7
>> extensional distance = 203
>> proper extension:  IsleofMan;
>> query: (?x1515, ?x1856) <- ?x1515[ a Island; has locatedIn ?x120[ is locatedIn of ?x146[ is locatedInWater of ?x145;]; is locatedIn of ?x1359[ a Island; has belongsToIslands ?x1856;];];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 2
EVAL	Fohr	belongsToIslands	NordfriesischeInseln	CNN-1.+1._MA	0.000	1.000	1.000	0.500	97.000	97.000	47.000	0.400	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

#504-RA
PRED entity: RA

PRED relation: neighbor!
PRED expected values: ROU BR
=> 39 concepts (28 used for prediction)
PRED predicted values (max 10 best out of 213): BR (0.93 #2395, 0.91 #2394, 0.90 #3196), ROU (0.93 #2395, 0.91 #2394, 0.90 #3196), PE (0.33 #210, 0.33 #50, 0.27 #321), RA (0.33 #226, 0.33 #66, 0.27 #321), CO (0.33 #38, 0.27 #321, 0.25 #359), YV (0.33 #59, 0.27 #321, 0.11 #699), GUY (0.33 #61, 0.20 #861, 0.11 #1019), SME (0.27 #321, 0.11 #2716, 0.08 #2555), EC (0.25 #456, 0.11 #1093, 0.08 #1916), RG (0.20 #1226, 0.15 #1386, 0.11 #748)
>> best conf = 0.93
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #2395 for best value:
>> intensional similarity = 6
>> extensional distance = 57
>> proper extension:  F; NAM; I; DK; FGU; PNG;
>> query: (?x379, ?x202) <- ?x379[ has language ?x796; has neighbor ?x202[ has ethnicGroup ?x197; has religion ?x352<RomanCatholic>;]; is locatedIn of ?x182;]
ranks of expected_values: 1, 2
EVAL	RA	neighbor!	BR	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	39.000	28.000	213.000	0.930	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	RA	neighbor!	ROU	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	39.000	28.000	213.000	0.930	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: ROU BR
=> 95 concepts (95 used for prediction)
PRED predicted values (max 10 best out of 233): BR (0.93 #12599, 0.93 #12603, 0.92 #12600), ROU (0.93 #12599, 0.93 #12603, 0.92 #12598), RA (0.60 #1200, 0.50 #162, 0.33 #2010), PE (0.50 #162, 0.43 #2154, 0.40 #1184), CO (0.50 #162, 0.33 #1134, 0.33 #200), YV (0.50 #162, 0.33 #1134, 0.33 #59), SME (0.50 #162, 0.33 #1134, 0.30 #4236), AND (0.40 #1583, 0.33 #1904, 0.25 #1095), E (0.40 #1481, 0.33 #646, 0.25 #993), GUY (0.33 #223, 0.33 #61, 0.33 #5230)
>> best conf = 0.93
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #12599 for best value:
>> intensional similarity = 14
>> extensional distance = 67
>> proper extension:  PK;
>> query: (?x379, ?x690) <- ?x379[ has government ?x435; has language ?x796; has neighbor ?x202[ has encompassed ?x521; has religion ?x95;]; has neighbor ?x542[ has neighbor ?x215[ has government ?x1377; is locatedIn of ?x282;]; is locatedIn of ?x48;]; has neighbor ?x690[ has ethnicGroup ?x197; is locatedIn of ?x274;]; is locatedIn of ?x182;]
ranks of expected_values: 1, 2
EVAL	RA	neighbor!	BR	CNN-1.+1._MA	1.000	1.000	1.000	1.000	95.000	95.000	233.000	0.929	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	RA	neighbor!	ROU	CNN-1.+1._MA	1.000	1.000	1.000	1.000	95.000	95.000	233.000	0.929	http://www.semwebtech.org/mondial/10/meta#neighbor

#503-Tongatapu
PRED entity: Tongatapu

PRED relation: type
PRED expected values: "coral"
=> 42 concepts (42 used for prediction)
PRED predicted values (max 10 best out of 9): "volcanic" (0.46 #82, 0.46 #114, 0.43 #146), "atoll" (0.12 #72, 0.11 #120, 0.10 #88), "volcano" (0.09 #214, 0.05 #534, 0.04 #486), "coral" (0.06 #73, 0.04 #153, 0.04 #169), "salt" (0.05 #247, 0.04 #311, 0.04 #327), "sand" (0.04 #212, 0.02 #308, 0.02 #324), "lime" (0.04 #197, 0.03 #229, 0.02 #277), "dam" (0.03 #241, 0.03 #209, 0.03 #465), "caldera" (0.02 #211)
>> best conf = 0.46
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #82 for best value:
>> intensional similarity = 6
>> extensional distance = 39
>> proper extension:  Hokkaido; Futuna; Tinian; Niihau; Tutuila; TeWaka-a-Maui-SouthIsland-; Okinawa; Oahu; Hawaii; Maui; ...
>> query: (?x205, "volcanic") <- ?x205[ a Island; has belongsToIslands ?x206[ a Islands;]; has locatedIn ?x1944; has locatedInWater ?x282<PacificOcean>;]

*> Best rule #73 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 31
*> proper extension:  Tasmania; Niue; Taiwan; Leyte; NewGuinea; VancouverIsland; EasterIsland; Banaba; Nauru;
*> query: (?x205, "coral") <- ?x205[ a Island; has locatedIn ?x1944[ has government ?x92; has religion ?x116;]; has locatedInWater ?x282<PacificOcean>;]
*> conf = 0.06
ranks of expected_values: 4
EVAL	Tongatapu	type	"coral"	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.250	42.000	42.000	9.000	0.463	http://www.semwebtech.org/mondial/10/meta#type

PRED relation: type
PRED expected values: "coral"
=> 122 concepts (122 used for prediction)
PRED predicted values (max 10 best out of 12): "volcanic" (0.48 #482, 0.46 #322, 0.46 #754), "atoll" (0.43 #184, 0.33 #280, 0.33 #264), "coral" (0.20 #89, 0.19 #1009, 0.14 #153), "volcano" (0.10 #1047, 0.09 #1671, 0.09 #1543), "lime" (0.10 #597, 0.09 #725, 0.05 #469), "sand" (0.09 #676, 0.04 #1045, 0.04 #1077), "salt" (0.07 #823, 0.05 #1160, 0.04 #951), "dam" (0.07 #673, 0.04 #689, 0.04 #1282), "caldera" (0.02 #1044, 0.02 #1076, 0.01 #1268), "granite" (0.02 #702, 0.02 #830, 0.02 #862)
>> best conf = 0.48
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #482 for best value:
>> intensional similarity = 10
>> extensional distance = 21
>> proper extension:  Futuna; Tutuila; Uvea; Pitcairn; GrandeTerre; Tahiti;
>> query: (?x205, "volcanic") <- ?x205[ a Island; has belongsToIslands ?x206; has locatedIn ?x1944[ a Country; has encompassed ?x211; has government ?x92; has religion ?x116[ a Religion;];]; has locatedInWater ?x282<PacificOcean>;]

*> Best rule #89 for first EXPECTED value:
*> intensional similarity = 11
*> extensional distance = 3
*> proper extension:  Nauru;
*> query: (?x205, "coral") <- ?x205[ a Island; has locatedIn ?x1944[ a Country; has encompassed ?x211<Australia-Oceania>; has government ?x92; has religion ?x116<Christian>; has wasDependentOf ?x81;]; has locatedInWater ?x282<PacificOcean>;]
*> conf = 0.20
ranks of expected_values: 3
EVAL	Tongatapu	type	"coral"	CNN-1.+1._MA	0.000	1.000	1.000	0.333	122.000	122.000	12.000	0.478	http://www.semwebtech.org/mondial/10/meta#type

#502-PR
PRED entity: PR

PRED relation: ethnicGroup
PRED expected values: Asian White
=> 39 concepts (39 used for prediction)
PRED predicted values (max 10 best out of 205): European (0.85 #1799, 0.83 #2055, 0.62 #263), Mestizo (0.46 #291, 0.24 #2083, 0.24 #1059), African (0.42 #2565, 0.40 #1797, 0.39 #2053), Chinese (0.29 #14, 0.25 #782, 0.18 #1550), White (0.29 #65, 0.23 #577, 0.21 #833), Mixed (0.15 #638, 0.14 #126, 0.12 #894), Polynesian (0.15 #599, 0.12 #855, 0.11 #2903), African-white-Indian (0.14 #62, 0.08 #574, 0.04 #830), Mulatto (0.14 #1081, 0.12 #1337, 0.10 #1849), German (0.11 #4617, 0.10 #3081, 0.10 #4105)
>> best conf = 0.85
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1799 for best value:
>> intensional similarity = 7
>> extensional distance = 38
>> proper extension:  SD;
>> query: (?x899, European) <- ?x899[ a Country; has ethnicGroup ?x79[ is ethnicGroup of ?x181<GCA>; is ethnicGroup of ?x408<NIC>;]; has government ?x2535;]

*> Best rule #65 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 5
*> proper extension:  SMAR;
*> query: (?x899, White) <- ?x899[ has dependentOf ?x315; has encompassed ?x521; has government ?x2535; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x317<CaribbeanSea>;]
*> conf = 0.29
ranks of expected_values: 5, 21
EVAL	PR	ethnicGroup	White	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.200	39.000	39.000	205.000	0.850	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	PR	ethnicGroup	Asian	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.050	39.000	39.000	205.000	0.850	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: Asian White
=> 78 concepts (78 used for prediction)
PRED predicted values (max 10 best out of 219): European (0.73 #6673, 0.67 #8209, 0.62 #3086), Mestizo (0.60 #4908, 0.50 #3114, 0.43 #2345), White (0.60 #1092, 0.43 #2118, 0.40 #834), Chinese (0.59 #7192, 0.39 #1285, 0.38 #1284), African (0.51 #8207, 0.47 #5390, 0.47 #4878), Mixed (0.40 #1153, 0.39 #1285, 0.38 #1284), EastIndian (0.39 #1285, 0.38 #1284, 0.33 #1677), Mulatto (0.39 #1285, 0.38 #1284, 0.21 #5442), Creole (0.39 #1285, 0.38 #1284, 0.17 #15901), Europeans (0.39 #1285, 0.38 #1284, 0.17 #15901)
>> best conf = 0.73
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #6673 for best value:
>> intensional similarity = 20
>> extensional distance = 24
>> proper extension:  PY;
>> query: (?x899, European) <- ?x899[ has ethnicGroup ?x79[ is ethnicGroup of ?x315[ is locatedIn of ?x282<PacificOcean>; is locatedIn of ?x1808<Chihuahua>;]; is ethnicGroup of ?x318<CR>; is ethnicGroup of ?x482<MEX>;]; has ethnicGroup ?x1009[ a EthnicGroup; is ethnicGroup of ?x1008[ has encompassed ?x521; has religion ?x429; is locatedIn of ?x182;];]; has religion ?x95<Protestant>; has religion ?x352<RomanCatholic>;]

*> Best rule #1092 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 3
*> proper extension:  AXA; BERM;
*> query: (?x899, White) <- ?x899[ a Country; has ethnicGroup ?x1009<Black>; has government ?x2535; has religion ?x95<Protestant>; is locatedIn of ?x182<AtlanticOcean>;]
*> conf = 0.60
ranks of expected_values: 3, 12
EVAL	PR	ethnicGroup	White	CNN-1.+1._MA	0.000	1.000	1.000	0.333	78.000	78.000	219.000	0.731	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	PR	ethnicGroup	Asian	CNN-1.+1._MA	0.000	0.000	0.000	0.091	78.000	78.000	219.000	0.731	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#501-JavaSea
PRED entity: JavaSea

PRED relation: locatedInWater!
PRED expected values: Borneo
=> 30 concepts (27 used for prediction)
PRED predicted values (max 10 best out of 289): Kyushu (0.33 #121, 0.25 #652, 0.25 #386), Taiwan (0.33 #56, 0.25 #587, 0.25 #321), Okinawa (0.33 #48, 0.25 #579, 0.25 #313), NewGuinea (0.33 #100, 0.25 #365, 0.20 #896), Tasmania (0.33 #23, 0.25 #288, 0.20 #819), Hokkaido (0.33 #26, 0.25 #291, 0.19 #1087), Mindanao (0.33 #111, 0.25 #376, 0.12 #1172), Leyte (0.33 #61, 0.25 #326, 0.12 #1122), Paramuschir (0.33 #149, 0.25 #414, 0.12 #1210), Unalaska (0.33 #183, 0.25 #448, 0.12 #1244)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #121 for best value:
>> intensional similarity = 5
>> extensional distance = 1
>> proper extension:  PacificOcean;
>> query: (?x241, Kyushu) <- ?x241[ has mergesWith ?x625<SulawesiSea>; is locatedInWater of ?x1768[ a Island; has locatedIn ?x217;];]

*> Best rule #531 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 2
*> proper extension:  SuluSea;
*> query: (?x241, ?x216) <- ?x241[ has mergesWith ?x60[ has locatedIn ?x61;]; has mergesWith ?x625<SulawesiSea>; has mergesWith ?x770[ is locatedInWater of ?x216;];]
*> conf = 0.09
ranks of expected_values: 110
EVAL	JavaSea	locatedInWater!	Borneo	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.009	30.000	27.000	289.000	0.333	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater!
PRED expected values: Borneo
=> 116 concepts (116 used for prediction)
PRED predicted values (max 10 best out of 425): Timor (0.63 #3999, 0.28 #5871, 0.27 #1866), Labuan (0.63 #3999, 0.27 #1866, 0.25 #5336), Borneo (0.63 #3999, 0.27 #1866, 0.25 #5336), Tasmania (0.33 #1357, 0.33 #23, 0.29 #1623), NewGuinea (0.33 #1434, 0.29 #1700, 0.28 #5871), Mindanao (0.33 #1445, 0.29 #1711, 0.27 #1866), Leyte (0.33 #1395, 0.29 #1661, 0.27 #1866), SriLanka (0.33 #14, 0.27 #1866, 0.25 #5336), GrandComoro (0.33 #181, 0.27 #1866, 0.25 #5336), Madagaskar (0.33 #167, 0.27 #1866, 0.25 #5336)
>> best conf = 0.63
=> the first rule below is the first best rule for 3 predicted values
>> Best rule #3999 for best value:
>> intensional similarity = 12
>> extensional distance = 20
>> proper extension:  HudsonBay;
>> query: (?x241, ?x375) <- ?x241[ a Sea; has mergesWith ?x60; is locatedInWater of ?x333[ is locatedOnIsland of ?x1571;]; is locatedInWater of ?x1157[ has locatedIn ?x217;]; is locatedInWater of ?x1768[ has belongsToIslands ?x875[ is belongsToIslands of ?x375;];]; is mergesWith of ?x770[ is locatedInWater of ?x216; is mergesWith of ?x282;];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 3
EVAL	JavaSea	locatedInWater!	Borneo	CNN-1.+1._MA	0.000	1.000	1.000	0.333	116.000	116.000	425.000	0.628	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#500-KaraSea
PRED entity: KaraSea

PRED relation: mergesWith!
PRED expected values: BarentsSea
=> 48 concepts (46 used for prediction)
PRED predicted values (max 10 best out of 131): AtlanticOcean (0.56 #403, 0.26 #639, 0.25 #1194), BarentsSea (0.47 #1068, 0.46 #1433, 0.46 #1069), KaraSea (0.46 #1433, 0.46 #1069, 0.41 #275), PacificOcean (0.41 #530, 0.36 #371, 0.23 #1124), LabradorSea (0.25 #87, 0.22 #324, 0.19 #406), GreenlandSea (0.25 #112, 0.19 #431, 0.17 #1311), BeringSea (0.25 #106, 0.18 #543, 0.17 #1311), EastSibirianSea (0.25 #99, 0.17 #1311, 0.17 #118), HudsonBay (0.25 #86, 0.17 #1311, 0.17 #118), JavaSea (0.24 #522, 0.18 #363, 0.13 #1116)
>> best conf = 0.56
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #403 for best value:
>> intensional similarity = 7
>> extensional distance = 14
>> proper extension:  IrishSea;
>> query: (?x801, AtlanticOcean) <- ?x801[ is locatedInWater of ?x931; is mergesWith of ?x263[ is locatedInWater of ?x1075<Greenland>; is locatedInWater of ?x2220[ has belongsToIslands ?x479;]; is mergesWith of ?x248;];]

*> Best rule #1068 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 27
*> proper extension:  Donau; Save; LakeHuron; OzeroBaikal; Zambezi;
*> query: (?x801, ?x251) <- ?x801[ is flowsInto of ?x800[ a River;]; is locatedInWater of ?x931[ a Island; has locatedInWater ?x251[ has locatedIn ?x73;];];]
*> conf = 0.47
ranks of expected_values: 2
EVAL	KaraSea	mergesWith!	BarentsSea	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	48.000	46.000	131.000	0.562	http://www.semwebtech.org/mondial/10/meta#mergesWith

PRED relation: mergesWith!
PRED expected values: BarentsSea
=> 73 concepts (73 used for prediction)
PRED predicted values (max 10 best out of 247): AtlanticOcean (0.64 #785, 0.50 #656, 0.47 #914), PacificOcean (0.47 #879, 0.38 #157, 0.36 #706), BarentsSea (0.44 #1355, 0.43 #1072, 0.41 #320), KaraSea (0.44 #1355, 0.43 #1072, 0.41 #320), BeringSea (0.38 #157, 0.30 #636, 0.30 #593), NorwegianSea (0.38 #157, 0.28 #774, 0.27 #904), EastSibirianSea (0.38 #157, 0.28 #774, 0.27 #731), HudsonBay (0.38 #157, 0.25 #125, 0.24 #776), LabradorSea (0.28 #774, 0.27 #731, 0.25 #126), JavaSea (0.27 #871, 0.27 #828, 0.18 #698)
>> best conf = 0.64
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #785 for best value:
>> intensional similarity = 15
>> extensional distance = 12
>> proper extension:  IrishSea;
>> query: (?x801, AtlanticOcean) <- ?x801[ a Sea; is locatedInWater of ?x931; is mergesWith of ?x263[ has locatedIn ?x73[ is neighbor of ?x170;]; has locatedIn ?x315<USA>; has locatedIn ?x792<GROX>; has mergesWith ?x452[ a Sea; has mergesWith ?x809; is flowsInto of ?x919;]; is locatedInWater of ?x478[ has belongsToIslands ?x479;];];]

*> Best rule #1355 for first EXPECTED value:
*> intensional similarity = 12
*> extensional distance = 21
*> proper extension:  Save; LakeHuron; Zambezi;
*> query: (?x801, ?x251) <- ?x801[ is flowsInto of ?x800[ a River; has hasSource ?x2232[ a Source;]; has locatedIn ?x73[ has encompassed ?x195; has ethnicGroup ?x58; has neighbor ?x170; has religion ?x56;];]; is locatedInWater of ?x931[ a Island; has locatedInWater ?x251;];]
*> conf = 0.44
ranks of expected_values: 3
EVAL	KaraSea	mergesWith!	BarentsSea	CNN-1.+1._MA	0.000	1.000	1.000	0.333	73.000	73.000	247.000	0.643	http://www.semwebtech.org/mondial/10/meta#mergesWith

#499-USA
PRED entity: USA

PRED relation: dependentOf!
PRED expected values: GUAM
=> 24 concepts (24 used for prediction)
PRED predicted values (max 10 best out of 36): XMAS (0.09 #69, 0.05 #106, 0.04 #143), COCO (0.09 #61, 0.05 #98, 0.04 #135), NORF (0.09 #41, 0.05 #78, 0.04 #115), TOK (0.05 #107, 0.04 #144, 0.04 #181), COOK (0.05 #105, 0.04 #142, 0.04 #179), HONX (0.03 #203, 0.02 #316, 0.01 #392), MACX (0.03 #199, 0.02 #312, 0.01 #388), CEU (0.01 #408, 0.01 #446, 0.01 #483), GBZ (0.01 #407, 0.01 #445, 0.01 #482), FALK (0.01 #402, 0.01 #440, 0.01 #477)
>> best conf = 0.09
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #69 for best value:
>> intensional similarity = 5
>> extensional distance = 9
>> proper extension:  RI;
>> query: (?x315, XMAS) <- ?x315[ has religion ?x95; is locatedIn of ?x282<PacificOcean>; is locatedIn of ?x361[ a River;];]

No rule for expected values
ranks of expected_values: 
EVAL	USA	dependentOf!	GUAM	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	24.000	24.000	36.000	0.091	http://www.semwebtech.org/mondial/10/meta#dependentOf

PRED relation: dependentOf!
PRED expected values: GUAM
=> 104 concepts (104 used for prediction)
PRED predicted values (max 10 best out of 181): NORF (0.33 #42, 0.17 #297, 0.17 #261), XMAS (0.33 #70, 0.17 #325, 0.17 #289), COCO (0.33 #62, 0.17 #317, 0.17 #281), NCA (0.33 #100, 0.14 #391, 0.12 #505), MART (0.33 #92, 0.14 #383, 0.12 #497), GUAD (0.33 #88, 0.14 #379, 0.12 #493), WAFU (0.33 #86, 0.14 #377, 0.12 #491), FPOL (0.33 #81, 0.14 #372, 0.12 #486), SPMI (0.33 #76, 0.14 #367, 0.12 #481), FGU (0.33 #96, 0.14 #387, 0.12 #501)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #42 for best value:
>> intensional similarity = 15
>> extensional distance = 1
>> proper extension:  AUS;
>> query: (?x315, NORF) <- ?x315[ has ethnicGroup ?x79; has religion ?x95; is dependentOf of ?x322; is locatedIn of ?x268[ a River;]; is locatedIn of ?x282<PacificOcean>; is locatedIn of ?x294[ a Mountain;]; is locatedIn of ?x1085[ a Lake;]; is locatedIn of ?x2018[ has hasEstuary ?x2245;]; is locatedIn of ?x2450[ a Source;];]

*> Best rule #515 for first EXPECTED value:
*> intensional similarity = 12
*> extensional distance = 6
*> proper extension:  NZ;
*> query: (?x315, ?x272) <- ?x315[ a Country; has encompassed ?x521; has religion ?x95; is dependentOf of ?x322; is locatedIn of ?x294[ has inMountains ?x1405;]; is locatedIn of ?x809[ a Sea;]; is locatedIn of ?x1313[ a Island;]; is locatedIn of ?x1925[ has locatedIn ?x272;];]
*> conf = 0.06
ranks of expected_values: 110
EVAL	USA	dependentOf!	GUAM	CNN-1.+1._MA	0.000	0.000	0.000	0.009	104.000	104.000	181.000	0.333	http://www.semwebtech.org/mondial/10/meta#dependentOf

#498-Jordan
PRED entity: Jordan

PRED relation: locatedIn
PRED expected values: JOR
=> 45 concepts (32 used for prediction)
PRED predicted values (max 10 best out of 199): SF (0.78 #2027, 0.62 #603, 0.48 #2500), IL (0.71 #295, 0.61 #5680, 0.61 #5679), SYR (0.66 #1895, 0.61 #5680, 0.61 #5679), JOR (0.61 #5680, 0.61 #5679, 0.61 #5678), RL (0.61 #5680, 0.61 #5679, 0.61 #5678), USA (0.51 #6226, 0.51 #6462, 0.17 #7173), DZ (0.50 #1556, 0.14 #369, 0.09 #843), SA (0.45 #870, 0.23 #2367, 0.19 #1420), IR (0.33 #2439, 0.23 #2367, 0.12 #542), CN (0.31 #4078, 0.29 #4787, 0.26 #5974)
>> best conf = 0.78
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2027 for best value:
>> intensional similarity = 7
>> extensional distance = 30
>> proper extension:  Paatsjoki; Kemijoki; Aland; Paatsjoki; Inari; Ounasjoki; Kokemaeenjoki; Kemijoki; Kallavesi; Vuoksi; ...
>> query: (?x420, SF) <- ?x420[ has locatedIn ?x568[ has language ?x1848<Arabic>; has religion ?x109; is locatedIn of ?x419[ has flowsThrough ?x1999;]; is neighbor of ?x239;];]

*> Best rule #5680 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 207
*> proper extension:  Indus; Akagera; Dnjestr; Arno; Fulda; MurrumbidgeeRiver; Angara; StraitsofMackinac; Volga; Petschora; ...
*> query: (?x420, ?x239) <- ?x420[ a Estuary; is hasEstuary of ?x419[ a River; has locatedIn ?x115[ has encompassed ?x175; has religion ?x116;]; has locatedIn ?x239;];]
*> conf = 0.61
ranks of expected_values: 4
EVAL	Jordan	locatedIn	JOR	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.250	45.000	32.000	199.000	0.781	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: JOR
=> 115 concepts (80 used for prediction)
PRED predicted values (max 10 best out of 213): IL (0.88 #2621, 0.79 #10760, 0.74 #13151), JOR (0.88 #2621, 0.79 #10760, 0.74 #13151), CN (0.71 #10580, 0.67 #10817, 0.49 #12491), SYR (0.70 #5009, 0.67 #3816, 0.66 #5248), MAL (0.67 #2469, 0.07 #2858, 0.07 #12999), RL (0.65 #9800, 0.64 #2383, 0.64 #2380), WAN (0.57 #2647, 0.22 #5752, 0.21 #6229), MA (0.56 #2082, 0.28 #3341, 0.28 #3276), SF (0.54 #7534, 0.49 #4772, 0.49 #8013), IND (0.54 #4721, 0.10 #14536, 0.07 #2858)
>> best conf = 0.88
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #2621 for best value:
>> intensional similarity = 15
>> extensional distance = 10
>> proper extension:  Tahan; Borneo; MalakkaStrait; SulawesiSea; SuluSea; Labuan; Kinabalu;
>> query: (?x420, ?x239) <- ?x420[ has locatedIn ?x568[ has encompassed ?x175; has religion ?x109[ a Religion; is religion of ?x315<USA>;]; has religion ?x116<Christian>; has religion ?x187<Muslim>; is locatedIn of ?x419[ is flowsInto of ?x1999;]; is locatedIn of ?x567[ has locatedIn ?x239; has type ?x762;];];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 2
EVAL	Jordan	locatedIn	JOR	CNN-1.+1._MA	0.000	1.000	1.000	0.500	115.000	80.000	213.000	0.875	http://www.semwebtech.org/mondial/10/meta#locatedIn

#497-GulfofAden
PRED entity: GulfofAden

PRED relation: locatedInWater!
PRED expected values: Sokotra
=> 30 concepts (20 used for prediction)
PRED predicted values (max 10 best out of 289): Sumatra (0.25 #334, 0.14 #1701, 0.14 #1427), Sokotra (0.25 #531, 0.10 #1078, 0.07 #3008), Tasmania (0.25 #298, 0.10 #845, 0.07 #1640), Lombok (0.25 #473, 0.10 #1020, 0.07 #1640), Sumbawa (0.25 #390, 0.10 #937, 0.07 #1640), Bali (0.25 #375, 0.10 #922, 0.07 #1640), Krakatau (0.25 #289, 0.10 #836, 0.07 #1640), SriLanka (0.25 #288, 0.10 #835, 0.07 #1640), Java (0.25 #285, 0.10 #832, 0.07 #1640), Pemba (0.25 #534, 0.10 #1081, 0.07 #1640)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #334 for best value:
>> intensional similarity = 8
>> extensional distance = 2
>> proper extension:  IndianOcean;
>> query: (?x2407, Sumatra) <- ?x2407[ a Sea; has locatedIn ?x94[ has ethnicGroup ?x1593; is neighbor of ?x476<ETH>;]; has locatedIn ?x668<YE>; has mergesWith ?x60;]

*> Best rule #531 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 2
*> proper extension:  IndianOcean;
*> query: (?x2407, Sokotra) <- ?x2407[ a Sea; has locatedIn ?x94[ has ethnicGroup ?x1593; is neighbor of ?x476<ETH>;]; has locatedIn ?x668<YE>; has mergesWith ?x60;]
*> conf = 0.25
ranks of expected_values: 2
EVAL	GulfofAden	locatedInWater!	Sokotra	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	30.000	20.000	289.000	0.250	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater!
PRED expected values: Sokotra
=> 86 concepts (86 used for prediction)
PRED predicted values (max 10 best out of 397): Sokotra (0.50 #1080, 0.33 #258, 0.25 #2176), SouthAndamanIsland (0.50 #1512, 0.25 #1786, 0.22 #2610), Sumatra (0.33 #2529, 0.33 #61, 0.30 #2804), SriLanka (0.33 #15, 0.25 #1385, 0.25 #837), Sumbawa (0.33 #117, 0.25 #939, 0.25 #664), Lombok (0.33 #200, 0.25 #1022, 0.25 #747), Bali (0.33 #102, 0.25 #924, 0.25 #649), Krakatau (0.33 #16, 0.25 #838, 0.25 #563), Java (0.33 #12, 0.25 #834, 0.25 #559), GrandComoro (0.33 #188, 0.25 #1010, 0.25 #735)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1080 for best value:
>> intensional similarity = 14
>> extensional distance = 2
>> proper extension:  ArabianSea;
>> query: (?x2407, Sokotra) <- ?x2407[ a Sea; has locatedIn ?x94[ a Country; has ethnicGroup ?x1593; has wasDependentOf ?x78;]; has locatedIn ?x668<YE>; has mergesWith ?x1333[ a Sea; has locatedIn ?x220; is locatedInWater of ?x1476;]; has mergesWith ?x1552[ has locatedIn ?x803[ has neighbor ?x302;];];]
ranks of expected_values: 1
EVAL	GulfofAden	locatedInWater!	Sokotra	CNN-1.+1._MA	1.000	1.000	1.000	1.000	86.000	86.000	397.000	0.500	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#496-Tortola
PRED entity: Tortola

PRED relation: belongsToIslands
PRED expected values: LesserAntilles
=> 59 concepts (59 used for prediction)
PRED predicted values (max 10 best out of 56): LesserAntilles (0.78 #491, 0.78 #219, 0.75 #423), Azores (0.23 #752, 0.19 #820, 0.12 #1296), HawaiiIslands (0.17 #981, 0.14 #1117, 0.11 #1253), Canares (0.17 #771, 0.16 #839, 0.12 #1043), GreaterAntilles (0.17 #115, 0.09 #319, 0.08 #387), MarianaIslands (0.12 #613, 0.02 #1021, 0.02 #1225), Japan (0.11 #978, 0.09 #1046, 0.09 #1114), LipariIslands (0.11 #1294, 0.10 #1362, 0.10 #1498), SundaIslands (0.09 #1034, 0.09 #1102, 0.07 #1238), CaymanIslands (0.08 #370, 0.04 #642, 0.03 #914)
>> best conf = 0.78
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #491 for best value:
>> intensional similarity = 5
>> extensional distance = 16
>> proper extension:  St.Barthelemy;
>> query: (?x1185, LesserAntilles) <- ?x1185[ a Island; has locatedInWater ?x182<AtlanticOcean>; has locatedInWater ?x317<CaribbeanSea>;]
>> Best rule #219 for best value:
>> intensional similarity = 6
>> extensional distance = 7
>> proper extension:  Grande-Terre;
>> query: (?x1185, LesserAntilles) <- ?x1185[ a Island; has locatedInWater ?x182<AtlanticOcean>; has locatedInWater ?x317<CaribbeanSea>; has type ?x150;]
ranks of expected_values: 1
EVAL	Tortola	belongsToIslands	LesserAntilles	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	59.000	59.000	56.000	0.778	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

PRED relation: belongsToIslands
PRED expected values: LesserAntilles
=> 101 concepts (101 used for prediction)
PRED predicted values (max 10 best out of 61): LesserAntilles (0.78 #1579, 0.78 #1171, 0.75 #1035), Azores (0.23 #2044, 0.20 #2112, 0.14 #2656), CanadianArcticIslands (0.21 #1912, 0.15 #2320, 0.08 #3000), GreaterAntilles (0.20 #659, 0.17 #727, 0.12 #999), HawaiiIslands (0.19 #2069, 0.15 #2273, 0.13 #2545), Canares (0.16 #2063, 0.14 #2131, 0.13 #2199), TurksandCaicosIslands (0.14 #1343, 0.13 #1411, 0.07 #2023), FalklandIslands (0.14 #1357, 0.13 #1425, 0.07 #2037), Philipines (0.13 #2795, 0.06 #3815, 0.04 #4497), LipariIslands (0.12 #2654, 0.08 #3266, 0.08 #2994)
>> best conf = 0.78
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1579 for best value:
>> intensional similarity = 5
>> extensional distance = 16
>> proper extension:  St.Barthelemy;
>> query: (?x1185, LesserAntilles) <- ?x1185[ a Island; has locatedInWater ?x182<AtlanticOcean>; has locatedInWater ?x317<CaribbeanSea>;]
>> Best rule #1171 for best value:
>> intensional similarity = 6
>> extensional distance = 7
>> proper extension:  Grande-Terre;
>> query: (?x1185, LesserAntilles) <- ?x1185[ a Island; has locatedInWater ?x182<AtlanticOcean>; has locatedInWater ?x317<CaribbeanSea>; has type ?x150;]
ranks of expected_values: 1
EVAL	Tortola	belongsToIslands	LesserAntilles	CNN-1.+1._MA	1.000	1.000	1.000	1.000	101.000	101.000	61.000	0.778	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

#495-Tobol
PRED entity: Tobol

PRED relation: hasEstuary!
PRED expected values: Tobol
=> 32 concepts (27 used for prediction)
PRED predicted values (max 10 best out of 168): Katun (0.04 #217, 0.04 #443, 0.04 #670), Schilka (0.04 #214, 0.04 #440, 0.04 #667), Kama (0.04 #199, 0.04 #425, 0.04 #652), Irtysch (0.04 #194, 0.04 #420, 0.04 #647), Amur (0.04 #181, 0.04 #407, 0.04 #634), Oka (0.04 #179, 0.04 #405, 0.04 #632), Kolyma (0.04 #163, 0.04 #389, 0.04 #616), Don (0.04 #147, 0.04 #373, 0.04 #600), Lena (0.04 #107, 0.04 #333, 0.04 #560), Chatanga (0.04 #97, 0.04 #323, 0.04 #550)
>> best conf = 0.04
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #217 for best value:
>> intensional similarity = 3
>> extensional distance = 21
>> proper extension:  Lena; Swir; Kama; Paatsjoki; Suchona; Chatanga; Vuoksi; Irtysch; Jenissej; Argun; ...
>> query: (?x1818, Katun) <- ?x1818[ a Estuary; has locatedIn ?x73<R>;]

*> Best rule #2500 for first EXPECTED value:
*> intensional similarity = 4
*> extensional distance = 219
*> proper extension:  Pibor; Bahrel-Ghasal; Busira; Ruzizi; Ubangi; Jubba; BlueNile; Lomami; Lualaba; Bomu; ...
*> query: (?x1818, ?x72) <- ?x1818[ a Estuary; has locatedIn ?x73[ has neighbor ?x170; is locatedIn of ?x72;];]
*> conf = 0.02
ranks of expected_values: 21
EVAL	Tobol	hasEstuary!	Tobol	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.048	32.000	27.000	168.000	0.043	http://www.semwebtech.org/mondial/10/meta#hasEstuary

PRED relation: hasEstuary!
PRED expected values: Tobol
=> 85 concepts (85 used for prediction)
PRED predicted values (max 10 best out of 241): Volga (0.08 #6173, 0.08 #6863, 0.08 #6632), Angara (0.08 #6173, 0.08 #6863, 0.08 #6632), Dnepr (0.08 #6173, 0.08 #6863, 0.08 #6632), Amur (0.08 #6173, 0.08 #6863, 0.08 #6632), Jenissej (0.08 #6173, 0.08 #6863, 0.08 #6632), Newa (0.08 #6173, 0.08 #6863, 0.08 #6632), Swir (0.08 #6173, 0.08 #6863, 0.08 #6632), Kolyma (0.08 #6173, 0.08 #6863, 0.08 #6632), Lena (0.08 #6173, 0.08 #6863, 0.08 #6632), Chatanga (0.08 #6173, 0.08 #6863, 0.08 #6632)
>> best conf = 0.08
=> the first rule below is the first best rule for 22 predicted values
>> Best rule #6173 for best value:
>> intensional similarity = 9
>> extensional distance = 198
>> proper extension:  Kura;
>> query: (?x1818, ?x1227) <- ?x1818[ a Estuary; has locatedIn ?x73[ has ethnicGroup ?x58; has neighbor ?x403; is locatedIn of ?x1227[ has hasEstuary ?x2338;]; is neighbor of ?x170[ a Country; is locatedIn of ?x121;];];]

*> Best rule #6863 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 218
*> proper extension:  Bahrel-Ghasal; Bahrel-Djebel-Albert-Nil; Sobat;
*> query: (?x1818, ?x679) <- ?x1818[ a Estuary; has locatedIn ?x73[ a Country; has neighbor ?x403; is locatedIn of ?x679[ a River;]; is neighbor of ?x303[ is locatedIn of ?x133;];];]
*> conf = 0.08
ranks of expected_values: 27
EVAL	Tobol	hasEstuary!	Tobol	CNN-1.+1._MA	0.000	0.000	0.000	0.037	85.000	85.000	241.000	0.082	http://www.semwebtech.org/mondial/10/meta#hasEstuary

#494-NorthSea
PRED entity: NorthSea

PRED relation: locatedInWater!
PRED expected values: Ameland Texel Spiekeroog
=> 33 concepts (29 used for prediction)
PRED predicted values (max 10 best out of 382): Sumatra (0.21 #818, 0.19 #2337, 0.18 #1325), Svalbard (0.20 #99, 0.11 #2377, 0.10 #1111), NowajaSemlja (0.20 #82, 0.09 #2613, 0.09 #1601), Taiwan (0.16 #815, 0.14 #1322, 0.13 #1575), Cuba (0.16 #961, 0.14 #1468, 0.13 #1721), Greenland (0.12 #2633, 0.11 #2380, 0.10 #3646), Kyushu (0.11 #2399, 0.11 #880, 0.09 #2905), Hokkaido (0.11 #2306, 0.11 #787, 0.09 #2812), Sulawesi (0.11 #2368, 0.09 #2621, 0.08 #3127), BaffinIsland (0.11 #2353, 0.09 #2606, 0.07 #3873)
>> best conf = 0.21
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #818 for best value:
>> intensional similarity = 5
>> extensional distance = 17
>> proper extension:  IndianOcean; AtlanticOcean; HudsonBay; BarentsSea; GulfofBengal; MediterraneanSea; PacificOcean; CaribbeanSea; AndamanSea; SouthChinaSea; ...
>> query: (?x121, Sumatra) <- ?x121[ a Sea; is flowsInto of ?x829; is locatedInWater of ?x2081[ has belongsToIslands ?x795;]; is mergesWith of ?x1211;]

*> Best rule #3798 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 39
*> proper extension:  LakeManicouagan;
*> query: (?x121, ?x70) <- ?x121[ has locatedIn ?x120[ a Country; is locatedIn of ?x70;]; is locatedInWater of ?x495; is locatedInWater of ?x848[ a Island;];]
*> conf = 0.07
ranks of expected_values: 122, 124, 151
EVAL	NorthSea	locatedInWater!	Spiekeroog	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.007	33.000	29.000	382.000	0.211	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	NorthSea	locatedInWater!	Texel	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.008	33.000	29.000	382.000	0.211	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	NorthSea	locatedInWater!	Ameland	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.008	33.000	29.000	382.000	0.211	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater!
PRED expected values: Ameland Texel Spiekeroog
=> 100 concepts (98 used for prediction)
PRED predicted values (max 10 best out of 899): Seeland (0.50 #1191, 0.40 #1446, 0.33 #173), Fuenen (0.50 #1099, 0.40 #1354, 0.33 #81), Ireland (0.49 #3310, 0.40 #1534, 0.32 #254), Skye (0.49 #3310, 0.32 #254, 0.20 #1779), Jura (0.49 #3310, 0.32 #254, 0.20 #1774), LewisandHarris (0.49 #3310, 0.32 #254, 0.20 #1747), Barra (0.49 #3310, 0.32 #254, 0.20 #1582), Rhum (0.49 #3310, 0.32 #254, 0.20 #1577), Benbecula (0.49 #3310, 0.32 #254, 0.20 #1565), BishopRock (0.49 #3310, 0.32 #254, 0.20 #1560)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1191 for best value:
>> intensional similarity = 13
>> extensional distance = 2
>> proper extension:  Kattegat;
>> query: (?x121, Seeland) <- ?x121[ has locatedIn ?x120[ has neighbor ?x424[ has ethnicGroup ?x160; is locatedIn of ?x155;]; has neighbor ?x718[ has ethnicGroup ?x1673; has religion ?x95;];]; has locatedIn ?x793<DK>; is flowsInto of ?x829; is locatedInWater of ?x495; is locatedInWater of ?x634[ a Island;];]

*> Best rule #3310 for first EXPECTED value:
*> intensional similarity = 12
*> extensional distance = 8
*> proper extension:  Seine; Saone;
*> query: (?x121, ?x2081) <- ?x121[ has locatedIn ?x78<F>; has locatedIn ?x575[ has religion ?x95; is locatedIn of ?x2081[ a Island;]; is wasDependentOf of ?x217[ has neighbor ?x376; is locatedIn of ?x60;];]; has locatedIn ?x793[ is dependentOf of ?x357;]; is flowsInto of ?x829;]
*> conf = 0.49
ranks of expected_values: 17, 18, 187
EVAL	NorthSea	locatedInWater!	Spiekeroog	CNN-1.+1._MA	0.000	0.000	0.000	0.005	100.000	98.000	899.000	0.500	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	NorthSea	locatedInWater!	Texel	CNN-1.+1._MA	0.000	0.000	0.000	0.059	100.000	98.000	899.000	0.500	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	NorthSea	locatedInWater!	Ameland	CNN-1.+1._MA	0.000	0.000	0.000	0.059	100.000	98.000	899.000	0.500	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#493-French
PRED entity: French

PRED relation: ethnicGroup!
PRED expected values: L
=> 30 concepts (30 used for prediction)
PRED predicted values (max 10 best out of 226): CR (0.40 #815, 0.37 #1517, 0.33 #1005), F (0.40 #947, 0.39 #1137, 0.27 #2650), A (0.40 #947, 0.39 #1137, 0.27 #2650), D (0.40 #947, 0.39 #1137, 0.27 #2650), FL (0.40 #947, 0.39 #1137, 0.27 #2650), E (0.40 #947, 0.39 #1137, 0.27 #2650), I (0.40 #947, 0.39 #1137, 0.27 #2650), MACX (0.37 #1517, 0.33 #309, 0.33 #120), HONX (0.37 #1517, 0.33 #139, 0.30 #897), XMAS (0.37 #1517, 0.33 #185, 0.25 #1133)
>> best conf = 0.40
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #815 for best value:
>> intensional similarity = 10
>> extensional distance = 8
>> proper extension:  Amerindian; African; European; Asian; Indonesian; Norwegian; Filipino;
>> query: (?x1672, CR) <- ?x1672[ a EthnicGroup; is ethnicGroup of ?x297[ has dependentOf ?x78; has encompassed ?x211; has government ?x2145; is locatedIn of ?x282;]; is ethnicGroup of ?x789[ has neighbor ?x149;]; is ethnicGroup of ?x1577[ has language ?x51;];]

*> Best rule #322 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 1
*> proper extension:  Portuguese;
*> query: (?x1672, L) <- ?x1672[ a EthnicGroup; is ethnicGroup of ?x297[ has dependentOf ?x78; has encompassed ?x211; is locatedIn of ?x282;]; is ethnicGroup of ?x789<AND>;]
*> conf = 0.33
ranks of expected_values: 32
EVAL	French	ethnicGroup!	L	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.031	30.000	30.000	226.000	0.400	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup!
PRED expected values: L
=> 78 concepts (78 used for prediction)
PRED predicted values (max 10 best out of 227): USA (0.60 #626, 0.47 #3609, 0.38 #1004), A (0.44 #759, 0.40 #462, 0.40 #191), D (0.44 #759, 0.40 #395, 0.40 #191), F (0.44 #759, 0.40 #191, 0.38 #380), FL (0.44 #759, 0.40 #191, 0.34 #569), I (0.44 #759, 0.40 #191, 0.34 #569), E (0.44 #759, 0.38 #380, 0.27 #570), SK (0.41 #3254, 0.33 #216, 0.26 #7981), NZ (0.40 #1228, 0.40 #661, 0.38 #380), CR (0.40 #627, 0.38 #1005, 0.38 #380)
>> best conf = 0.60
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #626 for best value:
>> intensional similarity = 20
>> extensional distance = 3
>> proper extension:  Amerindian; Asian;
>> query: (?x1672, USA) <- ?x1672[ a EthnicGroup; is ethnicGroup of ?x272[ has religion ?x95; is locatedIn of ?x219<DetroitRiver>; is locatedIn of ?x615<MtBona>; is locatedIn of ?x658<SaintLawrenceRiver>; is locatedIn of ?x733<YukonRiver>; is locatedIn of ?x895<NiagaraRiver>; is locatedIn of ?x1077<LakeSuperior>; is locatedIn of ?x2299<DetroitRiver>;]; is ethnicGroup of ?x789[ a Country; has neighbor ?x78;];]

*> Best rule #324 for first EXPECTED value:
*> intensional similarity = 15
*> extensional distance = 1
*> proper extension:  German;
*> query: (?x1672, L) <- ?x1672[ a EthnicGroup; is ethnicGroup of ?x234<CH>; is ethnicGroup of ?x272[ has religion ?x95<Protestant>; is locatedIn of ?x717[ a Lake;]; is locatedIn of ?x1433[ a Mountain; has inMountains ?x616;]; is locatedIn of ?x1891[ has locatedInWater ?x248;]; is locatedIn of ?x1949[ has belongsToIslands ?x479;];];]
*> conf = 0.33
ranks of expected_values: 105
EVAL	French	ethnicGroup!	L	CNN-1.+1._MA	0.000	0.000	0.000	0.010	78.000	78.000	227.000	0.600	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#492-RioSaoFrancisco
PRED entity: RioSaoFrancisco

PRED relation: locatedIn
PRED expected values: BR
=> 35 concepts (35 used for prediction)
PRED predicted values (max 10 best out of 107): BR (0.61 #4274, 0.57 #7593, 0.56 #7119), ZRE (0.40 #3800, 0.40 #3641, 0.17 #316), USA (0.29 #2685, 0.26 #3396, 0.22 #783), CDN (0.22 #3562, 0.22 #3387, 0.17 #300), CH (0.17 #2432, 0.12 #2908, 0.12 #3145), CAM (0.17 #360, 0.06 #6644, 0.06 #6881), R (0.15 #4753, 0.13 #2380, 0.13 #4279), E (0.15 #1926, 0.11 #738, 0.11 #1450), PE (0.13 #2204, 0.12 #541, 0.08 #2136), BOL (0.12 #627, 0.09 #2290, 0.08 #2136)
>> best conf = 0.61
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4274 for best value:
>> intensional similarity = 7
>> extensional distance = 49
>> proper extension:  Narva; MackenzieRiver;
>> query: (?x1704, ?x542) <- ?x1704[ a Source; is hasSource of ?x1703[ a River; has hasEstuary ?x2051; has locatedIn ?x542; is flowsInto of ?x2165[ a Lake;];];]
ranks of expected_values: 1
EVAL	RioSaoFrancisco	locatedIn	BR	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	35.000	35.000	107.000	0.613	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: BR
=> 96 concepts (96 used for prediction)
PRED predicted values (max 10 best out of 108): BR (0.71 #18579, 0.66 #16440, 0.65 #7860), ZRE (0.50 #6670, 0.50 #6510, 0.50 #4044), USA (0.50 #6908, 0.50 #6742, 0.50 #5787), CDN (0.42 #5953, 0.42 #5778, 0.33 #5715), E (0.38 #2405, 0.14 #11462, 0.12 #7174), PE (0.33 #67, 0.25 #5066, 0.25 #779), CAM (0.25 #598, 0.07 #16441, 0.06 #17391), CH (0.22 #7680, 0.21 #9345, 0.20 #4101), BOL (0.20 #1102, 0.17 #2056, 0.17 #1818), CO (0.20 #4333, 0.17 #1954, 0.12 #2668)
>> best conf = 0.71
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #18579 for best value:
>> intensional similarity = 10
>> extensional distance = 58
>> proper extension:  Thjorsa;
>> query: (?x1704, ?x542) <- ?x1704[ a Source; is hasSource of ?x1703[ a River; has hasEstuary ?x2051[ a Estuary;]; has locatedIn ?x542[ has ethnicGroup ?x162; has religion ?x95; is locatedIn of ?x182<AtlanticOcean>;];];]
ranks of expected_values: 1
EVAL	RioSaoFrancisco	locatedIn	BR	CNN-1.+1._MA	1.000	1.000	1.000	1.000	96.000	96.000	108.000	0.708	http://www.semwebtech.org/mondial/10/meta#locatedIn

#491-Basse-Terre
PRED entity: Basse-Terre

PRED relation: locatedInWater
PRED expected values: AtlanticOcean
=> 48 concepts (48 used for prediction)
PRED predicted values (max 10 best out of 31): AtlanticOcean (0.94 #893, 0.90 #1229, 0.90 #1193), PacificOcean (0.76 #1074, 0.75 #988, 0.68 #1458), MediterraneanSea (0.34 #858, 0.12 #1754, 0.12 #1798), IndianOcean (0.28 #718, 0.28 #676, 0.28 #844), JavaSea (0.28 #725, 0.24 #683, 0.23 #767), GulfofMexico (0.20 #38, 0.18 #1951, 0.18 #1782), SouthChinaSea (0.12 #737, 0.10 #949, 0.09 #1122), SulawesiSea (0.12 #743, 0.09 #1128, 0.09 #1170), NorthSea (0.09 #1613, 0.08 #1656, 0.08 #1741), IrishSea (0.07 #1227, 0.06 #546, 0.03 #1440)
>> best conf = 0.94
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #893 for best value:
>> intensional similarity = 8
>> extensional distance = 29
>> proper extension:  SaintHelena; Ascension; Faial;
>> query: (?x2152, AtlanticOcean) <- ?x2152[ a Island; has locatedInWater ?x317[ has locatedIn ?x482[ has government ?x140;]; has locatedIn ?x667<TT>;]; has type ?x150<"volcanic">;]
ranks of expected_values: 1
EVAL	Basse-Terre	locatedInWater	AtlanticOcean	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	48.000	48.000	31.000	0.935	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater
PRED expected values: AtlanticOcean
=> 76 concepts (76 used for prediction)
PRED predicted values (max 10 best out of 81): AtlanticOcean (0.94 #1384, 0.89 #1682, 0.89 #1645), PacificOcean (0.76 #1742, 0.75 #1438, 0.68 #2356), LaSoufriere (0.33 #43, 0.22 #172, 0.20 #86), JavaSea (0.32 #1084, 0.30 #1170, 0.27 #1301), IndianOcean (0.31 #1249, 0.30 #1163, 0.27 #1077), GulfofMexico (0.21 #1811, 0.19 #3215, 0.18 #3083), Martinique (0.16 #2815, 0.08 #3038), Pelee (0.16 #2815, 0.08 #3038), St.Martin (0.16 #2815, 0.08 #3038), MediterraneanSea (0.15 #2879, 0.15 #3009, 0.12 #3055)
>> best conf = 0.94
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1384 for best value:
>> intensional similarity = 25
>> extensional distance = 29
>> proper extension:  SaintHelena; Ascension; Faial;
>> query: (?x2152, AtlanticOcean) <- ?x2152[ a Island; has locatedInWater ?x317[ has locatedIn ?x246[ has encompassed ?x521;]; has locatedIn ?x628<SMAR>; has locatedIn ?x899<PR>; has mergesWith ?x182[ has locatedIn ?x315<USA>; is flowsInto of ?x137; is locatedInWater of ?x112;]; is locatedInWater of ?x817<Montserrat>; is locatedInWater of ?x1132<Grande-Terre>; is locatedInWater of ?x1397<SaintThomas>; is locatedInWater of ?x1847<Martinique>; is locatedInWater of ?x1928<Cuba>;]; has type ?x150<"volcanic">;]
ranks of expected_values: 1
EVAL	Basse-Terre	locatedInWater	AtlanticOcean	CNN-1.+1._MA	1.000	1.000	1.000	1.000	76.000	76.000	81.000	0.935	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#490-Dravidian
PRED entity: Dravidian

PRED relation: ethnicGroup!
PRED expected values: IND
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 96): EAU (0.03 #132), ETH (0.03 #100), UA (0.03 #57), EAK (0.03 #99), CZ (0.02 #98), SN (0.02 #83), MYA (0.02 #70), IR (0.02 #58), SK (0.02 #25), RCA (0.02 #139)
>> best conf = 0.03
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #132 for best value:
>> intensional similarity = 1
>> extensional distance = 279
>> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
>> query: (?x1824, EAU) <- ?x1824[ a EthnicGroup;]

No rule for expected values
ranks of expected_values: 
EVAL	Dravidian	ethnicGroup!	IND	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	2.000	2.000	96.000	0.032	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup!
PRED expected values: IND
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 96): EAU (0.03 #132), ETH (0.03 #100), UA (0.03 #57), EAK (0.03 #99), CZ (0.02 #98), SN (0.02 #83), MYA (0.02 #70), IR (0.02 #58), SK (0.02 #25), RCA (0.02 #139)
>> best conf = 0.03
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #132 for best value:
>> intensional similarity = 1
>> extensional distance = 279
>> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
>> query: (?x1824, EAU) <- ?x1824[ a EthnicGroup;]

No rule for expected values
ranks of expected_values: 
EVAL	Dravidian	ethnicGroup!	IND	CNN-1.+1._MA	0.000	0.000	0.000	0.000	2.000	2.000	96.000	0.032	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#489-NiagaraRiver
PRED entity: NiagaraRiver

PRED relation: locatedIn
PRED expected values: CDN
=> 43 concepts (43 used for prediction)
PRED predicted values (max 10 best out of 190): CDN (0.92 #3779, 0.90 #6612, 0.90 #6611), R (0.46 #6852, 0.29 #478, 0.25 #8032), RCH (0.18 #283, 0.13 #755, 0.08 #1227), CN (0.17 #7847, 0.15 #3363, 0.14 #8083), F (0.16 #3314, 0.11 #7798, 0.09 #244), ZRE (0.12 #1967, 0.12 #4330, 0.11 #4094), MEX (0.12 #7318, 0.12 #1296, 0.12 #7554), AUS (0.10 #3352, 0.09 #7836, 0.09 #282), E (0.10 #7818, 0.09 #264, 0.07 #3334), D (0.10 #6632, 0.09 #6395, 0.09 #7102)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3779 for best value:
>> intensional similarity = 5
>> extensional distance = 85
>> proper extension:  Tocantins;
>> query: (?x1084, ?x272) <- ?x1084[ a River; has flowsInto ?x1085; has hasEstuary ?x2458[ has locatedIn ?x272;]; is flowsInto of ?x2166;]
ranks of expected_values: 1
EVAL	NiagaraRiver	locatedIn	CDN	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	43.000	43.000	190.000	0.919	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: CDN
=> 157 concepts (151 used for prediction)
PRED predicted values (max 10 best out of 231): CDN (0.92 #16538, 0.91 #17011, 0.91 #17720), R (0.57 #10636, 0.45 #10871, 0.42 #3544), ZRE (0.35 #12359, 0.25 #1257, 0.24 #8346), F (0.29 #7802, 0.25 #1186, 0.24 #20326), CN (0.29 #8560, 0.26 #9267, 0.26 #9739), MEX (0.26 #16774, 0.17 #6017, 0.14 #5546), RCH (0.25 #2877, 0.25 #1225, 0.14 #2405), GB (0.25 #1188, 0.19 #19382, 0.19 #19619), C (0.25 #1205, 0.17 #3565, 0.14 #2385), E (0.25 #1206, 0.14 #2386, 0.14 #23658)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #16538 for best value:
>> intensional similarity = 9
>> extensional distance = 114
>> proper extension:  Leine; Neckar; Luapula;
>> query: (?x1084, ?x272) <- ?x1084[ a River; has hasEstuary ?x2458[ a Estuary; has locatedIn ?x272; has locatedIn ?x315[ has ethnicGroup ?x79; has religion ?x95<Protestant>; is neighbor of ?x482;];];]
ranks of expected_values: 1
EVAL	NiagaraRiver	locatedIn	CDN	CNN-1.+1._MA	1.000	1.000	1.000	1.000	157.000	151.000	231.000	0.920	http://www.semwebtech.org/mondial/10/meta#locatedIn

#488-BI
PRED entity: BI

PRED relation: neighbor!
PRED expected values: ZRE
=> 40 concepts (38 used for prediction)
PRED predicted values (max 10 best out of 231): ZRE (0.90 #5170, 0.89 #4200, 0.89 #3716), Z (0.60 #1293, 0.46 #3067, 0.35 #970), BI (0.60 #1293, 0.46 #3067, 0.35 #970), SSD (0.50 #850, 0.50 #366, 0.33 #1176), EAU (0.35 #970, 0.33 #1246, 0.33 #113), MW (0.35 #970, 0.33 #775, 0.30 #1133), MOC (0.35 #970, 0.30 #1133, 0.28 #2261), EAK (0.35 #970, 0.30 #1133, 0.28 #2261), RCA (0.33 #120, 0.30 #1133, 0.28 #2261), RCB (0.33 #91, 0.30 #1133, 0.28 #2261)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5170 for best value:
>> intensional similarity = 7
>> extensional distance = 141
>> proper extension:  LS; THA; DJI; UAE; TN; NEP; RL; KGZ; G; Q; ...
>> query: (?x359, ?x348) <- ?x359[ a Country; has neighbor ?x348[ has ethnicGroup ?x2121;]; has neighbor ?x820[ is locatedIn of ?x60;]; has religion ?x116; is locatedIn of ?x284;]
ranks of expected_values: 1
EVAL	BI	neighbor!	ZRE	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	40.000	38.000	231.000	0.899	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: ZRE
=> 94 concepts (94 used for prediction)
PRED predicted values (max 10 best out of 236): ZRE (0.94 #8581, 0.93 #7416, 0.92 #8083), EAU (0.56 #10579, 0.50 #605, 0.46 #13085), Z (0.56 #10579, 0.48 #7582, 0.48 #7583), BI (0.56 #10579, 0.48 #7582, 0.48 #7583), RCB (0.56 #10579, 0.46 #13085, 0.40 #327), MW (0.50 #458, 0.40 #327, 0.33 #296), EAK (0.40 #327, 0.33 #576, 0.33 #250), SSD (0.40 #327, 0.33 #535, 0.33 #43), MOC (0.40 #327, 0.33 #199, 0.33 #817), RCA (0.40 #327, 0.32 #3124, 0.30 #5433)
>> best conf = 0.94
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #8581 for best value:
>> intensional similarity = 15
>> extensional distance = 48
>> proper extension:  MA;
>> query: (?x359, ?x546) <- ?x359[ a Country; has neighbor ?x348[ is locatedIn of ?x113; is neighbor of ?x525[ has encompassed ?x213;];]; has neighbor ?x546[ has ethnicGroup ?x1946; has neighbor ?x688; has religion ?x95; is locatedIn of ?x545[ has inMountains ?x1066; has type ?x706;];]; has religion ?x116; has wasDependentOf ?x485; is locatedIn of ?x284;]
ranks of expected_values: 1
EVAL	BI	neighbor!	ZRE	CNN-1.+1._MA	1.000	1.000	1.000	1.000	94.000	94.000	236.000	0.937	http://www.semwebtech.org/mondial/10/meta#neighbor

#487-I
PRED entity: I

PRED relation: neighbor
PRED expected values: A SLO
=> 42 concepts (42 used for prediction)
PRED predicted values (max 10 best out of 210): SLO (0.94 #1262, 0.91 #1104, 0.91 #945), H (0.50 #515, 0.50 #358, 0.33 #201), D (0.50 #642, 0.25 #5684, 0.25 #6004), HR (0.33 #492, 0.33 #178, 0.33 #20), SK (0.33 #493, 0.33 #21, 0.09 #2866), I (0.33 #194, 0.27 #3003, 0.25 #5684), A (0.33 #233, 0.25 #5684, 0.25 #390), UA (0.33 #50, 0.17 #522, 0.14 #837), SRB (0.33 #136, 0.17 #608, 0.09 #2981), RO (0.33 #25, 0.17 #497, 0.05 #2399)
>> best conf = 0.94
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1262 for best value:
>> intensional similarity = 6
>> extensional distance = 8
>> proper extension:  D;
>> query: (?x207, ?x78) <- ?x207[ has religion ?x56; is locatedIn of ?x569[ has type ?x706;]; is locatedIn of ?x2190[ has belongsToIslands ?x87;]; is neighbor of ?x78;]
ranks of expected_values: 1, 7
EVAL	I	neighbor	SLO	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	42.000	42.000	210.000	0.941	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	I	neighbor	A	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.167	42.000	42.000	210.000	0.941	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: A SLO
=> 123 concepts (123 used for prediction)
PRED predicted values (max 10 best out of 219): SLO (0.93 #14469, 0.93 #5450, 0.93 #2087), AL (0.60 #674, 0.57 #159, 0.31 #2572), H (0.57 #159, 0.50 #1163, 0.33 #4532), I (0.57 #159, 0.42 #3048, 0.33 #319), DZ (0.57 #159, 0.38 #799, 0.31 #2572), TN (0.57 #159, 0.38 #799, 0.31 #2572), MA (0.57 #159, 0.38 #799, 0.31 #2572), A (0.57 #159, 0.33 #319, 0.33 #75), HR (0.57 #159, 0.33 #1140, 0.33 #319), E (0.57 #159, 0.33 #319, 0.31 #5451)
>> best conf = 0.93
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #14469 for best value:
>> intensional similarity = 10
>> extensional distance = 42
>> proper extension:  RN;
>> query: (?x207, ?x234) <- ?x207[ has encompassed ?x195; is locatedIn of ?x275[ is flowsInto of ?x698;]; is locatedIn of ?x1267[ has inMountains ?x261;]; is neighbor of ?x234[ has ethnicGroup ?x237; has language ?x51; has religion ?x56; is locatedIn of ?x233;];]
ranks of expected_values: 1, 8
EVAL	I	neighbor	SLO	CNN-1.+1._MA	1.000	1.000	1.000	1.000	123.000	123.000	219.000	0.934	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	I	neighbor	A	CNN-1.+1._MA	0.000	0.000	1.000	0.143	123.000	123.000	219.000	0.934	http://www.semwebtech.org/mondial/10/meta#neighbor

#486-USA
PRED entity: USA

PRED relation: locatedIn!
PRED expected values: MaunaLoa MtRedoubt LakeTahoe LakeOahe LakeWinnipesaukee StraitsofMackinac LakeSakakawea GranitePeak
=> 32 concepts (32 used for prediction)
PRED predicted values (max 10 best out of 1308): MaunaLoa (0.82 #5213), CaribbeanSea (0.33 #97, 0.26 #18340, 0.26 #4006), NevadodeColima (0.33 #1218, 0.03 #3824, 0.02 #9037), Popocatepetl (0.33 #1191, 0.03 #3797, 0.02 #9010), NevadodeToluca (0.33 #990, 0.03 #3596, 0.02 #8809), BajaCaliforniaDesert (0.33 #850, 0.03 #3456, 0.02 #8669), Iztaccihuatl (0.33 #839, 0.03 #3445, 0.02 #8658), RioLerma (0.33 #676, 0.03 #3282, 0.02 #8495), Citlaltepetl-PicodeOrizaba- (0.33 #609, 0.03 #3215, 0.02 #8428), LagodeChapala (0.33 #408, 0.03 #3014, 0.02 #8227)
>> best conf = 0.82
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5213 for best value:
>> intensional similarity = 3
>> extensional distance = 33
>> proper extension:  WV;
>> query: (?x315, ?x588) <- ?x315[ has encompassed ?x521; is locatedIn of ?x723[ is locatedOnIsland of ?x588;];]
ranks of expected_values: 1
EVAL	USA	locatedIn!	GranitePeak	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	32.000	32.000	1308.000	0.817	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	USA	locatedIn!	LakeSakakawea	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	32.000	32.000	1308.000	0.817	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	USA	locatedIn!	StraitsofMackinac	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	32.000	32.000	1308.000	0.817	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	USA	locatedIn!	LakeWinnipesaukee	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	32.000	32.000	1308.000	0.817	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	USA	locatedIn!	LakeOahe	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	32.000	32.000	1308.000	0.817	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	USA	locatedIn!	LakeTahoe	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	32.000	32.000	1308.000	0.817	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	USA	locatedIn!	MtRedoubt	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	32.000	32.000	1308.000	0.817	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	USA	locatedIn!	MaunaLoa	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	32.000	32.000	1308.000	0.817	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: MaunaLoa MtRedoubt LakeTahoe LakeOahe LakeWinnipesaukee StraitsofMackinac LakeSakakawea GranitePeak
=> 102 concepts (102 used for prediction)
PRED predicted values (max 10 best out of 1338): SaintLawrenceRiver (0.76 #92637, 0.67 #101773, 0.60 #123960), Colorado (0.76 #92637, 0.67 #101773, 0.60 #123960), StraitsofMackinac (0.76 #92637, 0.67 #101773, 0.60 #123960), YukonRiver (0.75 #10430, 0.33 #5761, 0.20 #8370), ColumbiaRiver (0.75 #10430, 0.33 #5582, 0.20 #8191), CaribbeanSea (0.44 #60113, 0.38 #40534, 0.35 #37921), NorthSea (0.44 #16975, 0.27 #18258, 0.25 #61320), RiviereRichelieu (0.34 #82197, 0.33 #50879, 0.33 #6349), Manicouagan (0.34 #82197, 0.33 #50879, 0.33 #5860), AtlinLake (0.34 #82197, 0.33 #50879, 0.33 #5835)
>> best conf = 0.76
=> the first rule below is the first best rule for 3 predicted values
>> Best rule #92637 for best value:
>> intensional similarity = 9
>> extensional distance = 40
>> proper extension:  HR; SP; MYA; VN; BD; WAG;
>> query: (?x315, ?x288) <- ?x315[ has ethnicGroup ?x79; has religion ?x95; is locatedIn of ?x263[ has mergesWith ?x248; is locatedInWater of ?x478;]; is locatedIn of ?x324[ a Estuary;]; is locatedIn of ?x1220[ has hasEstuary ?x288;];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 3, 30, 31, 32, 33
EVAL	USA	locatedIn!	GranitePeak	CNN-1.+1._MA	0.000	0.000	0.000	0.000	102.000	102.000	1338.000	0.760	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	USA	locatedIn!	LakeSakakawea	CNN-1.+1._MA	0.000	0.000	0.000	0.034	102.000	102.000	1338.000	0.760	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	USA	locatedIn!	StraitsofMackinac	CNN-1.+1._MA	0.000	1.000	1.000	0.333	102.000	102.000	1338.000	0.760	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	USA	locatedIn!	LakeWinnipesaukee	CNN-1.+1._MA	0.000	0.000	0.000	0.034	102.000	102.000	1338.000	0.760	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	USA	locatedIn!	LakeOahe	CNN-1.+1._MA	0.000	0.000	0.000	0.034	102.000	102.000	1338.000	0.760	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	USA	locatedIn!	LakeTahoe	CNN-1.+1._MA	0.000	0.000	0.000	0.034	102.000	102.000	1338.000	0.760	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	USA	locatedIn!	MtRedoubt	CNN-1.+1._MA	0.000	0.000	0.000	0.000	102.000	102.000	1338.000	0.760	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	USA	locatedIn!	MaunaLoa	CNN-1.+1._MA	0.000	0.000	0.000	0.000	102.000	102.000	1338.000	0.760	http://www.semwebtech.org/mondial/10/meta#locatedIn

#485-USA
PRED entity: USA

PRED relation: neighbor
PRED expected values: CDN
=> 37 concepts (37 used for prediction)
PRED predicted values (max 10 best out of 224): RCB (0.21 #731, 0.13 #2888, 0.12 #2889), AND (0.18 #283, 0.17 #444, 0.15 #604), R (0.18 #163, 0.13 #2888, 0.12 #2889), B (0.18 #254, 0.11 #895, 0.08 #415), D (0.18 #174, 0.08 #2099, 0.08 #2581), E (0.17 #340, 0.15 #500, 0.13 #2888), PE (0.17 #371, 0.13 #2888, 0.12 #1814), BOL (0.17 #434, 0.10 #1074, 0.10 #1235), F (0.16 #805, 0.15 #485, 0.13 #2888), CH (0.16 #845, 0.15 #525, 0.09 #204)
>> best conf = 0.21
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #731 for best value:
>> intensional similarity = 6
>> extensional distance = 12
>> proper extension:  RG; ANG; CI;
>> query: (?x315, RCB) <- ?x315[ is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x219[ a River;]; is locatedIn of ?x895[ a Source;];]

*> Best rule #2888 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 65
*> proper extension:  DJI;
*> query: (?x315, ?x139) <- ?x315[ is locatedIn of ?x182[ has locatedIn ?x139[ has religion ?x116;];]; is locatedIn of ?x1221[ a Lake;];]
*> conf = 0.13
ranks of expected_values: 71
EVAL	USA	neighbor	CDN	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.014	37.000	37.000	224.000	0.214	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: CDN
=> 132 concepts (132 used for prediction)
PRED predicted values (max 10 best out of 232): USA (0.45 #14237, 0.40 #10025, 0.25 #19937), CDN (0.45 #14237, 0.20 #1125, 0.14 #17654), HCA (0.33 #152, 0.25 #955, 0.20 #2407), GCA (0.33 #672, 0.25 #19937, 0.20 #2284), ES (0.33 #110, 0.14 #17654, 0.14 #15212), BR (0.29 #3477, 0.28 #8171, 0.25 #5578), EC (0.29 #3520, 0.20 #4491, 0.17 #5621), SF (0.27 #5421, 0.25 #1384, 0.25 #1222), R (0.25 #1291, 0.25 #1129, 0.20 #2097), GB (0.25 #1449, 0.25 #1287, 0.20 #2094)
>> best conf = 0.45
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #14237 for best value:
>> intensional similarity = 10
>> extensional distance = 30
>> proper extension:  BI;
>> query: (?x315, ?x272) <- ?x315[ has encompassed ?x521; has religion ?x95; has wasDependentOf ?x81; is locatedIn of ?x218[ a Lake; has locatedIn ?x272;]; is locatedIn of ?x324[ a Estuary;]; is locatedIn of ?x1366[ a River;];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 2
EVAL	USA	neighbor	CDN	CNN-1.+1._MA	0.000	1.000	1.000	0.500	132.000	132.000	232.000	0.446	http://www.semwebtech.org/mondial/10/meta#neighbor

#484-SouthChinaSea
PRED entity: SouthChinaSea

PRED relation: mergesWith!
PRED expected values: PacificOcean SuluSea
=> 32 concepts (28 used for prediction)
PRED predicted values (max 10 best out of 147): PacificOcean (0.84 #466, 0.84 #311, 0.83 #467), SuluSea (0.84 #466, 0.83 #467, 0.83 #310), SulawesiSea (0.46 #545, 0.30 #181, 0.29 #218), SouthChinaSea (0.46 #545, 0.30 #178, 0.29 #215), IndianOcean (0.46 #545, 0.20 #160, 0.19 #157), AndamanSea (0.46 #545, 0.20 #176, 0.19 #157), BandaSea (0.30 #183, 0.21 #220, 0.19 #157), SeaofJapan (0.29 #209, 0.22 #89, 0.20 #130), AtlanticOcean (0.28 #241, 0.25 #317, 0.25 #278), YellowSea (0.22 #88, 0.20 #129, 0.14 #208)
>> best conf = 0.84
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #466 for best value:
>> intensional similarity = 6
>> extensional distance = 37
>> proper extension:  SeaofAzov; BlackSea; RedSea; Kattegat;
>> query: (?x384, ?x241) <- ?x384[ a Sea; has locatedIn ?x91; has mergesWith ?x241; has mergesWith ?x282[ has locatedIn ?x408[ has religion ?x95;];];]
ranks of expected_values: 1, 2
EVAL	SouthChinaSea	mergesWith!	SuluSea	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	32.000	28.000	147.000	0.837	http://www.semwebtech.org/mondial/10/meta#mergesWith
EVAL	SouthChinaSea	mergesWith!	PacificOcean	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	32.000	28.000	147.000	0.837	http://www.semwebtech.org/mondial/10/meta#mergesWith

PRED relation: mergesWith!
PRED expected values: PacificOcean SuluSea
=> 79 concepts (79 used for prediction)
PRED predicted values (max 10 best out of 469): PacificOcean (0.84 #873, 0.84 #628, 0.83 #833), SuluSea (0.84 #873, 0.84 #628, 0.83 #833), SouthChinaSea (0.51 #670, 0.51 #669, 0.46 #914), SulawesiSea (0.51 #670, 0.51 #669, 0.46 #914), IndianOcean (0.51 #670, 0.51 #669, 0.46 #914), AndamanSea (0.51 #670, 0.51 #669, 0.46 #914), SeaofJapan (0.50 #171, 0.33 #287, 0.17 #328), BandaSea (0.33 #376, 0.33 #24, 0.28 #794), AtlanticOcean (0.29 #636, 0.29 #719, 0.29 #478), LakeToba (0.28 #794, 0.05 #197, 0.04 #391)
>> best conf = 0.84
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #873 for best value:
>> intensional similarity = 12
>> extensional distance = 30
>> proper extension:  YellowSea;
>> query: (?x384, ?x241) <- ?x384[ has locatedIn ?x217[ has wasDependentOf ?x575;]; has locatedIn ?x460[ has ethnicGroup ?x298; has religion ?x187<Muslim>; has religion ?x462[ a Religion;];]; has locatedIn ?x871[ a Country;]; has mergesWith ?x241; is mergesWith of ?x620;]
ranks of expected_values: 1, 2
EVAL	SouthChinaSea	mergesWith!	SuluSea	CNN-1.+1._MA	1.000	1.000	1.000	1.000	79.000	79.000	469.000	0.843	http://www.semwebtech.org/mondial/10/meta#mergesWith
EVAL	SouthChinaSea	mergesWith!	PacificOcean	CNN-1.+1._MA	1.000	1.000	1.000	1.000	79.000	79.000	469.000	0.843	http://www.semwebtech.org/mondial/10/meta#mergesWith

#483-LakeSkutari
PRED entity: LakeSkutari

PRED relation: flowsInto
PRED expected values: Buna
=> 39 concepts (34 used for prediction)
PRED predicted values (max 10 best out of 119): MediterraneanSea (0.25 #355, 0.20 #686, 0.06 #332), BlackDrin (0.25 #236, 0.07 #1066, 0.06 #1233), Drin (0.20 #737, 0.12 #1237, 0.02 #3658), Drina (0.09 #916), Euphrat (0.07 #1140, 0.05 #1472, 0.05 #1638), Rhone (0.07 #1109, 0.01 #1774, 0.01 #1941), Ticino (0.07 #1105, 0.01 #1770, 0.01 #1937), Adda (0.07 #1094, 0.01 #1759), Mincio (0.07 #1091, 0.01 #1756), Piva (0.06 #332, 0.02 #3658, 0.02 #1163)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #355 for best value:
>> intensional similarity = 7
>> extensional distance = 2
>> proper extension:  Drin;
>> query: (?x104, MediterraneanSea) <- ?x104[ has locatedIn ?x106[ has neighbor ?x156;]; has locatedIn ?x204<AL>; is flowsInto of ?x2296[ a River; has hasSource ?x224;];]

*> Best rule #332 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 2
*> proper extension:  LakePrespa; LakeOhrid;
*> query: (?x104, ?x105) <- ?x104[ a Lake; has locatedIn ?x106[ is locatedIn of ?x105; is neighbor of ?x692<KOS>; is neighbor of ?x904<SRB>;];]
*> conf = 0.06
ranks of expected_values: 11
EVAL	LakeSkutari	flowsInto	Buna	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.091	39.000	34.000	119.000	0.250	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto
PRED expected values: Buna
=> 111 concepts (111 used for prediction)
PRED predicted values (max 10 best out of 165): Donau (0.33 #1511, 0.29 #1008, 0.12 #3856), MediterraneanSea (0.27 #1166, 0.20 #998, 0.20 #855), Drin (0.25 #406, 0.22 #1410, 0.20 #906), BlackDrin (0.25 #236, 0.12 #1239, 0.11 #1406), Morava (0.20 #671, 0.07 #3019, 0.05 #2685), Save (0.14 #1011, 0.11 #1514, 0.06 #2522), BlackSea (0.14 #1003, 0.11 #1506, 0.06 #3851), Drina (0.12 #2598, 0.09 #2093, 0.06 #3599), Volga (0.11 #1883, 0.11 #1716, 0.08 #2219), Newa (0.11 #1901, 0.11 #1734, 0.08 #2237)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1511 for best value:
>> intensional similarity = 11
>> extensional distance = 7
>> proper extension:  Drau;
>> query: (?x104, Donau) <- ?x104[ has locatedIn ?x106[ a Country; has ethnicGroup ?x775<Serb>; has language ?x1251; has religion ?x56<ChristianOrthodox>; is locatedIn of ?x306[ a River;]; is neighbor of ?x55;]; is flowsInto of ?x2296;]

*> Best rule #1670 for first EXPECTED value:
*> intensional similarity = 11
*> extensional distance = 7
*> proper extension:  Drau;
*> query: (?x104, ?x306) <- ?x104[ has locatedIn ?x106[ a Country; has ethnicGroup ?x775<Serb>; has language ?x1251; has religion ?x56<ChristianOrthodox>; is locatedIn of ?x306[ a River;]; is neighbor of ?x55;]; is flowsInto of ?x2296;]
*> conf = 0.09
ranks of expected_values: 16
EVAL	LakeSkutari	flowsInto	Buna	CNN-1.+1._MA	0.000	0.000	0.000	0.062	111.000	111.000	165.000	0.333	http://www.semwebtech.org/mondial/10/meta#flowsInto

#482-Saone
PRED entity: Saone

PRED relation: hasEstuary!
PRED expected values: Saone
=> 2 concepts (2 used for prediction)
No prediction
ranks of expected_values: 
EVAL	Saone	hasEstuary!	Saone	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	2.000	2.000	0.000	0.000	http://www.semwebtech.org/mondial/10/meta#hasEstuary

PRED relation: hasEstuary!
PRED expected values: Saone
=> 2 concepts (2 used for prediction)
No prediction
ranks of expected_values: 
EVAL	Saone	hasEstuary!	Saone	CNN-1.+1._MA	0.000	0.000	0.000	0.000	2.000	2.000	0.000	0.000	http://www.semwebtech.org/mondial/10/meta#hasEstuary

#481-RSA
PRED entity: RSA

PRED relation: ethnicGroup
PRED expected values: Indian
=> 29 concepts (29 used for prediction)
PRED predicted values (max 10 best out of 174): African (0.41 #2063, 0.40 #2578, 0.33 #6), European (0.33 #8, 0.32 #2065, 0.30 #1037), Bakongo (0.25 #437, 0.10 #1209, 0.09 #6177), Kimbundu (0.25 #320, 0.10 #1092, 0.09 #6177), Batswana (0.20 #1021, 0.20 #763, 0.19 #3859), Kgalagadi (0.20 #989, 0.20 #731, 0.19 #3859), Asian (0.20 #791, 0.19 #3859, 0.19 #3601), Indian (0.20 #846, 0.19 #3859, 0.19 #3601), Euro-African (0.20 #909, 0.19 #3859, 0.19 #3601), Arab (0.20 #1040, 0.17 #1297, 0.17 #1811)
>> best conf = 0.41
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2063 for best value:
>> intensional similarity = 5
>> extensional distance = 32
>> proper extension:  WV; BS; AG;
>> query: (?x243, African) <- ?x243[ has encompassed ?x213; has government ?x435; has wasDependentOf ?x81; is locatedIn of ?x182<AtlanticOcean>;]

*> Best rule #846 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 3
*> proper extension:  MOC; ZW;
*> query: (?x243, Indian) <- ?x243[ has encompassed ?x213; has government ?x435; has neighbor ?x89; has wasDependentOf ?x81; is locatedIn of ?x242<Limpopo>;]
*> conf = 0.20
ranks of expected_values: 8
EVAL	RSA	ethnicGroup	Indian	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.125	29.000	29.000	174.000	0.412	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: Indian
=> 105 concepts (105 used for prediction)
PRED predicted values (max 10 best out of 252): European (0.67 #3352, 0.60 #5927, 0.60 #2067), African (0.53 #7984, 0.50 #10823, 0.46 #15201), Indian (0.33 #74, 0.26 #16224, 0.25 #1360), French (0.33 #639, 0.17 #2955, 0.17 #2698), BritishIsles (0.33 #705, 0.17 #3021, 0.17 #2764), Inuit (0.33 #528, 0.17 #2844, 0.17 #2587), Pakistani (0.33 #130, 0.11 #26276, 0.10 #6049), NorthernIrish (0.33 #232, 0.11 #26276, 0.10 #6151), English (0.33 #229, 0.11 #26276, 0.10 #6148), Welsh (0.33 #158, 0.11 #26276, 0.10 #6077)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3352 for best value:
>> intensional similarity = 16
>> extensional distance = 4
>> proper extension:  STP;
>> query: (?x243, European) <- ?x243[ a Country; has encompassed ?x213; has ethnicGroup ?x2226[ a EthnicGroup;]; has government ?x435<"republic">; has religion ?x187; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x933[ has locatedIn ?x525[ has wasDependentOf ?x81;]; has locatedIn ?x1239[ is neighbor of ?x1576;];]; is locatedIn of ?x2036[ a Mountain;];]

*> Best rule #74 for first EXPECTED value:
*> intensional similarity = 17
*> extensional distance = 1
*> proper extension:  GB;
*> query: (?x243, Indian) <- ?x243[ a Country; has encompassed ?x213; has government ?x435; has neighbor ?x138[ has language ?x247; has religion ?x95;]; has neighbor ?x1239[ has government ?x1174;]; has religion ?x187<Muslim>; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x1015[ has inMountains ?x2374[ a Mountains;];]; is locatedIn of ?x2036[ a Mountain;];]
*> conf = 0.33
ranks of expected_values: 3
EVAL	RSA	ethnicGroup	Indian	CNN-1.+1._MA	0.000	1.000	1.000	0.333	105.000	105.000	252.000	0.667	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#480-SF
PRED entity: SF

PRED relation: ethnicGroup
PRED expected values: Estonian
=> 47 concepts (47 used for prediction)
PRED predicted values (max 10 best out of 235): European (0.55 #773, 0.38 #1283, 0.32 #4343), Ukrainian (0.50 #1021, 0.33 #1, 0.25 #5866), Tatar (0.33 #86, 0.25 #5866, 0.25 #341), Chuvash (0.33 #179, 0.25 #5866, 0.25 #434), Bashkir (0.33 #100, 0.25 #5866, 0.25 #355), Mestizo (0.33 #546, 0.18 #4371, 0.14 #6667), Uzbek (0.31 #1174, 0.12 #2959, 0.07 #4999), Belorussian (0.31 #1105, 0.08 #2380, 0.07 #5695), African (0.27 #771, 0.22 #1536, 0.21 #3576), Asian (0.27 #784, 0.22 #1549, 0.19 #1294)
>> best conf = 0.55
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #773 for best value:
>> intensional similarity = 5
>> extensional distance = 9
>> proper extension:  KN;
>> query: (?x565, European) <- ?x565[ has ethnicGroup ?x1193; has language ?x247<English>; has wasDependentOf ?x73; is locatedIn of ?x631;]

*> Best rule #1234 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 14
*> proper extension:  TAD;
*> query: (?x565, Estonian) <- ?x565[ has ethnicGroup ?x1193<Russian>; has government ?x435; is locatedIn of ?x631; is neighbor of ?x73[ is locatedIn of ?x72;];]
*> conf = 0.06
ranks of expected_values: 63
EVAL	SF	ethnicGroup	Estonian	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.016	47.000	47.000	235.000	0.545	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: Estonian
=> 118 concepts (118 used for prediction)
PRED predicted values (max 10 best out of 247): European (0.69 #11507, 0.58 #17639, 0.55 #14317), Ukrainian (0.67 #6902, 0.60 #4088, 0.50 #7668), Mestizo (0.50 #11535, 0.50 #2334, 0.40 #14345), Belorussian (0.50 #2895, 0.44 #7241, 0.40 #4172), Amerindian (0.50 #7414, 0.44 #11501, 0.35 #14311), Arab (0.50 #2054, 0.43 #5375, 0.40 #3843), Polish (0.50 #3014, 0.40 #4291, 0.33 #7360), Portuguese (0.50 #4978, 0.33 #1401, 0.28 #8945), Jewish (0.43 #5409, 0.40 #3877, 0.40 #3622), German (0.43 #9210, 0.33 #8699, 0.33 #1287)
>> best conf = 0.69
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #11507 for best value:
>> intensional similarity = 17
>> extensional distance = 14
>> proper extension:  GCA; ES; HCA;
>> query: (?x565, European) <- ?x565[ a Country; has ethnicGroup ?x1193; has language ?x247[ is language of ?x246<CUR>; is language of ?x671<BZ>;]; has language ?x566[ a Language;]; has language ?x1839[ is language of ?x591[ has encompassed ?x195;];]; has religion ?x95<Protestant>; is locatedIn of ?x1396[ is flowsInto of ?x1573;]; is neighbor of ?x73;]

*> Best rule #8945 for first EXPECTED value:
*> intensional similarity = 17
*> extensional distance = 10
*> proper extension:  BIH; SK; RO; SRB;
*> query: (?x565, ?x244) <- ?x565[ a Country; has ethnicGroup ?x1193; has language ?x566[ a Language;]; has language ?x1848[ is language of ?x239[ has ethnicGroup ?x244; has neighbor ?x63;];]; has religion ?x56<ChristianOrthodox>; is locatedIn of ?x661[ a Estuary;]; is locatedIn of ?x808[ a Source;]; is locatedIn of ?x1396[ has flowsInto ?x589;]; is neighbor of ?x73;]
*> conf = 0.28
ranks of expected_values: 43
EVAL	SF	ethnicGroup	Estonian	CNN-1.+1._MA	0.000	0.000	0.000	0.023	118.000	118.000	247.000	0.688	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#479-A
PRED entity: A

PRED relation: neighbor
PRED expected values: I
=> 37 concepts (37 used for prediction)
PRED predicted values (max 10 best out of 179): A (0.50 #72, 0.26 #3551, 0.25 #4633), PL (0.50 #32, 0.26 #3551, 0.25 #4633), GR (0.43 #528, 0.10 #836, 0.08 #1144), HR (0.33 #328, 0.26 #3551, 0.25 #4633), B (0.30 #705, 0.29 #551, 0.26 #3551), F (0.30 #621, 0.26 #3551, 0.25 #4633), BG (0.29 #488, 0.11 #5252, 0.10 #4632), UA (0.26 #3551, 0.25 #4633, 0.25 #48), I (0.26 #3551, 0.25 #4633, 0.24 #4323), L (0.26 #3551, 0.25 #4633, 0.24 #4323)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #72 for best value:
>> intensional similarity = 5
>> extensional distance = 2
>> proper extension:  SK; CZ;
>> query: (?x424, A) <- ?x424[ has ethnicGroup ?x160; has religion ?x95; is locatedIn of ?x1096<March>; is neighbor of ?x120;]

*> Best rule #3551 for first EXPECTED value:
*> intensional similarity = 4
*> extensional distance = 119
*> proper extension:  SD; BHT; LB; BZ; AND;
*> query: (?x424, ?x194) <- ?x424[ has encompassed ?x195; has ethnicGroup ?x160; is neighbor of ?x163[ has neighbor ?x194;];]
*> conf = 0.26
ranks of expected_values: 9
EVAL	A	neighbor	I	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.111	37.000	37.000	179.000	0.500	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: I
=> 81 concepts (81 used for prediction)
PRED predicted values (max 10 best out of 196): A (0.57 #313, 0.56 #2023, 0.50 #1163), UA (0.57 #313, 0.56 #2023, 0.50 #6590), RO (0.57 #313, 0.56 #2023, 0.50 #6590), HR (0.57 #313, 0.56 #2023, 0.50 #6590), SRB (0.57 #313, 0.56 #2023, 0.50 #6590), MD (0.57 #313, 0.56 #2023, 0.50 #6590), BG (0.57 #313, 0.56 #2023, 0.50 #6590), F (0.57 #313, 0.56 #2023, 0.50 #6590), I (0.57 #313, 0.56 #2023, 0.50 #6590), PL (0.50 #1123, 0.33 #502, 0.32 #9416)
>> best conf = 0.57
=> the first rule below is the first best rule for 9 predicted values
>> Best rule #313 for best value:
>> intensional similarity = 14
>> extensional distance = 1
>> proper extension:  CH;
>> query: (?x424, ?x156) <- ?x424[ has ethnicGroup ?x237<German>; has language ?x511; has religion ?x95<Protestant>; is locatedIn of ?x155[ has locatedIn ?x156;]; is locatedIn of ?x490[ a River;]; is locatedIn of ?x756<Inn>; is neighbor of ?x234[ has language ?x51; is locatedIn of ?x233;];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 9
EVAL	A	neighbor	I	CNN-1.+1._MA	0.000	0.000	1.000	0.111	81.000	81.000	196.000	0.571	http://www.semwebtech.org/mondial/10/meta#neighbor

#478-SRB
PRED entity: SRB

PRED relation: religion
PRED expected values: Muslim
=> 41 concepts (41 used for prediction)
PRED predicted values (max 10 best out of 37): Muslim (0.88 #744, 0.60 #81, 0.57 #939), Buddhist (0.40 #126, 0.23 #243, 0.19 #282), Christian (0.34 #743, 0.30 #431, 0.28 #1212), Jewish (0.20 #118, 0.15 #235, 0.14 #157), Hindu (0.20 #124, 0.15 #241, 0.14 #748), Anglican (0.14 #795, 0.12 #522, 0.09 #1381), Bahai (0.11 #224, 0.02 #419, 0.02 #1122), ArmenianApostolic (0.11 #221, 0.01 #845), Yezidi (0.11 #208, 0.01 #832), JehovasWitnesses (0.09 #525, 0.07 #564, 0.07 #837)
>> best conf = 0.88
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #744 for best value:
>> intensional similarity = 7
>> extensional distance = 96
>> proper extension:  TCH; G; BI; RM; RCB; OM; YE; MW; COM; GBZ;
>> query: (?x904, Muslim) <- ?x904[ has government ?x435; has religion ?x56[ is religion of ?x156<HR>; is religion of ?x277<UZB>;]; is locatedIn of ?x132;]
ranks of expected_values: 1
EVAL	SRB	religion	Muslim	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	41.000	41.000	37.000	0.878	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion
PRED expected values: Muslim
=> 100 concepts (100 used for prediction)
PRED predicted values (max 10 best out of 35): Muslim (0.85 #2854, 0.79 #1352, 0.79 #1315), Jewish (0.69 #1353, 0.68 #756, 0.67 #161), UkrainianGreekCatholic (0.47 #1591, 0.43 #597, 0.32 #3447), Christian (0.42 #1314, 0.35 #2853, 0.31 #3171), Buddhist (0.20 #686, 0.19 #1678, 0.17 #1121), JehovasWitnesses (0.18 #893, 0.16 #1530, 0.10 #933), Anglican (0.17 #1684, 0.15 #1763, 0.12 #531), Hindu (0.14 #484, 0.12 #523, 0.12 #1676), Sikh (0.12 #547, 0.10 #708, 0.07 #826), Bahai (0.09 #745, 0.04 #1182, 0.04 #1261)
>> best conf = 0.85
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2854 for best value:
>> intensional similarity = 17
>> extensional distance = 79
>> proper extension:  OM;
>> query: (?x904, Muslim) <- ?x904[ has government ?x435; has religion ?x56[ is religion of ?x156<HR>; is religion of ?x204<AL>; is religion of ?x290<TM>;]; has religion ?x95[ is religion of ?x246[ has language ?x544<Dutch>; is locatedIn of ?x317;];]; is locatedIn of ?x132; is neighbor of ?x177[ a Country; has wasDependentOf ?x1656;];]
ranks of expected_values: 1
EVAL	SRB	religion	Muslim	CNN-1.+1._MA	1.000	1.000	1.000	1.000	100.000	100.000	35.000	0.852	http://www.semwebtech.org/mondial/10/meta#religion

#477-Sachalin
PRED entity: Sachalin

PRED relation: locatedIn
PRED expected values: R
=> 25 concepts (25 used for prediction)
PRED predicted values (max 10 best out of 101): J (0.33 #19, 0.25 #255, 0.15 #491), USA (0.27 #544, 0.23 #1020, 0.22 #1258), R (0.25 #241, 0.07 #2379, 0.06 #2857), D (0.12 #3113, 0.05 #4539, 0.05 #5014), P (0.11 #3054, 0.11 #2576, 0.09 #3529), CDN (0.10 #1964, 0.10 #2203, 0.09 #2681), GR (0.10 #3658, 0.04 #4133, 0.04 #4371), I (0.10 #2427, 0.09 #3616, 0.09 #2905), KIR (0.09 #868, 0.05 #1106, 0.05 #1344), E (0.08 #2406, 0.07 #2884, 0.06 #3359)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #19 for best value:
>> intensional similarity = 5
>> extensional distance = 1
>> proper extension:  Hokkaido;
>> query: (?x1816, J) <- ?x1816[ a Island; has locatedInWater ?x507<SeaofOkhotsk>; has type ?x150<"volcanic">;]

*> Best rule #241 for first EXPECTED value:
*> intensional similarity = 3
*> extensional distance = 2
*> proper extension:  Paramuschir;
*> query: (?x1816, R) <- ?x1816[ a Island; has locatedInWater ?x507<SeaofOkhotsk>;]
*> conf = 0.25
ranks of expected_values: 3
EVAL	Sachalin	locatedIn	R	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.333	25.000	25.000	101.000	0.333	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: R
=> 45 concepts (45 used for prediction)
PRED predicted values (max 10 best out of 133): R (0.40 #711, 0.38 #709, 0.38 #477), J (0.40 #711, 0.33 #949, 0.33 #731), USA (0.27 #1024, 0.26 #1743, 0.26 #1503), CN (0.23 #712, 0.11 #768, 0.10 #3355), I (0.16 #2440, 0.10 #5331, 0.10 #6774), D (0.14 #5543, 0.14 #5785, 0.07 #8419), P (0.13 #4034, 0.13 #4276, 0.12 #4517), SVAX (0.12 #664, 0.02 #3067, 0.02 #3307), RI (0.12 #6536, 0.11 #7731, 0.10 #6058), GB (0.11 #8648, 0.11 #8408, 0.10 #8888)
>> best conf = 0.40
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #711 for best value:
>> intensional similarity = 21
>> extensional distance = 6
>> proper extension:  Olkhon;
>> query: (?x1816, ?x117) <- ?x1816[ a Island; has locatedInWater ?x507[ has locatedIn ?x73<R>; has locatedIn ?x117[ a Country; has encompassed ?x175; has ethnicGroup ?x2391; has language ?x118; has religion ?x462; is locatedIn of ?x271<SeaofJapan>; is locatedIn of ?x282<PacificOcean>; is wasDependentOf of ?x334;]; is flowsInto of ?x1585[ a River; has hasSource ?x2148; has locatedIn ?x73<R>;];];]
ranks of expected_values: 1
EVAL	Sachalin	locatedIn	R	CNN-1.+1._MA	1.000	1.000	1.000	1.000	45.000	45.000	133.000	0.400	http://www.semwebtech.org/mondial/10/meta#locatedIn

#476-Karun
PRED entity: Karun

PRED relation: flowsInto
PRED expected values: SchattalArab
=> 44 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 87): Donau (0.14 #1171, 0.14 #1006, 0.08 #1833), AtlanticOcean (0.10 #1671, 0.10 #1340, 0.10 #1506), SchattalArab (0.08 #497, 0.08 #464, 0.08 #2987), CaspianSea (0.08 #2987, 0.04 #5157, 0.03 #4822), MediterraneanSea (0.06 #1351, 0.05 #1848, 0.05 #1186), BalticSea (0.05 #2001, 0.05 #1504, 0.05 #1669), MurrayRiver (0.05 #956, 0.02 #1289, 0.01 #1620), OzeroAral (0.05 #320, 0.01 #1649, 0.01 #1814), Zaire (0.04 #1419, 0.03 #2911, 0.03 #2745), BlackSea (0.04 #1166, 0.04 #1001, 0.03 #1497)
>> best conf = 0.14
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1171 for best value:
>> intensional similarity = 6
>> extensional distance = 81
>> proper extension:  Oranje; Mur; Amazonas; Rhein; Lukuga; Pjandsh; Piva; Saar; Ili; Orinoco; ...
>> query: (?x1620, Donau) <- ?x1620[ a River; has hasEstuary ?x1140; has hasSource ?x1382[ a Source; has inMountains ?x574; has locatedIn ?x304;];]
>> Best rule #1006 for best value:
>> intensional similarity = 6
>> extensional distance = 81
>> proper extension:  SnowyRiver;
>> query: (?x1620, Donau) <- ?x1620[ a River; has hasEstuary ?x1140[ has locatedIn ?x304;]; has hasSource ?x1382[ a Source; has inMountains ?x574;];]

*> Best rule #497 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 23
*> proper extension:  Euphrat; SchattalArab; Tigris; SchattalArab;
*> query: (?x1620, ?x1422) <- ?x1620[ has locatedIn ?x304[ has ethnicGroup ?x244; has ethnicGroup ?x1062[ a EthnicGroup;]; is locatedIn of ?x918<PersianGulf>; is locatedIn of ?x1422<SchattalArab>;];]
*> conf = 0.08
ranks of expected_values: 3
EVAL	Karun	flowsInto	SchattalArab	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.333	44.000	31.000	87.000	0.145	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto
PRED expected values: SchattalArab
=> 123 concepts (123 used for prediction)
PRED predicted values (max 10 best out of 137): SchattalArab (0.25 #465, 0.07 #11560, 0.06 #6688), LakeKeban (0.25 #449, 0.05 #3462, 0.04 #3796), DeadSea (0.25 #224, 0.05 #2232, 0.05 #2065), OzeroAral (0.20 #823, 0.18 #1158, 0.18 #991), Donau (0.18 #4687, 0.17 #4019, 0.17 #4855), Ob (0.18 #1156, 0.12 #653, 0.10 #821), AtlanticOcean (0.14 #5524, 0.11 #6533, 0.10 #4691), CaspianSea (0.14 #1170, 0.14 #1169, 0.12 #667), HamuneJazMurian (0.14 #1170, 0.14 #1169, 0.05 #1003), LakeUrmia (0.14 #1170, 0.14 #1169, 0.05 #1003)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #465 for best value:
>> intensional similarity = 13
>> extensional distance = 6
>> proper extension:  Kura; Tigris; Murat; Karasu; Euphrat;
>> query: (?x1620, SchattalArab) <- ?x1620[ has hasEstuary ?x1140; has locatedIn ?x304[ has ethnicGroup ?x244; has ethnicGroup ?x305[ a EthnicGroup;]; has language ?x511; has neighbor ?x83; is locatedIn of ?x918[ is locatedInWater of ?x1736;]; is locatedIn of ?x1422[ has hasSource ?x596;]; is neighbor of ?x302<IRQ>;];]
ranks of expected_values: 1
EVAL	Karun	flowsInto	SchattalArab	CNN-1.+1._MA	1.000	1.000	1.000	1.000	123.000	123.000	137.000	0.250	http://www.semwebtech.org/mondial/10/meta#flowsInto

#475-MediterraneanSea
PRED entity: MediterraneanSea

PRED relation: flowsInto!
PRED expected values: Buna
=> 38 concepts (33 used for prediction)
PRED predicted values (max 10 best out of 391): Douro (0.25 #515, 0.20 #809, 0.09 #1104), Tajo (0.25 #510, 0.20 #804, 0.09 #1099), Loire (0.25 #472, 0.20 #766, 0.09 #1061), Guadiana (0.25 #463, 0.20 #757, 0.09 #1052), Guadalquivir (0.25 #454, 0.20 #748, 0.09 #1043), Garonne (0.25 #408, 0.20 #702, 0.09 #997), MerrimackRiver (0.25 #552, 0.20 #846, 0.09 #1141), RioSaoFrancisco (0.25 #535, 0.20 #829, 0.09 #1124), Sanaga (0.25 #516, 0.20 #810, 0.09 #1105), HudsonRiver (0.25 #487, 0.20 #781, 0.09 #1076)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #515 for best value:
>> intensional similarity = 8
>> extensional distance = 2
>> proper extension:  Vignemale;
>> query: (?x275, Douro) <- ?x275[ has locatedIn ?x63[ has ethnicGroup ?x197;]; has locatedIn ?x78<F>; has locatedIn ?x149<E>; has locatedIn ?x466[ is neighbor of ?x302;];]

*> Best rule #883 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 3
*> proper extension:  NorthSea; AtlanticOcean; TheChannel;
*> query: (?x275, ?x419) <- ?x275[ has locatedIn ?x78<F>; has locatedIn ?x466[ is locatedIn of ?x419;]; is flowsInto of ?x698; is locatedInWater of ?x68;]
*> conf = 0.03
ranks of expected_values: 274
EVAL	MediterraneanSea	flowsInto!	Buna	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.004	38.000	33.000	391.000	0.250	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto!
PRED expected values: Buna
=> 101 concepts (101 used for prediction)
PRED predicted values (max 10 best out of 559): LakeGenezareth (0.33 #566, 0.25 #1751, 0.17 #3825), Mur (0.33 #605, 0.25 #2380, 0.14 #3863), Jordan (0.25 #1527, 0.20 #3007, 0.17 #3601), BlackDrin (0.25 #1276, 0.20 #3348, 0.11 #1778), WhiteDrin (0.25 #1306, 0.20 #3378, 0.11 #1778), Moraca (0.25 #1478, 0.20 #3550, 0.02 #16591), Drau (0.25 #2751, 0.11 #1778, 0.10 #1780), Save (0.25 #2676, 0.11 #1778, 0.10 #1780), Drina (0.25 #2774, 0.11 #1778, 0.10 #1780), Ticino (0.25 #2540, 0.11 #1778, 0.10 #1780)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #566 for best value:
>> intensional similarity = 11
>> extensional distance = 1
>> proper extension:  Jordan;
>> query: (?x275, LakeGenezareth) <- ?x275[ has locatedIn ?x108[ a Country; has religion ?x109;]; has locatedIn ?x239<IL>; has locatedIn ?x466<SYR>; has locatedIn ?x1588[ a Country; has government ?x2527;]; is flowsInto of ?x698;]

*> Best rule #1778 for first EXPECTED value:
*> intensional similarity = 14
*> extensional distance = 2
*> proper extension:  DeadSea;
*> query: (?x275, ?x184) <- ?x275[ has locatedIn ?x185[ is locatedIn of ?x98[ has mergesWith ?x97;]; is locatedIn of ?x184;]; has locatedIn ?x207[ has neighbor ?x234; has religion ?x56;]; has locatedIn ?x239<IL>; has locatedIn ?x1184[ is neighbor of ?x169;]; has locatedIn ?x1588[ has encompassed ?x213;]; is flowsInto of ?x698;]
*> conf = 0.11
ranks of expected_values: 100
EVAL	MediterraneanSea	flowsInto!	Buna	CNN-1.+1._MA	0.000	0.000	0.000	0.010	101.000	101.000	559.000	0.333	http://www.semwebtech.org/mondial/10/meta#flowsInto

#474-GCA
PRED entity: GCA

PRED relation: neighbor
PRED expected values: BZ
=> 54 concepts (54 used for prediction)
PRED predicted values (max 10 best out of 218): BZ (0.89 #3025, 0.89 #7200, 0.89 #7199), PE (0.33 #686, 0.31 #1006, 0.25 #209), BR (0.33 #729, 0.31 #1049, 0.25 #252), CR (0.33 #55, 0.20 #373, 0.17 #850), GCA (0.30 #7202, 0.26 #7525, 0.26 #6394), NIC (0.30 #7202, 0.26 #7525, 0.26 #6394), USA (0.30 #7202, 0.26 #7525, 0.26 #6394), BOL (0.25 #748, 0.23 #1068, 0.17 #4144), RCH (0.25 #671, 0.17 #4144, 0.15 #991), YV (0.25 #218, 0.12 #4145, 0.10 #537)
>> best conf = 0.89
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3025 for best value:
>> intensional similarity = 6
>> extensional distance = 48
>> proper extension:  D; ETH;
>> query: (?x181, ?x1364) <- ?x181[ has ethnicGroup ?x79; has religion ?x95<Protestant>; is locatedIn of ?x282; is neighbor of ?x1364[ has government ?x1535;];]
ranks of expected_values: 1
EVAL	GCA	neighbor	BZ	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	54.000	54.000	218.000	0.894	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: BZ
=> 97 concepts (97 used for prediction)
PRED predicted values (max 10 best out of 232): BZ (0.93 #9390, 0.93 #8087, 0.92 #7599), PE (0.36 #1813, 0.29 #3591, 0.29 #526), GCA (0.33 #29, 0.31 #11348, 0.31 #9226), PA (0.33 #439, 0.29 #597, 0.22 #1398), BR (0.33 #3153, 0.27 #1856, 0.24 #3634), CO (0.33 #1154, 0.24 #3700, 0.24 #3579), NIC (0.31 #11348, 0.31 #9226, 0.30 #11836), USA (0.31 #11348, 0.31 #9226, 0.30 #11836), CR (0.29 #691, 0.22 #1011, 0.20 #1492), BOL (0.24 #3653, 0.22 #3815, 0.21 #2846)
>> best conf = 0.93
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #9390 for best value:
>> intensional similarity = 9
>> extensional distance = 45
>> proper extension:  A; MACX; GAZA;
>> query: (?x181, ?x671) <- ?x181[ a Country; has ethnicGroup ?x79; has language ?x796; has neighbor ?x482; has religion ?x95; is locatedIn of ?x282[ is flowsInto of ?x602; is locatedInWater of ?x205;]; is neighbor of ?x671;]
ranks of expected_values: 1
EVAL	GCA	neighbor	BZ	CNN-1.+1._MA	1.000	1.000	1.000	1.000	97.000	97.000	232.000	0.926	http://www.semwebtech.org/mondial/10/meta#neighbor

#473-GBM
PRED entity: GBM

PRED relation: encompassed
PRED expected values: Europe
=> 48 concepts (48 used for prediction)
PRED predicted values (max 10 best out of 5): America (0.58 #163, 0.56 #158, 0.50 #45), Europe (0.50 #2, 0.39 #230, 0.36 #195), Australia-Oceania (0.32 #89, 0.32 #204, 0.31 #38), Africa (0.32 #204, 0.30 #177, 0.28 #187), Asia (0.32 #204, 0.19 #128, 0.19 #236)
>> best conf = 0.58
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #163 for best value:
>> intensional similarity = 7
>> extensional distance = 46
>> proper extension:  USA; PY; ES;
>> query: (?x1045, America) <- ?x1045[ has language ?x247[ is language of ?x561[ a Country; has religion ?x280;]; is language of ?x783<PA>;]; is locatedIn of ?x1321;]

*> Best rule #2 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 2
*> proper extension:  GB; IRL;
*> query: (?x1045, Europe) <- ?x1045[ a Country; has language ?x247<English>; is locatedIn of ?x1321[ has belongsToIslands ?x945<BritishIsles>;]; is locatedIn of ?x1833<IrishSea>;]
*> conf = 0.50
ranks of expected_values: 2
EVAL	GBM	encompassed	Europe	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	48.000	48.000	5.000	0.583	http://www.semwebtech.org/mondial/10/meta#encompassed

PRED relation: encompassed
PRED expected values: Europe
=> 93 concepts (93 used for prediction)
PRED predicted values (max 10 best out of 5): Europe (0.67 #27, 0.51 #310, 0.51 #306), America (0.63 #167, 0.59 #216, 0.58 #243), Australia-Oceania (0.36 #142, 0.35 #277, 0.33 #95), Africa (0.31 #428, 0.30 #297, 0.30 #352), Asia (0.31 #428, 0.30 #352, 0.30 #351)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #27 for best value:
>> intensional similarity = 15
>> extensional distance = 4
>> proper extension:  M;
>> query: (?x1045, Europe) <- ?x1045[ a Country; has government ?x254; has language ?x247<English>; is locatedIn of ?x1321[ a Island;]; is locatedIn of ?x1833[ a Sea; has locatedIn ?x81[ has government ?x1854; is dependentOf of ?x80; is wasDependentOf of ?x63;]; is locatedInWater of ?x153; is mergesWith of ?x182<AtlanticOcean>;];]
ranks of expected_values: 1
EVAL	GBM	encompassed	Europe	CNN-1.+1._MA	1.000	1.000	1.000	1.000	93.000	93.000	5.000	0.667	http://www.semwebtech.org/mondial/10/meta#encompassed

#472-Andes
PRED entity: Andes

PRED relation: inMountains!
PRED expected values: NevadodelHuila RioDesaguadero Huallaga Tupungato
=> 21 concepts (21 used for prediction)
PRED predicted values (max 10 best out of 286): MtWashington (0.25 #170, 0.06 #1110, 0.05 #1580), MtMarcy (0.25 #167, 0.06 #1107, 0.05 #1577), AlleghenyRiver (0.25 #109, 0.06 #1049, 0.05 #1519), HudsonRiver (0.25 #55, 0.06 #995, 0.05 #1465), MtMitchell (0.25 #44, 0.06 #984, 0.05 #1454), Fogo (0.14 #265, 0.08 #500, 0.06 #735), CerroChirripo (0.14 #367, 0.08 #602, 0.06 #1307), Irazu (0.14 #267, 0.08 #502, 0.06 #1207), PicoCristobalColon (0.14 #450, 0.08 #1646, 0.08 #2822), PicoTurquino (0.14 #415, 0.04 #2531, 0.03 #3238)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #170 for best value:
>> intensional similarity = 9
>> extensional distance = 2
>> proper extension:  Adirondacks; AppalachianMountains;
>> query: (?x431, MtWashington) <- ?x431[ is inMountains of ?x295[ a Source;]; is inMountains of ?x1362[ has locatedIn ?x902[ has ethnicGroup ?x79<Amerindian>; has wasDependentOf ?x149;];]; is inMountains of ?x1651[ a Mountain;];]

*> Best rule #1646 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 20
*> proper extension:  GreatDividingRange; Jura; Vogesen; WaldaiHills; Antitaurus;
*> query: (?x431, ?x282) <- ?x431[ is inMountains of ?x264[ a Source; is hasSource of ?x1049;]; is inMountains of ?x430[ a Source; has locatedIn ?x296[ has language ?x702; is locatedIn of ?x282;];];]
*> conf = 0.08
ranks of expected_values: 50, 69, 71, 194
EVAL	Andes	inMountains!	Tupungato	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.005	21.000	21.000	286.000	0.250	http://www.semwebtech.org/mondial/10/meta#inMountains
EVAL	Andes	inMountains!	Huallaga	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.020	21.000	21.000	286.000	0.250	http://www.semwebtech.org/mondial/10/meta#inMountains
EVAL	Andes	inMountains!	RioDesaguadero	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.015	21.000	21.000	286.000	0.250	http://www.semwebtech.org/mondial/10/meta#inMountains
EVAL	Andes	inMountains!	NevadodelHuila	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.014	21.000	21.000	286.000	0.250	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains!
PRED expected values: NevadodelHuila RioDesaguadero Huallaga Tupungato
=> 57 concepts (57 used for prediction)
PRED predicted values (max 10 best out of 286): Coropuna (0.33 #999, 0.31 #712, 0.26 #1664), Orinoco (0.33 #1266, 0.26 #1661, 0.19 #1662), Popocatepetl (0.33 #1170, 0.25 #2120, 0.20 #4015), NevadodeToluca (0.33 #1127, 0.25 #2077, 0.20 #3972), Iztaccihuatl (0.33 #1095, 0.25 #2045, 0.20 #3940), Citlaltepetl-PicodeOrizaba- (0.33 #1059, 0.25 #2009, 0.20 #3904), Luvua (0.33 #693, 0.25 #2592, 0.20 #4013), VictoriaNile (0.33 #690, 0.25 #2589, 0.20 #4010), Lukuga (0.33 #686, 0.25 #2585, 0.20 #4006), Chire (0.33 #684, 0.25 #2583, 0.20 #4004)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #999 for best value:
>> intensional similarity = 20
>> extensional distance = 1
>> proper extension:  CordilleraVolcanica;
>> query: (?x431, Coropuna) <- ?x431[ is inMountains of ?x914[ a Mountain; a Volcano; has type ?x706;]; is inMountains of ?x995[ a Mountain; a Volcano; has locatedIn ?x202[ has ethnicGroup ?x197; has government ?x435; has neighbor ?x690;];]; is inMountains of ?x2240[ has locatedIn ?x296<PE>;]; is inMountains of ?x2451[ a Mountain; has locatedIn ?x345[ a Country; has encompassed ?x521; has religion ?x95;];];]

*> Best rule #712 for first EXPECTED value:
*> intensional similarity = 23
*> extensional distance = 1
*> proper extension:  EastAfricanRift;
*> query: (?x431, ?x182) <- ?x431[ is inMountains of ?x209[ a Mountain; a Volcano; has type ?x706;]; is inMountains of ?x729[ a Source; has locatedIn ?x215; is hasSource of ?x1186[ has hasEstuary ?x2231;];]; is inMountains of ?x1161[ has locatedIn ?x379[ a Country; has ethnicGroup ?x197; has government ?x435; is locatedIn of ?x182; is neighbor of ?x404;];]; is inMountains of ?x1691[ has locatedIn ?x296[ has encompassed ?x521; is neighbor of ?x202;]; is hasSource of ?x987[ a River; is flowsInto of ?x1207;];];]
*> conf = 0.31
ranks of expected_values: 38, 46, 55, 71
EVAL	Andes	inMountains!	Tupungato	CNN-1.+1._MA	0.000	0.000	0.000	0.015	57.000	57.000	286.000	0.333	http://www.semwebtech.org/mondial/10/meta#inMountains
EVAL	Andes	inMountains!	Huallaga	CNN-1.+1._MA	0.000	0.000	0.000	0.019	57.000	57.000	286.000	0.333	http://www.semwebtech.org/mondial/10/meta#inMountains
EVAL	Andes	inMountains!	RioDesaguadero	CNN-1.+1._MA	0.000	0.000	0.000	0.026	57.000	57.000	286.000	0.333	http://www.semwebtech.org/mondial/10/meta#inMountains
EVAL	Andes	inMountains!	NevadodelHuila	CNN-1.+1._MA	0.000	0.000	0.000	0.022	57.000	57.000	286.000	0.333	http://www.semwebtech.org/mondial/10/meta#inMountains

#471-Save
PRED entity: Save

PRED relation: hasEstuary!
PRED expected values: Save
=> 35 concepts (26 used for prediction)
PRED predicted values (max 10 best out of 23): Theiss (0.14 #73, 0.10 #299, 0.07 #453), SouthernMorava (0.14 #173, 0.10 #399, 0.07 #453), Drina (0.14 #84, 0.10 #310, 0.07 #453), WesternMorava (0.14 #29, 0.10 #255, 0.07 #453), Morava (0.14 #3, 0.10 #229, 0.07 #453), Mur (0.10 #232, 0.02 #685), Drau (0.10 #289), Piva (0.10 #246), Donau (0.07 #453, 0.02 #683, 0.02 #1135), Save (0.07 #453, 0.02 #1135, 0.02 #1818)
>> best conf = 0.14
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #73 for best value:
>> intensional similarity = 3
>> extensional distance = 5
>> proper extension:  Morava; WesternMorava; Drina; SouthernMorava; Theiss;
>> query: (?x2175, Theiss) <- ?x2175[ a Estuary; has locatedIn ?x904<SRB>;]

*> Best rule #453 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 8
*> proper extension:  Mur; Drau; Piva;
*> query: (?x2175, ?x132) <- ?x2175[ a Estuary; has locatedIn ?x904[ a Country; has ethnicGroup ?x164; has language ?x684; is locatedIn of ?x132; is locatedIn of ?x152<Save>;];]
*> conf = 0.07
ranks of expected_values: 10
EVAL	Save	hasEstuary!	Save	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.100	35.000	26.000	23.000	0.143	http://www.semwebtech.org/mondial/10/meta#hasEstuary

PRED relation: hasEstuary!
PRED expected values: Save
=> 94 concepts (94 used for prediction)
PRED predicted values (max 10 best out of 163): Drina (0.14 #84, 0.14 #2506, 0.11 #5032), Morava (0.14 #3, 0.14 #2506, 0.11 #5032), Theiss (0.14 #73, 0.14 #2506, 0.11 #5032), SouthernMorava (0.14 #173, 0.14 #2506, 0.11 #5032), WesternMorava (0.14 #29, 0.14 #2506, 0.11 #5032), Save (0.14 #2506, 0.11 #5032, 0.11 #5031), Donau (0.14 #2506, 0.11 #5032, 0.11 #5031), Maas (0.10 #311, 0.01 #3740, 0.01 #4427), Rhein (0.10 #239, 0.01 #3668, 0.01 #4355), Thames (0.10 #385, 0.01 #3814)
>> best conf = 0.14
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #84 for best value:
>> intensional similarity = 3
>> extensional distance = 5
>> proper extension:  Morava; WesternMorava; Drina; SouthernMorava; Theiss;
>> query: (?x2175, Drina) <- ?x2175[ a Estuary; has locatedIn ?x904<SRB>;]

*> Best rule #2506 for first EXPECTED value:
*> intensional similarity = 12
*> extensional distance = 52
*> proper extension:  Bahrel-Ghasal; Bahrel-Djebel-Albert-Nil; Sobat;
*> query: (?x2175, ?x1489) <- ?x2175[ a Estuary; has locatedIn ?x904[ a Country; has government ?x435<"republic">; has neighbor ?x55; is locatedIn of ?x1343[ a Source;]; is locatedIn of ?x1489[ a River; has hasSource ?x2099;]; is neighbor of ?x236;];]
*> conf = 0.14
ranks of expected_values: 6
EVAL	Save	hasEstuary!	Save	CNN-1.+1._MA	0.000	0.000	1.000	0.167	94.000	94.000	163.000	0.143	http://www.semwebtech.org/mondial/10/meta#hasEstuary

#470-Niger
PRED entity: Niger

PRED relation: flowsInto
PRED expected values: AtlanticOcean
=> 42 concepts (32 used for prediction)
PRED predicted values (max 10 best out of 106): AtlanticOcean (0.33 #12, 0.25 #178, 0.21 #510), PacificOcean (0.21 #523, 0.08 #690, 0.04 #1690), MediterraneanSea (0.08 #688, 0.07 #521, 0.04 #2693), Po (0.08 #740, 0.02 #1240, 0.02 #2244), BalticSea (0.07 #842, 0.06 #1175, 0.06 #1508), Donau (0.07 #3174, 0.07 #3009, 0.07 #2844), BlackSea (0.07 #501, 0.04 #668, 0.03 #835), Mississippi (0.07 #533, 0.04 #700, 0.01 #867), MurrayRiver (0.07 #624, 0.04 #791, 0.01 #1124), LakeJindabyne (0.07 #634, 0.04 #801, 0.01 #1134)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #12 for best value:
>> intensional similarity = 6
>> extensional distance = 1
>> proper extension:  Senegal;
>> query: (?x580, AtlanticOcean) <- ?x580[ a River; has hasEstuary ?x2393[ a Estuary;]; has hasSource ?x965; has locatedIn ?x651<RG>;]
ranks of expected_values: 1
EVAL	Niger	flowsInto	AtlanticOcean	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	42.000	32.000	106.000	0.333	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto
PRED expected values: AtlanticOcean
=> 116 concepts (116 used for prediction)
PRED predicted values (max 10 best out of 175): AtlanticOcean (0.60 #1004, 0.60 #850, 0.50 #1841), PacificOcean (0.29 #1030, 0.25 #2034, 0.18 #2535), Niger (0.25 #565, 0.20 #3075, 0.14 #1400), Zaire (0.17 #5288, 0.11 #2267, 0.11 #5960), ChadLake (0.14 #1005, 0.13 #11902, 0.09 #1842), BlackSea (0.14 #1008, 0.12 #2012, 0.09 #2513), LakeJindabyne (0.14 #1141, 0.12 #2145, 0.08 #2814), Senegal (0.14 #1005, 0.09 #671, 0.09 #1842), Gambia (0.14 #1005, 0.09 #671, 0.09 #1842), Niger (0.14 #1005, 0.09 #671, 0.09 #1842)
>> best conf = 0.60
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1004 for best value:
>> intensional similarity = 12
>> extensional distance = 3
>> proper extension:  Gambia;
>> query: (?x580, ?x182) <- ?x580[ a River; has hasEstuary ?x2393; has locatedIn ?x651[ has encompassed ?x213; has ethnicGroup ?x1685; has government ?x435; has religion ?x116<Christian>; has wasDependentOf ?x78; is locatedIn of ?x182<AtlanticOcean>; is neighbor of ?x416;];]
>> Best rule #850 for best value:
>> intensional similarity = 12
>> extensional distance = 3
>> proper extension:  Gambia;
>> query: (?x580, AtlanticOcean) <- ?x580[ a River; has hasEstuary ?x2393; has locatedIn ?x651[ has encompassed ?x213; has ethnicGroup ?x1685; has government ?x435; has religion ?x116<Christian>; has wasDependentOf ?x78; is locatedIn of ?x182<AtlanticOcean>; is neighbor of ?x416;];]
ranks of expected_values: 1
EVAL	Niger	flowsInto	AtlanticOcean	CNN-1.+1._MA	1.000	1.000	1.000	1.000	116.000	116.000	175.000	0.600	http://www.semwebtech.org/mondial/10/meta#flowsInto

#469-MEX
PRED entity: MEX

PRED relation: locatedIn!
PRED expected values: RioLerma
=> 38 concepts (37 used for prediction)
PRED predicted values (max 10 best out of 1407): AtlanticOcean (0.82 #15515, 0.63 #9887, 0.59 #11294), RioGrande (0.52 #15472, 0.48 #28134, 0.25 #839), Colorado (0.52 #15472, 0.48 #28134, 0.25 #732), IndianOcean (0.30 #5628, 0.16 #19695, 0.15 #21099), ColumbiaRiver (0.25 #1142, 0.20 #36575, 0.19 #43608), LakePowell (0.25 #1029, 0.20 #36575, 0.19 #43608), LakeMead (0.25 #657, 0.20 #36575, 0.19 #43608), Mississippi (0.25 #128, 0.20 #36575, 0.19 #43608), BeringSea (0.25 #384, 0.15 #21099, 0.12 #33762), ArcticOcean (0.25 #74, 0.14 #4293, 0.10 #5699)
>> best conf = 0.82
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #15515 for best value:
>> intensional similarity = 4
>> extensional distance = 59
>> proper extension:  GROX; FALK;
>> query: (?x482, AtlanticOcean) <- ?x482[ has ethnicGroup ?x79; is locatedIn of ?x1371[ is locatedInWater of ?x1928<Cuba>;];]

*> Best rule #36575 for first EXPECTED value:
*> intensional similarity = 4
*> extensional distance = 116
*> proper extension:  AZ;
*> query: (?x482, ?x602) <- ?x482[ has ethnicGroup ?x79; is locatedIn of ?x282[ is flowsInto of ?x602;]; is neighbor of ?x181;]
*> conf = 0.20
ranks of expected_values: 122
EVAL	MEX	locatedIn!	RioLerma	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.008	38.000	37.000	1407.000	0.820	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: RioLerma
=> 94 concepts (91 used for prediction)
PRED predicted values (max 10 best out of 1421): RioLerma (0.90 #68998, 0.76 #8445, 0.75 #83087), AtlanticOcean (0.83 #98623, 0.82 #78905, 0.73 #54958), LakePowell (0.69 #76046, 0.60 #45056, 0.55 #59141), LakeMead (0.69 #76046, 0.60 #45056, 0.55 #59141), RioSanJuan (0.50 #5731, 0.41 #15483, 0.33 #9955), RioSanJuan (0.50 #5875, 0.33 #10099, 0.33 #1654), LakeNicaragua (0.50 #5730, 0.33 #9954, 0.33 #1509), Colorado (0.45 #76045, 0.33 #3550, 0.27 #8449), ColumbiaRiver (0.41 #15483, 0.33 #3960, 0.29 #16891), Mississippi (0.41 #15483, 0.33 #2946, 0.29 #16891)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #68998 for best value:
>> intensional similarity = 9
>> extensional distance = 34
>> proper extension:  LS; CH;
>> query: (?x482, ?x602) <- ?x482[ has ethnicGroup ?x79; has religion ?x95; is locatedIn of ?x282[ has locatedIn ?x902[ has language ?x796;];]; is locatedIn of ?x1216[ a Mountain;]; is locatedIn of ?x1346[ is hasSource of ?x602;];]
ranks of expected_values: 1
EVAL	MEX	locatedIn!	RioLerma	CNN-1.+1._MA	1.000	1.000	1.000	1.000	94.000	91.000	1421.000	0.905	http://www.semwebtech.org/mondial/10/meta#locatedIn

#468-Faial
PRED entity: Faial

PRED relation: belongsToIslands
PRED expected values: Azores
=> 70 concepts (70 used for prediction)
PRED predicted values (max 10 best out of 52): Azores (0.70 #140, 0.67 #4, 0.64 #208), LesserAntilles (0.32 #695, 0.29 #491, 0.27 #423), LipariIslands (0.19 #546, 0.10 #818, 0.10 #1226), Madeira (0.19 #1429, 0.18 #2314, 0.17 #3882), Canares (0.17 #431, 0.16 #499, 0.16 #567), SundaIslands (0.12 #558, 0.12 #626, 0.10 #1851), Japan (0.12 #570, 0.06 #978, 0.05 #1318), HawaiiIslands (0.10 #845, 0.10 #913, 0.09 #981), InnerHebrides (0.07 #1220, 0.03 #472, 0.03 #540), CapeVerdes (0.07 #451, 0.06 #519, 0.06 #723)
>> best conf = 0.70
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #140 for best value:
>> intensional similarity = 5
>> extensional distance = 8
>> proper extension:  SaoMiguel;
>> query: (?x1162, Azores) <- ?x1162[ a Island; has locatedIn ?x1027<P>; has type ?x150<"volcanic">;]
ranks of expected_values: 1
EVAL	Faial	belongsToIslands	Azores	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	70.000	70.000	52.000	0.700	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

PRED relation: belongsToIslands
PRED expected values: Azores
=> 166 concepts (166 used for prediction)
PRED predicted values (max 10 best out of 63): Azores (0.70 #140, 0.67 #4, 0.64 #276), Madeira (0.60 #4295, 0.59 #4226, 0.55 #1021), LesserAntilles (0.35 #763, 0.33 #967, 0.32 #1921), Canares (0.31 #703, 0.22 #1113, 0.19 #1181), SundaIslands (0.30 #1444, 0.24 #2534, 0.22 #3078), LipariIslands (0.23 #1160, 0.19 #1636, 0.17 #1976), CanadianArcticIslands (0.14 #2460, 0.12 #3004, 0.09 #3346), CapeVerdes (0.12 #655, 0.12 #791, 0.10 #927), HawaiiIslands (0.11 #3571, 0.11 #4118, 0.10 #3707), MoluccanIslands (0.10 #1454, 0.07 #2476, 0.07 #2544)
>> best conf = 0.70
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #140 for best value:
>> intensional similarity = 5
>> extensional distance = 8
>> proper extension:  SaoMiguel;
>> query: (?x1162, Azores) <- ?x1162[ a Island; has locatedIn ?x1027<P>; has type ?x150<"volcanic">;]
ranks of expected_values: 1
EVAL	Faial	belongsToIslands	Azores	CNN-1.+1._MA	1.000	1.000	1.000	1.000	166.000	166.000	63.000	0.700	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

#467-PhilipineLanguage
PRED entity: PhilipineLanguage

PRED relation: language!
PRED expected values: NMIS GUAM
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 60): PK (0.08 #8), SF (0.07 #79), IR (0.07 #45), BZ (0.06 #88), AND (0.05 #97), L (0.05 #93), MK (0.05 #92), NZ (0.05 #70), I (0.05 #30), NLSM (0.05 #1)
>> best conf = 0.08
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #8 for best value:
>> intensional similarity = 1
>> extensional distance = 102
>> proper extension:  French; Japanese; Samoan; English; Tajik; KhalkhaMongol; Italian; Turkish; Portuguese; Dutch; ...
>> query: (?x1336, PK) <- ?x1336[ a Language;]

*> Best rule #48 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 102
*> proper extension:  French; Japanese; Samoan; English; Tajik; KhalkhaMongol; Italian; Turkish; Portuguese; Dutch; ...
*> query: (?x1336, NMIS) <- ?x1336[ a Language;]
*> conf = 0.04
ranks of expected_values: 14
EVAL	PhilipineLanguage	language!	GUAM	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	2.000	2.000	60.000	0.077	http://www.semwebtech.org/mondial/10/meta#language
EVAL	PhilipineLanguage	language!	NMIS	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.071	2.000	2.000	60.000	0.077	http://www.semwebtech.org/mondial/10/meta#language

PRED relation: language!
PRED expected values: NMIS GUAM
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 60): PK (0.08 #8), SF (0.07 #79), IR (0.07 #45), BZ (0.06 #88), AND (0.05 #97), L (0.05 #93), MK (0.05 #92), NZ (0.05 #70), I (0.05 #30), NLSM (0.05 #1)
>> best conf = 0.08
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #8 for best value:
>> intensional similarity = 1
>> extensional distance = 102
>> proper extension:  French; Japanese; Samoan; English; Tajik; KhalkhaMongol; Italian; Turkish; Portuguese; Dutch; ...
>> query: (?x1336, PK) <- ?x1336[ a Language;]

*> Best rule #48 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 102
*> proper extension:  French; Japanese; Samoan; English; Tajik; KhalkhaMongol; Italian; Turkish; Portuguese; Dutch; ...
*> query: (?x1336, NMIS) <- ?x1336[ a Language;]
*> conf = 0.04
ranks of expected_values: 14
EVAL	PhilipineLanguage	language!	GUAM	CNN-1.+1._MA	0.000	0.000	0.000	0.000	2.000	2.000	60.000	0.077	http://www.semwebtech.org/mondial/10/meta#language
EVAL	PhilipineLanguage	language!	NMIS	CNN-1.+1._MA	0.000	0.000	0.000	0.071	2.000	2.000	60.000	0.077	http://www.semwebtech.org/mondial/10/meta#language

#466-RMM
PRED entity: RMM

PRED relation: neighbor
PRED expected values: BF
=> 48 concepts (48 used for prediction)
PRED predicted values (max 10 best out of 183): BF (0.90 #2524, 0.90 #2207, 0.90 #2206), RMM (0.50 #598, 0.40 #754, 0.33 #1070), LB (0.33 #884, 0.33 #414, 0.31 #2050), LAR (0.33 #301, 0.33 #144, 0.31 #2050), WAN (0.33 #961, 0.33 #18, 0.31 #2050), WSA (0.33 #262, 0.31 #2050, 0.31 #2525), TN (0.33 #168, 0.31 #2050, 0.31 #2525), BEN (0.33 #122, 0.31 #2050, 0.31 #2525), GNB (0.33 #467, 0.31 #2050, 0.31 #2525), WAL (0.33 #457, 0.31 #2050, 0.31 #2525)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2524 for best value:
>> intensional similarity = 6
>> extensional distance = 56
>> proper extension:  ROK; PNG;
>> query: (?x839, ?x811) <- ?x839[ is locatedIn of ?x1618[ has type ?x578;]; is neighbor of ?x515[ is locatedIn of ?x182;]; is neighbor of ?x811[ is neighbor of ?x810;];]
ranks of expected_values: 1
EVAL	RMM	neighbor	BF	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	48.000	48.000	183.000	0.904	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: BF
=> 89 concepts (89 used for prediction)
PRED predicted values (max 10 best out of 219): BF (0.93 #2566, 0.93 #2563, 0.92 #2564), WAL (0.50 #1581, 0.46 #161, 0.38 #1600), RMM (0.49 #10502, 0.49 #10501, 0.48 #10500), WAN (0.49 #10502, 0.49 #10501, 0.48 #10500), GNB (0.46 #161, 0.38 #1600, 0.36 #3534), LAR (0.46 #161, 0.38 #1600, 0.36 #3534), LB (0.46 #161, 0.38 #1600, 0.36 #3534), TN (0.46 #161, 0.38 #1600, 0.36 #3534), BEN (0.46 #161, 0.38 #1600, 0.36 #3534), MA (0.46 #161, 0.38 #1600, 0.36 #3534)
>> best conf = 0.93
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2566 for best value:
>> intensional similarity = 14
>> extensional distance = 10
>> proper extension:  TCH;
>> query: (?x839, ?x1206) <- ?x839[ has religion ?x116; has wasDependentOf ?x78; is neighbor of ?x581[ has encompassed ?x213; has ethnicGroup ?x197; is locatedIn of ?x84;]; is neighbor of ?x651[ has ethnicGroup ?x1685; has neighbor ?x1072[ a Country;]; is locatedIn of ?x580<Niger>;]; is neighbor of ?x1206[ is neighbor of ?x483;];]
>> Best rule #2563 for best value:
>> intensional similarity = 15
>> extensional distance = 10
>> proper extension:  TCH;
>> query: (?x839, ?x515) <- ?x839[ has religion ?x116; has wasDependentOf ?x78; is neighbor of ?x515; is neighbor of ?x581[ has encompassed ?x213; has ethnicGroup ?x197; is locatedIn of ?x84;]; is neighbor of ?x651[ has ethnicGroup ?x1685; has neighbor ?x1072[ a Country;]; is locatedIn of ?x580<Niger>;]; is neighbor of ?x1206[ is neighbor of ?x483;];]
ranks of expected_values: 1
EVAL	RMM	neighbor	BF	CNN-1.+1._MA	1.000	1.000	1.000	1.000	89.000	89.000	219.000	0.929	http://www.semwebtech.org/mondial/10/meta#neighbor

#465-Pruth
PRED entity: Pruth

PRED relation: locatedIn
PRED expected values: RO
=> 36 concepts (26 used for prediction)
PRED predicted values (max 10 best out of 176): RO (0.91 #3756, 0.64 #506, 0.50 #2812), R (0.67 #1879, 0.61 #2347, 0.36 #3761), SK (0.50 #2812, 0.49 #2813, 0.28 #469), H (0.50 #2812, 0.49 #2813, 0.28 #469), D (0.50 #2812, 0.49 #2813, 0.20 #20), A (0.50 #2812, 0.49 #2813, 0.20 #98), SRB (0.50 #2812, 0.49 #2813, 0.20 #183), HR (0.50 #2812, 0.49 #2813, 0.20 #29), BG (0.49 #2813, 0.20 #38, 0.18 #507), SF (0.42 #834, 0.17 #2005, 0.13 #2577)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3756 for best value:
>> intensional similarity = 6
>> extensional distance = 194
>> proper extension:  Leine; Sobat;
>> query: (?x1292, ?x176) <- ?x1292[ a River; has hasEstuary ?x2078[ a Estuary; has locatedIn ?x176;]; has hasSource ?x2271[ a Source;];]
ranks of expected_values: 1
EVAL	Pruth	locatedIn	RO	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	36.000	26.000	176.000	0.906	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: RO
=> 141 concepts (114 used for prediction)
PRED predicted values (max 10 best out of 223): RO (0.88 #5191, 0.87 #5904, 0.78 #945), R (0.82 #11829, 0.77 #4725, 0.73 #5197), USA (0.66 #14976, 0.53 #15688, 0.43 #8585), D (0.63 #12080, 0.41 #6162, 0.33 #8057), A (0.53 #6478, 0.48 #1985, 0.30 #6377), SRB (0.46 #1598, 0.37 #1833, 0.33 #183), BY (0.38 #764, 0.30 #1180, 0.27 #999), H (0.36 #1004, 0.33 #59, 0.30 #6377), SK (0.30 #6377, 0.30 #1180, 0.29 #472), HR (0.30 #6377, 0.29 #472, 0.27 #974)
>> best conf = 0.88
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5191 for best value:
>> intensional similarity = 11
>> extensional distance = 38
>> proper extension:  Moraca;
>> query: (?x1292, ?x176) <- ?x1292[ a River; has flowsInto ?x133; has hasEstuary ?x2078[ has locatedIn ?x176;]; has hasSource ?x2271[ a Source; has inMountains ?x1675; has locatedIn ?x303[ has ethnicGroup ?x58; has government ?x435; has neighbor ?x73;];];]
ranks of expected_values: 1
EVAL	Pruth	locatedIn	RO	CNN-1.+1._MA	1.000	1.000	1.000	1.000	141.000	114.000	223.000	0.878	http://www.semwebtech.org/mondial/10/meta#locatedIn

#464-LesserAntilles
PRED entity: LesserAntilles

PRED relation: belongsToIslands!
PRED expected values: Curacao Tortola
=> 17 concepts (17 used for prediction)
PRED predicted values (max 10 best out of 201): Cuba (0.33 #148, 0.12 #685, 0.07 #1043), PuertoRico (0.33 #121, 0.12 #658, 0.07 #1016), Santiago (0.17 #326, 0.14 #505, 0.12 #863), SaoJorge (0.17 #262, 0.14 #441, 0.12 #799), Madeira (0.17 #253, 0.14 #432, 0.12 #790), SantaMaria (0.17 #247, 0.14 #426, 0.12 #784), Graciosa (0.17 #232, 0.14 #411, 0.12 #769), Terceira (0.17 #229, 0.14 #408, 0.12 #766), Corvo (0.17 #220, 0.14 #399, 0.12 #757), Fogo (0.17 #197, 0.14 #376, 0.12 #734)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #148 for best value:
>> intensional similarity = 8
>> extensional distance = 1
>> proper extension:  GreaterAntilles;
>> query: (?x877, Cuba) <- ?x877[ is belongsToIslands of ?x609[ a Island; is locatedOnIsland of ?x1972;]; is belongsToIslands of ?x2161[ has locatedInWater ?x182<AtlanticOcean>; has locatedInWater ?x317<CaribbeanSea>;];]

*> Best rule #1794 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 16
*> proper extension:  CanadianArcticIslands;
*> query: (?x877, ?x112) <- ?x877[ is belongsToIslands of ?x609[ is locatedOnIsland of ?x1972;]; is belongsToIslands of ?x2161[ has locatedInWater ?x182[ a Sea; has mergesWith ?x60; is flowsInto of ?x137; is locatedInWater of ?x112;];];]
*> conf = 0.06
ranks of expected_values: 56, 66
EVAL	LesserAntilles	belongsToIslands!	Tortola	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.018	17.000	17.000	201.000	0.333	http://www.semwebtech.org/mondial/10/meta#belongsToIslands
EVAL	LesserAntilles	belongsToIslands!	Curacao	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.015	17.000	17.000	201.000	0.333	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

PRED relation: belongsToIslands!
PRED expected values: Curacao Tortola
=> 30 concepts (30 used for prediction)
PRED predicted values (max 10 best out of 206): Cuba (0.33 #328, 0.25 #507, 0.17 #687), PuertoRico (0.33 #301, 0.25 #480, 0.17 #660), Isabela (0.33 #63), GrandCayman (0.25 #438, 0.09 #2527, 0.09 #1080), CaymanBrac (0.25 #394, 0.09 #2527, 0.09 #1080), Santiago (0.17 #866, 0.17 #686, 0.14 #1047), Fuerteventura (0.17 #862, 0.17 #682, 0.14 #1043), Gomera (0.17 #858, 0.17 #678, 0.14 #1039), Teneriffa (0.17 #848, 0.17 #668, 0.14 #1029), Lanzarote (0.17 #838, 0.17 #658, 0.14 #1019)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #328 for best value:
>> intensional similarity = 16
>> extensional distance = 1
>> proper extension:  GreaterAntilles;
>> query: (?x877, Cuba) <- ?x877[ a Islands; is belongsToIslands of ?x123[ a Island; has locatedInWater ?x182<AtlanticOcean>; has locatedInWater ?x317<CaribbeanSea>;]; is belongsToIslands of ?x727[ a Island; has locatedIn ?x407[ a Country; has language ?x247;];]; is belongsToIslands of ?x1847[ has locatedIn ?x745[ has dependentOf ?x78;]; is locatedOnIsland of ?x1846;];]

*> Best rule #2527 for first EXPECTED value:
*> intensional similarity = 22
*> extensional distance = 9
*> proper extension:  CanadianArcticIslands;
*> query: (?x877, ?x506) <- ?x877[ a Islands; is belongsToIslands of ?x123[ a Island; has locatedInWater ?x182[ a Sea; has locatedIn ?x77; has mergesWith ?x60;]; has locatedInWater ?x317[ a Sea; has locatedIn ?x50; is locatedInWater of ?x506; is mergesWith of ?x1371;]; is locatedOnIsland of ?x1806;]; is belongsToIslands of ?x1117[ a Island; has locatedIn ?x667;]; is belongsToIslands of ?x1847[ has locatedIn ?x745[ has ethnicGroup ?x298; has government ?x828; has religion ?x352;]; is locatedOnIsland of ?x1846;];]
*> conf = 0.09
ranks of expected_values: 103, 123
EVAL	LesserAntilles	belongsToIslands!	Tortola	CNN-1.+1._MA	0.000	0.000	0.000	0.010	30.000	30.000	206.000	0.333	http://www.semwebtech.org/mondial/10/meta#belongsToIslands
EVAL	LesserAntilles	belongsToIslands!	Curacao	CNN-1.+1._MA	0.000	0.000	0.000	0.008	30.000	30.000	206.000	0.333	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

#463-BeringSea
PRED entity: BeringSea

PRED relation: mergesWith!
PRED expected values: ArcticOcean
=> 29 concepts (26 used for prediction)
PRED predicted values (max 10 best out of 246): ArcticOcean (0.83 #469, 0.83 #470, 0.83 #389), BeringSea (0.46 #273, 0.45 #511, 0.45 #234), JavaSea (0.40 #7, 0.19 #47, 0.15 #163), AtlanticOcean (0.31 #46, 0.27 #318, 0.26 #162), SeaofJapan (0.20 #14, 0.19 #54, 0.17 #391), SulawesiSea (0.20 #23, 0.19 #63, 0.17 #391), EastChinaSea (0.20 #22, 0.17 #391, 0.17 #471), YellowSea (0.20 #13, 0.12 #53, 0.07 #169), IndianOcean (0.19 #41, 0.19 #157, 0.17 #391), BandaSea (0.19 #65, 0.17 #391, 0.17 #471)
>> best conf = 0.83
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #469 for best value:
>> intensional similarity = 4
>> extensional distance = 39
>> proper extension:  SeaofAzov; BlackSea; RedSea;
>> query: (?x809, ?x263) <- ?x809[ has mergesWith ?x263; has mergesWith ?x282[ has locatedIn ?x428[ has religion ?x429;];];]
ranks of expected_values: 1
EVAL	BeringSea	mergesWith!	ArcticOcean	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	29.000	26.000	246.000	0.833	http://www.semwebtech.org/mondial/10/meta#mergesWith

PRED relation: mergesWith!
PRED expected values: ArcticOcean
=> 117 concepts (117 used for prediction)
PRED predicted values (max 10 best out of 392): ArcticOcean (0.84 #1112, 0.83 #1111, 0.82 #952), BeringSea (0.46 #1353, 0.33 #27, 0.22 #199), JavaSea (0.44 #165, 0.33 #126, 0.29 #361), SeaofJapan (0.40 #53, 0.33 #133, 0.33 #14), IndianOcean (0.33 #79, 0.33 #1, 0.29 #355), EastChinaSea (0.33 #22, 0.22 #180, 0.22 #199), BandaSea (0.33 #25, 0.22 #199, 0.21 #394), SulawesiSea (0.33 #23, 0.22 #199, 0.21 #377), SeaofOkhotsk (0.33 #21, 0.22 #199, 0.18 #118), SuluSea (0.33 #24, 0.22 #199, 0.17 #1113)
>> best conf = 0.84
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1112 for best value:
>> intensional similarity = 8
>> extensional distance = 34
>> proper extension:  SuluSea;
>> query: (?x809, ?x263) <- ?x809[ a Sea; has locatedIn ?x315[ a Country; has ethnicGroup ?x79; has neighbor ?x482;]; has mergesWith ?x263[ a Sea; has mergesWith ?x248;];]
ranks of expected_values: 1
EVAL	BeringSea	mergesWith!	ArcticOcean	CNN-1.+1._MA	1.000	1.000	1.000	1.000	117.000	117.000	392.000	0.835	http://www.semwebtech.org/mondial/10/meta#mergesWith

#462-Philipines
PRED entity: Philipines

PRED relation: belongsToIslands!
PRED expected values: Leyte
=> 18 concepts (18 used for prediction)
PRED predicted values (max 10 best out of 211): Sumatra (0.33 #48, 0.26 #576, 0.25 #624), Labuan (0.33 #108, 0.26 #576, 0.25 #684), Bangka (0.33 #57, 0.26 #576, 0.25 #633), Sulawesi (0.33 #70, 0.25 #646, 0.25 #454), Borneo (0.33 #14, 0.25 #590, 0.25 #398), Lombok (0.33 #143, 0.25 #719, 0.25 #527), Timor (0.33 #90, 0.25 #666, 0.25 #474), Sumbawa (0.33 #87, 0.25 #663, 0.25 #471), Bali (0.33 #76, 0.25 #652, 0.25 #460), Krakatau (0.33 #12, 0.25 #588, 0.25 #396)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #48 for best value:
>> intensional similarity = 18
>> extensional distance = 1
>> proper extension:  SundaIslands;
>> query: (?x370, Sumatra) <- ?x370[ a Islands; is belongsToIslands of ?x824[ a Island; has locatedInWater ?x384<SouthChinaSea>;]; is belongsToIslands of ?x880[ a Island; has locatedInWater ?x625<SulawesiSea>;]; is belongsToIslands of ?x1575[ has locatedIn ?x460[ has encompassed ?x175; has ethnicGroup ?x298; has government ?x435; has religion ?x95<Protestant>; has religion ?x352<RomanCatholic>;];];]

*> Best rule #959 for first EXPECTED value:
*> intensional similarity = 13
*> extensional distance = 3
*> proper extension:  MoluccanIslands;
*> query: (?x370, ?x716) <- ?x370[ a Islands; is belongsToIslands of ?x624[ a Island;]; is belongsToIslands of ?x824[ has locatedInWater ?x384[ a Sea; has locatedIn ?x91; has mergesWith ?x241<JavaSea>; has mergesWith ?x282<PacificOcean>; is locatedInWater of ?x716; is mergesWith of ?x620;];];]
*> conf = 0.24
ranks of expected_values: 27
EVAL	Philipines	belongsToIslands!	Leyte	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.037	18.000	18.000	211.000	0.333	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

PRED relation: belongsToIslands!
PRED expected values: Leyte
=> 40 concepts (37 used for prediction)
PRED predicted values (max 10 best out of 208): Leyte (0.47 #6941, 0.31 #5786, 0.30 #2311), Sumatra (0.33 #240, 0.29 #191, 0.25 #625), Labuan (0.33 #300, 0.29 #191, 0.25 #685), Bangka (0.33 #249, 0.29 #191, 0.25 #634), Sulawesi (0.33 #262, 0.25 #647, 0.25 #454), Borneo (0.33 #206, 0.25 #591, 0.25 #398), Lombok (0.33 #335, 0.25 #720, 0.25 #527), Timor (0.33 #282, 0.25 #667, 0.25 #474), Sumbawa (0.33 #279, 0.25 #664, 0.25 #471), Bali (0.33 #268, 0.25 #653, 0.25 #460)
>> best conf = 0.47
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #6941 for best value:
>> intensional similarity = 22
>> extensional distance = 40
>> proper extension:  TurksandCaicosIslands;
>> query: (?x370, ?x765) <- ?x370[ a Islands; is belongsToIslands of ?x624[ a Island; has locatedIn ?x460[ a Country; has encompassed ?x175; has ethnicGroup ?x298; has government ?x435; has religion ?x95; is locatedIn of ?x765[ has locatedInWater ?x625[ a Sea; has mergesWith ?x677[ is mergesWith of ?x384[ a Sea; has locatedIn ?x91; is flowsInto of ?x1152; is locatedInWater of ?x1575[ a Island;]; is mergesWith of ?x241;];]; is locatedInWater of ?x1575;];];];]; is belongsToIslands of ?x1575;]
ranks of expected_values: 1
EVAL	Philipines	belongsToIslands!	Leyte	CNN-1.+1._MA	1.000	1.000	1.000	1.000	40.000	37.000	208.000	0.468	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

#461-Mincio
PRED entity: Mincio

PRED relation: hasSource!
PRED expected values: Mincio
=> 36 concepts (33 used for prediction)
PRED predicted values (max 10 best out of 117): Drau (0.17 #64, 0.12 #292, 0.08 #520), Adda (0.17 #112, 0.12 #340, 0.08 #568), Etsch (0.17 #87, 0.12 #315, 0.08 #543), Po (0.17 #74, 0.12 #302, 0.08 #530), Arno (0.12 #429, 0.07 #3889, 0.06 #885), Tiber (0.12 #335, 0.07 #3889, 0.06 #791), Ticino (0.08 #2973, 0.08 #2972, 0.07 #3889), Salzach (0.08 #2973, 0.08 #2972, 0.06 #852), Isar (0.08 #2973, 0.08 #2972, 0.06 #811), Lech (0.08 #2973, 0.08 #2972, 0.06 #778)
>> best conf = 0.17
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #64 for best value:
>> intensional similarity = 5
>> extensional distance = 4
>> proper extension:  Po; Etsch; Drau; Adda;
>> query: (?x437, Drau) <- ?x437[ a Source; has inMountains ?x261<Alps>; has locatedIn ?x207<I>;]

*> Best rule #3889 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 217
*> proper extension:  Bahrel-Ghasal; Sobat; Bani; SchattalArab; Luapula; Kasai; Cuilo; Zambezi; Okavango; WhiteNile; ...
*> query: (?x437, ?x614) <- ?x437[ a Source; has locatedIn ?x207[ has neighbor ?x78; is locatedIn of ?x614[ a River;];];]
*> conf = 0.07
ranks of expected_values: 20
EVAL	Mincio	hasSource!	Mincio	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.050	36.000	33.000	117.000	0.167	http://www.semwebtech.org/mondial/10/meta#hasSource

PRED relation: hasSource!
PRED expected values: Mincio
=> 144 concepts (144 used for prediction)
PRED predicted values (max 10 best out of 221): Drau (0.17 #64, 0.12 #292, 0.12 #4815), Adda (0.17 #112, 0.12 #340, 0.12 #4815), Po (0.17 #74, 0.12 #302, 0.12 #4815), Etsch (0.17 #87, 0.12 #315, 0.12 #4815), Arno (0.12 #429, 0.12 #4815, 0.11 #7571), Tiber (0.12 #335, 0.12 #4815, 0.11 #7571), Ticino (0.12 #4815, 0.12 #6653, 0.12 #6652), Mincio (0.12 #4815, 0.11 #7571, 0.03 #7572), Save (0.12 #6653, 0.12 #6652, 0.12 #6651), Isere (0.12 #6653, 0.12 #6652, 0.12 #6651)
>> best conf = 0.17
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #64 for best value:
>> intensional similarity = 5
>> extensional distance = 4
>> proper extension:  Po; Etsch; Drau; Adda;
>> query: (?x437, Drau) <- ?x437[ a Source; has inMountains ?x261<Alps>; has locatedIn ?x207<I>;]

*> Best rule #4815 for first EXPECTED value:
*> intensional similarity = 12
*> extensional distance = 57
*> proper extension:  JoekulsaaFjoellum; Thjorsa;
*> query: (?x437, ?x836) <- ?x437[ a Source; has locatedIn ?x207[ has government ?x435; has language ?x635[ a Language;]; has religion ?x56; is locatedIn of ?x166[ a Mountain;]; is locatedIn of ?x341[ a Island;]; is locatedIn of ?x836[ a River;];];]
*> conf = 0.12
ranks of expected_values: 8
EVAL	Mincio	hasSource!	Mincio	CNN-1.+1._MA	0.000	0.000	1.000	0.125	144.000	144.000	221.000	0.167	http://www.semwebtech.org/mondial/10/meta#hasSource

#460-RP
PRED entity: RP

PRED relation: wasDependentOf
PRED expected values: USA
=> 43 concepts (43 used for prediction)
PRED predicted values (max 10 best out of 29): GB (0.43 #92, 0.33 #508, 0.32 #122), NL (0.33 #18, 0.25 #47, 0.12 #77), E (0.26 #244, 0.24 #302, 0.23 #333), F (0.19 #447, 0.14 #774, 0.14 #805), SovietUnion (0.10 #377, 0.10 #168, 0.07 #465), UnitedNations (0.09 #817, 0.08 #371, 0.07 #133), CN (0.07 #99, 0.04 #1199, 0.03 #1105), J (0.06 #213, 0.05 #362, 0.03 #1105), P (0.05 #467, 0.04 #1199, 0.03 #1105), Yugoslavia (0.05 #764, 0.04 #616, 0.04 #1199)
>> best conf = 0.43
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #92 for best value:
>> intensional similarity = 5
>> extensional distance = 12
>> proper extension:  MNG;
>> query: (?x460, GB) <- ?x460[ has religion ?x187<Muslim>; has religion ?x462<Buddhist>; is locatedIn of ?x384;]

No rule for expected values
ranks of expected_values: 
EVAL	RP	wasDependentOf	USA	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	43.000	43.000	29.000	0.429	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

PRED relation: wasDependentOf
PRED expected values: USA
=> 109 concepts (109 used for prediction)
PRED predicted values (max 10 best out of 204): GB (0.62 #1012, 0.50 #100, 0.40 #296), NL (0.33 #51, 0.25 #179, 0.25 #147), SovietUnion (0.31 #1064, 0.17 #408, 0.12 #1492), F (0.29 #621, 0.25 #719, 0.25 #132), E (0.27 #1873, 0.26 #1937, 0.26 #1968), RI (0.17 #494, 0.10 #940, 0.10 #918), MAL (0.14 #663, 0.11 #3318, 0.10 #3070), UnitedNations (0.13 #1814, 0.11 #1654, 0.09 #1419), P (0.11 #867, 0.08 #999, 0.07 #2049), OttomanEmpire (0.10 #1729, 0.07 #3603, 0.07 #2518)
>> best conf = 0.62
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1012 for best value:
>> intensional similarity = 16
>> extensional distance = 11
>> proper extension:  BHT;
>> query: (?x460, GB) <- ?x460[ a Country; has encompassed ?x175; has ethnicGroup ?x298; has government ?x435; has religion ?x187[ is religion of ?x91<THA>; is religion of ?x111<NEP>; is religion of ?x376<MAL>; is religion of ?x434[ a Country; is locatedIn of ?x60;];]; has religion ?x462<Buddhist>;]

*> Best rule #30 for first EXPECTED value:
*> intensional similarity = 20
*> extensional distance = 1
*> proper extension:  MAL;
*> query: (?x460, ?x718) <- ?x460[ a Country; has encompassed ?x175<Asia>; has religion ?x187<Muslim>; has religion ?x352[ is religion of ?x81<GB>; is religion of ?x351<GUY>; is religion of ?x461<NZ>; is religion of ?x718[ a Country; has encompassed ?x195; has ethnicGroup ?x237;];]; is locatedIn of ?x384<SouthChinaSea>; is locatedIn of ?x624[ has belongsToIslands ?x370;];]
*> conf = 0.06
ranks of expected_values: 52
EVAL	RP	wasDependentOf	USA	CNN-1.+1._MA	0.000	0.000	0.000	0.019	109.000	109.000	204.000	0.615	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

#459-SUD
PRED entity: SUD

PRED relation: wasDependentOf!
PRED expected values: SSD
=> 30 concepts (30 used for prediction)
PRED predicted values (max 10 best out of 121): SUD (0.33 #27, 0.17 #829, 0.08 #160), LAR (0.08 #160, 0.06 #964, 0.04 #1105), RCA (0.08 #160, 0.06 #964, 0.04 #1077), TCH (0.08 #160, 0.06 #964, 0.04 #982), SSD (0.08 #160, 0.06 #964, 0.03 #1762), ETH (0.08 #160, 0.06 #964, 0.02 #1922), DZ (0.08 #160, 0.04 #1059, 0.03 #1220), RCB (0.08 #160, 0.04 #1051, 0.03 #1212), TN (0.08 #160, 0.04 #970, 0.03 #1131), DJI (0.08 #160, 0.04 #968, 0.03 #1129)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #27 for best value:
>> intensional similarity = 7
>> extensional distance = 1
>> proper extension:  ET;
>> query: (?x186, SUD) <- ?x186[ has ethnicGroup ?x244; has neighbor ?x63[ has neighbor ?x239; is locatedIn of ?x62;]; has religion ?x116; is locatedIn of ?x2124<Nile>;]

*> Best rule #160 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 1
*> proper extension:  ET;
*> query: (?x186, ?x239) <- ?x186[ has ethnicGroup ?x244; has neighbor ?x63[ has neighbor ?x239; is locatedIn of ?x62;]; has religion ?x116; is locatedIn of ?x2124<Nile>;]
*> conf = 0.08
ranks of expected_values: 5
EVAL	SUD	wasDependentOf!	SSD	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.200	30.000	30.000	121.000	0.333	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

PRED relation: wasDependentOf!
PRED expected values: SSD
=> 77 concepts (77 used for prediction)
PRED predicted values (max 10 best out of 143): SUD (0.33 #192, 0.25 #1500, 0.25 #1335), ER (0.33 #749, 0.20 #1900, 0.17 #325), LAR (0.17 #326, 0.17 #325, 0.12 #1637), ETH (0.17 #326, 0.17 #325, 0.12 #1637), RCA (0.17 #326, 0.17 #325, 0.12 #1637), SSD (0.17 #325, 0.12 #1637, 0.09 #816), TCH (0.17 #325, 0.12 #1637, 0.09 #816), TL (0.14 #2408, 0.11 #3556, 0.05 #5203), ET (0.09 #816, 0.05 #2456, 0.05 #2455), DJI (0.09 #816, 0.05 #4927, 0.05 #1634)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #192 for best value:
>> intensional similarity = 18
>> extensional distance = 1
>> proper extension:  ET;
>> query: (?x186, SUD) <- ?x186[ has encompassed ?x213<Africa>; has neighbor ?x629[ has ethnicGroup ?x996[ a EthnicGroup;]; has government ?x1090;]; has religion ?x187<Muslim>; is locatedIn of ?x531<LakeNasser>; is locatedIn of ?x990[ is flowsInto of ?x1170;]; is locatedIn of ?x1269<LibyanDesert>; is neighbor of ?x476[ has ethnicGroup ?x1179; has religion ?x56; is locatedIn of ?x228;];]

*> Best rule #325 for first EXPECTED value:
*> intensional similarity = 19
*> extensional distance = 1
*> proper extension:  ET;
*> query: (?x186, ?x169) <- ?x186[ has encompassed ?x213<Africa>; has neighbor ?x629[ has ethnicGroup ?x996[ a EthnicGroup;]; has government ?x1090;]; has religion ?x187<Muslim>; is locatedIn of ?x531<LakeNasser>; is locatedIn of ?x990[ is flowsInto of ?x1170;]; is locatedIn of ?x1269<LibyanDesert>; is neighbor of ?x169; is neighbor of ?x476[ has ethnicGroup ?x1179; has religion ?x56; is locatedIn of ?x228;];]
*> conf = 0.17
ranks of expected_values: 6
EVAL	SUD	wasDependentOf!	SSD	CNN-1.+1._MA	0.000	0.000	1.000	0.167	77.000	77.000	143.000	0.333	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

#458-OuterHebrides
PRED entity: OuterHebrides

PRED relation: belongsToIslands!
PRED expected values: SouthUist
=> 16 concepts (16 used for prediction)
PRED predicted values (max 10 best out of 223): Skye (0.21 #979, 0.21 #1175, 0.20 #194), Jura (0.21 #979, 0.21 #1175, 0.20 #188), Rhum (0.21 #979, 0.21 #1175, 0.20 #42), BishopRock (0.21 #979, 0.21 #1175, 0.20 #26), GreatBritain (0.21 #979, 0.21 #1175, 0.20 #25), Arran (0.21 #979, 0.21 #1175, 0.20 #22), Ireland (0.21 #979, 0.21 #1175, 0.20 #4), Anglesey (0.21 #979, 0.21 #1175, 0.20 #186), Tiree (0.21 #979, 0.21 #1175, 0.20 #134), Hoy (0.21 #979, 0.21 #1175, 0.15 #587)
>> best conf = 0.21
=> the first rule below is the first best rule for 25 predicted values
>> Best rule #979 for best value:
>> intensional similarity = 9
>> extensional distance = 10
>> proper extension:  FalklandIslands;
>> query: (?x1415, ?x121) <- ?x1415[ a Islands; is belongsToIslands of ?x526[ a Island; has locatedIn ?x81[ has ethnicGroup ?x1196; has government ?x1854; is locatedIn of ?x121;]; has locatedInWater ?x182<AtlanticOcean>;];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 14
EVAL	OuterHebrides	belongsToIslands!	SouthUist	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.071	16.000	16.000	223.000	0.209	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

PRED relation: belongsToIslands!
PRED expected values: SouthUist
=> 47 concepts (47 used for prediction)
PRED predicted values (max 10 best out of 223): GreatBritain (0.28 #8452, 0.21 #8058, 0.20 #25), Skye (0.28 #8452, 0.21 #8058, 0.20 #194), Jura (0.28 #8452, 0.21 #8058, 0.20 #188), Rhum (0.28 #8452, 0.21 #8058, 0.20 #42), BishopRock (0.28 #8452, 0.21 #8058, 0.20 #26), Arran (0.28 #8452, 0.21 #8058, 0.20 #22), Ireland (0.28 #8452, 0.21 #8058, 0.20 #4), Anglesey (0.28 #8452, 0.21 #8058, 0.20 #186), Tiree (0.28 #8452, 0.21 #8058, 0.20 #134), Hoy (0.28 #8452, 0.21 #8058, 0.19 #5106)
>> best conf = 0.28
=> the first rule below is the first best rule for 25 predicted values
>> Best rule #8452 for best value:
>> intensional similarity = 14
>> extensional distance = 32
>> proper extension:  Carolines;
>> query: (?x1415, ?x121) <- ?x1415[ a Islands; is belongsToIslands of ?x2069[ a Island; has locatedIn ?x81[ a Country; has government ?x1854; is locatedIn of ?x121;]; has locatedInWater ?x182[ a Sea; has locatedIn ?x315<USA>; has mergesWith ?x60<IndianOcean>; is flowsInto of ?x137;];];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 14
EVAL	OuterHebrides	belongsToIslands!	SouthUist	CNN-1.+1._MA	0.000	0.000	0.000	0.071	47.000	47.000	223.000	0.275	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

#457-RWA
PRED entity: RWA

PRED relation: neighbor!
PRED expected values: ZRE
=> 41 concepts (41 used for prediction)
PRED predicted values (max 10 best out of 210): ZRE (0.90 #3819, 0.89 #3659, 0.89 #4622), RWA (0.50 #95, 0.33 #255, 0.26 #4624), SYR (0.40 #401, 0.29 #559, 0.27 #717), EAK (0.33 #243, 0.26 #4624, 0.07 #1037), IL (0.30 #367, 0.21 #525, 0.20 #683), JOR (0.30 #445, 0.21 #603, 0.20 #761), SSD (0.26 #4624, 0.25 #43, 0.22 #203), RCA (0.26 #4624, 0.25 #119, 0.22 #279), Z (0.26 #4624, 0.25 #89, 0.11 #249), RCB (0.26 #4624, 0.25 #90, 0.11 #250)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3819 for best value:
>> intensional similarity = 6
>> extensional distance = 109
>> proper extension:  RN; ER;
>> query: (?x546, ?x348) <- ?x546[ has ethnicGroup ?x1946; has government ?x2266; has neighbor ?x348; is locatedIn of ?x545; is neighbor of ?x359[ has religion ?x116;];]
ranks of expected_values: 1
EVAL	RWA	neighbor!	ZRE	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	41.000	41.000	210.000	0.895	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: ZRE
=> 104 concepts (104 used for prediction)
PRED predicted values (max 10 best out of 238): ZRE (0.92 #5685, 0.91 #4876, 0.91 #14483), EAK (0.57 #6986, 0.50 #731, 0.43 #1620), SSD (0.50 #1016, 0.40 #2643, 0.40 #484), RWA (0.50 #743, 0.40 #905, 0.40 #484), DZ (0.44 #1884, 0.27 #3512, 0.24 #4004), Z (0.43 #1620, 0.40 #484, 0.35 #809), MW (0.43 #1620, 0.40 #484, 0.35 #809), ZW (0.43 #1620, 0.20 #964, 0.20 #6824), RSA (0.43 #1620, 0.20 #6824, 0.19 #5737), SD (0.43 #1620, 0.20 #6824, 0.19 #15626)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5685 for best value:
>> intensional similarity = 11
>> extensional distance = 24
>> proper extension:  BIH; MNE; RO; SLO; KOS;
>> query: (?x546, ?x688) <- ?x546[ a Country; has encompassed ?x213; has ethnicGroup ?x1946; has neighbor ?x688[ has ethnicGroup ?x529; has religion ?x187<Muslim>; is locatedIn of ?x600;]; has wasDependentOf ?x485; is locatedIn of ?x1803[ has inMountains ?x1066;];]
ranks of expected_values: 1
EVAL	RWA	neighbor!	ZRE	CNN-1.+1._MA	1.000	1.000	1.000	1.000	104.000	104.000	238.000	0.917	http://www.semwebtech.org/mondial/10/meta#neighbor

#456-IL
PRED entity: IL

PRED relation: religion
PRED expected values: Muslim
=> 39 concepts (39 used for prediction)
PRED predicted values (max 10 best out of 28): Muslim (0.91 #513, 0.67 #159, 0.65 #473), RomanCatholic (0.70 #673, 0.65 #712, 0.63 #634), CopticChristian (0.55 #863, 0.50 #589, 0.48 #1332), Protestant (0.53 #825, 0.53 #630, 0.51 #865), ChristianOrthodox (0.43 #392, 0.38 #431, 0.37 #511), Anglican (0.22 #605, 0.17 #290, 0.10 #683), Buddhist (0.17 #480, 0.17 #284, 0.17 #205), Hindu (0.17 #203, 0.12 #360, 0.10 #1183), Bahai (0.17 #304, 0.05 #460, 0.04 #500), JehovasWitnesses (0.11 #568, 0.10 #449, 0.09 #686)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #513 for best value:
>> intensional similarity = 7
>> extensional distance = 33
>> proper extension:  GB;
>> query: (?x239, Muslim) <- ?x239[ has ethnicGroup ?x244; has language ?x1398; has neighbor ?x63[ is locatedIn of ?x62;]; has religion ?x116[ is religion of ?x139<WAN>;];]
ranks of expected_values: 1
EVAL	IL	religion	Muslim	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	39.000	39.000	28.000	0.914	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion
PRED expected values: Muslim
=> 106 concepts (106 used for prediction)
PRED predicted values (max 10 best out of 39): RomanCatholic (0.93 #3838, 0.93 #3717, 0.88 #1951), Muslim (0.91 #1830, 0.88 #1356, 0.86 #1159), Protestant (0.70 #997, 0.68 #3593, 0.67 #1316), CopticChristian (0.62 #1432, 0.60 #1630, 0.59 #2267), ChristianOrthodox (0.56 #918, 0.51 #799, 0.50 #1513), Bahai (0.51 #799, 0.38 #1235, 0.33 #69), Anglican (0.40 #1330, 0.37 #1489, 0.33 #16), Buddhist (0.34 #3631, 0.33 #3550, 0.33 #10), Hindu (0.34 #3631, 0.33 #3550, 0.27 #4030), UkrainianGreekCatholic (0.25 #2909, 0.18 #2025, 0.12 #795)
>> best conf = 0.93
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3838 for best value:
>> intensional similarity = 20
>> extensional distance = 104
>> proper extension:  ARU;
>> query: (?x239, RomanCatholic) <- ?x239[ a Country; has religion ?x109[ is religion of ?x50<NLSM>; is religion of ?x78<F>; is religion of ?x177<BG>; is religion of ?x303<UA>; is religion of ?x363<ROU>; is religion of ?x718<L>;]; has religion ?x2142[ a Religion;]; is locatedIn of ?x419[ has locatedIn ?x466[ a Country; has encompassed ?x175;];];]

*> Best rule #1830 for first EXPECTED value:
*> intensional similarity = 14
*> extensional distance = 30
*> proper extension:  NEP; DK;
*> query: (?x239, Muslim) <- ?x239[ a Country; has government ?x254; has language ?x1848[ is language of ?x565[ has neighbor ?x170; is locatedIn of ?x631; is neighbor of ?x73;];]; has religion ?x116[ is religion of ?x302<IRQ>;]; is locatedIn of ?x238; is neighbor of ?x1495[ a Country; has ethnicGroup ?x852;];]
*> conf = 0.91
ranks of expected_values: 2
EVAL	IL	religion	Muslim	CNN-1.+1._MA	0.000	1.000	1.000	0.500	106.000	106.000	39.000	0.934	http://www.semwebtech.org/mondial/10/meta#religion

#455-Creole
PRED entity: Creole

PRED relation: language!
PRED expected values: NLSM
=> 23 concepts (19 used for prediction)
PRED predicted values (max 10 best out of 182): DOM (0.94 #599, 0.29 #719, 0.26 #1199), NLSM (0.45 #120, 0.33 #1, 0.20 #239), L (0.44 #91, 0.36 #210, 0.27 #329), CUR (0.36 #157, 0.22 #38, 0.13 #276), AND (0.33 #95, 0.27 #214, 0.16 #453), NAM (0.33 #13, 0.27 #132, 0.13 #251), I (0.33 #268, 0.22 #30, 0.20 #388), PK (0.32 #366, 0.18 #1207, 0.03 #1939), GCA (0.29 #719, 0.21 #2175, 0.11 #23), MEX (0.29 #719, 0.21 #2175, 0.11 #72)
>> best conf = 0.94
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #599 for best value:
>> intensional similarity = 7
>> extensional distance = 24
>> proper extension:  Maltese;
>> query: (?x2186, ?x520) <- ?x2186[ a Language; is language of ?x697[ has government ?x435<"republic">; is locatedIn of ?x2210[ a Island; has locatedIn ?x520;];];]

*> Best rule #120 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 9
*> proper extension:  Papiamento;
*> query: (?x2186, NLSM) <- ?x2186[ a Language; is language of ?x697[ a Country; has government ?x435; has religion ?x95<Protestant>; is locatedIn of ?x182<AtlanticOcean>;];]
*> conf = 0.45
ranks of expected_values: 2
EVAL	Creole	language!	NLSM	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	23.000	19.000	182.000	0.938	http://www.semwebtech.org/mondial/10/meta#language

PRED relation: language!
PRED expected values: NLSM
=> 54 concepts (54 used for prediction)
PRED predicted values (max 10 best out of 196): RT (0.72 #244, 0.28 #611, 0.27 #1965), BEN (0.72 #244, 0.28 #611, 0.27 #1965), RG (0.72 #244, 0.28 #611, 0.26 #983), CI (0.72 #244, 0.28 #611, 0.26 #983), MA (0.72 #244, 0.28 #611, 0.26 #983), RCB (0.72 #244, 0.28 #611, 0.26 #983), RIM (0.72 #244, 0.28 #611, 0.26 #983), SN (0.72 #244, 0.28 #611, 0.26 #983), G (0.72 #244, 0.28 #611, 0.26 #983), RMM (0.72 #244, 0.25 #464, 0.24 #4811)
>> best conf = 0.72
=> the first rule below is the first best rule for 21 predicted values
>> Best rule #244 for best value:
>> intensional similarity = 17
>> extensional distance = 1
>> proper extension:  Spanish;
>> query: (?x2186, ?x94) <- ?x2186[ a Language; is language of ?x697[ a Country; has encompassed ?x521<America>; has ethnicGroup ?x162; has religion ?x352<RomanCatholic>; has wasDependentOf ?x78[ a Country; has encompassed ?x195; is dependentOf of ?x61; is locatedIn of ?x121; is neighbor of ?x120; is wasDependentOf of ?x94;]; is locatedIn of ?x182; is wasDependentOf of ?x520;];]

*> Best rule #1108 for first EXPECTED value:
*> intensional similarity = 19
*> extensional distance = 4
*> proper extension:  Papiamento;
*> query: (?x2186, NLSM) <- ?x2186[ a Language; is language of ?x671[ has language ?x635[ a Language; is language of ?x718<L>;]; has language ?x796<Spanish>; has religion ?x95<Protestant>; has religion ?x1151<JehovasWitnesses>;]; is language of ?x697[ a Country; has government ?x435; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x317<CaribbeanSea>;];]
*> conf = 0.67
ranks of expected_values: 22
EVAL	Creole	language!	NLSM	CNN-1.+1._MA	0.000	0.000	0.000	0.045	54.000	54.000	196.000	0.722	http://www.semwebtech.org/mondial/10/meta#language

#454-Zaire
PRED entity: Zaire

PRED relation: hasEstuary!
PRED expected values: Zaire
=> 22 concepts (20 used for prediction)
PRED predicted values (max 10 best out of 72): Lomami (0.08 #1815, 0.05 #224, 0.04 #450), Luapula (0.08 #1815, 0.05 #220, 0.04 #446), Aruwimi (0.08 #1815, 0.05 #198, 0.04 #424), Tshuapa (0.08 #1815, 0.05 #195, 0.04 #421), Lukenie (0.08 #1815, 0.05 #188, 0.04 #414), Fimi (0.08 #1815, 0.05 #141, 0.04 #367), Ruzizi (0.08 #1815, 0.05 #120, 0.04 #346), Cuilo (0.08 #1815, 0.05 #98, 0.04 #324), Bomu (0.08 #1815, 0.05 #91, 0.04 #317), Busira (0.08 #1815, 0.05 #55, 0.04 #281)
>> best conf = 0.08
=> the first rule below is the first best rule for 20 predicted values
>> Best rule #1815 for best value:
>> intensional similarity = 5
>> extensional distance = 218
>> proper extension:  Leine; Moraca; Marne; Umeaelv; Lena; Pibor; Po; Hwangho; Aare; Euphrat; ...
>> query: (?x1506, ?x113) <- ?x1506[ a Estuary; has locatedIn ?x348[ is locatedIn of ?x113[ a River;]; is neighbor of ?x229;];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 18
EVAL	Zaire	hasEstuary!	Zaire	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.056	22.000	20.000	72.000	0.075	http://www.semwebtech.org/mondial/10/meta#hasEstuary

PRED relation: hasEstuary!
PRED expected values: Zaire
=> 60 concepts (60 used for prediction)
PRED predicted values (max 10 best out of 133): Ubangi (0.09 #2969, 0.07 #2968, 0.06 #5258), Fimi (0.09 #2969, 0.07 #2968, 0.06 #5258), Ruzizi (0.09 #2969, 0.07 #2968, 0.06 #5258), Lualaba (0.09 #2969, 0.07 #2968, 0.06 #5258), Luvua (0.09 #2969, 0.07 #2968, 0.06 #5258), Lukuga (0.09 #2969, 0.07 #2968, 0.06 #5258), Ruki (0.09 #2969, 0.07 #2968, 0.06 #5258), Lomami (0.09 #2969, 0.07 #2968, 0.06 #5258), Aruwimi (0.09 #2969, 0.07 #2968, 0.06 #5258), Tshuapa (0.09 #2969, 0.07 #2968, 0.06 #5258)
>> best conf = 0.09
=> the first rule below is the first best rule for 18 predicted values
>> Best rule #2969 for best value:
>> intensional similarity = 14
>> extensional distance = 76
>> proper extension:  Thjorsa; JoekulsaaFjoellum;
>> query: (?x1506, ?x601) <- ?x1506[ a Estuary; has locatedIn ?x348[ has religion ?x187[ is religion of ?x177<BG>; is religion of ?x819<MW>;]; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x281[ a River;]; is locatedIn of ?x1188[ a Estuary; is hasEstuary of ?x601;];];]

*> Best rule #2968 for first EXPECTED value:
*> intensional similarity = 14
*> extensional distance = 76
*> proper extension:  Thjorsa; JoekulsaaFjoellum;
*> query: (?x1506, ?x281) <- ?x1506[ a Estuary; has locatedIn ?x348[ has religion ?x187[ is religion of ?x177<BG>; is religion of ?x819<MW>;]; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x281[ a River;]; is locatedIn of ?x1188[ a Estuary; is hasEstuary of ?x601;];];]
*> conf = 0.07
ranks of expected_values: 19
EVAL	Zaire	hasEstuary!	Zaire	CNN-1.+1._MA	0.000	0.000	0.000	0.053	60.000	60.000	133.000	0.087	http://www.semwebtech.org/mondial/10/meta#hasEstuary

#453-D
PRED entity: D

PRED relation: neighbor!
PRED expected values: F
=> 34 concepts (34 used for prediction)
PRED predicted values (max 10 best out of 214): F (0.89 #3997, 0.89 #3996, 0.88 #2608), D (0.50 #321, 0.45 #627, 0.40 #474), R (0.40 #462, 0.28 #1228, 0.27 #3998), BY (0.40 #500, 0.28 #1228, 0.27 #3998), UA (0.33 #51, 0.28 #1228, 0.27 #3998), I (0.33 #190, 0.28 #1228, 0.27 #3998), AND (0.33 #271, 0.27 #3998, 0.19 #768), E (0.33 #173, 0.27 #3998, 0.19 #768), H (0.33 #44, 0.13 #3224, 0.11 #1578), MC (0.33 #303, 0.13 #3224, 0.09 #762)
>> best conf = 0.89
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3997 for best value:
>> intensional similarity = 5
>> extensional distance = 150
>> proper extension:  Q; YE;
>> query: (?x120, ?x234) <- ?x120[ has neighbor ?x234[ has government ?x2472; has neighbor ?x207; is locatedIn of ?x233;]; is locatedIn of ?x70;]
ranks of expected_values: 1
EVAL	D	neighbor!	F	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	34.000	34.000	214.000	0.890	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: F
=> 99 concepts (99 used for prediction)
PRED predicted values (max 10 best out of 212): F (0.90 #8522, 0.90 #11165, 0.90 #8523), H (0.57 #617, 0.53 #10386, 0.50 #1582), UA (0.57 #617, 0.53 #10386, 0.50 #2209), D (0.57 #617, 0.53 #10386, 0.50 #1092), FL (0.57 #617, 0.53 #10386, 0.50 #1151), SK (0.57 #617, 0.53 #10386, 0.49 #11164), RO (0.57 #617, 0.53 #10386, 0.49 #11164), SRB (0.57 #617, 0.53 #10386, 0.49 #11164), HR (0.57 #617, 0.53 #10386, 0.49 #11164), MD (0.57 #617, 0.53 #10386, 0.49 #11164)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #8522 for best value:
>> intensional similarity = 11
>> extensional distance = 32
>> proper extension:  RSM; V;
>> query: (?x120, ?x543) <- ?x120[ has encompassed ?x195<Europe>; has government ?x140; has neighbor ?x543[ has language ?x51; has religion ?x352<RomanCatholic>;]; has neighbor ?x793[ is locatedIn of ?x146[ is flowsInto of ?x590; is locatedInWater of ?x145;];];]
ranks of expected_values: 1
EVAL	D	neighbor!	F	CNN-1.+1._MA	1.000	1.000	1.000	1.000	99.000	99.000	212.000	0.899	http://www.semwebtech.org/mondial/10/meta#neighbor

#452-GQ
PRED entity: GQ

PRED relation: wasDependentOf
PRED expected values: E
=> 36 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 107): E (0.52 #96, 0.24 #279, 0.16 #460), F (0.40 #3, 0.29 #152, 0.29 #32), GB (0.26 #247, 0.23 #336, 0.23 #427), SovietUnion (0.17 #382, 0.14 #506, 0.13 #474), P (0.14 #52, 0.09 #266, 0.08 #736), UnitedNations (0.14 #44, 0.07 #347, 0.07 #881), USA (0.09 #549, 0.02 #767, 0.01 #768), Yugoslavia (0.09 #385, 0.08 #416, 0.06 #477), OttomanEmpire (0.08 #418, 0.07 #387, 0.06 #479), RH (0.08 #736, 0.08 #865, 0.07 #77)
>> best conf = 0.52
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #96 for best value:
>> intensional similarity = 5
>> extensional distance = 19
>> proper extension:  NLSM;
>> query: (?x1408, E) <- ?x1408[ a Country; has government ?x435; has language ?x796<Spanish>; has religion ?x352;]
ranks of expected_values: 1
EVAL	GQ	wasDependentOf	E	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	36.000	36.000	107.000	0.524	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

PRED relation: wasDependentOf
PRED expected values: E
=> 82 concepts (82 used for prediction)
PRED predicted values (max 10 best out of 125): F (0.67 #796, 0.67 #699, 0.60 #898), E (0.56 #1045, 0.50 #602, 0.50 #566), GB (0.55 #628, 0.50 #591, 0.40 #788), UnitedNations (0.50 #259, 0.49 #1972, 0.46 #1503), Yugoslavia (0.33 #885, 0.26 #1101, 0.16 #1821), P (0.33 #185, 0.25 #285, 0.25 #214), SovietUnion (0.30 #1201, 0.27 #1600, 0.24 #1712), B (0.17 #383, 0.12 #648, 0.12 #1074), BR (0.15 #1467, 0.12 #611, 0.12 #575), RH (0.15 #1467, 0.12 #614, 0.11 #745)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #796 for best value:
>> intensional similarity = 19
>> extensional distance = 10
>> proper extension:  DZ; BF;
>> query: (?x1408, F) <- ?x1408[ a Country; has encompassed ?x213<Africa>; is locatedIn of ?x182; is neighbor of ?x172[ has government ?x1721;]; is neighbor of ?x536[ a Country; has ethnicGroup ?x122; is locatedIn of ?x1745[ a Volcano;]; is locatedIn of ?x2238<ChadLake>; is neighbor of ?x139<WAN>; is neighbor of ?x736[ has ethnicGroup ?x992; has religion ?x187; is locatedIn of ?x388;];];]
>> Best rule #699 for best value:
>> intensional similarity = 20
>> extensional distance = 7
>> proper extension:  RT;
>> query: (?x1408, F) <- ?x1408[ a Country; has encompassed ?x213<Africa>; has religion ?x352; is locatedIn of ?x182; is neighbor of ?x536[ a Country; has ethnicGroup ?x122; has ethnicGroup ?x941[ a EthnicGroup;]; has neighbor ?x528[ has neighbor ?x348;]; has religion ?x116<Christian>; has wasDependentOf ?x485; is locatedIn of ?x286; is neighbor of ?x139<WAN>; is neighbor of ?x736[ is locatedIn of ?x388;];];]

*> Best rule #1045 for first EXPECTED value:
*> intensional similarity = 17
*> extensional distance = 16
*> proper extension:  AND;
*> query: (?x1408, E) <- ?x1408[ has encompassed ?x213[ is encompassed of ?x450[ a Country; has ethnicGroup ?x197; has government ?x435; is locatedIn of ?x449;]; is encompassed of ?x483[ has wasDependentOf ?x81;];]; has language ?x796<Spanish>; has religion ?x352<RomanCatholic>; is neighbor of ?x536[ has ethnicGroup ?x122; is locatedIn of ?x182; is locatedIn of ?x286[ a Lake;];];]
*> conf = 0.56
ranks of expected_values: 2
EVAL	GQ	wasDependentOf	E	CNN-1.+1._MA	0.000	1.000	1.000	0.500	82.000	82.000	125.000	0.667	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

#451-CaribbeanSea
PRED entity: CaribbeanSea

PRED relation: locatedIn
PRED expected values: BZ ARU VIRG
=> 40 concepts (40 used for prediction)
PRED predicted values (max 10 best out of 195): VIRG (0.88 #4331, 0.88 #4330, 0.87 #3503), USA (0.63 #5633, 0.33 #64, 0.20 #1094), RI (0.38 #1693, 0.36 #1487, 0.27 #3135), CDN (0.33 #55, 0.33 #5830, 0.20 #1085), SVAX (0.33 #168, 0.21 #1610, 0.20 #1198), IS (0.33 #95, 0.21 #1537, 0.20 #1125), GB (0.33 #7, 0.21 #2273, 0.20 #1037), F (0.33 #6, 0.20 #1860, 0.20 #1036), BR (0.33 #110, 0.20 #1140, 0.20 #934), GUY (0.33 #69, 0.20 #1099, 0.20 #893)
>> best conf = 0.88
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4331 for best value:
>> intensional similarity = 5
>> extensional distance = 41
>> proper extension:  Araguaia;
>> query: (?x317, ?x1444) <- ?x317[ is locatedInWater of ?x817[ has locatedIn ?x1444;]; is locatedInWater of ?x1829[ a Island; has locatedIn ?x1209;];]
>> Best rule #4330 for best value:
>> intensional similarity = 3
>> extensional distance = 41
>> proper extension:  Araguaia;
>> query: (?x317, ?x1209) <- ?x317[ is locatedInWater of ?x1829[ a Island; has locatedIn ?x1209;];]
ranks of expected_values: 1, 94
EVAL	CaribbeanSea	locatedIn	VIRG	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	40.000	40.000	195.000	0.880	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	CaribbeanSea	locatedIn	ARU	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	40.000	40.000	195.000	0.880	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	CaribbeanSea	locatedIn	BZ	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.011	40.000	40.000	195.000	0.880	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: BZ ARU VIRG
=> 89 concepts (89 used for prediction)
PRED predicted values (max 10 best out of 212): VIRG (0.89 #4970, 0.89 #5600, 0.89 #5599), USA (0.79 #5665, 0.56 #2482, 0.55 #2481), RI (0.67 #1698, 0.56 #1905, 0.50 #2112), BR (0.56 #2482, 0.55 #2481, 0.45 #3517), PE (0.56 #2482, 0.55 #2481, 0.45 #3517), ES (0.56 #2482, 0.55 #2481, 0.45 #3517), BZ (0.56 #2482, 0.55 #2481, 0.45 #3517), EC (0.56 #2482, 0.55 #2481, 0.45 #3517), CDN (0.49 #9595, 0.42 #5656, 0.38 #1860), GB (0.46 #1034, 0.46 #1033, 0.46 #1032)
>> best conf = 0.89
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4970 for best value:
>> intensional similarity = 13
>> extensional distance = 20
>> proper extension:  HudsonBay;
>> query: (?x317, ?x1073) <- ?x317[ has mergesWith ?x182[ has locatedIn ?x50; is locatedInWater of ?x112;]; is locatedInWater of ?x609[ a Island; is locatedOnIsland of ?x1972;]; is locatedInWater of ?x703[ a Island; has belongsToIslands ?x877; has locatedIn ?x1209;]; is locatedInWater of ?x1219[ a Island; has locatedIn ?x1073;];]
ranks of expected_values: 1, 7, 180
EVAL	CaribbeanSea	locatedIn	VIRG	CNN-1.+1._MA	1.000	1.000	1.000	1.000	89.000	89.000	212.000	0.886	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	CaribbeanSea	locatedIn	ARU	CNN-1.+1._MA	0.000	0.000	0.000	0.006	89.000	89.000	212.000	0.886	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	CaribbeanSea	locatedIn	BZ	CNN-1.+1._MA	0.000	0.000	1.000	0.167	89.000	89.000	212.000	0.886	http://www.semwebtech.org/mondial/10/meta#locatedIn

#450-Tahiti
PRED entity: Tahiti

PRED relation: locatedOnIsland!
PRED expected values: MontOrohena
=> 60 concepts (59 used for prediction)
PRED predicted values (max 10 best out of 53): MontOrohena (0.16 #258, 0.07 #259, 0.03 #193), QueenMarysPeak (0.10 #129, 0.03 #452, 0.03 #517), Pelee (0.08 #250, 0.04 #316, 0.02 #701), PitondelaFournaise (0.08 #218, 0.04 #284, 0.01 #1760), PitondesNeiges (0.08 #208, 0.04 #274, 0.01 #1750), Tahiti (0.07 #259, 0.03 #193, 0.01 #1800), PacificOcean (0.07 #259, 0.03 #193, 0.01 #1800), CerrodePunta (0.04 #269, 0.01 #1424), Haleakala (0.04 #376, 0.04 #440, 0.03 #633), MaunaKea (0.04 #334, 0.04 #398, 0.03 #591)
>> best conf = 0.16
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #258 for best value:
>> intensional similarity = 6
>> extensional distance = 11
>> proper extension:  PitondesNeiges; PitondelaFournaise;
>> query: (?x2009, ?x2130) <- ?x2009[ has locatedIn ?x297[ a Country; has dependentOf ?x78; has religion ?x352; is locatedIn of ?x2130[ a Volcano;];];]
ranks of expected_values: 1
EVAL	Tahiti	locatedOnIsland!	MontOrohena	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	60.000	59.000	53.000	0.158	http://www.semwebtech.org/mondial/10/meta#locatedOnIsland

PRED relation: locatedOnIsland!
PRED expected values: MontOrohena
=> 185 concepts (185 used for prediction)
PRED predicted values (max 10 best out of 65): Pelee (0.25 #186, 0.12 #958, 0.12 #894), YuShan (0.17 #479, 0.03 #3912, 0.03 #6547), MontOrohena (0.15 #966, 0.12 #194, 0.12 #4077), Tahiti (0.15 #966, 0.12 #194, 0.10 #1354), PacificOcean (0.15 #966, 0.12 #194, 0.10 #1354), PitondelaFournaise (0.12 #926, 0.10 #1314, 0.02 #5011), PitondesNeiges (0.12 #916, 0.10 #1304, 0.02 #5001), Ruapehu (0.09 #1471, 0.06 #2380, 0.05 #2705), Haleakala (0.08 #1537, 0.06 #2251, 0.05 #2900), MaunaKea (0.08 #1495, 0.06 #2209, 0.05 #2858)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #186 for best value:
>> intensional similarity = 10
>> extensional distance = 2
>> proper extension:  Nauru;
>> query: (?x2009, Pelee) <- ?x2009[ a Island; has locatedIn ?x297[ a Country; has encompassed ?x211; has ethnicGroup ?x298<Chinese>; has government ?x2145; has religion ?x352;]; has locatedInWater ?x282; has type ?x150;]

*> Best rule #966 for first EXPECTED value:
*> intensional similarity = 11
*> extensional distance = 6
*> proper extension:  Reunion;
*> query: (?x2009, ?x282) <- ?x2009[ a Island; has locatedIn ?x297[ has dependentOf ?x78; has encompassed ?x211[ a Continent;]; has government ?x2145; has religion ?x352<RomanCatholic>; is locatedIn of ?x282;]; has type ?x150<"volcanic">;]
*> conf = 0.15
ranks of expected_values: 3
EVAL	Tahiti	locatedOnIsland!	MontOrohena	CNN-1.+1._MA	0.000	1.000	1.000	0.333	185.000	185.000	65.000	0.250	http://www.semwebtech.org/mondial/10/meta#locatedOnIsland

#449-NZ
PRED entity: NZ

PRED relation: wasDependentOf!
PRED expected values: NIUE
=> 29 concepts (29 used for prediction)
PRED predicted values (max 10 best out of 203): NZ (0.07 #706, 0.06 #864, 0.02 #787), CDN (0.07 #669, 0.06 #827, 0.02 #787), AUS (0.07 #659, 0.06 #817, 0.02 #787), KIR (0.07 #740, 0.06 #898, 0.02 #787), USA (0.07 #676, 0.06 #834, 0.02 #787), FJI (0.07 #644, 0.06 #802, 0.02 #787), TO (0.07 #784, 0.06 #942, 0.02 #787), TUV (0.07 #733, 0.06 #891, 0.02 #787), BDS (0.07 #768, 0.06 #926, 0.02 #945), IND (0.07 #760, 0.06 #918, 0.02 #945)
>> best conf = 0.07
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #706 for best value:
>> intensional similarity = 5
>> extensional distance = 13
>> proper extension:  BS;
>> query: (?x461, NZ) <- ?x461[ has government ?x1947; has religion ?x713<Anglican>; is locatedIn of ?x282[ is locatedInWater of ?x205;];]

*> Best rule #787 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 13
*> proper extension:  BS;
*> query: (?x461, ?x73) <- ?x461[ has government ?x1947; has religion ?x713<Anglican>; is locatedIn of ?x282[ has locatedIn ?x73; is locatedInWater of ?x205;];]
*> conf = 0.02
ranks of expected_values: 106
EVAL	NZ	wasDependentOf!	NIUE	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.009	29.000	29.000	203.000	0.067	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

PRED relation: wasDependentOf!
PRED expected values: NIUE
=> 113 concepts (113 used for prediction)
PRED predicted values (max 10 best out of 213): AUS (0.50 #964, 0.44 #965, 0.33 #835), NZ (0.50 #964, 0.44 #965, 0.33 #882), CDN (0.50 #964, 0.44 #965, 0.33 #845), IRL (0.50 #964, 0.44 #965, 0.33 #818), BDS (0.50 #964, 0.44 #965, 0.33 #944), SLB (0.50 #964, 0.44 #965, 0.33 #867), USA (0.50 #964, 0.44 #965, 0.33 #852), ZW (0.50 #964, 0.44 #965, 0.33 #957), Z (0.50 #964, 0.44 #965, 0.33 #891), SD (0.50 #964, 0.44 #965, 0.33 #834)
>> best conf = 0.50
=> the first rule below is the first best rule for 40 predicted values
>> Best rule #964 for best value:
>> intensional similarity = 16
>> extensional distance = 1
>> proper extension:  GB;
>> query: (?x461, ?x1072) <- ?x461[ a Country; has ethnicGroup ?x380[ is ethnicGroup of ?x1072[ has religion ?x116;];]; has government ?x1947; has language ?x51; has religion ?x95<Protestant>; has religion ?x410<Hindu>; has religion ?x713<Anglican>; is dependentOf of ?x1334; is locatedIn of ?x897[ a Island; has belongsToIslands ?x1523;];]

*> Best rule #2917 for first EXPECTED value:
*> intensional similarity = 20
*> extensional distance = 6
*> proper extension:  E; N; USA; NL;
*> query: (?x461, ?x81) <- ?x461[ has ethnicGroup ?x197; has language ?x51; has religion ?x713[ a Religion; is religion of ?x81; is religion of ?x210<NORF>; is religion of ?x279<BS>; is religion of ?x667<TT>; is religion of ?x853<PNG>; is religion of ?x865<CAYM>; is religion of ?x1008<BERM>;]; is dependentOf of ?x1819[ a Country;]; is locatedIn of ?x282;]
*> conf = 0.06
ranks of expected_values: 157
EVAL	NZ	wasDependentOf!	NIUE	CNN-1.+1._MA	0.000	0.000	0.000	0.006	113.000	113.000	213.000	0.500	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

#448-Sanaga
PRED entity: Sanaga

PRED relation: locatedIn
PRED expected values: CAM
=> 47 concepts (47 used for prediction)
PRED predicted values (max 10 best out of 202): CAM (0.90 #9260, 0.88 #8550, 0.84 #4746), ZRE (0.57 #2922, 0.36 #7599, 0.35 #10922), WAN (0.40 #498, 0.16 #8073, 0.16 #8311), USA (0.36 #7599, 0.35 #4034, 0.35 #3869), RCB (0.36 #7599, 0.35 #10922, 0.35 #10921), BR (0.36 #7599, 0.35 #10922, 0.35 #10921), GH (0.36 #7599, 0.35 #10922, 0.35 #10921), BF (0.36 #7599, 0.35 #10922, 0.35 #10921), RG (0.36 #7599, 0.35 #10922, 0.35 #10921), RMM (0.36 #7599, 0.35 #10922, 0.35 #10921)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #9260 for best value:
>> intensional similarity = 5
>> extensional distance = 194
>> proper extension:  Suchona; Enns;
>> query: (?x1525, ?x536) <- ?x1525[ a River; has hasSource ?x1899[ has locatedIn ?x536[ has neighbor ?x139; has religion ?x116;];];]
ranks of expected_values: 1
EVAL	Sanaga	locatedIn	CAM	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	47.000	47.000	202.000	0.896	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: CAM
=> 115 concepts (115 used for prediction)
PRED predicted values (max 10 best out of 229): CAM (0.90 #25231, 0.88 #19197, 0.88 #10772), CDN (0.70 #5801, 0.50 #8429, 0.49 #22336), ZRE (0.56 #4382, 0.52 #9082, 0.52 #8919), USA (0.50 #5810, 0.49 #22336, 0.45 #8438), BR (0.49 #22336, 0.43 #3232, 0.40 #1321), RCB (0.49 #22336, 0.36 #22338, 0.36 #22337), CO (0.49 #22336, 0.36 #22338, 0.36 #22337), NAM (0.49 #22336, 0.36 #22338, 0.36 #22337), LS (0.49 #22336, 0.36 #22338, 0.36 #22337), GH (0.49 #22336, 0.36 #22338, 0.36 #22337)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #25231 for best value:
>> intensional similarity = 10
>> extensional distance = 179
>> proper extension:  Enns;
>> query: (?x1525, ?x536) <- ?x1525[ a River; has hasSource ?x1899[ a Source; has locatedIn ?x536[ a Country; has ethnicGroup ?x122; has neighbor ?x139; has neighbor ?x169; has religion ?x116; is neighbor of ?x169;];];]
ranks of expected_values: 1
EVAL	Sanaga	locatedIn	CAM	CNN-1.+1._MA	1.000	1.000	1.000	1.000	115.000	115.000	229.000	0.898	http://www.semwebtech.org/mondial/10/meta#locatedIn

#447-Indus
PRED entity: Indus

PRED relation: hasSource
PRED expected values: Indus
=> 31 concepts (23 used for prediction)
PRED predicted values (max 10 best out of 148): Ganges (0.12 #337, 0.02 #4582, 0.02 #2977), Limpopo (0.12 #392, 0.02 #1308, 0.01 #1767), MurrayRiver (0.12 #373, 0.02 #1289, 0.01 #1748), Zambezi (0.12 #344, 0.02 #1260), Mekong (0.08 #684, 0.02 #4582, 0.02 #2977), Jangtse (0.08 #540, 0.02 #4582, 0.02 #2977), Tarim-Yarkend (0.08 #618, 0.02 #4582, 0.02 #2977), Saluen (0.08 #606, 0.02 #4582, 0.02 #2977), Argun (0.08 #574, 0.02 #4582, 0.02 #2977), Ili (0.08 #507, 0.02 #4582, 0.02 #2977)
>> best conf = 0.12
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #337 for best value:
>> intensional similarity = 5
>> extensional distance = 6
>> proper extension:  MurrayRiver;
>> query: (?x411, Ganges) <- ?x411[ has flowsInto ?x1333[ has locatedIn ?x924<IND>; is locatedInWater of ?x1476; is mergesWith of ?x60;];]

*> Best rule #2977 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 200
*> proper extension:  DarlingRiver; GreatSandyDesert; Tasmania; EucumbeneRiver; Mt.Bogong; LakeBurleyGriffin; GreatVictoriaDesert; LakeEyre; Mt.Kosciuszko; DarlingRiver; ...
*> query: (?x411, ?x1478) <- ?x411[ has locatedIn ?x232[ has government ?x831; is dependentOf of ?x641; is locatedIn of ?x1478[ a Source;];];]
*> conf = 0.02
ranks of expected_values: 14
EVAL	Indus	hasSource	Indus	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.071	31.000	23.000	148.000	0.125	http://www.semwebtech.org/mondial/10/meta#hasSource

PRED relation: hasSource
PRED expected values: Indus
=> 135 concepts (135 used for prediction)
PRED predicted values (max 10 best out of 227): Ganges (0.33 #109, 0.14 #1939, 0.08 #2398), Limpopo (0.25 #622, 0.20 #1537, 0.20 #1308), MurrayRiver (0.20 #1518, 0.14 #1975, 0.12 #2204), Zambezi (0.20 #1032, 0.12 #2175, 0.06 #3779), Shabelle (0.20 #1303, 0.12 #2218, 0.06 #3822), Mekong (0.14 #1829, 0.08 #2517, 0.08 #2746), Ili (0.14 #1652, 0.07 #3029, 0.04 #5547), Akagera (0.08 #2433, 0.06 #3807, 0.06 #4494), Ruzizi (0.08 #2392, 0.06 #3766, 0.06 #4453), Asahan (0.08 #2380, 0.06 #4212, 0.04 #5587)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #109 for best value:
>> intensional similarity = 13
>> extensional distance = 1
>> proper extension:  Ganges;
>> query: (?x411, Ganges) <- ?x411[ has flowsInto ?x1333[ a Sea; has locatedIn ?x220[ has government ?x1766;]; has locatedIn ?x924<IND>; has mergesWith ?x926[ has locatedIn ?x83; is locatedInWater of ?x2355;]; is locatedInWater of ?x1476; is mergesWith of ?x60<IndianOcean>;]; has hasEstuary ?x383;]

*> Best rule #18573 for first EXPECTED value:
*> intensional similarity = 11
*> extensional distance = 157
*> proper extension:  Kasai; Vuoksi;
*> query: (?x411, ?x231) <- ?x411[ a River; has flowsInto ?x1333[ has locatedIn ?x220[ has religion ?x187; is neighbor of ?x94;]; has locatedIn ?x668[ has encompassed ?x175; has neighbor ?x639;];]; has locatedIn ?x232[ has government ?x831; is locatedIn of ?x231;];]
*> conf = 0.02
ranks of expected_values: 137
EVAL	Indus	hasSource	Indus	CNN-1.+1._MA	0.000	0.000	0.000	0.007	135.000	135.000	227.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasSource

#446-L
PRED entity: L

PRED relation: religion
PRED expected values: Muslim
=> 43 concepts (43 used for prediction)
PRED predicted values (max 10 best out of 34): Muslim (0.64 #275, 0.63 #1414, 0.55 #1181), ChristianOrthodox (0.34 #394, 0.30 #316, 0.29 #629), Christian (0.33 #237, 0.33 #748, 0.32 #984), Anglican (0.20 #172, 0.12 #604, 0.12 #447), Buddhist (0.18 #324, 0.15 #284, 0.14 #48), Hindu (0.12 #322, 0.11 #556, 0.10 #164), JehovasWitnesses (0.11 #135, 0.11 #293, 0.10 #450), Seventh-DayAdventist (0.11 #125, 0.10 #165, 0.03 #597), Presbyterian (0.11 #123, 0.10 #163, 0.02 #360), Sikh (0.10 #188, 0.02 #541, 0.02 #659)
>> best conf = 0.64
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #275 for best value:
>> intensional similarity = 5
>> extensional distance = 13
>> proper extension:  GBZ;
>> query: (?x718, ?x187) <- ?x718[ has religion ?x109<Jewish>; is neighbor of ?x120[ has religion ?x187; is locatedIn of ?x70;];]
ranks of expected_values: 1
EVAL	L	religion	Muslim	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	43.000	43.000	34.000	0.640	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion
PRED expected values: Muslim
=> 115 concepts (115 used for prediction)
PRED predicted values (max 10 best out of 37): Muslim (0.93 #3694, 0.83 #2667, 0.83 #3772), Buddhist (0.78 #1604, 0.51 #1722, 0.46 #2428), Mormon (0.78 #1604, 0.19 #3216, 0.15 #3414), ChristianOrthodox (0.71 #1526, 0.50 #2389, 0.50 #1253), Christian (0.60 #3612, 0.51 #1722, 0.42 #2743), Anglican (0.51 #1722, 0.50 #1306, 0.42 #2743), Seventh-DayAdventist (0.51 #1722, 0.42 #2743, 0.33 #282), Presbyterian (0.51 #1722, 0.42 #2743, 0.33 #280), HoaHao (0.51 #1722, 0.25 #510, 0.15 #3414), CaoDai (0.51 #1722, 0.25 #510, 0.15 #3414)
>> best conf = 0.93
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3694 for best value:
>> intensional similarity = 19
>> extensional distance = 88
>> proper extension:  TCH; G; BI; RCB; OM; WSA; YE;
>> query: (?x718, Muslim) <- ?x718[ has neighbor ?x120[ is locatedIn of ?x70;]; has religion ?x95[ is religion of ?x246[ has language ?x247;]; is religion of ?x272<CDN>; is religion of ?x934[ is locatedIn of ?x436<Cuango>; is locatedIn of ?x927<Cuilo>;]; is religion of ?x962[ has ethnicGroup ?x963;]; is religion of ?x1364[ has encompassed ?x521;];]; has religion ?x109[ is religion of ?x851<MA>;];]
ranks of expected_values: 1
EVAL	L	religion	Muslim	CNN-1.+1._MA	1.000	1.000	1.000	1.000	115.000	115.000	37.000	0.933	http://www.semwebtech.org/mondial/10/meta#religion

#445-Silisili
PRED entity: Silisili

PRED relation: locatedIn
PRED expected values: WS
=> 46 concepts (46 used for prediction)
PRED predicted values (max 10 best out of 76): WS (0.86 #6893, 0.86 #7367, 0.86 #4514), RI (0.69 #3853, 0.67 #3139, 0.64 #4329), RM (0.50 #2004, 0.09 #8417, 0.06 #8653), FJI (0.33 #30, 0.25 #982, 0.25 #744), RP (0.29 #2249, 0.20 #2960, 0.18 #6765), IS (0.25 #3433, 0.20 #1772, 0.14 #2485), MAL (0.25 #800, 0.06 #5553, 0.04 #7216), RC (0.25 #1176, 0.02 #9011, 0.02 #9248), P (0.20 #1623, 0.12 #5426, 0.11 #6140), CDN (0.15 #6481, 0.10 #7667, 0.07 #4340)
>> best conf = 0.86
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #6893 for best value:
>> intensional similarity = 12
>> extensional distance = 20
>> proper extension:  Kanlaon;
>> query: (?x1229, ?x453) <- ?x1229[ a Mountain; a Volcano; has locatedOnIsland ?x1205[ a Island; has belongsToIslands ?x586[ a Islands;]; has locatedIn ?x453[ a Country; has encompassed ?x211; has ethnicGroup ?x454; has government ?x254; has religion ?x116;];];]
ranks of expected_values: 1
EVAL	Silisili	locatedIn	WS	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	46.000	46.000	76.000	0.864	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: WS
=> 46 concepts (46 used for prediction)
PRED predicted values (max 10 best out of 76): WS (0.86 #6893, 0.86 #7367, 0.86 #4514), RI (0.69 #3853, 0.67 #3139, 0.64 #4329), RM (0.50 #2004, 0.09 #8417, 0.06 #8653), FJI (0.33 #30, 0.25 #982, 0.25 #744), RP (0.29 #2249, 0.20 #2960, 0.18 #6765), IS (0.25 #3433, 0.20 #1772, 0.14 #2485), MAL (0.25 #800, 0.06 #5315, 0.04 #7216), RC (0.25 #1176, 0.02 #9011, 0.02 #9248), P (0.20 #1623, 0.12 #5664, 0.11 #6140), CDN (0.15 #6481, 0.10 #7667, 0.07 #4340)
>> best conf = 0.86
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #6893 for best value:
>> intensional similarity = 12
>> extensional distance = 20
>> proper extension:  Kanlaon;
>> query: (?x1229, ?x453) <- ?x1229[ a Mountain; a Volcano; has locatedOnIsland ?x1205[ a Island; has belongsToIslands ?x586[ a Islands;]; has locatedIn ?x453[ a Country; has encompassed ?x211; has ethnicGroup ?x454; has government ?x254; has religion ?x116;];];]
ranks of expected_values: 1
EVAL	Silisili	locatedIn	WS	CNN-1.+1._MA	1.000	1.000	1.000	1.000	46.000	46.000	76.000	0.864	http://www.semwebtech.org/mondial/10/meta#locatedIn

#444-Sokotra
PRED entity: Sokotra

PRED relation: locatedInWater
PRED expected values: GulfofAden
=> 35 concepts (29 used for prediction)
PRED predicted values (max 10 best out of 32): GulfofAden (0.41 #678, 0.09 #252, 0.08 #167), RedSea (0.41 #678, 0.09 #252, 0.07 #1063), AtlanticOcean (0.31 #386, 0.31 #430, 0.29 #557), SulawesiSea (0.26 #237, 0.04 #749, 0.04 #494), JavaSea (0.25 #132, 0.21 #176, 0.08 #167), PacificOcean (0.24 #268, 0.24 #440, 0.24 #396), SouthChinaSea (0.23 #231, 0.06 #401, 0.06 #358), EastChinaSea (0.16 #236, 0.03 #363, 0.02 #406), MediterraneanSea (0.15 #310, 0.13 #439, 0.12 #395), PersianGulf (0.14 #201, 0.10 #243, 0.02 #755)
>> best conf = 0.41
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #678 for best value:
>> intensional similarity = 5
>> extensional distance = 242
>> proper extension:  Jersey;
>> query: (?x2223, ?x1333) <- ?x2223[ a Island; has locatedIn ?x668[ has encompassed ?x175; is locatedIn of ?x1333[ has mergesWith ?x926;];];]
ranks of expected_values: 1
EVAL	Sokotra	locatedInWater	GulfofAden	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	35.000	29.000	32.000	0.410	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater
PRED expected values: GulfofAden
=> 119 concepts (119 used for prediction)
PRED predicted values (max 10 best out of 160): PacificOcean (0.73 #1456, 0.71 #1720, 0.45 #1853), RedSea (0.65 #867, 0.60 #86, 0.49 #781), MediterraneanSea (0.65 #839, 0.38 #1364, 0.29 #1943), GulfofAden (0.60 #86, 0.49 #781, 0.44 #3706), JavaSea (0.50 #222, 0.40 #352, 0.33 #397), AtlanticOcean (0.44 #2959, 0.42 #2871, 0.39 #1052), RubAlChali (0.38 #87, 0.12 #130, 0.11 #43), JabalShuayb (0.38 #87, 0.12 #130, 0.11 #43), Sokotra (0.38 #87, 0.12 #130, 0.11 #43), SulawesiSea (0.29 #937, 0.27 #765, 0.14 #114)
>> best conf = 0.73
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1456 for best value:
>> intensional similarity = 13
>> extensional distance = 64
>> proper extension:  Tinian; Rota;
>> query: (?x2223, PacificOcean) <- ?x2223[ a Island; has locatedIn ?x668[ has encompassed ?x175;]; has locatedInWater ?x60[ has locatedIn ?x196<AUS>; has locatedIn ?x758[ a Country;]; is flowsInto of ?x242; is locatedInWater of ?x1666[ has belongsToIslands ?x227;]; is locatedInWater of ?x1768[ is locatedOnIsland of ?x1545;];];]

*> Best rule #86 for first EXPECTED value:
*> intensional similarity = 13
*> extensional distance = 2
*> proper extension:  Male;
*> query: (?x2223, ?x1552) <- ?x2223[ has locatedIn ?x668[ has encompassed ?x175<Asia>; has government ?x435<"republic">; has wasDependentOf ?x2153; is locatedIn of ?x1552[ a Sea;];]; has locatedInWater ?x60<IndianOcean>; has locatedInWater ?x1333[ has locatedIn ?x220; has mergesWith ?x926;];]
*> conf = 0.60
ranks of expected_values: 4
EVAL	Sokotra	locatedInWater	GulfofAden	CNN-1.+1._MA	0.000	0.000	1.000	0.250	119.000	119.000	160.000	0.727	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#443-SchattalArab
PRED entity: SchattalArab

PRED relation: flowsInto!
PRED expected values: Karun
=> 41 concepts (33 used for prediction)
PRED predicted values (max 10 best out of 322): Kura (0.33 #53, 0.07 #1254, 0.06 #1555), Ural (0.33 #121, 0.07 #1322, 0.06 #1623), Volga (0.33 #48, 0.07 #1249, 0.06 #1550), LakeKeban (0.25 #785, 0.10 #1085, 0.07 #1386), Atbara (0.03 #2065, 0.02 #2667, 0.02 #2366), BlueNile (0.03 #2038, 0.02 #2640, 0.02 #2339), WhiteNile (0.03 #1944, 0.02 #2546, 0.02 #2245), LakeNasser (0.03 #1870, 0.02 #2472, 0.02 #2171), Bahrel-Djebel-Albert-Nil (0.03 #2050, 0.02 #2652, 0.02 #2351), Bahrel-Ghasal (0.03 #1968, 0.02 #2570, 0.02 #2269)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #53 for best value:
>> intensional similarity = 6
>> extensional distance = 1
>> proper extension:  CaspianSea;
>> query: (?x1422, Kura) <- ?x1422[ has locatedIn ?x302[ has neighbor ?x185;]; has locatedIn ?x304<IR>; is flowsInto of ?x666[ a River;];]

*> Best rule #5408 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 100
*> proper extension:  IndianOcean; SeaofAzov; BlackSea; LakeSkutari; NorthSea; BalticSea; AtlanticOcean; LakeHuron; BarentsSea; GulfofBengal; ...
*> query: (?x1422, ?x573) <- ?x1422[ has locatedIn ?x304[ is locatedIn of ?x573; is neighbor of ?x185;]; is flowsInto of ?x666[ a River;];]
*> conf = 0.03
ranks of expected_values: 105
EVAL	SchattalArab	flowsInto!	Karun	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.010	41.000	33.000	322.000	0.333	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto!
PRED expected values: Karun
=> 115 concepts (115 used for prediction)
PRED predicted values (max 10 best out of 342): LakeKeban (0.25 #785, 0.20 #1991, 0.20 #1690), Breg (0.25 #1168, 0.20 #1470, 0.06 #5701), Olt (0.25 #1125, 0.20 #1427, 0.06 #5658), Pruth (0.25 #1092, 0.20 #1394, 0.06 #5625), Isar (0.25 #1064, 0.20 #1366, 0.06 #5597), March (0.25 #1059, 0.20 #1361, 0.06 #5592), Brigach (0.25 #1035, 0.20 #1337, 0.06 #5568), Lech (0.25 #1022, 0.20 #1324, 0.06 #5555), Waag (0.25 #1015, 0.20 #1317, 0.06 #5548), Inn (0.25 #1005, 0.20 #1307, 0.06 #5538)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #785 for best value:
>> intensional similarity = 13
>> extensional distance = 2
>> proper extension:  Tigris; Euphrat;
>> query: (?x1422, LakeKeban) <- ?x1422[ a River; has hasEstuary ?x1756; has locatedIn ?x302<IRQ>; has locatedIn ?x304[ has encompassed ?x175; has ethnicGroup ?x244; has language ?x511<Turkish>; is neighbor of ?x332<AZ>; is neighbor of ?x381[ is locatedIn of ?x82;];];]

*> Best rule #21153 for first EXPECTED value:
*> intensional similarity = 14
*> extensional distance = 158
*> proper extension:  Guadalquivir; Guadiana; Tajo; Douro; Ebro;
*> query: (?x1422, ?x666) <- ?x1422[ a River; has locatedIn ?x302[ a Country; has ethnicGroup ?x557; has government ?x254; has religion ?x116[ a Religion;]; is locatedIn of ?x666[ a River; has hasSource ?x1669;]; is neighbor of ?x751[ has ethnicGroup ?x244;]; is neighbor of ?x1963[ has wasDependentOf ?x81;];];]
*> conf = 0.05
ranks of expected_values: 127
EVAL	SchattalArab	flowsInto!	Karun	CNN-1.+1._MA	0.000	0.000	0.000	0.008	115.000	115.000	342.000	0.250	http://www.semwebtech.org/mondial/10/meta#flowsInto

#442-BalticSea
PRED entity: BalticSea

PRED relation: locatedIn
PRED expected values: R S LV SF EW
=> 40 concepts (40 used for prediction)
PRED predicted values (max 10 best out of 220): SF (0.91 #2097, 0.90 #3492, 0.90 #3491), S (0.91 #2097, 0.90 #3492, 0.90 #3491), EW (0.91 #2097, 0.90 #3492, 0.90 #3491), BY (0.71 #6062, 0.71 #6061, 0.70 #5826), CZ (0.71 #6062, 0.71 #6061, 0.70 #5826), LV (0.71 #6062, 0.71 #6061, 0.70 #5826), R (0.71 #6062, 0.71 #6061, 0.70 #5826), A (0.56 #1029, 0.22 #932, 0.22 #931), USA (0.49 #6367, 0.35 #6601, 0.19 #1467), N (0.40 #33, 0.17 #731, 0.16 #5827)
>> best conf = 0.91
=> the first rule below is the first best rule for 3 predicted values
>> Best rule #2097 for best value:
>> intensional similarity = 6
>> extensional distance = 26
>> proper extension:  AndamanSea; ArabianSea;
>> query: (?x146, ?x591) <- ?x146[ is flowsInto of ?x590[ has hasEstuary ?x1612;]; is flowsInto of ?x1462[ a River;]; is locatedInWater of ?x145[ has locatedIn ?x591;];]
ranks of expected_values: 1, 2, 3, 6, 7
EVAL	BalticSea	locatedIn	EW	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	40.000	40.000	220.000	0.915	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	BalticSea	locatedIn	SF	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	40.000	40.000	220.000	0.915	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	BalticSea	locatedIn	LV	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.333	40.000	40.000	220.000	0.915	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	BalticSea	locatedIn	S	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	40.000	40.000	220.000	0.915	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	BalticSea	locatedIn	R	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.333	40.000	40.000	220.000	0.915	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: R S LV SF EW
=> 109 concepts (109 used for prediction)
PRED predicted values (max 10 best out of 238): S (0.93 #7262, 0.93 #6792, 0.92 #5624), SF (0.93 #7262, 0.93 #6792, 0.92 #5624), EW (0.93 #7262, 0.93 #6792, 0.92 #5624), R (0.79 #5862, 0.76 #3987, 0.72 #12908), CZ (0.76 #3987, 0.72 #12908, 0.71 #13615), BY (0.76 #3987, 0.72 #12908, 0.71 #13615), LV (0.76 #3987, 0.72 #12908, 0.71 #13615), F (0.73 #8916, 0.43 #710, 0.31 #7731), UA (0.63 #8035, 0.50 #1173, 0.50 #1006), SK (0.47 #1873, 0.46 #3752, 0.33 #4923)
>> best conf = 0.93
=> the first rule below is the first best rule for 3 predicted values
>> Best rule #7262 for best value:
>> intensional similarity = 7
>> extensional distance = 30
>> proper extension:  LakeManicouagan;
>> query: (?x146, ?x565) <- ?x146[ has locatedIn ?x194[ has encompassed ?x195; has government ?x435;]; is locatedInWater of ?x804[ a Island; has locatedIn ?x565[ has language ?x247;];];]
ranks of expected_values: 1, 2, 3, 4, 7
EVAL	BalticSea	locatedIn	EW	CNN-1.+1._MA	1.000	1.000	1.000	1.000	109.000	109.000	238.000	0.931	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	BalticSea	locatedIn	SF	CNN-1.+1._MA	1.000	1.000	1.000	1.000	109.000	109.000	238.000	0.931	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	BalticSea	locatedIn	LV	CNN-1.+1._MA	0.000	1.000	1.000	0.333	109.000	109.000	238.000	0.931	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	BalticSea	locatedIn	S	CNN-1.+1._MA	1.000	1.000	1.000	1.000	109.000	109.000	238.000	0.931	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	BalticSea	locatedIn	R	CNN-1.+1._MA	1.000	1.000	1.000	1.000	109.000	109.000	238.000	0.931	http://www.semwebtech.org/mondial/10/meta#locatedIn

#441-Angara
PRED entity: Angara

PRED relation: locatedIn
PRED expected values: R
=> 44 concepts (44 used for prediction)
PRED predicted values (max 10 best out of 75): R (0.75 #711, 0.75 #479, 0.74 #5222), USA (0.36 #4818, 0.31 #5057, 0.14 #2680), UA (0.25 #70, 0.12 #781, 0.11 #1018), MEX (0.25 #827, 0.09 #2724, 0.05 #5101), ZRE (0.22 #5301, 0.19 #4588, 0.16 #7200), D (0.18 #1679, 0.17 #5954, 0.15 #7141), I (0.18 #1707, 0.09 #2656, 0.09 #2418), CN (0.17 #293, 0.15 #3850, 0.12 #530), SF (0.17 #3451, 0.07 #8069, 0.07 #2607), CDN (0.15 #5048, 0.08 #4809, 0.05 #5285)
>> best conf = 0.75
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #711 for best value:
>> intensional similarity = 8
>> extensional distance = 6
>> proper extension:  Kama;
>> query: (?x2211, ?x73) <- ?x2211[ is hasEstuary of ?x465[ a River; has flowsInto ?x800[ has flowsInto ?x801;]; has hasSource ?x1467[ a Source;]; has locatedIn ?x73<R>;];]
>> Best rule #479 for best value:
>> intensional similarity = 8
>> extensional distance = 6
>> proper extension:  Kama;
>> query: (?x2211, R) <- ?x2211[ is hasEstuary of ?x465[ a River; has flowsInto ?x800[ has flowsInto ?x801;]; has hasSource ?x1467[ a Source;]; has locatedIn ?x73<R>;];]
ranks of expected_values: 1
EVAL	Angara	locatedIn	R	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	44.000	44.000	75.000	0.750	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: R
=> 113 concepts (113 used for prediction)
PRED predicted values (max 10 best out of 82): R (0.80 #5461, 0.78 #19526, 0.76 #19287), D (0.48 #16449, 0.35 #11437, 0.33 #19546), I (0.43 #4080, 0.14 #8852, 0.14 #8377), USA (0.40 #11729, 0.39 #10301, 0.37 #11014), CDN (0.33 #63, 0.25 #4572, 0.20 #11959), IRQ (0.33 #307, 0.08 #7681, 0.06 #9585), UA (0.25 #1256, 0.20 #5531, 0.20 #1730), S (0.25 #7226, 0.10 #16999, 0.10 #5075), MEX (0.25 #4862, 0.08 #15351, 0.06 #17976), ZRE (0.24 #12213, 0.24 #20320, 0.24 #21988)
>> best conf = 0.80
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5461 for best value:
>> intensional similarity = 13
>> extensional distance = 8
>> proper extension:  Karasu; Murat;
>> query: (?x2211, ?x73) <- ?x2211[ a Estuary; is hasEstuary of ?x465[ a River; has flowsInto ?x800[ has flowsInto ?x801;]; has hasSource ?x1467[ a Source;]; has locatedIn ?x73[ has ethnicGroup ?x58; has neighbor ?x353<GE>; is locatedIn of ?x98<BlackSea>;];];]
ranks of expected_values: 1
EVAL	Angara	locatedIn	R	CNN-1.+1._MA	1.000	1.000	1.000	1.000	113.000	113.000	82.000	0.800	http://www.semwebtech.org/mondial/10/meta#locatedIn

#440-CraterLake
PRED entity: CraterLake

PRED relation: type
PRED expected values: "caldera"
=> 34 concepts (34 used for prediction)
PRED predicted values (max 10 best out of 8): "salt" (0.30 #23, 0.22 #71, 0.20 #103), "dam" (0.24 #1, 0.16 #17, 0.15 #33), "volcanic" (0.12 #243, 0.12 #259, 0.11 #227), "volcano" (0.10 #274, 0.08 #86, 0.07 #199), "caldera" (0.06 #35, 0.05 #99, 0.03 #67), "impact" (0.03 #42, 0.02 #74, 0.02 #106), "sand" (0.03 #116, 0.02 #164, 0.02 #148), "crater" (0.02 #29)
>> best conf = 0.30
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #23 for best value:
>> intensional similarity = 5
>> extensional distance = 48
>> proper extension:  EtoschaSaltPan;
>> query: (?x1315, "salt") <- ?x1315[ a Lake; has locatedIn ?x315[ has religion ?x95; is locatedIn of ?x2337[ a Desert;];];]

*> Best rule #35 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 106
*> proper extension:  LagodiBolsena; LagodiComo; LagoTrasimeno; LagodiGarda; Arresoe; LakeMaracaibo;
*> query: (?x1315, "caldera") <- ?x1315[ a Lake; has locatedIn ?x315[ has religion ?x95; is locatedIn of ?x809[ has mergesWith ?x452;];];]
*> conf = 0.06
ranks of expected_values: 5
EVAL	CraterLake	type	"caldera"	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.200	34.000	34.000	8.000	0.300	http://www.semwebtech.org/mondial/10/meta#type

PRED relation: type
PRED expected values: "caldera"
=> 97 concepts (97 used for prediction)
PRED predicted values (max 10 best out of 12): "salt" (0.25 #266, 0.24 #382, 0.24 #282), "dam" (0.24 #1, 0.22 #17, 0.19 #211), "volcanic" (0.19 #293, 0.19 #292, 0.17 #375), "volcano" (0.19 #293, 0.19 #292, 0.17 #375), "caldera" (0.12 #148, 0.09 #296, 0.07 #99), "impact" (0.04 #42, 0.02 #497, 0.02 #220), "crater" (0.02 #174, 0.02 #191, 0.02 #223), "acid" (0.02 #274, 0.01 #390, 0.01 #406), "sand" (0.01 #653, 0.01 #817, 0.01 #701), "naturaldam" (0.01 #391, 0.01 #374, 0.01 #423)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #266 for best value:
>> intensional similarity = 9
>> extensional distance = 59
>> proper extension:  LakeIrazu;
>> query: (?x1315, "salt") <- ?x1315[ a Lake; has locatedIn ?x315[ has ethnicGroup ?x79; has neighbor ?x482; has religion ?x95; is locatedIn of ?x219[ a River;]; is locatedIn of ?x294[ a Volcano;];];]

*> Best rule #148 for first EXPECTED value:
*> intensional similarity = 10
*> extensional distance = 32
*> proper extension:  LagunadeBay;
*> query: (?x1315, "caldera") <- ?x1315[ a Lake; has locatedIn ?x315[ a Country; is locatedIn of ?x314[ a Mountain;]; is locatedIn of ?x823[ a Mountain; a Volcano;]; is locatedIn of ?x2222[ has belongsToIslands ?x2237;];];]
*> conf = 0.12
ranks of expected_values: 5
EVAL	CraterLake	type	"caldera"	CNN-1.+1._MA	0.000	0.000	1.000	0.200	97.000	97.000	12.000	0.246	http://www.semwebtech.org/mondial/10/meta#type

#439-SaintKitts
PRED entity: SaintKitts

PRED relation: locatedInWater
PRED expected values: CaribbeanSea
=> 33 concepts (33 used for prediction)
PRED predicted values (max 10 best out of 26): CaribbeanSea (0.74 #102, 0.71 #144, 0.70 #635), PacificOcean (0.36 #310, 0.35 #735, 0.34 #479), NorthSea (0.18 #1316, 0.09 #892, 0.09 #1063), LabradorSea (0.18 #1316, 0.06 #1103, 0.05 #762), TheChannel (0.18 #1316, 0.06 #1103, 0.05 #762), GreenlandSea (0.18 #1316, 0.06 #1103, 0.05 #762), GulfofMexico (0.18 #1316, 0.06 #1103, 0.05 #762), IrishSea (0.18 #1316, 0.05 #762, 0.03 #548), NorwegianSea (0.18 #1316, 0.05 #762, 0.02 #951), ArcticOcean (0.18 #1316, 0.04 #1117, 0.03 #1202)
>> best conf = 0.74
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #102 for best value:
>> intensional similarity = 5
>> extensional distance = 17
>> proper extension:  Tobago; Anguilla; Trinidad; St.Martin; SaintThomas; Antigua; St.Barthelemy;
>> query: (?x1843, CaribbeanSea) <- ?x1843[ a Island; has belongsToIslands ?x877<LesserAntilles>; has locatedInWater ?x182<AtlanticOcean>;]
ranks of expected_values: 1
EVAL	SaintKitts	locatedInWater	CaribbeanSea	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	33.000	33.000	26.000	0.737	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater
PRED expected values: CaribbeanSea
=> 60 concepts (60 used for prediction)
PRED predicted values (max 10 best out of 289): CaribbeanSea (0.91 #561, 0.91 #690, 0.90 #475), PacificOcean (0.48 #934, 0.44 #706, 0.44 #1879), NorthSea (0.25 #2357, 0.22 #1780, 0.19 #2581), GulfofMexico (0.25 #2357, 0.19 #2581, 0.18 #2312), TheChannel (0.25 #2357, 0.19 #2581, 0.18 #2312), IrishSea (0.25 #2357, 0.19 #2581, 0.18 #2312), GreenlandSea (0.19 #2581, 0.18 #2312, 0.18 #2265), LabradorSea (0.19 #2581, 0.18 #2312, 0.18 #2265), ArcticOcean (0.19 #2581, 0.18 #2312, 0.18 #2265), MediterraneanSea (0.19 #2014, 0.17 #256, 0.17 #255)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #561 for best value:
>> intensional similarity = 15
>> extensional distance = 38
>> proper extension:  Arran; Mull; Jura; Skye;
>> query: (?x1843, ?x317) <- ?x1843[ a Island; has belongsToIslands ?x877[ a Islands; is belongsToIslands of ?x123[ has type ?x150<"volcanic">;]; is belongsToIslands of ?x727[ has locatedIn ?x407; has locatedInWater ?x317;]; is belongsToIslands of ?x1046[ a Island; has locatedIn ?x1130; has type ?x704;];]; has locatedInWater ?x182<AtlanticOcean>;]
ranks of expected_values: 1
EVAL	SaintKitts	locatedInWater	CaribbeanSea	CNN-1.+1._MA	1.000	1.000	1.000	1.000	60.000	60.000	289.000	0.912	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#438-Hazara
PRED entity: Hazara

PRED relation: ethnicGroup!
PRED expected values: AFG
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 96): EAU (0.03 #132), ETH (0.03 #100), UA (0.03 #57), EAK (0.03 #99), CZ (0.02 #98), SN (0.02 #83), MYA (0.02 #70), IR (0.02 #58), SK (0.02 #25), RCA (0.02 #139)
>> best conf = 0.03
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #132 for best value:
>> intensional similarity = 1
>> extensional distance = 279
>> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
>> query: (?x2410, EAU) <- ?x2410[ a EthnicGroup;]

No rule for expected values
ranks of expected_values: 
EVAL	Hazara	ethnicGroup!	AFG	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	2.000	2.000	96.000	0.032	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup!
PRED expected values: AFG
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 96): EAU (0.03 #132), ETH (0.03 #100), UA (0.03 #57), EAK (0.03 #99), CZ (0.02 #98), SN (0.02 #83), MYA (0.02 #70), IR (0.02 #58), SK (0.02 #25), RCA (0.02 #139)
>> best conf = 0.03
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #132 for best value:
>> intensional similarity = 1
>> extensional distance = 279
>> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
>> query: (?x2410, EAU) <- ?x2410[ a EthnicGroup;]

No rule for expected values
ranks of expected_values: 
EVAL	Hazara	ethnicGroup!	AFG	CNN-1.+1._MA	0.000	0.000	0.000	0.000	2.000	2.000	96.000	0.032	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#437-AtlanticOcean
PRED entity: AtlanticOcean

PRED relation: mergesWith!
PRED expected values: NorwegianSea IrishSea
=> 32 concepts (32 used for prediction)
PRED predicted values (max 10 best out of 508): NorwegianSea (0.87 #196, 0.84 #625, 0.81 #493), IrishSea (0.87 #196, 0.84 #625, 0.81 #493), AtlanticOcean (0.51 #426, 0.48 #558, 0.38 #397), ArcticOcean (0.51 #426, 0.48 #558, 0.21 #534), BeringSea (0.50 #187, 0.33 #320, 0.17 #384), HudsonBay (0.33 #366, 0.25 #169, 0.17 #302), SeaofJapan (0.25 #174, 0.19 #404, 0.17 #307), EastSibirianSea (0.25 #180, 0.17 #377, 0.17 #313), BarentsSea (0.25 #170, 0.17 #367, 0.17 #303), KaraSea (0.25 #186, 0.17 #383, 0.17 #319)
>> best conf = 0.87
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #196 for best value:
>> intensional similarity = 5
>> extensional distance = 2
>> proper extension:  ArcticOcean;
>> query: (?x182, ?x60) <- ?x182[ a Sea; has locatedIn ?x272<CDN>; has mergesWith ?x60; is locatedInWater of ?x112;]
ranks of expected_values: 1, 2
EVAL	AtlanticOcean	mergesWith!	IrishSea	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	32.000	32.000	508.000	0.867	http://www.semwebtech.org/mondial/10/meta#mergesWith
EVAL	AtlanticOcean	mergesWith!	NorwegianSea	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	32.000	32.000	508.000	0.867	http://www.semwebtech.org/mondial/10/meta#mergesWith

PRED relation: mergesWith!
PRED expected values: NorwegianSea IrishSea
=> 72 concepts (72 used for prediction)
PRED predicted values (max 10 best out of 508): IrishSea (0.83 #1179, 0.83 #1038, 0.83 #1073), NorwegianSea (0.83 #1179, 0.83 #1038, 0.83 #1073), AtlanticOcean (0.50 #346, 0.44 #414, 0.44 #1002), ArcticOcean (0.44 #1002, 0.44 #1001, 0.33 #180), Skagerrak (0.25 #269, 0.17 #373, 0.11 #441), PacificOcean (0.24 #839, 0.24 #530, 0.23 #912), JavaSea (0.22 #415, 0.19 #628, 0.16 #832), BandaSea (0.22 #432, 0.14 #645, 0.12 #849), EastChinaSea (0.22 #429, 0.13 #747, 0.12 #1022), SeaofJapan (0.18 #564, 0.18 #529, 0.14 #669)
>> best conf = 0.83
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #1179 for best value:
>> intensional similarity = 9
>> extensional distance = 35
>> proper extension:  MarmaraSea;
>> query: (?x182, ?x60) <- ?x182[ has locatedIn ?x50[ a Country; has religion ?x95;]; has locatedIn ?x455[ has ethnicGroup ?x1309;]; has locatedIn ?x697[ has encompassed ?x521[ is encompassed of ?x181;];]; has mergesWith ?x60;]
ranks of expected_values: 1, 2
EVAL	AtlanticOcean	mergesWith!	IrishSea	CNN-1.+1._MA	1.000	1.000	1.000	1.000	72.000	72.000	508.000	0.832	http://www.semwebtech.org/mondial/10/meta#mergesWith
EVAL	AtlanticOcean	mergesWith!	NorwegianSea	CNN-1.+1._MA	1.000	1.000	1.000	1.000	72.000	72.000	508.000	0.832	http://www.semwebtech.org/mondial/10/meta#mergesWith

#436-SP
PRED entity: SP

PRED relation: neighbor
PRED expected values: ETH
=> 26 concepts (26 used for prediction)
PRED predicted values (max 10 best out of 169): ETH (0.53 #481, 0.42 #480, 0.30 #1281), SP (0.53 #481, 0.42 #480, 0.30 #1281), SUD (0.50 #189, 0.33 #350, 0.33 #30), ZRE (0.50 #379, 0.10 #3207, 0.09 #1020), EAT (0.42 #480, 0.30 #1281, 0.28 #1601), IND (0.42 #480, 0.18 #641, 0.16 #3208), YE (0.42 #480, 0.18 #641, 0.16 #3208), MOC (0.42 #480, 0.17 #351, 0.16 #3208), RI (0.42 #480, 0.16 #3208, 0.11 #2243), TL (0.42 #480, 0.16 #3208, 0.11 #2243)
>> best conf = 0.53
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #481 for best value:
>> intensional similarity = 6
>> extensional distance = 4
>> proper extension:  SSD; EAU; EAT;
>> query: (?x220, ?x476) <- ?x220[ has neighbor ?x474<EAK>; is locatedIn of ?x2035[ a River; has hasSource ?x1917; has locatedIn ?x476;];]
ranks of expected_values: 1
EVAL	SP	neighbor	ETH	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	26.000	26.000	169.000	0.533	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: ETH
=> 84 concepts (84 used for prediction)
PRED predicted values (max 10 best out of 236): SP (0.57 #488, 0.46 #1135, 0.46 #1136), ETH (0.57 #488, 0.46 #1135, 0.46 #1136), SSD (0.57 #488, 0.44 #489, 0.40 #1016), ER (0.57 #488, 0.44 #489, 0.32 #1941), SUD (0.57 #488, 0.33 #195, 0.33 #30), CL (0.50 #325, 0.46 #1135, 0.46 #1136), SA (0.50 #2226, 0.33 #609, 0.18 #2926), IND (0.46 #1135, 0.46 #1136, 0.41 #1137), MOC (0.46 #1135, 0.46 #1136, 0.41 #1137), RI (0.46 #1135, 0.46 #1136, 0.41 #1137)
>> best conf = 0.57
=> the first rule below is the first best rule for 5 predicted values
>> Best rule #488 for best value:
>> intensional similarity = 20
>> extensional distance = 1
>> proper extension:  IND;
>> query: (?x220, ?x186) <- ?x220[ a Country; has encompassed ?x213; has ethnicGroup ?x1593[ is ethnicGroup of ?x476[ has neighbor ?x186; has religion ?x56; is locatedIn of ?x228; is neighbor of ?x629;];]; has government ?x1766; has neighbor ?x94; has religion ?x187<Muslim>; is locatedIn of ?x60<IndianOcean>; is locatedIn of ?x510[ a Estuary;]; is locatedIn of ?x1333<ArabianSea>; is locatedIn of ?x2035[ has hasSource ?x1917;];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 2
EVAL	SP	neighbor	ETH	CNN-1.+1._MA	0.000	1.000	1.000	0.500	84.000	84.000	236.000	0.571	http://www.semwebtech.org/mondial/10/meta#neighbor

#435-PalestinianArab
PRED entity: PalestinianArab

PRED relation: ethnicGroup!
PRED expected values: WEST
=> 27 concepts (16 used for prediction)
PRED predicted values (max 10 best out of 213): IL (0.33 #242, 0.33 #49, 0.32 #777), WEST (0.33 #116, 0.32 #777, 0.17 #309), UA (0.33 #57, 0.17 #250, 0.10 #2403), TN (0.33 #12, 0.17 #205, 0.08 #401), MA (0.33 #153, 0.17 #346, 0.06 #971), SUD (0.33 #226, 0.04 #616, 0.02 #2932), IR (0.32 #777, 0.17 #251, 0.09 #1029), SA (0.32 #777, 0.17 #334, 0.06 #2540), AUS (0.32 #777, 0.04 #426, 0.04 #620), SF (0.32 #777, 0.03 #2852, 0.03 #3049)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #242 for best value:
>> intensional similarity = 9
>> extensional distance = 4
>> proper extension:  BerberArab; Beja;
>> query: (?x2485, IL) <- ?x2485[ a EthnicGroup; is ethnicGroup of ?x1495[ has encompassed ?x175; has neighbor ?x63<ET>; has neighbor ?x239[ a Country; has wasDependentOf ?x485;]; has religion ?x116;];]
>> Best rule #49 for best value:
>> intensional similarity = 3
>> extensional distance = 1
>> proper extension:  Jewish;
>> query: (?x2485, IL) <- ?x2485[ a EthnicGroup; is ethnicGroup of ?x1495<GAZA>;]

*> Best rule #116 for first EXPECTED value:
*> intensional similarity = 3
*> extensional distance = 1
*> proper extension:  Jewish;
*> query: (?x2485, WEST) <- ?x2485[ a EthnicGroup; is ethnicGroup of ?x1495<GAZA>;]
*> conf = 0.33
ranks of expected_values: 2
EVAL	PalestinianArab	ethnicGroup!	WEST	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	27.000	16.000	213.000	0.333	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup!
PRED expected values: WEST
=> 67 concepts (67 used for prediction)
PRED predicted values (max 10 best out of 214): MYA (0.58 #2047, 0.47 #2245, 0.33 #3235), ET (0.51 #3953, 0.45 #5150, 0.40 #592), JOR (0.50 #935, 0.43 #1329, 0.40 #541), ETH (0.47 #3067, 0.25 #6052, 0.07 #10455), IL (0.47 #981, 0.44 #588, 0.43 #8743), WEST (0.47 #981, 0.43 #8743, 0.42 #5751), TN (0.47 #981, 0.42 #5751, 0.40 #602), MA (0.47 #981, 0.42 #5751, 0.38 #4555), UA (0.47 #981, 0.42 #5751, 0.38 #4555), RMM (0.45 #1731, 0.23 #3514, 0.22 #3911)
>> best conf = 0.58
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2047 for best value:
>> intensional similarity = 20
>> extensional distance = 10
>> proper extension:  Chinese; Indian; Rakhine; Euro-African; Karen; Mon; Burman; Shan;
>> query: (?x2485, MYA) <- ?x2485[ a EthnicGroup; is ethnicGroup of ?x1495[ has encompassed ?x175; has language ?x1398; has neighbor ?x63[ a Country; has ethnicGroup ?x197; has government ?x435;]; has neighbor ?x239[ has ethnicGroup ?x244; has government ?x254<"parliamentary democracy">; has neighbor ?x115; has wasDependentOf ?x485; is locatedIn of ?x238;]; has religion ?x116<Christian>; has religion ?x187<Muslim>; is locatedIn of ?x275;];]

*> Best rule #981 for first EXPECTED value:
*> intensional similarity = 20
*> extensional distance = 4
*> proper extension:  Circassian;
*> query: (?x2485, ?x303) <- ?x2485[ a EthnicGroup; is ethnicGroup of ?x1495[ a Country; has ethnicGroup ?x852[ a EthnicGroup; is ethnicGroup of ?x303;]; has neighbor ?x63[ has ethnicGroup ?x197; has wasDependentOf ?x81; is locatedIn of ?x1552<RedSea>;]; has neighbor ?x239<IL>; has religion ?x116<Christian>; has religion ?x187<Muslim>; is locatedIn of ?x275[ a Sea; has mergesWith ?x182;];];]
*> conf = 0.47
ranks of expected_values: 6
EVAL	PalestinianArab	ethnicGroup!	WEST	CNN-1.+1._MA	0.000	0.000	1.000	0.167	67.000	67.000	214.000	0.583	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#434-BF
PRED entity: BF

PRED relation: religion
PRED expected values: Muslim
=> 33 concepts (33 used for prediction)
PRED predicted values (max 10 best out of 30): Muslim (0.87 #538, 0.84 #579, 0.73 #210), RomanCatholic (0.57 #500, 0.50 #459, 0.49 #1201), Protestant (0.47 #495, 0.44 #454, 0.41 #701), ChristianOrthodox (0.20 #658, 0.20 #700, 0.20 #742), Jewish (0.20 #168, 0.15 #1277, 0.14 #85), Hindu (0.16 #256, 0.15 #1277, 0.13 #584), Anglican (0.15 #1277, 0.13 #469, 0.13 #510), Catholic (0.15 #1277, 0.05 #325, 0.04 #448), Kimbanguist (0.15 #1277, 0.03 #323, 0.03 #364), CopticChristian (0.15 #1277, 0.03 #318, 0.03 #359)
>> best conf = 0.87
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #538 for best value:
>> intensional similarity = 6
>> extensional distance = 92
>> proper extension:  GBZ;
>> query: (?x811, Muslim) <- ?x811[ a Country; has religion ?x116[ is religion of ?x1576<ZW>;]; is neighbor of ?x426[ is neighbor of ?x139;];]
ranks of expected_values: 1
EVAL	BF	religion	Muslim	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	33.000	33.000	30.000	0.872	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion
PRED expected values: Muslim
=> 69 concepts (69 used for prediction)
PRED predicted values (max 10 best out of 36): Muslim (0.87 #1887, 0.84 #922, 0.84 #2264), RomanCatholic (0.53 #2815, 0.50 #1469, 0.49 #1763), Protestant (0.48 #2685, 0.44 #1464, 0.44 #1591), Jewish (0.42 #2050, 0.33 #169, 0.25 #1043), ChristianOrthodox (0.28 #1800, 0.26 #1842, 0.25 #1967), Buddhist (0.25 #1043, 0.24 #805, 0.20 #887), HoaHao (0.25 #1043, 0.12 #1547, 0.02 #1453), CaoDai (0.25 #1043, 0.12 #1547, 0.02 #1434), Hindu (0.24 #803, 0.20 #885, 0.17 #1010), Anglican (0.16 #2683, 0.15 #1128, 0.13 #1479)
>> best conf = 0.87
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1887 for best value:
>> intensional similarity = 29
>> extensional distance = 92
>> proper extension:  GBZ;
>> query: (?x811, Muslim) <- ?x811[ a Country; has religion ?x116[ is religion of ?x91<THA>; is religion of ?x115<RL>; is religion of ?x158<FJI>; is religion of ?x376<MAL>; is religion of ?x536<CAM>; is religion of ?x538<BRU>; is religion of ?x773[ has ethnicGroup ?x774<Indonesian>;]; is religion of ?x810[ a Country; is locatedIn of ?x182;]; is religion of ?x851<MA>; is religion of ?x1072<WAL>; is religion of ?x1307<RT>; is religion of ?x1576<ZW>;]; is neighbor of ?x426;]
ranks of expected_values: 1
EVAL	BF	religion	Muslim	CNN-1.+1._MA	1.000	1.000	1.000	1.000	69.000	69.000	36.000	0.872	http://www.semwebtech.org/mondial/10/meta#religion

#433-Mull
PRED entity: Mull

PRED relation: locatedIn
PRED expected values: GB
=> 29 concepts (29 used for prediction)
PRED predicted values (max 10 best out of 137): GB (0.83 #709, 0.83 #481, 0.75 #946), P (0.18 #1618, 0.17 #1381, 0.13 #1855), E (0.15 #1211, 0.14 #1448, 0.12 #1685), RI (0.09 #2893, 0.09 #3368, 0.09 #3130), USA (0.08 #2913, 0.08 #3388, 0.08 #3150), IRL (0.08 #500, 0.05 #6407, 0.05 #6406), I (0.06 #2889, 0.06 #3364, 0.06 #3126), GR (0.06 #3880, 0.06 #4118, 0.05 #4830), D (0.05 #5471, 0.05 #5708, 0.05 #5234), TT (0.05 #6407, 0.05 #6406, 0.05 #6405)
>> best conf = 0.83
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #709 for best value:
>> intensional similarity = 9
>> extensional distance = 10
>> proper extension:  Ireland; GreatBritain; BishopRock; Benbecula; Barra; LewisandHarris;
>> query: (?x2113, ?x81) <- ?x2113[ a Island; has belongsToIslands ?x2364[ a Islands; is belongsToIslands of ?x1599[ a Island; has locatedIn ?x81<GB>;];]; has locatedInWater ?x182<AtlanticOcean>;]
>> Best rule #481 for best value:
>> intensional similarity = 9
>> extensional distance = 10
>> proper extension:  Ireland; GreatBritain; BishopRock; Benbecula; Barra; LewisandHarris;
>> query: (?x2113, GB) <- ?x2113[ a Island; has belongsToIslands ?x2364[ a Islands; is belongsToIslands of ?x1599[ a Island; has locatedIn ?x81<GB>;];]; has locatedInWater ?x182<AtlanticOcean>;]
ranks of expected_values: 1
EVAL	Mull	locatedIn	GB	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	29.000	29.000	137.000	0.833	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: GB
=> 46 concepts (46 used for prediction)
PRED predicted values (max 10 best out of 139): GB (0.83 #709, 0.83 #481, 0.71 #245), P (0.18 #1617, 0.17 #1143, 0.17 #1380), E (0.15 #973, 0.15 #1210, 0.14 #1447), USA (0.13 #5084, 0.11 #4840, 0.10 #5573), GR (0.12 #2937, 0.06 #5347, 0.06 #5833), CDN (0.10 #3395, 0.05 #10591, 0.05 #10590), RI (0.10 #3872, 0.10 #4109, 0.10 #4346), F (0.09 #5253, 0.08 #10342, 0.08 #5010), IRL (0.08 #500, 0.06 #737, 0.05 #10591), I (0.08 #2895, 0.07 #3868, 0.07 #4105)
>> best conf = 0.83
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #709 for best value:
>> intensional similarity = 13
>> extensional distance = 10
>> proper extension:  Ireland; GreatBritain; BishopRock; Benbecula; Barra; LewisandHarris;
>> query: (?x2113, ?x81) <- ?x2113[ a Island; has belongsToIslands ?x2364[ a Islands; is belongsToIslands of ?x467[ a Island; has locatedIn ?x81<GB>;]; is belongsToIslands of ?x674[ a Island; has locatedIn ?x81<GB>; has locatedInWater ?x182<AtlanticOcean>;];]; has locatedInWater ?x182<AtlanticOcean>;]
>> Best rule #481 for best value:
>> intensional similarity = 13
>> extensional distance = 10
>> proper extension:  Ireland; GreatBritain; BishopRock; Benbecula; Barra; LewisandHarris;
>> query: (?x2113, GB) <- ?x2113[ a Island; has belongsToIslands ?x2364[ a Islands; is belongsToIslands of ?x467[ a Island; has locatedIn ?x81<GB>;]; is belongsToIslands of ?x674[ a Island; has locatedIn ?x81<GB>; has locatedInWater ?x182<AtlanticOcean>;];]; has locatedInWater ?x182<AtlanticOcean>;]
ranks of expected_values: 1
EVAL	Mull	locatedIn	GB	CNN-1.+1._MA	1.000	1.000	1.000	1.000	46.000	46.000	139.000	0.833	http://www.semwebtech.org/mondial/10/meta#locatedIn

#432-Christian
PRED entity: Christian

PRED relation: religion!
PRED expected values: SYR WAG CI
=> 16 concepts (16 used for prediction)
PRED predicted values (max 10 best out of 189): GB (0.57 #499, 0.40 #831, 0.36 #997), VN (0.54 #1482, 0.54 #987, 0.53 #1152), KGZ (0.54 #1482, 0.54 #987, 0.53 #1152), R (0.54 #1482, 0.54 #987, 0.53 #1152), AFG (0.54 #1482, 0.54 #987, 0.53 #1152), BHT (0.54 #1482, 0.54 #987, 0.53 #1152), ET (0.54 #1482, 0.54 #987, 0.53 #1152), RI (0.54 #1482, 0.54 #987, 0.53 #1152), CI (0.54 #1482, 0.54 #987, 0.53 #1152), SYR (0.54 #1482, 0.54 #987, 0.53 #1152)
>> best conf = 0.57
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #499 for best value:
>> intensional similarity = 12
>> extensional distance = 5
>> proper extension:  Hindu; Buddhist; Jains; Sikh;
>> query: (?x116, GB) <- ?x116[ is religion of ?x232[ is locatedIn of ?x231; is neighbor of ?x409<BHT>; is neighbor of ?x463[ has ethnicGroup ?x1647; is neighbor of ?x871;];]; is religion of ?x525[ has wasDependentOf ?x81;]; is religion of ?x536[ has ethnicGroup ?x122; is locatedIn of ?x182;];]

*> Best rule #1482 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 21
*> proper extension:  Confucianism; Chondogyo;
*> query: (?x116, ?x409) <- ?x116[ is religion of ?x172[ has encompassed ?x213;]; is religion of ?x232[ has government ?x831; is locatedIn of ?x231; is neighbor of ?x409[ has wasDependentOf ?x81;];];]
*> conf = 0.54
ranks of expected_values: 9, 10, 22
EVAL	Christian	religion!	CI	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.111	16.000	16.000	189.000	0.571	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Christian	religion!	WAG	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.050	16.000	16.000	189.000	0.571	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Christian	religion!	SYR	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.111	16.000	16.000	189.000	0.571	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion!
PRED expected values: SYR WAG CI
=> 30 concepts (30 used for prediction)
PRED predicted values (max 10 best out of 188): RI (0.67 #873, 0.63 #1006, 0.62 #664), GB (0.67 #845, 0.60 #1005, 0.56 #1176), I (0.67 #870, 0.56 #1040, 0.50 #699), AUS (0.67 #867, 0.50 #696, 0.44 #1037), RP (0.67 #918, 0.50 #747, 0.44 #1088), LAR (0.66 #832, 0.63 #1006, 0.62 #664), WAG (0.66 #832, 0.63 #1006, 0.62 #664), CI (0.66 #832, 0.63 #1006, 0.62 #664), SYR (0.66 #832, 0.63 #1006, 0.62 #664), RSA (0.66 #832, 0.63 #1006, 0.62 #664)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #873 for best value:
>> intensional similarity = 26
>> extensional distance = 4
>> proper extension:  Protestant; RomanCatholic;
>> query: (?x116, RI) <- ?x116[ is religion of ?x94[ has ethnicGroup ?x1593; has neighbor ?x220; is locatedIn of ?x415;]; is religion of ?x232[ has neighbor ?x641[ has ethnicGroup ?x298;]; is locatedIn of ?x384<SouthChinaSea>;]; is religion of ?x239[ a Country; is neighbor of ?x63;]; is religion of ?x538[ has religion ?x462<Buddhist>; has wasDependentOf ?x81; is locatedIn of ?x375;]; is religion of ?x621[ a Country; has ethnicGroup ?x162;]; is religion of ?x1010[ has government ?x2058<"parliamentary">; is locatedIn of ?x72;]; is religion of ?x1731[ has encompassed ?x211;];]

*> Best rule #832 for first EXPECTED value:
*> intensional similarity = 24
*> extensional distance = 2
*> proper extension:  Buddhist;
*> query: (?x116, ?x426) <- ?x116[ is religion of ?x186[ a Country; has neighbor ?x229[ has neighbor ?x348; is locatedIn of ?x53;]; is locatedIn of ?x531;]; is religion of ?x538<BRU>; is religion of ?x803[ has ethnicGroup ?x244; has government ?x92<"constitutional monarchy">;]; is religion of ?x839[ has encompassed ?x213; has ethnicGroup ?x1537; has government ?x435<"republic">; has wasDependentOf ?x78; is locatedIn of ?x456; is neighbor of ?x426;]; is religion of ?x1944[ has wasDependentOf ?x81; is locatedIn of ?x282<PacificOcean>;];]
*> conf = 0.66
ranks of expected_values: 7, 8, 9
EVAL	Christian	religion!	CI	CNN-1.+1._MA	0.000	0.000	1.000	0.143	30.000	30.000	188.000	0.667	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Christian	religion!	WAG	CNN-1.+1._MA	0.000	0.000	1.000	0.143	30.000	30.000	188.000	0.667	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Christian	religion!	SYR	CNN-1.+1._MA	0.000	0.000	1.000	0.143	30.000	30.000	188.000	0.667	http://www.semwebtech.org/mondial/10/meta#religion

#431-PK
PRED entity: PK

PRED relation: wasDependentOf
PRED expected values: GB
=> 39 concepts (39 used for prediction)
PRED predicted values (max 10 best out of 32): GB (0.33 #4, 0.32 #660, 0.30 #253), SovietUnion (0.25 #143, 0.20 #237, 0.15 #649), E (0.21 #413, 0.20 #256, 0.19 #445), P (0.20 #84, 0.11 #208, 0.04 #776), F (0.15 #787, 0.15 #756, 0.14 #849), UnitedNations (0.11 #200, 0.08 #799, 0.07 #1018), J (0.10 #285, 0.03 #572, 0.02 #759), OttomanEmpire (0.08 #432, 0.07 #464, 0.06 #654), Yugoslavia (0.08 #492, 0.07 #621, 0.06 #652), NL (0.08 #393, 0.05 #297, 0.04 #895)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4 for best value:
>> intensional similarity = 6
>> extensional distance = 1
>> proper extension:  AFG;
>> query: (?x83, GB) <- ?x83[ has language ?x1033<Pashtu>; has neighbor ?x381[ is locatedIn of ?x276;]; is locatedIn of ?x82<Rigestan>;]
ranks of expected_values: 1
EVAL	PK	wasDependentOf	GB	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	39.000	39.000	32.000	0.333	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

PRED relation: wasDependentOf
PRED expected values: GB
=> 85 concepts (85 used for prediction)
PRED predicted values (max 10 best out of 49): GB (0.50 #238, 0.43 #496, 0.43 #468), SovietUnion (0.40 #1178, 0.37 #1429, 0.33 #1213), E (0.33 #1273, 0.26 #1491, 0.25 #1619), F (0.26 #1444, 0.24 #1855, 0.23 #2200), NL (0.25 #148, 0.14 #518, 0.14 #482), UnitedNations (0.20 #1353, 0.16 #1461, 0.15 #1135), J (0.20 #2728, 0.06 #1452, 0.05 #1235), OttomanEmpire (0.15 #1146, 0.14 #1045, 0.12 #1364), P (0.14 #594, 0.12 #1677, 0.10 #1848), CN (0.14 #1815, 0.06 #2863, 0.05 #1168)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #238 for best value:
>> intensional similarity = 15
>> extensional distance = 2
>> proper extension:  BHT;
>> query: (?x83, GB) <- ?x83[ is neighbor of ?x232<CN>; is neighbor of ?x381[ has encompassed ?x175; has government ?x2442; has language ?x1033; has religion ?x187<Muslim>; is locatedIn of ?x82; is neighbor of ?x290[ has ethnicGroup ?x1193; has language ?x555; has religion ?x56;];]; is neighbor of ?x924<IND>;]
ranks of expected_values: 1
EVAL	PK	wasDependentOf	GB	CNN-1.+1._MA	1.000	1.000	1.000	1.000	85.000	85.000	49.000	0.500	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

#430-WAL
PRED entity: WAL

PRED relation: encompassed
PRED expected values: Africa
=> 45 concepts (45 used for prediction)
PRED predicted values (max 10 best out of 5): Africa (0.81 #24, 0.78 #117, 0.77 #149), America (0.61 #40, 0.57 #15, 0.53 #95), Asia (0.34 #133, 0.34 #86, 0.33 #96), Europe (0.34 #133, 0.27 #108, 0.27 #124), Australia-Oceania (0.34 #133, 0.16 #63, 0.16 #48)
>> best conf = 0.81
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #24 for best value:
>> intensional similarity = 6
>> extensional distance = 14
>> proper extension:  TN; WAN; TCH; GH; RIM; DZ; RG; BEN; MA; LAR;
>> query: (?x1072, Africa) <- ?x1072[ has government ?x180; has neighbor ?x651[ is locatedIn of ?x182; is neighbor of ?x839<RMM>;]; has wasDependentOf ?x81;]
ranks of expected_values: 1
EVAL	WAL	encompassed	Africa	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	45.000	45.000	5.000	0.812	http://www.semwebtech.org/mondial/10/meta#encompassed

PRED relation: encompassed
PRED expected values: Africa
=> 91 concepts (91 used for prediction)
PRED predicted values (max 10 best out of 5): Africa (0.92 #261, 0.91 #210, 0.87 #232), America (0.64 #64, 0.61 #181, 0.60 #170), Europe (0.44 #273, 0.38 #288, 0.38 #423), Asia (0.38 #288, 0.38 #423, 0.37 #317), Australia-Oceania (0.38 #288, 0.38 #423, 0.37 #412)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #261 for best value:
>> intensional similarity = 15
>> extensional distance = 49
>> proper extension:  SMAR;
>> query: (?x1072, ?x213) <- ?x1072[ a Country; has government ?x180; has neighbor ?x651[ a Country; has government ?x435; has neighbor ?x1206[ a Country; has neighbor ?x483; is locatedIn of ?x350;]; has neighbor ?x1755[ has encompassed ?x213;]; is locatedIn of ?x580[ has locatedIn ?x139<WAN>; is flowsInto of ?x456;];];]
ranks of expected_values: 1
EVAL	WAL	encompassed	Africa	CNN-1.+1._MA	1.000	1.000	1.000	1.000	91.000	91.000	5.000	0.922	http://www.semwebtech.org/mondial/10/meta#encompassed

#429-NorwegianSea
PRED entity: NorwegianSea

PRED relation: mergesWith
PRED expected values: NorthSea AtlanticOcean BarentsSea
=> 29 concepts (25 used for prediction)
PRED predicted values (max 10 best out of 89): NorthSea (0.85 #655, 0.84 #613, 0.84 #486), BarentsSea (0.85 #655, 0.84 #613, 0.84 #486), AtlanticOcean (0.85 #655, 0.84 #613, 0.84 #486), NorwegianSea (0.50 #182, 0.50 #59, 0.46 #776), ArcticOcean (0.46 #776, 0.46 #775, 0.33 #175), TheChannel (0.33 #154, 0.33 #31, 0.25 #72), IndianOcean (0.33 #1, 0.25 #42, 0.21 #407), GulfofMexico (0.33 #33, 0.25 #74, 0.17 #197), CaribbeanSea (0.33 #16, 0.25 #57, 0.17 #180), LabradorSea (0.33 #8, 0.25 #49, 0.17 #172)
>> best conf = 0.85
=> the first rule below is the first best rule for 3 predicted values
>> Best rule #655 for best value:
>> intensional similarity = 7
>> extensional distance = 34
>> proper extension:  MarmaraSea;
>> query: (?x373, ?x121) <- ?x373[ has locatedIn ?x81[ has ethnicGroup ?x1196;]; has locatedIn ?x170[ has neighbor ?x73;]; has locatedIn ?x455[ a Country;]; is mergesWith of ?x121;]
ranks of expected_values: 1, 2, 3
EVAL	NorwegianSea	mergesWith	BarentsSea	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	29.000	25.000	89.000	0.845	http://www.semwebtech.org/mondial/10/meta#mergesWith
EVAL	NorwegianSea	mergesWith	AtlanticOcean	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	29.000	25.000	89.000	0.845	http://www.semwebtech.org/mondial/10/meta#mergesWith
EVAL	NorwegianSea	mergesWith	NorthSea	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	29.000	25.000	89.000	0.845	http://www.semwebtech.org/mondial/10/meta#mergesWith

PRED relation: mergesWith
PRED expected values: NorthSea AtlanticOcean BarentsSea
=> 107 concepts (107 used for prediction)
PRED predicted values (max 10 best out of 873): AtlanticOcean (0.86 #511, 0.85 #1147, 0.84 #1150), BarentsSea (0.85 #1147, 0.84 #1150, 0.84 #1148), NorthSea (0.85 #1147, 0.84 #1150, 0.84 #1148), ArcticOcean (0.63 #640, 0.54 #641, 0.53 #385), NorwegianSea (0.54 #641, 0.53 #385, 0.53 #639), PacificOcean (0.42 #485, 0.40 #358, 0.35 #613), TheChannel (0.33 #126, 0.33 #115, 0.33 #31), LabradorSea (0.33 #126, 0.33 #8, 0.29 #170), GulfofMexico (0.33 #126, 0.33 #33, 0.29 #170), CaribbeanSea (0.33 #126, 0.33 #16, 0.29 #170)
>> best conf = 0.86
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #511 for best value:
>> intensional similarity = 8
>> extensional distance = 17
>> proper extension:  SeaofJapan; SeaofOkhotsk; EastChinaSea; SulawesiSea; SuluSea; BandaSea; BeringSea;
>> query: (?x373, ?x1419) <- ?x373[ has locatedIn ?x81; is mergesWith of ?x1419[ a Sea; has locatedIn ?x455[ has encompassed ?x195; has government ?x700<"constitutional republic">;]; is locatedInWater of ?x1075;];]
ranks of expected_values: 1, 2, 3
EVAL	NorwegianSea	mergesWith	BarentsSea	CNN-1.+1._MA	1.000	1.000	1.000	1.000	107.000	107.000	873.000	0.857	http://www.semwebtech.org/mondial/10/meta#mergesWith
EVAL	NorwegianSea	mergesWith	AtlanticOcean	CNN-1.+1._MA	1.000	1.000	1.000	1.000	107.000	107.000	873.000	0.857	http://www.semwebtech.org/mondial/10/meta#mergesWith
EVAL	NorwegianSea	mergesWith	NorthSea	CNN-1.+1._MA	1.000	1.000	1.000	1.000	107.000	107.000	873.000	0.857	http://www.semwebtech.org/mondial/10/meta#mergesWith

#428-MC
PRED entity: MC

PRED relation: neighbor!
PRED expected values: F
=> 44 concepts (44 used for prediction)
PRED predicted values (max 10 best out of 221): F (0.91 #3425, 0.90 #2605, 0.90 #3588), D (0.50 #338, 0.40 #499, 0.33 #1640), A (0.50 #238, 0.40 #1538, 0.33 #1702), I (0.33 #38, 0.26 #3426, 0.25 #2607), FL (0.33 #76, 0.25 #399, 0.25 #237), R (0.28 #2283, 0.21 #2772, 0.16 #3266), UA (0.27 #1678, 0.27 #1514, 0.09 #2333), SK (0.27 #1647, 0.20 #1483, 0.07 #2302), CH (0.26 #3426, 0.25 #2607, 0.25 #2606), B (0.26 #3426, 0.25 #2607, 0.25 #2606)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3425 for best value:
>> intensional similarity = 6
>> extensional distance = 72
>> proper extension:  NAM; DK; FGU; GQ;
>> query: (?x1577, ?x78) <- ?x1577[ has government ?x92; has language ?x51; has neighbor ?x78[ has neighbor ?x149; is locatedIn of ?x121;]; has religion ?x352;]
ranks of expected_values: 1
EVAL	MC	neighbor!	F	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	44.000	44.000	221.000	0.912	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: F
=> 72 concepts (72 used for prediction)
PRED predicted values (max 10 best out of 226): F (0.90 #7012, 0.90 #8018, 0.88 #10062), CH (0.40 #1212, 0.40 #1040, 0.33 #210), I (0.40 #2209, 0.33 #992, 0.33 #165), HR (0.40 #681, 0.33 #165, 0.33 #164), D (0.40 #844, 0.33 #15, 0.30 #8020), AND (0.40 #1120, 0.30 #8022, 0.30 #8021), SLO (0.33 #244, 0.33 #165, 0.33 #164), E (0.33 #165, 0.33 #164, 0.30 #8020), MC (0.33 #165, 0.33 #164, 0.30 #8022), MNE (0.33 #165, 0.33 #164, 0.22 #2013)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #7012 for best value:
>> intensional similarity = 15
>> extensional distance = 67
>> proper extension:  NOK;
>> query: (?x1577, ?x78) <- ?x1577[ a Country; has encompassed ?x195[ is encompassed of ?x106[ has ethnicGroup ?x775; has religion ?x56;]; is encompassed of ?x565[ has language ?x247; is locatedIn of ?x631;]; is encompassed of ?x1027[ a Country; is locatedIn of ?x182; is wasDependentOf of ?x192;];]; has government ?x92; has language ?x51; has neighbor ?x78;]
ranks of expected_values: 1
EVAL	MC	neighbor!	F	CNN-1.+1._MA	1.000	1.000	1.000	1.000	72.000	72.000	226.000	0.903	http://www.semwebtech.org/mondial/10/meta#neighbor

#427-IndianOcean
PRED entity: IndianOcean

PRED relation: mergesWith
PRED expected values: JavaSea AndamanSea
=> 34 concepts (30 used for prediction)
PRED predicted values (max 10 best out of 151): JavaSea (0.82 #245, 0.46 #488, 0.12 #179), AndamanSea (0.82 #245, 0.46 #488, 0.08 #120), IndianOcean (0.46 #488, 0.25 #37, 0.20 #246), SouthChinaSea (0.46 #488, 0.12 #190, 0.10 #261), MalakkaStrait (0.46 #488, 0.08 #123, 0.08 #191), PersianGulf (0.25 #61, 0.03 #270, 0.03 #235), RedSea (0.25 #66, 0.03 #449, 0.02 #314), ArcticOcean (0.22 #323, 0.17 #219, 0.17 #115), NorwegianSea (0.17 #121, 0.13 #225, 0.13 #294), SuluSea (0.12 #196, 0.10 #267, 0.10 #232)
>> best conf = 0.82
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #245 for best value:
>> intensional similarity = 6
>> extensional distance = 28
>> proper extension:  NorthSea; AtlanticOcean; JavaSea; LabradorSea; BarentsSea; GulfofBengal; ArcticOcean; SeaofJapan; MediterraneanSea; PacificOcean; ...
>> query: (?x60, ?x241) <- ?x60[ has locatedIn ?x61; has mergesWith ?x182; is locatedInWater of ?x226; is locatedInWater of ?x1619[ a Island;]; is mergesWith of ?x241;]
ranks of expected_values: 1, 2
EVAL	IndianOcean	mergesWith	AndamanSea	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	34.000	30.000	151.000	0.824	http://www.semwebtech.org/mondial/10/meta#mergesWith
EVAL	IndianOcean	mergesWith	JavaSea	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	34.000	30.000	151.000	0.824	http://www.semwebtech.org/mondial/10/meta#mergesWith

PRED relation: mergesWith
PRED expected values: JavaSea AndamanSea
=> 91 concepts (91 used for prediction)
PRED predicted values (max 10 best out of 324): AndamanSea (0.87 #1002, 0.87 #1001, 0.85 #1077), JavaSea (0.87 #1002, 0.87 #1001, 0.85 #1077), IndianOcean (0.56 #820, 0.50 #1377, 0.50 #1376), SouthChinaSea (0.56 #820, 0.50 #1377, 0.50 #1376), MalakkaStrait (0.56 #820, 0.50 #1377, 0.50 #1376), GulfofOman (0.33 #26, 0.25 #326, 0.20 #401), YellowSea (0.33 #496, 0.20 #423, 0.10 #647), GulfofMexico (0.29 #588, 0.20 #665, 0.17 #1040), MarmaraSea (0.29 #591, 0.11 #995, 0.10 #668), ArcticOcean (0.27 #1387, 0.20 #646, 0.20 #422)
>> best conf = 0.87
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #1002 for best value:
>> intensional similarity = 14
>> extensional distance = 16
>> proper extension:  SeaofAzov;
>> query: (?x60, ?x339) <- ?x60[ a Sea; has locatedIn ?x217[ has neighbor ?x376; is locatedIn of ?x1005[ has type ?x150;]; is locatedIn of ?x1253[ a Volcano;];]; has locatedIn ?x906[ has encompassed ?x211;]; is flowsInto of ?x1977[ has locatedIn ?x138;]; is mergesWith of ?x339[ a Sea; has locatedIn ?x91;];]
>> Best rule #1001 for best value:
>> intensional similarity = 15
>> extensional distance = 16
>> proper extension:  SeaofAzov;
>> query: (?x60, ?x182) <- ?x60[ a Sea; has locatedIn ?x217[ has neighbor ?x376; is locatedIn of ?x1005[ has type ?x150;]; is locatedIn of ?x1253[ a Volcano;];]; has locatedIn ?x906[ has encompassed ?x211;]; is flowsInto of ?x1977[ has locatedIn ?x138;]; is mergesWith of ?x182; is mergesWith of ?x339[ a Sea; has locatedIn ?x91;];]
ranks of expected_values: 1, 2
EVAL	IndianOcean	mergesWith	AndamanSea	CNN-1.+1._MA	1.000	1.000	1.000	1.000	91.000	91.000	324.000	0.870	http://www.semwebtech.org/mondial/10/meta#mergesWith
EVAL	IndianOcean	mergesWith	JavaSea	CNN-1.+1._MA	1.000	1.000	1.000	1.000	91.000	91.000	324.000	0.870	http://www.semwebtech.org/mondial/10/meta#mergesWith

#426-PuertoRico
PRED entity: PuertoRico

PRED relation: locatedInWater
PRED expected values: AtlanticOcean
=> 41 concepts (38 used for prediction)
PRED predicted values (max 10 best out of 31): AtlanticOcean (0.83 #598, 0.83 #561, 0.77 #127), GulfofMexico (0.77 #127, 0.76 #597, 0.62 #981), PacificOcean (0.68 #615, 0.29 #657, 0.28 #699), PuertoRico (0.48 #170, 0.42 #213, 0.05 #299), CerrodePunta (0.48 #170, 0.42 #213, 0.05 #299), JavaSea (0.27 #264, 0.15 #392, 0.15 #435), IndianOcean (0.23 #257, 0.17 #385, 0.17 #428), ArcticOcean (0.18 #355, 0.04 #738, 0.04 #397), SulawesiSea (0.15 #282, 0.09 #410, 0.08 #453), NorthSea (0.12 #685, 0.12 #643, 0.10 #813)
>> best conf = 0.83
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #598 for best value:
>> intensional similarity = 6
>> extensional distance = 61
>> proper extension:  NorthUist;
>> query: (?x1557, ?x182) <- ?x1557[ a Island; has belongsToIslands ?x1962[ a Islands; is belongsToIslands of ?x1928[ has locatedInWater ?x182<AtlanticOcean>;];];]
>> Best rule #561 for best value:
>> intensional similarity = 6
>> extensional distance = 61
>> proper extension:  NorthUist;
>> query: (?x1557, AtlanticOcean) <- ?x1557[ a Island; has belongsToIslands ?x1962[ a Islands; is belongsToIslands of ?x1928[ has locatedInWater ?x182<AtlanticOcean>;];];]
ranks of expected_values: 1
EVAL	PuertoRico	locatedInWater	AtlanticOcean	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	41.000	38.000	31.000	0.825	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater
PRED expected values: AtlanticOcean
=> 100 concepts (98 used for prediction)
PRED predicted values (max 10 best out of 152): AtlanticOcean (0.93 #1551, 0.92 #816, 0.91 #684), GulfofMexico (0.92 #816, 0.91 #684, 0.76 #2016), PacificOcean (0.81 #1819, 0.78 #1776, 0.74 #1947), PuertoRico (0.45 #469, 0.43 #342, 0.38 #945), CerrodePunta (0.45 #469, 0.43 #342, 0.38 #945), JavaSea (0.29 #997, 0.27 #1168, 0.27 #1125), IndianOcean (0.29 #947, 0.27 #1075, 0.25 #990), NorthSea (0.18 #2150, 0.16 #2326, 0.14 #215), SulawesiSea (0.17 #1015, 0.15 #1143, 0.14 #1316), MediterraneanSea (0.16 #2899, 0.14 #2814, 0.13 #2943)
>> best conf = 0.93
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1551 for best value:
>> intensional similarity = 9
>> extensional distance = 38
>> proper extension:  Corvo; Terceira; Graciosa; SantaMaria; SaoJorge;
>> query: (?x1557, AtlanticOcean) <- ?x1557[ a Island; has belongsToIslands ?x1962[ a Islands;]; has locatedIn ?x899[ has encompassed ?x521; has religion ?x95;]; has locatedInWater ?x317[ has locatedIn ?x80<BVIR>;];]
ranks of expected_values: 1
EVAL	PuertoRico	locatedInWater	AtlanticOcean	CNN-1.+1._MA	1.000	1.000	1.000	1.000	100.000	98.000	152.000	0.925	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#425-KN
PRED entity: KN

PRED relation: locatedIn!
PRED expected values: SaintKitts
=> 32 concepts (23 used for prediction)
PRED predicted values (max 10 best out of 1239): PacificOcean (0.50 #17139, 0.38 #21404, 0.35 #10032), MediterraneanSea (0.18 #29934, 0.16 #25668, 0.13 #18476), Hispaniola (0.17 #5529, 0.17 #4108, 0.12 #9792), Zambezi (0.15 #6822, 0.07 #16771, 0.04 #32697), LakeKariba (0.15 #6633, 0.07 #16582), IndianOcean (0.14 #29855, 0.13 #18476, 0.06 #17057), Uruguay (0.14 #14763, 0.12 #9078, 0.08 #19028), GulfofMexico (0.13 #18476, 0.12 #754, 0.12 #10701), IrishSea (0.13 #18476, 0.09 #15256, 0.08 #6729), NorwegianSea (0.13 #18476, 0.09 #14345, 0.06 #17188)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #17139 for best value:
>> intensional similarity = 6
>> extensional distance = 34
>> proper extension:  IL; S; GUAM;
>> query: (?x161, PacificOcean) <- ?x161[ has ethnicGroup ?x162; has language ?x247; is locatedIn of ?x182[ has mergesWith ?x60;]; is locatedIn of ?x1753[ has type ?x150;];]

*> Best rule #15633 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 20
*> proper extension:  FALK;
*> query: (?x161, ?x123) <- ?x161[ has ethnicGroup ?x162; has government ?x1947; has language ?x247; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x317[ is locatedInWater of ?x123;];]
*> conf = 0.03
ranks of expected_values: 567
EVAL	KN	locatedIn!	SaintKitts	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.002	32.000	23.000	1239.000	0.500	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: SaintKitts
=> 91 concepts (88 used for prediction)
PRED predicted values (max 10 best out of 1396): PacificOcean (0.67 #11458, 0.43 #85500, 0.40 #86924), SaintLawrenceRiver (0.33 #12098, 0.29 #16368, 0.24 #32713), ArcticOcean (0.33 #11446, 0.29 #15716, 0.14 #31364), NiagaraRiver (0.33 #12770, 0.29 #17040, 0.14 #32688), DetroitRiver (0.33 #12687, 0.29 #16957, 0.14 #32605), LakeErie (0.33 #12609, 0.29 #16879, 0.14 #32527), LakeChamplain (0.33 #12606, 0.29 #16876, 0.14 #32524), MtFairweather (0.33 #12590, 0.29 #16860, 0.14 #32508), ColumbiaRiver (0.33 #12528, 0.29 #16798, 0.14 #32446), SaintMarysRiver (0.33 #12478, 0.29 #16748, 0.14 #32396)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #11458 for best value:
>> intensional similarity = 13
>> extensional distance = 4
>> proper extension:  AUS; NZ;
>> query: (?x161, PacificOcean) <- ?x161[ a Country; has ethnicGroup ?x162; has language ?x247; has wasDependentOf ?x81<GB>; is locatedIn of ?x317[ has locatedIn ?x246[ has encompassed ?x521; has language ?x544;]; has locatedIn ?x482<MEX>; has mergesWith ?x1371; is locatedInWater of ?x123;];]

*> Best rule #32716 for first EXPECTED value:
*> intensional similarity = 16
*> extensional distance = 12
*> proper extension:  M;
*> query: (?x161, ?x123) <- ?x161[ a Country; has language ?x247; has wasDependentOf ?x81<GB>; is locatedIn of ?x182[ is flowsInto of ?x137;]; is locatedIn of ?x317[ has locatedIn ?x246[ has encompassed ?x521; has language ?x544;]; has locatedIn ?x745[ has religion ?x352;]; has mergesWith ?x1371; is locatedInWater of ?x123; is locatedInWater of ?x599[ a Island;];];]
*> conf = 0.05
ranks of expected_values: 581
EVAL	KN	locatedIn!	SaintKitts	CNN-1.+1._MA	0.000	0.000	0.000	0.002	91.000	88.000	1396.000	0.667	http://www.semwebtech.org/mondial/10/meta#locatedIn

#424-MNE
PRED entity: MNE

PRED relation: religion
PRED expected values: RomanCatholic
=> 31 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 32): RomanCatholic (0.79 #695, 0.68 #444, 0.68 #409), Protestant (0.67 #690, 0.64 #81, 0.62 #404), Christian (0.41 #366, 0.30 #892, 0.29 #772), Jewish (0.16 #285, 0.14 #245, 0.14 #203), CopticChristian (0.14 #1174, 0.10 #1215, 0.02 #352), Druze (0.14 #1174, 0.07 #276, 0.02 #316), Anglican (0.11 #705, 0.10 #1215, 0.09 #299), Buddhist (0.11 #373, 0.11 #859, 0.10 #1215), Hindu (0.10 #1215, 0.10 #857, 0.09 #371), JehovasWitnesses (0.10 #1215, 0.09 #422, 0.07 #504)
>> best conf = 0.79
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #695 for best value:
>> intensional similarity = 6
>> extensional distance = 123
>> proper extension:  NLSM; REUN; NORF; CUR; ARU; VIRG;
>> query: (?x106, RomanCatholic) <- ?x106[ has religion ?x56[ is religion of ?x222<BY>; is religion of ?x904<SRB>;]; is locatedIn of ?x104;]
ranks of expected_values: 1
EVAL	MNE	religion	RomanCatholic	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	31.000	31.000	32.000	0.792	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion
PRED expected values: RomanCatholic
=> 104 concepts (104 used for prediction)
PRED predicted values (max 10 best out of 36): RomanCatholic (0.83 #534, 0.80 #657, 0.79 #1721), Protestant (0.80 #652, 0.79 #2218, 0.75 #1183), Buddhist (0.50 #528, 0.30 #2380, 0.27 #621), Hindu (0.50 #528, 0.30 #2380, 0.26 #3652), Christian (0.43 #3000, 0.41 #1351, 0.39 #3489), Jewish (0.36 #2177, 0.26 #3652, 0.19 #1020), CopticChristian (0.19 #2096, 0.18 #4182, 0.16 #733), Druze (0.19 #2096, 0.18 #4182, 0.16 #733), Anglican (0.13 #627, 0.13 #3527, 0.12 #708), Sikh (0.13 #643, 0.13 #3527, 0.10 #970)
>> best conf = 0.83
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #534 for best value:
>> intensional similarity = 16
>> extensional distance = 10
>> proper extension:  I;
>> query: (?x106, RomanCatholic) <- ?x106[ has government ?x435; is locatedIn of ?x104; is locatedIn of ?x224[ a Source;]; is neighbor of ?x156[ has encompassed ?x195<Europe>; has ethnicGroup ?x160; is locatedIn of ?x155<Mur>;]; is neighbor of ?x904[ has ethnicGroup ?x2300[ a EthnicGroup;]; has religion ?x56; is locatedIn of ?x132; is neighbor of ?x176;];]
ranks of expected_values: 1
EVAL	MNE	religion	RomanCatholic	CNN-1.+1._MA	1.000	1.000	1.000	1.000	104.000	104.000	36.000	0.833	http://www.semwebtech.org/mondial/10/meta#religion

#423-Irawaddy
PRED entity: Irawaddy

PRED relation: hasSource!
PRED expected values: Irawaddy
=> 29 concepts (22 used for prediction)
PRED predicted values (max 10 best out of 77): Mekong (0.09 #128, 0.08 #586, 0.08 #356), Jangtse (0.09 #76, 0.08 #534, 0.08 #304), Tarim-Yarkend (0.09 #47, 0.08 #505, 0.08 #275), Argun (0.09 #42, 0.08 #500, 0.08 #270), Saluen (0.09 #228, 0.08 #686, 0.08 #456), Ili (0.09 #28, 0.08 #486, 0.08 #256), Ganges (0.09 #143, 0.08 #601, 0.08 #371), Selenge (0.08 #460, 0.04 #918), Asahan (0.08 #280), Amudarja (0.07 #711, 0.01 #1858)
>> best conf = 0.09
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #128 for best value:
>> intensional similarity = 3
>> extensional distance = 9
>> proper extension:  Ili; Jangtse; Ganges; Argun; Saluen; Tarim-Yarkend; Indus; Brahmaputra; Mekong;
>> query: (?x1795, Mekong) <- ?x1795[ a Source; has locatedIn ?x232<CN>;]

*> Best rule #1145 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 21
*> proper extension:  SchattalArab; Senegal; Luapula; Niger; Gambia; Jordan; Zambezi; Sanaga; Oranje; Nile; ...
*> query: (?x1795, ?x231) <- ?x1795[ a Source; has locatedIn ?x232[ has neighbor ?x73[ is locatedIn of ?x72;]; has religion ?x116<Christian>; is locatedIn of ?x231;];]
*> conf = 0.02
ranks of expected_values: 70
EVAL	Irawaddy	hasSource!	Irawaddy	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.014	29.000	22.000	77.000	0.091	http://www.semwebtech.org/mondial/10/meta#hasSource

PRED relation: hasSource!
PRED expected values: Irawaddy
=> 71 concepts (71 used for prediction)
PRED predicted values (max 10 best out of 103): Mekong (0.12 #6949, 0.11 #6948, 0.11 #6946), Jangtse (0.12 #6949, 0.11 #6948, 0.11 #6946), Tarim-Yarkend (0.12 #6949, 0.11 #6948, 0.11 #6946), Argun (0.12 #6949, 0.11 #6948, 0.11 #6946), Ili (0.12 #6949, 0.11 #6948, 0.11 #6946), Amur (0.12 #6949, 0.11 #6948, 0.11 #6946), Brahmaputra (0.12 #6949, 0.11 #6948, 0.11 #6946), Irtysch (0.12 #6949, 0.11 #6948, 0.11 #6946), Indus (0.12 #6949, 0.11 #6948, 0.11 #6946), Irawaddy (0.12 #6949, 0.11 #6948, 0.11 #6946)
>> best conf = 0.12
=> the first rule below is the first best rule for 12 predicted values
>> Best rule #6949 for best value:
>> intensional similarity = 11
>> extensional distance = 163
>> proper extension:  DarlingRiver; JoekulsaaFjoellum; ColumbiaRiver; YukonRiver; EucumbeneRiver; RiviereRichelieu; MurrumbidgeeRiver; SaskatchewanRiver; MurrayRiver; Thjorsa; ...
>> query: (?x1795, ?x411) <- ?x1795[ a Source; has locatedIn ?x232[ has government ?x831; has religion ?x116; is locatedIn of ?x319[ has hasEstuary ?x1344;]; is locatedIn of ?x411[ a River;]; is locatedIn of ?x1585[ a River; has flowsInto ?x507;];];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 10
EVAL	Irawaddy	hasSource!	Irawaddy	CNN-1.+1._MA	0.000	0.000	1.000	0.100	71.000	71.000	103.000	0.115	http://www.semwebtech.org/mondial/10/meta#hasSource

#422-CEU
PRED entity: CEU

PRED relation: government
PRED expected values: "dependent territory of Spain"
=> 22 concepts (22 used for prediction)
PRED predicted values (max 10 best out of 46): "republic" (0.46 #150, 0.38 #294, 0.35 #439), "dependent territory of Spain" (0.25 #64, 0.20 #136, 0.08 #208), "overseas department of France" (0.24 #228, 0.13 #373, 0.05 #361), "British Overseas Territories" (0.20 #368, 0.04 #1010, 0.04 #1233), "parliamentary democracy" (0.16 #582, 0.13 #366, 0.11 #438), "parliamentary representative democratic French overseas collectivity" (0.12 #263, 0.07 #408, 0.01 #1129), "parliamentary representative democracy" (0.12 #232, 0.07 #377, 0.01 #1098), "parliamentary republic" (0.08 #307, 0.07 #1371, 0.06 #1517), "operates under a transitional government" (0.08 #168, 0.07 #1371, 0.06 #1517), "federal republic" (0.08 #147, 0.07 #652, 0.07 #724)
>> best conf = 0.46
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #150 for best value:
>> intensional similarity = 7
>> extensional distance = 11
>> proper extension:  ET; TN; TCH; SUD; RN; RIM; RMM; LAR;
>> query: (?x2084, "republic") <- ?x2084[ a Country; has encompassed ?x213<Africa>; is neighbor of ?x851[ has ethnicGroup ?x582; is locatedIn of ?x275<MediterraneanSea>;];]

*> Best rule #64 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 2
*> proper extension:  DZ; MEL;
*> query: (?x2084, "dependent territory of Spain") <- ?x2084[ a Country; has encompassed ?x213<Africa>; is locatedIn of ?x275<MediterraneanSea>; is neighbor of ?x851<MA>;]
*> conf = 0.25
ranks of expected_values: 2
EVAL	CEU	government	"dependent territory of Spain"	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	22.000	22.000	46.000	0.462	http://www.semwebtech.org/mondial/10/meta#government

PRED relation: government
PRED expected values: "dependent territory of Spain"
=> 49 concepts (49 used for prediction)
PRED predicted values (max 10 best out of 68): "territory of Australia" (0.60 #233, 0.04 #2642, 0.01 #3446), "republic" (0.52 #2122, 0.50 #1538, 0.50 #662), "dependent territory of Spain" (0.37 #583, 0.26 #1239, 0.25 #64), "British Overseas Territories" (0.36 #1099, 0.22 #1975, 0.21 #1684), "overseas department of France" (0.33 #958, 0.31 #1031, 0.17 #1689), "constitutional monarchy" (0.31 #945, 0.30 #2189, 0.27 #656), "parliamentary democracy" (0.21 #1097, 0.20 #77, 0.19 #2412), "parliamentary" (0.20 #112, 0.04 #2008, 0.03 #2082), "parliamentary representative democratic French overseas collectivity" (0.17 #993, 0.15 #1066, 0.08 #1724), "parliamentary representative democracy" (0.17 #962, 0.15 #1035, 0.08 #1693)
>> best conf = 0.60
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #233 for best value:
>> intensional similarity = 18
>> extensional distance = 3
>> proper extension:  NORF; COCO; XMAS;
>> query: (?x2084, "territory of Australia") <- ?x2084[ a Country; has dependentOf ?x149[ a Country; has ethnicGroup ?x2540; has government ?x1657; has language ?x790; is locatedIn of ?x68[ a Island;]; is locatedIn of ?x1020[ has type ?x150;]; is locatedIn of ?x1198[ a River;]; is locatedIn of ?x1726[ a Source; has inMountains ?x1701;]; is locatedIn of ?x1762[ a Mountain;];]; has encompassed ?x213;]

*> Best rule #583 for first EXPECTED value:
*> intensional similarity = 19
*> extensional distance = 8
*> proper extension:  SUD;
*> query: (?x2084, ?x2527) <- ?x2084[ has encompassed ?x213<Africa>; is neighbor of ?x851[ has government ?x92; has neighbor ?x1588[ a Country; has government ?x2527;]; has religion ?x109[ a Religion;]; has religion ?x187<Muslim>; is locatedIn of ?x182[ has locatedIn ?x149<E>; has mergesWith ?x60; is flowsInto of ?x137; is locatedInWater of ?x112;]; is locatedIn of ?x275<MediterraneanSea>;];]
*> conf = 0.37
ranks of expected_values: 3
EVAL	CEU	government	"dependent territory of Spain"	CNN-1.+1._MA	0.000	1.000	1.000	0.333	49.000	49.000	68.000	0.600	http://www.semwebtech.org/mondial/10/meta#government

#421-Russian
PRED entity: Russian

PRED relation: ethnicGroup!
PRED expected values: AZ
=> 23 concepts (23 used for prediction)
PRED predicted values (max 10 best out of 174): S (0.50 #179, 0.50 #178, 0.47 #535), CN (0.50 #179, 0.50 #178, 0.47 #535), SRB (0.50 #179, 0.50 #178, 0.47 #535), SK (0.50 #179, 0.50 #178, 0.47 #535), H (0.50 #179, 0.50 #178, 0.47 #535), PL (0.50 #179, 0.50 #178, 0.47 #535), AZ (0.50 #179, 0.50 #178, 0.47 #535), TR (0.50 #179, 0.50 #178, 0.47 #535), N (0.50 #179, 0.50 #178, 0.47 #535), MNG (0.50 #179, 0.50 #178, 0.47 #535)
>> best conf = 0.50
=> the first rule below is the first best rule for 14 predicted values
>> Best rule #179 for best value:
>> intensional similarity = 12
>> extensional distance = 1
>> proper extension:  Uzbek;
>> query: (?x1193, ?x185) <- ?x1193[ a EthnicGroup; is ethnicGroup of ?x129<TAD>; is ethnicGroup of ?x290<TM>; is ethnicGroup of ?x303[ has language ?x1108; has neighbor ?x163; is locatedIn of ?x97;]; is ethnicGroup of ?x331[ has neighbor ?x185; has religion ?x670;];]
>> Best rule #178 for best value:
>> intensional similarity = 12
>> extensional distance = 1
>> proper extension:  Uzbek;
>> query: (?x1193, ?x163) <- ?x1193[ a EthnicGroup; is ethnicGroup of ?x129<TAD>; is ethnicGroup of ?x290<TM>; is ethnicGroup of ?x303[ has language ?x1108; has neighbor ?x163; is locatedIn of ?x97;]; is ethnicGroup of ?x331[ has neighbor ?x185; has religion ?x670;];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 7
EVAL	Russian	ethnicGroup!	AZ	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.143	23.000	23.000	174.000	0.500	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup!
PRED expected values: AZ
=> 59 concepts (59 used for prediction)
PRED predicted values (max 10 best out of 219): PL (0.50 #1620, 0.50 #934, 0.46 #902), MK (0.50 #2462, 0.50 #1620, 0.46 #902), SK (0.50 #1620, 0.50 #925, 0.43 #1261), SRB (0.50 #1620, 0.46 #902, 0.43 #1441), TR (0.50 #1620, 0.46 #902, 0.43 #1441), H (0.50 #1620, 0.43 #1261, 0.43 #2339), AFG (0.50 #1620, 0.43 #1261, 0.43 #2339), AZ (0.50 #1620, 0.43 #1261, 0.41 #1262), N (0.50 #1620, 0.43 #1261, 0.41 #1262), S (0.50 #1620, 0.43 #1261, 0.41 #1262)
>> best conf = 0.50
=> the first rule below is the first best rule for 12 predicted values
>> Best rule #1620 for best value:
>> intensional similarity = 23
>> extensional distance = 3
>> proper extension:  Uzbek; Uighur;
>> query: (?x1193, ?x185) <- ?x1193[ is ethnicGroup of ?x130<KGZ>; is ethnicGroup of ?x177[ has government ?x254; has language ?x511; has neighbor ?x185; is locatedIn of ?x98;]; is ethnicGroup of ?x331[ has religion ?x670;]; is ethnicGroup of ?x403<KAZ>; is ethnicGroup of ?x591[ a Country; is locatedIn of ?x145;]; is ethnicGroup of ?x886[ has government ?x435<"republic">; has language ?x1108; is locatedIn of ?x133;]; is ethnicGroup of ?x962[ has encompassed ?x195; has language ?x555; is neighbor of ?x194;];]
>> Best rule #934 for best value:
>> intensional similarity = 21
>> extensional distance = 2
>> proper extension:  German;
>> query: (?x1193, PL) <- ?x1193[ is ethnicGroup of ?x130[ has religion ?x187; is locatedIn of ?x662;]; is ethnicGroup of ?x177[ has government ?x254; has language ?x511; is locatedIn of ?x98;]; is ethnicGroup of ?x403<KAZ>; is ethnicGroup of ?x591[ a Country; is locatedIn of ?x145;]; is ethnicGroup of ?x886[ has government ?x435; is locatedIn of ?x133<Donau>;]; is ethnicGroup of ?x962[ has encompassed ?x195; has language ?x555; has religion ?x56; is neighbor of ?x194;];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 8
EVAL	Russian	ethnicGroup!	AZ	CNN-1.+1._MA	0.000	0.000	1.000	0.125	59.000	59.000	219.000	0.500	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#420-Roma
PRED entity: Roma

PRED relation: language!
PRED expected values: SRB
=> 23 concepts (23 used for prediction)
PRED predicted values (max 10 best out of 206): MD (0.57 #576, 0.29 #236, 0.25 #1313), KOS (0.33 #324, 0.33 #87, 0.32 #237), A (0.33 #299, 0.33 #62, 0.32 #237), MNE (0.33 #246, 0.33 #9, 0.29 #365), SRB (0.33 #102, 0.32 #237, 0.31 #356), HR (0.33 #17, 0.17 #254, 0.16 #1672), AL (0.32 #237, 0.31 #356, 0.31 #355), GR (0.32 #237, 0.31 #356, 0.31 #355), UA (0.32 #237, 0.31 #356, 0.31 #355), CZ (0.32 #237, 0.31 #356, 0.31 #355)
>> best conf = 0.57
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #576 for best value:
>> intensional similarity = 11
>> extensional distance = 5
>> proper extension:  Russian; Ukrainian; Romanian; Gagauz;
>> query: (?x1504, MD) <- ?x1504[ is language of ?x177[ has ethnicGroup ?x1780<Bulgarian>; has neighbor ?x185; has wasDependentOf ?x1656;]; is language of ?x701[ has encompassed ?x195; has government ?x254; has religion ?x56; is locatedIn of ?x656; is neighbor of ?x204;];]

*> Best rule #102 for first EXPECTED value:
*> intensional similarity = 12
*> extensional distance = 1
*> proper extension:  Serbian;
*> query: (?x1504, SRB) <- ?x1504[ is language of ?x163[ has government ?x254; has language ?x684<Hungarian>; has religion ?x56<ChristianOrthodox>; has religion ?x95<Protestant>; is locatedIn of ?x133; is neighbor of ?x194;]; is language of ?x701<MK>;]
*> conf = 0.33
ranks of expected_values: 5
EVAL	Roma	language!	SRB	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.200	23.000	23.000	206.000	0.571	http://www.semwebtech.org/mondial/10/meta#language

PRED relation: language!
PRED expected values: SRB
=> 46 concepts (46 used for prediction)
PRED predicted values (max 10 best out of 212): MD (0.57 #1086, 0.51 #1602, 0.41 #1851), A (0.51 #1602, 0.50 #801, 0.50 #308), SRB (0.51 #1602, 0.50 #841, 0.41 #1851), IR (0.51 #1602, 0.44 #2220, 0.41 #1851), TR (0.51 #1602, 0.44 #2220, 0.41 #1851), CY (0.51 #1602, 0.44 #2220, 0.41 #1851), H (0.51 #1602, 0.41 #1851, 0.38 #365), GE (0.50 #1406, 0.25 #2593, 0.22 #1900), PK (0.44 #1980, 0.08 #5454, 0.05 #3840), GR (0.38 #365, 0.36 #2218, 0.34 #1723)
>> best conf = 0.57
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1086 for best value:
>> intensional similarity = 21
>> extensional distance = 5
>> proper extension:  Romanian; Gagauz;
>> query: (?x1504, MD) <- ?x1504[ is language of ?x177[ has ethnicGroup ?x1780<Bulgarian>; has neighbor ?x185[ has neighbor ?x302; is locatedIn of ?x184;]; has neighbor ?x904[ a Country; has language ?x684; has religion ?x95; is locatedIn of ?x132; is neighbor of ?x236<H>;]; has wasDependentOf ?x1656; is locatedIn of ?x98;]; is language of ?x701[ has encompassed ?x195; has government ?x254; has neighbor ?x692; has religion ?x56; is locatedIn of ?x656;];]

*> Best rule #1602 for first EXPECTED value:
*> intensional similarity = 22
*> extensional distance = 7
*> proper extension:  Hungarian;
*> query: (?x1504, ?x185) <- ?x1504[ a Language; is language of ?x177[ has ethnicGroup ?x1193<Russian>; has ethnicGroup ?x1780[ a EthnicGroup;]; has language ?x511[ a Language; is language of ?x185;]; has religion ?x56; has wasDependentOf ?x1656; is locatedIn of ?x98<BlackSea>;]; is language of ?x701[ has ethnicGroup ?x775; has neighbor ?x399[ a Country; has encompassed ?x195; has government ?x1174; is locatedIn of ?x275;]; has wasDependentOf ?x1197; is locatedIn of ?x656;];]
*> conf = 0.51
ranks of expected_values: 3
EVAL	Roma	language!	SRB	CNN-1.+1._MA	0.000	1.000	1.000	0.333	46.000	46.000	212.000	0.571	http://www.semwebtech.org/mondial/10/meta#language

#419-Q
PRED entity: Q

PRED relation: government
PRED expected values: "emirate"
=> 28 concepts (28 used for prediction)
PRED predicted values (max 10 best out of 61): "republic" (0.35 #1158, 0.33 #1446, 0.33 #510), "monarchy" (0.25 #8, 0.17 #80, 0.14 #152), "parliamentary democracy" (0.22 #293, 0.12 #1229, 0.11 #365), "constitutional monarchy" (0.18 #650, 0.17 #434, 0.17 #74), "theocratic republic" (0.14 #194, 0.12 #266, 0.11 #410), "republic under an authoritarian regime" (0.14 #213, 0.12 #285, 0.11 #429), "federal republic" (0.11 #363, 0.10 #1874, 0.08 #507), "federation with specified powers delegated to the UAE federal government and other powers reserved to member emirates" (0.11 #306, 0.08 #522, 0.05 #1801), "constitutional monarchy and Commonwealth realm" (0.10 #1874, 0.01 #1258), "Communist state" (0.09 #805, 0.09 #877, 0.08 #949)
>> best conf = 0.35
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1158 for best value:
>> intensional similarity = 8
>> extensional distance = 87
>> proper extension:  RM;
>> query: (?x174, "republic") <- ?x174[ has wasDependentOf ?x81[ has religion ?x95; is dependentOf of ?x80; is locatedIn of ?x1509[ has inMountains ?x2469;]; is wasDependentOf of ?x366[ has ethnicGroup ?x298; has religion ?x116;];];]

No rule for expected values
ranks of expected_values: 
EVAL	Q	government	"emirate"	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	28.000	28.000	61.000	0.348	http://www.semwebtech.org/mondial/10/meta#government

PRED relation: government
PRED expected values: "emirate"
=> 80 concepts (80 used for prediction)
PRED predicted values (max 10 best out of 63): "republic" (0.42 #946, 0.41 #2833, 0.40 #1092), "constitutional monarchy" (0.33 #2, 0.25 #870, 0.25 #291), "parliamentary democracy" (0.33 #77, 0.20 #1382, 0.19 #2541), "constitutional monarchy and Commonwealth realm" (0.33 #178, 0.19 #434, 0.10 #5520), "monarchy" (0.25 #225, 0.20 #369, 0.19 #434), "theocratic republic" (0.25 #267, 0.20 #411, 0.19 #434), "federal republic" (0.25 #292, 0.19 #434, 0.17 #1013), "federation with specified powers delegated to the UAE federal government and other powers reserved to member emirates" (0.20 #379, 0.18 #3482, 0.16 #796), "republic under an authoritarian regime" (0.19 #434, 0.17 #1013, 0.16 #1014), "parliamentary democracy and a Commonwealth realm" (0.15 #2937, 0.14 #2426, 0.11 #3700)
>> best conf = 0.42
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #946 for best value:
>> intensional similarity = 17
>> extensional distance = 10
>> proper extension:  ARM;
>> query: (?x174, "republic") <- ?x174[ a Country; has ethnicGroup ?x244[ is ethnicGroup of ?x115<RL>; is ethnicGroup of ?x474[ has government ?x435; has neighbor ?x220; has religion ?x95; is locatedIn of ?x60;]; is ethnicGroup of ?x751[ a Country; has encompassed ?x175; has government ?x640; is locatedIn of ?x637; is neighbor of ?x107;];]; has religion ?x187; has wasDependentOf ?x81;]

No rule for expected values
ranks of expected_values: 
EVAL	Q	government	"emirate"	CNN-1.+1._MA	0.000	0.000	0.000	0.000	80.000	80.000	63.000	0.417	http://www.semwebtech.org/mondial/10/meta#government

#418-VN
PRED entity: VN

PRED relation: ethnicGroup
PRED expected values: Nung Viet-Kinh
=> 36 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 234): Chinese (0.91 #521, 0.17 #7344, 0.14 #6346), European (0.38 #767, 0.29 #1780, 0.27 #2033), Russian (0.33 #325, 0.33 #72, 0.17 #2856), Ukrainian (0.33 #1, 0.22 #254, 0.14 #1520), Uzbek (0.33 #407, 0.17 #154, 0.10 #1419), Amerindian (0.31 #761, 0.18 #1521, 0.17 #1774), Mestizo (0.31 #795, 0.16 #1555, 0.15 #1808), African (0.25 #1778, 0.20 #6337, 0.19 #3804), Malay (0.23 #604, 0.05 #3389, 0.04 #6429), Uighur (0.22 #417, 0.17 #164, 0.04 #1429)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #521 for best value:
>> intensional similarity = 6
>> extensional distance = 20
>> proper extension:  HELX; SGP;
>> query: (?x617, Chinese) <- ?x617[ has encompassed ?x175; has ethnicGroup ?x2363[ a EthnicGroup; is ethnicGroup of ?x91<THA>;]; is locatedIn of ?x384;]

No rule for expected values
ranks of expected_values: 
EVAL	VN	ethnicGroup	Viet-Kinh	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	36.000	36.000	234.000	0.909	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	VN	ethnicGroup	Nung	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	36.000	36.000	234.000	0.909	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: Nung Viet-Kinh
=> 92 concepts (92 used for prediction)
PRED predicted values (max 10 best out of 252): Chinese (0.64 #3565, 0.58 #4071, 0.50 #3057), European (0.50 #3811, 0.49 #8379, 0.42 #8126), African (0.33 #8377, 0.28 #8883, 0.27 #11929), Vietnamese (0.33 #585, 0.26 #11162, 0.25 #760), Indian (0.33 #833, 0.25 #3115, 0.23 #4382), Amerindian (0.33 #3805, 0.24 #7359, 0.23 #6342), Mestizo (0.33 #3839, 0.24 #7393, 0.23 #8407), Shan (0.33 #1001, 0.20 #1508, 0.17 #2269), Burman (0.33 #996, 0.20 #1503, 0.17 #2264), Mon (0.33 #929, 0.20 #1436, 0.17 #2197)
>> best conf = 0.64
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3565 for best value:
>> intensional similarity = 14
>> extensional distance = 9
>> proper extension:  RC;
>> query: (?x617, Chinese) <- ?x617[ has ethnicGroup ?x2261[ a EthnicGroup;]; has government ?x831; has religion ?x95[ is religion of ?x81[ is locatedIn of ?x153; is wasDependentOf of ?x63;];]; has religion ?x618[ a Religion;]; is locatedIn of ?x384<SouthChinaSea>; is locatedIn of ?x1152[ has locatedIn ?x232<CN>;];]

No rule for expected values
ranks of expected_values: 
EVAL	VN	ethnicGroup	Viet-Kinh	CNN-1.+1._MA	0.000	0.000	0.000	0.000	92.000	92.000	252.000	0.636	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	VN	ethnicGroup	Nung	CNN-1.+1._MA	0.000	0.000	0.000	0.000	92.000	92.000	252.000	0.636	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#417-LakeKariba
PRED entity: LakeKariba

PRED relation: flowsInto
PRED expected values: Zambezi
=> 33 concepts (28 used for prediction)
PRED predicted values (max 10 best out of 85): Zambezi (0.95 #1832, 0.95 #1664, 0.94 #1496), IndianOcean (0.33 #167, 0.25 #332, 0.11 #831), VictoriaNile (0.12 #745, 0.04 #1078, 0.03 #1748), Lukuga (0.11 #524, 0.06 #690, 0.06 #857), Zaire (0.10 #2092, 0.06 #754, 0.03 #1253), Dnepr (0.10 #1234, 0.08 #1402, 0.08 #1570), Missouri (0.08 #1457, 0.08 #1793, 0.03 #1625), Aare (0.06 #1255, 0.06 #1423, 0.05 #1591), Colorado (0.06 #1274, 0.06 #1442, 0.05 #1610), Vuoksi (0.06 #1293, 0.06 #1461, 0.05 #1797)
>> best conf = 0.95
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1832 for best value:
>> intensional similarity = 4
>> extensional distance = 37
>> proper extension:  LakeKainji; LakeKioga; Bodensee;
>> query: (?x1676, ?x1977) <- ?x1676[ a Lake; is flowsThrough of ?x1977[ has locatedIn ?x525[ has neighbor ?x192;];];]
ranks of expected_values: 1
EVAL	LakeKariba	flowsInto	Zambezi	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	33.000	28.000	85.000	0.949	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto
PRED expected values: Zambezi
=> 94 concepts (94 used for prediction)
PRED predicted values (max 10 best out of 173): Zambezi (0.94 #4522, 0.94 #4352, 0.92 #3682), IndianOcean (0.50 #666, 0.33 #335, 0.12 #2003), Lukuga (0.33 #195, 0.25 #526, 0.12 #1194), Colorado (0.25 #943, 0.18 #1778, 0.14 #1945), Limpopo (0.20 #4354, 0.04 #2337, 0.03 #4186), MurrayRiver (0.20 #4354, 0.02 #6499, 0.02 #6832), Jubba (0.20 #4354), Tennessee (0.18 #1687, 0.10 #2360, 0.10 #2695), Dnepr (0.18 #2241, 0.15 #2411, 0.14 #2746), VictoriaNile (0.12 #1249, 0.10 #1581, 0.05 #2589)
>> best conf = 0.94
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4522 for best value:
>> intensional similarity = 11
>> extensional distance = 31
>> proper extension:  LakeVolta; MaleboPool; LakeNasser; Chiemsee; LakeKioga; Ammersee;
>> query: (?x1676, ?x1977) <- ?x1676[ a Lake; has locatedIn ?x1576[ has religion ?x116;]; is flowsThrough of ?x1977[ a River; has hasSource ?x1596; has locatedIn ?x138[ has encompassed ?x213; has neighbor ?x243; has religion ?x95;]; is flowsInto of ?x387;];]
ranks of expected_values: 1
EVAL	LakeKariba	flowsInto	Zambezi	CNN-1.+1._MA	1.000	1.000	1.000	1.000	94.000	94.000	173.000	0.939	http://www.semwebtech.org/mondial/10/meta#flowsInto

#416-NLSM
PRED entity: NLSM

PRED relation: neighbor
PRED expected values: SMAR
=> 42 concepts (42 used for prediction)
PRED predicted values (max 10 best out of 215): F (0.36 #1138, 0.33 #1300, 0.33 #491), D (0.33 #14, 0.22 #1963, 0.21 #1148), B (0.33 #95, 0.17 #582, 0.14 #3422), BR (0.21 #2864, 0.13 #4890, 0.13 #4073), CO (0.20 #361, 0.13 #4890, 0.13 #4073), CR (0.20 #378, 0.13 #4890, 0.13 #4073), CH (0.17 #2111, 0.17 #1993, 0.14 #3422), E (0.17 #506, 0.13 #4890, 0.13 #4073), BOL (0.17 #2884, 0.13 #4890, 0.13 #4073), PE (0.17 #2820, 0.13 #4890, 0.13 #4073)
>> best conf = 0.36
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1138 for best value:
>> intensional similarity = 7
>> extensional distance = 12
>> proper extension:  SPMI; F; I; CH; FPOL; B; WAFU; FGU; MC;
>> query: (?x50, F) <- ?x50[ has language ?x51<French>; has language ?x247[ a Language;]; has religion ?x352<RomanCatholic>; is locatedIn of ?x182;]

*> Best rule #1564 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 13
*> proper extension:  SMAR;
*> query: (?x50, SMAR) <- ?x50[ a Country; has dependentOf ?x575[ is wasDependentOf of ?x179;]; has government ?x2058; is locatedIn of ?x182<AtlanticOcean>;]
*> conf = 0.07
ranks of expected_values: 89
EVAL	NLSM	neighbor	SMAR	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.011	42.000	42.000	215.000	0.357	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: SMAR
=> 72 concepts (72 used for prediction)
PRED predicted values (max 10 best out of 223): B (0.50 #751, 0.16 #818, 0.15 #2415), F (0.40 #2661, 0.25 #660, 0.21 #3665), GCA (0.33 #191, 0.17 #2181, 0.14 #8671), MEX (0.33 #248, 0.14 #8671, 0.14 #9180), SMAR (0.33 #597, 0.13 #9351, 0.13 #9349), D (0.25 #670, 0.20 #2671, 0.16 #3675), CH (0.25 #700, 0.16 #3827, 0.16 #3705), E (0.25 #675, 0.16 #818, 0.14 #8671), I (0.25 #693, 0.16 #3698, 0.14 #8671), L (0.25 #774, 0.14 #8671, 0.13 #8672)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #751 for best value:
>> intensional similarity = 11
>> extensional distance = 2
>> proper extension:  L;
>> query: (?x50, B) <- ?x50[ a Country; has language ?x51<French>; has religion ?x109<Jewish>; is locatedIn of ?x317[ has locatedIn ?x215[ has neighbor ?x296;]; has locatedIn ?x321[ a Country; has wasDependentOf ?x81;];];]

*> Best rule #597 for first EXPECTED value:
*> intensional similarity = 18
*> extensional distance = 1
*> proper extension:  SMAR;
*> query: (?x50, SMAR) <- ?x50[ a Country; has dependentOf ?x575[ has government ?x92; has neighbor ?x543<B>; has religion ?x352<RomanCatholic>; is locatedIn of ?x731[ a Island;]; is locatedIn of ?x829<Maas>;]; has government ?x2058; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x317<CaribbeanSea>; is locatedIn of ?x1380<St.Martin>;]
*> conf = 0.33
ranks of expected_values: 5
EVAL	NLSM	neighbor	SMAR	CNN-1.+1._MA	0.000	0.000	1.000	0.200	72.000	72.000	223.000	0.500	http://www.semwebtech.org/mondial/10/meta#neighbor

#415-EW
PRED entity: EW

PRED relation: wasDependentOf
PRED expected values: SovietUnion
=> 40 concepts (40 used for prediction)
PRED predicted values (max 10 best out of 43): SovietUnion (0.59 #144, 0.56 #80, 0.43 #112), GB (0.30 #188, 0.27 #403, 0.26 #675), Yugoslavia (0.21 #178, 0.17 #54, 0.14 #363), E (0.17 #498, 0.16 #467, 0.15 #530), F (0.17 #33, 0.13 #928, 0.12 #768), OttomanEmpire (0.17 #56, 0.12 #240, 0.12 #149), R (0.11 #91, 0.11 #61, 0.07 #123), UnitedNations (0.10 #199, 0.10 #716, 0.08 #385), Czechoslovakia (0.08 #270, 0.08 #239, 0.05 #364), S (0.07 #104, 0.03 #291, 0.03 #321)
>> best conf = 0.59
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #144 for best value:
>> intensional similarity = 7
>> extensional distance = 15
>> proper extension:  R;
>> query: (?x591, SovietUnion) <- ?x591[ a Country; has ethnicGroup ?x1193<Russian>; has ethnicGroup ?x1322[ a EthnicGroup;]; has neighbor ?x73; is locatedIn of ?x145;]
ranks of expected_values: 1
EVAL	EW	wasDependentOf	SovietUnion	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	40.000	40.000	43.000	0.588	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

PRED relation: wasDependentOf
PRED expected values: SovietUnion
=> 92 concepts (92 used for prediction)
PRED predicted values (max 10 best out of 65): SovietUnion (0.60 #411, 0.60 #250, 0.60 #184), R (0.33 #66, 0.33 #35, 0.25 #98), GB (0.29 #1244, 0.23 #940, 0.22 #3004), E (0.26 #802, 0.26 #977, 0.25 #1212), S (0.24 #2226, 0.23 #1954, 0.20 #209), CN (0.24 #2226, 0.23 #1954, 0.06 #534), Yugoslavia (0.21 #481, 0.20 #514, 0.15 #853), LV (0.20 #65, 0.14 #130, 0.14 #129), Czechoslovakia (0.18 #448, 0.18 #2468, 0.16 #2891), OttomanEmpire (0.18 #2468, 0.16 #2891, 0.15 #1671)
>> best conf = 0.60
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #411 for best value:
>> intensional similarity = 15
>> extensional distance = 8
>> proper extension:  KGZ; RO; KAZ; MD;
>> query: (?x591, SovietUnion) <- ?x591[ has ethnicGroup ?x58<Ukrainian>; has ethnicGroup ?x1193<Russian>; has ethnicGroup ?x1322[ is ethnicGroup of ?x303<UA>;]; has language ?x555; is locatedIn of ?x145; is neighbor of ?x448[ has ethnicGroup ?x963[ a EthnicGroup;]; has government ?x254; has religion ?x56<ChristianOrthodox>;];]
>> Best rule #250 for best value:
>> intensional similarity = 18
>> extensional distance = 3
>> proper extension:  LT;
>> query: (?x591, SovietUnion) <- ?x591[ a Country; has ethnicGroup ?x58[ a EthnicGroup; is ethnicGroup of ?x176<RO>; is ethnicGroup of ?x403<KAZ>;]; has ethnicGroup ?x1193<Russian>; has ethnicGroup ?x1322<Belorussian>; has government ?x1174; has language ?x555<Russian>; has language ?x1839[ a Language;]; has neighbor ?x73; has religion ?x56;]
>> Best rule #184 for best value:
>> intensional similarity = 16
>> extensional distance = 3
>> proper extension:  UA;
>> query: (?x591, SovietUnion) <- ?x591[ has ethnicGroup ?x58<Ukrainian>; has ethnicGroup ?x1322<Belorussian>; has ethnicGroup ?x1473[ a EthnicGroup;]; has language ?x555[ a Language; is language of ?x222[ has government ?x1621; is neighbor of ?x194;]; is language of ?x331[ has religion ?x670;];]; has neighbor ?x73; is locatedIn of ?x802[ has hasSource ?x1794;];]
ranks of expected_values: 1
EVAL	EW	wasDependentOf	SovietUnion	CNN-1.+1._MA	1.000	1.000	1.000	1.000	92.000	92.000	65.000	0.600	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

#414-LakeMweru
PRED entity: LakeMweru

PRED relation: locatedIn
PRED expected values: Z
=> 39 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 132): EAU (0.29 #386, 0.17 #2973, 0.14 #4710), USA (0.25 #5962, 0.17 #1717, 0.16 #2659), RCB (0.17 #121, 0.14 #356, 0.14 #4710), Z (0.17 #120, 0.14 #355, 0.14 #4710), EAT (0.17 #174, 0.14 #409, 0.14 #4710), BI (0.17 #82, 0.14 #317, 0.14 #4710), ANG (0.17 #658, 0.14 #2304, 0.12 #893), R (0.15 #4479, 0.14 #4716, 0.11 #4952), RCA (0.14 #4710, 0.12 #5183, 0.09 #2040), SSD (0.14 #4710, 0.12 #5183, 0.08 #3294)
>> best conf = 0.29
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #386 for best value:
>> intensional similarity = 5
>> extensional distance = 5
>> proper extension:  MaleboPool; LakeTanganjika; Rutanzige-Eduardsee; LakeSeseSeko-Albertsee; LakeKivu;
>> query: (?x364, EAU) <- ?x364[ a Lake; has flowsInto ?x365[ has flowsInto ?x527;]; has locatedIn ?x348<ZRE>;]

*> Best rule #120 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 4
*> proper extension:  LakeMaiNdombe;
*> query: (?x364, Z) <- ?x364[ a Lake; has flowsInto ?x365[ a River; has locatedIn ?x348<ZRE>;];]
*> conf = 0.17
ranks of expected_values: 4
EVAL	LakeMweru	locatedIn	Z	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.250	39.000	36.000	132.000	0.286	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: Z
=> 114 concepts (109 used for prediction)
PRED predicted values (max 10 best out of 206): USA (0.57 #16179, 0.50 #3846, 0.27 #17852), EAT (0.50 #2059, 0.47 #21833, 0.47 #12785), BI (0.48 #15632, 0.47 #21833, 0.47 #12785), EAU (0.48 #15632, 0.47 #21833, 0.47 #12785), Z (0.48 #15632, 0.47 #21833, 0.47 #12785), RCB (0.48 #15632, 0.47 #21833, 0.47 #12785), RCA (0.48 #15632, 0.18 #17778, 0.17 #1646), R (0.27 #17544, 0.20 #18979, 0.18 #15164), MOC (0.27 #1690, 0.18 #17778, 0.16 #17296), ETH (0.26 #3651, 0.17 #11947, 0.14 #4010)
>> best conf = 0.57
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #16179 for best value:
>> intensional similarity = 10
>> extensional distance = 210
>> proper extension:  Namib; Impalila;
>> query: (?x364, USA) <- ?x364[ has locatedIn ?x348[ has neighbor ?x229[ is locatedIn of ?x53;]; has religion ?x95<Protestant>; has wasDependentOf ?x543; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x834[ a Estuary;];];]

*> Best rule #15632 for first EXPECTED value:
*> intensional similarity = 11
*> extensional distance = 168
*> proper extension:  EucumbeneRiver;
*> query: (?x364, ?x688) <- ?x364[ has flowsInto ?x365[ has flowsInto ?x527;]; has locatedIn ?x348[ has religion ?x95; is locatedIn of ?x509[ is flowsInto of ?x1057;]; is locatedIn of ?x732[ a Source;]; is locatedIn of ?x1188[ a Estuary; has locatedIn ?x688;];];]
*> conf = 0.48
ranks of expected_values: 5
EVAL	LakeMweru	locatedIn	Z	CNN-1.+1._MA	0.000	0.000	1.000	0.200	114.000	109.000	206.000	0.571	http://www.semwebtech.org/mondial/10/meta#locatedIn

#413-Oesel
PRED entity: Oesel

PRED relation: locatedIn
PRED expected values: EW
=> 24 concepts (24 used for prediction)
PRED predicted values (max 10 best out of 67): D (0.39 #256, 0.15 #20, 0.15 #477), DK (0.38 #168, 0.15 #476, 0.15 #404), GR (0.22 #567, 0.09 #1285, 0.07 #1525), I (0.20 #525, 0.08 #1243, 0.07 #1483), USA (0.18 #788, 0.16 #1027, 0.12 #1267), GB (0.15 #245, 0.12 #1687, 0.12 #1444), PL (0.15 #477, 0.08 #44, 0.05 #5286), LT (0.15 #477, 0.05 #716, 0.04 #954), NL (0.09 #370, 0.02 #1569, 0.02 #1812), CDN (0.09 #1018, 0.06 #1258, 0.04 #5109)
>> best conf = 0.39
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #256 for best value:
>> intensional similarity = 13
>> extensional distance = 31
>> proper extension:  GreatBritain; ShetlandMainland; OrkneyMainland; Pellworm; Westray; Langeoog; Schiermonnikoog; Amrum; Norderney; Helgoland; ...
>> query: (?x1436, D) <- ?x1436[ a Island; has locatedInWater ?x146[ a Sea; has locatedIn ?x793<DK>; has locatedIn ?x962[ has ethnicGroup ?x963; has language ?x555; has religion ?x56; is neighbor of ?x73<R>;]; is flowsInto of ?x1725[ a River;];];]

*> Best rule #136 for first EXPECTED value:
*> intensional similarity = 3
*> extensional distance = 11
*> proper extension:  Dagoe; Rugen; Falster; Aland; Fuenen; Seeland; Bornholm; Oeland; Usedom; Fehmarn; ...
*> query: (?x1436, EW) <- ?x1436[ a Island; has locatedInWater ?x146<BalticSea>;]
*> conf = 0.08
ranks of expected_values: 13
EVAL	Oesel	locatedIn	EW	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.077	24.000	24.000	67.000	0.394	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: EW
=> 25 concepts (25 used for prediction)
PRED predicted values (max 10 best out of 67): DK (0.38 #168, 0.31 #404, 0.17 #475), D (0.35 #716, 0.35 #497, 0.17 #475), USA (0.18 #791, 0.17 #1033, 0.08 #1520), PL (0.17 #475, 0.17 #474, 0.15 #718), LT (0.17 #475, 0.17 #474, 0.15 #718), GB (0.14 #486, 0.12 #1214, 0.08 #1457), CDN (0.09 #1024, 0.04 #4417, 0.04 #5393), NL (0.08 #611, 0.05 #717, 0.02 #1339), SF (0.08 #132, 0.06 #368, 0.05 #477), S (0.08 #92, 0.06 #328, 0.05 #477)
>> best conf = 0.38
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #168 for best value:
>> intensional similarity = 3
>> extensional distance = 11
>> proper extension:  Dagoe; Rugen; Falster; Aland; Fuenen; Seeland; Bornholm; Oeland; Usedom; Fehmarn; ...
>> query: (?x1436, DK) <- ?x1436[ a Island; has locatedInWater ?x146<BalticSea>;]

*> Best rule #136 for first EXPECTED value:
*> intensional similarity = 3
*> extensional distance = 11
*> proper extension:  Dagoe; Rugen; Falster; Aland; Fuenen; Seeland; Bornholm; Oeland; Usedom; Fehmarn; ...
*> query: (?x1436, EW) <- ?x1436[ a Island; has locatedInWater ?x146<BalticSea>;]
*> conf = 0.08
ranks of expected_values: 11
EVAL	Oesel	locatedIn	EW	CNN-1.+1._MA	0.000	0.000	0.000	0.091	25.000	25.000	67.000	0.385	http://www.semwebtech.org/mondial/10/meta#locatedIn

#412-BIH
PRED entity: BIH

PRED relation: neighbor!
PRED expected values: MNE
=> 38 concepts (38 used for prediction)
PRED predicted values (max 10 best out of 209): MNE (0.92 #1921, 0.91 #481, 0.91 #1283), AL (0.43 #36, 0.20 #961, 0.13 #962), R (0.31 #484, 0.26 #644, 0.15 #1286), BG (0.29 #27, 0.12 #960, 0.12 #508), KOS (0.29 #115, 0.12 #960, 0.12 #596), MK (0.29 #117, 0.12 #960, 0.11 #277), Yugoslavia (0.29 #160, 0.11 #3683, 0.09 #2882), BIH (0.20 #961, 0.19 #641, 0.14 #1), SLO (0.20 #961, 0.19 #641, 0.13 #962), LAR (0.20 #961, 0.17 #308, 0.16 #469)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1921 for best value:
>> intensional similarity = 6
>> extensional distance = 79
>> proper extension:  R; TR; USA; PY; A; WEST; MACX; ES; L; GAZA;
>> query: (?x55, ?x106) <- ?x55[ has language ?x1241; has neighbor ?x106; is locatedIn of ?x152; is neighbor of ?x904[ has ethnicGroup ?x164; is locatedIn of ?x132;];]
ranks of expected_values: 1
EVAL	BIH	neighbor!	MNE	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	38.000	38.000	209.000	0.918	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: MNE
=> 118 concepts (118 used for prediction)
PRED predicted values (max 10 best out of 222): MNE (0.93 #12177, 0.93 #11352, 0.93 #11354), TR (0.44 #2156, 0.24 #5082, 0.19 #16659), H (0.40 #2010, 0.40 #1845, 0.35 #3599), BIH (0.35 #3599, 0.34 #13173, 0.34 #2289), RO (0.35 #3599, 0.34 #13173, 0.34 #2289), BG (0.35 #3599, 0.34 #13173, 0.34 #2289), KOS (0.35 #3599, 0.34 #13173, 0.34 #2289), AL (0.35 #3599, 0.34 #13173, 0.33 #325), SLO (0.35 #3599, 0.34 #13173, 0.33 #404), MK (0.34 #2289, 0.33 #769, 0.32 #1310)
>> best conf = 0.93
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #12177 for best value:
>> intensional similarity = 16
>> extensional distance = 59
>> proper extension:  A;
>> query: (?x55, ?x106) <- ?x55[ a Country; has ethnicGroup ?x160; has language ?x1241; has neighbor ?x106[ has ethnicGroup ?x775; is locatedIn of ?x104[ a Lake;]; is locatedIn of ?x105[ a Estuary;];]; has religion ?x56[ is religion of ?x204[ has encompassed ?x195; is locatedIn of ?x183;]; is religion of ?x234<CH>;]; is locatedIn of ?x152;]
ranks of expected_values: 1
EVAL	BIH	neighbor!	MNE	CNN-1.+1._MA	1.000	1.000	1.000	1.000	118.000	118.000	222.000	0.935	http://www.semwebtech.org/mondial/10/meta#neighbor

#411-BF
PRED entity: BF

PRED relation: neighbor!
PRED expected values: GH RMM
=> 45 concepts (45 used for prediction)
PRED predicted values (max 10 best out of 174): GH (0.90 #5569, 0.90 #5729, 0.90 #4927), RMM (0.90 #5569, 0.90 #5729, 0.90 #4927), BF (0.40 #761, 0.40 #1743, 0.33 #127), SN (0.40 #551, 0.29 #5248, 0.29 #4446), RIM (0.40 #721, 0.29 #5248, 0.29 #4446), RG (0.33 #901, 0.29 #5248, 0.29 #4446), LB (0.29 #5248, 0.29 #4446, 0.28 #1109), DZ (0.29 #5248, 0.29 #4446, 0.28 #1109), WAN (0.29 #5248, 0.29 #4446, 0.28 #1109), LAR (0.29 #4446, 0.28 #1109, 0.26 #4928)
>> best conf = 0.90
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #5569 for best value:
>> intensional similarity = 7
>> extensional distance = 145
>> proper extension:  ARM;
>> query: (?x811, ?x426) <- ?x811[ has neighbor ?x426[ has ethnicGroup ?x1109; has neighbor ?x139;]; has neighbor ?x483[ has ethnicGroup ?x162; is locatedIn of ?x135;]; has religion ?x116;]
ranks of expected_values: 1, 2
EVAL	BF	neighbor!	RMM	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	45.000	45.000	174.000	0.901	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	BF	neighbor!	GH	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	45.000	45.000	174.000	0.901	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: GH RMM
=> 90 concepts (90 used for prediction)
PRED predicted values (max 10 best out of 225): RMM (0.96 #7384, 0.96 #3935, 0.94 #2622), GH (0.93 #9024, 0.93 #2456, 0.93 #2455), RG (0.55 #1915, 0.42 #2134, 0.42 #2081), D (0.53 #2312, 0.12 #6089, 0.08 #9700), WAN (0.49 #3447, 0.48 #11000, 0.40 #653), TCH (0.49 #3447, 0.48 #11000, 0.34 #3112), CAM (0.49 #3447, 0.48 #11000, 0.27 #652), DZ (0.45 #1740, 0.40 #653, 0.38 #1803), SN (0.40 #2459, 0.40 #1551, 0.40 #816), RIM (0.40 #1071, 0.40 #905, 0.38 #1803)
>> best conf = 0.96
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #7384 for best value:
>> intensional similarity = 16
>> extensional distance = 59
>> proper extension:  ET; RL; WEST; ES; JOR; GAZA;
>> query: (?x811, ?x839) <- ?x811[ has ethnicGroup ?x2156; has neighbor ?x839[ a Country; has ethnicGroup ?x1537[ a EthnicGroup;]; has language ?x1228; has neighbor ?x416[ a Country; has ethnicGroup ?x122; has neighbor ?x1051; is locatedIn of ?x182;]; has wasDependentOf ?x78; is locatedIn of ?x1618[ has type ?x578;];]; has religion ?x116; is neighbor of ?x1307;]
ranks of expected_values: 1, 2
EVAL	BF	neighbor!	RMM	CNN-1.+1._MA	1.000	1.000	1.000	1.000	90.000	90.000	225.000	0.958	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	BF	neighbor!	GH	CNN-1.+1._MA	1.000	1.000	1.000	1.000	90.000	90.000	225.000	0.958	http://www.semwebtech.org/mondial/10/meta#neighbor

#410-Maas
PRED entity: Maas

PRED relation: hasSource!
PRED expected values: Maas
=> 27 concepts (23 used for prediction)
PRED predicted values (max 10 best out of 117): Seine (0.11 #159, 0.08 #2748, 0.07 #2518), Marne (0.11 #146, 0.08 #2748, 0.07 #2518), Isere (0.11 #85, 0.08 #2748, 0.07 #2518), Mosel (0.11 #78, 0.08 #2748, 0.07 #2518), Saar (0.11 #27, 0.08 #2748, 0.07 #2518), Saone (0.11 #160, 0.08 #2748, 0.07 #2518), Doubs (0.11 #125, 0.03 #353, 0.02 #581), Loire (0.08 #2748, 0.07 #2518, 0.02 #2749), Rhone (0.08 #2748, 0.07 #2518, 0.02 #2749), Garonne (0.08 #2748, 0.07 #2518, 0.02 #2749)
>> best conf = 0.11
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #159 for best value:
>> intensional similarity = 3
>> extensional distance = 7
>> proper extension:  Doubs; Marne; Saar; Saone; Seine; Mosel; Isere;
>> query: (?x1682, Seine) <- ?x1682[ a Source; has locatedIn ?x78<F>;]

*> Best rule #2518 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 213
*> proper extension:  Breg; Buna; Moraca; Ene; Apurimac; Piva; Bani; Waag; Neckar; Werra; ...
*> query: (?x1682, ?x1257) <- ?x1682[ a Source; has locatedIn ?x78[ has religion ?x95; is locatedIn of ?x1257[ a River;]; is neighbor of ?x120;];]
*> conf = 0.07
ranks of expected_values: 12
EVAL	Maas	hasSource!	Maas	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.083	27.000	23.000	117.000	0.111	http://www.semwebtech.org/mondial/10/meta#hasSource

PRED relation: hasSource!
PRED expected values: Maas
=> 82 concepts (82 used for prediction)
PRED predicted values (max 10 best out of 57): Seine (0.11 #159, 0.08 #3214, 0.04 #387), Mosel (0.11 #78, 0.08 #3214, 0.04 #306), Marne (0.11 #146, 0.08 #3214, 0.04 #374), Isere (0.11 #85, 0.08 #3214, 0.04 #313), Saar (0.11 #27, 0.08 #3214, 0.04 #255), Saone (0.11 #160, 0.08 #3214, 0.04 #388), Doubs (0.11 #125, 0.04 #353, 0.03 #582), Rhone (0.08 #3214, 0.03 #7371, 0.02 #8993), Rhein (0.08 #3214, 0.03 #7371, 0.02 #8993), Loire (0.08 #3214, 0.03 #7371, 0.02 #8993)
>> best conf = 0.11
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #159 for best value:
>> intensional similarity = 3
>> extensional distance = 7
>> proper extension:  Doubs; Marne; Saar; Saone; Seine; Mosel; Isere;
>> query: (?x1682, Seine) <- ?x1682[ a Source; has locatedIn ?x78<F>;]

*> Best rule #8993 for first EXPECTED value:
*> intensional similarity = 10
*> extensional distance = 431
*> proper extension:  ReneLevasseurIsland; DarlingRiver; GreatSandyDesert; DarlingRiver; Tasmania; VictoriaIsland; EucumbeneRiver; JoekulsaaFjoellum; MtColumbia; JoekulsaaFjoellum; ...
*> query: (?x1682, ?x742) <- ?x1682[ has locatedIn ?x78[ has government ?x435; has language ?x51; has religion ?x95; is locatedIn of ?x275[ is locatedInWater of ?x68;]; is locatedIn of ?x742[ a River;]; is locatedIn of ?x1275[ a Source;];];]
*> conf = 0.02
ranks of expected_values: 24
EVAL	Maas	hasSource!	Maas	CNN-1.+1._MA	0.000	0.000	0.000	0.042	82.000	82.000	57.000	0.111	http://www.semwebtech.org/mondial/10/meta#hasSource

#409-BG
PRED entity: BG

PRED relation: neighbor!
PRED expected values: GR
=> 32 concepts (32 used for prediction)
PRED predicted values (max 10 best out of 204): GR (0.89 #1588, 0.89 #635, 0.89 #3332), H (0.43 #680, 0.33 #44, 0.31 #796), UA (0.43 #687, 0.31 #796, 0.25 #3491), A (0.43 #711, 0.18 #954, 0.16 #475), BG (0.40 #504, 0.33 #186, 0.33 #27), KOS (0.40 #591, 0.33 #273, 0.31 #796), MNE (0.33 #10, 0.31 #796, 0.25 #3491), AL (0.33 #194, 0.31 #796, 0.25 #3491), BIH (0.33 #1, 0.31 #796, 0.25 #3491), R (0.33 #799, 0.25 #320, 0.18 #954)
>> best conf = 0.89
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1588 for best value:
>> intensional similarity = 5
>> extensional distance = 73
>> proper extension:  YE;
>> query: (?x177, ?x399) <- ?x177[ has neighbor ?x399; has wasDependentOf ?x1656; is locatedIn of ?x98[ has mergesWith ?x97; is flowsInto of ?x679;];]
ranks of expected_values: 1
EVAL	BG	neighbor!	GR	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	32.000	32.000	204.000	0.893	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: GR
=> 90 concepts (90 used for prediction)
PRED predicted values (max 10 best out of 229): GR (0.92 #4700, 0.91 #5188, 0.91 #5187), R (0.62 #1295, 0.50 #2588, 0.47 #2108), UA (0.50 #535, 0.50 #483, 0.49 #10072), H (0.50 #528, 0.50 #483, 0.49 #10072), HR (0.50 #483, 0.49 #10072, 0.40 #825), SK (0.50 #483, 0.49 #10072, 0.33 #1476), A (0.50 #483, 0.49 #10072, 0.33 #1529), MD (0.50 #483, 0.49 #10072, 0.33 #482), D (0.50 #483, 0.49 #10072, 0.33 #482), BIH (0.50 #483, 0.34 #6656, 0.33 #3561)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4700 for best value:
>> intensional similarity = 16
>> extensional distance = 36
>> proper extension:  MOC; MYA; AFG; RG; KOS; SA; RMM; IND; MNG;
>> query: (?x177, ?x185) <- ?x177[ a Country; has ethnicGroup ?x164; has government ?x254; has language ?x511; has neighbor ?x185[ has ethnicGroup ?x638; has neighbor ?x302; is locatedIn of ?x184;]; has religion ?x56[ is religion of ?x55[ has wasDependentOf ?x1197;]; is religion of ?x353<GE>; is religion of ?x403<KAZ>;]; is locatedIn of ?x98;]
ranks of expected_values: 1
EVAL	BG	neighbor!	GR	CNN-1.+1._MA	1.000	1.000	1.000	1.000	90.000	90.000	229.000	0.918	http://www.semwebtech.org/mondial/10/meta#neighbor

#408-GUAM
PRED entity: GUAM

PRED relation: encompassed
PRED expected values: Australia-Oceania
=> 33 concepts (33 used for prediction)
PRED predicted values (max 10 best out of 5): Australia-Oceania (0.71 #8, 0.70 #23, 0.67 #13), America (0.59 #30, 0.58 #20, 0.57 #70), Europe (0.38 #112, 0.36 #92, 0.35 #103), Asia (0.38 #112, 0.20 #133, 0.20 #113), Africa (0.22 #161, 0.21 #156, 0.20 #136)
>> best conf = 0.71
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #8 for best value:
>> intensional similarity = 7
>> extensional distance = 12
>> proper extension:  PAL;
>> query: (?x1154, Australia-Oceania) <- ?x1154[ a Country; has government ?x2344; is locatedIn of ?x282<PacificOcean>; is locatedIn of ?x1401[ has belongsToIslands ?x66; has type ?x1402;];]
ranks of expected_values: 1
EVAL	GUAM	encompassed	Australia-Oceania	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	33.000	33.000	5.000	0.714	http://www.semwebtech.org/mondial/10/meta#encompassed

PRED relation: encompassed
PRED expected values: Australia-Oceania
=> 63 concepts (63 used for prediction)
PRED predicted values (max 10 best out of 5): Australia-Oceania (0.71 #105, 0.71 #93, 0.70 #132), Asia (0.71 #145, 0.56 #102, 0.47 #113), America (0.69 #118, 0.68 #129, 0.66 #144), Europe (0.37 #338, 0.37 #345, 0.36 #215), Africa (0.27 #299, 0.26 #206, 0.24 #223)
>> best conf = 0.71
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #105 for best value:
>> intensional similarity = 9
>> extensional distance = 12
>> proper extension:  PAL;
>> query: (?x1154, Australia-Oceania) <- ?x1154[ a Country; has government ?x2344; is locatedIn of ?x282<PacificOcean>; is locatedIn of ?x1401[ a Island; has belongsToIslands ?x66[ a Islands;]; has type ?x1402;];]
>> Best rule #93 for best value:
>> intensional similarity = 14
>> extensional distance = 12
>> proper extension:  FSM;
>> query: (?x1154, Australia-Oceania) <- ?x1154[ a Country; has ethnicGroup ?x2149[ a EthnicGroup; is ethnicGroup of ?x773[ has encompassed ?x175; has religion ?x116<Christian>; is locatedIn of ?x384;];]; has government ?x2344; is locatedIn of ?x282<PacificOcean>; is locatedIn of ?x1401[ a Island; has belongsToIslands ?x66;];]
ranks of expected_values: 1
EVAL	GUAM	encompassed	Australia-Oceania	CNN-1.+1._MA	1.000	1.000	1.000	1.000	63.000	63.000	5.000	0.714	http://www.semwebtech.org/mondial/10/meta#encompassed

#407-PikPobeda
PRED entity: PikPobeda

PRED relation: inMountains
PRED expected values: TianShan
=> 32 concepts (32 used for prediction)
PRED predicted values (max 10 best out of 28): TianShan (0.33 #31, 0.17 #118, 0.11 #262), Pamir (0.17 #104, 0.11 #262, 0.04 #366), Himalaya (0.17 #180, 0.06 #268, 0.05 #529), Andes (0.07 #360, 0.06 #534, 0.05 #621), Alps (0.06 #527, 0.06 #353, 0.05 #875), RockyMountains (0.06 #704, 0.05 #878, 0.01 #1313), Karakorum (0.06 #182), EastAfricanRift (0.05 #377, 0.04 #551, 0.03 #638), CordilleraVolcanica (0.04 #414, 0.03 #588, 0.03 #675), Kurdistan (0.03 #297, 0.03 #471, 0.02 #819)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #31 for best value:
>> intensional similarity = 5
>> extensional distance = 1
>> proper extension:  PikChan-Tengri;
>> query: (?x2122, TianShan) <- ?x2122[ a Mountain; has locatedIn ?x130<KGZ>; has locatedIn ?x232<CN>;]
ranks of expected_values: 1
EVAL	PikPobeda	inMountains	TianShan	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	32.000	32.000	28.000	0.333	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains
PRED expected values: TianShan
=> 85 concepts (85 used for prediction)
PRED predicted values (max 10 best out of 45): TianShan (0.33 #31, 0.25 #610, 0.23 #1224), Himalaya (0.30 #791, 0.25 #528, 0.22 #2272), Pamir (0.25 #610, 0.22 #2272, 0.13 #874), Karakorum (0.23 #1224, 0.22 #2272, 0.13 #874), Kunlun (0.22 #2272, 0.13 #874, 0.13 #873), Transhimalaya (0.22 #2272, 0.13 #874, 0.13 #873), Alps (0.16 #1140, 0.08 #2188, 0.05 #2537), SnowyMountains (0.08 #982, 0.03 #1157, 0.03 #2380), RockyMountains (0.08 #2104, 0.06 #2191, 0.05 #2540), Andes (0.07 #2195, 0.05 #2108, 0.05 #2544)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #31 for best value:
>> intensional similarity = 5
>> extensional distance = 1
>> proper extension:  PikChan-Tengri;
>> query: (?x2122, TianShan) <- ?x2122[ a Mountain; has locatedIn ?x130<KGZ>; has locatedIn ?x232<CN>;]
ranks of expected_values: 1
EVAL	PikPobeda	inMountains	TianShan	CNN-1.+1._MA	1.000	1.000	1.000	1.000	85.000	85.000	45.000	0.333	http://www.semwebtech.org/mondial/10/meta#inMountains

#406-Texel
PRED entity: Texel

PRED relation: locatedInWater
PRED expected values: NorthSea
=> 50 concepts (47 used for prediction)
PRED predicted values (max 10 best out of 47): NorthSea (0.67 #174, 0.67 #133, 0.64 #1373), PacificOcean (0.57 #192, 0.25 #369, 0.23 #1346), AtlanticOcean (0.54 #271, 0.43 #315, 0.39 #226), BalticSea (0.16 #224, 0.10 #135, 0.08 #357), MediterraneanSea (0.16 #813, 0.16 #588, 0.15 #634), JavaSea (0.12 #361, 0.12 #405, 0.10 #581), IndianOcean (0.11 #177, 0.09 #354, 0.09 #574), CaribbeanSea (0.10 #1171, 0.10 #1261, 0.10 #1305), IrishSea (0.10 #261, 0.09 #306, 0.07 #350), SouthChinaSea (0.09 #594, 0.09 #418, 0.09 #640)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #174 for best value:
>> intensional similarity = 7
>> extensional distance = 19
>> proper extension:  Rugen; Helgoland; Fohr; Baltrum; Fehmarn; Wangerooge;
>> query: (?x764, ?x121) <- ?x764[ a Island; has locatedIn ?x575[ has ethnicGroup ?x734; has government ?x92; is locatedIn of ?x121<NorthSea>; is neighbor of ?x120;];]
>> Best rule #133 for best value:
>> intensional similarity = 7
>> extensional distance = 19
>> proper extension:  Rugen; Helgoland; Fohr; Baltrum; Fehmarn; Wangerooge;
>> query: (?x764, NorthSea) <- ?x764[ a Island; has locatedIn ?x575[ has ethnicGroup ?x734; has government ?x92; is locatedIn of ?x121<NorthSea>; is neighbor of ?x120;];]
ranks of expected_values: 1
EVAL	Texel	locatedInWater	NorthSea	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	50.000	47.000	47.000	0.667	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater
PRED expected values: NorthSea
=> 163 concepts (163 used for prediction)
PRED predicted values (max 10 best out of 158): NorthSea (0.89 #1026, 0.81 #1655, 0.79 #2681), AtlanticOcean (0.55 #2466, 0.54 #1437, 0.54 #1393), MediterraneanSea (0.50 #326, 0.40 #906, 0.24 #2429), PacificOcean (0.48 #1269, 0.48 #998, 0.41 #1627), BalticSea (0.42 #269, 0.20 #895, 0.09 #2108), SulawesiSea (0.39 #428, 0.10 #2799, 0.10 #2844), Maas (0.36 #176, 0.36 #3817, 0.10 #5890), JavaSea (0.31 #945, 0.29 #1035, 0.25 #1305), IndianOcean (0.27 #938, 0.21 #1028, 0.19 #1342), IrishSea (0.23 #397, 0.18 #798, 0.17 #888)
>> best conf = 0.89
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1026 for best value:
>> intensional similarity = 13
>> extensional distance = 25
>> proper extension:  Niihau; Oahu; Hawaii; Maui; Kauai; SantaRosaIsland; SantaCruzIsland; Lanai; Unalaska; Aust-Vagoey; ...
>> query: (?x764, ?x121) <- ?x764[ a Island; has belongsToIslands ?x795[ is belongsToIslands of ?x2081[ a Island; has locatedInWater ?x121;];]; has locatedIn ?x575[ has ethnicGroup ?x734[ a EthnicGroup;]; has religion ?x95; is dependentOf of ?x1171[ a Country; is locatedIn of ?x1865;]; is neighbor of ?x120;];]
ranks of expected_values: 1
EVAL	Texel	locatedInWater	NorthSea	CNN-1.+1._MA	1.000	1.000	1.000	1.000	163.000	163.000	158.000	0.893	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#405-Greek
PRED entity: Greek

PRED relation: language!
PRED expected values: AUS
=> 23 concepts (23 used for prediction)
PRED predicted values (max 10 best out of 203): MK (0.71 #449, 0.50 #119, 0.41 #238), MNE (0.60 #128, 0.50 #119, 0.41 #238), KOS (0.50 #119, 0.41 #238, 0.40 #206), BG (0.50 #119, 0.29 #382, 0.27 #838), TR (0.50 #119, 0.27 #838, 0.27 #1079), SRB (0.43 #342, 0.20 #222, 0.14 #463), I (0.33 #29, 0.28 #510, 0.20 #148), A (0.29 #422, 0.29 #301, 0.20 #181), HR (0.29 #256, 0.20 #136, 0.14 #377), IR (0.19 #1123, 0.15 #1242, 0.14 #403)
>> best conf = 0.71
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #449 for best value:
>> intensional similarity = 10
>> extensional distance = 5
>> proper extension:  Turkish; Roma; Macedonian;
>> query: (?x1567, MK) <- ?x1567[ is language of ?x204[ has wasDependentOf ?x1656; is locatedIn of ?x1004[ has inMountains ?x785;]; is locatedIn of ?x1374<LakeOhrid>; is neighbor of ?x692<KOS>;]; is language of ?x399[ has neighbor ?x185;];]

*> Best rule #866 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 29
*> proper extension:  French; Samoan; English; Vietnamese; Arabic; Chinese;
*> query: (?x1567, AUS) <- ?x1567[ is language of ?x204[ has religion ?x352<RomanCatholic>; has wasDependentOf ?x1656; is locatedIn of ?x104[ is flowsInto of ?x2296;]; is locatedIn of ?x1004[ has inMountains ?x785;];];]
*> conf = 0.10
ranks of expected_values: 52
EVAL	Greek	language!	AUS	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.019	23.000	23.000	203.000	0.714	http://www.semwebtech.org/mondial/10/meta#language

PRED relation: language!
PRED expected values: AUS
=> 52 concepts (52 used for prediction)
PRED predicted values (max 10 best out of 216): MK (0.71 #697, 0.59 #4430, 0.48 #363), MNE (0.60 #372, 0.59 #4430, 0.48 #363), I (0.50 #1004, 0.42 #1493, 0.38 #1617), KOS (0.48 #363, 0.44 #4429, 0.44 #1217), M (0.40 #584, 0.22 #729, 0.17 #2810), BG (0.33 #874, 0.33 #22, 0.32 #1095), TR (0.33 #24, 0.32 #1095, 0.29 #607), CH (0.33 #1497, 0.31 #1621, 0.30 #1008), A (0.33 #62, 0.29 #607, 0.29 #670), IR (0.33 #43, 0.29 #607, 0.26 #3605)
>> best conf = 0.71
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #697 for best value:
>> intensional similarity = 21
>> extensional distance = 5
>> proper extension:  Serbian; Roma; Macedonian;
>> query: (?x1567, MK) <- ?x1567[ is language of ?x204[ a Country; has encompassed ?x195<Europe>; has ethnicGroup ?x1472<Albanian>; has religion ?x352<RomanCatholic>; is locatedIn of ?x183[ a Source;]; is locatedIn of ?x275[ is flowsInto of ?x699;]; is locatedIn of ?x656<BlackDrin>; is locatedIn of ?x1516<Korab>; is neighbor of ?x692<KOS>;]; is language of ?x235[ a Country; has government ?x435;];]

*> Best rule #1370 for first EXPECTED value:
*> intensional similarity = 20
*> extensional distance = 10
*> proper extension:  English; Vietnamese; Arabic;
*> query: (?x1567, AUS) <- ?x1567[ a Language; is language of ?x204[ a Country; has religion ?x56<ChristianOrthodox>; has religion ?x187<Muslim>; is locatedIn of ?x183[ a Source;]; is locatedIn of ?x203[ a River;]; is locatedIn of ?x275[ a Sea; is flowsInto of ?x699; is locatedInWater of ?x68;];]; is language of ?x235[ a Country; has encompassed ?x195; has ethnicGroup ?x595; has wasDependentOf ?x81;];]
*> conf = 0.25
ranks of expected_values: 20
EVAL	Greek	language!	AUS	CNN-1.+1._MA	0.000	0.000	0.000	0.050	52.000	52.000	216.000	0.714	http://www.semwebtech.org/mondial/10/meta#language

#404-Kwa
PRED entity: Kwa

PRED relation: hasEstuary!
PRED expected values: Kwa
=> 26 concepts (24 used for prediction)
PRED predicted values (max 10 best out of 67): Lomami (0.08 #1588, 0.05 #224, 0.02 #1589), Luapula (0.08 #1588, 0.05 #220, 0.02 #1589), Aruwimi (0.08 #1588, 0.05 #198, 0.02 #1589), Tshuapa (0.08 #1588, 0.05 #195, 0.02 #1589), Lukenie (0.08 #1588, 0.05 #188, 0.02 #1589), Fimi (0.08 #1588, 0.05 #141, 0.02 #1589), Ruzizi (0.08 #1588, 0.05 #120, 0.02 #1589), Cuilo (0.08 #1588, 0.05 #98, 0.02 #1589), Bomu (0.08 #1588, 0.05 #91, 0.02 #1589), Busira (0.08 #1588, 0.05 #55, 0.02 #1589)
>> best conf = 0.08
=> the first rule below is the first best rule for 20 predicted values
>> Best rule #1588 for best value:
>> intensional similarity = 4
>> extensional distance = 230
>> proper extension:  Bahrel-Ghasal; Bahrel-Djebel-Albert-Nil; Sobat;
>> query: (?x1089, ?x1791) <- ?x1089[ a Estuary; has locatedIn ?x348[ is locatedIn of ?x1791[ a River;];];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 20
EVAL	Kwa	hasEstuary!	Kwa	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.050	26.000	24.000	67.000	0.077	http://www.semwebtech.org/mondial/10/meta#hasEstuary

PRED relation: hasEstuary!
PRED expected values: Kwa
=> 75 concepts (75 used for prediction)
PRED predicted values (max 10 best out of 198): Ruzizi (0.08 #4570, 0.06 #1597, 0.06 #5719), Fimi (0.08 #4570, 0.06 #1597, 0.06 #5719), Busira (0.08 #4570, 0.06 #1597, 0.06 #5719), Lualaba (0.08 #4570, 0.06 #1597, 0.06 #5719), Ubangi (0.08 #4570, 0.06 #1597, 0.06 #5719), Luvua (0.08 #4570, 0.06 #1597, 0.06 #5719), Lukuga (0.08 #4570, 0.06 #1597, 0.06 #5719), Ruki (0.08 #4570, 0.06 #1597, 0.06 #5719), Zaire (0.08 #4570, 0.06 #1597, 0.06 #5719), Kasai (0.08 #4570, 0.06 #1597, 0.06 #5719)
>> best conf = 0.08
=> the first rule below is the first best rule for 20 predicted values
>> Best rule #4570 for best value:
>> intensional similarity = 10
>> extensional distance = 85
>> proper extension:  WesternDwina;
>> query: (?x1089, ?x929) <- ?x1089[ a Estuary; has locatedIn ?x348[ has neighbor ?x934[ a Country; is locatedIn of ?x933;]; has religion ?x95<Protestant>; has wasDependentOf ?x543; is locatedIn of ?x929[ a River;];];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 11
EVAL	Kwa	hasEstuary!	Kwa	CNN-1.+1._MA	0.000	0.000	0.000	0.091	75.000	75.000	198.000	0.078	http://www.semwebtech.org/mondial/10/meta#hasEstuary

#403-H
PRED entity: H

PRED relation: neighbor
PRED expected values: A
=> 40 concepts (40 used for prediction)
PRED predicted values (max 10 best out of 197): A (0.90 #934, 0.89 #2961, 0.89 #3742), D (0.50 #324, 0.50 #169, 0.40 #14), PL (0.40 #31, 0.33 #186, 0.29 #341), R (0.40 #3, 0.33 #158, 0.27 #4837), BY (0.40 #38, 0.33 #193, 0.27 #4837), H (0.33 #197, 0.29 #352, 0.28 #467), CZ (0.33 #233, 0.29 #388, 0.28 #467), F (0.29 #314, 0.09 #782, 0.07 #1871), I (0.28 #467, 0.27 #4837, 0.26 #4368), MD (0.27 #4837, 0.27 #1711, 0.26 #4368)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #934 for best value:
>> intensional similarity = 6
>> extensional distance = 45
>> proper extension:  IRL; ROU;
>> query: (?x236, ?x156) <- ?x236[ has ethnicGroup ?x164; has government ?x254; has religion ?x352<RomanCatholic>; is locatedIn of ?x133; is neighbor of ?x156;]
ranks of expected_values: 1
EVAL	H	neighbor	A	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	40.000	40.000	197.000	0.902	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: A
=> 84 concepts (84 used for prediction)
PRED predicted values (max 10 best out of 217): A (0.92 #9414, 0.91 #6361, 0.91 #7801), D (0.60 #960, 0.50 #491, 0.49 #5883), H (0.50 #674, 0.49 #5882, 0.49 #5883), CZ (0.50 #867, 0.43 #1343, 0.40 #1024), MNE (0.50 #168, 0.38 #1578, 0.34 #3326), BIH (0.50 #160, 0.38 #1578, 0.34 #3326), MD (0.49 #5882, 0.49 #5883, 0.48 #5881), BG (0.49 #5882, 0.49 #5883, 0.48 #3009), I (0.49 #5883, 0.48 #3009, 0.45 #2687), PL (0.43 #1296, 0.42 #2367, 0.42 #2242)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #9414 for best value:
>> intensional similarity = 12
>> extensional distance = 95
>> proper extension:  ROK;
>> query: (?x236, ?x303) <- ?x236[ a Country; has neighbor ?x904[ has government ?x435; has language ?x684; has neighbor ?x701[ is locatedIn of ?x656;]; is locatedIn of ?x132;]; is locatedIn of ?x133; is neighbor of ?x303[ has encompassed ?x195; has language ?x1108; is locatedIn of ?x97;];]
ranks of expected_values: 1
EVAL	H	neighbor	A	CNN-1.+1._MA	1.000	1.000	1.000	1.000	84.000	84.000	217.000	0.917	http://www.semwebtech.org/mondial/10/meta#neighbor

#402-Amudarja
PRED entity: Amudarja

PRED relation: locatedIn
PRED expected values: TAD
=> 45 concepts (41 used for prediction)
PRED predicted values (max 10 best out of 196): CN (0.74 #2168, 0.67 #993, 0.36 #758), TAD (0.72 #3052, 0.72 #3051, 0.68 #5630), KAZ (0.50 #90, 0.36 #792, 0.31 #468), IND (0.50 #1357, 0.33 #1593, 0.17 #2297), R (0.46 #7040, 0.21 #707, 0.21 #4930), IR (0.44 #1477, 0.31 #468, 0.31 #234), KGZ (0.31 #468, 0.31 #234, 0.25 #257), PK (0.31 #468, 0.31 #234, 0.21 #4457), USA (0.23 #1947, 0.20 #3823, 0.18 #4057), BD (0.15 #1359, 0.10 #1595, 0.06 #1124)
>> best conf = 0.74
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2168 for best value:
>> intensional similarity = 7
>> extensional distance = 75
>> proper extension:  Hwangho; YellowSea; Lhotse; Zhoushan; Ordos; UlugMuztag; GasherbrumII; Tarim-Yarkend; ChoOyu; Muztagata; ...
>> query: (?x301, CN) <- ?x301[ has locatedIn ?x381[ has neighbor ?x83<PK>; has neighbor ?x232[ is locatedIn of ?x411<Indus>;]; is neighbor of ?x129;];]

*> Best rule #3052 for first EXPECTED value:
*> intensional similarity = 4
*> extensional distance = 93
*> proper extension:  Sanaga;
*> query: (?x301, ?x129) <- ?x301[ has flowsInto ?x1971; has hasSource ?x2401; is flowsInto of ?x300[ has locatedIn ?x129;];]
*> conf = 0.72
ranks of expected_values: 2
EVAL	Amudarja	locatedIn	TAD	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	45.000	41.000	196.000	0.740	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: TAD
=> 148 concepts (141 used for prediction)
PRED predicted values (max 10 best out of 230): TAD (0.90 #20492, 0.82 #9176, 0.82 #9175), R (0.86 #4240, 0.77 #31824, 0.76 #18611), KAZ (0.77 #31824, 0.69 #29702, 0.62 #1974), IR (0.77 #31824, 0.69 #29702, 0.29 #941), AZ (0.77 #31824, 0.69 #29702, 0.25 #545), CN (0.60 #16306, 0.44 #3821, 0.29 #941), IRQ (0.33 #1477, 0.20 #1645, 0.20 #1242), SF (0.32 #13787, 0.17 #19909, 0.17 #3424), UA (0.31 #13726, 0.24 #5242, 0.21 #6182), USA (0.29 #23161, 0.25 #1011, 0.22 #25047)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #20492 for best value:
>> intensional similarity = 9
>> extensional distance = 154
>> proper extension:  Lulua;
>> query: (?x301, ?x129) <- ?x301[ a River; has flowsInto ?x1971[ has locatedIn ?x277[ has ethnicGroup ?x1193; has neighbor ?x130;];]; has hasEstuary ?x968; has hasSource ?x2401[ a Source; has locatedIn ?x129;];]
ranks of expected_values: 1
EVAL	Amudarja	locatedIn	TAD	CNN-1.+1._MA	1.000	1.000	1.000	1.000	148.000	141.000	230.000	0.901	http://www.semwebtech.org/mondial/10/meta#locatedIn

#401-NOK
PRED entity: NOK

PRED relation: neighbor!
PRED expected values: R
=> 30 concepts (30 used for prediction)
PRED predicted values (max 10 best out of 165): UZB (0.40 #368, 0.33 #528, 0.31 #848), LAO (0.33 #240, 0.33 #80, 0.20 #719), BD (0.33 #300, 0.33 #140, 0.20 #460), IND (0.33 #138, 0.25 #3529, 0.25 #3530), MYA (0.27 #703, 0.25 #3529, 0.25 #3530), KGZ (0.25 #3529, 0.25 #3530, 0.25 #817), AFG (0.25 #3529, 0.25 #3530, 0.25 #867), TAD (0.25 #3529, 0.25 #3530, 0.25 #816), KAZ (0.25 #3529, 0.25 #3530, 0.25 #3528), NEP (0.25 #3529, 0.25 #3530, 0.25 #3528)
>> best conf = 0.40
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #368 for best value:
>> intensional similarity = 9
>> extensional distance = 8
>> proper extension:  TAD; KGZ; AFG; KAZ;
>> query: (?x334, UZB) <- ?x334[ a Country; has encompassed ?x175; is locatedIn of ?x270; is neighbor of ?x232<CN>; is neighbor of ?x626[ a Country; has wasDependentOf ?x117; is locatedIn of ?x619;];]

*> Best rule #3529 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 137
*> proper extension:  RT;
*> query: (?x334, ?x617) <- ?x334[ a Country; has encompassed ?x175; is locatedIn of ?x271; is neighbor of ?x232[ has neighbor ?x617[ has religion ?x95;]; is locatedIn of ?x231;];]
*> conf = 0.25
ranks of expected_values: 11
EVAL	NOK	neighbor!	R	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.091	30.000	30.000	165.000	0.400	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: R
=> 75 concepts (75 used for prediction)
PRED predicted values (max 10 best out of 197): PK (0.50 #484, 0.40 #965, 0.33 #1931), UZB (0.50 #1812, 0.33 #2294, 0.33 #1973), R (0.46 #4049, 0.39 #2085, 0.35 #2407), BD (0.40 #1423, 0.40 #1099, 0.33 #2065), J (0.39 #2085, 0.35 #2407, 0.35 #2406), NOK (0.39 #2085, 0.35 #2407, 0.35 #1925), KGZ (0.33 #1781, 0.33 #17, 0.28 #11087), TAD (0.33 #1941, 0.28 #11087, 0.28 #11085), TM (0.33 #49, 0.25 #527, 0.23 #4858), PL (0.33 #194, 0.23 #4858, 0.23 #6985)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #484 for best value:
>> intensional similarity = 16
>> extensional distance = 2
>> proper extension:  AFG; IND;
>> query: (?x334, PK) <- ?x334[ a Country; has encompassed ?x175<Asia>; has government ?x1979; has language ?x2244; has wasDependentOf ?x117[ has ethnicGroup ?x2391; has language ?x118; is locatedIn of ?x282[ is flowsInto of ?x602; is locatedInWater of ?x205; is mergesWith of ?x60;];]; is locatedIn of ?x2111[ has inMountains ?x898;]; is neighbor of ?x232<CN>;]

*> Best rule #4049 for first EXPECTED value:
*> intensional similarity = 18
*> extensional distance = 26
*> proper extension:  MEX;
*> query: (?x334, R) <- ?x334[ is neighbor of ?x232[ a Country; has encompassed ?x175; is locatedIn of ?x338[ has flowsInto ?x339; has hasEstuary ?x1481;]; is locatedIn of ?x340[ a Island;]; is locatedIn of ?x349[ a Desert;]; is locatedIn of ?x421[ a Mountain;]; is neighbor of ?x111[ has religion ?x187; is locatedIn of ?x110;]; is neighbor of ?x641[ a Country; has language ?x539;];];]
*> conf = 0.46
ranks of expected_values: 3
EVAL	NOK	neighbor!	R	CNN-1.+1._MA	0.000	1.000	1.000	0.333	75.000	75.000	197.000	0.500	http://www.semwebtech.org/mondial/10/meta#neighbor

#400-OM
PRED entity: OM

PRED relation: neighbor
PRED expected values: YE
=> 23 concepts (23 used for prediction)
PRED predicted values (max 10 best out of 161): YE (0.89 #1771, 0.89 #1772, 0.89 #1609), IRQ (0.43 #851, 0.33 #211, 0.20 #692), AFG (0.40 #548, 0.14 #868, 0.06 #2743), OM (0.33 #267, 0.29 #907, 0.24 #1287), KWT (0.33 #319, 0.24 #1287, 0.23 #2420), RSA (0.33 #46, 0.05 #1657, 0.05 #1495), MOC (0.33 #31, 0.05 #1642, 0.05 #1480), TR (0.29 #830, 0.20 #510, 0.06 #1640), JOR (0.24 #1287, 0.23 #2420, 0.23 #2421), Q (0.24 #1287, 0.23 #2420, 0.23 #2421)
>> best conf = 0.89
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1771 for best value:
>> intensional similarity = 11
>> extensional distance = 106
>> proper extension:  V;
>> query: (?x639, ?x107) <- ?x639[ has government ?x640; has wasDependentOf ?x1027; is neighbor of ?x107[ has government ?x1136;]; is neighbor of ?x668[ a Country; is locatedIn of ?x60;]; is neighbor of ?x751[ a Country; has encompassed ?x175; has religion ?x187;];]
ranks of expected_values: 1
EVAL	OM	neighbor	YE	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	23.000	23.000	161.000	0.894	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: YE
=> 83 concepts (83 used for prediction)
PRED predicted values (max 10 best out of 191): YE (0.94 #8218, 0.90 #9900, 0.90 #10239), CN (0.67 #4764, 0.33 #366, 0.33 #203), IR (0.60 #2271, 0.60 #2163, 0.43 #3305), IL (0.60 #3786, 0.57 #2483, 0.36 #4603), OM (0.50 #1241, 0.40 #973, 0.39 #5044), IRQ (0.43 #3303, 0.43 #2488, 0.40 #973), JOR (0.43 #3251, 0.43 #3215, 0.39 #5044), PK (0.40 #973, 0.38 #2109, 0.33 #2437), KWT (0.40 #973, 0.33 #2437, 0.33 #969), TR (0.40 #2140, 0.33 #679, 0.31 #4913)
>> best conf = 0.94
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #8218 for best value:
>> intensional similarity = 20
>> extensional distance = 54
>> proper extension:  V;
>> query: (?x639, ?x668) <- ?x639[ has encompassed ?x175[ is encompassed of ?x304[ has language ?x511; has religion ?x2031;]; is encompassed of ?x617[ has ethnicGroup ?x872;];]; has government ?x640; has neighbor ?x107[ a Country;]; has religion ?x187; has wasDependentOf ?x1027; is neighbor of ?x668[ has government ?x435; is locatedIn of ?x60[ a Sea; has mergesWith ?x182<AtlanticOcean>; is flowsInto of ?x242; is locatedInWater of ?x226; is mergesWith of ?x241;];];]
ranks of expected_values: 1
EVAL	OM	neighbor	YE	CNN-1.+1._MA	1.000	1.000	1.000	1.000	83.000	83.000	191.000	0.940	http://www.semwebtech.org/mondial/10/meta#neighbor

#399-SRB
PRED entity: SRB

PRED relation: ethnicGroup
PRED expected values: Croat
=> 25 concepts (25 used for prediction)
PRED predicted values (max 10 best out of 144): Russian (0.54 #1082, 0.23 #2784, 0.21 #1841), Albanian (0.40 #347, 0.23 #2784, 0.18 #4303), Ukrainian (0.36 #1013, 0.23 #2784, 0.18 #4303), Croat (0.33 #5, 0.23 #2784, 0.20 #258), Muslim (0.33 #105, 0.23 #2784, 0.20 #358), Slovene (0.33 #11, 0.20 #264, 0.07 #5824), European (0.27 #2284, 0.23 #2031, 0.22 #3044), German (0.23 #2784, 0.18 #4303, 0.16 #5064), Slovak (0.23 #2784, 0.18 #4303, 0.16 #5064), Romanian (0.23 #2784, 0.18 #4303, 0.16 #5064)
>> best conf = 0.54
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1082 for best value:
>> intensional similarity = 6
>> extensional distance = 26
>> proper extension:  AUS; CY;
>> query: (?x904, Russian) <- ?x904[ has language ?x684; has religion ?x56<ChristianOrthodox>; is locatedIn of ?x1489[ has locatedIn ?x692[ has encompassed ?x195;];];]

*> Best rule #5 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 1
*> proper extension:  HR;
*> query: (?x904, Croat) <- ?x904[ has wasDependentOf ?x1197; is locatedIn of ?x132; is neighbor of ?x55<BIH>; is neighbor of ?x106<MNE>; is neighbor of ?x236<H>;]
*> conf = 0.33
ranks of expected_values: 4
EVAL	SRB	ethnicGroup	Croat	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.250	25.000	25.000	144.000	0.536	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: Croat
=> 89 concepts (89 used for prediction)
PRED predicted values (max 10 best out of 247): German (0.60 #2291, 0.50 #3306, 0.33 #9), Turkish (0.50 #1197, 0.33 #690, 0.33 #437), Ukrainian (0.40 #3298, 0.40 #2283, 0.33 #1), Albanian (0.40 #1616, 0.33 #600, 0.31 #4152), Croat (0.40 #2033, 0.31 #10147, 0.30 #3044), Muslim (0.40 #2133, 0.31 #10147, 0.30 #3044), African (0.40 #4572, 0.27 #12947, 0.26 #18788), Slovak (0.40 #2490, 0.27 #4820, 0.26 #11418), Greek (0.40 #1803, 0.20 #2056, 0.20 #1550), European (0.37 #6856, 0.36 #7109, 0.33 #4573)
>> best conf = 0.60
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2291 for best value:
>> intensional similarity = 14
>> extensional distance = 3
>> proper extension:  SK; H;
>> query: (?x904, German) <- ?x904[ has ethnicGroup ?x164; has language ?x684<Hungarian>; has neighbor ?x177[ a Country; has government ?x254;]; has neighbor ?x701[ has encompassed ?x195; has ethnicGroup ?x354; has neighbor ?x204;]; has wasDependentOf ?x1197; is locatedIn of ?x132; is locatedIn of ?x378[ a Estuary;];]

*> Best rule #2033 for first EXPECTED value:
*> intensional similarity = 15
*> extensional distance = 3
*> proper extension:  BIH; HR;
*> query: (?x904, Croat) <- ?x904[ has ethnicGroup ?x164; has language ?x684; has neighbor ?x106<MNE>; has neighbor ?x177[ a Country; has government ?x254;]; has neighbor ?x701[ has encompassed ?x195; has ethnicGroup ?x354; has neighbor ?x204;]; has wasDependentOf ?x1197; is locatedIn of ?x132; is locatedIn of ?x378[ a Estuary;];]
*> conf = 0.40
ranks of expected_values: 5
EVAL	SRB	ethnicGroup	Croat	CNN-1.+1._MA	0.000	0.000	1.000	0.200	89.000	89.000	247.000	0.600	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#398-Buddhist
PRED entity: Buddhist

PRED relation: religion!
PRED expected values: THA ROK XMAS
=> 17 concepts (17 used for prediction)
PRED predicted values (max 10 best out of 207): THA (0.66 #792, 0.63 #793, 0.59 #198), CN (0.66 #792, 0.63 #793, 0.59 #198), BD (0.66 #792, 0.63 #793, 0.59 #198), PK (0.66 #792, 0.63 #793, 0.59 #198), PNG (0.66 #792, 0.63 #793, 0.59 #198), TL (0.66 #792, 0.63 #793, 0.59 #198), MEX (0.66 #792, 0.59 #198, 0.56 #593), IRL (0.66 #792, 0.59 #198, 0.55 #991), CH (0.63 #793, 0.60 #641, 0.59 #198), F (0.63 #793, 0.59 #198, 0.56 #593)
>> best conf = 0.66
=> the first rule below is the first best rule for 8 predicted values
>> Best rule #792 for best value:
>> intensional similarity = 11
>> extensional distance = 3
>> proper extension:  Protestant; RomanCatholic;
>> query: (?x462, ?x91) <- ?x462[ is religion of ?x366[ has encompassed ?x175; has ethnicGroup ?x298; has neighbor ?x91; is locatedIn of ?x262[ a Sea;];]; is religion of ?x409[ has government ?x92; is neighbor of ?x232;]; is religion of ?x460<RP>;]
ranks of expected_values: 1, 37, 57
EVAL	Buddhist	religion!	XMAS	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.028	17.000	17.000	207.000	0.663	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Buddhist	religion!	ROK	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.018	17.000	17.000	207.000	0.663	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Buddhist	religion!	THA	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	17.000	17.000	207.000	0.663	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion!
PRED expected values: THA ROK XMAS
=> 31 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 221): PNG (0.93 #1791, 0.90 #2784, 0.65 #792), TL (0.90 #2784, 0.65 #792, 0.60 #992), IRL (0.90 #2784, 0.60 #992, 0.59 #1790), TT (0.67 #1115, 0.57 #1516, 0.50 #2112), NL (0.67 #1391, 0.50 #707, 0.42 #594), THA (0.65 #792, 0.60 #992, 0.59 #1193), MEX (0.65 #792, 0.60 #992, 0.59 #1193), CN (0.65 #792, 0.60 #992, 0.59 #1193), BD (0.65 #792, 0.60 #992, 0.59 #1193), PK (0.65 #792, 0.60 #992, 0.59 #1193)
>> best conf = 0.93
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1791 for best value:
>> intensional similarity = 24
>> extensional distance = 5
>> proper extension:  JehovasWitnesses;
>> query: (?x462, ?x853) <- ?x462[ is religion of ?x217[ has religion ?x352<RomanCatholic>; is locatedIn of ?x1074[ a Island; has locatedIn ?x853; is locatedOnIsland of ?x1697;];]; is religion of ?x315[ is locatedIn of ?x823[ has inMountains ?x1405;]; is locatedIn of ?x844[ has type ?x150;]; is locatedIn of ?x1371<GulfofMexico>;]; is religion of ?x538[ a Country; has ethnicGroup ?x298;]; is religion of ?x617[ has government ?x831; is locatedIn of ?x975;]; is religion of ?x1010[ a Country; has language ?x335; has neighbor ?x73;];]

*> Best rule #792 for first EXPECTED value:
*> intensional similarity = 22
*> extensional distance = 2
*> proper extension:  Protestant; RomanCatholic;
*> query: (?x462, ?x83) <- ?x462[ is religion of ?x117[ is locatedIn of ?x271[ a Sea;]; is wasDependentOf of ?x334;]; is religion of ?x217<RI>; is religion of ?x315<USA>; is religion of ?x376[ has encompassed ?x175; has ethnicGroup ?x298;]; is religion of ?x461<NZ>; is religion of ?x538[ is locatedIn of ?x375[ has locatedInWater ?x625;]; is locatedIn of ?x384<SouthChinaSea>;]; is religion of ?x924[ is locatedIn of ?x923[ a Mountain;]; is neighbor of ?x83;];]
*> conf = 0.65
ranks of expected_values: 6, 53, 170
EVAL	Buddhist	religion!	XMAS	CNN-1.+1._MA	0.000	0.000	0.000	0.019	31.000	31.000	221.000	0.932	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Buddhist	religion!	ROK	CNN-1.+1._MA	0.000	0.000	0.000	0.006	31.000	31.000	221.000	0.932	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Buddhist	religion!	THA	CNN-1.+1._MA	0.000	0.000	1.000	0.167	31.000	31.000	221.000	0.932	http://www.semwebtech.org/mondial/10/meta#religion

#397-BZ
PRED entity: BZ

PRED relation: locatedIn!
PRED expected values: CaribbeanSea
=> 51 concepts (45 used for prediction)
PRED predicted values (max 10 best out of 1250): PacificOcean (0.75 #14327, 0.64 #12903, 0.62 #10055), CaribbeanSea (0.69 #18514, 0.69 #17195, 0.58 #38562), AtlanticOcean (0.67 #5738, 0.65 #38498, 0.63 #25679), Hispaniola (0.33 #1269, 0.12 #18358, 0.09 #22634), St.Martin (0.33 #2187, 0.08 #49849, 0.06 #17852), GulfofMexico (0.27 #18515, 0.12 #16421, 0.12 #10725), Sonora (0.27 #18515, 0.12 #11069, 0.09 #22465), Chihuahua (0.27 #18515, 0.12 #11004, 0.09 #22400), RioGrande (0.27 #18515, 0.12 #10788, 0.09 #22184), Colorado (0.27 #18515, 0.12 #10633, 0.09 #22029)
>> best conf = 0.75
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #14327 for best value:
>> intensional similarity = 7
>> extensional distance = 10
>> proper extension:  PY; ES;
>> query: (?x671, PacificOcean) <- ?x671[ has ethnicGroup ?x676<Mestizo>; has ethnicGroup ?x2207[ a EthnicGroup;]; has language ?x247; has neighbor ?x181; has religion ?x95;]

*> Best rule #18514 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 14
*> proper extension:  SMAR;
*> query: (?x671, ?x317) <- ?x671[ a Country; has government ?x1947; is neighbor of ?x482[ has encompassed ?x521; is locatedIn of ?x317<CaribbeanSea>;];]
*> conf = 0.69
ranks of expected_values: 2
EVAL	BZ	locatedIn!	CaribbeanSea	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	51.000	45.000	1250.000	0.750	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: CaribbeanSea
=> 86 concepts (79 used for prediction)
PRED predicted values (max 10 best out of 1405): PacificOcean (0.80 #17182, 0.78 #28506, 0.73 #18608), AtlanticOcean (0.67 #12865, 0.65 #61352, 0.63 #44230), CaribbeanSea (0.62 #28503, 0.62 #27184, 0.60 #17202), ColumbiaRiver (0.33 #2583, 0.17 #12556, 0.10 #94118), ArcticOcean (0.33 #1499, 0.17 #11472, 0.10 #94118), NiagaraRiver (0.33 #2825, 0.17 #12798, 0.10 #94118), DetroitRiver (0.33 #2742, 0.17 #12715, 0.10 #94118), LakeErie (0.33 #2664, 0.17 #12637, 0.10 #94118), LakeChamplain (0.33 #2661, 0.17 #12634, 0.10 #94118), MtFairweather (0.33 #2645, 0.17 #12618, 0.10 #94118)
>> best conf = 0.80
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #17182 for best value:
>> intensional similarity = 15
>> extensional distance = 8
>> proper extension:  CR;
>> query: (?x671, PacificOcean) <- ?x671[ a Country; has encompassed ?x521; has ethnicGroup ?x676<Mestizo>; has government ?x1947; has language ?x635[ a Language;]; has language ?x796<Spanish>; has neighbor ?x181[ is locatedIn of ?x2037;]; has religion ?x95; is neighbor of ?x482[ has ethnicGroup ?x79; is locatedIn of ?x282;];]

*> Best rule #28503 for first EXPECTED value:
*> intensional similarity = 14
*> extensional distance = 14
*> proper extension:  SMAR;
*> query: (?x671, ?x317) <- ?x671[ a Country; has encompassed ?x521<America>; has government ?x1947; has neighbor ?x181[ a Country; has government ?x724; is locatedIn of ?x282[ has locatedIn ?x272<CDN>; has mergesWith ?x60; is locatedInWater of ?x205;]; is locatedIn of ?x317<CaribbeanSea>;];]
*> conf = 0.62
ranks of expected_values: 3
EVAL	BZ	locatedIn!	CaribbeanSea	CNN-1.+1._MA	0.000	1.000	1.000	0.333	86.000	79.000	1405.000	0.800	http://www.semwebtech.org/mondial/10/meta#locatedIn

#396-LaPalma
PRED entity: LaPalma

PRED relation: belongsToIslands
PRED expected values: Canares
=> 53 concepts (53 used for prediction)
PRED predicted values (max 10 best out of 55): Canares (0.75 #91, 0.71 #23, 0.60 #159), Baleares (0.40 #1634, 0.38 #1497, 0.16 #2248), LesserAntilles (0.32 #355, 0.25 #763, 0.21 #287), SundaIslands (0.21 #286, 0.17 #558, 0.16 #626), Azores (0.21 #344, 0.12 #684, 0.10 #752), HawaiiIslands (0.13 #505, 0.12 #709, 0.11 #301), LipariIslands (0.13 #478, 0.10 #818, 0.08 #954), Japan (0.09 #502, 0.05 #842, 0.05 #298), InnerHebrides (0.08 #744, 0.07 #812, 0.06 #948), CalifornianChannelIslands (0.08 #739, 0.06 #943, 0.03 #1351)
>> best conf = 0.75
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #91 for best value:
>> intensional similarity = 5
>> extensional distance = 6
>> proper extension:  Lanzarote;
>> query: (?x1935, Canares) <- ?x1935[ a Island; has locatedIn ?x149<E>; has locatedInWater ?x182<AtlanticOcean>;]
ranks of expected_values: 1
EVAL	LaPalma	belongsToIslands	Canares	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	53.000	53.000	55.000	0.750	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

PRED relation: belongsToIslands
PRED expected values: Canares
=> 122 concepts (122 used for prediction)
PRED predicted values (max 10 best out of 61): Canares (0.75 #432, 0.71 #228, 0.57 #546), Baleares (0.57 #546, 0.45 #205, 0.41 #1024), LesserAntilles (0.36 #629, 0.32 #1722, 0.30 #492), Azores (0.35 #959, 0.23 #1438, 0.23 #1506), SundaIslands (0.23 #1038, 0.21 #1106, 0.20 #2266), HawaiiIslands (0.19 #1531, 0.17 #1940, 0.08 #2896), LipariIslands (0.18 #1572, 0.08 #2869, 0.08 #3278), Sporades (0.15 #1591, 0.06 #3228, 0.04 #3569), Madeira (0.14 #181, 0.12 #318, 0.10 #522), CapeVerdes (0.14 #179, 0.10 #520, 0.09 #657)
>> best conf = 0.75
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #432 for best value:
>> intensional similarity = 5
>> extensional distance = 6
>> proper extension:  Lanzarote;
>> query: (?x1935, Canares) <- ?x1935[ a Island; has locatedIn ?x149<E>; has locatedInWater ?x182<AtlanticOcean>;]
ranks of expected_values: 1
EVAL	LaPalma	belongsToIslands	Canares	CNN-1.+1._MA	1.000	1.000	1.000	1.000	122.000	122.000	61.000	0.750	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

#395-Cevennes
PRED entity: Cevennes

PRED relation: inMountains!
PRED expected values: Loire
=> 25 concepts (25 used for prediction)
PRED predicted values (max 10 best out of 285): Vignemale (0.27 #1030, 0.20 #250, 0.14 #507), Isere (0.27 #1030, 0.20 #206, 0.14 #463), MonteCinto (0.27 #1030, 0.20 #95, 0.14 #352), MontBlanc (0.27 #1030, 0.20 #22, 0.14 #279), Mosel (0.27 #1030, 0.14 #456, 0.09 #1229), Saone (0.27 #1030, 0.14 #388, 0.09 #1161), Saar (0.27 #1030, 0.14 #380, 0.09 #1153), Doubs (0.27 #1030, 0.14 #357, 0.09 #1130), Loire (0.27 #1030, 0.08 #5156, 0.08 #2836), Seine (0.27 #1030, 0.08 #5156, 0.08 #2836)
>> best conf = 0.27
=> the first rule below is the first best rule for 36 predicted values
>> Best rule #1030 for best value:
>> intensional similarity = 8
>> extensional distance = 6
>> proper extension:  Tibesti;
>> query: (?x2350, ?x121) <- ?x2350[ a Mountains; is inMountains of ?x1091[ a Volcano; has locatedIn ?x78[ has government ?x435<"republic">; has neighbor ?x149; is locatedIn of ?x121;];];]

No rule for expected values
ranks of expected_values: 
EVAL	Cevennes	inMountains!	Loire	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	25.000	25.000	285.000	0.268	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains!
PRED expected values: Loire
=> 59 concepts (59 used for prediction)
PRED predicted values (max 10 best out of 285): Dychtau (0.33 #507, 0.20 #2313, 0.14 #5925), Zachwoa (0.33 #465, 0.20 #2271, 0.14 #5883), Kasbek (0.33 #333, 0.20 #2139, 0.14 #5751), Elbrus (0.33 #271, 0.20 #2077, 0.14 #5689), Schchara (0.33 #264, 0.20 #2070, 0.14 #5682), RoquedelosMuchachos (0.33 #178, 0.20 #2242, 0.11 #7917), PicodeTeide (0.33 #111, 0.20 #2175, 0.11 #7850), PicodelosNieves (0.33 #87, 0.20 #2151, 0.11 #7826), MtRobson (0.33 #1289, 0.17 #3611, 0.12 #7480), GranitePeak (0.33 #1280, 0.17 #3602, 0.12 #7471)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #507 for best value:
>> intensional similarity = 14
>> extensional distance = 1
>> proper extension:  Kaukasus;
>> query: (?x2350, Dychtau) <- ?x2350[ a Mountains; is inMountains of ?x440[ a Mountain; a Volcano; has locatedIn ?x78[ a Country; has encompassed ?x195<Europe>; has language ?x51; has religion ?x95; is neighbor of ?x120; is wasDependentOf of ?x94;]; has type ?x150<"volcanic">;];]

No rule for expected values
ranks of expected_values: 
EVAL	Cevennes	inMountains!	Loire	CNN-1.+1._MA	0.000	0.000	0.000	0.000	59.000	59.000	285.000	0.333	http://www.semwebtech.org/mondial/10/meta#inMountains

#394-RIM
PRED entity: RIM

PRED relation: locatedIn!
PRED expected values: Senegal
=> 36 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 1279): MediterraneanSea (0.50 #8591, 0.33 #11430, 0.33 #82), Talak (0.50 #6585, 0.33 #2331, 0.23 #18439), Senegal (0.40 #7498, 0.33 #4661, 0.33 #3243), CaribbeanSea (0.39 #22798, 0.30 #19962, 0.24 #15707), Tanezrouft (0.33 #1353, 0.25 #7025, 0.23 #18439), ErgChech (0.33 #1059, 0.25 #6731, 0.23 #18439), Niger (0.33 #4505, 0.25 #5923, 0.23 #18439), ChadLake (0.33 #2697, 0.25 #6951, 0.22 #12627), Tenere (0.33 #1882, 0.25 #6136, 0.22 #11812), Tamgak (0.33 #1652, 0.25 #5906, 0.12 #10162)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #8591 for best value:
>> intensional similarity = 8
>> extensional distance = 4
>> proper extension:  TN; MA; LAR;
>> query: (?x515, MediterraneanSea) <- ?x515[ has neighbor ?x581<DZ>; has neighbor ?x839[ has religion ?x116<Christian>; is locatedIn of ?x456;]; has wasDependentOf ?x78; is locatedIn of ?x182;]

*> Best rule #7498 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 3
*> proper extension:  CI;
*> query: (?x515, Senegal) <- ?x515[ has government ?x2373; has neighbor ?x416[ a Country;]; has neighbor ?x839<RMM>; is locatedIn of ?x182<AtlanticOcean>;]
*> conf = 0.40
ranks of expected_values: 3
EVAL	RIM	locatedIn!	Senegal	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.333	36.000	31.000	1279.000	0.500	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Senegal
=> 89 concepts (89 used for prediction)
PRED predicted values (max 10 best out of 1409): MediterraneanSea (0.60 #5683, 0.50 #52574, 0.50 #51236), Senegal (0.40 #4669, 0.38 #11366, 0.38 #10353), CaribbeanSea (0.39 #88206, 0.36 #46992, 0.27 #81102), Tanezrouft (0.33 #2775, 0.27 #1422, 0.25 #4261), ErgChech (0.33 #2481, 0.27 #1422, 0.25 #3901), HamadaduDraa (0.33 #2373, 0.27 #1422, 0.25 #3793), ErgIsaouane (0.33 #2475, 0.27 #1422, 0.23 #2841), GrandErgEst (0.33 #2168, 0.27 #1422, 0.23 #2841), GrandErgOuest (0.33 #2133, 0.27 #1422, 0.23 #2841), Ferlo (0.33 #1307, 0.27 #1422, 0.23 #2841)
>> best conf = 0.60
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5683 for best value:
>> intensional similarity = 13
>> extensional distance = 3
>> proper extension:  RG; CI;
>> query: (?x515, ?x275) <- ?x515[ has government ?x2373; has religion ?x187; has wasDependentOf ?x78<F>; is locatedIn of ?x182<AtlanticOcean>; is neighbor of ?x581[ is locatedIn of ?x275[ a Sea; is flowsInto of ?x698; is locatedInWater of ?x68;];]; is neighbor of ?x839<RMM>;]

*> Best rule #4669 for first EXPECTED value:
*> intensional similarity = 13
*> extensional distance = 3
*> proper extension:  RG; CI;
*> query: (?x515, Senegal) <- ?x515[ has government ?x2373; has religion ?x187; has wasDependentOf ?x78<F>; is locatedIn of ?x182<AtlanticOcean>; is neighbor of ?x581[ is locatedIn of ?x275[ a Sea; is flowsInto of ?x698; is locatedInWater of ?x68;];]; is neighbor of ?x839<RMM>;]
*> conf = 0.40
ranks of expected_values: 2
EVAL	RIM	locatedIn!	Senegal	CNN-1.+1._MA	0.000	1.000	1.000	0.500	89.000	89.000	1409.000	0.600	http://www.semwebtech.org/mondial/10/meta#locatedIn

#393-GreatBearLake
PRED entity: GreatBearLake

PRED relation: locatedIn
PRED expected values: CDN
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 37): USA (0.11 #72, 0.05 #308), CDN (0.08 #63, 0.03 #299), R (0.06 #5, 0.04 #241), CH (0.05 #57, 0.01 #293), ZRE (0.04 #79, 0.03 #315), EAT (0.04 #175), D (0.03 #20, 0.03 #256), I (0.03 #48, 0.02 #284), AUS (0.03 #45, 0.01 #281), CN (0.03 #56, 0.02 #292)
>> best conf = 0.11
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #72 for best value:
>> intensional similarity = 1
>> extensional distance = 142
>> proper extension:  LagunaMarChiquita; LakeSkutari; StarnbergerSee; LakeVolta; LakeHuron; MaleboPool; ChickamaugaLake; Poopo; LakeTanganjika; LakeNyos; ...
>> query: (?x1591, USA) <- ?x1591[ a Lake;]

*> Best rule #63 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 142
*> proper extension:  LagunaMarChiquita; LakeSkutari; StarnbergerSee; LakeVolta; LakeHuron; MaleboPool; ChickamaugaLake; Poopo; LakeTanganjika; LakeNyos; ...
*> query: (?x1591, CDN) <- ?x1591[ a Lake;]
*> conf = 0.08
ranks of expected_values: 2
EVAL	GreatBearLake	locatedIn	CDN	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	2.000	2.000	37.000	0.111	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: CDN
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 37): USA (0.11 #72, 0.05 #308), CDN (0.08 #63, 0.03 #299), R (0.06 #5, 0.04 #241), CH (0.05 #57, 0.01 #293), ZRE (0.04 #79, 0.03 #315), EAT (0.04 #175), D (0.03 #20, 0.03 #256), I (0.03 #48, 0.02 #284), AUS (0.03 #45, 0.01 #281), CN (0.03 #56, 0.02 #292)
>> best conf = 0.11
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #72 for best value:
>> intensional similarity = 1
>> extensional distance = 142
>> proper extension:  LagunaMarChiquita; LakeSkutari; StarnbergerSee; LakeVolta; LakeHuron; MaleboPool; ChickamaugaLake; Poopo; LakeTanganjika; LakeNyos; ...
>> query: (?x1591, USA) <- ?x1591[ a Lake;]

*> Best rule #63 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 142
*> proper extension:  LagunaMarChiquita; LakeSkutari; StarnbergerSee; LakeVolta; LakeHuron; MaleboPool; ChickamaugaLake; Poopo; LakeTanganjika; LakeNyos; ...
*> query: (?x1591, CDN) <- ?x1591[ a Lake;]
*> conf = 0.08
ranks of expected_values: 2
EVAL	GreatBearLake	locatedIn	CDN	CNN-1.+1._MA	0.000	1.000	1.000	0.500	2.000	2.000	37.000	0.111	http://www.semwebtech.org/mondial/10/meta#locatedIn

#392-E
PRED entity: E

PRED relation: language
PRED expected values: Galician
=> 42 concepts (42 used for prediction)
PRED predicted values (max 10 best out of 93): French (0.71 #191, 0.40 #1, 0.27 #381), German (0.57 #205, 0.21 #870, 0.20 #15), Italian (0.29 #197, 0.20 #7, 0.10 #292), Portuguese (0.29 #199, 0.10 #294, 0.09 #484), English (0.28 #1999, 0.23 #2855, 0.21 #2189), Albanian (0.20 #34, 0.19 #1079, 0.14 #1364), Slovenian (0.20 #19, 0.14 #209, 0.12 #1064), Greek (0.19 #1092, 0.12 #1187, 0.11 #1282), Dutch (0.18 #390, 0.14 #865, 0.14 #200), Russian (0.16 #1911, 0.15 #2291, 0.10 #296)
>> best conf = 0.71
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #191 for best value:
>> intensional similarity = 5
>> extensional distance = 5
>> proper extension:  AND;
>> query: (?x149, French) <- ?x149[ has language ?x790; has neighbor ?x78<F>; has neighbor ?x789[ has ethnicGroup ?x746;];]

*> Best rule #238 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 5
*> proper extension:  AND;
*> query: (?x149, Galician) <- ?x149[ has language ?x790; has neighbor ?x78<F>; has neighbor ?x789[ has ethnicGroup ?x746;];]
*> conf = 0.14
ranks of expected_values: 13
EVAL	E	language	Galician	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.077	42.000	42.000	93.000	0.714	http://www.semwebtech.org/mondial/10/meta#language

PRED relation: language
PRED expected values: Galician
=> 124 concepts (124 used for prediction)
PRED predicted values (max 10 best out of 95): English (0.62 #7045, 0.60 #1336, 0.56 #8471), French (0.56 #2949, 0.50 #4472, 0.50 #3615), Portuguese (0.50 #476, 0.33 #389, 0.28 #3044), Galician (0.50 #476, 0.33 #428, 0.28 #3044), German (0.40 #3629, 0.33 #4486, 0.33 #2963), Albanian (0.33 #3078, 0.28 #3044, 0.25 #2792), Italian (0.33 #2955, 0.28 #3044, 0.25 #4478), Monegasque (0.33 #74, 0.28 #3044, 0.20 #10655), Turkish (0.33 #103, 0.25 #675, 0.20 #10655), Arabic (0.29 #2148, 0.20 #10655, 0.20 #1388)
>> best conf = 0.62
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #7045 for best value:
>> intensional similarity = 12
>> extensional distance = 27
>> proper extension:  IRL; AXA; TUCA; GBJ; GBM; FALK;
>> query: (?x149, English) <- ?x149[ a Country; has government ?x1657; has language ?x796[ is language of ?x363[ has encompassed ?x521;]; is language of ?x379[ has ethnicGroup ?x197; has religion ?x95;]; is language of ?x1408[ is neighbor of ?x172;];]; is locatedIn of ?x2304[ has belongsToIslands ?x1715;];]

*> Best rule #476 for first EXPECTED value:
*> intensional similarity = 12
*> extensional distance = 1
*> proper extension:  AND;
*> query: (?x149, ?x51) <- ?x149[ has ethnicGroup ?x2540; has language ?x790<Catalan>; has language ?x1112[ a Language;]; has neighbor ?x1826; is neighbor of ?x789[ has language ?x51;]; is neighbor of ?x1027[ is locatedIn of ?x1739<Guadiana>; is wasDependentOf of ?x192;];]
*> conf = 0.50
ranks of expected_values: 4
EVAL	E	language	Galician	CNN-1.+1._MA	0.000	0.000	1.000	0.250	124.000	124.000	95.000	0.621	http://www.semwebtech.org/mondial/10/meta#language

#391-LakeOahe
PRED entity: LakeOahe

PRED relation: locatedIn
PRED expected values: USA
=> 36 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 87): USA (0.82 #5922, 0.74 #3553, 0.71 #1421), CDN (0.30 #1721, 0.29 #1247, 0.25 #4090), SUD (0.17 #1937, 0.17 #2174, 0.06 #1463), UA (0.17 #1491, 0.13 #1965, 0.12 #2202), ZRE (0.15 #6001, 0.15 #7424, 0.09 #6714), CH (0.15 #3373, 0.15 #3136, 0.14 #2663), D (0.12 #7128, 0.11 #2626, 0.10 #3336), R (0.11 #7587, 0.10 #8062, 0.09 #7825), AUS (0.11 #1466, 0.09 #1940, 0.08 #2651), F (0.10 #6404, 0.06 #6166, 0.05 #5455)
>> best conf = 0.82
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5922 for best value:
>> intensional similarity = 7
>> extensional distance = 78
>> proper extension:  DarlingRiver; MurrumbidgeeRiver;
>> query: (?x1989, ?x315) <- ?x1989[ has flowsInto ?x1366[ has hasEstuary ?x1254; has hasSource ?x2450[ a Source;]; is flowsInto of ?x1113[ a Lake; has locatedIn ?x315;];];]
ranks of expected_values: 1
EVAL	LakeOahe	locatedIn	USA	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	36.000	36.000	87.000	0.819	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: USA
=> 91 concepts (91 used for prediction)
PRED predicted values (max 10 best out of 105): USA (0.93 #16882, 0.90 #19266, 0.87 #15691), ZRE (0.50 #7919, 0.44 #11726, 0.39 #4111), CDN (0.33 #5284, 0.29 #3382, 0.29 #3145), R (0.27 #10463, 0.26 #17841, 0.26 #9272), CH (0.21 #8135, 0.21 #8372, 0.20 #6470), D (0.21 #11429, 0.19 #18333, 0.19 #18095), UA (0.20 #2203, 0.20 #1966, 0.16 #4340), F (0.20 #1665, 0.19 #8559, 0.19 #8797), SUD (0.17 #5738, 0.11 #9548, 0.08 #11928), RCB (0.17 #4154, 0.15 #4629, 0.06 #11769)
>> best conf = 0.93
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #16882 for best value:
>> intensional similarity = 10
>> extensional distance = 79
>> proper extension:  Baro; Pibor;
>> query: (?x1989, ?x315) <- ?x1989[ has flowsInto ?x1366[ a River; has hasEstuary ?x1254[ a Estuary; has locatedIn ?x315[ is locatedIn of ?x1113; is locatedIn of ?x2450[ a Source;];];]; has hasSource ?x2450; is flowsInto of ?x1113;];]
ranks of expected_values: 1
EVAL	LakeOahe	locatedIn	USA	CNN-1.+1._MA	1.000	1.000	1.000	1.000	91.000	91.000	105.000	0.927	http://www.semwebtech.org/mondial/10/meta#locatedIn

#390-SRB
PRED entity: SRB

PRED relation: neighbor
PRED expected values: HR H
=> 40 concepts (39 used for prediction)
PRED predicted values (max 10 best out of 154): H (0.90 #2802, 0.90 #3115, 0.90 #4989), UA (0.50 #359, 0.26 #3271, 0.25 #2958), AL (0.50 #188, 0.26 #3271, 0.25 #2958), SRB (0.33 #443, 0.26 #3271, 0.25 #2958), SK (0.33 #331, 0.16 #5304, 0.12 #1870), A (0.33 #384, 0.16 #5304, 0.12 #1870), PL (0.33 #341, 0.09 #5303, 0.09 #1743), GR (0.26 #3271, 0.25 #2958, 0.25 #221), HR (0.26 #3271, 0.25 #2958, 0.17 #330), MD (0.26 #3271, 0.25 #2958, 0.17 #441)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2802 for best value:
>> intensional similarity = 5
>> extensional distance = 74
>> proper extension:  ARM; LB; BZ; AND;
>> query: (?x904, ?x55) <- ?x904[ a Country; has ethnicGroup ?x164; has language ?x684; has neighbor ?x692; is neighbor of ?x55;]
ranks of expected_values: 1, 9
EVAL	SRB	neighbor	H	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	40.000	39.000	154.000	0.905	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	SRB	neighbor	HR	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.125	40.000	39.000	154.000	0.905	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: HR H
=> 113 concepts (113 used for prediction)
PRED predicted values (max 10 best out of 214): H (0.91 #9680, 0.91 #7115, 0.91 #6958), UA (0.69 #1408, 0.45 #7430, 0.44 #4408), SK (0.50 #645, 0.45 #7430, 0.44 #4408), SRB (0.50 #912, 0.45 #7430, 0.44 #4408), HR (0.45 #7430, 0.44 #4408, 0.41 #2035), MD (0.45 #7430, 0.44 #4408, 0.34 #2982), A (0.45 #7430, 0.44 #4408, 0.34 #2982), D (0.45 #7430, 0.44 #4408, 0.34 #2982), R (0.44 #3937, 0.43 #2984, 0.33 #2513), SLO (0.43 #1953, 0.33 #231, 0.29 #2271)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #9680 for best value:
>> intensional similarity = 13
>> extensional distance = 55
>> proper extension:  ES; L;
>> query: (?x904, ?x236) <- ?x904[ a Country; has ethnicGroup ?x517[ a EthnicGroup;]; has language ?x684; has neighbor ?x55[ has encompassed ?x195; has ethnicGroup ?x160;]; has neighbor ?x106[ a Country; is locatedIn of ?x104;]; has wasDependentOf ?x1197; is locatedIn of ?x132; is neighbor of ?x236;]
ranks of expected_values: 1, 5
EVAL	SRB	neighbor	H	CNN-1.+1._MA	1.000	1.000	1.000	1.000	113.000	113.000	214.000	0.913	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	SRB	neighbor	HR	CNN-1.+1._MA	0.000	0.000	1.000	0.250	113.000	113.000	214.000	0.913	http://www.semwebtech.org/mondial/10/meta#neighbor

#389-NorthSea
PRED entity: NorthSea

PRED relation: mergesWith!
PRED expected values: AtlanticOcean NorwegianSea
=> 37 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 198): AtlanticOcean (0.84 #664, 0.84 #663, 0.83 #505), NorwegianSea (0.84 #664, 0.84 #663, 0.83 #505), NorthSea (0.46 #783, 0.46 #584, 0.46 #704), IrishSea (0.46 #783, 0.46 #584, 0.46 #704), PacificOcean (0.27 #326, 0.23 #443, 0.23 #403), Kattegat (0.20 #77, 0.20 #37, 0.16 #665), ArcticOcean (0.20 #52, 0.18 #361, 0.15 #478), BalticSea (0.20 #5, 0.05 #198, 0.04 #276), IndianOcean (0.19 #311, 0.18 #194, 0.17 #233), GreenlandSea (0.18 #112, 0.16 #665, 0.12 #344)
>> best conf = 0.84
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #664 for best value:
>> intensional similarity = 5
>> extensional distance = 37
>> proper extension:  SeaofAzov; BlackSea; YellowSea; SuluSea; RedSea; MarmaraSea; GulfofAden;
>> query: (?x121, ?x1664) <- ?x121[ a Sea; has locatedIn ?x793[ has religion ?x95;]; has mergesWith ?x1664[ has mergesWith ?x1663;];]
ranks of expected_values: 1, 2
EVAL	NorthSea	mergesWith!	NorwegianSea	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	37.000	36.000	198.000	0.845	http://www.semwebtech.org/mondial/10/meta#mergesWith
EVAL	NorthSea	mergesWith!	AtlanticOcean	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	37.000	36.000	198.000	0.845	http://www.semwebtech.org/mondial/10/meta#mergesWith

PRED relation: mergesWith!
PRED expected values: AtlanticOcean NorwegianSea
=> 118 concepts (118 used for prediction)
PRED predicted values (max 10 best out of 489): AtlanticOcean (0.85 #1423, 0.84 #1340, 0.83 #1217), NorwegianSea (0.85 #1423, 0.84 #1340, 0.83 #1217), NorthSea (0.46 #1620, 0.40 #168, 0.33 #247), IrishSea (0.46 #1620, 0.25 #81, 0.18 #1381), ArcticOcean (0.40 #133, 0.25 #379, 0.21 #1065), PacificOcean (0.36 #506, 0.29 #987, 0.27 #1108), Kattegat (0.33 #37, 0.29 #1338, 0.17 #1218), BalticSea (0.33 #46, 0.29 #1338, 0.15 #82), MediterraneanSea (0.29 #1338, 0.20 #179, 0.20 #136), BarentsSea (0.29 #1338, 0.18 #1381, 0.17 #326)
>> best conf = 0.85
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #1423 for best value:
>> intensional similarity = 10
>> extensional distance = 31
>> proper extension:  MarmaraSea;
>> query: (?x121, ?x182) <- ?x121[ a Sea; has locatedIn ?x120[ has neighbor ?x194; is locatedIn of ?x475[ a River;];]; has locatedIn ?x575[ has ethnicGroup ?x734; has religion ?x95;]; has mergesWith ?x182; is mergesWith of ?x1211;]
ranks of expected_values: 1, 2
EVAL	NorthSea	mergesWith!	NorwegianSea	CNN-1.+1._MA	1.000	1.000	1.000	1.000	118.000	118.000	489.000	0.846	http://www.semwebtech.org/mondial/10/meta#mergesWith
EVAL	NorthSea	mergesWith!	AtlanticOcean	CNN-1.+1._MA	1.000	1.000	1.000	1.000	118.000	118.000	489.000	0.846	http://www.semwebtech.org/mondial/10/meta#mergesWith

#388-Jubba
PRED entity: Jubba

PRED relation: hasSource
PRED expected values: Jubba
=> 37 concepts (24 used for prediction)
PRED predicted values (max 10 best out of 147): Shabelle (0.33 #159, 0.17 #616, 0.14 #844), Ganges (0.17 #566, 0.11 #1023, 0.04 #1252), Asahan (0.11 #1005, 0.01 #1691, 0.01 #1919), Limpopo (0.11 #1078, 0.01 #1764), Chire (0.11 #1097, 0.01 #2011), BlueNile (0.03 #1564, 0.02 #914, 0.01 #5265), Atbara (0.03 #1524, 0.02 #914, 0.01 #5265), Baro (0.03 #1428, 0.02 #914, 0.01 #5265), Semliki (0.03 #1484, 0.01 #1941), VictoriaNile (0.03 #1562)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #159 for best value:
>> intensional similarity = 7
>> extensional distance = 1
>> proper extension:  Shabelle;
>> query: (?x750, Shabelle) <- ?x750[ a River; has flowsInto ?x60[ has locatedIn ?x61;]; has hasEstuary ?x510[ a Estuary;]; has locatedIn ?x220<SP>;]

*> Best rule #914 for first EXPECTED value:
*> intensional similarity = 9
*> extensional distance = 5
*> proper extension:  Jubba; Shabelle;
*> query: (?x750, ?x228) <- ?x750[ has locatedIn ?x220<SP>; has locatedIn ?x476[ has ethnicGroup ?x1179; has neighbor ?x229[ has neighbor ?x348;]; is locatedIn of ?x228; is locatedIn of ?x2035<Shabelle>;];]
*> conf = 0.02
ranks of expected_values: 21
EVAL	Jubba	hasSource	Jubba	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.048	37.000	24.000	147.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasSource

PRED relation: hasSource
PRED expected values: Jubba
=> 131 concepts (124 used for prediction)
PRED predicted values (max 10 best out of 336): Zambezi (0.36 #2062, 0.36 #2061, 0.33 #345), Limpopo (0.36 #2062, 0.36 #2061, 0.14 #12389), MurrayRiver (0.36 #2062, 0.33 #145, 0.25 #1059), Ganges (0.25 #1023, 0.12 #2859, 0.06 #4235), Nile (0.25 #1335, 0.06 #4088, 0.06 #3859), Sobat (0.25 #1150, 0.06 #3903, 0.04 #4591), Shabelle (0.20 #1532, 0.17 #1991, 0.14 #2680), BlueNile (0.20 #1566, 0.17 #2025, 0.14 #2714), Atbara (0.20 #1526, 0.17 #1985, 0.14 #2674), VictoriaNile (0.20 #1794, 0.04 #5005, 0.02 #10287)
>> best conf = 0.36
=> the first rule below is the first best rule for 3 predicted values
>> Best rule #2062 for best value:
>> intensional similarity = 13
>> extensional distance = 4
>> proper extension:  Pibor;
>> query: (?x750, ?x1820) <- ?x750[ a River; has flowsInto ?x60[ is flowsInto of ?x1356[ a River; has hasSource ?x1820;]; is flowsInto of ?x1977[ has hasSource ?x1596; has locatedIn ?x138;];]; has hasEstuary ?x510; has locatedIn ?x220[ is neighbor of ?x94;]; has locatedIn ?x476<ETH>;]
>> Best rule #2061 for best value:
>> intensional similarity = 13
>> extensional distance = 4
>> proper extension:  Pibor;
>> query: (?x750, ?x1596) <- ?x750[ a River; has flowsInto ?x60[ is flowsInto of ?x1356[ a River; has hasSource ?x1820;]; is flowsInto of ?x1977[ has hasSource ?x1596; has locatedIn ?x138;];]; has hasEstuary ?x510; has locatedIn ?x220[ is neighbor of ?x94;]; has locatedIn ?x476<ETH>;]

*> Best rule #3667 for first EXPECTED value:
*> intensional similarity = 12
*> extensional distance = 9
*> proper extension:  Sanga;
*> query: (?x750, ?x228) <- ?x750[ a River; has flowsInto ?x60; has locatedIn ?x476[ has ethnicGroup ?x1179[ a EthnicGroup;]; has neighbor ?x186<SUD>; has religion ?x187<Muslim>; is locatedIn of ?x228; is locatedIn of ?x2035[ has hasEstuary ?x2345;];];]
*> conf = 0.05
ranks of expected_values: 44
EVAL	Jubba	hasSource	Jubba	CNN-1.+1._MA	0.000	0.000	0.000	0.023	131.000	124.000	336.000	0.364	http://www.semwebtech.org/mondial/10/meta#hasSource

#387-Gobi
PRED entity: Gobi

PRED relation: locatedIn
PRED expected values: MNG
=> 26 concepts (21 used for prediction)
PRED predicted values (max 10 best out of 118): R (0.61 #2138, 0.23 #2376, 0.13 #2132), NEP (0.56 #3086, 0.11 #962, 0.09 #4989), KAZ (0.56 #3086, 0.11 #2225, 0.09 #4989), KGZ (0.56 #3086, 0.09 #4989, 0.08 #4512), PK (0.56 #3086, 0.09 #4989, 0.08 #4512), USA (0.28 #2442, 0.23 #306, 0.14 #2681), AUS (0.27 #280, 0.13 #1228, 0.04 #2895), UA (0.23 #1726, 0.19 #1964, 0.09 #4989), N (0.19 #1691, 0.16 #1929, 0.09 #4989), IND (0.19 #1605, 0.09 #4989, 0.08 #4512)
>> best conf = 0.61
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2138 for best value:
>> intensional similarity = 7
>> extensional distance = 164
>> proper extension:  Selenge; SeaofAzov; BlackSea; Suchona; Bjelucha; Schchara; Lena; Elbrus; BarentsSea; ArcticOcean; ...
>> query: (?x791, R) <- ?x791[ has locatedIn ?x232[ has ethnicGroup ?x2285; has neighbor ?x409[ has ethnicGroup ?x1920;]; is locatedIn of ?x1748<Irtysch>; is neighbor of ?x463;];]

*> Best rule #4989 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 1082
*> proper extension:  Kwa; LakeVolta; Oranje; Tahan; Pibor; MaleboPool; Ruki; Lukuga; Uelle; Busira; ...
*> query: (?x791, ?x170) <- ?x791[ has locatedIn ?x232[ has ethnicGroup ?x2285; has neighbor ?x73[ is locatedIn of ?x72; is neighbor of ?x170;]; has neighbor ?x409[ has ethnicGroup ?x1920;];];]
*> conf = 0.09
ranks of expected_values: 33
EVAL	Gobi	locatedIn	MNG	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.030	26.000	21.000	118.000	0.608	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: MNG
=> 77 concepts (71 used for prediction)
PRED predicted values (max 10 best out of 209): R (0.65 #5976, 0.22 #1191, 0.19 #8125), KAZ (0.65 #13857, 0.65 #13856, 0.60 #9785), PK (0.65 #13857, 0.65 #13856, 0.60 #9785), KGZ (0.65 #13857, 0.65 #13856, 0.60 #9785), NEP (0.65 #13857, 0.65 #13856, 0.60 #9785), AUS (0.53 #998, 0.20 #6253, 0.17 #6968), USA (0.28 #1739, 0.27 #1979, 0.25 #2218), UA (0.26 #5562, 0.13 #9546, 0.10 #6920), UZB (0.25 #775, 0.18 #537, 0.13 #4837), F (0.24 #6215, 0.20 #6930, 0.10 #7168)
>> best conf = 0.65
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5976 for best value:
>> intensional similarity = 15
>> extensional distance = 153
>> proper extension:  Selenge; SeaofAzov; BlackSea; Suchona; Bjelucha; Schchara; Lena; Elbrus; BarentsSea; ArcticOcean; ...
>> query: (?x791, R) <- ?x791[ has locatedIn ?x232[ has encompassed ?x175; has neighbor ?x403<KAZ>; is locatedIn of ?x497[ a River;]; is locatedIn of ?x576[ a Mountain; has inMountains ?x309;]; is locatedIn of ?x620[ is locatedInWater of ?x619;]; is locatedIn of ?x874[ a Source;]; is locatedIn of ?x1493<Argun>;];]

*> Best rule #9546 for first EXPECTED value:
*> intensional similarity = 12
*> extensional distance = 568
*> proper extension:  NangaParbat; TirichMir;
*> query: (?x791, ?x170) <- ?x791[ has locatedIn ?x232[ a Country; has government ?x831; has neighbor ?x73[ has neighbor ?x170; has wasDependentOf ?x903; is locatedIn of ?x72;]; has neighbor ?x641[ has religion ?x95;]; is locatedIn of ?x1771[ a Mountain; has inMountains ?x309;];];]
*> conf = 0.13
ranks of expected_values: 40
EVAL	Gobi	locatedIn	MNG	CNN-1.+1._MA	0.000	0.000	0.000	0.025	77.000	71.000	209.000	0.652	http://www.semwebtech.org/mondial/10/meta#locatedIn

#386-BarentsSea
PRED entity: BarentsSea

PRED relation: mergesWith!
PRED expected values: NorwegianSea KaraSea
=> 37 concepts (29 used for prediction)
PRED predicted values (max 10 best out of 147): NorwegianSea (0.85 #122, 0.84 #162, 0.81 #521), BarentsSea (0.53 #40, 0.51 #401, 0.46 #920), KaraSea (0.53 #40, 0.51 #401, 0.46 #920), AtlanticOcean (0.39 #81, 0.33 #46, 0.29 #367), PacificOcean (0.39 #81, 0.33 #97, 0.25 #137), GreenlandSea (0.39 #81, 0.33 #74, 0.25 #34), BeringSea (0.39 #81, 0.33 #110, 0.25 #150), NorthSea (0.39 #81, 0.33 #44, 0.25 #4), SeaofJapan (0.39 #81, 0.33 #95, 0.25 #135), EastSibirianSea (0.39 #81, 0.17 #799, 0.17 #103)
>> best conf = 0.85
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #122 for best value:
>> intensional similarity = 6
>> extensional distance = 4
>> proper extension:  ArcticOcean; SeaofJapan;
>> query: (?x251, ?x263) <- ?x251[ has locatedIn ?x73<R>; has locatedIn ?x973[ has government ?x1319;]; has mergesWith ?x263; is locatedInWater of ?x931;]
ranks of expected_values: 1, 3
EVAL	BarentsSea	mergesWith!	KaraSea	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	37.000	29.000	147.000	0.850	http://www.semwebtech.org/mondial/10/meta#mergesWith
EVAL	BarentsSea	mergesWith!	NorwegianSea	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	37.000	29.000	147.000	0.850	http://www.semwebtech.org/mondial/10/meta#mergesWith

PRED relation: mergesWith!
PRED expected values: NorwegianSea KaraSea
=> 107 concepts (99 used for prediction)
PRED predicted values (max 10 best out of 267): NorwegianSea (0.80 #1021, 0.79 #857, 0.78 #733), AtlanticOcean (0.50 #453, 0.39 #243, 0.38 #536), BarentsSea (0.45 #1266, 0.45 #1265, 0.44 #940), KaraSea (0.45 #1266, 0.45 #1265, 0.44 #940), NorthSea (0.39 #243, 0.38 #451, 0.33 #207), GreenlandSea (0.39 #243, 0.33 #237, 0.33 #76), PacificOcean (0.39 #243, 0.26 #995, 0.25 #422), SeaofJapan (0.39 #243, 0.25 #420, 0.21 #897), BeringSea (0.39 #243, 0.25 #435, 0.21 #897), EastSibirianSea (0.39 #243, 0.21 #897, 0.18 #652)
>> best conf = 0.80
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1021 for best value:
>> intensional similarity = 9
>> extensional distance = 25
>> proper extension:  JavaSea; SulawesiSea; BandaSea;
>> query: (?x251, ?x263) <- ?x251[ a Sea; has locatedIn ?x170[ a Country; has encompassed ?x195; has ethnicGroup ?x979; has religion ?x187<Muslim>;]; has mergesWith ?x263; is locatedInWater of ?x931;]
ranks of expected_values: 1, 4
EVAL	BarentsSea	mergesWith!	KaraSea	CNN-1.+1._MA	0.000	1.000	1.000	0.333	107.000	99.000	267.000	0.805	http://www.semwebtech.org/mondial/10/meta#mergesWith
EVAL	BarentsSea	mergesWith!	NorwegianSea	CNN-1.+1._MA	1.000	1.000	1.000	1.000	107.000	99.000	267.000	0.805	http://www.semwebtech.org/mondial/10/meta#mergesWith

#385-BF
PRED entity: BF

PRED relation: neighbor
PRED expected values: RT
=> 43 concepts (42 used for prediction)
PRED predicted values (max 10 best out of 183): RT (0.90 #3957, 0.90 #3956, 0.89 #4761), BF (0.50 #283, 0.33 #125, 0.26 #3641), RIM (0.40 #560, 0.40 #402, 0.29 #1034), RG (0.33 #740, 0.29 #897, 0.26 #3641), SN (0.33 #707, 0.29 #864, 0.26 #3641), WAL (0.33 #775, 0.20 #459, 0.14 #932), LB (0.29 #890, 0.26 #3641, 0.26 #3958), DZ (0.29 #1046, 0.26 #3641, 0.26 #3958), TCH (0.29 #971, 0.26 #4762, 0.06 #5882), WSA (0.29 #896, 0.20 #581, 0.20 #423)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3957 for best value:
>> intensional similarity = 6
>> extensional distance = 113
>> proper extension:  BIH; ET; R; DJI; MNE; TN; RL; KGZ; NAM; HR; ...
>> query: (?x811, ?x426) <- ?x811[ has neighbor ?x839[ is locatedIn of ?x456;]; has religion ?x116; has wasDependentOf ?x78; is neighbor of ?x426[ has ethnicGroup ?x1109;];]
ranks of expected_values: 1
EVAL	BF	neighbor	RT	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	43.000	42.000	183.000	0.896	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: RT
=> 83 concepts (83 used for prediction)
PRED predicted values (max 10 best out of 222): RT (0.90 #11240, 0.90 #11403, 0.90 #12048), BF (0.50 #762, 0.40 #1284, 0.37 #1283), WAN (0.40 #1284, 0.37 #1283, 0.34 #960), RG (0.40 #1284, 0.37 #1283, 0.34 #960), LB (0.40 #1284, 0.37 #1283, 0.34 #960), RIM (0.34 #960, 0.34 #12045, 0.33 #887), DZ (0.34 #960, 0.34 #12045, 0.33 #575), SN (0.34 #960, 0.34 #12045, 0.33 #552), TCH (0.33 #1280, 0.33 #1145, 0.33 #23), CAM (0.33 #1050, 0.33 #90, 0.33 #2722)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #11240 for best value:
>> intensional similarity = 14
>> extensional distance = 113
>> proper extension:  ROU;
>> query: (?x811, ?x426) <- ?x811[ a Country; has ethnicGroup ?x2156; has religion ?x116; is locatedIn of ?x610[ is flowsInto of ?x135;]; is neighbor of ?x426[ has ethnicGroup ?x1109; is locatedIn of ?x535;]; is neighbor of ?x810[ a Country; has encompassed ?x213; has ethnicGroup ?x162;]; is neighbor of ?x1206[ is neighbor of ?x621;];]
ranks of expected_values: 1
EVAL	BF	neighbor	RT	CNN-1.+1._MA	1.000	1.000	1.000	1.000	83.000	83.000	222.000	0.903	http://www.semwebtech.org/mondial/10/meta#neighbor

#384-RP
PRED entity: RP

PRED relation: locatedIn!
PRED expected values: PacificOcean Cebu SuluSea
=> 33 concepts (27 used for prediction)
PRED predicted values (max 10 best out of 1367): PacificOcean (0.91 #12667, 0.90 #22519, 0.89 #11259), AtlanticOcean (0.50 #11301, 0.38 #36636, 0.36 #9893), MalakkaStrait (0.50 #1548, 0.33 #2955, 0.33 #141), Mekong (0.38 #6236, 0.33 #4828, 0.33 #3420), JavaSea (0.33 #64, 0.29 #4222, 0.25 #5630), BandaSea (0.33 #360, 0.29 #4222, 0.25 #1767), IndianOcean (0.33 #3, 0.29 #7040, 0.25 #1410), AndamanSea (0.33 #119, 0.25 #1526, 0.21 #7156), NewGuinea (0.33 #551, 0.25 #1958, 0.09 #10402), Timor (0.33 #636, 0.25 #2043, 0.08 #3450)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #12667 for best value:
>> intensional similarity = 8
>> extensional distance = 32
>> proper extension:  NLSM;
>> query: (?x460, ?x282) <- ?x460[ has religion ?x352<RomanCatholic>; is locatedIn of ?x765[ a Island; has locatedInWater ?x282;]; is locatedIn of ?x2129[ a Island; has belongsToIslands ?x370;];]
ranks of expected_values: 1, 40, 805
EVAL	RP	locatedIn!	SuluSea	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.026	33.000	27.000	1367.000	0.906	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	RP	locatedIn!	Cebu	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.001	33.000	27.000	1367.000	0.906	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	RP	locatedIn!	PacificOcean	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	33.000	27.000	1367.000	0.906	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: PacificOcean Cebu SuluSea
=> 119 concepts (117 used for prediction)
PRED predicted values (max 10 best out of 1415): PacificOcean (0.93 #31004, 0.92 #85980, 0.92 #109953), IndianOcean (0.70 #59201, 0.60 #36643, 0.55 #19731), AtlanticOcean (0.69 #129725, 0.62 #109995, 0.59 #56416), Cebu (0.58 #36640, 0.53 #2817, 0.48 #85981), MalakkaStrait (0.50 #1549, 0.40 #4365, 0.33 #5774), JavaSea (0.48 #7043, 0.42 #7041, 0.35 #43686), SuluSea (0.48 #7043, 0.42 #7041, 0.35 #43686), CaribbeanSea (0.43 #9967, 0.38 #40976, 0.33 #26881), BandaSea (0.42 #7041, 0.35 #43686, 0.33 #360), Borneo (0.40 #4361, 0.25 #1545, 0.17 #5770)
>> best conf = 0.93
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #31004 for best value:
>> intensional similarity = 11
>> extensional distance = 11
>> proper extension:  WV; MNTS;
>> query: (?x460, ?x282) <- ?x460[ has government ?x435; is locatedIn of ?x765[ has locatedInWater ?x282;]; is locatedIn of ?x1575[ has belongsToIslands ?x370[ a Islands;];]; is locatedIn of ?x1670[ a Mountain; a Volcano; has type ?x706<"volcano">;];]
ranks of expected_values: 1, 4, 7
EVAL	RP	locatedIn!	SuluSea	CNN-1.+1._MA	0.000	0.000	1.000	0.200	119.000	117.000	1415.000	0.929	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	RP	locatedIn!	Cebu	CNN-1.+1._MA	0.000	1.000	1.000	0.333	119.000	117.000	1415.000	0.929	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	RP	locatedIn!	PacificOcean	CNN-1.+1._MA	1.000	1.000	1.000	1.000	119.000	117.000	1415.000	0.929	http://www.semwebtech.org/mondial/10/meta#locatedIn

#383-USA
PRED entity: USA

PRED relation: language
PRED expected values: English
=> 34 concepts (34 used for prediction)
PRED predicted values (max 10 best out of 96): English (0.36 #974, 0.33 #295, 0.33 #101), French (0.33 #98, 0.18 #680, 0.18 #486), German (0.25 #209, 0.11 #403, 0.09 #1567), Creole (0.25 #273, 0.03 #1631, 0.03 #952), Amerindian (0.25 #250, 0.02 #1123, 0.02 #1317), Garifuna (0.25 #255, 0.02 #1613, 0.01 #2292), MayanDialects (0.25 #233, 0.02 #1591, 0.01 #2270), Arabic (0.17 #349, 0.12 #543, 0.11 #834), Dutch (0.17 #301, 0.11 #398, 0.06 #495), Norwegian (0.17 #328, 0.11 #425, 0.04 #813)
>> best conf = 0.36
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #974 for best value:
>> intensional similarity = 4
>> extensional distance = 37
>> proper extension:  GROX;
>> query: (?x315, English) <- ?x315[ is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x2276[ a Island;];]
ranks of expected_values: 1
EVAL	USA	language	English	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	34.000	34.000	96.000	0.359	http://www.semwebtech.org/mondial/10/meta#language

PRED relation: language
PRED expected values: English
=> 133 concepts (133 used for prediction)
PRED predicted values (max 10 best out of 89): English (0.67 #2626, 0.50 #3693, 0.41 #6216), French (0.33 #98, 0.25 #778, 0.25 #681), Catalan (0.25 #701, 0.25 #506, 0.25 #409), Basque (0.25 #710, 0.25 #515, 0.25 #418), Hindi (0.25 #866, 0.25 #283, 0.20 #1157), Arabic (0.25 #446, 0.25 #349, 0.20 #2291), Vietnamese (0.25 #432, 0.25 #335, 0.17 #1306), German (0.25 #792, 0.20 #889, 0.17 #1374), Samoan (0.25 #197, 0.20 #1071, 0.17 #1168), Chinese (0.25 #257, 0.20 #1131, 0.17 #1228)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2626 for best value:
>> intensional similarity = 8
>> extensional distance = 10
>> proper extension:  FALK;
>> query: (?x315, English) <- ?x315[ a Country; has ethnicGroup ?x197[ a EthnicGroup;]; has language ?x796; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x2325[ has belongsToIslands ?x2237;];]
ranks of expected_values: 1
EVAL	USA	language	English	CNN-1.+1._MA	1.000	1.000	1.000	1.000	133.000	133.000	89.000	0.667	http://www.semwebtech.org/mondial/10/meta#language

#382-PL
PRED entity: PL

PRED relation: locatedIn!
PRED expected values: Schneekoppe
=> 38 concepts (29 used for prediction)
PRED predicted values (max 10 best out of 1328): Donau (0.67 #2854, 0.57 #4267, 0.54 #7093), AtlanticOcean (0.40 #14177, 0.40 #9938, 0.38 #12764), PacificOcean (0.35 #9981, 0.29 #12807, 0.26 #14220), Drau (0.33 #1687, 0.31 #7340, 0.17 #3101), Mur (0.33 #1448, 0.31 #7101, 0.17 #2862), MalyZitnyOstrov (0.33 #4222, 0.29 #5635, 0.17 #2808), Pruth (0.33 #4011, 0.29 #5424, 0.15 #8250), Theiss (0.33 #1738, 0.17 #3152, 0.16 #8806), March (0.33 #1991, 0.17 #3405, 0.15 #7644), Neusiedlersee (0.33 #1708, 0.17 #3122, 0.15 #7361)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2854 for best value:
>> intensional similarity = 6
>> extensional distance = 4
>> proper extension:  SK; RO; H; MD;
>> query: (?x194, Donau) <- ?x194[ has encompassed ?x195; has government ?x435; has language ?x1314; has neighbor ?x303<UA>; is locatedIn of ?x146;]

*> Best rule #2644 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 4
*> proper extension:  CZ;
*> query: (?x194, Schneekoppe) <- ?x194[ has encompassed ?x195; has language ?x1314; has neighbor ?x163<SK>; has neighbor ?x303[ is locatedIn of ?x97;]; is locatedIn of ?x146;]
*> conf = 0.17
ranks of expected_values: 76
EVAL	PL	locatedIn!	Schneekoppe	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.013	38.000	29.000	1328.000	0.667	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Schneekoppe
=> 68 concepts (68 used for prediction)
PRED predicted values (max 10 best out of 1409): Narew (0.67 #26934, 0.34 #2832, 0.33 #1379), WesternBug (0.67 #26934, 0.34 #2832, 0.25 #5460), Oder (0.67 #26934, 0.17 #7258, 0.13 #59516), PacificOcean (0.57 #45441, 0.55 #46856, 0.39 #24185), NorthSea (0.57 #9954, 0.40 #12794, 0.34 #2832), AtlanticOcean (0.55 #51060, 0.50 #82238, 0.41 #52476), WesternDwina (0.50 #3640, 0.50 #2829, 0.50 #2223), Dnepr (0.50 #4554, 0.50 #1721, 0.40 #5970), Donau (0.50 #11376, 0.50 #7113, 0.36 #15631), Prypjat (0.50 #4544, 0.34 #2832, 0.33 #296)
>> best conf = 0.67
=> the first rule below is the first best rule for 3 predicted values
>> Best rule #26934 for best value:
>> intensional similarity = 15
>> extensional distance = 17
>> proper extension:  TAD; MYA; AFG; IND;
>> query: (?x194, ?x442) <- ?x194[ has ethnicGroup ?x58; is locatedIn of ?x1094[ has hasSource ?x442;]; is neighbor of ?x73[ is locatedIn of ?x472<Argun>; is locatedIn of ?x1585<Amur>; is neighbor of ?x170[ is locatedIn of ?x121;]; is neighbor of ?x591[ has ethnicGroup ?x1193;];]; is neighbor of ?x962[ has language ?x555; has religion ?x56;];]

*> Best rule #8317 for first EXPECTED value:
*> intensional similarity = 14
*> extensional distance = 4
*> proper extension:  A; CZ;
*> query: (?x194, Schneekoppe) <- ?x194[ has ethnicGroup ?x58; is locatedIn of ?x146; is neighbor of ?x73[ is locatedIn of ?x282[ has mergesWith ?x60; is locatedInWater of ?x205;]; is locatedIn of ?x590[ a River;]; is locatedIn of ?x631[ is flowsInto of ?x884;]; is neighbor of ?x353[ has ethnicGroup ?x908;];]; is neighbor of ?x163<SK>;]
*> conf = 0.17
ranks of expected_values: 256
EVAL	PL	locatedIn!	Schneekoppe	CNN-1.+1._MA	0.000	0.000	0.000	0.004	68.000	68.000	1409.000	0.667	http://www.semwebtech.org/mondial/10/meta#locatedIn

#381-F
PRED entity: F

PRED relation: religion
PRED expected values: Muslim
=> 42 concepts (42 used for prediction)
PRED predicted values (max 10 best out of 32): Muslim (0.58 #1017, 0.57 #666, 0.57 #237), Buddhist (0.44 #321, 0.33 #360, 0.20 #438), ChristianOrthodox (0.44 #820, 0.32 #976, 0.28 #1132), Anglican (0.33 #327, 0.22 #366, 0.14 #249), Christian (0.32 #626, 0.29 #782, 0.28 #1406), Hindu (0.22 #319, 0.14 #241, 0.14 #1060), Mormon (0.20 #218, 0.12 #296, 0.11 #374), Sikh (0.14 #265, 0.11 #382, 0.11 #343), JehovasWitnesses (0.10 #720, 0.08 #1110, 0.08 #798), CopticChristian (0.07 #418, 0.06 #496, 0.06 #535)
>> best conf = 0.58
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1017 for best value:
>> intensional similarity = 5
>> extensional distance = 74
>> proper extension:  PY; A; WAG;
>> query: (?x78, Muslim) <- ?x78[ is locatedIn of ?x165[ a Estuary;]; is locatedIn of ?x1784[ is hasEstuary of ?x1114;]; is neighbor of ?x120;]
ranks of expected_values: 1
EVAL	F	religion	Muslim	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	42.000	42.000	32.000	0.579	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion
PRED expected values: Muslim
=> 86 concepts (86 used for prediction)
PRED predicted values (max 10 best out of 37): Muslim (0.67 #594, 0.66 #1308, 0.64 #3113), ChristianOrthodox (0.57 #2050, 0.56 #2834, 0.55 #2521), Buddhist (0.57 #2050, 0.55 #2521, 0.54 #2090), Hindu (0.57 #2050, 0.55 #2521, 0.54 #2090), Christian (0.50 #40, 0.44 #631, 0.40 #1030), HoaHao (0.50 #40, 0.44 #631, 0.40 #1030), CaoDai (0.50 #40, 0.44 #631, 0.40 #1030), Anglican (0.50 #40, 0.44 #631, 0.40 #1030), Seventh-DayAdventist (0.50 #40, 0.44 #631, 0.40 #1030), Presbyterian (0.50 #40, 0.44 #631, 0.40 #1030)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #594 for best value:
>> intensional similarity = 14
>> extensional distance = 4
>> proper extension:  DK;
>> query: (?x78, Muslim) <- ?x78[ has government ?x435; is dependentOf of ?x816[ has government ?x828; has religion ?x352;]; is locatedIn of ?x121; is wasDependentOf of ?x434[ has religion ?x116; is locatedIn of ?x60;]; is wasDependentOf of ?x528[ a Country; is locatedIn of ?x265;]; is wasDependentOf of ?x617[ a Country; has ethnicGroup ?x872;];]
ranks of expected_values: 1
EVAL	F	religion	Muslim	CNN-1.+1._MA	1.000	1.000	1.000	1.000	86.000	86.000	37.000	0.667	http://www.semwebtech.org/mondial/10/meta#religion

#380-SP
PRED entity: SP

PRED relation: locatedIn!
PRED expected values: GulfofAden
=> 32 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 1288): PacificOcean (0.65 #14270, 0.21 #39816, 0.20 #38396), Shabelle (0.57 #17027, 0.33 #1100, 0.25 #6773), AtlanticOcean (0.42 #11390, 0.40 #18487, 0.38 #38352), Atbara (0.33 #1073, 0.25 #6746, 0.20 #8164), BlueNile (0.33 #900, 0.25 #6573, 0.20 #7991), Baro (0.33 #1381, 0.25 #7054, 0.20 #8472), Pibor (0.33 #1083, 0.25 #6756, 0.20 #8174), Baro (0.33 #348, 0.25 #6021, 0.20 #7439), Pibor (0.33 #57, 0.25 #5730, 0.20 #7148), GulfofBengal (0.33 #1490, 0.25 #4327, 0.17 #11348)
>> best conf = 0.65
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #14270 for best value:
>> intensional similarity = 6
>> extensional distance = 49
>> proper extension:  VU; GUAM;
>> query: (?x220, PacificOcean) <- ?x220[ has government ?x1766; is locatedIn of ?x60[ is locatedInWater of ?x433<Tasmania>; is mergesWith of ?x770<BandaSea>;];]

*> Best rule #5622 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 2
*> proper extension:  YE;
*> query: (?x220, GulfofAden) <- ?x220[ a Country; has encompassed ?x213; has neighbor ?x94; has religion ?x187; is locatedIn of ?x1333<ArabianSea>;]
*> conf = 0.25
ranks of expected_values: 42
EVAL	SP	locatedIn!	GulfofAden	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.024	32.000	31.000	1288.000	0.647	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: GulfofAden
=> 70 concepts (69 used for prediction)
PRED predicted values (max 10 best out of 1368): Limpopo (0.78 #5684, 0.33 #4263, 0.33 #64), Zambezi (0.78 #5684, 0.33 #4263, 0.30 #26715), Indus (0.78 #5684, 0.33 #4263, 0.30 #71063), MurrayRiver (0.78 #5684, 0.30 #71063, 0.21 #51158), Shabelle (0.65 #5682, 0.59 #59689, 0.42 #51159), Atbara (0.65 #5682, 0.40 #11018, 0.33 #3917), BlueNile (0.65 #5682, 0.40 #10845, 0.33 #3744), Elgon (0.65 #5682, 0.33 #12787, 0.33 #11707), ChewBahir (0.65 #5682, 0.33 #12188, 0.33 #3665), LakeVictoria (0.65 #5682, 0.33 #12012, 0.33 #2066)
>> best conf = 0.78
=> the first rule below is the first best rule for 4 predicted values
>> Best rule #5684 for best value:
>> intensional similarity = 13
>> extensional distance = 2
>> proper extension:  EAU;
>> query: (?x220, ?x242) <- ?x220[ a Country; has encompassed ?x213<Africa>; has religion ?x187; is locatedIn of ?x60[ is flowsInto of ?x242;]; is locatedIn of ?x2035[ has locatedIn ?x476[ has religion ?x56; is locatedIn of ?x1468<ChewBahir>;];]; is neighbor of ?x474<EAK>;]

*> Best rule #7051 for first EXPECTED value:
*> intensional similarity = 18
*> extensional distance = 2
*> proper extension:  YE;
*> query: (?x220, GulfofAden) <- ?x220[ has government ?x1766; has neighbor ?x94[ a Country;]; has neighbor ?x474[ has encompassed ?x213; has ethnicGroup ?x244; has ethnicGroup ?x1459[ a EthnicGroup;]; has government ?x435; has neighbor ?x229[ has neighbor ?x186; is locatedIn of ?x53;]; has religion ?x95; is locatedIn of ?x598;]; is locatedIn of ?x60<IndianOcean>; is locatedIn of ?x1333<ArabianSea>;]
*> conf = 0.25
ranks of expected_values: 82
EVAL	SP	locatedIn!	GulfofAden	CNN-1.+1._MA	0.000	0.000	0.000	0.012	70.000	69.000	1368.000	0.778	http://www.semwebtech.org/mondial/10/meta#locatedIn

#379-Tobol
PRED entity: Tobol

PRED relation: hasEstuary
PRED expected values: Tobol
=> 28 concepts (27 used for prediction)
PRED predicted values (max 10 best out of 202): Irtysch (0.33 #85, 0.25 #538, 0.25 #311), Ural (0.25 #506, 0.14 #732, 0.05 #958), Katun (0.25 #405, 0.03 #1311, 0.01 #3396), Ischim (0.05 #1094, 0.01 #3396, 0.01 #2943), Ili (0.05 #1006, 0.01 #3396), Syrdarja (0.05 #1103), Schilka (0.03 #1357, 0.01 #3396, 0.01 #2943), Oka (0.03 #1304, 0.01 #3396, 0.01 #2943), Argun (0.03 #1244, 0.01 #3396, 0.01 #2943), Kolyma (0.03 #1322, 0.01 #3396, 0.01 #2943)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #85 for best value:
>> intensional similarity = 6
>> extensional distance = 1
>> proper extension:  Irtysch;
>> query: (?x2102, Irtysch) <- ?x2102[ has flowsInto ?x1845<Ob>; has locatedIn ?x73<R>; has locatedIn ?x403<KAZ>;]

*> Best rule #3396 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 220
*> proper extension:  MackenzieRiver; Thjorsa;
*> query: (?x2102, ?x72) <- ?x2102[ a River; has locatedIn ?x73[ has ethnicGroup ?x58; has religion ?x56; is locatedIn of ?x72;];]
*> conf = 0.01
ranks of expected_values: 47
EVAL	Tobol	hasEstuary	Tobol	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.021	28.000	27.000	202.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasEstuary

PRED relation: hasEstuary
PRED expected values: Tobol
=> 91 concepts (91 used for prediction)
PRED predicted values (max 10 best out of 244): Irtysch (0.34 #2725, 0.33 #85, 0.29 #680), Katun (0.34 #2725, 0.29 #680, 0.26 #4094), Ural (0.25 #506, 0.14 #961, 0.14 #733), Ili (0.14 #781, 0.08 #1462, 0.06 #1917), Ischim (0.14 #869, 0.08 #1550, 0.06 #2005), Syrdarja (0.14 #878, 0.08 #1559, 0.06 #2014), Argun (0.09 #1246, 0.08 #1701, 0.06 #1928), Amur (0.09 #1309, 0.06 #1991, 0.05 #907), Paatsjoki (0.09 #1175, 0.05 #907, 0.04 #2085), Dnepr (0.09 #1195, 0.04 #2105, 0.03 #2332)
>> best conf = 0.34
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #2725 for best value:
>> intensional similarity = 10
>> extensional distance = 35
>> proper extension:  Ob;
>> query: (?x2102, ?x1213) <- ?x2102[ a River; has flowsInto ?x1845[ is flowsInto of ?x1748[ a River; has hasEstuary ?x1213; has locatedIn ?x73<R>;]; is flowsInto of ?x2143[ has hasEstuary ?x2144; has hasSource ?x1038;];];]

*> Best rule #907 for first EXPECTED value:
*> intensional similarity = 10
*> extensional distance = 5
*> proper extension:  Ili; Syrdarja; Ischim;
*> query: (?x2102, ?x397) <- ?x2102[ a River; has flowsInto ?x1845; has locatedIn ?x73[ has ethnicGroup ?x58; is locatedIn of ?x397[ a Estuary;]; is neighbor of ?x232<CN>;]; has locatedIn ?x403<KAZ>;]
*> conf = 0.05
ranks of expected_values: 39
EVAL	Tobol	hasEstuary	Tobol	CNN-1.+1._MA	0.000	0.000	0.000	0.026	91.000	91.000	244.000	0.339	http://www.semwebtech.org/mondial/10/meta#hasEstuary

#378-Semliki
PRED entity: Semliki

PRED relation: locatedIn
PRED expected values: ZRE
=> 40 concepts (37 used for prediction)
PRED predicted values (max 10 best out of 204): ZRE (0.92 #1417, 0.92 #4725, 0.91 #1181), SSD (0.43 #472, 0.19 #2365, 0.19 #2838), EAT (0.29 #646, 0.25 #174, 0.20 #410), EAK (0.25 #114, 0.19 #2365, 0.19 #2838), USA (0.23 #5506, 0.17 #5742, 0.16 #1962), RWA (0.20 #363, 0.19 #2365, 0.19 #2838), R (0.19 #5439, 0.18 #1895, 0.15 #2134), RCA (0.19 #2126, 0.12 #2836, 0.11 #1102), RCB (0.19 #2126, 0.12 #2836, 0.10 #4724), Z (0.19 #2126, 0.12 #2836, 0.10 #4724)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1417 for best value:
>> intensional similarity = 5
>> extensional distance = 90
>> proper extension:  Save; DetroitRiver; RioDesaguadero; Kasai; Zaire; Reuss; Saone; Vuoksi; Zambezi; Kymijoki;
>> query: (?x601, ?x348) <- ?x601[ a River; has hasSource ?x1532[ has locatedIn ?x348;]; has locatedIn ?x688; is flowsInto of ?x600;]
ranks of expected_values: 1
EVAL	Semliki	locatedIn	ZRE	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	40.000	37.000	204.000	0.919	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: ZRE
=> 147 concepts (137 used for prediction)
PRED predicted values (max 10 best out of 236): ZRE (0.95 #20181, 0.95 #17805, 0.94 #9730), EAK (0.91 #17330, 0.39 #31363, 0.33 #4141), USA (0.77 #17165, 0.48 #20730, 0.45 #5765), RI (0.60 #11444, 0.24 #18094, 0.23 #19284), EAT (0.50 #6342, 0.39 #31363, 0.33 #1833), RWA (0.50 #1312, 0.33 #2025, 0.33 #711), SSD (0.50 #474, 0.33 #768, 0.30 #8070), CDN (0.40 #17868, 0.38 #18820, 0.30 #20484), BI (0.33 #557, 0.25 #1268, 0.22 #1897), RCB (0.29 #236, 0.23 #1895, 0.22 #1897)
>> best conf = 0.95
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #20181 for best value:
>> intensional similarity = 7
>> extensional distance = 178
>> proper extension:  Suchona;
>> query: (?x601, ?x348) <- ?x601[ a River; has hasSource ?x1532[ a Source; has locatedIn ?x348;]; has locatedIn ?x688[ has ethnicGroup ?x529; has neighbor ?x229;];]
ranks of expected_values: 1
EVAL	Semliki	locatedIn	ZRE	CNN-1.+1._MA	1.000	1.000	1.000	1.000	147.000	137.000	236.000	0.947	http://www.semwebtech.org/mondial/10/meta#locatedIn

#377-Indian
PRED entity: Indian

PRED relation: ethnicGroup!
PRED expected values: Q RSA
=> 24 concepts (13 used for prediction)
PRED predicted values (max 10 best out of 231): BRU (0.50 #673, 0.37 #569, 0.33 #293), XMAS (0.50 #755, 0.33 #375, 0.33 #185), RP (0.50 #659, 0.33 #279, 0.19 #190), THA (0.37 #569, 0.33 #197, 0.33 #189), RI (0.37 #569, 0.33 #189, 0.31 #762), IND (0.37 #569, 0.33 #189, 0.31 #762), BD (0.37 #569, 0.33 #189, 0.31 #762), Z (0.37 #569, 0.33 #101, 0.31 #762), SD (0.37 #569, 0.33 #32, 0.31 #762), LAO (0.37 #569, 0.33 #189, 0.31 #762)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #673 for best value:
>> intensional similarity = 11
>> extensional distance = 2
>> proper extension:  Malay;
>> query: (?x1196, BRU) <- ?x1196[ a EthnicGroup; is ethnicGroup of ?x192[ has neighbor ?x193; has wasDependentOf ?x1027; is locatedIn of ?x60;]; is ethnicGroup of ?x366[ has neighbor ?x91<THA>; is locatedIn of ?x262;]; is ethnicGroup of ?x1404<SGP>;]

*> Best rule #569 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 2
*> proper extension:  Euro-African;
*> query: (?x1196, ?x91) <- ?x1196[ a EthnicGroup; is ethnicGroup of ?x192<MOC>; is ethnicGroup of ?x376[ a Country; has religion ?x116; is locatedIn of ?x178; is neighbor of ?x91;];]
*> conf = 0.37
ranks of expected_values: 14, 91
EVAL	Indian	ethnicGroup!	RSA	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.071	24.000	13.000	231.000	0.500	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	Indian	ethnicGroup!	Q	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.011	24.000	13.000	231.000	0.500	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup!
PRED expected values: Q RSA
=> 61 concepts (55 used for prediction)
PRED predicted values (max 10 best out of 237): BRU (0.64 #193, 0.50 #873, 0.47 #962), RP (0.64 #193, 0.50 #859, 0.47 #962), RI (0.64 #193, 0.50 #1382, 0.47 #962), VN (0.64 #193, 0.47 #962, 0.45 #961), K (0.64 #193, 0.47 #962, 0.45 #961), THA (0.64 #193, 0.47 #962, 0.45 #961), USA (0.64 #193, 0.47 #962, 0.45 #961), GH (0.64 #193, 0.47 #962, 0.45 #961), BDS (0.64 #193, 0.47 #962, 0.45 #961), CDN (0.64 #193, 0.47 #962, 0.45 #961)
>> best conf = 0.64
=> the first rule below is the first best rule for 83 predicted values
>> Best rule #193 for best value:
>> intensional similarity = 19
>> extensional distance = 1
>> proper extension:  Chinese;
>> query: (?x1196, ?x78) <- ?x1196[ a EthnicGroup; is ethnicGroup of ?x81[ is locatedIn of ?x121[ has locatedIn ?x78; has mergesWith ?x1664; is locatedInWater of ?x848;]; is locatedIn of ?x467[ a Island; has belongsToIslands ?x2364;]; is wasDependentOf of ?x381[ is locatedIn of ?x82;]; is wasDependentOf of ?x1051[ has ethnicGroup ?x162;];]; is ethnicGroup of ?x158[ has encompassed ?x211<Australia-Oceania>; has religion ?x116;]; is ethnicGroup of ?x366<MYA>;]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 31, 119
EVAL	Indian	ethnicGroup!	RSA	CNN-1.+1._MA	0.000	0.000	0.000	0.032	61.000	55.000	237.000	0.643	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	Indian	ethnicGroup!	Q	CNN-1.+1._MA	0.000	0.000	0.000	0.008	61.000	55.000	237.000	0.643	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#376-Ural
PRED entity: Ural

PRED relation: inMountains!
PRED expected values: Ural
=> 20 concepts (20 used for prediction)
PRED predicted values (max 10 best out of 345): Katun (0.20 #90, 0.17 #346, 0.11 #603), Bjelucha (0.20 #5, 0.17 #261, 0.11 #518), Zachwoa (0.20 #207, 0.17 #463, 0.11 #720), KljutschewskajaSopka (0.20 #202, 0.17 #458, 0.11 #715), Kasbek (0.20 #75, 0.17 #331, 0.11 #588), Elbrus (0.20 #13, 0.17 #269, 0.11 #526), Schchara (0.20 #6, 0.17 #262, 0.11 #519), Irtysch (0.20 #146, 0.17 #402, 0.11 #659), Dychtau (0.20 #248, 0.17 #504, 0.11 #761), ChangbaiShan (0.17 #478, 0.02 #3302, 0.01 #4587)
>> best conf = 0.20
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #90 for best value:
>> intensional similarity = 5
>> extensional distance = 3
>> proper extension:  Kaukasus; Kamchatka;
>> query: (?x2187, Katun) <- ?x2187[ a Mountains; is inMountains of ?x2107[ a Mountain; has locatedIn ?x73<R>;];]

*> Best rule #1541 for first EXPECTED value:
*> intensional similarity = 9
*> extensional distance = 19
*> proper extension:  CordilleraBetica;
*> query: (?x2187, ?x72) <- ?x2187[ a Mountains; is inMountains of ?x693[ a Source; has locatedIn ?x73[ has ethnicGroup ?x58; has neighbor ?x170; is locatedIn of ?x72;];]; is inMountains of ?x2107[ a Mountain;];]
*> conf = 0.10
ranks of expected_values: 63
EVAL	Ural	inMountains!	Ural	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.016	20.000	20.000	345.000	0.200	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains!
PRED expected values: Ural
=> 53 concepts (53 used for prediction)
PRED predicted values (max 10 best out of 345): Katun (0.33 #90, 0.25 #603, 0.25 #346), Bjelucha (0.33 #5, 0.25 #518, 0.25 #261), Irtysch (0.33 #146, 0.25 #659, 0.25 #402), Karasu (0.25 #500, 0.20 #1527, 0.14 #3325), Sabalan (0.25 #422, 0.20 #1449, 0.14 #3247), Ararat (0.25 #360, 0.20 #1387, 0.14 #3185), Kura (0.25 #289, 0.20 #1316, 0.14 #3114), Dnepr (0.25 #727, 0.12 #4066, 0.12 #5394), WesternDwina (0.25 #586, 0.12 #3925, 0.12 #5394), Zachwoa (0.20 #2004, 0.17 #3031, 0.17 #2517)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #90 for best value:
>> intensional similarity = 13
>> extensional distance = 1
>> proper extension:  Altai;
>> query: (?x2187, Katun) <- ?x2187[ a Mountains; is inMountains of ?x1416[ a Source; has locatedIn ?x73<R>; is hasSource of ?x1227[ a River; has flowsInto ?x251; has hasEstuary ?x2338; has locatedIn ?x73<R>;];]; is inMountains of ?x2107[ a Mountain; has locatedIn ?x73<R>;];]

*> Best rule #5394 for first EXPECTED value:
*> intensional similarity = 15
*> extensional distance = 8
*> proper extension:  EastAfricanRift; Drakensberge;
*> query: (?x2187, ?x72) <- ?x2187[ a Mountains; is inMountains of ?x693[ has locatedIn ?x73[ a Country; has ethnicGroup ?x58; has neighbor ?x403; has religion ?x187<Muslim>; has wasDependentOf ?x903; is locatedIn of ?x72; is neighbor of ?x170;];]; is inMountains of ?x1416[ a Source;]; is inMountains of ?x2107[ a Mountain;];]
*> conf = 0.12
ranks of expected_values: 133
EVAL	Ural	inMountains!	Ural	CNN-1.+1._MA	0.000	0.000	0.000	0.008	53.000	53.000	345.000	0.333	http://www.semwebtech.org/mondial/10/meta#inMountains

#375-Lulua
PRED entity: Lulua

PRED relation: locatedIn
PRED expected values: ZRE
=> 43 concepts (43 used for prediction)
PRED predicted values (max 10 best out of 87): ZRE (0.92 #6424, 0.91 #5708, 0.91 #3568), ANG (0.87 #3331, 0.75 #2855, 0.72 #3093), USA (0.27 #2928, 0.26 #2690, 0.19 #1501), R (0.20 #4998, 0.18 #5474, 0.15 #6191), D (0.16 #3588, 0.16 #4537, 0.15 #4298), CDN (0.15 #2919, 0.15 #2681, 0.07 #6249), BR (0.14 #1554, 0.07 #1792, 0.06 #2266), RCA (0.12 #635, 0.12 #397, 0.11 #872), RCB (0.12 #597, 0.12 #5945, 0.11 #6184), Z (0.12 #5945, 0.11 #6184, 0.10 #1311)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #6424 for best value:
>> intensional similarity = 6
>> extensional distance = 126
>> proper extension:  MackenzieRiver; Thjorsa;
>> query: (?x1057, ?x348) <- ?x1057[ a River; has hasEstuary ?x2406[ a Estuary;]; has hasSource ?x1058[ has locatedIn ?x348[ has wasDependentOf ?x543;];];]
ranks of expected_values: 1
EVAL	Lulua	locatedIn	ZRE	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	43.000	43.000	87.000	0.917	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: ZRE
=> 113 concepts (113 used for prediction)
PRED predicted values (max 10 best out of 101): ZRE (0.93 #10779, 0.92 #14124, 0.92 #25375), ANG (0.90 #11733, 0.88 #12927, 0.88 #12210), USA (0.39 #10613, 0.39 #10136, 0.34 #20178), CDN (0.38 #6776, 0.22 #10604, 0.15 #13707), RCB (0.33 #122, 0.22 #2395, 0.20 #475), R (0.30 #16286, 0.30 #15086, 0.30 #15326), SUD (0.27 #2199, 0.12 #9389, 0.12 #2679), D (0.26 #11037, 0.25 #17502, 0.24 #17742), Z (0.22 #2395, 0.20 #475, 0.19 #4315), NAM (0.22 #2395, 0.19 #4315, 0.14 #715)
>> best conf = 0.93
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #10779 for best value:
>> intensional similarity = 14
>> extensional distance = 39
>> proper extension:  Thjorsa;
>> query: (?x1057, ?x348) <- ?x1057[ a River; has hasEstuary ?x2406[ a Estuary;]; has hasSource ?x1058[ a Source; has locatedIn ?x348[ a Country; has encompassed ?x213; has ethnicGroup ?x2121; has religion ?x95<Protestant>; has wasDependentOf ?x543; is locatedIn of ?x182<AtlanticOcean>;];];]
ranks of expected_values: 1
EVAL	Lulua	locatedIn	ZRE	CNN-1.+1._MA	1.000	1.000	1.000	1.000	113.000	113.000	101.000	0.930	http://www.semwebtech.org/mondial/10/meta#locatedIn

#374-FGU
PRED entity: FGU

PRED relation: neighbor!
PRED expected values: BR
=> 51 concepts (51 used for prediction)
PRED predicted values (max 10 best out of 220): F (0.40 #1290, 0.38 #1458, 0.33 #1452), CN (0.40 #1172, 0.33 #1334, 0.12 #4738), D (0.38 #1469, 0.22 #1629, 0.17 #4858), GUY (0.33 #1454, 0.33 #162, 0.32 #3079), BR (0.33 #162, 0.32 #3079, 0.29 #4693), FGU (0.33 #162, 0.32 #3079, 0.29 #4693), L (0.33 #1732, 0.25 #1572, 0.17 #4858), PE (0.33 #1454, 0.20 #2967, 0.19 #7298), CO (0.33 #1454, 0.20 #2955, 0.19 #7298), GCA (0.33 #1454, 0.12 #2946, 0.11 #6974)
>> best conf = 0.40
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1290 for best value:
>> intensional similarity = 7
>> extensional distance = 3
>> proper extension:  MACX; HONX; GBZ;
>> query: (?x816, ?x78) <- ?x816[ has dependentOf ?x78; has religion ?x352; is neighbor of ?x179[ a Country; has ethnicGroup ?x79; has religion ?x95; is locatedIn of ?x182;];]

*> Best rule #162 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 1
*> proper extension:  SMAR;
*> query: (?x816, ?x351) <- ?x816[ has dependentOf ?x78<F>; has encompassed ?x521; is neighbor of ?x179[ has government ?x180; is locatedIn of ?x182<AtlanticOcean>; is neighbor of ?x351;];]
*> conf = 0.33
ranks of expected_values: 5
EVAL	FGU	neighbor!	BR	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.200	51.000	51.000	220.000	0.400	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: BR
=> 49 concepts (49 used for prediction)
PRED predicted values (max 10 best out of 234): GUY (0.50 #2064, 0.50 #1320, 0.41 #3703), D (0.50 #1328, 0.50 #1174, 0.33 #4215), L (0.50 #1277, 0.41 #4218, 0.30 #4221), BR (0.50 #1320, 0.34 #5225, 0.33 #2096), FGU (0.50 #1320, 0.34 #5225, 0.33 #4730), CH (0.41 #4218, 0.37 #4217, 0.30 #4221), CO (0.41 #3703, 0.40 #1698, 0.36 #4046), YV (0.41 #3703, 0.36 #4046, 0.35 #4728), PE (0.41 #3703, 0.36 #4046, 0.35 #4728), BOL (0.41 #3703, 0.36 #4046, 0.35 #4728)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2064 for best value:
>> intensional similarity = 24
>> extensional distance = 4
>> proper extension:  SME; YV;
>> query: (?x816, GUY) <- ?x816[ a Country; has encompassed ?x521<America>; has government ?x828; has religion ?x352<RomanCatholic>; is locatedIn of ?x182<AtlanticOcean>; is neighbor of ?x179[ has ethnicGroup ?x79<Amerindian>; has ethnicGroup ?x298[ a EthnicGroup;]; has wasDependentOf ?x575[ a Country; has ethnicGroup ?x734; has government ?x92; has neighbor ?x120; has religion ?x95<Protestant>; has religion ?x187<Muslim>; is locatedIn of ?x121;]; is neighbor of ?x351;];]
>> Best rule #1320 for best value:
>> intensional similarity = 35
>> extensional distance = 2
>> proper extension:  B;
>> query: (?x816, ?x542) <- ?x816[ a Country; has government ?x828; has language ?x51<French>; has religion ?x352<RomanCatholic>; is neighbor of ?x179[ has ethnicGroup ?x79[ a EthnicGroup;]; has ethnicGroup ?x298[ is ethnicGroup of ?x641<MACX>;]; has religion ?x187[ is religion of ?x81<GB>; is religion of ?x120<D>; is religion of ?x170<N>; is religion of ?x207<I>; is religion of ?x217<RI>; is religion of ?x234<CH>; is religion of ?x476<ETH>; is religion of ?x575<NL>; is religion of ?x793<DK>; is religion of ?x1826<GBZ>;]; is locatedIn of ?x182; is neighbor of ?x542;];]

*> Best rule #1320 for first EXPECTED value:
*> intensional similarity = 35
*> extensional distance = 2
*> proper extension:  B;
*> query: (?x816, ?x542) <- ?x816[ a Country; has government ?x828; has language ?x51<French>; has religion ?x352<RomanCatholic>; is neighbor of ?x179[ has ethnicGroup ?x79[ a EthnicGroup;]; has ethnicGroup ?x298[ is ethnicGroup of ?x641<MACX>;]; has religion ?x187[ is religion of ?x81<GB>; is religion of ?x120<D>; is religion of ?x170<N>; is religion of ?x207<I>; is religion of ?x217<RI>; is religion of ?x234<CH>; is religion of ?x476<ETH>; is religion of ?x575<NL>; is religion of ?x793<DK>; is religion of ?x1826<GBZ>;]; is locatedIn of ?x182; is neighbor of ?x542;];]
*> conf = 0.50
ranks of expected_values: 4
EVAL	FGU	neighbor!	BR	CNN-1.+1._MA	0.000	0.000	1.000	0.250	49.000	49.000	234.000	0.500	http://www.semwebtech.org/mondial/10/meta#neighbor

#373-PE
PRED entity: PE

PRED relation: religion
PRED expected values: RomanCatholic
=> 29 concepts (29 used for prediction)
PRED predicted values (max 10 best out of 35): RomanCatholic (0.80 #171, 0.77 #130, 0.70 #89), Muslim (0.61 #578, 0.58 #783, 0.57 #660), ChristianOrthodox (0.39 #493, 0.30 #534, 0.24 #780), JehovasWitnesses (0.33 #184, 0.33 #20, 0.23 #143), Christian (0.31 #577, 0.30 #659, 0.28 #700), Buddhist (0.30 #93, 0.26 #216, 0.23 #134), Anglican (0.22 #821, 0.20 #1068, 0.16 #1110), Jewish (0.22 #821, 0.20 #1068, 0.16 #1110), Mormon (0.22 #821, 0.20 #1068, 0.16 #1110), Hindu (0.22 #821, 0.20 #1068, 0.16 #1110)
>> best conf = 0.80
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #171 for best value:
>> intensional similarity = 5
>> extensional distance = 13
>> proper extension:  R; GCA; CO; RI; USA; CR; NIC; MEX; ES; HCA;
>> query: (?x296, RomanCatholic) <- ?x296[ has neighbor ?x215[ has ethnicGroup ?x79; has wasDependentOf ?x149;]; is locatedIn of ?x282<PacificOcean>;]
ranks of expected_values: 1
EVAL	PE	religion	RomanCatholic	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	29.000	29.000	35.000	0.800	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion
PRED expected values: RomanCatholic
=> 117 concepts (117 used for prediction)
PRED predicted values (max 10 best out of 41): RomanCatholic (0.91 #1611, 0.91 #1364, 0.88 #912), Muslim (0.66 #2637, 0.63 #2185, 0.61 #3093), JehovasWitnesses (0.64 #494, 0.60 #3089, 0.56 #2799), ChristianOrthodox (0.45 #2428, 0.42 #2017, 0.39 #2141), Christian (0.37 #2677, 0.34 #2225, 0.32 #3591), Jewish (0.33 #289, 0.32 #2510, 0.30 #330), Hindu (0.32 #2510, 0.29 #3462, 0.26 #4416), Buddhist (0.32 #2510, 0.29 #3462, 0.26 #4416), Mormon (0.32 #2510, 0.29 #3462, 0.26 #4416), Anglican (0.19 #4668, 0.19 #922, 0.19 #1869)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1611 for best value:
>> intensional similarity = 10
>> extensional distance = 21
>> proper extension:  NCA;
>> query: (?x296, RomanCatholic) <- ?x296[ a Country; has ethnicGroup ?x79[ is ethnicGroup of ?x181<GCA>; is ethnicGroup of ?x902<EC>;]; has government ?x700; has religion ?x95<Protestant>; is locatedIn of ?x264;]
ranks of expected_values: 1
EVAL	PE	religion	RomanCatholic	CNN-1.+1._MA	1.000	1.000	1.000	1.000	117.000	117.000	41.000	0.913	http://www.semwebtech.org/mondial/10/meta#religion

#372-Rhein
PRED entity: Rhein

PRED relation: flowsInto
PRED expected values: NorthSea
=> 44 concepts (44 used for prediction)
PRED predicted values (max 10 best out of 270): Donau (0.40 #175, 0.29 #1505, 0.29 #673), Rhein (0.25 #19, 0.17 #519, 0.17 #352), AtlanticOcean (0.24 #3501, 0.24 #3347, 0.20 #843), BlackSea (0.20 #170, 0.17 #503, 0.17 #336), NorthSea (0.17 #506, 0.14 #671, 0.10 #5837), BalticSea (0.17 #510, 0.10 #5837, 0.10 #2676), Weser (0.14 #1635, 0.14 #1800, 0.10 #5837), PacificOcean (0.13 #2019, 0.11 #2187, 0.07 #1852), MediterraneanSea (0.11 #2521, 0.10 #5837, 0.10 #854), Inn (0.10 #5837, 0.10 #1077, 0.10 #1577)
>> best conf = 0.40
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #175 for best value:
>> intensional similarity = 7
>> extensional distance = 3
>> proper extension:  Donau; Inn; Isar;
>> query: (?x256, Donau) <- ?x256[ has hasEstuary ?x257; has hasSource ?x1695; has locatedIn ?x120<D>; has locatedIn ?x424<A>; is flowsInto of ?x613;]

*> Best rule #506 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 4
*> proper extension:  Oder;
*> query: (?x256, NorthSea) <- ?x256[ has hasEstuary ?x257; has hasSource ?x1695; has locatedIn ?x120<D>; has locatedIn ?x423[ has religion ?x95; has wasDependentOf ?x2516;];]
*> conf = 0.17
ranks of expected_values: 5
EVAL	Rhein	flowsInto	NorthSea	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.200	44.000	44.000	270.000	0.400	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto
PRED expected values: NorthSea
=> 112 concepts (112 used for prediction)
PRED predicted values (max 10 best out of 237): Donau (0.50 #4195, 0.50 #4036, 0.50 #1516), AtlanticOcean (0.50 #2178, 0.50 #2021, 0.18 #1675), PacificOcean (0.33 #359, 0.29 #1197, 0.20 #2706), MediterraneanSea (0.33 #189, 0.22 #2371, 0.20 #3209), BlackSea (0.33 #3, 0.22 #2181, 0.12 #1679), Po (0.30 #3091, 0.18 #3430, 0.17 #3934), Rhein (0.25 #691, 0.25 #525, 0.20 #1024), Mosel (0.25 #750, 0.20 #1083, 0.12 #1754), Inn (0.20 #918, 0.12 #1588, 0.10 #2929), Isar (0.20 #943, 0.11 #2453, 0.10 #2954)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4195 for best value:
>> intensional similarity = 14
>> extensional distance = 10
>> proper extension:  Mur; Enns; Drau; March;
>> query: (?x256, ?x133) <- ?x256[ a River; has hasSource ?x1695; has locatedIn ?x120[ has neighbor ?x194; is locatedIn of ?x133<Donau>; is locatedIn of ?x313[ a Estuary;]; is locatedIn of ?x1124<Isar>; is locatedIn of ?x1278<Inn>;]; has locatedIn ?x424<A>;]
>> Best rule #4036 for best value:
>> intensional similarity = 14
>> extensional distance = 10
>> proper extension:  Mur; Enns; Drau; March;
>> query: (?x256, Donau) <- ?x256[ a River; has hasSource ?x1695; has locatedIn ?x120[ has neighbor ?x194; is locatedIn of ?x133<Donau>; is locatedIn of ?x313[ a Estuary;]; is locatedIn of ?x1124<Isar>; is locatedIn of ?x1278<Inn>;]; has locatedIn ?x424<A>;]
>> Best rule #1516 for best value:
>> intensional similarity = 11
>> extensional distance = 6
>> proper extension:  Iller; Inn; Lech; Isar; Salzach;
>> query: (?x256, Donau) <- ?x256[ a River; has hasSource ?x1695; has locatedIn ?x78[ is locatedIn of ?x182[ is flowsInto of ?x137; is locatedInWater of ?x112;]; is neighbor of ?x149;]; has locatedIn ?x120<D>; has locatedIn ?x424<A>;]

*> Best rule #1675 for first EXPECTED value:
*> intensional similarity = 11
*> extensional distance = 6
*> proper extension:  Iller; Inn; Lech; Isar; Salzach;
*> query: (?x256, ?x182) <- ?x256[ a River; has hasSource ?x1695; has locatedIn ?x78[ is locatedIn of ?x182[ is flowsInto of ?x137; is locatedInWater of ?x112;]; is neighbor of ?x149;]; has locatedIn ?x120<D>; has locatedIn ?x424<A>;]
*> conf = 0.18
ranks of expected_values: 11
EVAL	Rhein	flowsInto	NorthSea	CNN-1.+1._MA	0.000	0.000	0.000	0.091	112.000	112.000	237.000	0.500	http://www.semwebtech.org/mondial/10/meta#flowsInto

#371-RN
PRED entity: RN

PRED relation: wasDependentOf
PRED expected values: F
=> 29 concepts (29 used for prediction)
PRED predicted values (max 10 best out of 27): F (0.50 #62, 0.33 #32, 0.33 #3), GB (0.30 #524, 0.29 #95, 0.29 #690), I (0.28 #657, 0.17 #67, 0.03 #99), E (0.26 #129, 0.24 #159, 0.22 #189), SovietUnion (0.12 #262, 0.09 #513, 0.09 #579), UnitedNations (0.11 #287, 0.11 #256, 0.10 #380), P (0.09 #114, 0.05 #295, 0.03 #614), OttomanEmpire (0.05 #238, 0.04 #454, 0.04 #552), Yugoslavia (0.04 #420, 0.04 #452, 0.04 #550), Czechoslovakia (0.04 #237, 0.02 #421, 0.02 #453)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #62 for best value:
>> intensional similarity = 5
>> extensional distance = 4
>> proper extension:  TN; RIM; LAR;
>> query: (?x426, F) <- ?x426[ has encompassed ?x213; has ethnicGroup ?x1109; is locatedIn of ?x535; is neighbor of ?x581<DZ>;]
ranks of expected_values: 1
EVAL	RN	wasDependentOf	F	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	29.000	29.000	27.000	0.500	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

PRED relation: wasDependentOf
PRED expected values: F
=> 80 concepts (80 used for prediction)
PRED predicted values (max 10 best out of 81): F (0.89 #928, 0.73 #537, 0.71 #371), GB (0.89 #928, 0.39 #762, 0.36 #601), UnitedNations (0.89 #928, 0.36 #1655, 0.28 #1993), I (0.36 #1655, 0.31 #1694, 0.28 #2068), ET (0.36 #1655, 0.28 #1993, 0.27 #1802), E (0.30 #510, 0.25 #1006, 0.21 #797), P (0.25 #209, 0.17 #334, 0.11 #781), SovietUnion (0.16 #811, 0.15 #917, 0.13 #1392), Yugoslavia (0.15 #985, 0.08 #1395, 0.07 #1753), B (0.12 #389, 0.09 #1336, 0.08 #2707)
>> best conf = 0.89
=> the first rule below is the first best rule for 3 predicted values
>> Best rule #928 for best value:
>> intensional similarity = 15
>> extensional distance = 24
>> proper extension:  RM;
>> query: (?x426, ?x78) <- ?x426[ a Country; has government ?x435<"republic">; is locatedIn of ?x930[ has locatedIn ?x169[ a Country; has religion ?x116; has wasDependentOf ?x78;]; has type ?x578;]; is locatedIn of ?x1618[ has locatedIn ?x839[ a Country; has ethnicGroup ?x1537; has language ?x1228; is neighbor of ?x416;];];]
ranks of expected_values: 1
EVAL	RN	wasDependentOf	F	CNN-1.+1._MA	1.000	1.000	1.000	1.000	80.000	80.000	81.000	0.889	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

#370-CDN
PRED entity: CDN

PRED relation: neighbor
PRED expected values: USA
=> 29 concepts (29 used for prediction)
PRED predicted values (max 10 best out of 234): MEX (0.33 #86, 0.20 #247, 0.13 #1616), PL (0.20 #195, 0.14 #356, 0.11 #1003), UA (0.20 #213, 0.14 #374, 0.09 #1021), SF (0.20 #258, 0.14 #419, 0.05 #1389), N (0.20 #185, 0.14 #346, 0.05 #1316), KAZ (0.20 #232, 0.09 #393, 0.09 #1525), AZ (0.20 #218, 0.09 #379, 0.06 #1835), BY (0.20 #202, 0.09 #363, 0.05 #1333), LT (0.20 #303, 0.09 #464, 0.05 #1434), CN (0.20 #204, 0.08 #3114, 0.08 #2952)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #86 for best value:
>> intensional similarity = 7
>> extensional distance = 1
>> proper extension:  USA;
>> query: (?x272, MEX) <- ?x272[ has language ?x51; is locatedIn of ?x263<ArcticOcean>; is locatedIn of ?x494<SaintMarysRiver>; is locatedIn of ?x1680[ a Lake;];]

*> Best rule #1616 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 56
*> proper extension:  RN;
*> query: (?x272, ?x315) <- ?x272[ has ethnicGroup ?x197; is locatedIn of ?x1680[ a Lake;]; is locatedIn of ?x2166[ has locatedIn ?x315;];]
*> conf = 0.13
ranks of expected_values: 27
EVAL	CDN	neighbor	USA	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.037	29.000	29.000	234.000	0.333	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: USA
=> 85 concepts (85 used for prediction)
PRED predicted values (max 10 best out of 243): F (0.50 #2113, 0.17 #1300, 0.17 #1136), BOL (0.40 #597, 0.33 #1572, 0.25 #2877), PE (0.40 #533, 0.25 #2813, 0.25 #211), ROU (0.33 #1522, 0.25 #225, 0.20 #2989), PY (0.33 #1530, 0.25 #233, 0.20 #2997), MEX (0.33 #86, 0.25 #247, 0.20 #569), AND (0.29 #2398, 0.25 #2562, 0.18 #3374), RCB (0.27 #3670, 0.18 #4003, 0.17 #1712), RMM (0.27 #6004, 0.20 #7150, 0.20 #454), USA (0.27 #2600, 0.14 #10975, 0.14 #10809)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2113 for best value:
>> intensional similarity = 9
>> extensional distance = 4
>> proper extension:  AND;
>> query: (?x272, F) <- ?x272[ a Country; has encompassed ?x521; has ethnicGroup ?x197[ is ethnicGroup of ?x483[ has government ?x180;];]; has ethnicGroup ?x1672<French>; has language ?x51; has religion ?x95;]

*> Best rule #2600 for first EXPECTED value:
*> intensional similarity = 14
*> extensional distance = 6
*> proper extension:  YV;
*> query: (?x272, ?x315) <- ?x272[ a Country; has encompassed ?x521; has religion ?x95; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x494[ a Source;]; is locatedIn of ?x1077[ a Lake; has locatedIn ?x315;]; is locatedIn of ?x2007[ a River;]; is locatedIn of ?x2526[ has inMountains ?x337;];]
*> conf = 0.27
ranks of expected_values: 10
EVAL	CDN	neighbor	USA	CNN-1.+1._MA	0.000	0.000	1.000	0.100	85.000	85.000	243.000	0.500	http://www.semwebtech.org/mondial/10/meta#neighbor

#369-ChristmasIsland
PRED entity: ChristmasIsland

PRED relation: locatedIn
PRED expected values: XMAS
=> 25 concepts (25 used for prediction)
PRED predicted values (max 10 best out of 124): RI (0.40 #949, 0.40 #764, 0.33 #528), NAU (0.20 #218, 0.17 #458, 0.02 #2118), NIUE (0.20 #129, 0.17 #369, 0.02 #2029), BERM (0.20 #195, 0.17 #435, 0.01 #2337), RP (0.17 #1771, 0.03 #4394, 0.03 #3202), USA (0.17 #1972, 0.08 #2214, 0.08 #3165), GUAM (0.17 #444, 0.01 #2823), AUS (0.12 #995, 0.12 #1469, 0.12 #1231), MS (0.11 #645, 0.10 #881, 0.08 #1900), COCO (0.11 #662, 0.10 #898, 0.08 #1900)
>> best conf = 0.40
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #949 for best value:
>> intensional similarity = 13
>> extensional distance = 8
>> proper extension:  Sulawesi;
>> query: (?x2050, ?x217) <- ?x2050[ a Island; has locatedInWater ?x60[ has locatedIn ?x217<RI>; is locatedInWater of ?x240<Java>; is locatedInWater of ?x1047<Bali>; is locatedInWater of ?x1666[ a Island; has belongsToIslands ?x227;]; is mergesWith of ?x182;]; has type ?x1402;]
>> Best rule #764 for best value:
>> intensional similarity = 13
>> extensional distance = 8
>> proper extension:  Sulawesi;
>> query: (?x2050, RI) <- ?x2050[ a Island; has locatedInWater ?x60[ has locatedIn ?x217<RI>; is locatedInWater of ?x240<Java>; is locatedInWater of ?x1047<Bali>; is locatedInWater of ?x1666[ a Island; has belongsToIslands ?x227;]; is mergesWith of ?x182;]; has type ?x1402;]

*> Best rule #1900 for first EXPECTED value:
*> intensional similarity = 12
*> extensional distance = 38
*> proper extension:  Ambon; Cebu; Borneo; Panay; Samar; Ternate; Negros; Timor; Ceram; Bohol;
*> query: (?x2050, ?x61) <- ?x2050[ a Island; has locatedInWater ?x60[ has locatedIn ?x61; has locatedIn ?x217<RI>; has mergesWith ?x182; is locatedInWater of ?x740[ has locatedInWater ?x241<JavaSea>;]; is locatedInWater of ?x2233[ a Island; has type ?x1160;];];]
*> conf = 0.08
ranks of expected_values: 26
EVAL	ChristmasIsland	locatedIn	XMAS	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.038	25.000	25.000	124.000	0.400	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: XMAS
=> 36 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 138): RI (0.33 #288, 0.25 #524, 0.25 #1985), I (0.25 #1247, 0.15 #2226, 0.10 #2964), RP (0.21 #1552, 0.16 #2042, 0.06 #3517), USA (0.17 #1758, 0.17 #2497, 0.14 #3480), GR (0.17 #2268, 0.11 #3006, 0.10 #3742), NAU (0.17 #218, 0.03 #1904, 0.01 #2643), NIUE (0.17 #129, 0.03 #1815, 0.01 #2554), BERM (0.17 #195, 0.01 #2868, 0.01 #3358), GUAM (0.17 #204, 0.01 #4099), AUS (0.12 #517, 0.12 #996, 0.12 #753)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #288 for best value:
>> intensional similarity = 4
>> extensional distance = 7
>> proper extension:  Krakatau; Mayotte; Bali; Male; Mauritius; Lombok; PulauPanjang;
>> query: (?x2050, RI) <- ?x2050[ a Island; has locatedInWater ?x60<IndianOcean>; has type ?x1402;]

*> Best rule #2170 for first EXPECTED value:
*> intensional similarity = 23
*> extensional distance = 55
*> proper extension:  Ambon; Ternate; Timor; Ceram;
*> query: (?x2050, ?x1731) <- ?x2050[ has locatedInWater ?x60[ a Sea; has locatedIn ?x61[ a Country;]; has locatedIn ?x192[ has neighbor ?x193;]; has locatedIn ?x1731[ has ethnicGroup ?x197; has religion ?x116;]; has mergesWith ?x182[ has locatedIn ?x50; has mergesWith ?x249; is flowsInto of ?x137; is locatedInWater of ?x112;]; has mergesWith ?x282<PacificOcean>; has mergesWith ?x770[ is locatedInWater of ?x1074<NewGuinea>;]; is locatedInWater of ?x226[ a Island;]; is locatedInWater of ?x1442[ has belongsToIslands ?x1059;];];]
*> conf = 0.10
ranks of expected_values: 25
EVAL	ChristmasIsland	locatedIn	XMAS	CNN-1.+1._MA	0.000	0.000	0.000	0.040	36.000	36.000	138.000	0.333	http://www.semwebtech.org/mondial/10/meta#locatedIn

#368-MAYO
PRED entity: MAYO

PRED relation: religion
PRED expected values: Muslim
=> 40 concepts (40 used for prediction)
PRED predicted values (max 10 best out of 37): Muslim (0.59 #512, 0.58 #806, 0.57 #848), RomanCatholic (0.54 #599, 0.51 #641, 0.50 #767), Christian (0.45 #721, 0.44 #847, 0.44 #511), Protestant (0.37 #929, 0.37 #1478, 0.37 #1349), Hindu (0.29 #431, 0.29 #388, 0.25 #94), Buddhist (0.29 #390, 0.22 #476, 0.16 #1390), Anglican (0.25 #60, 0.16 #421, 0.16 #1390), ChristianOrthodox (0.16 #421, 0.16 #1390, 0.16 #1433), Jewish (0.16 #421, 0.15 #507, 0.14 #1476), Catholic (0.16 #421, 0.15 #507, 0.14 #1476)
>> best conf = 0.59
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #512 for best value:
>> intensional similarity = 7
>> extensional distance = 25
>> proper extension:  ER;
>> query: (?x787, Muslim) <- ?x787[ a Country; has government ?x2534; is locatedIn of ?x60[ a Sea; has locatedIn ?x668<YE>; is mergesWith of ?x182;];]
ranks of expected_values: 1
EVAL	MAYO	religion	Muslim	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	40.000	40.000	37.000	0.593	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion
PRED expected values: Muslim
=> 61 concepts (61 used for prediction)
PRED predicted values (max 10 best out of 37): RomanCatholic (0.71 #563, 0.69 #434, 0.64 #303), Muslim (0.65 #905, 0.64 #818, 0.62 #732), Christian (0.47 #1119, 0.45 #817, 0.45 #1161), Protestant (0.41 #771, 0.40 #1332, 0.40 #2193), Jewish (0.39 #598, 0.37 #469, 0.32 #812), Hindu (0.33 #10, 0.28 #1457, 0.26 #910), Buddhist (0.32 #1127, 0.28 #1457, 0.26 #997), Sikh (0.28 #1457, 0.26 #1028, 0.25 #2494), Jains (0.28 #1457, 0.26 #1028, 0.25 #2494), Anglican (0.26 #1028, 0.25 #102, 0.25 #943)
>> best conf = 0.71
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #563 for best value:
>> intensional similarity = 13
>> extensional distance = 12
>> proper extension:  NLSM;
>> query: (?x787, RomanCatholic) <- ?x787[ has dependentOf ?x78[ a Country; has encompassed ?x195; has religion ?x95; is locatedIn of ?x165[ a Estuary;]; is locatedIn of ?x829<Maas>; is neighbor of ?x120; is wasDependentOf of ?x1206[ has ethnicGroup ?x2201; has government ?x2531; has neighbor ?x621;];];]

*> Best rule #905 for first EXPECTED value:
*> intensional similarity = 15
*> extensional distance = 21
*> proper extension:  BD;
*> query: (?x787, Muslim) <- ?x787[ a Country; has government ?x2534; is locatedIn of ?x60[ has locatedIn ?x61[ has encompassed ?x213;]; has locatedIn ?x217[ has ethnicGroup ?x425; has religion ?x95;]; has locatedIn ?x924<IND>; is locatedInWater of ?x226; is mergesWith of ?x282[ has locatedIn ?x73; is locatedInWater of ?x205; is mergesWith of ?x271;];];]
*> conf = 0.65
ranks of expected_values: 2
EVAL	MAYO	religion	Muslim	CNN-1.+1._MA	0.000	1.000	1.000	0.500	61.000	61.000	37.000	0.714	http://www.semwebtech.org/mondial/10/meta#religion

#367-IndianOcean
PRED entity: IndianOcean

PRED relation: mergesWith!
PRED expected values: ArabianSea
=> 39 concepts (38 used for prediction)
PRED predicted values (max 10 best out of 151): ArabianSea (0.84 #413, 0.82 #728, 0.82 #727), MalakkaStrait (0.57 #448, 0.52 #588, 0.51 #449), IndianOcean (0.57 #448, 0.52 #588, 0.51 #449), SouthChinaSea (0.57 #448, 0.52 #588, 0.51 #449), SulawesiSea (0.33 #90, 0.20 #327, 0.13 #471), GulfofOman (0.33 #25, 0.12 #414, 0.12 #265), EastChinaSea (0.20 #326, 0.20 #191, 0.13 #470), SeaofJapan (0.20 #318, 0.14 #354, 0.13 #462), NorthSea (0.20 #278, 0.13 #418, 0.13 #382), YellowSea (0.20 #317, 0.09 #461, 0.09 #424)
>> best conf = 0.84
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #413 for best value:
>> intensional similarity = 6
>> extensional distance = 21
>> proper extension:  SeaofAzov; BlackSea; Skagerrak;
>> query: (?x60, ?x182) <- ?x60[ has locatedIn ?x61; has mergesWith ?x182[ has locatedIn ?x50; has mergesWith ?x249; is flowsInto of ?x137;]; is flowsInto of ?x242;]
ranks of expected_values: 1
EVAL	IndianOcean	mergesWith!	ArabianSea	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	39.000	38.000	151.000	0.841	http://www.semwebtech.org/mondial/10/meta#mergesWith

PRED relation: mergesWith!
PRED expected values: ArabianSea
=> 102 concepts (102 used for prediction)
PRED predicted values (max 10 best out of 151): ArabianSea (0.81 #1356, 0.79 #1355, 0.78 #1213), IndianOcean (0.49 #1358, 0.45 #1750, 0.33 #521), SouthChinaSea (0.49 #1358, 0.45 #1750, 0.33 #740), MalakkaStrait (0.49 #1358, 0.45 #1750, 0.25 #1248), SulawesiSea (0.40 #367, 0.33 #745, 0.33 #506), EastChinaSea (0.33 #52, 0.25 #296, 0.22 #744), SuluSea (0.33 #54, 0.25 #298, 0.22 #746), SeaofJapan (0.33 #44, 0.25 #288, 0.19 #1502), BeringSea (0.33 #56, 0.25 #300, 0.19 #1502), SeaofOkhotsk (0.33 #51, 0.25 #295, 0.19 #1502)
>> best conf = 0.81
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1356 for best value:
>> intensional similarity = 6
>> extensional distance = 22
>> proper extension:  KaraSea;
>> query: (?x60, ?x262) <- ?x60[ has mergesWith ?x262[ has mergesWith ?x339;]; is flowsInto of ?x242; is locatedInWater of ?x1694[ a Island; has locatedIn ?x196;];]
ranks of expected_values: 1
EVAL	IndianOcean	mergesWith!	ArabianSea	CNN-1.+1._MA	1.000	1.000	1.000	1.000	102.000	102.000	151.000	0.806	http://www.semwebtech.org/mondial/10/meta#mergesWith

#366-SLO
PRED entity: SLO

PRED relation: locatedIn!
PRED expected values: Save
=> 40 concepts (35 used for prediction)
PRED predicted values (max 10 best out of 1364): Donau (0.60 #1446, 0.29 #2866, 0.17 #12810), Mur (0.40 #1819, 0.14 #5682, 0.13 #11363), Neusiedlersee (0.40 #1714, 0.14 #5682, 0.13 #11363), AtlanticOcean (0.39 #28450, 0.35 #48342, 0.35 #24189), Bodensee (0.33 #903, 0.29 #3743, 0.20 #2323), Inn (0.33 #354, 0.29 #3194, 0.20 #1774), Rhone (0.33 #665, 0.21 #38358, 0.20 #32673), LagoMaggiore (0.33 #1049, 0.14 #5682, 0.14 #3889), Ticino (0.33 #648, 0.14 #5682, 0.14 #3488), PizBernina (0.33 #597, 0.14 #5682, 0.14 #3437)
>> best conf = 0.60
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1446 for best value:
>> intensional similarity = 6
>> extensional distance = 3
>> proper extension:  HR; H; A;
>> query: (?x446, Donau) <- ?x446[ has ethnicGroup ?x160; has language ?x738; has neighbor ?x156; has religion ?x187; is locatedIn of ?x614<Drau>;]

*> Best rule #1453 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 3
*> proper extension:  HR; H; A;
*> query: (?x446, Save) <- ?x446[ has ethnicGroup ?x160; has language ?x738; has neighbor ?x156; has religion ?x187; is locatedIn of ?x614<Drau>;]
*> conf = 0.20
ranks of expected_values: 47
EVAL	SLO	locatedIn!	Save	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.021	40.000	35.000	1364.000	0.600	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Save
=> 105 concepts (97 used for prediction)
PRED predicted values (max 10 best out of 1416): Save (0.87 #45496, 0.86 #66845, 0.64 #31276), Donau (0.78 #68270, 0.72 #61149, 0.69 #73959), AtlanticOcean (0.76 #105302, 0.42 #56915, 0.40 #62613), Mur (0.67 #7104, 0.61 #66844, 0.50 #14209), Drau (0.67 #7104, 0.61 #66844, 0.50 #14209), PizBernina (0.67 #7104, 0.50 #14209, 0.49 #12786), MonteRosa (0.67 #7104, 0.50 #14209, 0.49 #12786), Rhein (0.67 #7104, 0.50 #14209, 0.49 #12786), Inn (0.67 #7104, 0.50 #14209, 0.49 #12786), CrapSognGion (0.67 #7104, 0.50 #14209, 0.49 #12786)
>> best conf = 0.87
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #45496 for best value:
>> intensional similarity = 14
>> extensional distance = 28
>> proper extension:  S;
>> query: (?x446, ?x152) <- ?x446[ a Country; has ethnicGroup ?x160; has government ?x1174; has language ?x738; has neighbor ?x207[ has neighbor ?x78; has religion ?x56; is locatedIn of ?x86;]; has religion ?x187; is locatedIn of ?x1363[ is hasSource of ?x152;]; is neighbor of ?x424[ has encompassed ?x195; is locatedIn of ?x133;];]
ranks of expected_values: 1
EVAL	SLO	locatedIn!	Save	CNN-1.+1._MA	1.000	1.000	1.000	1.000	105.000	97.000	1416.000	0.872	http://www.semwebtech.org/mondial/10/meta#locatedIn

#365-ARU
PRED entity: ARU

PRED relation: locatedIn!
PRED expected values: CaribbeanSea
=> 45 concepts (45 used for prediction)
PRED predicted values (max 10 best out of 1104): AtlanticOcean (0.82 #17123, 0.80 #12851, 0.75 #25667), CaribbeanSea (0.73 #21457, 0.60 #5798, 0.60 #45658), TheChannel (0.40 #7773, 0.33 #9197, 0.12 #24857), Trinidad (0.33 #2018, 0.33 #51246, 0.26 #18505), Tobago (0.33 #1617, 0.33 #51246, 0.26 #18505), St.Martin (0.33 #3609, 0.33 #51246, 0.26 #18505), PacificOcean (0.33 #37100, 0.31 #31404, 0.30 #34252), Donau (0.33 #18531, 0.22 #27074, 0.21 #28497), Curacao (0.33 #217, 0.09 #15872, 0.06 #24416), Dominica (0.33 #51246, 0.26 #18505, 0.25 #4541)
>> best conf = 0.82
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #17123 for best value:
>> intensional similarity = 10
>> extensional distance = 9
>> proper extension:  USA;
>> query: (?x1171, AtlanticOcean) <- ?x1171[ a Country; has encompassed ?x521<America>; has religion ?x95<Protestant>; has religion ?x352<RomanCatholic>; is locatedIn of ?x1865[ a Island; has belongsToIslands ?x877;];]

*> Best rule #21457 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 13
*> proper extension:  WV; KN; WG; AG; MNTS;
*> query: (?x1171, CaribbeanSea) <- ?x1171[ a Country; has encompassed ?x521<America>; has government ?x254; is locatedIn of ?x1865[ a Island; has belongsToIslands ?x877<LesserAntilles>;];]
*> conf = 0.73
ranks of expected_values: 2
EVAL	ARU	locatedIn!	CaribbeanSea	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	45.000	45.000	1104.000	0.818	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: CaribbeanSea
=> 65 concepts (65 used for prediction)
PRED predicted values (max 10 best out of 1227): AtlanticOcean (0.82 #29984, 0.80 #27133, 0.75 #35686), CaribbeanSea (0.73 #32898, 0.60 #52855, 0.54 #40026), Donau (0.40 #11431, 0.38 #22841, 0.30 #24268), MalyZitnyOstrov (0.40 #12809, 0.25 #24219, 0.20 #25646), PacificOcean (0.39 #49984, 0.35 #54258, 0.33 #45710), Anguilla (0.34 #62735, 0.33 #3194, 0.33 #64163), Trinidad (0.34 #62735, 0.33 #6299, 0.33 #64163), Tobago (0.34 #62735, 0.33 #5898, 0.33 #64163), St.Martin (0.34 #62735, 0.33 #5044, 0.33 #64163), Martinique (0.34 #62735, 0.33 #64163, 0.26 #31366)
>> best conf = 0.82
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #29984 for best value:
>> intensional similarity = 11
>> extensional distance = 9
>> proper extension:  USA;
>> query: (?x1171, AtlanticOcean) <- ?x1171[ a Country; has encompassed ?x521<America>; has religion ?x95<Protestant>; has religion ?x352<RomanCatholic>; is locatedIn of ?x1865[ a Island; has belongsToIslands ?x877[ a Islands;];];]

*> Best rule #32898 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 13
*> proper extension:  WV; KN; WG; AG; MNTS;
*> query: (?x1171, CaribbeanSea) <- ?x1171[ a Country; has encompassed ?x521<America>; has government ?x254; is locatedIn of ?x1865[ a Island; has belongsToIslands ?x877<LesserAntilles>;];]
*> conf = 0.73
ranks of expected_values: 2
EVAL	ARU	locatedIn!	CaribbeanSea	CNN-1.+1._MA	0.000	1.000	1.000	0.500	65.000	65.000	1227.000	0.818	http://www.semwebtech.org/mondial/10/meta#locatedIn

#364-Ob
PRED entity: Ob

PRED relation: locatedIn
PRED expected values: R
=> 40 concepts (40 used for prediction)
PRED predicted values (max 10 best out of 64): R (0.64 #6641, 0.63 #2140, 0.63 #1908), CN (0.59 #3799, 0.56 #4748, 0.54 #6642), KAZ (0.59 #3799, 0.56 #4748, 0.54 #6642), BD (0.25 #190, 0.03 #3752, 0.03 #3989), IND (0.25 #188, 0.03 #3750, 0.03 #3987), AUS (0.18 #1474, 0.14 #1711, 0.06 #3607), UA (0.14 #1736, 0.06 #3395, 0.06 #4105), D (0.12 #2634, 0.12 #4768, 0.11 #6187), USA (0.12 #6003, 0.12 #5293, 0.12 #5530), ZRE (0.12 #4827, 0.11 #6958, 0.11 #6483)
>> best conf = 0.64
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #6641 for best value:
>> intensional similarity = 8
>> extensional distance = 74
>> proper extension:  MackenzieRiver;
>> query: (?x1765, ?x73) <- ?x1765[ a Estuary; is hasEstuary of ?x1845[ a River; has hasSource ?x976[ a Source; has locatedIn ?x73[ is locatedIn of ?x1748;];]; is flowsInto of ?x1748;];]
ranks of expected_values: 1
EVAL	Ob	locatedIn	R	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	40.000	40.000	64.000	0.637	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: R
=> 70 concepts (70 used for prediction)
PRED predicted values (max 10 best out of 64): R (0.67 #7156, 0.65 #9064, 0.65 #7873), CN (0.56 #11454, 0.56 #11453, 0.56 #11452), KAZ (0.56 #11454, 0.56 #11453, 0.56 #11452), N (0.25 #990, 0.11 #3137, 0.10 #4808), BD (0.25 #907, 0.05 #8302, 0.04 #9256), IND (0.25 #905, 0.05 #8300, 0.04 #9254), D (0.18 #5507, 0.14 #8610, 0.14 #8371), AUS (0.18 #5771, 0.14 #6725, 0.12 #2431), F (0.14 #6450, 0.14 #6211, 0.12 #7165), BR (0.14 #6568, 0.12 #2511, 0.11 #3706)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #7156 for best value:
>> intensional similarity = 13
>> extensional distance = 13
>> proper extension:  Morava; Drina;
>> query: (?x1765, ?x73) <- ?x1765[ a Estuary; is hasEstuary of ?x1845[ a River; has flowsInto ?x801[ is locatedInWater of ?x931;]; has hasSource ?x976[ a Source; has locatedIn ?x73[ is locatedIn of ?x2143[ a River; has hasEstuary ?x2144; has hasSource ?x1038;];];]; is flowsInto of ?x2143;];]
ranks of expected_values: 1
EVAL	Ob	locatedIn	R	CNN-1.+1._MA	1.000	1.000	1.000	1.000	70.000	70.000	64.000	0.667	http://www.semwebtech.org/mondial/10/meta#locatedIn

#363-NiagaraRiver
PRED entity: NiagaraRiver

PRED relation: hasSource
PRED expected values: NiagaraRiver
=> 40 concepts (33 used for prediction)
PRED predicted values (max 10 best out of 254): DetroitRiver (0.06 #433, 0.05 #661, 0.03 #889), StraitsofMackinac (0.06 #418, 0.05 #646, 0.03 #874), SaintMarysRiver (0.06 #253, 0.05 #481, 0.03 #709), Swir (0.06 #437, 0.02 #1578, 0.02 #1806), Semliki (0.06 #341, 0.02 #1482, 0.02 #1710), EucumbeneRiver (0.06 #333, 0.02 #1474, 0.02 #1702), Ruzizi (0.06 #331, 0.02 #1472, 0.02 #1700), Vuoksi (0.06 #431, 0.02 #1572, 0.02 #1800), Kama (0.06 #425, 0.01 #2022), Akagera (0.06 #372, 0.01 #1969)
>> best conf = 0.06
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #433 for best value:
>> intensional similarity = 4
>> extensional distance = 15
>> proper extension:  Selenge; DetroitRiver; Swir; SaintMarysRiver; Semliki; Prypjat; Ruzizi; Akagera; EucumbeneRiver; Murat; ...
>> query: (?x1084, DetroitRiver) <- ?x1084[ a River; has flowsInto ?x1085[ a Lake; has flowsInto ?x1325;];]

*> Best rule #5708 for first EXPECTED value:
*> intensional similarity = 4
*> extensional distance = 213
*> proper extension:  Neckar; Buna; Enns; Hwangho; Uruguay; RioNegro; Perene; Okavango; Karun; Raab; ...
*> query: (?x1084, ?x182) <- ?x1084[ a River; has hasEstuary ?x2458; has locatedIn ?x315[ is locatedIn of ?x182;];]
*> conf = 0.01
ranks of expected_values: 179
EVAL	NiagaraRiver	hasSource	NiagaraRiver	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.006	40.000	33.000	254.000	0.059	http://www.semwebtech.org/mondial/10/meta#hasSource

PRED relation: hasSource
PRED expected values: NiagaraRiver
=> 156 concepts (156 used for prediction)
PRED predicted values (max 10 best out of 358): DetroitRiver (0.25 #661, 0.25 #433, 0.14 #2487), SaintMarysRiver (0.25 #710, 0.20 #1166, 0.20 #938), StraitsofMackinac (0.25 #875, 0.20 #1331, 0.20 #1103), SaintLawrenceRiver (0.25 #259, 0.12 #2997, 0.11 #3225), Missouri (0.20 #1137, 0.17 #1593, 0.09 #4332), TruckeeRiver (0.20 #1357, 0.08 #4552, 0.07 #20388), Arkansas (0.17 #1481, 0.07 #20388, 0.06 #19470), RiviereRichelieu (0.11 #3309, 0.07 #5139, 0.06 #5368), MackenzieRiver (0.11 #3398, 0.07 #5228, 0.05 #7517), SaskatchewanRiver (0.11 #3320, 0.06 #5607, 0.05 #6293)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #661 for best value:
>> intensional similarity = 9
>> extensional distance = 2
>> proper extension:  AtlanticOcean;
>> query: (?x1084, DetroitRiver) <- ?x1084[ is flowsInto of ?x2166[ has locatedIn ?x272<CDN>; has locatedIn ?x315<USA>; is flowsInto of ?x219[ a River; has hasSource ?x2299; is flowsInto of ?x218;];];]
>> Best rule #433 for best value:
>> intensional similarity = 8
>> extensional distance = 2
>> proper extension:  DetroitRiver;
>> query: (?x1084, DetroitRiver) <- ?x1084[ a River; has locatedIn ?x315; is flowsInto of ?x2166[ a Lake; has locatedIn ?x272<CDN>; is flowsInto of ?x219[ a River;];];]

*> Best rule #20388 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 171
*> proper extension:  Bahrel-Ghasal; Bahrel-Djebel-Albert-Nil;
*> query: (?x1084, ?x760) <- ?x1084[ a River; has flowsInto ?x1085[ has locatedIn ?x272;]; has hasEstuary ?x2458; has locatedIn ?x315[ is locatedIn of ?x760[ a Source;]; is neighbor of ?x482;];]
*> conf = 0.07
ranks of expected_values: 23
EVAL	NiagaraRiver	hasSource	NiagaraRiver	CNN-1.+1._MA	0.000	0.000	0.000	0.043	156.000	156.000	358.000	0.250	http://www.semwebtech.org/mondial/10/meta#hasSource

#362-IL
PRED entity: IL

PRED relation: encompassed
PRED expected values: Asia
=> 43 concepts (43 used for prediction)
PRED predicted values (max 10 best out of 5): Africa (0.78 #81, 0.48 #64, 0.43 #14), Asia (0.78 #81, 0.38 #142, 0.33 #46), Europe (0.73 #27, 0.52 #42, 0.50 #37), America (0.42 #60, 0.39 #75, 0.34 #70), Australia-Oceania (0.20 #119, 0.17 #68, 0.14 #58)
>> best conf = 0.78
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #81 for best value:
>> intensional similarity = 6
>> extensional distance = 55
>> proper extension:  ARM; BZ;
>> query: (?x239, ?x175) <- ?x239[ has government ?x254; has language ?x1398; has religion ?x109; has wasDependentOf ?x485; is neighbor of ?x1495[ has encompassed ?x175;];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 2
EVAL	IL	encompassed	Asia	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	43.000	43.000	5.000	0.781	http://www.semwebtech.org/mondial/10/meta#encompassed

PRED relation: encompassed
PRED expected values: Asia
=> 82 concepts (82 used for prediction)
PRED predicted values (max 10 best out of 5): Asia (0.87 #242, 0.87 #410, 0.86 #168), Africa (0.87 #242, 0.87 #410, 0.86 #168), Europe (0.82 #111, 0.80 #87, 0.70 #92), America (0.59 #151, 0.55 #156, 0.46 #167), Australia-Oceania (0.38 #78, 0.33 #46, 0.24 #312)
>> best conf = 0.87
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #242 for best value:
>> intensional similarity = 15
>> extensional distance = 49
>> proper extension:  RN;
>> query: (?x239, ?x175) <- ?x239[ a Country; has government ?x254; has neighbor ?x568[ has ethnicGroup ?x852; has language ?x1398;]; has neighbor ?x803[ has encompassed ?x175[ a Continent;]; has ethnicGroup ?x244;]; is locatedIn of ?x275[ has locatedIn ?x78[ a Country; is locatedIn of ?x182<AtlanticOcean>;]; is flowsInto of ?x698;];]
ranks of expected_values: 1
EVAL	IL	encompassed	Asia	CNN-1.+1._MA	1.000	1.000	1.000	1.000	82.000	82.000	5.000	0.870	http://www.semwebtech.org/mondial/10/meta#encompassed

#361-JOR
PRED entity: JOR

PRED relation: locatedIn!
PRED expected values: Jordan
=> 37 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 1265): MediterraneanSea (0.78 #14285, 0.75 #11445, 0.38 #7184), Euphrat (0.50 #5188, 0.33 #928, 0.25 #6609), AtlanticOcean (0.35 #36970, 0.34 #41235, 0.33 #15665), PersianGulf (0.33 #3296, 0.25 #6137, 0.25 #4716), LakeGenezareth (0.33 #1149, 0.25 #6830, 0.25 #5409), Negev (0.33 #1482, 0.12 #9942, 0.12 #7163), RubAlChali (0.33 #3126, 0.11 #8807, 0.08 #13068), Nefud (0.33 #3759, 0.11 #9440, 0.04 #36928), PacificOcean (0.30 #10028, 0.23 #41279, 0.23 #37014), SchattalArab (0.25 #6468, 0.25 #5047, 0.19 #41192)
>> best conf = 0.78
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #14285 for best value:
>> intensional similarity = 5
>> extensional distance = 30
>> proper extension:  M;
>> query: (?x803, MediterraneanSea) <- ?x803[ a Country; is locatedIn of ?x1552[ has locatedIn ?x239<IL>; is mergesWith of ?x2407;];]

*> Best rule #9942 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 7
*> proper extension:  RL; SUD; EAK;
*> query: (?x803, ?x255) <- ?x803[ has ethnicGroup ?x244<Arab>; is locatedIn of ?x419; is neighbor of ?x302[ is locatedIn of ?x255;]; is neighbor of ?x568[ has encompassed ?x175; has religion ?x109;];]
*> conf = 0.12
ranks of expected_values: 54
EVAL	JOR	locatedIn!	Jordan	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.019	37.000	31.000	1265.000	0.781	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Jordan
=> 99 concepts (97 used for prediction)
PRED predicted values (max 10 best out of 1370): MediterraneanSea (0.89 #44165, 0.87 #37049, 0.57 #28517), IndianOcean (0.77 #49773, 0.33 #5687, 0.26 #75375), Jordan (0.70 #61143, 0.65 #76798, 0.61 #62568), LakeGenezareth (0.57 #44079, 0.55 #39811, 0.34 #69682), Euphrat (0.50 #15142, 0.50 #13720, 0.40 #19408), PersianGulf (0.50 #24625, 0.42 #128010, 0.40 #8528), BlackSea (0.43 #27027, 0.25 #31292, 0.25 #12805), RubAlChali (0.40 #8528, 0.33 #24455, 0.33 #8527), Nefud (0.40 #8528, 0.33 #8024, 0.29 #29856), AtlanticOcean (0.38 #135166, 0.37 #130899, 0.35 #132322)
>> best conf = 0.89
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #44165 for best value:
>> intensional similarity = 17
>> extensional distance = 16
>> proper extension:  M;
>> query: (?x803, MediterraneanSea) <- ?x803[ a Country; has government ?x92; has wasDependentOf ?x485; is locatedIn of ?x953[ has locatedIn ?x466<SYR>;]; is locatedIn of ?x1552[ has locatedIn ?x239<IL>; has locatedIn ?x629[ has ethnicGroup ?x996;]; has locatedIn ?x668[ a Country; has religion ?x187<Muslim>; has wasDependentOf ?x2153;]; is mergesWith of ?x2407;];]

*> Best rule #61143 for first EXPECTED value:
*> intensional similarity = 17
*> extensional distance = 29
*> proper extension:  TCH; YV; MW;
*> query: (?x803, ?x420) <- ?x803[ a Country; has encompassed ?x175; has neighbor ?x302[ has ethnicGroup ?x557[ a EthnicGroup;]; has government ?x254; has neighbor ?x185; is locatedIn of ?x255[ a Estuary;]; is locatedIn of ?x1422[ a River;];]; has religion ?x116; has wasDependentOf ?x485; is locatedIn of ?x419[ has hasEstuary ?x420;]; is locatedIn of ?x567[ a Lake;];]
*> conf = 0.70
ranks of expected_values: 3
EVAL	JOR	locatedIn!	Jordan	CNN-1.+1._MA	0.000	1.000	1.000	0.333	99.000	97.000	1370.000	0.889	http://www.semwebtech.org/mondial/10/meta#locatedIn

#360-Leine
PRED entity: Leine

PRED relation: locatedIn
PRED expected values: D
=> 25 concepts (25 used for prediction)
PRED predicted values (max 10 best out of 96): D (0.93 #2380, 0.92 #1425, 0.92 #2141), R (0.39 #2146, 0.21 #2861, 0.19 #2623), F (0.33 #1432, 0.32 #481, 0.29 #1671), A (0.26 #811, 0.25 #1763, 0.25 #1048), I (0.17 #1950, 0.07 #2666, 0.04 #3379), USA (0.17 #2452, 0.09 #4355, 0.09 #4831), CH (0.14 #3568, 0.13 #3806, 0.13 #2617), CZ (0.14 #3568, 0.13 #3806, 0.13 #2617), L (0.14 #3568, 0.13 #3806, 0.13 #2617), PL (0.14 #3568, 0.13 #3806, 0.13 #2617)
>> best conf = 0.93
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2380 for best value:
>> intensional similarity = 8
>> extensional distance = 42
>> proper extension:  Irtysch;
>> query: (?x100, ?x120) <- ?x100[ a River; has hasEstuary ?x101[ a Estuary; has locatedIn ?x120[ a Country; has ethnicGroup ?x237; has neighbor ?x194<PL>;];];]
ranks of expected_values: 1
EVAL	Leine	locatedIn	D	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	25.000	25.000	96.000	0.932	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: D
=> 82 concepts (82 used for prediction)
PRED predicted values (max 10 best out of 104): D (0.92 #7163, 0.92 #6683, 0.92 #6444), R (0.61 #9553, 0.58 #8837, 0.43 #10271), A (0.33 #5965, 0.33 #5826, 0.33 #3677), ZRE (0.33 #11777, 0.33 #12254, 0.29 #11298), I (0.33 #760, 0.23 #6971, 0.16 #8641), F (0.32 #1199, 0.31 #6690, 0.26 #8600), CH (0.32 #1489, 0.30 #2203, 0.26 #4829), UA (0.32 #1022, 0.16 #6275, 0.13 #11529), CDN (0.24 #10569, 0.17 #10806, 0.04 #15103), USA (0.22 #15112, 0.22 #16790, 0.22 #14397)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #7163 for best value:
>> intensional similarity = 15
>> extensional distance = 24
>> proper extension:  Mincio;
>> query: (?x100, ?x120) <- ?x100[ a River; has hasEstuary ?x101[ a Estuary; has locatedIn ?x120[ a Country; has encompassed ?x195; has neighbor ?x78; has religion ?x95<Protestant>; has religion ?x187<Muslim>; has religion ?x352<RomanCatholic>; is neighbor of ?x234<CH>;];];]
ranks of expected_values: 1
EVAL	Leine	locatedIn	D	CNN-1.+1._MA	1.000	1.000	1.000	1.000	82.000	82.000	104.000	0.923	http://www.semwebtech.org/mondial/10/meta#locatedIn

#359-CaymanBrac
PRED entity: CaymanBrac

PRED relation: locatedIn
PRED expected values: CAYM
=> 26 concepts (26 used for prediction)
PRED predicted values (max 10 best out of 96): CAYM (0.35 #3805, 0.34 #4042, 0.33 #180), USA (0.20 #1495, 0.16 #1974, 0.09 #2213), GB (0.18 #955, 0.15 #1672, 0.09 #2626), GR (0.14 #1275, 0.07 #2231, 0.06 #2469), P (0.12 #1143, 0.11 #1860, 0.05 #2338), E (0.11 #973, 0.09 #1690, 0.05 #2168), TT (0.10 #384, 0.08 #621, 0.08 #858), AG (0.10 #444, 0.08 #681, 0.08 #918), RI (0.09 #1237, 0.08 #2431, 0.08 #2908), I (0.09 #1233, 0.04 #2189, 0.04 #4090)
>> best conf = 0.35
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3805 for best value:
>> intensional similarity = 7
>> extensional distance = 190
>> proper extension:  Saipan; Jersey; Ameland; Texel; Savaii; SaoMiguel; Guam; Samos; Tiree; Spiekeroog; ...
>> query: (?x599, ?x865) <- ?x599[ a Island; has belongsToIslands ?x1357[ a Islands; is belongsToIslands of ?x1093[ a Island; has locatedIn ?x865; has locatedInWater ?x317;];];]
ranks of expected_values: 1
EVAL	CaymanBrac	locatedIn	CAYM	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	26.000	26.000	96.000	0.345	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: CAYM
=> 37 concepts (37 used for prediction)
PRED predicted values (max 10 best out of 108): CAYM (0.35 #5873, 0.34 #6110, 0.33 #180), USA (0.21 #1761, 0.20 #2007, 0.16 #2505), GB (0.18 #1205, 0.18 #1451, 0.15 #2194), P (0.12 #1393, 0.12 #1639, 0.11 #2382), RI (0.12 #998, 0.08 #5439, 0.08 #3720), GR (0.11 #3019, 0.08 #3512, 0.06 #4494), E (0.11 #1223, 0.11 #1469, 0.09 #2212), I (0.10 #2977, 0.06 #4452, 0.05 #3470), AG (0.10 #444, 0.08 #681, 0.08 #918), TT (0.10 #384, 0.08 #621, 0.08 #858)
>> best conf = 0.35
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5873 for best value:
>> intensional similarity = 7
>> extensional distance = 190
>> proper extension:  Saipan; Jersey; Ameland; Texel; Savaii; SaoMiguel; Guam; Samos; Tiree; Spiekeroog; ...
>> query: (?x599, ?x865) <- ?x599[ a Island; has belongsToIslands ?x1357[ a Islands; is belongsToIslands of ?x1093[ a Island; has locatedIn ?x865; has locatedInWater ?x317;];];]
ranks of expected_values: 1
EVAL	CaymanBrac	locatedIn	CAYM	CNN-1.+1._MA	1.000	1.000	1.000	1.000	37.000	37.000	108.000	0.345	http://www.semwebtech.org/mondial/10/meta#locatedIn

#358-MW
PRED entity: MW

PRED relation: locatedIn!
PRED expected values: LakeChilwa Chire
=> 36 concepts (25 used for prediction)
PRED predicted values (max 10 best out of 1376): IndianOcean (0.80 #3, 0.62 #1425, 0.17 #17070), AtlanticOcean (0.38 #27025, 0.38 #25644, 0.37 #17109), Limpopo (0.31 #1487, 0.15 #5754, 0.13 #7176), PacificOcean (0.23 #28535, 0.22 #27113, 0.20 #22842), CaribbeanSea (0.22 #27133, 0.21 #28555, 0.20 #14328), LakeVictoria (0.20 #648, 0.15 #6337, 0.13 #7759), GulfofBengal (0.20 #73, 0.12 #8606, 0.11 #2917), NorthSea (0.19 #11400, 0.17 #14244, 0.12 #21356), MediterraneanSea (0.19 #11461, 0.14 #19995, 0.14 #22839), Donau (0.17 #14248, 0.14 #19938, 0.14 #21360)
>> best conf = 0.80
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3 for best value:
>> intensional similarity = 8
>> extensional distance = 8
>> proper extension:  MV;
>> query: (?x819, IndianOcean) <- ?x819[ a Country; has wasDependentOf ?x81<GB>; is locatedIn of ?x1650[ has locatedIn ?x192<MOC>; has locatedIn ?x820<EAT>;];]

*> Best rule #11378 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 24
*> proper extension:  SD; BHT; BZ;
*> query: (?x819, ?x284) <- ?x819[ a Country; has wasDependentOf ?x81<GB>; is neighbor of ?x525[ has encompassed ?x213; has religion ?x116; is locatedIn of ?x284;];]
*> conf = 0.10
ranks of expected_values: 80
EVAL	MW	locatedIn!	Chire	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	36.000	25.000	1376.000	0.800	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	MW	locatedIn!	LakeChilwa	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.013	36.000	25.000	1376.000	0.800	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: LakeChilwa Chire
=> 84 concepts (83 used for prediction)
PRED predicted values (max 10 best out of 1407): IndianOcean (0.80 #24229, 0.67 #7124, 0.58 #27075), AtlanticOcean (0.70 #89843, 0.63 #42795, 0.62 #14294), Zambezi (0.69 #55589, 0.62 #7120, 0.55 #47027), Chire (0.60 #7119, 0.50 #11398, 0.23 #48456), Limpopo (0.50 #11398, 0.44 #15741, 0.33 #17167), PacificOcean (0.50 #32856, 0.40 #5782, 0.39 #48542), CaribbeanSea (0.50 #32876, 0.33 #48562, 0.32 #57124), LakeCabora-Bassa (0.50 #11398, 0.27 #5694, 0.23 #48456), LakeChilwa (0.50 #11398, 0.23 #48456, 0.22 #17102), Zambezi (0.50 #11398, 0.23 #48456, 0.22 #17102)
>> best conf = 0.80
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #24229 for best value:
>> intensional similarity = 9
>> extensional distance = 8
>> proper extension:  MAYO;
>> query: (?x819, IndianOcean) <- ?x819[ a Country; has encompassed ?x213<Africa>; has government ?x2064; is locatedIn of ?x1650[ has locatedIn ?x192<MOC>; has locatedIn ?x820<EAT>;];]

*> Best rule #11398 for first EXPECTED value:
*> intensional similarity = 18
*> extensional distance = 4
*> proper extension:  BR;
*> query: (?x819, ?x60) <- ?x819[ has neighbor ?x192[ has ethnicGroup ?x1196[ is ethnicGroup of ?x1404<SGP>;]; has government ?x435; has neighbor ?x193; has wasDependentOf ?x1027; is locatedIn of ?x60;]; has neighbor ?x525[ a Country; has ethnicGroup ?x162; is locatedIn of ?x284; is neighbor of ?x1576[ has encompassed ?x213; has government ?x254; is locatedIn of ?x1676;];]; has religion ?x95<Protestant>;]
*> conf = 0.50
ranks of expected_values: 9, 12
EVAL	MW	locatedIn!	Chire	CNN-1.+1._MA	0.000	0.000	0.000	0.091	84.000	83.000	1407.000	0.800	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	MW	locatedIn!	LakeChilwa	CNN-1.+1._MA	0.000	0.000	1.000	0.111	84.000	83.000	1407.000	0.800	http://www.semwebtech.org/mondial/10/meta#locatedIn

#357-K
PRED entity: K

PRED relation: neighbor!
PRED expected values: LAO
=> 40 concepts (40 used for prediction)
PRED predicted values (max 10 best out of 172): LAO (0.91 #3067, 0.91 #2092, 0.90 #4360), CN (0.69 #685, 0.50 #43, 0.30 #367), K (0.50 #297, 0.30 #3231, 0.29 #2580), MAL (0.30 #3231, 0.29 #2580, 0.28 #2904), MYA (0.30 #3231, 0.29 #2580, 0.28 #2904), IND (0.25 #780, 0.25 #138, 0.10 #621), BD (0.25 #140, 0.12 #782, 0.10 #623), UZB (0.25 #690, 0.07 #4844, 0.06 #1492), TAD (0.19 #657, 0.07 #4844, 0.06 #4845), KGZ (0.19 #658, 0.07 #4844, 0.06 #4845)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3067 for best value:
>> intensional similarity = 6
>> extensional distance = 110
>> proper extension:  E;
>> query: (?x871, ?x463) <- ?x871[ has ethnicGroup ?x298; has government ?x2490; has neighbor ?x463[ has ethnicGroup ?x1647; has neighbor ?x232;]; has religion ?x462;]
ranks of expected_values: 1
EVAL	K	neighbor!	LAO	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	40.000	40.000	172.000	0.913	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: LAO
=> 78 concepts (78 used for prediction)
PRED predicted values (max 10 best out of 229): LAO (0.91 #7891, 0.90 #8059, 0.90 #6892), CN (0.67 #2170, 0.42 #818, 0.39 #3111), K (0.50 #460, 0.42 #818, 0.39 #3111), MYA (0.42 #818, 0.32 #6727, 0.31 #6893), EAT (0.35 #2585, 0.14 #489, 0.13 #2422), RI (0.33 #202, 0.25 #860, 0.22 #1679), BRU (0.33 #255, 0.25 #913, 0.21 #7892), IL (0.30 #2996, 0.20 #3324, 0.17 #3812), MAL (0.30 #7893, 0.30 #9216, 0.29 #6563), RMM (0.29 #2752, 0.24 #3733, 0.14 #489)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #7891 for best value:
>> intensional similarity = 15
>> extensional distance = 75
>> proper extension:  F; NAM; TCH; I; YV; BI; RCB; MW; P;
>> query: (?x871, ?x617) <- ?x871[ a Country; has encompassed ?x175; has government ?x2490; has neighbor ?x617[ a Country; has ethnicGroup ?x872; has government ?x831; has neighbor ?x232[ has religion ?x116; is locatedIn of ?x231;]; has religion ?x95; is locatedIn of ?x975;]; is locatedIn of ?x1152[ a River; has hasSource ?x2532;];]
ranks of expected_values: 1
EVAL	K	neighbor!	LAO	CNN-1.+1._MA	1.000	1.000	1.000	1.000	78.000	78.000	229.000	0.911	http://www.semwebtech.org/mondial/10/meta#neighbor

#356-Usedom
PRED entity: Usedom

PRED relation: locatedIn
PRED expected values: D
=> 39 concepts (30 used for prediction)
PRED predicted values (max 10 best out of 173): NL (0.71 #610, 0.38 #1082, 0.11 #3081), UA (0.65 #1490, 0.21 #1894, 0.21 #1727), R (0.60 #5, 0.17 #1895, 0.11 #3081), DK (0.46 #1116, 0.38 #1353, 0.11 #3081), H (0.40 #297, 0.26 #1716, 0.11 #3081), SK (0.40 #271, 0.24 #1690, 0.17 #1895), D (0.25 #734, 0.17 #1895, 0.15 #1206), RI (0.23 #3844, 0.17 #2185, 0.15 #5262), GB (0.19 #2143, 0.18 #2378, 0.14 #3092), USA (0.19 #1966, 0.10 #3627, 0.07 #4808)
>> best conf = 0.71
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #610 for best value:
>> intensional similarity = 6
>> extensional distance = 5
>> proper extension:  Ameland; Texel; Schiermonnikoog; Terschelling; Vlieland;
>> query: (?x2027, NL) <- ?x2027[ a Island; has locatedIn ?x194[ has ethnicGroup ?x58; has government ?x435; has neighbor ?x120<D>;];]

*> Best rule #734 for first EXPECTED value:
*> intensional similarity = 2
*> extensional distance = 10
*> proper extension:  BalticSea; WesternBug; WesternBug; Oder; Oder; Narew; Weichsel; Weichsel; Narew; Weichsel;
*> query: (?x2027, D) <- ?x2027[ has locatedIn ?x194<PL>;]
*> conf = 0.25
ranks of expected_values: 7
EVAL	Usedom	locatedIn	D	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.143	39.000	30.000	173.000	0.714	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: D
=> 79 concepts (73 used for prediction)
PRED predicted values (max 10 best out of 208): D (0.90 #4073, 0.66 #6226, 0.55 #7162), R (0.89 #5733, 0.60 #5, 0.38 #11698), CDN (0.84 #3636, 0.16 #3398, 0.12 #13431), A (0.69 #1764, 0.24 #2618, 0.18 #13845), CZ (0.48 #1539, 0.24 #2618, 0.20 #3573), F (0.44 #3819, 0.37 #4537, 0.20 #9079), SK (0.43 #1219, 0.24 #2618, 0.18 #13845), UA (0.41 #2451, 0.24 #2618, 0.20 #3573), DK (0.38 #642, 0.17 #9308, 0.14 #7160), BY (0.38 #1001, 0.21 #947, 0.21 #763)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4073 for best value:
>> intensional similarity = 13
>> extensional distance = 84
>> proper extension:  Breg; Leine; StarnbergerSee; NorthSea; Donau; Neckar; Zugspitze; Rhein; Main; Saar; ...
>> query: (?x2027, D) <- ?x2027[ has locatedIn ?x194[ has ethnicGroup ?x237<German>; has religion ?x352; is locatedIn of ?x737<Oder>; is locatedIn of ?x1347[ a River;]; is neighbor of ?x962[ a Country; has ethnicGroup ?x963; has language ?x1314<Polish>;];];]
ranks of expected_values: 1
EVAL	Usedom	locatedIn	D	CNN-1.+1._MA	1.000	1.000	1.000	1.000	79.000	73.000	208.000	0.895	http://www.semwebtech.org/mondial/10/meta#locatedIn

#355-Chinese
PRED entity: Chinese

PRED relation: language!
PRED expected values: AUS
=> 28 concepts (23 used for prediction)
PRED predicted values (max 10 best out of 223): NMIS (0.50 #167, 0.33 #48, 0.25 #287), NLSM (0.44 #603, 0.40 #361, 0.36 #845), I (0.42 #1234, 0.20 #390, 0.17 #512), L (0.40 #452, 0.33 #574, 0.33 #92), CDN (0.40 #399, 0.33 #521, 0.33 #39), CUR (0.33 #640, 0.33 #38, 0.27 #882), AUS (0.33 #27, 0.27 #871, 0.25 #1351), SF (0.33 #78, 0.25 #317, 0.25 #197), BZ (0.33 #87, 0.25 #326, 0.25 #206), PA (0.33 #94, 0.25 #333, 0.25 #213)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #167 for best value:
>> intensional similarity = 10
>> extensional distance = 2
>> proper extension:  Chamorro;
>> query: (?x1905, NMIS) <- ?x1905[ a Language; is language of ?x461[ has encompassed ?x211; has government ?x1947; is locatedIn of ?x282;]; is language of ?x773[ a Country; has ethnicGroup ?x2149<Filipino>; has government ?x2356;];]

*> Best rule #27 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 1
*> proper extension:  English;
*> query: (?x1905, AUS) <- ?x1905[ a Language; is language of ?x461<NZ>; is language of ?x773<HONX>;]
*> conf = 0.33
ranks of expected_values: 7
EVAL	Chinese	language!	AUS	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.143	28.000	23.000	223.000	0.500	http://www.semwebtech.org/mondial/10/meta#language

PRED relation: language!
PRED expected values: AUS
=> 51 concepts (51 used for prediction)
PRED predicted values (max 10 best out of 223): BZ (0.95 #365, 0.94 #2103, 0.94 #1856), L (0.95 #365, 0.94 #2103, 0.94 #1856), SF (0.95 #365, 0.94 #2103, 0.94 #1856), NMIS (0.95 #365, 0.94 #2103, 0.94 #1856), NAM (0.95 #365, 0.94 #2103, 0.94 #1856), NLSM (0.95 #365, 0.94 #2103, 0.94 #1856), CUR (0.95 #365, 0.94 #2103, 0.94 #1856), CAYM (0.95 #365, 0.94 #2103, 0.94 #1856), PA (0.95 #365, 0.94 #2103, 0.94 #1856), CDN (0.95 #365, 0.94 #2103, 0.94 #1856)
>> best conf = 0.95
=> the first rule below is the first best rule for 28 predicted values
>> Best rule #365 for best value:
>> intensional similarity = 24
>> extensional distance = 2
>> proper extension:  Portuguese;
>> query: (?x1905, ?x50) <- ?x1905[ is language of ?x461[ a Country; has encompassed ?x211; has ethnicGroup ?x197; has government ?x1947; is locatedIn of ?x282[ has mergesWith ?x60; is locatedInWater of ?x205;]; is locatedIn of ?x587[ has belongsToIslands ?x1523;];]; is language of ?x773[ a Country; has dependentOf ?x232<CN>; has encompassed ?x175<Asia>; has ethnicGroup ?x298<Chinese>; has ethnicGroup ?x2149[ a EthnicGroup;]; has language ?x247[ is language of ?x50;]; is locatedIn of ?x384<SouthChinaSea>;];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 12
EVAL	Chinese	language!	AUS	CNN-1.+1._MA	0.000	0.000	0.000	0.083	51.000	51.000	223.000	0.947	http://www.semwebtech.org/mondial/10/meta#language

#354-CUR
PRED entity: CUR

PRED relation: government
PRED expected values: "parliamentary"
=> 42 concepts (42 used for prediction)
PRED predicted values (max 10 best out of 60): "parliamentary democracy" (0.33 #77, 0.33 #5, 0.26 #869), "parliamentary" (0.33 #184, 0.25 #256, 0.17 #328), "British Overseas Territories" (0.32 #871, 0.30 #1015, 0.27 #1159), "republic" (0.27 #2671, 0.27 #2599, 0.27 #2455), "overseas department of France" (0.25 #516, 0.21 #948, 0.07 #1309), "commonwealth" (0.25 #284, 0.11 #500, 0.06 #644), "parliamentary democracy and a Commonwealth realm" (0.19 #1116, 0.17 #684, 0.16 #1405), "limited democracy" (0.17 #340, 0.11 #484, 0.06 #556), "territory of Australia" (0.14 #1167, 0.01 #2608, 0.01 #2680), "federal republic" (0.12 #363, 0.12 #579, 0.11 #795)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #77 for best value:
>> intensional similarity = 8
>> extensional distance = 1
>> proper extension:  CAYM;
>> query: (?x246, "parliamentary democracy") <- ?x246[ has dependentOf ?x575; has encompassed ?x521; has language ?x544[ a Language;]; has language ?x796<Spanish>; has religion ?x95; is locatedIn of ?x317;]
>> Best rule #5 for best value:
>> intensional similarity = 7
>> extensional distance = 1
>> proper extension:  ARU;
>> query: (?x246, "parliamentary democracy") <- ?x246[ a Country; has dependentOf ?x575<NL>; has encompassed ?x521; is locatedIn of ?x317[ has locatedIn ?x345[ has religion ?x95;];];]

*> Best rule #184 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 1
*> proper extension:  NLSM;
*> query: (?x246, "parliamentary") <- ?x246[ has dependentOf ?x575; has language ?x611<Papiamento>; has language ?x796<Spanish>; has religion ?x95; is locatedIn of ?x317<CaribbeanSea>;]
*> conf = 0.33
ranks of expected_values: 2
EVAL	CUR	government	"parliamentary"	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	42.000	42.000	60.000	0.333	http://www.semwebtech.org/mondial/10/meta#government

PRED relation: government
PRED expected values: "parliamentary"
=> 83 concepts (83 used for prediction)
PRED predicted values (max 10 best out of 61): "British Overseas Territories" (0.50 #805, 0.40 #369, 0.40 #225), "overseas department of France" (0.40 #448, 0.33 #1464, 0.22 #1100), "parliamentary" (0.33 #113, 0.20 #330, 0.10 #4862), "territory of Norway administered by the Ministry of Industry" (0.33 #21, 0.11 #1037, 0.10 #1256), "parliamentary democracy" (0.31 #1602, 0.25 #803, 0.25 #150), "republic" (0.29 #659, 0.28 #5232, 0.27 #5596), "parliamentary democracy and a Commonwealth realm" (0.29 #1705, 0.25 #1850, 0.25 #761), "parliamentary representative democratic French overseas collectivity" (0.20 #1208, 0.17 #1499, 0.08 #2511), "constitutional monarchy" (0.20 #292, 0.12 #3190, 0.10 #4862), "commonwealth" (0.20 #286, 0.11 #1156, 0.11 #1084)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #805 for best value:
>> intensional similarity = 14
>> extensional distance = 6
>> proper extension:  HELX; GBJ;
>> query: (?x246, "British Overseas Territories") <- ?x246[ has dependentOf ?x575[ a Country; has encompassed ?x195; has ethnicGroup ?x734; has government ?x92; has language ?x544; has religion ?x352<RomanCatholic>; is locatedIn of ?x764[ a Island;];]; has encompassed ?x521; has language ?x247<English>; is locatedIn of ?x317;]

*> Best rule #113 for first EXPECTED value:
*> intensional similarity = 26
*> extensional distance = 1
*> proper extension:  NLSM;
*> query: (?x246, "parliamentary") <- ?x246[ a Country; has dependentOf ?x575; has language ?x247<English>; has language ?x544<Dutch>; has language ?x611<Papiamento>; has language ?x796<Spanish>; has religion ?x95<Protestant>; has religion ?x352<RomanCatholic>; has religion ?x1151[ a Religion; is religion of ?x408<NIC>; is religion of ?x482<MEX>; is religion of ?x654<ES>; is religion of ?x671<BZ>;]; is locatedIn of ?x506[ a Island;];]
*> conf = 0.33
ranks of expected_values: 3
EVAL	CUR	government	"parliamentary"	CNN-1.+1._MA	0.000	1.000	1.000	0.333	83.000	83.000	61.000	0.500	http://www.semwebtech.org/mondial/10/meta#government

#353-Spiekeroog
PRED entity: Spiekeroog

PRED relation: locatedInWater
PRED expected values: NorthSea
=> 35 concepts (32 used for prediction)
PRED predicted values (max 10 best out of 102): NorthSea (0.78 #87, 0.78 #46, 0.73 #90), BalticSea (0.37 #746, 0.13 #92, 0.08 #487), Donau (0.37 #746, 0.08 #1410, 0.04 #747), PacificOcean (0.29 #147, 0.27 #543, 0.24 #675), AtlanticOcean (0.28 #401, 0.27 #842, 0.26 #709), MediterraneanSea (0.20 #233, 0.17 #454, 0.17 #498), JavaSea (0.15 #139, 0.10 #226, 0.08 #315), IndianOcean (0.12 #132, 0.08 #793, 0.08 #1100), CaribbeanSea (0.10 #854, 0.10 #898, 0.10 #941), SouthChinaSea (0.08 #152, 0.06 #239, 0.06 #548)
>> best conf = 0.78
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #87 for best value:
>> intensional similarity = 7
>> extensional distance = 7
>> proper extension:  Pellworm; Amrum; Sylt;
>> query: (?x1760, ?x121) <- ?x1760[ has belongsToIslands ?x1856[ is belongsToIslands of ?x1100[ a Island;]; is belongsToIslands of ?x1658[ has locatedInWater ?x121;];]; has locatedIn ?x120<D>;]
>> Best rule #46 for best value:
>> intensional similarity = 7
>> extensional distance = 7
>> proper extension:  Pellworm; Amrum; Sylt;
>> query: (?x1760, NorthSea) <- ?x1760[ has belongsToIslands ?x1856[ is belongsToIslands of ?x1100[ a Island;]; is belongsToIslands of ?x1658[ has locatedInWater ?x121;];]; has locatedIn ?x120<D>;]
ranks of expected_values: 1
EVAL	Spiekeroog	locatedInWater	NorthSea	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	35.000	32.000	102.000	0.778	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater
PRED expected values: NorthSea
=> 111 concepts (111 used for prediction)
PRED predicted values (max 10 best out of 216): NorthSea (0.78 #87, 0.78 #46, 0.73 #90), PacificOcean (0.48 #191, 0.45 #286, 0.39 #334), AtlanticOcean (0.47 #1165, 0.40 #654, 0.39 #701), BalticSea (0.45 #1987, 0.41 #2033, 0.39 #1892), Donau (0.39 #1892, 0.33 #1846, 0.30 #831), Mosel (0.36 #1153, 0.36 #1014, 0.30 #691), Rhein (0.36 #1153, 0.36 #1014, 0.30 #691), Saar (0.36 #1153, 0.36 #1014, 0.30 #691), MediterraneanSea (0.27 #710, 0.21 #190, 0.19 #333), Inn (0.27 #739, 0.18 #267, 0.16 #646)
>> best conf = 0.78
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #87 for best value:
>> intensional similarity = 10
>> extensional distance = 7
>> proper extension:  Pellworm; Amrum; Sylt;
>> query: (?x1760, ?x121) <- ?x1760[ a Island; has belongsToIslands ?x1856[ a Islands; is belongsToIslands of ?x1658[ a Island; has locatedIn ?x120<D>; has locatedInWater ?x121<NorthSea>;];]; has locatedIn ?x120<D>;]
>> Best rule #46 for best value:
>> intensional similarity = 10
>> extensional distance = 7
>> proper extension:  Pellworm; Amrum; Sylt;
>> query: (?x1760, NorthSea) <- ?x1760[ a Island; has belongsToIslands ?x1856[ a Islands; is belongsToIslands of ?x1658[ a Island; has locatedIn ?x120<D>; has locatedInWater ?x121<NorthSea>;];]; has locatedIn ?x120<D>;]
ranks of expected_values: 1
EVAL	Spiekeroog	locatedInWater	NorthSea	CNN-1.+1._MA	1.000	1.000	1.000	1.000	111.000	111.000	216.000	0.778	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#352-LB
PRED entity: LB

PRED relation: locatedIn!
PRED expected values: AtlanticOcean
=> 35 concepts (30 used for prediction)
PRED predicted values (max 10 best out of 1344): AtlanticOcean (0.67 #2890, 0.60 #5738, 0.54 #8586), CaribbeanSea (0.37 #5802, 0.31 #8650, 0.29 #7226), Niger (0.33 #1677, 0.08 #9969, 0.08 #11646), Volta (0.33 #1697, 0.08 #9969, 0.07 #38456), Senegal (0.33 #1833, 0.08 #11802, 0.08 #10378), PacificOcean (0.23 #8630, 0.18 #25721, 0.18 #41391), IndianOcean (0.18 #12820, 0.18 #4275, 0.15 #17092), MediterraneanSea (0.17 #21444, 0.16 #18596, 0.16 #20020), IrishSea (0.17 #3896, 0.10 #8168, 0.08 #9969), Ireland (0.17 #2882, 0.08 #9969, 0.07 #38456)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2890 for best value:
>> intensional similarity = 6
>> extensional distance = 4
>> proper extension:  KN;
>> query: (?x621, AtlanticOcean) <- ?x621[ a Country; has encompassed ?x213; has ethnicGroup ?x162<African>; has language ?x247<English>;]
ranks of expected_values: 1
EVAL	LB	locatedIn!	AtlanticOcean	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	35.000	30.000	1344.000	0.667	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: AtlanticOcean
=> 92 concepts (92 used for prediction)
PRED predicted values (max 10 best out of 1407): AtlanticOcean (0.70 #20008, 0.67 #8595, 0.62 #44265), CaribbeanSea (0.54 #1425, 0.50 #28636, 0.50 #25780), PacificOcean (0.54 #1425, 0.48 #42883, 0.44 #50013), Amazonas (0.54 #1425, 0.29 #14311, 0.17 #21445), RioNegro (0.54 #1425, 0.29 #14902, 0.17 #22036), IrishSea (0.54 #1425, 0.25 #3898, 0.11 #32431), Ireland (0.54 #1425, 0.25 #2884, 0.11 #31417), Barbados (0.54 #1425, 0.25 #3398, 0.09 #35661), Uruguay (0.54 #1425, 0.17 #21948, 0.14 #26228), RioSanJuan (0.54 #1425, 0.17 #21641, 0.14 #25921)
>> best conf = 0.70
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #20008 for best value:
>> intensional similarity = 16
>> extensional distance = 8
>> proper extension:  WAG;
>> query: (?x621, AtlanticOcean) <- ?x621[ has ethnicGroup ?x162; has religion ?x116; is neighbor of ?x651[ has ethnicGroup ?x1685; is locatedIn of ?x580[ a River; has hasEstuary ?x2393;]; is neighbor of ?x839<RMM>; is neighbor of ?x1755<GNB>;]; is neighbor of ?x1206[ has ethnicGroup ?x2201; has government ?x2531; has wasDependentOf ?x78; is locatedIn of ?x350;];]
ranks of expected_values: 1
EVAL	LB	locatedIn!	AtlanticOcean	CNN-1.+1._MA	1.000	1.000	1.000	1.000	92.000	92.000	1407.000	0.700	http://www.semwebtech.org/mondial/10/meta#locatedIn

#351-LakeBangweulu
PRED entity: LakeBangweulu

PRED relation: locatedIn
PRED expected values: Z
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 37): USA (0.11 #72, 0.05 #308), CDN (0.08 #63, 0.03 #299), R (0.06 #5, 0.04 #241), CH (0.05 #57, 0.01 #293), ZRE (0.04 #79, 0.03 #315), EAT (0.04 #175), D (0.03 #20, 0.03 #256), I (0.03 #48, 0.02 #284), AUS (0.03 #45, 0.01 #281), CN (0.03 #56, 0.02 #292)
>> best conf = 0.11
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #72 for best value:
>> intensional similarity = 1
>> extensional distance = 142
>> proper extension:  LagunaMarChiquita; LakeSkutari; StarnbergerSee; LakeVolta; LakeHuron; MaleboPool; ChickamaugaLake; Poopo; LakeTanganjika; LakeNyos; ...
>> query: (?x2008, USA) <- ?x2008[ a Lake;]

*> Best rule #121 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 142
*> proper extension:  LagunaMarChiquita; LakeSkutari; StarnbergerSee; LakeVolta; LakeHuron; MaleboPool; ChickamaugaLake; Poopo; LakeTanganjika; LakeNyos; ...
*> query: (?x2008, Z) <- ?x2008[ a Lake;]
*> conf = 0.01
ranks of expected_values: 29
EVAL	LakeBangweulu	locatedIn	Z	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.034	2.000	2.000	37.000	0.111	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: Z
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 37): USA (0.11 #72, 0.05 #308), CDN (0.08 #63, 0.03 #299), R (0.06 #5, 0.04 #241), CH (0.05 #57, 0.01 #293), ZRE (0.04 #79, 0.03 #315), EAT (0.04 #175), D (0.03 #20, 0.03 #256), I (0.03 #48, 0.02 #284), AUS (0.03 #45, 0.01 #281), CN (0.03 #56, 0.02 #292)
>> best conf = 0.11
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #72 for best value:
>> intensional similarity = 1
>> extensional distance = 142
>> proper extension:  LagunaMarChiquita; LakeSkutari; StarnbergerSee; LakeVolta; LakeHuron; MaleboPool; ChickamaugaLake; Poopo; LakeTanganjika; LakeNyos; ...
>> query: (?x2008, USA) <- ?x2008[ a Lake;]

*> Best rule #121 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 142
*> proper extension:  LagunaMarChiquita; LakeSkutari; StarnbergerSee; LakeVolta; LakeHuron; MaleboPool; ChickamaugaLake; Poopo; LakeTanganjika; LakeNyos; ...
*> query: (?x2008, Z) <- ?x2008[ a Lake;]
*> conf = 0.01
ranks of expected_values: 29
EVAL	LakeBangweulu	locatedIn	Z	CNN-1.+1._MA	0.000	0.000	0.000	0.034	2.000	2.000	37.000	0.111	http://www.semwebtech.org/mondial/10/meta#locatedIn

#350-IR
PRED entity: IR

PRED relation: neighbor
PRED expected values: ARM
=> 41 concepts (41 used for prediction)
PRED predicted values (max 10 best out of 167): ARM (0.90 #4051, 0.88 #4052, 0.88 #4050), IR (0.46 #2026, 0.40 #205, 0.33 #515), R (0.46 #2026, 0.27 #4989, 0.25 #5457), KAZ (0.46 #2026, 0.22 #1869, 0.21 #841), UZB (0.40 #202, 0.27 #4989, 0.25 #5457), CN (0.33 #41, 0.29 #817, 0.27 #4989), IND (0.33 #133, 0.27 #4989, 0.25 #5457), SYR (0.30 #697, 0.27 #4989, 0.25 #5457), IL (0.30 #665, 0.17 #1402, 0.12 #2961), JOR (0.27 #4989, 0.25 #5457, 0.22 #586)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4051 for best value:
>> intensional similarity = 6
>> extensional distance = 95
>> proper extension:  ROK;
>> query: (?x304, ?x331) <- ?x304[ has encompassed ?x175; is locatedIn of ?x573; is neighbor of ?x302[ has government ?x254;]; is neighbor of ?x331[ has language ?x555;];]
ranks of expected_values: 1
EVAL	IR	neighbor	ARM	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	41.000	41.000	167.000	0.897	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: ARM
=> 119 concepts (119 used for prediction)
PRED predicted values (max 10 best out of 204): ARM (0.90 #16781, 0.89 #12961, 0.89 #15182), KAZ (0.62 #1727, 0.50 #3685, 0.44 #1569), R (0.62 #1727, 0.44 #4100, 0.44 #1569), IR (0.62 #1727, 0.44 #1569, 0.44 #3776), UAE (0.62 #1727, 0.44 #1569, 0.40 #2685), SA (0.62 #1727, 0.44 #1569, 0.36 #10101), KWT (0.62 #1727, 0.44 #1569, 0.36 #10101), OM (0.62 #1727, 0.44 #1569, 0.33 #3461), Q (0.62 #1727, 0.44 #1569, 0.33 #633), BRN (0.62 #1727, 0.44 #1569, 0.33 #633)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #16781 for best value:
>> intensional similarity = 15
>> extensional distance = 85
>> proper extension:  G;
>> query: (?x304, ?x331) <- ?x304[ has government ?x2318; has neighbor ?x290[ has religion ?x56; has wasDependentOf ?x903;]; has neighbor ?x302[ a Country; has ethnicGroup ?x557; has neighbor ?x466;]; has neighbor ?x381[ has government ?x2442; has language ?x1033; is locatedIn of ?x82;]; has religion ?x187; is locatedIn of ?x573; is neighbor of ?x331;]
ranks of expected_values: 1
EVAL	IR	neighbor	ARM	CNN-1.+1._MA	1.000	1.000	1.000	1.000	119.000	119.000	204.000	0.896	http://www.semwebtech.org/mondial/10/meta#neighbor

#349-Leuser
PRED entity: Leuser

PRED relation: type
PRED expected values: "volcanic"
=> 52 concepts (52 used for prediction)
PRED predicted values (max 10 best out of 9): "volcano" (0.66 #70, 0.63 #118, 0.60 #134), "volcanic" (0.33 #210, 0.29 #130, 0.24 #114), "caldera" (0.17 #257, 0.15 #482, 0.10 #775), "granite" (0.08 #46, 0.03 #94, 0.02 #174), "salt" (0.04 #392, 0.04 #472, 0.03 #732), "dam" (0.02 #466, 0.02 #500, 0.02 #402), "sand" (0.02 #469, 0.01 #405, 0.01 #389), "atoll" (0.01 #473, 0.01 #523, 0.01 #393), "lime" (0.01 #470)
>> best conf = 0.66
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #70 for best value:
>> intensional similarity = 6
>> extensional distance = 27
>> proper extension:  Fogo; Pulog; MaunaKea; Tambora; Mantalingajan; PicodelosNieves; Pico; PicoRuivo; Kanlaon; Popomanaseu; ...
>> query: (?x584, "volcano") <- ?x584[ a Mountain; a Volcano; has locatedIn ?x217; has locatedOnIsland ?x740[ a Island; has belongsToIslands ?x875;];]

*> Best rule #210 for first EXPECTED value:
*> intensional similarity = 2
*> extensional distance = 100
*> proper extension:  Cayambe; MtRedoubt; Sajama; Hotaka-Dake;
*> query: (?x584, "volcanic") <- ?x584[ a Mountain; a Volcano;]
*> conf = 0.33
ranks of expected_values: 2
EVAL	Leuser	type	"volcanic"	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	52.000	52.000	9.000	0.655	http://www.semwebtech.org/mondial/10/meta#type

PRED relation: type
PRED expected values: "volcanic"
=> 152 concepts (152 used for prediction)
PRED predicted values (max 10 best out of 11): "volcano" (0.79 #327, 0.79 #311, 0.75 #391), "volcanic" (0.38 #50, 0.37 #807, 0.36 #98), "caldera" (0.37 #807, 0.36 #790, 0.36 #789), "salt" (0.09 #944, 0.07 #1333, 0.05 #1235), "granite" (0.08 #190, 0.08 #174, 0.06 #303), "atoll" (0.06 #1058, 0.03 #1010, 0.02 #1402), "sand" (0.05 #1232, 0.04 #1330, 0.02 #1478), "dam" (0.04 #1327, 0.04 #1229, 0.03 #1558), "coral" (0.02 #1483), "lime" (0.02 #1399, 0.01 #1233)
>> best conf = 0.79
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #327 for best value:
>> intensional similarity = 10
>> extensional distance = 17
>> proper extension:  Vesuv;
>> query: (?x584, "volcano") <- ?x584[ a Mountain; a Volcano; has locatedIn ?x217[ a Country; has encompassed ?x211; has religion ?x462<Buddhist>; is neighbor of ?x376[ has ethnicGroup ?x298; has religion ?x116;];];]
>> Best rule #311 for best value:
>> intensional similarity = 11
>> extensional distance = 17
>> proper extension:  Etna;
>> query: (?x584, "volcano") <- ?x584[ a Mountain; a Volcano; has locatedIn ?x217[ has neighbor ?x376; is locatedIn of ?x384[ is flowsInto of ?x1152; is locatedInWater of ?x518;]; is locatedIn of ?x1074[ is locatedOnIsland of ?x1697;];]; has locatedOnIsland ?x740[ a Island;];]

*> Best rule #50 for first EXPECTED value:
*> intensional similarity = 11
*> extensional distance = 6
*> proper extension:  Kanlaon; Rantekombola;
*> query: (?x584, "volcanic") <- ?x584[ a Mountain; a Volcano; has locatedIn ?x217; has locatedOnIsland ?x740[ has belongsToIslands ?x875; has locatedInWater ?x384[ a Sea; has locatedIn ?x376<MAL>; has mergesWith ?x677<SuluSea>;];];]
*> conf = 0.38
ranks of expected_values: 2
EVAL	Leuser	type	"volcanic"	CNN-1.+1._MA	0.000	1.000	1.000	0.500	152.000	152.000	11.000	0.789	http://www.semwebtech.org/mondial/10/meta#type

#348-Kwa
PRED entity: Kwa

PRED relation: locatedIn
PRED expected values: ZRE
=> 37 concepts (37 used for prediction)
PRED predicted values (max 10 best out of 55): ZRE (0.71 #948, 0.71 #790, 0.71 #2853), RCB (0.54 #2140, 0.46 #1185, 0.23 #5943), ANG (0.51 #3803, 0.50 #5468, 0.50 #5467), RCA (0.20 #397, 0.14 #871, 0.12 #2062), R (0.17 #3095, 0.16 #6424, 0.13 #6662), PE (0.12 #2920, 0.09 #3870, 0.08 #3157), USA (0.11 #5063, 0.10 #6015, 0.10 #4588), CH (0.11 #3622, 0.08 #3147, 0.08 #2910), ETH (0.10 #3442, 0.06 #1539, 0.06 #1778), D (0.08 #5726, 0.07 #6439, 0.07 #6202)
>> best conf = 0.71
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #948 for best value:
>> intensional similarity = 7
>> extensional distance = 5
>> proper extension:  Lomami; Aruwimi;
>> query: (?x114, ?x348) <- ?x114[ a Source; is hasSource of ?x113[ a River; has flowsInto ?x929<Zaire>; has locatedIn ?x348<ZRE>;];]
>> Best rule #790 for best value:
>> intensional similarity = 7
>> extensional distance = 5
>> proper extension:  Lomami; Aruwimi;
>> query: (?x114, ZRE) <- ?x114[ a Source; is hasSource of ?x113[ a River; has flowsInto ?x929<Zaire>; has locatedIn ?x348<ZRE>;];]
ranks of expected_values: 1
EVAL	Kwa	locatedIn	ZRE	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	37.000	37.000	55.000	0.714	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: ZRE
=> 97 concepts (97 used for prediction)
PRED predicted values (max 10 best out of 61): ZRE (0.80 #15306, 0.78 #12910, 0.77 #16025), ANG (0.70 #2869, 0.67 #957, 0.60 #2154), RCB (0.54 #6694, 0.54 #2868, 0.54 #6931), USA (0.37 #14900, 0.34 #15140, 0.33 #12504), CDN (0.33 #8671, 0.30 #12735, 0.26 #13694), CH (0.27 #6037, 0.22 #2687, 0.21 #6989), RCA (0.25 #2552, 0.20 #879, 0.14 #1597), R (0.23 #19863, 0.19 #12198, 0.16 #20822), EAU (0.20 #240, 0.20 #152, 0.14 #1349), PE (0.17 #4373, 0.16 #6999, 0.15 #7238)
>> best conf = 0.80
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #15306 for best value:
>> intensional similarity = 13
>> extensional distance = 39
>> proper extension:  JoekulsaaFjoellum;
>> query: (?x114, ?x348) <- ?x114[ a Source; is hasSource of ?x113[ a River; has flowsInto ?x929[ has locatedIn ?x528;]; has locatedIn ?x348[ a Country; has ethnicGroup ?x2121; has religion ?x95<Protestant>; has wasDependentOf ?x543; is locatedIn of ?x182<AtlanticOcean>;];];]
ranks of expected_values: 1
EVAL	Kwa	locatedIn	ZRE	CNN-1.+1._MA	1.000	1.000	1.000	1.000	97.000	97.000	61.000	0.796	http://www.semwebtech.org/mondial/10/meta#locatedIn

#347-TheChannel
PRED entity: TheChannel

PRED relation: locatedInWater!
PRED expected values: Jersey
=> 36 concepts (23 used for prediction)
PRED predicted values (max 10 best out of 316): Greenland (0.24 #1731, 0.20 #5150, 0.20 #5149), Taiwan (0.21 #1412, 0.17 #1141, 0.14 #1953), Svalbard (0.21 #1458, 0.14 #1999, 0.12 #1728), Cuba (0.20 #5150, 0.20 #5149, 0.20 #1029), ShetlandMainland (0.20 #5150, 0.20 #5149, 0.20 #593), Streymoy (0.20 #5150, 0.20 #5149, 0.20 #781), Ireland (0.20 #5150, 0.20 #5149, 0.20 #550), Hispaniola (0.20 #5150, 0.20 #5149, 0.20 #795), St.Barthelemy (0.20 #5150, 0.20 #5149, 0.20 #789), Martinique (0.20 #5150, 0.20 #5149, 0.20 #751)
>> best conf = 0.24
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1731 for best value:
>> intensional similarity = 5
>> extensional distance = 15
>> proper extension:  LakeManicouagan;
>> query: (?x1211, Greenland) <- ?x1211[ has locatedIn ?x81[ has religion ?x95; is locatedIn of ?x182<AtlanticOcean>;]; is locatedInWater of ?x495;]

*> Best rule #1084 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 8
*> proper extension:  LabradorSea; GreenlandSea;
*> query: (?x1211, ?x165) <- ?x1211[ has locatedIn ?x78[ is locatedIn of ?x165;]; is locatedInWater of ?x495; is mergesWith of ?x182<AtlanticOcean>;]
*> conf = 0.12
ranks of expected_values: 177
EVAL	TheChannel	locatedInWater!	Jersey	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.006	36.000	23.000	316.000	0.235	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater!
PRED expected values: Jersey
=> 89 concepts (89 used for prediction)
PRED predicted values (max 10 best out of 775): ShetlandMainland (0.54 #812, 0.51 #1083, 0.50 #271), Hoy (0.54 #812, 0.51 #1083, 0.50 #271), Westray (0.54 #812, 0.51 #1083, 0.50 #271), OrkneyMainland (0.54 #812, 0.51 #1083, 0.50 #271), Ireland (0.54 #812, 0.51 #1083, 0.44 #2440), Anglesey (0.54 #812, 0.51 #1083, 0.44 #2440), Skye (0.54 #812, 0.51 #1083, 0.44 #2440), Jura (0.54 #812, 0.51 #1083, 0.44 #2440), LewisandHarris (0.54 #812, 0.51 #1083, 0.44 #2440), Barra (0.54 #812, 0.51 #1083, 0.44 #2440)
>> best conf = 0.54
=> the first rule below is the first best rule for 16 predicted values
>> Best rule #812 for best value:
>> intensional similarity = 13
>> extensional distance = 4
>> proper extension:  Rhein;
>> query: (?x1211, ?x1210) <- ?x1211[ has locatedIn ?x78<F>; has locatedIn ?x643[ has government ?x254; has language ?x247[ is language of ?x138<NAM>;];]; has locatedIn ?x678[ a Country; is locatedIn of ?x1210[ has belongsToIslands ?x2310;];]; is flowsInto of ?x1383[ has hasSource ?x1799;];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 16
EVAL	TheChannel	locatedInWater!	Jersey	CNN-1.+1._MA	0.000	0.000	0.000	0.062	89.000	89.000	775.000	0.543	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#346-Kattegat
PRED entity: Kattegat

PRED relation: locatedIn
PRED expected values: S
=> 52 concepts (44 used for prediction)
PRED predicted values (max 10 best out of 223): S (0.92 #6849, 0.88 #7321, 0.79 #3072), F (0.68 #5673, 0.22 #8745, 0.19 #1423), R (0.62 #2839, 0.29 #2365, 0.25 #4963), USA (0.48 #9045, 0.43 #9282, 0.22 #3615), D (0.45 #8758, 0.33 #20, 0.32 #3543), PL (0.33 #44, 0.27 #2834, 0.21 #4723), LT (0.33 #190, 0.27 #2834, 0.21 #4723), RI (0.28 #3359, 0.28 #3124, 0.24 #1468), N (0.27 #979, 0.24 #6612, 0.21 #8029), SF (0.24 #6612, 0.21 #8029, 0.17 #2966)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #6849 for best value:
>> intensional similarity = 6
>> extensional distance = 90
>> proper extension:  LakeSkutari; Morava; Oranje; OzeroBalchash; LopNor; Ubangi; KuybyshevReservoir; Volga; Kasai; Parana; ...
>> query: (?x1663, ?x402) <- ?x1663[ has locatedIn ?x793; is flowsInto of ?x1069[ a River; has hasEstuary ?x1070[ has locatedIn ?x402;]; has hasSource ?x2265;];]
ranks of expected_values: 1
EVAL	Kattegat	locatedIn	S	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	52.000	44.000	223.000	0.916	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: S
=> 111 concepts (108 used for prediction)
PRED predicted values (max 10 best out of 239): S (0.69 #1183, 0.65 #6400, 0.65 #7113), N (0.67 #5009, 0.57 #1892, 0.53 #6162), F (0.58 #7120, 0.51 #7357, 0.50 #480), USA (0.56 #11710, 0.54 #6472, 0.38 #1255), D (0.55 #10231, 0.53 #10469, 0.53 #10707), GB (0.50 #482, 0.38 #1428, 0.33 #9), RI (0.50 #2182, 0.28 #4554, 0.26 #5265), CN (0.47 #9556, 0.13 #18343, 0.12 #18582), R (0.43 #1660, 0.37 #14258, 0.28 #4269), PL (0.33 #281, 0.33 #237, 0.33 #236)
>> best conf = 0.69
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1183 for best value:
>> intensional similarity = 12
>> extensional distance = 11
>> proper extension:  Sobat;
>> query: (?x1663, ?x402) <- ?x1663[ is flowsInto of ?x1069[ a River; has hasEstuary ?x1070[ has locatedIn ?x402[ a Country; has ethnicGroup ?x1473; has government ?x92; has neighbor ?x565; has religion ?x95<Protestant>; is wasDependentOf of ?x170;];]; has locatedIn ?x402;];]
ranks of expected_values: 1
EVAL	Kattegat	locatedIn	S	CNN-1.+1._MA	1.000	1.000	1.000	1.000	111.000	108.000	239.000	0.692	http://www.semwebtech.org/mondial/10/meta#locatedIn

#345-Bougainville
PRED entity: Bougainville

PRED relation: locatedIn
PRED expected values: PNG
=> 34 concepts (34 used for prediction)
PRED predicted values (max 10 best out of 119): PNG (0.86 #3079, 0.85 #2132, 0.85 #1657), USA (0.35 #1255, 0.33 #545, 0.29 #782), RI (0.33 #52, 0.32 #1471, 0.27 #1947), J (0.20 #1202, 0.14 #729, 0.09 #3813), NZ (0.17 #583, 0.14 #820, 0.12 #1056), RP (0.13 #4614, 0.12 #2004, 0.09 #2714), RC (0.12 #1170, 0.05 #7594, 0.05 #7593), E (0.12 #1446, 0.12 #1684, 0.09 #2869), GB (0.10 #5224, 0.08 #5700, 0.08 #5462), P (0.09 #5176, 0.08 #1616, 0.08 #2092)
>> best conf = 0.86
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3079 for best value:
>> intensional similarity = 6
>> extensional distance = 33
>> proper extension:  Reunion; SaoTome;
>> query: (?x1403, ?x853) <- ?x1403[ a Island; is locatedOnIsland of ?x1964[ a Mountain; a Volcano; has locatedIn ?x853[ has encompassed ?x211;];];]
ranks of expected_values: 1
EVAL	Bougainville	locatedIn	PNG	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	34.000	34.000	119.000	0.857	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: PNG
=> 62 concepts (62 used for prediction)
PRED predicted values (max 10 best out of 137): PNG (0.90 #7442, 0.88 #7198, 0.86 #5257), USA (0.50 #547, 0.44 #3647, 0.41 #4126), RI (0.42 #4772, 0.42 #4587, 0.39 #4342), E (0.33 #2167, 0.16 #4804, 0.15 #5047), J (0.29 #4073, 0.25 #3594, 0.25 #971), NZ (0.25 #824, 0.17 #2013, 0.12 #3685), RP (0.22 #2487, 0.20 #2967, 0.18 #3203), NIC (0.20 #1522, 0.17 #1760, 0.11 #2236), I (0.20 #1474, 0.11 #2188, 0.05 #12353), FJI (0.20 #1218, 0.10 #2888, 0.09 #3124)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #7442 for best value:
>> intensional similarity = 13
>> extensional distance = 38
>> proper extension:  ReneLevasseurIsland;
>> query: (?x1403, ?x853) <- ?x1403[ a Island; has locatedInWater ?x282[ has locatedIn ?x564[ a Country; has language ?x51;]; has locatedIn ?x1514[ a Country; has encompassed ?x211;]; is locatedInWater of ?x1281[ has locatedIn ?x400;];]; is locatedOnIsland of ?x1964[ a Mountain; has locatedIn ?x853;];]
ranks of expected_values: 1
EVAL	Bougainville	locatedIn	PNG	CNN-1.+1._MA	1.000	1.000	1.000	1.000	62.000	62.000	137.000	0.902	http://www.semwebtech.org/mondial/10/meta#locatedIn

#344-Save
PRED entity: Save

PRED relation: hasEstuary
PRED expected values: Save
=> 59 concepts (47 used for prediction)
PRED predicted values (max 10 best out of 176): Drau (0.33 #163, 0.25 #617, 0.18 #10206), Drina (0.25 #384, 0.20 #1290, 0.20 #1064), Morava (0.25 #472, 0.18 #10206, 0.17 #1377), Waag (0.20 #729, 0.18 #10206, 0.12 #3174), Donau (0.20 #801, 0.11 #1706, 0.10 #2160), Piva (0.20 #1107, 0.11 #2013, 0.10 #2239), Tara (0.20 #1128, 0.11 #2034, 0.10 #2260), Inn (0.18 #10206, 0.17 #1452, 0.12 #3174), Isar (0.18 #10206, 0.17 #1433, 0.12 #3174), Theiss (0.18 #10206, 0.12 #3174, 0.10 #2261)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #163 for best value:
>> intensional similarity = 5
>> extensional distance = 1
>> proper extension:  Drau;
>> query: (?x152, Drau) <- ?x152[ has flowsInto ?x133<Donau>; has locatedIn ?x156<HR>; is flowsInto of ?x813;]

*> Best rule #2267 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 8
*> proper extension:  WesternMorava; Theiss; SouthernMorava;
*> query: (?x152, ?x275) <- ?x152[ a River; has flowsInto ?x133; has locatedIn ?x55[ has ethnicGroup ?x160; is locatedIn of ?x275; is locatedIn of ?x1939<Drina>;];]
*> conf = 0.05
ranks of expected_values: 29
EVAL	Save	hasEstuary	Save	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.034	59.000	47.000	176.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasEstuary

PRED relation: hasEstuary
PRED expected values: Save
=> 141 concepts (141 used for prediction)
PRED predicted values (max 10 best out of 339): Donau (0.43 #1818, 0.33 #121, 0.25 #1029), Drau (0.27 #13867, 0.27 #13866, 0.25 #844), Waag (0.27 #13867, 0.27 #13866, 0.25 #503), Theiss (0.27 #13867, 0.27 #13866, 0.17 #14322), Morava (0.27 #13867, 0.27 #13866, 0.17 #14322), Inn (0.27 #13867, 0.27 #13866, 0.10 #12498), Isar (0.27 #13867, 0.27 #13866, 0.10 #12498), March (0.27 #13867, 0.27 #13866, 0.10 #12498), Iller (0.27 #13867, 0.27 #13866, 0.10 #12498), Lech (0.27 #13867, 0.27 #13866, 0.10 #12498)
>> best conf = 0.43
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1818 for best value:
>> intensional similarity = 9
>> extensional distance = 3
>> proper extension:  Araguaia;
>> query: (?x152, ?x1556) <- ?x152[ a River; has hasSource ?x1363; is locatedInWater of ?x151[ a Island; has locatedIn ?x904; has locatedInWater ?x133[ has flowsInto ?x98; has hasEstuary ?x1556; has hasSource ?x1190;];];]

*> Best rule #2045 for first EXPECTED value:
*> intensional similarity = 14
*> extensional distance = 4
*> proper extension:  Mur;
*> query: (?x152, ?x2184) <- ?x152[ has locatedIn ?x55[ has wasDependentOf ?x1197;]; has locatedIn ?x156<HR>; has locatedIn ?x904[ has neighbor ?x106[ has government ?x435; is locatedIn of ?x104;]; has neighbor ?x176<RO>; has neighbor ?x692[ has ethnicGroup ?x223;]; is locatedIn of ?x2184[ a Estuary;];];]
*> conf = 0.10
ranks of expected_values: 22
EVAL	Save	hasEstuary	Save	CNN-1.+1._MA	0.000	0.000	0.000	0.045	141.000	141.000	339.000	0.429	http://www.semwebtech.org/mondial/10/meta#hasEstuary

#343-Saluen
PRED entity: Saluen

PRED relation: flowsInto
PRED expected values: AndamanSea
=> 33 concepts (24 used for prediction)
PRED predicted values (max 10 best out of 115): AndamanSea (0.33 #34, 0.25 #199, 0.20 #533), SouthChinaSea (0.20 #536, 0.04 #1036, 0.04 #832), AtlanticOcean (0.09 #2005, 0.08 #2171, 0.08 #2834), Donau (0.08 #2167, 0.07 #2001, 0.07 #2333), Amur (0.08 #973, 0.02 #1304, 0.02 #1636), Ob (0.08 #986, 0.02 #1317, 0.02 #1649), ArabianSea (0.08 #956, 0.02 #1287, 0.02 #1619), Ganges (0.08 #949, 0.02 #1280, 0.02 #1612), SeaofOkhotsk (0.08 #882, 0.02 #1213, 0.02 #1545), OzeroBalchash (0.08 #866, 0.02 #1197, 0.02 #1529)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #34 for best value:
>> intensional similarity = 8
>> extensional distance = 1
>> proper extension:  Irawaddy;
>> query: (?x2428, AndamanSea) <- ?x2428[ a River; has hasEstuary ?x2433[ a Estuary; has locatedIn ?x366<MYA>;]; has locatedIn ?x232<CN>; has locatedIn ?x366<MYA>;]
ranks of expected_values: 1
EVAL	Saluen	flowsInto	AndamanSea	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	33.000	24.000	115.000	0.333	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto
PRED expected values: AndamanSea
=> 100 concepts (100 used for prediction)
PRED predicted values (max 10 best out of 171): AndamanSea (0.33 #34, 0.29 #332, 0.25 #199), GulfofBengal (0.29 #332, 0.18 #6014, 0.18 #4840), SouthChinaSea (0.25 #202, 0.18 #6014, 0.18 #7680), Ganges (0.20 #615, 0.18 #7680, 0.08 #1448), ArabianSea (0.20 #622, 0.18 #7680, 0.08 #1455), AtlanticOcean (0.20 #2010, 0.12 #4352, 0.11 #1012), BlackSea (0.20 #1836, 0.05 #4677, 0.04 #5182), Donau (0.19 #4682, 0.12 #5856, 0.10 #1841), EastChinaSea (0.18 #6014, 0.18 #7680, 0.18 #4840), Amur (0.18 #7680, 0.07 #2805, 0.05 #1806)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #34 for best value:
>> intensional similarity = 8
>> extensional distance = 1
>> proper extension:  Irawaddy;
>> query: (?x2428, AndamanSea) <- ?x2428[ a River; has hasEstuary ?x2433[ a Estuary; has locatedIn ?x366<MYA>;]; has locatedIn ?x232<CN>; has locatedIn ?x366<MYA>;]
ranks of expected_values: 1
EVAL	Saluen	flowsInto	AndamanSea	CNN-1.+1._MA	1.000	1.000	1.000	1.000	100.000	100.000	171.000	0.333	http://www.semwebtech.org/mondial/10/meta#flowsInto

#342-I
PRED entity: I

PRED relation: neighbor!
PRED expected values: A
=> 43 concepts (43 used for prediction)
PRED predicted values (max 10 best out of 212): H (0.50 #200, 0.33 #356, 0.18 #824), D (0.43 #638, 0.33 #14, 0.18 #1420), CN (0.40 #979, 0.22 #2391, 0.19 #3643), A (0.33 #387, 0.33 #75, 0.25 #231), HR (0.33 #332, 0.27 #800, 0.25 #176), FL (0.33 #74, 0.17 #2973, 0.17 #386), I (0.33 #37, 0.17 #2973, 0.14 #661), SK (0.33 #333, 0.14 #1427, 0.12 #1739), R (0.32 #2195, 0.24 #3132, 0.14 #3916), THA (0.27 #943, 0.04 #3607, 0.03 #2355)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #200 for best value:
>> intensional similarity = 7
>> extensional distance = 2
>> proper extension:  HR;
>> query: (?x207, H) <- ?x207[ has encompassed ?x195; has neighbor ?x78; has religion ?x56; is locatedIn of ?x275<MediterraneanSea>; is locatedIn of ?x614<Drau>;]

*> Best rule #387 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 4
*> proper extension:  H;
*> query: (?x207, A) <- ?x207[ has encompassed ?x195; has neighbor ?x78; has religion ?x56; is locatedIn of ?x275[ is locatedInWater of ?x68;]; is locatedIn of ?x614<Drau>;]
*> conf = 0.33
ranks of expected_values: 4
EVAL	I	neighbor!	A	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.250	43.000	43.000	212.000	0.500	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: A
=> 126 concepts (126 used for prediction)
PRED predicted values (max 10 best out of 220): D (0.60 #2533, 0.50 #3487, 0.43 #3647), I (0.52 #10933, 0.50 #2239, 0.47 #1103), A (0.52 #10933, 0.33 #391, 0.28 #12689), H (0.52 #10933, 0.27 #9501, 0.27 #5735), HR (0.52 #10933, 0.27 #9501, 0.26 #13320), KOS (0.50 #3427, 0.50 #2159, 0.50 #1059), AL (0.50 #3350, 0.47 #1103, 0.41 #3473), CN (0.50 #1462, 0.38 #4311, 0.25 #1776), GR (0.50 #2113, 0.33 #3381, 0.25 #1013), SRB (0.50 #3450, 0.27 #5827, 0.27 #7575)
>> best conf = 0.60
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2533 for best value:
>> intensional similarity = 13
>> extensional distance = 3
>> proper extension:  L;
>> query: (?x207, D) <- ?x207[ has language ?x635<German>; has neighbor ?x78<F>; has neighbor ?x234[ has encompassed ?x195;]; has religion ?x56[ is religion of ?x177<BG>;]; is locatedIn of ?x983[ a River;]; is locatedIn of ?x1812[ has hasEstuary ?x1813;];]

*> Best rule #10933 for first EXPECTED value:
*> intensional similarity = 10
*> extensional distance = 23
*> proper extension:  NAM; MOC; UZB; AZ; WEST; RMM;
*> query: (?x207, ?x156) <- ?x207[ has language ?x51; has neighbor ?x78; has religion ?x56; is locatedIn of ?x614[ a River; has locatedIn ?x156;]; is locatedIn of ?x1517[ a Estuary;]; is locatedIn of ?x2067[ has type ?x150;];]
*> conf = 0.52
ranks of expected_values: 3
EVAL	I	neighbor!	A	CNN-1.+1._MA	0.000	1.000	1.000	0.333	126.000	126.000	220.000	0.600	http://www.semwebtech.org/mondial/10/meta#neighbor

#341-USA
PRED entity: USA

PRED relation: religion
PRED expected values: Muslim
=> 29 concepts (29 used for prediction)
PRED predicted values (max 10 best out of 33): Muslim (0.56 #706, 0.56 #225, 0.53 #743), Christian (0.34 #520, 0.32 #446, 0.31 #742), JehovasWitnesses (0.33 #128, 0.33 #54, 0.25 #165), ChristianOrthodox (0.33 #1, 0.18 #297, 0.18 #371), EkalesiaNiue (0.25 #157, 0.01 #601, 0.01 #638), Anglican (0.22 #236, 0.14 #680, 0.14 #495), Hindu (0.14 #562, 0.12 #414, 0.11 #229), Sikh (0.11 #251, 0.10 #288, 0.09 #325), UkrainianGreekCatholic (0.10 #293, 0.09 #330, 0.06 #404), Kimbanguist (0.08 #364, 0.04 #438, 0.03 #475)
>> best conf = 0.56
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #706 for best value:
>> intensional similarity = 3
>> extensional distance = 106
>> proper extension:  ER;
>> query: (?x315, Muslim) <- ?x315[ has neighbor ?x482; is locatedIn of ?x263[ is mergesWith of ?x248;];]
ranks of expected_values: 1
EVAL	USA	religion	Muslim	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	29.000	29.000	33.000	0.565	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion
PRED expected values: Muslim
=> 77 concepts (77 used for prediction)
PRED predicted values (max 10 best out of 35): Muslim (0.61 #2118, 0.61 #2455, 0.56 #854), Anglican (0.40 #1087, 0.39 #976, 0.33 #51), JehovasWitnesses (0.40 #387, 0.33 #165, 0.17 #1164), ChristianOrthodox (0.33 #186, 0.26 #1000, 0.25 #852), Christian (0.31 #2117, 0.27 #1892, 0.26 #2676), Hindu (0.25 #229, 0.22 #1228, 0.20 #377), Sikh (0.25 #251, 0.17 #2264, 0.16 #2827), Methodist (0.17 #2264, 0.16 #2827, 0.16 #2826), Baptist (0.17 #2264, 0.16 #2827, 0.16 #2826), ChurchofGod (0.17 #2264, 0.16 #2827, 0.16 #2826)
>> best conf = 0.61
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2118 for best value:
>> intensional similarity = 6
>> extensional distance = 87
>> proper extension:  DJI; SUD; ER; JOR;
>> query: (?x315, Muslim) <- ?x315[ has ethnicGroup ?x79; has neighbor ?x482; is locatedIn of ?x182[ has locatedIn ?x202[ has encompassed ?x521;]; is mergesWith of ?x60;];]
ranks of expected_values: 1
EVAL	USA	religion	Muslim	CNN-1.+1._MA	1.000	1.000	1.000	1.000	77.000	77.000	35.000	0.607	http://www.semwebtech.org/mondial/10/meta#religion

#340-Huascaran
PRED entity: Huascaran

PRED relation: locatedIn
PRED expected values: PE
=> 10 concepts (10 used for prediction)
PRED predicted values (max 10 best out of 47): PE (0.45 #947, 0.38 #710, 0.38 #540), MEX (0.18 #352, 0.12 #589, 0.03 #826), RA (0.18 #323, 0.12 #560, 0.03 #797), USA (0.15 #782, 0.15 #1019, 0.11 #1255), RCH (0.14 #282, 0.09 #519, 0.02 #756), EC (0.09 #420, 0.06 #657, 0.01 #894), CO (0.09 #524, 0.05 #287, 0.02 #1234), I (0.06 #1231, 0.06 #1468, 0.06 #1940), CN (0.06 #1712, 0.06 #766, 0.06 #1003), BOL (0.06 #626, 0.05 #389, 0.02 #863)
>> best conf = 0.45
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #947 for best value:
>> intensional similarity = 6
>> extensional distance = 152
>> proper extension:  JabalKatrina; Tahat; Annapurna; Bjelucha; Schchara; GranSasso; EmiKussi; Zugspitze; Demirkazik; Elbrus; ...
>> query: (?x1286, ?x296) <- ?x1286[ a Mountain; has inMountains ?x1287[ a Mountains; is inMountains of ?x1646[ a Mountain; has locatedIn ?x296;];];]
ranks of expected_values: 1
EVAL	Huascaran	locatedIn	PE	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	10.000	10.000	47.000	0.446	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: PE
=> 10 concepts (10 used for prediction)
PRED predicted values (max 10 best out of 47): PE (0.45 #947, 0.38 #710, 0.38 #540), MEX (0.18 #352, 0.12 #589, 0.03 #826), RA (0.18 #323, 0.12 #560, 0.03 #797), USA (0.15 #782, 0.15 #1019, 0.11 #1255), RCH (0.14 #282, 0.09 #519, 0.02 #756), EC (0.09 #420, 0.06 #657, 0.01 #894), CO (0.09 #524, 0.05 #287, 0.02 #1234), I (0.06 #1231, 0.06 #1468, 0.06 #1940), CN (0.06 #1712, 0.06 #766, 0.06 #1003), BOL (0.06 #626, 0.05 #389, 0.02 #863)
>> best conf = 0.45
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #947 for best value:
>> intensional similarity = 6
>> extensional distance = 152
>> proper extension:  JabalKatrina; Tahat; Annapurna; Bjelucha; Schchara; GranSasso; EmiKussi; Zugspitze; Demirkazik; Elbrus; ...
>> query: (?x1286, ?x296) <- ?x1286[ a Mountain; has inMountains ?x1287[ a Mountains; is inMountains of ?x1646[ a Mountain; has locatedIn ?x296;];];]
ranks of expected_values: 1
EVAL	Huascaran	locatedIn	PE	CNN-1.+1._MA	1.000	1.000	1.000	1.000	10.000	10.000	47.000	0.446	http://www.semwebtech.org/mondial/10/meta#locatedIn

#339-Andorran
PRED entity: Andorran

PRED relation: ethnicGroup!
PRED expected values: AND
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 96): EAU (0.03 #132), ETH (0.03 #100), UA (0.03 #57), EAK (0.03 #99), CZ (0.02 #98), SN (0.02 #83), MYA (0.02 #70), IR (0.02 #58), SK (0.02 #25), RCA (0.02 #139)
>> best conf = 0.03
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #132 for best value:
>> intensional similarity = 1
>> extensional distance = 279
>> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
>> query: (?x2104, EAU) <- ?x2104[ a EthnicGroup;]

*> Best rule #145 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 279
*> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
*> query: (?x2104, AND) <- ?x2104[ a EthnicGroup;]
*> conf = 0.01
ranks of expected_values: 65
EVAL	Andorran	ethnicGroup!	AND	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.015	2.000	2.000	96.000	0.032	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup!
PRED expected values: AND
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 96): EAU (0.03 #132), ETH (0.03 #100), UA (0.03 #57), EAK (0.03 #99), CZ (0.02 #98), SN (0.02 #83), MYA (0.02 #70), IR (0.02 #58), SK (0.02 #25), RCA (0.02 #139)
>> best conf = 0.03
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #132 for best value:
>> intensional similarity = 1
>> extensional distance = 279
>> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
>> query: (?x2104, EAU) <- ?x2104[ a EthnicGroup;]

*> Best rule #145 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 279
*> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
*> query: (?x2104, AND) <- ?x2104[ a EthnicGroup;]
*> conf = 0.01
ranks of expected_values: 65
EVAL	Andorran	ethnicGroup!	AND	CNN-1.+1._MA	0.000	0.000	0.000	0.015	2.000	2.000	96.000	0.032	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#338-FARX
PRED entity: FARX

PRED relation: locatedIn!
PRED expected values: Streymoy
=> 33 concepts (22 used for prediction)
PRED predicted values (max 10 best out of 1233): CaribbeanSea (0.46 #2949, 0.34 #12907, 0.28 #4372), GreenlandSea (0.40 #786, 0.20 #2208, 0.15 #4267), NorthSea (0.40 #1444, 0.20 #22, 0.11 #24190), PacificOcean (0.26 #17157, 0.26 #20004, 0.25 #18580), TheChannel (0.20 #2078, 0.20 #656, 0.15 #4267), IrishSea (0.20 #2468, 0.20 #1046, 0.15 #4267), BarentsSea (0.20 #1488, 0.20 #66, 0.11 #24190), Ireland (0.20 #1456, 0.20 #34, 0.05 #7146), Skye (0.20 #2796, 0.20 #1374, 0.03 #31306), Jura (0.20 #2738, 0.20 #1316, 0.03 #31306)
>> best conf = 0.46
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2949 for best value:
>> intensional similarity = 6
>> extensional distance = 11
>> proper extension:  NLSM; BVIR; CUR; AXA; GUAD; MACX; GROX; PR; NCA; BERM; ...
>> query: (?x357, CaribbeanSea) <- ?x357[ a Country; has dependentOf ?x793; has religion ?x95<Protestant>; is locatedIn of ?x373[ has mergesWith ?x1419;];]

*> Best rule #19919 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 128
*> proper extension:  BIH; ET; R; THA; MNE; UAE; TN; RL; J; D; ...
*> query: (?x357, ?x634) <- ?x357[ has encompassed ?x195; has ethnicGroup ?x2465; has religion ?x95; is locatedIn of ?x373[ is locatedInWater of ?x634;];]
*> conf = 0.03
ranks of expected_values: 548
EVAL	FARX	locatedIn!	Streymoy	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.002	33.000	22.000	1233.000	0.462	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Streymoy
=> 55 concepts (52 used for prediction)
PRED predicted values (max 10 best out of 1298): GreenlandSea (0.43 #5058, 0.43 #4270, 0.33 #786), NorthSea (0.43 #4270, 0.40 #2869, 0.40 #1424), BarentsSea (0.43 #4270, 0.33 #66, 0.21 #42752), TheChannel (0.40 #3503, 0.25 #7783, 0.25 #2081), BalticSea (0.40 #1424, 0.09 #1425, 0.05 #11401), Kattegat (0.40 #1424, 0.09 #1425, 0.05 #11401), CaribbeanSea (0.39 #27180, 0.30 #10083, 0.30 #20053), PacificOcean (0.35 #28587, 0.32 #31437, 0.30 #32864), Svalbard (0.33 #558, 0.28 #5699, 0.14 #6257), IrishSea (0.29 #6745, 0.25 #8173, 0.25 #2471)
>> best conf = 0.43
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5058 for best value:
>> intensional similarity = 13
>> extensional distance = 5
>> proper extension:  GROX;
>> query: (?x357, GreenlandSea) <- ?x357[ a Country; has ethnicGroup ?x2465; has government ?x2553; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x373[ a Sea; has locatedIn ?x170[ has religion ?x95; is locatedIn of ?x251<BarentsSea>;]; is locatedInWater of ?x1065<Svalbard>;];]
>> Best rule #4270 for best value:
>> intensional similarity = 16
>> extensional distance = 3
>> proper extension:  F;
>> query: (?x357, ?x121) <- ?x357[ a Country; has encompassed ?x195<Europe>; has government ?x2553; has religion ?x95; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x373[ has locatedIn ?x170<N>; has locatedIn ?x973[ a Country; has ethnicGroup ?x798;]; is locatedInWater of ?x2103[ has belongsToIslands ?x2214;]; is mergesWith of ?x121;];]

*> Best rule #5699 for first EXPECTED value:
*> intensional similarity = 14
*> extensional distance = 5
*> proper extension:  GROX;
*> query: (?x357, ?x634) <- ?x357[ a Country; has ethnicGroup ?x2465; has government ?x2553; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x373[ a Sea; has locatedIn ?x170[ has religion ?x95; is locatedIn of ?x251<BarentsSea>;]; is locatedInWater of ?x634; is locatedInWater of ?x1065<Svalbard>;];]
*> conf = 0.28
ranks of expected_values: 16
EVAL	FARX	locatedIn!	Streymoy	CNN-1.+1._MA	0.000	0.000	0.000	0.062	55.000	52.000	1298.000	0.429	http://www.semwebtech.org/mondial/10/meta#locatedIn

#337-Borneo
PRED entity: Borneo

PRED relation: locatedIn
PRED expected values: RI
=> 44 concepts (33 used for prediction)
PRED predicted values (max 10 best out of 197): RI (0.75 #520, 0.54 #755, 0.42 #1225), CN (0.61 #3577, 0.35 #3811, 0.33 #56), THA (0.40 #951, 0.33 #12, 0.23 #1408), TL (0.37 #4224, 0.33 #4694, 0.23 #1408), K (0.33 #179, 0.23 #1408, 0.21 #1880), RP (0.33 #108, 0.22 #342, 0.13 #1047), SGP (0.33 #214, 0.22 #448, 0.13 #1153), RC (0.33 #222, 0.13 #1161, 0.11 #456), VN (0.33 #135, 0.13 #1074, 0.11 #369), HONX (0.33 #162, 0.11 #396, 0.07 #1101)
>> best conf = 0.75
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #520 for best value:
>> intensional similarity = 7
>> extensional distance = 10
>> proper extension:  Bangka; Timor; Labuan;
>> query: (?x375, RI) <- ?x375[ a Island; has belongsToIslands ?x875<SundaIslands>; has locatedIn ?x376; has locatedInWater ?x625[ has locatedIn ?x217; has mergesWith ?x241;];]
ranks of expected_values: 1
EVAL	Borneo	locatedIn	RI	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	44.000	33.000	197.000	0.750	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: RI
=> 159 concepts (147 used for prediction)
PRED predicted values (max 10 best out of 233): RI (0.80 #6893, 0.80 #2882, 0.75 #3826), RP (0.75 #4119, 0.71 #8130, 0.60 #3411), TL (0.71 #20083, 0.68 #17951, 0.57 #3303), USA (0.70 #7857, 0.40 #10691, 0.40 #1720), AUS (0.68 #12083, 0.51 #14920, 0.34 #20604), CN (0.66 #19194, 0.39 #25579, 0.34 #26763), I (0.63 #17527, 0.36 #24149, 0.28 #26519), CDN (0.50 #24635, 0.46 #25111, 0.38 #2657), IND (0.46 #8681, 0.33 #2543, 0.28 #11985), MYA (0.40 #5984, 0.29 #4009, 0.21 #8580)
>> best conf = 0.80
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #6893 for best value:
>> intensional similarity = 11
>> extensional distance = 18
>> proper extension:  Singapore;
>> query: (?x375, RI) <- ?x375[ a Island; has locatedIn ?x376[ a Country; has ethnicGroup ?x298; is locatedIn of ?x385<MalakkaStrait>;]; has locatedIn ?x538[ a Country; has encompassed ?x175; has wasDependentOf ?x81[ is locatedIn of ?x121;];];]
>> Best rule #2882 for best value:
>> intensional similarity = 12
>> extensional distance = 8
>> proper extension:  Java; Krakatau; Sumatra; Sulawesi; Bali; Sumbawa; Lombok;
>> query: (?x375, RI) <- ?x375[ has locatedIn ?x376[ a Country; has religion ?x116[ is religion of ?x851<MA>;]; is neighbor of ?x217[ has religion ?x95; is locatedIn of ?x241<JavaSea>;];]; has locatedInWater ?x625; is locatedOnIsland of ?x1526[ a Mountain;];]
ranks of expected_values: 1
EVAL	Borneo	locatedIn	RI	CNN-1.+1._MA	1.000	1.000	1.000	1.000	159.000	147.000	233.000	0.800	http://www.semwebtech.org/mondial/10/meta#locatedIn

#336-RioDesaguadero
PRED entity: RioDesaguadero

PRED relation: inMountains
PRED expected values: Andes
=> 43 concepts (43 used for prediction)
PRED predicted values (max 10 best out of 27): Andes (0.25 #11, 0.18 #272, 0.12 #185), CordilleraReal (0.12 #235, 0.09 #322, 0.07 #1654), Alps (0.11 #526, 0.11 #613, 0.10 #700), EastAfricanRift (0.11 #463, 0.07 #724, 0.07 #550), SnowyMountains (0.09 #108, 0.06 #456, 0.04 #717), Kurdistan (0.09 #122, 0.01 #818, 0.01 #905), TianShan (0.09 #118, 0.01 #988), Karakorum (0.09 #95), RockyMountains (0.04 #1399, 0.03 #1486, 0.02 #1573), Balkan (0.04 #1064, 0.03 #890, 0.03 #1151)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #11 for best value:
>> intensional similarity = 6
>> extensional distance = 2
>> proper extension:  RioMamore;
>> query: (?x780, Andes) <- ?x780[ a Source; has locatedIn ?x690<BOL>; is hasSource of ?x481[ a River; has flowsInto ?x274;];]
ranks of expected_values: 1
EVAL	RioDesaguadero	inMountains	Andes	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	43.000	43.000	27.000	0.250	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains
PRED expected values: Andes
=> 168 concepts (168 used for prediction)
PRED predicted values (max 10 best out of 50): Andes (0.69 #1751, 0.67 #1664, 0.60 #707), SnowyMountains (0.25 #369, 0.10 #1587, 0.10 #1500), CordilleraReal (0.24 #2959, 0.21 #4093, 0.21 #4092), Alps (0.21 #2875, 0.16 #3660, 0.14 #3921), EastAfricanRift (0.20 #1594, 0.13 #2725, 0.12 #3510), SierraParima (0.17 #863, 0.06 #2081, 0.05 #2255), Kurdistan (0.14 #905, 0.11 #1340, 0.11 #1253), TianShan (0.14 #901, 0.11 #1336, 0.10 #1423), CordilleraIberica (0.14 #2317, 0.04 #4322, 0.04 #4409), Karakorum (0.11 #1313, 0.01 #4884)
>> best conf = 0.69
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1751 for best value:
>> intensional similarity = 13
>> extensional distance = 11
>> proper extension:  Mantaro;
>> query: (?x780, Andes) <- ?x780[ a Source; has locatedIn ?x690[ has language ?x702; has wasDependentOf ?x149; is locatedIn of ?x1578[ has hasEstuary ?x1579;]; is neighbor of ?x202<RCH>; is neighbor of ?x379[ has ethnicGroup ?x197; is locatedIn of ?x182;]; is neighbor of ?x542<BR>;];]
ranks of expected_values: 1
EVAL	RioDesaguadero	inMountains	Andes	CNN-1.+1._MA	1.000	1.000	1.000	1.000	168.000	168.000	50.000	0.692	http://www.semwebtech.org/mondial/10/meta#inMountains

#335-A
PRED entity: A

PRED relation: religion
PRED expected values: RomanCatholic
=> 38 concepts (38 used for prediction)
PRED predicted values (max 10 best out of 29): RomanCatholic (0.84 #417, 0.70 #212, 0.67 #130), Muslim (0.64 #373, 0.63 #291, 0.62 #332), ChristianOrthodox (0.53 #1067, 0.50 #124, 0.40 #83), Christian (0.31 #864, 0.29 #1111, 0.28 #1193), Jewish (0.14 #412, 0.14 #166, 0.13 #617), Buddhist (0.12 #216, 0.11 #872, 0.11 #421), JehovasWitnesses (0.11 #430, 0.08 #184, 0.08 #717), Anglican (0.11 #427, 0.08 #1248, 0.08 #960), Hindu (0.09 #1240, 0.09 #1404, 0.08 #1445), Sikh (0.05 #197, 0.05 #238, 0.03 #525)
>> best conf = 0.84
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #417 for best value:
>> intensional similarity = 4
>> extensional distance = 54
>> proper extension:  NLSM; CUR;
>> query: (?x424, RomanCatholic) <- ?x424[ has language ?x511; has religion ?x95<Protestant>; is locatedIn of ?x133;]
ranks of expected_values: 1
EVAL	A	religion	RomanCatholic	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	38.000	38.000	29.000	0.839	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion
PRED expected values: RomanCatholic
=> 72 concepts (72 used for prediction)
PRED predicted values (max 10 best out of 40): RomanCatholic (0.84 #1175, 0.81 #839, 0.79 #2175), ChristianOrthodox (0.70 #584, 0.65 #832, 0.64 #417), Muslim (0.67 #462, 0.65 #832, 0.64 #417), Jewish (0.65 #832, 0.61 #1293, 0.51 #708), UkrainianGreekCatholic (0.65 #832, 0.61 #1293, 0.51 #708), Buddhist (0.61 #1293, 0.21 #2919, 0.19 #677), Hindu (0.61 #1293, 0.21 #2919, 0.17 #2499), Christian (0.32 #628, 0.29 #2712, 0.29 #2627), Anglican (0.17 #2499, 0.12 #2185, 0.10 #1935), Sikh (0.17 #2499, 0.05 #699, 0.05 #992)
>> best conf = 0.84
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1175 for best value:
>> intensional similarity = 18
>> extensional distance = 48
>> proper extension:  AND;
>> query: (?x424, RomanCatholic) <- ?x424[ has encompassed ?x195; has ethnicGroup ?x1966[ a EthnicGroup;]; has language ?x511; has religion ?x95[ is religion of ?x156<HR>; is religion of ?x170<N>; is religion of ?x236<H>; is religion of ?x390<SLB>; is religion of ?x404<PY>; is religion of ?x546<RWA>;]; is neighbor of ?x120;]
ranks of expected_values: 1
EVAL	A	religion	RomanCatholic	CNN-1.+1._MA	1.000	1.000	1.000	1.000	72.000	72.000	40.000	0.840	http://www.semwebtech.org/mondial/10/meta#religion

#334-Suchona
PRED entity: Suchona

PRED relation: hasEstuary!
PRED expected values: Suchona
=> 26 concepts (21 used for prediction)
PRED predicted values (max 10 best out of 154): Amur (0.04 #181, 0.04 #682, 0.04 #407), Jenissej (0.04 #81, 0.04 #682, 0.04 #307), Newa (0.04 #57, 0.04 #682, 0.04 #283), Swir (0.04 #17, 0.04 #682, 0.04 #243), Katun (0.04 #217, 0.04 #682, 0.04 #443), Schilka (0.04 #214, 0.04 #682, 0.04 #440), Kama (0.04 #199, 0.04 #682, 0.04 #425), Irtysch (0.04 #194, 0.04 #682, 0.04 #420), Oka (0.04 #179, 0.04 #682, 0.04 #405), Kolyma (0.04 #163, 0.04 #682, 0.04 #389)
>> best conf = 0.04
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #181 for best value:
>> intensional similarity = 3
>> extensional distance = 21
>> proper extension:  Lena; Swir; Kama; Paatsjoki; Chatanga; Vuoksi; Irtysch; Jenissej; Argun; Don; ...
>> query: (?x720, Amur) <- ?x720[ a Estuary; has locatedIn ?x73<R>;]

*> Best rule #682 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 43
*> proper extension:  SaintLawrenceRiver; Manicouagan; MackenzieRiver; RiviereRichelieu; SaskatchewanRiver; NelsonRiver;
*> query: (?x720, ?x800) <- ?x720[ a Estuary; has locatedIn ?x73[ has ethnicGroup ?x58; is locatedIn of ?x263<ArcticOcean>; is locatedIn of ?x800[ a River;];];]
*> conf = 0.04
ranks of expected_values: 28
EVAL	Suchona	hasEstuary!	Suchona	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.036	26.000	21.000	154.000	0.043	http://www.semwebtech.org/mondial/10/meta#hasEstuary

PRED relation: hasEstuary!
PRED expected values: Suchona
=> 78 concepts (77 used for prediction)
PRED predicted values (max 10 best out of 37): Irtysch (0.04 #194, 0.04 #687, 0.04 #453), Katun (0.04 #217, 0.04 #687, 0.04 #443), Schilka (0.04 #214, 0.04 #687, 0.04 #440), Kama (0.04 #199, 0.04 #687, 0.04 #425), Amur (0.04 #181, 0.04 #687, 0.04 #407), Oka (0.04 #179, 0.04 #687, 0.04 #405), Kolyma (0.04 #163, 0.04 #687, 0.04 #389), Don (0.04 #147, 0.04 #687, 0.04 #373), Lena (0.04 #107, 0.04 #687, 0.04 #333), Chatanga (0.04 #97, 0.04 #687, 0.04 #323)
>> best conf = 0.04
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #194 for best value:
>> intensional similarity = 3
>> extensional distance = 21
>> proper extension:  Lena; Swir; Kama; Paatsjoki; Chatanga; Vuoksi; Irtysch; Jenissej; Argun; Don; ...
>> query: (?x720, Irtysch) <- ?x720[ a Estuary; has locatedIn ?x73<R>;]

*> Best rule #685 for first EXPECTED value:
*> intensional similarity = 12
*> extensional distance = 26
*> proper extension:  Hwangho; Tarim-Yarkend; Jangtse;
*> query: (?x720, ?x1396) <- ?x720[ a Estuary; has locatedIn ?x73[ has ethnicGroup ?x58; has neighbor ?x170; is locatedIn of ?x225[ a Mountain;]; is locatedIn of ?x679[ has hasEstuary ?x939;]; is locatedIn of ?x1396[ a River;]; is locatedIn of ?x1748<Irtysch>;];]
*> conf = 0.03
ranks of expected_values: 31
EVAL	Suchona	hasEstuary!	Suchona	CNN-1.+1._MA	0.000	0.000	0.000	0.032	78.000	77.000	37.000	0.043	http://www.semwebtech.org/mondial/10/meta#hasEstuary

#333-CordilleraCantabrica
PRED entity: CordilleraCantabrica

PRED relation: inMountains!
PRED expected values: Ebro
=> 33 concepts (33 used for prediction)
PRED predicted values (max 10 best out of 279): Vignemale (0.14 #251, 0.12 #768, 0.12 #509), Guadalquivir (0.14 #238, 0.12 #755, 0.12 #496), Douro (0.14 #231, 0.12 #748, 0.12 #489), Mulhacen (0.14 #201, 0.12 #718, 0.12 #459), RoquedelosMuchachos (0.14 #179, 0.12 #696, 0.12 #437), PicodeAlmanzor (0.14 #178, 0.12 #695, 0.12 #436), Moncayo (0.14 #173, 0.12 #690, 0.12 #431), Guadiana (0.14 #172, 0.12 #689, 0.12 #430), Tajo (0.14 #169, 0.12 #686, 0.12 #427), PicodeAneto (0.14 #123, 0.12 #640, 0.12 #381)
>> best conf = 0.14
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #251 for best value:
>> intensional similarity = 5
>> extensional distance = 5
>> proper extension:  CordilleraBetica; CordilleraIberica; CanaryIslands; Pyrenees; CordilleraCentral;
>> query: (?x2280, Vignemale) <- ?x2280[ a Mountains; is inMountains of ?x500[ a Mountain; has locatedIn ?x149<E>;];]

*> Best rule #5438 for first EXPECTED value:
*> intensional similarity = 10
*> extensional distance = 33
*> proper extension:  Changbai;
*> query: (?x2280, ?x68) <- ?x2280[ a Mountains; is inMountains of ?x500[ a Mountain; has locatedIn ?x149[ a Country; has encompassed ?x195; has government ?x1657; has language ?x790; is locatedIn of ?x68; is neighbor of ?x78;];];]
*> conf = 0.11
ranks of expected_values: 47
EVAL	CordilleraCantabrica	inMountains!	Ebro	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.021	33.000	33.000	279.000	0.143	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains!
PRED expected values: Ebro
=> 45 concepts (45 used for prediction)
PRED predicted values (max 10 best out of 279): Vignemale (0.14 #251, 0.12 #1804, 0.12 #1545), Guadalquivir (0.14 #238, 0.12 #1791, 0.12 #1532), Douro (0.14 #231, 0.12 #1784, 0.12 #1525), Mulhacen (0.14 #201, 0.12 #1754, 0.12 #1495), RoquedelosMuchachos (0.14 #179, 0.12 #1732, 0.12 #1473), PicodeAlmanzor (0.14 #178, 0.12 #1731, 0.12 #1472), Moncayo (0.14 #173, 0.12 #1726, 0.12 #1467), Guadiana (0.14 #172, 0.12 #1725, 0.12 #1466), Tajo (0.14 #169, 0.12 #1722, 0.12 #1463), PicodeAneto (0.14 #123, 0.12 #1676, 0.12 #1417)
>> best conf = 0.14
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #251 for best value:
>> intensional similarity = 5
>> extensional distance = 5
>> proper extension:  CordilleraBetica; CordilleraIberica; CanaryIslands; Pyrenees; CordilleraCentral;
>> query: (?x2280, Vignemale) <- ?x2280[ a Mountains; is inMountains of ?x500[ a Mountain; has locatedIn ?x149<E>;];]

*> Best rule #6735 for first EXPECTED value:
*> intensional similarity = 16
*> extensional distance = 11
*> proper extension:  CordilleradeTalamanca; SierraMadre; CordilleraReal; CordilleraVolcanica;
*> query: (?x2280, ?x68) <- ?x2280[ a Mountains; is inMountains of ?x500[ a Mountain; has locatedIn ?x149[ a Country; has encompassed ?x195; has ethnicGroup ?x2540; has government ?x1657; has language ?x796<Spanish>; has neighbor ?x1027[ a Country;]; has religion ?x352<RomanCatholic>; is locatedIn of ?x68; is neighbor of ?x1027;];];]
*> conf = 0.12
ranks of expected_values: 34
EVAL	CordilleraCantabrica	inMountains!	Ebro	CNN-1.+1._MA	0.000	0.000	0.000	0.029	45.000	45.000	279.000	0.143	http://www.semwebtech.org/mondial/10/meta#inMountains

#332-CDN
PRED entity: CDN

PRED relation: locatedIn!
PRED expected values: HudsonBay LabradorSea NiagaraRiver GreatBearLake MtWaddington
=> 30 concepts (26 used for prediction)
PRED predicted values (max 10 best out of 1359): NiagaraRiver (0.90 #17677, 0.89 #9516, 0.66 #23122), BeringSea (0.66 #23122, 0.65 #24482, 0.50 #3089), HudsonBay (0.66 #23122, 0.65 #24482, 0.16 #9517), ColumbiaRiver (0.60 #23121, 0.33 #2376, 0.25 #3735), YukonRiver (0.60 #23121, 0.33 #1765, 0.25 #3124), CaribbeanSea (0.38 #5538, 0.38 #17780, 0.31 #6898), Colorado (0.33 #1995, 0.27 #17678, 0.25 #3354), StraitsofMackinac (0.33 #2477, 0.27 #17678, 0.25 #3836), MerrimackRiver (0.33 #2398, 0.27 #17678, 0.25 #3757), HudsonRiver (0.33 #2072, 0.27 #17678, 0.25 #3431)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #17677 for best value:
>> intensional similarity = 4
>> extensional distance = 77
>> proper extension:  TCH; SSD; BI;
>> query: (?x272, ?x1084) <- ?x272[ is locatedIn of ?x182[ is flowsInto of ?x137;]; is locatedIn of ?x2458[ is hasEstuary of ?x1084;];]
ranks of expected_values: 1, 3, 102
EVAL	CDN	locatedIn!	MtWaddington	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	30.000	26.000	1359.000	0.902	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	CDN	locatedIn!	GreatBearLake	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	30.000	26.000	1359.000	0.902	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	CDN	locatedIn!	NiagaraRiver	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	30.000	26.000	1359.000	0.902	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	CDN	locatedIn!	LabradorSea	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.010	30.000	26.000	1359.000	0.902	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	CDN	locatedIn!	HudsonBay	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	30.000	26.000	1359.000	0.902	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: HudsonBay LabradorSea NiagaraRiver GreatBearLake MtWaddington
=> 99 concepts (99 used for prediction)
PRED predicted values (max 10 best out of 1386): HudsonBay (0.94 #65348, 0.80 #21771, 0.32 #13603), LabradorSea (0.94 #65348, 0.32 #13603, 0.30 #13604), NiagaraRiver (0.80 #21771, 0.45 #8160, 0.35 #6799), BeringSea (0.80 #21771, 0.40 #7171, 0.33 #8531), ColumbiaRiver (0.74 #10880, 0.73 #20409, 0.71 #13602), YukonRiver (0.74 #10880, 0.73 #20409, 0.71 #13602), CaribbeanSea (0.60 #16427, 0.46 #49107, 0.45 #53193), Colorado (0.45 #8160, 0.35 #6799, 0.33 #4716), MerrimackRiver (0.45 #8160, 0.35 #6799, 0.33 #5119), HudsonRiver (0.45 #8160, 0.35 #6799, 0.33 #4793)
>> best conf = 0.94
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #65348 for best value:
>> intensional similarity = 8
>> extensional distance = 34
>> proper extension:  BRN;
>> query: (?x272, ?x249) <- ?x272[ has ethnicGroup ?x273; has religion ?x95; has wasDependentOf ?x81; is locatedIn of ?x282[ has mergesWith ?x60; is locatedInWater of ?x205;]; is locatedIn of ?x1949[ has locatedInWater ?x249;];]
ranks of expected_values: 1, 2, 3
EVAL	CDN	locatedIn!	MtWaddington	CNN-1.+1._MA	0.000	0.000	0.000	0.000	99.000	99.000	1386.000	0.935	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	CDN	locatedIn!	GreatBearLake	CNN-1.+1._MA	0.000	0.000	0.000	0.000	99.000	99.000	1386.000	0.935	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	CDN	locatedIn!	NiagaraRiver	CNN-1.+1._MA	1.000	1.000	1.000	1.000	99.000	99.000	1386.000	0.935	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	CDN	locatedIn!	LabradorSea	CNN-1.+1._MA	1.000	1.000	1.000	1.000	99.000	99.000	1386.000	0.935	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	CDN	locatedIn!	HudsonBay	CNN-1.+1._MA	1.000	1.000	1.000	1.000	99.000	99.000	1386.000	0.935	http://www.semwebtech.org/mondial/10/meta#locatedIn

#331-R
PRED entity: R

PRED relation: neighbor!
PRED expected values: NOK KAZ SF
=> 30 concepts (30 used for prediction)
PRED predicted values (max 10 best out of 170): SF (0.90 #1658, 0.89 #3467, 0.89 #2865), KAZ (0.90 #1658, 0.89 #3467, 0.89 #2865), R (0.60 #304, 0.33 #3, 0.29 #605), UZB (0.44 #797, 0.26 #2866, 0.08 #1809), RO (0.29 #628, 0.27 #2713, 0.26 #2866), ARM (0.29 #654, 0.27 #2713, 0.26 #2866), TR (0.29 #632, 0.27 #2713, 0.26 #2866), H (0.29 #644, 0.27 #2713, 0.26 #2866), BG (0.29 #629, 0.08 #1809, 0.07 #3919), SRB (0.29 #732, 0.06 #1788, 0.06 #1637)
>> best conf = 0.90
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #1658 for best value:
>> intensional similarity = 5
>> extensional distance = 99
>> proper extension:  F; NAM; TCH; I; SSD; YV; BI; RCB; MW; P;
>> query: (?x73, ?x170) <- ?x73[ has neighbor ?x170; is locatedIn of ?x631[ a River;]; is locatedIn of ?x800[ has flowsInto ?x801;];]
ranks of expected_values: 1, 2, 29
EVAL	R	neighbor!	SF	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	30.000	30.000	170.000	0.899	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	R	neighbor!	KAZ	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	30.000	30.000	170.000	0.899	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	R	neighbor!	NOK	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.037	30.000	30.000	170.000	0.899	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: NOK KAZ SF
=> 99 concepts (99 used for prediction)
PRED predicted values (max 10 best out of 229): SF (0.93 #4920, 0.93 #6311, 0.93 #4921), KAZ (0.93 #4920, 0.93 #6311, 0.93 #4921), R (0.56 #1692, 0.50 #4158, 0.50 #1691), RO (0.56 #1692, 0.50 #1691, 0.45 #2305), TM (0.56 #1692, 0.50 #1691, 0.45 #2305), MEX (0.56 #1692, 0.50 #1691, 0.45 #2305), TR (0.56 #1692, 0.50 #1691, 0.45 #2305), BG (0.56 #1692, 0.50 #1691, 0.45 #2305), EC (0.56 #1692, 0.50 #1691, 0.45 #2305), PE (0.56 #1692, 0.50 #1691, 0.45 #2305)
>> best conf = 0.93
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #4920 for best value:
>> intensional similarity = 14
>> extensional distance = 11
>> proper extension:  EAK;
>> query: (?x73, ?x170) <- ?x73[ has ethnicGroup ?x58; has neighbor ?x170; has neighbor ?x565[ a Country; is locatedIn of ?x660;]; is locatedIn of ?x282[ has locatedIn ?x196<AUS>; has locatedIn ?x217<RI>; is locatedInWater of ?x205;]; is locatedIn of ?x2107[ a Mountain; has inMountains ?x2187;];]
ranks of expected_values: 1, 2, 21
EVAL	R	neighbor!	SF	CNN-1.+1._MA	1.000	1.000	1.000	1.000	99.000	99.000	229.000	0.930	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	R	neighbor!	KAZ	CNN-1.+1._MA	1.000	1.000	1.000	1.000	99.000	99.000	229.000	0.930	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	R	neighbor!	NOK	CNN-1.+1._MA	0.000	0.000	0.000	0.053	99.000	99.000	229.000	0.930	http://www.semwebtech.org/mondial/10/meta#neighbor

#330-IonicIslands
PRED entity: IonicIslands

PRED relation: belongsToIslands!
PRED expected values: Kefallinia
=> 22 concepts (22 used for prediction)
PRED predicted values (max 10 best out of 211): Chios (0.25 #170, 0.21 #393, 0.20 #366), Kos (0.25 #153, 0.21 #393, 0.20 #349), Mykonos (0.25 #127, 0.21 #393, 0.20 #323), Syros (0.25 #108, 0.21 #393, 0.20 #304), Ikaria (0.25 #81, 0.21 #393, 0.20 #277), Rhodos (0.25 #27, 0.21 #393, 0.20 #223), Samos (0.25 #122, 0.21 #393, 0.20 #318), Lesbos (0.21 #393, 0.19 #3352, 0.18 #3946), Athos (0.21 #393, 0.19 #3352, 0.18 #3946), Psiloritis (0.21 #393, 0.19 #3352, 0.18 #3946)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #170 for best value:
>> intensional similarity = 7
>> extensional distance = 2
>> proper extension:  Sporades; Kyklades;
>> query: (?x978, Chios) <- ?x978[ a Islands; is belongsToIslands of ?x398[ a Island; has locatedIn ?x399<GR>; has locatedInWater ?x275<MediterraneanSea>;];]

*> Best rule #2166 for first EXPECTED value:
*> intensional similarity = 10
*> extensional distance = 23
*> proper extension:  Philipines;
*> query: (?x978, ?x68) <- ?x978[ a Islands; is belongsToIslands of ?x977[ has locatedIn ?x399[ has encompassed ?x195; has religion ?x187<Muslim>;]; has locatedInWater ?x275[ has locatedIn ?x55; has mergesWith ?x182; is locatedInWater of ?x68;];];]
*> conf = 0.11
ranks of expected_values: 28
EVAL	IonicIslands	belongsToIslands!	Kefallinia	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.036	22.000	22.000	211.000	0.250	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

PRED relation: belongsToIslands!
PRED expected values: Kefallinia
=> 37 concepts (37 used for prediction)
PRED predicted values (max 10 best out of 211): Chios (0.25 #170, 0.21 #787, 0.21 #2363), Kos (0.25 #153, 0.21 #787, 0.21 #2363), Mykonos (0.25 #127, 0.21 #787, 0.21 #2363), Syros (0.25 #108, 0.21 #787, 0.21 #2363), Ikaria (0.25 #81, 0.21 #787, 0.21 #2363), Rhodos (0.25 #27, 0.21 #787, 0.21 #2363), Samos (0.25 #122, 0.21 #787, 0.21 #2363), Lesbos (0.21 #787, 0.21 #2363, 0.21 #984), Athos (0.21 #787, 0.21 #2363, 0.21 #984), Psiloritis (0.21 #787, 0.21 #2363, 0.21 #984)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #170 for best value:
>> intensional similarity = 7
>> extensional distance = 2
>> proper extension:  Sporades; Kyklades;
>> query: (?x978, Chios) <- ?x978[ a Islands; is belongsToIslands of ?x398[ a Island; has locatedIn ?x399<GR>; has locatedInWater ?x275<MediterraneanSea>;];]

*> Best rule #4530 for first EXPECTED value:
*> intensional similarity = 15
*> extensional distance = 19
*> proper extension:  FaroeIslands;
*> query: (?x978, ?x68) <- ?x978[ a Islands; is belongsToIslands of ?x398[ a Island; has locatedInWater ?x275[ a Sea; has locatedIn ?x78<F>; has locatedIn ?x149<E>; has locatedIn ?x851<MA>; has mergesWith ?x182; is flowsInto of ?x698; is locatedInWater of ?x68; is mergesWith of ?x182;];];]
*> conf = 0.06
ranks of expected_values: 152
EVAL	IonicIslands	belongsToIslands!	Kefallinia	CNN-1.+1._MA	0.000	0.000	0.000	0.007	37.000	37.000	211.000	0.250	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

#329-Parana
PRED entity: Parana

PRED relation: locatedIn
PRED expected values: BR
=> 43 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 223): BR (0.71 #3518, 0.70 #3282, 0.68 #1295), USA (0.57 #5706, 0.50 #235, 0.45 #1641), RCH (0.50 #235, 0.44 #983, 0.23 #749), ROU (0.50 #235, 0.23 #787, 0.17 #1021), CDN (0.50 #235, 0.20 #6639, 0.17 #1469), F (0.50 #235, 0.19 #6575, 0.18 #5641), GUY (0.50 #235, 0.19 #6575, 0.18 #3281), YV (0.50 #235, 0.19 #6575, 0.18 #3281), ZRE (0.50 #235, 0.19 #6575, 0.13 #6576), E (0.50 #235, 0.19 #6575, 0.13 #6576)
>> best conf = 0.71
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3518 for best value:
>> intensional similarity = 4
>> extensional distance = 120
>> proper extension:  HudsonBay;
>> query: (?x513, ?x404) <- ?x513[ is flowsInto of ?x512[ has locatedIn ?x404[ a Country; has language ?x796;];];]
ranks of expected_values: 1
EVAL	Parana	locatedIn	BR	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	43.000	36.000	223.000	0.708	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: BR
=> 145 concepts (137 used for prediction)
PRED predicted values (max 10 best out of 229): PE (0.89 #3841, 0.73 #1007, 0.50 #1952), BR (0.73 #10152, 0.72 #10151, 0.68 #2594), USA (0.67 #542, 0.55 #18032, 0.52 #6913), CDN (0.50 #236, 0.50 #235, 0.43 #7077), ROU (0.50 #236, 0.50 #235, 0.31 #24108), ZRE (0.50 #236, 0.50 #235, 0.31 #17245), E (0.50 #236, 0.50 #235, 0.31 #17245), F (0.50 #236, 0.50 #235, 0.31 #17245), YV (0.50 #236, 0.50 #235, 0.31 #17245), SN (0.50 #236, 0.50 #235, 0.31 #17245)
>> best conf = 0.89
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3841 for best value:
>> intensional similarity = 11
>> extensional distance = 36
>> proper extension:  Ene; PacificOcean; Apurimac; Tambo; Mantaro; Ampato; Coropuna; Ausangate; Tambo; Perene; ...
>> query: (?x513, PE) <- ?x513[ has locatedIn ?x379[ has wasDependentOf ?x149;]; has locatedIn ?x404[ a Country; has ethnicGroup ?x676<Mestizo>; has language ?x796; has neighbor ?x690<BOL>; is locatedIn of ?x512[ has hasSource ?x938;];];]

*> Best rule #10152 for first EXPECTED value:
*> intensional similarity = 10
*> extensional distance = 75
*> proper extension:  Würm;
*> query: (?x513, ?x379) <- ?x513[ a River; has flowsInto ?x182; has hasEstuary ?x1150[ a Estuary;]; has hasSource ?x1625[ a Source;]; is flowsInto of ?x512[ has locatedIn ?x379; has locatedIn ?x542[ has neighbor ?x179;];];]
*> conf = 0.73
ranks of expected_values: 2
EVAL	Parana	locatedIn	BR	CNN-1.+1._MA	0.000	1.000	1.000	0.500	145.000	137.000	229.000	0.895	http://www.semwebtech.org/mondial/10/meta#locatedIn

#328-Benue
PRED entity: Benue

PRED relation: hasEstuary
PRED expected values: Benue
=> 41 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 47): Schari (0.25 #331, 0.20 #783, 0.08 #1010), Sanaga (0.07 #3853, 0.02 #906, 0.01 #3854), Niger (0.07 #3853, 0.02 #906, 0.01 #3854), Benue (0.07 #3853, 0.02 #906, 0.01 #3854), Bomu (0.05 #1181), Ubangi (0.05 #1157), Bani (0.04 #1430, 0.02 #1656), Amazonas (0.04 #1583), Loire (0.04 #1576), Garonne (0.04 #1567)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #331 for best value:
>> intensional similarity = 7
>> extensional distance = 2
>> proper extension:  Schari; Sanga;
>> query: (?x1858, Schari) <- ?x1858[ a River; has flowsInto ?x580; has locatedIn ?x139[ a Country; has neighbor ?x169;]; has locatedIn ?x536<CAM>;]

*> Best rule #3853 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 209
*> proper extension:  Bahrel-Ghasal; Bahrel-Djebel-Albert-Nil;
*> query: (?x1858, ?x1376) <- ?x1858[ a River; has flowsInto ?x580; has locatedIn ?x536[ a Country; is locatedIn of ?x1376[ a Estuary;];];]
*> conf = 0.07
ranks of expected_values: 4
EVAL	Benue	hasEstuary	Benue	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.250	41.000	31.000	47.000	0.250	http://www.semwebtech.org/mondial/10/meta#hasEstuary

PRED relation: hasEstuary
PRED expected values: Benue
=> 115 concepts (115 used for prediction)
PRED predicted values (max 10 best out of 226): Schari (0.33 #785, 0.17 #2723, 0.17 #2146), Niger (0.33 #437, 0.09 #227, 0.07 #13878), Bani (0.33 #70, 0.06 #3475, 0.05 #4841), Ubangi (0.25 #1385, 0.20 #1838, 0.06 #4112), Bomu (0.20 #1862, 0.05 #4363, 0.04 #5046), Sanaga (0.17 #2723, 0.09 #227, 0.05 #454), WhiteNile (0.12 #2424, 0.10 #2651, 0.08 #2879), Nile (0.12 #2374, 0.10 #2601, 0.08 #2829), Bahrel-Ghasal (0.12 #2286, 0.04 #5015, 0.01 #8427), Pibor (0.12 #2275, 0.01 #9328, 0.01 #9783)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #785 for best value:
>> intensional similarity = 11
>> extensional distance = 1
>> proper extension:  Schari;
>> query: (?x1858, Schari) <- ?x1858[ a River; has flowsInto ?x580[ has locatedIn ?x839[ a Country; has ethnicGroup ?x1537; is neighbor of ?x426<RN>;];]; has hasSource ?x2448[ a Source;]; has locatedIn ?x536<CAM>;]

*> Best rule #227 for first EXPECTED value:
*> intensional similarity = 11
*> extensional distance = 1
*> proper extension:  Bani;
*> query: (?x1858, ?x182) <- ?x1858[ a River; has flowsInto ?x580<Niger>; has hasSource ?x2448; has locatedIn ?x536[ a Country; has ethnicGroup ?x122; has religion ?x116; has wasDependentOf ?x485; is locatedIn of ?x182; is neighbor of ?x169;];]
*> conf = 0.09
ranks of expected_values: 14
EVAL	Benue	hasEstuary	Benue	CNN-1.+1._MA	0.000	0.000	0.000	0.071	115.000	115.000	226.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasEstuary

#327-GulfofAden
PRED entity: GulfofAden

PRED relation: locatedIn
PRED expected values: SP
=> 24 concepts (20 used for prediction)
PRED predicted values (max 10 best out of 219): SA (0.50 #237, 0.33 #397, 0.33 #236), SP (0.50 #237, 0.33 #289, 0.33 #236), ER (0.50 #237, 0.33 #236, 0.33 #139), IND (0.50 #237, 0.33 #423, 0.33 #236), SUD (0.50 #237, 0.33 #236, 0.33 #41), ET (0.50 #237, 0.33 #236, 0.33 #4), JOR (0.50 #237, 0.33 #236, 0.33 #168), IL (0.50 #237, 0.33 #236, 0.33 #59), RI (0.50 #237, 0.33 #236, 0.30 #1706), EAK (0.50 #237, 0.33 #236, 0.20 #235)
>> best conf = 0.50
=> the first rule below is the first best rule for 24 predicted values
>> Best rule #237 for best value:
>> intensional similarity = 7
>> extensional distance = 1
>> proper extension:  RedSea;
>> query: (?x2407, ?x63) <- ?x2407[ has locatedIn ?x94<DJI>; has mergesWith ?x1333; is mergesWith of ?x1552[ has locatedIn ?x63; has locatedIn ?x186[ is neighbor of ?x169;];];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 2
EVAL	GulfofAden	locatedIn	SP	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	24.000	20.000	219.000	0.500	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: SP
=> 95 concepts (88 used for prediction)
PRED predicted values (max 10 best out of 233): R (0.84 #9995, 0.35 #12843, 0.24 #4996), IND (0.76 #3042, 0.50 #1140, 0.50 #475), RI (0.72 #6468, 0.66 #6948, 0.66 #6708), SP (0.60 #2432, 0.50 #1006, 0.50 #475), CN (0.55 #9093, 0.42 #10996, 0.40 #11236), SUD (0.50 #475, 0.48 #3609, 0.33 #474), ET (0.50 #475, 0.44 #3336, 0.44 #3098), TL (0.50 #475, 0.44 #2301, 0.33 #157), ETH (0.50 #1782, 0.40 #1306, 0.38 #2733), AUS (0.50 #475, 0.38 #8131, 0.34 #8368)
>> best conf = 0.84
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #9995 for best value:
>> intensional similarity = 13
>> extensional distance = 118
>> proper extension:  Selenge; Suchona; Bjelucha; Schchara; Lena; Elbrus; Swir; Swir; NorthernDwina; KuybyshevReservoir; ...
>> query: (?x2407, R) <- ?x2407[ has locatedIn ?x668[ a Country; has encompassed ?x175[ is encompassed of ?x185<TR>;]; is locatedIn of ?x60[ a Sea; has mergesWith ?x182; is flowsInto of ?x242; is locatedInWater of ?x433<Tasmania>;]; is locatedIn of ?x637[ a Desert;];];]

*> Best rule #2432 for first EXPECTED value:
*> intensional similarity = 14
*> extensional distance = 8
*> proper extension:  Jubba; Shabelle;
*> query: (?x2407, SP) <- ?x2407[ has locatedIn ?x94[ has ethnicGroup ?x1593<Somali>; has religion ?x116; is neighbor of ?x220[ is locatedIn of ?x510;]; is neighbor of ?x629[ has ethnicGroup ?x996;];]; has locatedIn ?x668[ has government ?x435; has neighbor ?x639; has religion ?x187; is locatedIn of ?x60<IndianOcean>;];]
*> conf = 0.60
ranks of expected_values: 4
EVAL	GulfofAden	locatedIn	SP	CNN-1.+1._MA	0.000	0.000	1.000	0.250	95.000	88.000	233.000	0.842	http://www.semwebtech.org/mondial/10/meta#locatedIn

#326-SN
PRED entity: SN

PRED relation: neighbor
PRED expected values: GNB
=> 35 concepts (35 used for prediction)
PRED predicted values (max 10 best out of 209): GNB (0.89 #1261, 0.89 #1739, 0.89 #1738), WSA (0.26 #1740, 0.25 #3792, 0.11 #108), DZ (0.26 #1740, 0.25 #3792, 0.11 #99), SN (0.26 #1740, 0.25 #3792, 0.09 #232), CI (0.26 #1740, 0.25 #3792, 0.07 #3160), WAL (0.26 #1740, 0.25 #3792, 0.07 #3160), RN (0.26 #1740, 0.25 #3792, 0.06 #235), LB (0.26 #1740, 0.25 #3792, 0.06 #259), NAM (0.14 #17, 0.07 #3160, 0.07 #4902), R (0.11 #791, 0.10 #949, 0.09 #1265)
>> best conf = 0.89
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1261 for best value:
>> intensional similarity = 6
>> extensional distance = 91
>> proper extension:  SD; BHT; BZ;
>> query: (?x416, ?x1755) <- ?x416[ a Country; has encompassed ?x213; has ethnicGroup ?x122; has neighbor ?x515; has wasDependentOf ?x78; is neighbor of ?x1755;]
ranks of expected_values: 1
EVAL	SN	neighbor	GNB	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	35.000	35.000	209.000	0.893	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: GNB
=> 91 concepts (91 used for prediction)
PRED predicted values (max 10 best out of 227): GNB (0.93 #6482, 0.92 #6478, 0.92 #6479), SN (0.50 #1685, 0.50 #1206, 0.50 #882), CI (0.50 #791, 0.36 #326, 0.36 #323), WAN (0.42 #2255, 0.33 #181, 0.18 #12043), CAM (0.40 #645, 0.17 #8924, 0.16 #3873), MW (0.38 #1577, 0.15 #3840, 0.14 #2868), DZ (0.36 #326, 0.36 #323, 0.34 #6966), RN (0.36 #326, 0.36 #323, 0.34 #6966), LB (0.36 #326, 0.36 #323, 0.34 #6966), WAL (0.36 #326, 0.36 #323, 0.34 #6966)
>> best conf = 0.93
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #6482 for best value:
>> intensional similarity = 14
>> extensional distance = 36
>> proper extension:  ARM;
>> query: (?x416, ?x1051) <- ?x416[ a Country; has ethnicGroup ?x122[ a EthnicGroup;]; has government ?x435<"republic">; has religion ?x116; is neighbor of ?x651[ has ethnicGroup ?x1685; has wasDependentOf ?x78; is locatedIn of ?x182; is neighbor of ?x621;]; is neighbor of ?x1051[ has encompassed ?x213; is locatedIn of ?x952;];]
ranks of expected_values: 1
EVAL	SN	neighbor	GNB	CNN-1.+1._MA	1.000	1.000	1.000	1.000	91.000	91.000	227.000	0.927	http://www.semwebtech.org/mondial/10/meta#neighbor

#325-Spanish
PRED entity: Spanish

PRED relation: language!
PRED expected values: YV
=> 21 concepts (21 used for prediction)
PRED predicted values (max 10 best out of 201): L (0.33 #74, 0.29 #554, 0.25 #266), NAM (0.33 #12, 0.29 #492, 0.25 #204), SF (0.33 #63, 0.25 #255, 0.20 #447), NZ (0.33 #56, 0.25 #248, 0.20 #440), AUS (0.33 #23, 0.25 #215, 0.20 #407), CDN (0.33 #32, 0.25 #224, 0.20 #416), BDS (0.33 #90, 0.25 #282, 0.20 #474), AXA (0.33 #49, 0.25 #241, 0.20 #433), IRL (0.33 #13, 0.25 #205, 0.20 #397), BVIR (0.33 #5, 0.25 #197, 0.20 #389)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #74 for best value:
>> intensional similarity = 11
>> extensional distance = 1
>> proper extension:  English;
>> query: (?x796, L) <- ?x796[ is language of ?x408[ is locatedIn of ?x282;]; is language of ?x654[ has ethnicGroup ?x79; has religion ?x95;]; is language of ?x783<PA>; is language of ?x789[ has encompassed ?x195<Europe>; has ethnicGroup ?x746;];]

*> Best rule #1447 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 69
*> proper extension:  Galician;
*> query: (?x796, ?x542) <- ?x796[ is language of ?x296[ has neighbor ?x542;]; is language of ?x654[ has encompassed ?x521; has ethnicGroup ?x79; has religion ?x95;];]
*> conf = 0.18
ranks of expected_values: 36
EVAL	Spanish	language!	YV	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.028	21.000	21.000	201.000	0.333	http://www.semwebtech.org/mondial/10/meta#language

PRED relation: language!
PRED expected values: YV
=> 41 concepts (41 used for prediction)
PRED predicted values (max 10 best out of 201): L (0.60 #74, 0.33 #371, 0.33 #174), NZ (0.56 #747, 0.40 #56, 0.36 #942), SRB (0.43 #478, 0.09 #3046, 0.08 #2352), CDN (0.40 #32, 0.33 #132, 0.29 #427), GBJ (0.40 #69, 0.17 #366, 0.17 #267), B (0.33 #160, 0.23 #1044, 0.22 #654), AUS (0.33 #714, 0.20 #811, 0.20 #23), SF (0.30 #851, 0.23 #1146, 0.22 #754), NAM (0.29 #507, 0.20 #1194, 0.20 #12), A (0.29 #446, 0.14 #2418, 0.10 #2022)
>> best conf = 0.60
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #74 for best value:
>> intensional similarity = 22
>> extensional distance = 3
>> proper extension:  Portuguese;
>> query: (?x796, L) <- ?x796[ a Language; is language of ?x50[ has dependentOf ?x575;]; is language of ?x408[ has ethnicGroup ?x197<European>; has religion ?x95; is locatedIn of ?x310;]; is language of ?x1364[ a Country; is locatedIn of ?x282[ has mergesWith ?x60<IndianOcean>; is locatedInWater of ?x205;]; is locatedIn of ?x317[ is locatedInWater of ?x1117<Trinidad>; is locatedInWater of ?x1397<SaintThomas>;];]; is language of ?x1408[ has government ?x435; has neighbor ?x172;];]

*> Best rule #3060 for first EXPECTED value:
*> intensional similarity = 23
*> extensional distance = 31
*> proper extension:  Slovak; Gagauz;
*> query: (?x796, ?x78) <- ?x796[ a Language; is language of ?x149[ a Country; has neighbor ?x78; is locatedIn of ?x68;]; is language of ?x315[ a Country; has religion ?x95; has wasDependentOf ?x81; is locatedIn of ?x182; is locatedIn of ?x361[ a River;];]; is language of ?x408[ has encompassed ?x521; has religion ?x1151; is locatedIn of ?x310;]; is language of ?x1364[ a Country; is locatedIn of ?x282[ is locatedInWater of ?x205;];]; is language of ?x1408[ has encompassed ?x213; is neighbor of ?x172;];]
*> conf = 0.23
ranks of expected_values: 25
EVAL	Spanish	language!	YV	CNN-1.+1._MA	0.000	0.000	0.000	0.040	41.000	41.000	201.000	0.600	http://www.semwebtech.org/mondial/10/meta#language

#324-Hotaka-Dake
PRED entity: Hotaka-Dake

PRED relation: locatedIn
PRED expected values: J
=> 7 concepts (7 used for prediction)
PRED predicted values (max 10 best out of 67): RI (0.12 #52, 0.09 #760, 0.09 #288), USA (0.10 #72, 0.10 #1016, 0.08 #308), MEX (0.07 #116, 0.05 #352, 0.05 #588), CN (0.06 #1000, 0.02 #1472, 0.02 #1236), E (0.05 #27, 0.04 #971, 0.03 #1207), RP (0.05 #817, 0.04 #345, 0.04 #581), I (0.04 #1228, 0.04 #992, 0.03 #48), P (0.04 #1377, 0.02 #433, 0.02 #669), R (0.04 #1421, 0.03 #241, 0.03 #949), RA (0.04 #795, 0.03 #87, 0.03 #323)
>> best conf = 0.12
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #52 for best value:
>> intensional similarity = 4
>> extensional distance = 56
>> proper extension:  Cayambe; QueenMarysPeak; MtAdams; Fogo; Irazu; Karisimbi; Etna; MaunaLoa; NevadodelHuila; Ampato; ...
>> query: (?x2252, RI) <- ?x2252[ a Mountain; a Volcano; has type ?x706<"volcano">;]

*> Best rule #1199 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 261
*> proper extension:  Saipan; LagunaMarChiquita; Stromboli; SaintVincent; LakeVolta; Pico; Poopo; LakeNyos; Flores; OzeroBalchash; ...
*> query: (?x2252, J) <- ?x2252[ has type ?x706;]
*> conf = 0.02
ranks of expected_values: 35
EVAL	Hotaka-Dake	locatedIn	J	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.029	7.000	7.000	67.000	0.121	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: J
=> 7 concepts (7 used for prediction)
PRED predicted values (max 10 best out of 67): RI (0.12 #52, 0.09 #760, 0.09 #288), USA (0.10 #72, 0.10 #1016, 0.08 #308), MEX (0.07 #116, 0.05 #352, 0.05 #588), CN (0.06 #1000, 0.02 #1472, 0.02 #1236), E (0.05 #27, 0.04 #971, 0.03 #1207), RP (0.05 #817, 0.04 #345, 0.04 #581), I (0.04 #1228, 0.04 #992, 0.03 #48), P (0.04 #1377, 0.02 #433, 0.02 #669), R (0.04 #1421, 0.03 #241, 0.03 #949), RA (0.04 #795, 0.03 #87, 0.03 #323)
>> best conf = 0.12
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #52 for best value:
>> intensional similarity = 4
>> extensional distance = 56
>> proper extension:  Cayambe; QueenMarysPeak; MtAdams; Fogo; Irazu; Karisimbi; Etna; MaunaLoa; NevadodelHuila; Ampato; ...
>> query: (?x2252, RI) <- ?x2252[ a Mountain; a Volcano; has type ?x706<"volcano">;]

*> Best rule #1199 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 261
*> proper extension:  Saipan; LagunaMarChiquita; Stromboli; SaintVincent; LakeVolta; Pico; Poopo; LakeNyos; Flores; OzeroBalchash; ...
*> query: (?x2252, J) <- ?x2252[ has type ?x706;]
*> conf = 0.02
ranks of expected_values: 35
EVAL	Hotaka-Dake	locatedIn	J	CNN-1.+1._MA	0.000	0.000	0.000	0.029	7.000	7.000	67.000	0.121	http://www.semwebtech.org/mondial/10/meta#locatedIn

#323-GBZ
PRED entity: GBZ

PRED relation: locatedIn!
PRED expected values: AtlanticOcean
=> 30 concepts (22 used for prediction)
PRED predicted values (max 10 best out of 1381): AtlanticOcean (0.56 #7159, 0.50 #2888, 0.47 #14275), NorthSea (0.33 #4291, 0.33 #1445, 0.23 #9985), TheChannel (0.33 #4926, 0.33 #2080, 0.22 #7774), CaribbeanSea (0.33 #105, 0.23 #14338, 0.22 #17189), Mosel (0.33 #4617, 0.22 #7465, 0.17 #3194), Uruguay (0.33 #3399, 0.22 #7670, 0.07 #17084), Uruguay (0.33 #4115, 0.22 #8386, 0.07 #17084), NorwegianSea (0.33 #1558, 0.17 #4404, 0.11 #7252), IrishSea (0.33 #2470, 0.17 #5316, 0.11 #8164), Ireland (0.33 #1457, 0.17 #4303, 0.11 #7151)
>> best conf = 0.56
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #7159 for best value:
>> intensional similarity = 8
>> extensional distance = 7
>> proper extension:  USA;
>> query: (?x1826, AtlanticOcean) <- ?x1826[ has encompassed ?x195[ is encompassed of ?x156[ has ethnicGroup ?x160;];]; has religion ?x95<Protestant>; has religion ?x109<Jewish>; is locatedIn of ?x275;]
ranks of expected_values: 1
EVAL	GBZ	locatedIn!	AtlanticOcean	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	30.000	22.000	1381.000	0.556	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: AtlanticOcean
=> 66 concepts (66 used for prediction)
PRED predicted values (max 10 best out of 1424): AtlanticOcean (0.81 #22885, 0.64 #11422, 0.56 #30028), Drau (0.43 #10270, 0.40 #7413, 0.21 #17410), NorthSea (0.38 #15731, 0.38 #11445, 0.27 #18580), CaribbeanSea (0.33 #22948, 0.32 #30091, 0.23 #52929), Donau (0.33 #4312, 0.25 #11449, 0.25 #5737), BlackSea (0.33 #4299, 0.25 #14295, 0.25 #5724), Musala (0.33 #5407, 0.25 #6832, 0.12 #12544), IndianOcean (0.33 #1433, 0.17 #55680, 0.12 #38554), Jordan (0.33 #3018, 0.14 #27287, 0.12 #31578), DeadSea (0.33 #3102, 0.11 #8563, 0.11 #9993)
>> best conf = 0.81
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #22885 for best value:
>> intensional similarity = 17
>> extensional distance = 19
>> proper extension:  SVAX;
>> query: (?x1826, AtlanticOcean) <- ?x1826[ has dependentOf ?x81[ has religion ?x95<Protestant>; is locatedIn of ?x121;]; is locatedIn of ?x275[ a Sea; has locatedIn ?x55[ has encompassed ?x195; has ethnicGroup ?x160;]; has locatedIn ?x78<F>; has locatedIn ?x399[ has government ?x1174;]; is flowsInto of ?x698; is locatedInWater of ?x777[ a Island; has belongsToIslands ?x1302;];];]
ranks of expected_values: 1
EVAL	GBZ	locatedIn!	AtlanticOcean	CNN-1.+1._MA	1.000	1.000	1.000	1.000	66.000	66.000	1424.000	0.810	http://www.semwebtech.org/mondial/10/meta#locatedIn

#322-NEP
PRED entity: NEP

PRED relation: neighbor!
PRED expected values: IND
=> 35 concepts (35 used for prediction)
PRED predicted values (max 10 best out of 179): IND (0.91 #1935, 0.90 #2746, 0.90 #1612), UZB (0.44 #531, 0.40 #692, 0.33 #855), NEP (0.37 #1611, 0.35 #1934, 0.33 #13), PK (0.33 #6, 0.26 #4208, 0.26 #4371), MYA (0.33 #64, 0.26 #4208, 0.26 #4371), BHT (0.33 #73, 0.26 #4208, 0.26 #4371), BD (0.33 #141, 0.26 #4208, 0.26 #4371), TAD (0.26 #4208, 0.26 #4371, 0.26 #4207), KGZ (0.26 #4208, 0.26 #4371, 0.26 #4207), R (0.26 #4208, 0.26 #4371, 0.26 #4207)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1935 for best value:
>> intensional similarity = 5
>> extensional distance = 37
>> proper extension:  NOK;
>> query: (?x111, ?x924) <- ?x111[ has encompassed ?x175; has language ?x2295; has neighbor ?x924; is locatedIn of ?x1771[ a Mountain;];]
ranks of expected_values: 1
EVAL	NEP	neighbor!	IND	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	35.000	35.000	179.000	0.906	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: IND
=> 101 concepts (101 used for prediction)
PRED predicted values (max 10 best out of 207): IND (0.92 #9622, 0.91 #6116, 0.91 #7457), AFG (0.89 #1153, 0.79 #1155, 0.50 #1154), R (0.89 #1153, 0.79 #1155, 0.43 #6291), BHT (0.89 #1153, 0.79 #1155, 0.40 #162), NEP (0.89 #1153, 0.79 #1155, 0.40 #162), PK (0.89 #1153, 0.79 #1155, 0.40 #162), VN (0.89 #1153, 0.79 #1155, 0.40 #162), KGZ (0.89 #1153, 0.79 #1155, 0.40 #162), TAD (0.89 #1153, 0.79 #1155, 0.40 #162), KAZ (0.89 #1153, 0.79 #1155, 0.40 #162)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #9622 for best value:
>> intensional similarity = 17
>> extensional distance = 50
>> proper extension:  WAL;
>> query: (?x111, ?x232) <- ?x111[ has neighbor ?x232; has neighbor ?x924[ a Country; has government ?x140; has language ?x2392; has religion ?x116; is locatedIn of ?x411[ a River;]; is neighbor of ?x409[ a Country; has encompassed ?x175;];]; has religion ?x462[ is religion of ?x81<GB>; is religion of ?x538<BRU>;]; is locatedIn of ?x110;]
ranks of expected_values: 1
EVAL	NEP	neighbor!	IND	CNN-1.+1._MA	1.000	1.000	1.000	1.000	101.000	101.000	207.000	0.916	http://www.semwebtech.org/mondial/10/meta#neighbor

#321-AXA
PRED entity: AXA

PRED relation: ethnicGroup
PRED expected values: Mulatto
=> 28 concepts (28 used for prediction)
PRED predicted values (max 10 best out of 183): African (0.46 #1286, 0.39 #1542, 0.33 #518), European (0.46 #1288, 0.30 #2824, 0.30 #1544), Amerindian (0.30 #1282, 0.29 #258, 0.17 #514), Chinese (0.29 #271, 0.25 #527, 0.17 #1039), Mestizo (0.27 #1316, 0.12 #2852, 0.10 #3108), Mulatto (0.25 #570, 0.17 #1082, 0.14 #1338), Mixed (0.17 #126, 0.14 #894, 0.14 #382), EastIndian (0.17 #648, 0.11 #1160, 0.08 #1416), African-white-Indian (0.14 #319, 0.08 #575, 0.07 #831), German (0.10 #2826, 0.09 #3082, 0.06 #4874)
>> best conf = 0.46
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1286 for best value:
>> intensional similarity = 4
>> extensional distance = 35
>> proper extension:  BZ;
>> query: (?x407, African) <- ?x407[ has encompassed ?x521<America>; has ethnicGroup ?x1009; has government ?x562;]

*> Best rule #570 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 10
*> proper extension:  BVIR; C; YV; DOM; GUAD; TT; RH; MART; PR; WL;
*> query: (?x407, Mulatto) <- ?x407[ has encompassed ?x521; has government ?x562; has religion ?x95; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x317<CaribbeanSea>;]
*> conf = 0.25
ranks of expected_values: 6
EVAL	AXA	ethnicGroup	Mulatto	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.167	28.000	28.000	183.000	0.459	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: Mulatto
=> 82 concepts (82 used for prediction)
PRED predicted values (max 10 best out of 218): European (0.67 #9244, 0.64 #9501, 0.53 #10015), African (0.47 #10013, 0.45 #5903, 0.43 #12577), Mestizo (0.44 #9272, 0.43 #9529, 0.29 #4135), Mixed (0.40 #2176, 0.40 #1664, 0.33 #2688), Amerindian (0.40 #2052, 0.37 #9238, 0.36 #8469), EastIndian (0.33 #2954, 0.20 #5775, 0.18 #6033), Indian (0.28 #9750, 0.12 #7697, 0.10 #5711), Pakistani (0.28 #9750, 0.12 #7697, 0.10 #5767), NorthernIrish (0.28 #9750, 0.12 #7697, 0.10 #5870), English (0.28 #9750, 0.12 #7697, 0.10 #5867)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #9244 for best value:
>> intensional similarity = 14
>> extensional distance = 25
>> proper extension:  C; GCA; RCH; CO; PE; USA; CR; ROU; RA; PY; ...
>> query: (?x407, European) <- ?x407[ has encompassed ?x521<America>; has language ?x247[ a Language; is language of ?x81[ has ethnicGroup ?x1196; has religion ?x109; is dependentOf of ?x428; is locatedIn of ?x182<AtlanticOcean>; is wasDependentOf of ?x63;];]; has religion ?x280[ is religion of ?x279<BS>;];]

*> Best rule #5955 for first EXPECTED value:
*> intensional similarity = 17
*> extensional distance = 9
*> proper extension:  KN;
*> query: (?x407, Mulatto) <- ?x407[ a Country; has ethnicGroup ?x1009[ a EthnicGroup;]; has ethnicGroup ?x1147[ is ethnicGroup of ?x212[ a Country; has encompassed ?x213<Africa>; has government ?x562; is locatedIn of ?x283;]; is ethnicGroup of ?x1008[ has religion ?x95; is locatedIn of ?x405;];]; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x317<CaribbeanSea>;]
*> conf = 0.27
ranks of expected_values: 14
EVAL	AXA	ethnicGroup	Mulatto	CNN-1.+1._MA	0.000	0.000	0.000	0.071	82.000	82.000	218.000	0.667	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#320-Uelle
PRED entity: Uelle

PRED relation: hasSource!
PRED expected values: Uelle
=> 36 concepts (33 used for prediction)
PRED predicted values (max 10 best out of 157): Ruzizi (0.17 #121, 0.14 #2974, 0.12 #349), Luvua (0.17 #30, 0.14 #2974, 0.12 #258), Lukuga (0.17 #21, 0.14 #2974, 0.12 #249), Semliki (0.17 #61, 0.14 #2974, 0.12 #289), Bahrel-Djebel-Albert-Nil (0.14 #2974, 0.11 #651, 0.10 #880), VictoriaNile (0.14 #2974, 0.11 #538, 0.10 #767), Akagera (0.14 #2974, 0.11 #589, 0.10 #818), Chire (0.14 #2974, 0.10 #899, 0.08 #3431), Lomami (0.06 #1827, 0.04 #1371, 0.02 #3660), Aruwimi (0.06 #1800, 0.04 #1371, 0.02 #3660)
>> best conf = 0.17
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #121 for best value:
>> intensional similarity = 5
>> extensional distance = 4
>> proper extension:  Ruzizi; Semliki; Lukuga; Luvua;
>> query: (?x1189, Ruzizi) <- ?x1189[ a Source; has inMountains ?x1066<EastAfricanRift>; has locatedIn ?x348<ZRE>;]

*> Best rule #1371 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 12
*> proper extension:  Meru; Kilimanjaro;
*> query: (?x1189, ?x509) <- ?x1189[ has inMountains ?x1066<EastAfricanRift>; has locatedIn ?x348[ is locatedIn of ?x509[ a River;];];]
*> conf = 0.04
ranks of expected_values: 25
EVAL	Uelle	hasSource!	Uelle	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.040	36.000	33.000	157.000	0.167	http://www.semwebtech.org/mondial/10/meta#hasSource

PRED relation: hasSource!
PRED expected values: Uelle
=> 99 concepts (94 used for prediction)
PRED predicted values (max 10 best out of 205): Ruzizi (0.17 #121, 0.14 #349, 0.12 #807), Luvua (0.17 #30, 0.14 #258, 0.12 #716), Lukuga (0.17 #21, 0.14 #249, 0.12 #707), Semliki (0.17 #61, 0.14 #289, 0.12 #747), Akagera (0.14 #361, 0.12 #6424, 0.10 #2060), Bahrel-Djebel-Albert-Nil (0.12 #652, 0.12 #6424, 0.10 #2060), VictoriaNile (0.12 #539, 0.12 #6424, 0.10 #2060), Chire (0.12 #6424, 0.10 #2060, 0.10 #1357), Fimi (0.08 #2747, 0.08 #5964, 0.08 #6883), Zaire (0.08 #2747, 0.08 #5964, 0.08 #6883)
>> best conf = 0.17
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #121 for best value:
>> intensional similarity = 5
>> extensional distance = 4
>> proper extension:  Ruzizi; Semliki; Lukuga; Luvua;
>> query: (?x1189, Ruzizi) <- ?x1189[ a Source; has inMountains ?x1066<EastAfricanRift>; has locatedIn ?x348<ZRE>;]

*> Best rule #2747 for first EXPECTED value:
*> intensional similarity = 11
*> extensional distance = 44
*> proper extension:  Orinoco;
*> query: (?x1189, ?x1749) <- ?x1189[ a Source; has inMountains ?x1066; has locatedIn ?x348[ has neighbor ?x934; has religion ?x95; has wasDependentOf ?x543; is locatedIn of ?x358[ a Estuary;]; is locatedIn of ?x1749[ a River;]; is neighbor of ?x229;];]
*> conf = 0.08
ranks of expected_values: 25
EVAL	Uelle	hasSource!	Uelle	CNN-1.+1._MA	0.000	0.000	0.000	0.040	99.000	94.000	205.000	0.167	http://www.semwebtech.org/mondial/10/meta#hasSource

#319-German
PRED entity: German

PRED relation: language!
PRED expected values: D A
=> 24 concepts (24 used for prediction)
PRED predicted values (max 10 best out of 215): AND (0.56 #544, 0.33 #431, 0.33 #90), F (0.53 #228, 0.44 #341, 0.33 #5), D (0.53 #228, 0.44 #341, 0.32 #681), A (0.53 #228, 0.44 #341, 0.31 #1030), NLSM (0.50 #115, 0.33 #455, 0.33 #342), PK (0.36 #1151, 0.08 #2521, 0.04 #1266), NZ (0.33 #407, 0.33 #66, 0.25 #294), CDN (0.33 #377, 0.33 #36, 0.25 #264), RG (0.33 #81, 0.25 #309, 0.25 #195), WAFU (0.33 #73, 0.25 #301, 0.25 #187)
>> best conf = 0.56
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #544 for best value:
>> intensional similarity = 9
>> extensional distance = 7
>> proper extension:  Galician;
>> query: (?x635, AND) <- ?x635[ is language of ?x234[ has religion ?x56;]; is language of ?x718[ has encompassed ?x195; has ethnicGroup ?x237; has language ?x539<Portuguese>; is neighbor of ?x120[ is locatedIn of ?x70;];];]

*> Best rule #228 for first EXPECTED value:
*> intensional similarity = 10
*> extensional distance = 2
*> proper extension:  Dutch;
*> query: (?x635, ?x78) <- ?x635[ a Language; is language of ?x543<B>; is language of ?x718[ has encompassed ?x195; has religion ?x95<Protestant>; is neighbor of ?x78; is neighbor of ?x120<D>;];]
*> conf = 0.53
ranks of expected_values: 3, 4
EVAL	German	language!	A	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.333	24.000	24.000	215.000	0.556	http://www.semwebtech.org/mondial/10/meta#language
EVAL	German	language!	D	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.333	24.000	24.000	215.000	0.556	http://www.semwebtech.org/mondial/10/meta#language

PRED relation: language!
PRED expected values: D A
=> 63 concepts (63 used for prediction)
PRED predicted values (max 10 best out of 223): NZ (0.71 #1218, 0.45 #2490, 0.40 #2028), NLSM (0.62 #1618, 0.60 #462, 0.40 #810), F (0.57 #577, 0.57 #576, 0.50 #1613), A (0.57 #577, 0.57 #576, 0.50 #1613), D (0.57 #577, 0.57 #576, 0.50 #1613), V (0.57 #577, 0.50 #1613, 0.50 #1612), RSM (0.57 #577, 0.50 #1613, 0.50 #1612), AND (0.56 #1820, 0.48 #575, 0.40 #899), NL (0.50 #1613, 0.50 #1612, 0.48 #575), SLO (0.50 #1613, 0.50 #1612, 0.48 #575)
>> best conf = 0.71
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1218 for best value:
>> intensional similarity = 20
>> extensional distance = 5
>> proper extension:  Samoan;
>> query: (?x635, NZ) <- ?x635[ is language of ?x207[ has religion ?x462<Buddhist>; is locatedIn of ?x1285[ a Island;]; is locatedIn of ?x2191[ a Mountain;];]; is language of ?x234[ has ethnicGroup ?x237; has language ?x51<French>; has religion ?x95<Protestant>; is locatedIn of ?x847[ a Mountain; has inMountains ?x261;];]; is language of ?x671[ a Country;]; is language of ?x718[ has wasDependentOf ?x575;];]

*> Best rule #577 for first EXPECTED value:
*> intensional similarity = 23
*> extensional distance = 3
*> proper extension:  Spanish;
*> query: (?x635, ?x78) <- ?x635[ is language of ?x207[ is locatedIn of ?x86[ has belongsToIslands ?x87;]; is locatedIn of ?x614[ is flowsInto of ?x155;]; is wasDependentOf of ?x1165;]; is language of ?x234[ has neighbor ?x78; has neighbor ?x120[ has neighbor ?x194; is locatedIn of ?x70;]; is locatedIn of ?x847[ a Mountain;];]; is language of ?x718[ a Country; has religion ?x109<Jewish>; has religion ?x352<RomanCatholic>;]; is language of ?x793[ has religion ?x187; is locatedIn of ?x754[ a Island;];];]
*> conf = 0.57
ranks of expected_values: 4, 5
EVAL	German	language!	A	CNN-1.+1._MA	0.000	0.000	1.000	0.250	63.000	63.000	223.000	0.714	http://www.semwebtech.org/mondial/10/meta#language
EVAL	German	language!	D	CNN-1.+1._MA	0.000	0.000	1.000	0.250	63.000	63.000	223.000	0.714	http://www.semwebtech.org/mondial/10/meta#language

#318-TN
PRED entity: TN

PRED relation: locatedIn!
PRED expected values: GrandErgEst
=> 34 concepts (29 used for prediction)
PRED predicted values (max 10 best out of 1310): AtlanticOcean (0.55 #10006, 0.48 #18548, 0.41 #15700), HamadaduDraa (0.40 #5224, 0.33 #2378, 0.33 #954), Nile (0.33 #6910, 0.23 #7117, 0.16 #18506), Tanezrouft (0.33 #2782, 0.23 #1424, 0.20 #5628), ErgChech (0.33 #2487, 0.23 #1424, 0.20 #5333), ErgIgidi (0.33 #1671, 0.23 #1424, 0.20 #4517), ErgIsaouane (0.33 #2481, 0.23 #1424, 0.20 #5327), GrandErgEst (0.33 #2171, 0.23 #1424, 0.20 #5017), GrandErgOuest (0.33 #2136, 0.23 #1424, 0.20 #4982), Tahat (0.33 #1434, 0.23 #1424, 0.20 #4280)
>> best conf = 0.55
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #10006 for best value:
>> intensional similarity = 7
>> extensional distance = 29
>> proper extension:  MAYO;
>> query: (?x108, AtlanticOcean) <- ?x108[ a Country; has encompassed ?x213<Africa>; has government ?x435; is locatedIn of ?x275[ a Sea; is locatedInWater of ?x68;];]

*> Best rule #2171 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 1
*> proper extension:  DZ;
*> query: (?x108, GrandErgEst) <- ?x108[ a Country; has ethnicGroup ?x582<Arab-Berber>; has neighbor ?x1184<LAR>; has religion ?x109; has wasDependentOf ?x78; is locatedIn of ?x275;]
*> conf = 0.33
ranks of expected_values: 8
EVAL	TN	locatedIn!	GrandErgEst	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.125	34.000	29.000	1310.000	0.548	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: GrandErgEst
=> 99 concepts (99 used for prediction)
PRED predicted values (max 10 best out of 1400): AtlanticOcean (0.67 #99842, 0.57 #11442, 0.55 #54199), PacificOcean (0.53 #31431, 0.50 #35708, 0.43 #39986), HamadaduDraa (0.50 #2378, 0.40 #5229, 0.33 #954), Talak (0.40 #9466, 0.25 #16590, 0.25 #3767), CaribbeanSea (0.35 #31451, 0.30 #40006, 0.28 #85639), Tanezrouft (0.33 #1358, 0.25 #17032, 0.25 #4209), ErgChech (0.33 #1063, 0.25 #16737, 0.25 #3914), ErgIgidi (0.33 #247, 0.25 #15921, 0.25 #1671), ErgIsaouane (0.33 #1057, 0.25 #2481, 0.24 #9974), GrandErgEst (0.33 #747, 0.25 #2171, 0.24 #9974)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #99842 for best value:
>> intensional similarity = 14
>> extensional distance = 90
>> proper extension:  GBM;
>> query: (?x108, AtlanticOcean) <- ?x108[ a Country; has government ?x435; is locatedIn of ?x275[ has locatedIn ?x78[ has language ?x51; is locatedIn of ?x121<NorthSea>; is wasDependentOf of ?x94;]; has locatedIn ?x207[ has religion ?x56;]; has locatedIn ?x1826[ has encompassed ?x195;]; is locatedInWater of ?x1379[ has belongsToIslands ?x978;];];]

*> Best rule #747 for first EXPECTED value:
*> intensional similarity = 15
*> extensional distance = 1
*> proper extension:  DZ;
*> query: (?x108, GrandErgEst) <- ?x108[ a Country; has encompassed ?x213<Africa>; has ethnicGroup ?x197<European>; has ethnicGroup ?x582<Arab-Berber>; has government ?x435<"republic">; has neighbor ?x581[ has neighbor ?x646; is locatedIn of ?x84;]; has neighbor ?x1184<LAR>; is locatedIn of ?x275;]
*> conf = 0.33
ranks of expected_values: 10
EVAL	TN	locatedIn!	GrandErgEst	CNN-1.+1._MA	0.000	0.000	1.000	0.100	99.000	99.000	1400.000	0.674	http://www.semwebtech.org/mondial/10/meta#locatedIn

#317-Sanga
PRED entity: Sanga

PRED relation: hasEstuary
PRED expected values: Sanga
=> 34 concepts (24 used for prediction)
PRED predicted values (max 10 best out of 27): Schari (0.33 #104, 0.11 #331, 0.08 #558), Bomu (0.11 #275, 0.08 #502, 0.04 #682), Ubangi (0.11 #251, 0.08 #478, 0.04 #227), Bahrel-Djebel-Albert-Nil (0.08 #632, 0.03 #860), WhiteNile (0.08 #609, 0.03 #837), Nile (0.08 #559, 0.03 #787), Bahrel-Ghasal (0.08 #471, 0.03 #699), Pibor (0.08 #460, 0.03 #688), Sanga (0.04 #682, 0.04 #227, 0.03 #681), Sanga (0.04 #682, 0.04 #227, 0.03 #681)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #104 for best value:
>> intensional similarity = 7
>> extensional distance = 1
>> proper extension:  Schari;
>> query: (?x2087, Schari) <- ?x2087[ has flowsInto ?x929; has locatedIn ?x528[ has neighbor ?x172;]; has locatedIn ?x536<CAM>; has locatedIn ?x736<RCA>;]

*> Best rule #227 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 1
*> proper extension:  Schari;
*> query: (?x2087, ?x182) <- ?x2087[ has flowsInto ?x929; has locatedIn ?x528[ has neighbor ?x172; is locatedIn of ?x182;]; has locatedIn ?x536<CAM>; has locatedIn ?x736<RCA>;]
*> conf = 0.04
ranks of expected_values: 25
EVAL	Sanga	hasEstuary	Sanga	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.040	34.000	24.000	27.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasEstuary

PRED relation: hasEstuary
PRED expected values: Sanga
=> 78 concepts (78 used for prediction)
PRED predicted values (max 10 best out of 204): Ubangi (0.33 #24, 0.27 #5015, 0.26 #6381), Ruki (0.27 #5015, 0.26 #6381, 0.25 #6380), Lualaba (0.27 #5015, 0.26 #6381, 0.25 #6380), Aruwimi (0.26 #6381, 0.25 #6380, 0.19 #5016), Lomami (0.26 #6381, 0.25 #6380, 0.19 #5016), Schari (0.25 #332, 0.23 #1825, 0.20 #788), Bomu (0.23 #1825, 0.20 #732, 0.11 #1645), Sanga (0.23 #1825, 0.07 #8888, 0.06 #911), Sanaga (0.23 #1825, 0.07 #8888, 0.05 #9118), Cuango (0.17 #1103, 0.11 #1561, 0.08 #2245)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #24 for best value:
>> intensional similarity = 11
>> extensional distance = 1
>> proper extension:  Ubangi;
>> query: (?x2087, Ubangi) <- ?x2087[ a River; has flowsInto ?x929<Zaire>; has locatedIn ?x528<RCB>; has locatedIn ?x536[ has ethnicGroup ?x122; has government ?x1721; is neighbor of ?x139;]; has locatedIn ?x736<RCA>;]

*> Best rule #1825 for first EXPECTED value:
*> intensional similarity = 12
*> extensional distance = 7
*> proper extension:  Ubangi; Bomu; Bomu; Sanga;
*> query: (?x2087, ?x1378) <- ?x2087[ has locatedIn ?x528[ has neighbor ?x348<ZRE>;]; has locatedIn ?x536[ has government ?x1721; has neighbor ?x139; has religion ?x116; has wasDependentOf ?x485; is locatedIn of ?x1378[ a Estuary;];]; has locatedIn ?x736<RCA>;]
*> conf = 0.23
ranks of expected_values: 8
EVAL	Sanga	hasEstuary	Sanga	CNN-1.+1._MA	0.000	0.000	1.000	0.125	78.000	78.000	204.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasEstuary

#316-Babelthuap
PRED entity: Babelthuap

PRED relation: locatedIn
PRED expected values: PAL
=> 39 concepts (39 used for prediction)
PRED predicted values (max 10 best out of 114): PAL (0.91 #2366, 0.87 #3075, 0.82 #1183), USA (0.32 #545, 0.32 #308, 0.31 #2201), J (0.18 #255, 0.16 #729, 0.16 #492), RP (0.14 #2947, 0.07 #3896, 0.05 #8789), P (0.13 #3272, 0.12 #3509, 0.11 #4463), I (0.10 #3123, 0.09 #3360, 0.09 #4314), KIR (0.09 #1814, 0.09 #2050, 0.08 #1104), RI (0.09 #5032, 0.09 #3839, 0.08 #5930), WAFU (0.09 #367, 0.08 #841, 0.08 #604), GB (0.08 #6414, 0.08 #5701, 0.08 #5940)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2366 for best value:
>> intensional similarity = 9
>> extensional distance = 40
>> proper extension:  Tongatapu; TeWaka-a-Maui-SouthIsland-; TeIka-a-Maui-NorthIsland-; Halmahera; Mindanao; SantaRosaIsland; SantaCruzIsland; Paramuschir; Efate; Ponape; ...
>> query: (?x1203, ?x2188) <- ?x1203[ a Island; has belongsToIslands ?x1169[ a Islands; is belongsToIslands of ?x1168[ a Island; has locatedIn ?x2188; has locatedInWater ?x282<PacificOcean>;];]; has locatedInWater ?x282<PacificOcean>;]
ranks of expected_values: 1
EVAL	Babelthuap	locatedIn	PAL	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	39.000	39.000	114.000	0.909	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: PAL
=> 69 concepts (69 used for prediction)
PRED predicted values (max 10 best out of 133): PAL (0.91 #4769, 0.85 #1895, 0.83 #710), USA (0.36 #545, 0.33 #1493, 0.33 #308), J (0.21 #1440, 0.19 #255, 0.18 #966), RI (0.17 #8625, 0.17 #8435, 0.10 #9356), P (0.13 #7381, 0.13 #5933, 0.12 #7618), RP (0.11 #5601, 0.08 #8492, 0.07 #5357), GB (0.11 #10324, 0.10 #10803, 0.09 #11046), CDN (0.10 #14676, 0.08 #12985, 0.08 #13945), AUS (0.10 #14676, 0.08 #12985, 0.08 #13945), I (0.10 #6745, 0.10 #6987, 0.10 #8188)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4769 for best value:
>> intensional similarity = 9
>> extensional distance = 40
>> proper extension:  Tongatapu; TeWaka-a-Maui-SouthIsland-; TeIka-a-Maui-NorthIsland-; Halmahera; Mindanao; SantaRosaIsland; SantaCruzIsland; Paramuschir; Efate; Ponape; ...
>> query: (?x1203, ?x2188) <- ?x1203[ a Island; has belongsToIslands ?x1169[ a Islands; is belongsToIslands of ?x1168[ a Island; has locatedIn ?x2188; has locatedInWater ?x282<PacificOcean>;];]; has locatedInWater ?x282<PacificOcean>;]
ranks of expected_values: 1
EVAL	Babelthuap	locatedIn	PAL	CNN-1.+1._MA	1.000	1.000	1.000	1.000	69.000	69.000	133.000	0.909	http://www.semwebtech.org/mondial/10/meta#locatedIn

#315-Streymoy
PRED entity: Streymoy

PRED relation: locatedIn
PRED expected values: FARX
=> 21 concepts (21 used for prediction)
PRED predicted values (max 10 best out of 96): GB (0.33 #9, 0.20 #476, 0.20 #475), IS (0.20 #476, 0.20 #475, 0.20 #346), SVAX (0.20 #476, 0.20 #475, 0.20 #430), P (0.20 #476, 0.20 #475, 0.13 #673), USA (0.20 #476, 0.20 #475, 0.12 #2146), E (0.20 #476, 0.20 #475, 0.11 #503), FARX (0.20 #476, 0.20 #475, 0.11 #238), CDN (0.20 #476, 0.20 #475, 0.10 #2389), RH (0.20 #476, 0.20 #475, 0.06 #2150), BR (0.20 #476, 0.20 #475, 0.06 #2150)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #9 for best value:
>> intensional similarity = 8
>> extensional distance = 1
>> proper extension:  GreatBritain;
>> query: (?x2103, GB) <- ?x2103[ a Island; has belongsToIslands ?x2214[ a Islands;]; has locatedInWater ?x182<AtlanticOcean>; has locatedInWater ?x373[ has locatedIn ?x170<N>;];]

*> Best rule #476 for first EXPECTED value:
*> intensional similarity = 9
*> extensional distance = 3
*> proper extension:  Iceland;
*> query: (?x2103, ?x50) <- ?x2103[ a Island; has locatedInWater ?x182[ has locatedIn ?x50; has locatedIn ?x279[ a Country;]; has mergesWith ?x60; is flowsInto of ?x137;]; has locatedInWater ?x373<NorwegianSea>;]
*> conf = 0.20
ranks of expected_values: 7
EVAL	Streymoy	locatedIn	FARX	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.143	21.000	21.000	96.000	0.333	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: FARX
=> 37 concepts (37 used for prediction)
PRED predicted values (max 10 best out of 136): GROX (0.33 #167, 0.05 #8597, 0.05 #8596), GB (0.25 #240, 0.20 #731, 0.20 #241), N (0.25 #240, 0.20 #241, 0.05 #8597), IS (0.25 #240, 0.20 #241, 0.05 #8597), FARX (0.25 #240, 0.20 #241, 0.03 #8345), SVAX (0.20 #241, 0.05 #8597, 0.05 #8596), P (0.13 #438, 0.13 #679, 0.13 #919), USA (0.13 #3246, 0.13 #3493, 0.10 #5215), GR (0.12 #2036, 0.10 #2772, 0.07 #3758), E (0.11 #268, 0.11 #509, 0.11 #749)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #167 for best value:
>> intensional similarity = 20
>> extensional distance = 1
>> proper extension:  Greenland;
>> query: (?x2103, GROX) <- ?x2103[ a Island; has locatedInWater ?x182<AtlanticOcean>; has locatedInWater ?x373[ a Sea; has locatedIn ?x170[ has religion ?x95;]; has locatedIn ?x973<SVAX>; is locatedInWater of ?x807<Iceland>; is mergesWith of ?x121[ a Sea; has locatedIn ?x78<F>; has mergesWith ?x1211<TheChannel>; is flowsInto of ?x829; is locatedInWater of ?x495<GreatBritain>;];];]

*> Best rule #240 for first EXPECTED value:
*> intensional similarity = 20
*> extensional distance = 1
*> proper extension:  Greenland;
*> query: (?x2103, ?x170) <- ?x2103[ a Island; has locatedInWater ?x182<AtlanticOcean>; has locatedInWater ?x373[ a Sea; has locatedIn ?x170[ has religion ?x95;]; has locatedIn ?x973<SVAX>; is locatedInWater of ?x807<Iceland>; is mergesWith of ?x121[ a Sea; has locatedIn ?x78<F>; has mergesWith ?x1211<TheChannel>; is flowsInto of ?x829; is locatedInWater of ?x495<GreatBritain>;];];]
*> conf = 0.25
ranks of expected_values: 5
EVAL	Streymoy	locatedIn	FARX	CNN-1.+1._MA	0.000	0.000	1.000	0.200	37.000	37.000	136.000	0.333	http://www.semwebtech.org/mondial/10/meta#locatedIn

#314-BalticSea
PRED entity: BalticSea

PRED relation: mergesWith!
PRED expected values: Kattegat
=> 39 concepts (35 used for prediction)
PRED predicted values (max 10 best out of 130): Skagerrak (0.33 #39, 0.17 #164, 0.15 #43), BalticSea (0.33 #5, 0.15 #43, 0.15 #42), PacificOcean (0.26 #463, 0.23 #422, 0.21 #584), AtlanticOcean (0.24 #292, 0.23 #412, 0.23 #574), ArcticOcean (0.19 #218, 0.19 #378, 0.12 #298), IndianOcean (0.17 #407, 0.16 #488, 0.15 #569), NorthSea (0.15 #43, 0.15 #42, 0.15 #41), Kattegat (0.15 #43, 0.15 #42, 0.15 #41), Aller (0.15 #43, 0.15 #42, 0.15 #41), Leine (0.15 #43, 0.15 #42, 0.15 #41)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #39 for best value:
>> intensional similarity = 3
>> extensional distance = 1
>> proper extension:  Kattegat;
>> query: (?x146, Skagerrak) <- ?x146[ has locatedIn ?x120; is locatedInWater of ?x917<Fuenen>;]

*> Best rule #43 for first EXPECTED value:
*> intensional similarity = 4
*> extensional distance = 1
*> proper extension:  Kattegat;
*> query: (?x146, ?x447) <- ?x146[ has locatedIn ?x194[ is locatedIn of ?x447;]; is locatedInWater of ?x917<Fuenen>;]
*> conf = 0.15
ranks of expected_values: 8
EVAL	BalticSea	mergesWith!	Kattegat	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.125	39.000	35.000	130.000	0.333	http://www.semwebtech.org/mondial/10/meta#mergesWith

PRED relation: mergesWith!
PRED expected values: Kattegat
=> 97 concepts (97 used for prediction)
PRED predicted values (max 10 best out of 358): Kattegat (0.83 #576, 0.83 #913, 0.83 #1206), BalticSea (0.47 #1164, 0.47 #1290, 0.13 #578), ArcticOcean (0.38 #220, 0.33 #179, 0.21 #717), NorthSea (0.33 #4, 0.19 #539, 0.14 #876), AtlanticOcean (0.31 #541, 0.28 #794, 0.28 #752), PacificOcean (0.25 #931, 0.24 #972, 0.24 #762), IndianOcean (0.20 #747, 0.18 #1166, 0.18 #916), Skagerrak (0.17 #830, 0.17 #164, 0.16 #577), SeaofJapan (0.17 #181, 0.12 #719, 0.12 #676), MarmaraSea (0.17 #162, 0.12 #572, 0.12 #286)
>> best conf = 0.83
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #576 for best value:
>> intensional similarity = 8
>> extensional distance = 14
>> proper extension:  BlackSea;
>> query: (?x146, ?x1663) <- ?x146[ has locatedIn ?x120[ has neighbor ?x78; has religion ?x352<RomanCatholic>;]; has mergesWith ?x1663[ has mergesWith ?x1664;]; is flowsInto of ?x660[ is flowsInto of ?x905;];]
ranks of expected_values: 1
EVAL	BalticSea	mergesWith!	Kattegat	CNN-1.+1._MA	1.000	1.000	1.000	1.000	97.000	97.000	358.000	0.833	http://www.semwebtech.org/mondial/10/meta#mergesWith

#313-F
PRED entity: F

PRED relation: wasDependentOf!
PRED expected values: RN LAO
=> 34 concepts (34 used for prediction)
PRED predicted values (max 10 best out of 183): ZRE (0.33 #185, 0.27 #2451, 0.24 #1907), B (0.33 #81, 0.25 #136, 0.18 #682), L (0.33 #97, 0.25 #136, 0.15 #1634), SME (0.33 #20, 0.18 #682, 0.14 #1109), RI (0.33 #30, 0.14 #1119, 0.14 #983), WAL (0.27 #2451, 0.24 #1907, 0.20 #665), WAG (0.27 #2451, 0.24 #1907, 0.20 #664), GH (0.27 #2451, 0.24 #1907, 0.20 #621), WAN (0.27 #2451, 0.24 #1907, 0.20 #555), GNB (0.27 #2451, 0.24 #1907, 0.18 #682)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #185 for best value:
>> intensional similarity = 5
>> extensional distance = 1
>> proper extension:  B;
>> query: (?x78, ZRE) <- ?x78[ has government ?x435; is locatedIn of ?x121; is neighbor of ?x718<L>; is wasDependentOf of ?x94;]

*> Best rule #2451 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 25
*> proper extension:  MergerofNorth-SouthYemen;
*> query: (?x78, ?x426) <- ?x78[ is wasDependentOf of ?x94[ a Country;]; is wasDependentOf of ?x581[ has neighbor ?x426; is locatedIn of ?x84;];]
*> conf = 0.27
ranks of expected_values: 19, 20
EVAL	F	wasDependentOf!	LAO	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.053	34.000	34.000	183.000	0.333	http://www.semwebtech.org/mondial/10/meta#wasDependentOf
EVAL	F	wasDependentOf!	RN	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.053	34.000	34.000	183.000	0.333	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

PRED relation: wasDependentOf!
PRED expected values: RN LAO
=> 104 concepts (104 used for prediction)
PRED predicted values (max 10 best out of 191): GNB (0.36 #1931, 0.35 #5256, 0.33 #5255), LAR (0.36 #1931, 0.35 #5256, 0.33 #5255), WAN (0.36 #1931, 0.35 #5256, 0.33 #5255), WAG (0.36 #1931, 0.35 #5256, 0.33 #5255), WAL (0.36 #1931, 0.35 #5256, 0.33 #5255), SUD (0.36 #1931, 0.35 #5256, 0.33 #5255), DOM (0.36 #1931, 0.35 #5256, 0.33 #5255), CAM (0.36 #1931, 0.35 #5256, 0.33 #5255), ZRE (0.36 #1931, 0.35 #5256, 0.33 #5255), LB (0.36 #1931, 0.35 #5256, 0.33 #5255)
>> best conf = 0.36
=> the first rule below is the first best rule for 21 predicted values
>> Best rule #1931 for best value:
>> intensional similarity = 13
>> extensional distance = 4
>> proper extension:  RH;
>> query: (?x78, ?x1051) <- ?x78[ is locatedIn of ?x182<AtlanticOcean>; is neighbor of ?x120[ has religion ?x95; is locatedIn of ?x133[ is flowsInto of ?x132;]; is neighbor of ?x194;]; is wasDependentOf of ?x416[ has ethnicGroup ?x122; is neighbor of ?x1051;]; is wasDependentOf of ?x871[ has religion ?x462; is locatedIn of ?x384;];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 11, 18
EVAL	F	wasDependentOf!	LAO	CNN-1.+1._MA	0.000	0.000	0.000	0.059	104.000	104.000	191.000	0.364	http://www.semwebtech.org/mondial/10/meta#wasDependentOf
EVAL	F	wasDependentOf!	RN	CNN-1.+1._MA	0.000	0.000	0.000	0.091	104.000	104.000	191.000	0.364	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

#312-Vosges
PRED entity: Vosges

PRED relation: inMountains!
PRED expected values: GrandBallon
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 259): MtRobson (0.01 #259), BarredesEcrins (0.01 #258), Grossglockner (0.01 #257), PicodeAgulhasNegras (0.01 #256), Mantaro (0.01 #255), PicoBolivar (0.01 #254), Drin (0.01 #253), Vignemale (0.01 #252), Dychtau (0.01 #251), GranitePeak (0.01 #250)
>> best conf = 0.01
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #259 for best value:
>> intensional similarity = 1
>> extensional distance = 91
>> proper extension:  BlackForest; Ahaggar; Apennin; Alps; Troodos; Himalaya; RockyMountains; Karakorum; GreatDividingRange; Kunlun; ...
>> query: (?x2541, MtRobson) <- ?x2541[ a Mountains;]

No rule for expected values
ranks of expected_values: 
EVAL	Vosges	inMountains!	GrandBallon	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	2.000	2.000	259.000	0.011	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains!
PRED expected values: GrandBallon
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 259): MtRobson (0.01 #259), BarredesEcrins (0.01 #258), Grossglockner (0.01 #257), PicodeAgulhasNegras (0.01 #256), Mantaro (0.01 #255), PicoBolivar (0.01 #254), Drin (0.01 #253), Vignemale (0.01 #252), Dychtau (0.01 #251), GranitePeak (0.01 #250)
>> best conf = 0.01
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #259 for best value:
>> intensional similarity = 1
>> extensional distance = 91
>> proper extension:  BlackForest; Ahaggar; Apennin; Alps; Troodos; Himalaya; RockyMountains; Karakorum; GreatDividingRange; Kunlun; ...
>> query: (?x2541, MtRobson) <- ?x2541[ a Mountains;]

No rule for expected values
ranks of expected_values: 
EVAL	Vosges	inMountains!	GrandBallon	CNN-1.+1._MA	0.000	0.000	0.000	0.000	2.000	2.000	259.000	0.011	http://www.semwebtech.org/mondial/10/meta#inMountains

#311-LakeNgami
PRED entity: LakeNgami

PRED relation: type
PRED expected values: "salt"
=> 29 concepts (29 used for prediction)
PRED predicted values (max 10 best out of 7): "salt" (0.33 #39, 0.33 #7, 0.20 #151), "dam" (0.12 #145, 0.04 #97, 0.04 #129), "volcanic" (0.09 #210, 0.09 #322, 0.08 #434), "sand" (0.07 #84, 0.03 #116, 0.02 #196), "caldera" (0.05 #147), "volcano" (0.04 #326, 0.04 #246, 0.04 #422), "impact" (0.02 #154)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #39 for best value:
>> intensional similarity = 7
>> extensional distance = 4
>> proper extension:  EtoschaSaltPan;
>> query: (?x1902, "salt") <- ?x1902[ a Lake; has locatedIn ?x1239[ has neighbor ?x138; has religion ?x116; is locatedIn of ?x933<Kalahari>; is neighbor of ?x1576;];]
>> Best rule #7 for best value:
>> intensional similarity = 3
>> extensional distance = 1
>> proper extension:  MakarikariSaltPan;
>> query: (?x1902, "salt") <- ?x1902[ a Lake; has locatedIn ?x1239<RB>;]
ranks of expected_values: 1
EVAL	LakeNgami	type	"salt"	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	29.000	29.000	7.000	0.333	http://www.semwebtech.org/mondial/10/meta#type

PRED relation: type
PRED expected values: "salt"
=> 76 concepts (76 used for prediction)
PRED predicted values (max 10 best out of 14): "salt" (0.39 #103, 0.36 #232, 0.33 #7), "dam" (0.27 #49, 0.22 #97, 0.17 #292), "volcanic" (0.16 #244, 0.12 #374, 0.11 #599), "impact" (0.09 #58, 0.06 #106, 0.04 #301), "volcano" (0.08 #554, 0.08 #603, 0.06 #669), "caldera" (0.06 #228, 0.05 #147, 0.05 #423), "sand" (0.05 #116, 0.04 #440, 0.04 #536), "lime" (0.03 #505, 0.01 #734), "monolith" (0.02 #171, 0.02 #204, 0.01 #253), "atoll" (0.02 #508, 0.01 #524, 0.01 #671)
>> best conf = 0.39
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #103 for best value:
>> intensional similarity = 8
>> extensional distance = 16
>> proper extension:  LopNor; LakeCabora-Bassa; LakeChilwa; QinghaiLake; LakeMalawi; NamCo; OzeroChanka;
>> query: (?x1902, "salt") <- ?x1902[ a Lake; has locatedIn ?x1239[ has encompassed ?x213; has ethnicGroup ?x2322; has government ?x1174; has religion ?x116<Christian>; is neighbor of ?x243;];]
ranks of expected_values: 1
EVAL	LakeNgami	type	"salt"	CNN-1.+1._MA	1.000	1.000	1.000	1.000	76.000	76.000	14.000	0.389	http://www.semwebtech.org/mondial/10/meta#type

#310-STP
PRED entity: STP

PRED relation: wasDependentOf
PRED expected values: P
=> 33 concepts (33 used for prediction)
PRED predicted values (max 10 best out of 86): F (0.38 #61, 0.33 #264, 0.32 #206), GB (0.35 #323, 0.34 #178, 0.31 #149), E (0.22 #326, 0.11 #349, 0.10 #94), P (0.14 #81, 0.11 #168, 0.11 #349), UnitedNations (0.11 #349, 0.10 #455, 0.09 #396), RH (0.11 #349, 0.03 #106, 0.02 #338), BR (0.11 #349, 0.02 #335, 0.02 #426), CO (0.11 #349, 0.02 #328), SovietUnion (0.07 #787, 0.06 #725, 0.06 #756), B (0.06 #46, 0.05 #75, 0.03 #191)
>> best conf = 0.38
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #61 for best value:
>> intensional similarity = 6
>> extensional distance = 19
>> proper extension:  WSA; WAG; RT;
>> query: (?x994, F) <- ?x994[ a Country; has encompassed ?x213<Africa>; has religion ?x316; is locatedIn of ?x182<AtlanticOcean>;]

*> Best rule #81 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 19
*> proper extension:  WSA; WAG; RT;
*> query: (?x994, P) <- ?x994[ a Country; has encompassed ?x213<Africa>; has religion ?x316; is locatedIn of ?x182<AtlanticOcean>;]
*> conf = 0.14
ranks of expected_values: 4
EVAL	STP	wasDependentOf	P	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.250	33.000	33.000	86.000	0.381	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

PRED relation: wasDependentOf
PRED expected values: P
=> 86 concepts (86 used for prediction)
PRED predicted values (max 10 best out of 76): F (0.43 #649, 0.39 #773, 0.38 #337), GB (0.43 #277, 0.38 #964, 0.38 #308), P (0.40 #235, 0.25 #176, 0.25 #146), E (0.30 #1234, 0.29 #249, 0.25 #311), UnitedNations (0.25 #168, 0.16 #2460, 0.14 #257), B (0.25 #80, 0.12 #580, 0.12 #1388), SovietUnion (0.16 #1013, 0.08 #2447, 0.08 #2488), BR (0.16 #2460, 0.13 #1190, 0.12 #580), RH (0.16 #2460, 0.13 #1190, 0.12 #580), CO (0.16 #2460, 0.13 #1190, 0.03 #1236)
>> best conf = 0.43
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #649 for best value:
>> intensional similarity = 10
>> extensional distance = 12
>> proper extension:  G;
>> query: (?x994, F) <- ?x994[ a Country; has encompassed ?x213<Africa>; has government ?x435; has religion ?x2256[ a Religion; is religion of ?x476<ETH>;]; is locatedIn of ?x182<AtlanticOcean>;]

*> Best rule #235 for first EXPECTED value:
*> intensional similarity = 12
*> extensional distance = 3
*> proper extension:  ANG; GNB;
*> query: (?x994, P) <- ?x994[ a Country; has encompassed ?x213<Africa>; has ethnicGroup ?x197<European>; has government ?x435; has religion ?x2256[ a Religion; is religion of ?x476<ETH>;]; is locatedIn of ?x182<AtlanticOcean>;]
*> conf = 0.40
ranks of expected_values: 3
EVAL	STP	wasDependentOf	P	CNN-1.+1._MA	0.000	1.000	1.000	0.333	86.000	86.000	76.000	0.429	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

#309-WAG
PRED entity: WAG

PRED relation: government
PRED expected values: "republic"
=> 36 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 64): "republic" (0.50 #870, 0.50 #582, 0.50 #438), "military junta" (0.20 #197, 0.12 #269, 0.11 #413), "parliamentary democracy and a Commonwealth realm" (0.18 #684, 0.16 #756, 0.16 #1044), "constitutional democracy" (0.12 #292, 0.11 #364, 0.10 #508), "federal republic" (0.12 #291, 0.11 #723, 0.07 #795), "republic; multiparty presidential regime established 1960" (0.11 #425, 0.10 #569, 0.10 #497), "parliamentary democracy" (0.11 #1590, 0.11 #1302, 0.11 #1230), "constitutional monarchy" (0.09 #1227, 0.08 #1515, 0.08 #1299), "British Overseas Territories" (0.09 #1376, 0.05 #727, 0.05 #1081), "democratic republic" (0.07 #802, 0.06 #658, 0.05 #730)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #870 for best value:
>> intensional similarity = 6
>> extensional distance = 26
>> proper extension:  COM;
>> query: (?x1051, "republic") <- ?x1051[ a Country; has encompassed ?x213<Africa>; has religion ?x187<Muslim>; has wasDependentOf ?x81;]
>> Best rule #582 for best value:
>> intensional similarity = 8
>> extensional distance = 10
>> proper extension:  RN;
>> query: (?x1051, "republic") <- ?x1051[ is neighbor of ?x416[ has encompassed ?x213; has ethnicGroup ?x122; has religion ?x116; has wasDependentOf ?x78; is locatedIn of ?x838<Senegal>; is neighbor of ?x515;];]
>> Best rule #438 for best value:
>> intensional similarity = 8
>> extensional distance = 8
>> proper extension:  DZ;
>> query: (?x1051, "republic") <- ?x1051[ has ethnicGroup ?x162; has wasDependentOf ?x81; is neighbor of ?x416[ has encompassed ?x213; has ethnicGroup ?x122; is locatedIn of ?x838<Senegal>; is neighbor of ?x515;];]
>> Best rule #222 for best value:
>> intensional similarity = 8
>> extensional distance = 6
>> proper extension:  WSA;
>> query: (?x1051, "republic") <- ?x1051[ a Country; is neighbor of ?x416[ has encompassed ?x213; has ethnicGroup ?x122; has religion ?x116; is locatedIn of ?x1801<Senegal>; is neighbor of ?x515;];]
>> Best rule #78 for best value:
>> intensional similarity = 9
>> extensional distance = 2
>> proper extension:  RMM;
>> query: (?x1051, "republic") <- ?x1051[ has ethnicGroup ?x162; is locatedIn of ?x182[ has locatedIn ?x154[ has language ?x247;]; is flowsInto of ?x137;]; is locatedIn of ?x952[ a River;]; is neighbor of ?x416<SN>;]
ranks of expected_values: 1
EVAL	WAG	government	"republic"	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	36.000	36.000	64.000	0.500	http://www.semwebtech.org/mondial/10/meta#government

PRED relation: government
PRED expected values: "republic"
=> 87 concepts (87 used for prediction)
PRED predicted values (max 10 best out of 69): "republic" (0.55 #361, 0.55 #294, 0.50 #1810), "military junta" (0.33 #53, 0.21 #1949, 0.14 #197), "parliamentary democracy and a Commonwealth realm" (0.27 #973, 0.17 #469, 0.14 #1550), "parliamentary democracy" (0.21 #2747, 0.13 #4415, 0.13 #3185), "constitutional democracy" (0.18 #4410, 0.15 #797, 0.13 #869), "federal republic" (0.18 #4410, 0.15 #580, 0.11 #5640), "republic; multiparty presidential regime" (0.18 #4410, 0.11 #5640, 0.11 #1038), "constitutional republic" (0.18 #4410, 0.11 #5640, 0.08 #2529), "republic; multiparty presidential regime established 1960" (0.14 #209, 0.11 #5640, 0.09 #426), "constitutional parliamentary democracy and a Commonwealth realm" (0.13 #879, 0.08 #447, 0.08 #2529)
>> best conf = 0.55
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #361 for best value:
>> intensional similarity = 13
>> extensional distance = 9
>> proper extension:  RN;
>> query: (?x1051, ?x435) <- ?x1051[ a Country; has encompassed ?x213; has ethnicGroup ?x162; is neighbor of ?x416[ a Country; has ethnicGroup ?x122; has ethnicGroup ?x417[ a EthnicGroup;]; has government ?x435; has religion ?x116; is locatedIn of ?x182; is neighbor of ?x515<RIM>;];]
>> Best rule #294 for best value:
>> intensional similarity = 13
>> extensional distance = 9
>> proper extension:  RN;
>> query: (?x1051, "republic") <- ?x1051[ a Country; has encompassed ?x213; has ethnicGroup ?x162; is neighbor of ?x416[ a Country; has ethnicGroup ?x122; has ethnicGroup ?x417[ a EthnicGroup;]; has government ?x435; has religion ?x116; is locatedIn of ?x182; is neighbor of ?x515<RIM>;];]
ranks of expected_values: 1
EVAL	WAG	government	"republic"	CNN-1.+1._MA	1.000	1.000	1.000	1.000	87.000	87.000	69.000	0.545	http://www.semwebtech.org/mondial/10/meta#government

#308-LV
PRED entity: LV

PRED relation: locatedIn!
PRED expected values: BalticSea
=> 45 concepts (41 used for prediction)
PRED predicted values (max 10 best out of 1388): PacificOcean (0.66 #12885, 0.33 #86, 0.25 #10040), BalticSea (0.63 #19911, 0.25 #1453, 0.10 #12799), WesternDwina (0.56 #19912, 0.33 #428, 0.09 #44091), WesternBug (0.38 #1477, 0.10 #12799, 0.09 #44091), Dnepr (0.33 #309, 0.25 #1731, 0.10 #12799), BlackSea (0.33 #13, 0.12 #1435, 0.09 #44091), SeaofAzov (0.33 #12, 0.12 #1434, 0.09 #44091), OzeroPskovskoje (0.33 #1183, 0.10 #12799, 0.09 #44091), Narva (0.33 #382, 0.10 #12799, 0.09 #44091), CaspianSea (0.33 #732, 0.09 #44091, 0.09 #42668)
>> best conf = 0.66
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #12885 for best value:
>> intensional similarity = 6
>> extensional distance = 45
>> proper extension:  PAL;
>> query: (?x448, PacificOcean) <- ?x448[ a Country; has encompassed ?x195; has government ?x254; is locatedIn of ?x1457[ has locatedIn ?x73<R>;];]

*> Best rule #19911 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 62
*> proper extension:  F; I;
*> query: (?x448, ?x146) <- ?x448[ has encompassed ?x195; has religion ?x56; is locatedIn of ?x1457[ has flowsInto ?x146; has hasSource ?x868;]; is neighbor of ?x591[ has language ?x555;];]
*> conf = 0.63
ranks of expected_values: 2
EVAL	LV	locatedIn!	BalticSea	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	45.000	41.000	1388.000	0.660	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: BalticSea
=> 87 concepts (87 used for prediction)
PRED predicted values (max 10 best out of 1404): WesternBug (0.50 #7174, 0.50 #2902, 0.40 #5749), Dnepr (0.50 #3156, 0.40 #6003, 0.40 #4580), Prypjat (0.50 #3146, 0.40 #5993, 0.40 #4570), Donau (0.46 #21382, 0.45 #18534, 0.44 #14260), AtlanticOcean (0.34 #81251, 0.33 #65561, 0.33 #62711), PacificOcean (0.34 #111133, 0.33 #86, 0.33 #89754), BarentsSea (0.34 #111133, 0.33 #67, 0.33 #89754), EastSibirianSea (0.34 #111133, 0.33 #180, 0.33 #89754), SeaofOkhotsk (0.34 #111133, 0.33 #218, 0.33 #89754), BeringSea (0.34 #111133, 0.33 #388, 0.33 #89754)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #7174 for best value:
>> intensional similarity = 15
>> extensional distance = 4
>> proper extension:  PL;
>> query: (?x448, WesternBug) <- ?x448[ a Country; has encompassed ?x195; has ethnicGroup ?x58<Ukrainian>; has ethnicGroup ?x2273<Polish>; has language ?x555; has religion ?x56[ is religion of ?x176<RO>; is religion of ?x204<AL>;]; is locatedIn of ?x885; is neighbor of ?x591[ has government ?x1174;];]
>> Best rule #2902 for best value:
>> intensional similarity = 15
>> extensional distance = 2
>> proper extension:  UA;
>> query: (?x448, WesternBug) <- ?x448[ a Country; has ethnicGroup ?x58<Ukrainian>; has ethnicGroup ?x1193<Russian>; has ethnicGroup ?x1322<Belorussian>; has religion ?x56; has wasDependentOf ?x903<SovietUnion>; is locatedIn of ?x885; is neighbor of ?x962[ has language ?x555; has religion ?x352; is locatedIn of ?x146;];]

*> Best rule #111133 for first EXPECTED value:
*> intensional similarity = 15
*> extensional distance = 138
*> proper extension:  SMAR;
*> query: (?x448, ?x507) <- ?x448[ is locatedIn of ?x885; is neighbor of ?x73[ is locatedIn of ?x507[ a Sea; is locatedInWater of ?x451;]; is neighbor of ?x232;]; is neighbor of ?x222[ is locatedIn of ?x679[ is flowsInto of ?x457;]; is neighbor of ?x303[ is locatedIn of ?x97;];]; is neighbor of ?x962[ a Country; has encompassed ?x195; has government ?x254;];]
*> conf = 0.34
ranks of expected_values: 13
EVAL	LV	locatedIn!	BalticSea	CNN-1.+1._MA	0.000	0.000	0.000	0.077	87.000	87.000	1404.000	0.500	http://www.semwebtech.org/mondial/10/meta#locatedIn

#307-Selenge
PRED entity: Selenge

PRED relation: hasEstuary!
PRED expected values: Selenge
=> 29 concepts (24 used for prediction)
PRED predicted values (max 10 best out of 114): Katun (0.08 #2499, 0.04 #217, 0.04 #443), Schilka (0.08 #2499, 0.04 #214, 0.04 #440), Kama (0.08 #2499, 0.04 #199, 0.04 #425), Amur (0.08 #2499, 0.04 #181, 0.04 #407), Oka (0.08 #2499, 0.04 #179, 0.04 #405), Kolyma (0.08 #2499, 0.04 #163, 0.04 #389), Don (0.08 #2499, 0.04 #147, 0.04 #373), Lena (0.08 #2499, 0.04 #107, 0.04 #333), Chatanga (0.08 #2499, 0.04 #97, 0.04 #323), Narva (0.08 #2499, 0.04 #82, 0.04 #308)
>> best conf = 0.08
=> the first rule below is the first best rule for 27 predicted values
>> Best rule #2499 for best value:
>> intensional similarity = 5
>> extensional distance = 218
>> proper extension:  Marne; Po; Bahrel-Ghasal; Orinoco; Mincio; Rhone; Douro; Isere; Adda; Ticino; ...
>> query: (?x2504, ?x72) <- ?x2504[ a Estuary; has locatedIn ?x73[ is locatedIn of ?x72[ a River;]; is neighbor of ?x170;];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 26
EVAL	Selenge	hasEstuary!	Selenge	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.038	29.000	24.000	114.000	0.075	http://www.semwebtech.org/mondial/10/meta#hasEstuary

PRED relation: hasEstuary!
PRED expected values: Selenge
=> 86 concepts (86 used for prediction)
PRED predicted values (max 10 best out of 205): Paatsjoki (0.08 #7098, 0.06 #2744, 0.05 #2287), Amur (0.08 #7098, 0.06 #2744, 0.05 #2287), Swir (0.08 #7098, 0.06 #2744, 0.05 #2287), Jenissej (0.08 #7098, 0.06 #2744, 0.05 #2287), Newa (0.08 #7098, 0.06 #2744, 0.05 #2287), Irtysch (0.08 #7098, 0.06 #2744, 0.05 #2287), Dnepr (0.08 #7098, 0.06 #2744, 0.05 #2287), Angara (0.08 #7098, 0.06 #2744, 0.05 #2287), Volga (0.08 #7098, 0.06 #2744, 0.05 #2287), Kolyma (0.08 #7098, 0.06 #2744, 0.05 #2287)
>> best conf = 0.08
=> the first rule below is the first best rule for 22 predicted values
>> Best rule #7098 for best value:
>> intensional similarity = 9
>> extensional distance = 197
>> proper extension:  Leine; Moraca; Umeaelv; Pibor; Aare; Rhein; Main; Colorado; Saar; Tennessee; ...
>> query: (?x2504, ?x465) <- ?x2504[ a Estuary; has locatedIn ?x73[ has ethnicGroup ?x58; has neighbor ?x332[ has neighbor ?x185;]; is locatedIn of ?x98[ is flowsInto of ?x133;]; is locatedIn of ?x465[ has hasEstuary ?x2211;];];]

*> Best rule #5952 for first EXPECTED value:
*> intensional similarity = 9
*> extensional distance = 172
*> proper extension:  Thjorsa; JoekulsaaFjoellum;
*> query: (?x2504, ?x590) <- ?x2504[ a Estuary; has locatedIn ?x73[ has ethnicGroup ?x58; has religion ?x56; is locatedIn of ?x282[ is flowsInto of ?x602; is locatedInWater of ?x205;]; is locatedIn of ?x590[ has flowsInto ?x146;];];]
*> conf = 0.05
ranks of expected_values: 28
EVAL	Selenge	hasEstuary!	Selenge	CNN-1.+1._MA	0.000	0.000	0.000	0.036	86.000	86.000	205.000	0.081	http://www.semwebtech.org/mondial/10/meta#hasEstuary

#306-SaoMiguel
PRED entity: SaoMiguel

PRED relation: locatedInWater
PRED expected values: AtlanticOcean
=> 69 concepts (66 used for prediction)
PRED predicted values (max 10 best out of 46): AtlanticOcean (0.82 #180, 0.81 #353, 0.80 #137), PacificOcean (0.34 #984, 0.33 #939, 0.33 #1028), JavaSea (0.31 #311, 0.15 #619, 0.15 #664), IndianOcean (0.23 #304, 0.13 #746, 0.11 #612), MediterraneanSea (0.21 #493, 0.20 #1289, 0.19 #1158), NorthSea (0.19 #613, 0.18 #1145, 0.16 #1057), CaribbeanSea (0.12 #584, 0.11 #1030, 0.11 #986), SouthChinaSea (0.11 #766, 0.09 #677, 0.08 #722), SulawesiSea (0.11 #772, 0.08 #330, 0.05 #1785), BandaSea (0.08 #331, 0.08 #639, 0.07 #684)
>> best conf = 0.82
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #180 for best value:
>> intensional similarity = 3
>> extensional distance = 9
>> proper extension:  PortoSanto;
>> query: (?x1338, AtlanticOcean) <- ?x1338[ a Island; has locatedIn ?x1027<P>;]
ranks of expected_values: 1
EVAL	SaoMiguel	locatedInWater	AtlanticOcean	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	69.000	66.000	46.000	0.818	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater
PRED expected values: AtlanticOcean
=> 179 concepts (179 used for prediction)
PRED predicted values (max 10 best out of 80): AtlanticOcean (0.90 #969, 0.90 #932, 0.90 #924), CaribbeanSea (0.39 #1390, 0.38 #1657, 0.34 #1568), PacificOcean (0.39 #2373, 0.37 #1209, 0.34 #3310), MediterraneanSea (0.35 #1076, 0.33 #987, 0.29 #451), JavaSea (0.27 #708, 0.22 #1246, 0.22 #1291), IndianOcean (0.20 #701, 0.17 #1239, 0.16 #1284), NorthSea (0.17 #1685, 0.17 #2982, 0.15 #3204), BandaSea (0.17 #728, 0.14 #1266, 0.10 #1891), Tajo (0.15 #3836, 0.08 #3474, 0.08 #3520), Guadiana (0.15 #3836, 0.08 #3474, 0.08 #3520)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #969 for best value:
>> intensional similarity = 10
>> extensional distance = 29
>> proper extension:  Antigua;
>> query: (?x1338, ?x182) <- ?x1338[ a Island; has belongsToIslands ?x200[ is belongsToIslands of ?x827[ has type ?x150;];]; has locatedIn ?x1027[ has government ?x2551; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x1026[ has type ?x706;];];]
>> Best rule #932 for best value:
>> intensional similarity = 10
>> extensional distance = 29
>> proper extension:  Antigua;
>> query: (?x1338, AtlanticOcean) <- ?x1338[ a Island; has belongsToIslands ?x200[ is belongsToIslands of ?x827[ has type ?x150;];]; has locatedIn ?x1027[ has government ?x2551; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x1026[ has type ?x706;];];]
>> Best rule #924 for best value:
>> intensional similarity = 9
>> extensional distance = 29
>> proper extension:  Tobago; Anguilla; Trinidad; St.Martin; SaintThomas;
>> query: (?x1338, ?x182) <- ?x1338[ a Island; has belongsToIslands ?x200[ is belongsToIslands of ?x827[ has type ?x150;];]; has locatedIn ?x1027[ has government ?x2551; has religion ?x352; is locatedIn of ?x182<AtlanticOcean>;];]
>> Best rule #887 for best value:
>> intensional similarity = 9
>> extensional distance = 29
>> proper extension:  Tobago; Anguilla; Trinidad; St.Martin; SaintThomas;
>> query: (?x1338, AtlanticOcean) <- ?x1338[ a Island; has belongsToIslands ?x200[ is belongsToIslands of ?x827[ has type ?x150;];]; has locatedIn ?x1027[ has government ?x2551; has religion ?x352; is locatedIn of ?x182<AtlanticOcean>;];]
ranks of expected_values: 1
EVAL	SaoMiguel	locatedInWater	AtlanticOcean	CNN-1.+1._MA	1.000	1.000	1.000	1.000	179.000	179.000	80.000	0.903	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#305-LakeChilwa
PRED entity: LakeChilwa

PRED relation: locatedIn
PRED expected values: MW
=> 42 concepts (37 used for prediction)
PRED predicted values (max 10 best out of 216): BR (0.64 #2483, 0.08 #7081, 0.07 #8506), GB (0.61 #2841, 0.08 #7081, 0.07 #8506), ZRE (0.55 #783, 0.40 #1020, 0.17 #6212), USA (0.47 #2667, 0.18 #3848, 0.16 #2194), RI (0.47 #3591, 0.32 #5007, 0.10 #521), EAT (0.40 #1116, 0.25 #409, 0.20 #644), RB (0.35 #1389, 0.11 #6607, 0.10 #679), CDN (0.34 #2185, 0.15 #2658, 0.11 #4311), RMM (0.28 #1828, 0.05 #4189, 0.04 #5602), EAK (0.27 #1531, 0.11 #6607, 0.10 #583)
>> best conf = 0.64
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2483 for best value:
>> intensional similarity = 6
>> extensional distance = 31
>> proper extension:  Araguaia; AtlanticOcean; Amazonas; SouthChinaSea; Paraguay; Mosel; Tocantins; Paraguay; Uruguay; Tocantins; ...
>> query: (?x1048, BR) <- ?x1048[ has locatedIn ?x192[ has ethnicGroup ?x197; has language ?x539<Portuguese>; has neighbor ?x819[ has religion ?x95;];];]

*> Best rule #408 for first EXPECTED value:
*> intensional similarity = 3
*> extensional distance = 2
*> proper extension:  LakeMalawi;
*> query: (?x1048, MW) <- ?x1048[ a Lake; has locatedIn ?x192<MOC>;]
*> conf = 0.25
ranks of expected_values: 13
EVAL	LakeChilwa	locatedIn	MW	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.077	42.000	37.000	216.000	0.636	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: MW
=> 133 concepts (127 used for prediction)
PRED predicted values (max 10 best out of 226): ZRE (0.73 #16767, 0.40 #3404, 0.33 #9832), RI (0.68 #6469, 0.65 #6946, 0.62 #7184), EAT (0.67 #2075, 0.60 #6353, 0.60 #884), EAK (0.57 #1538, 0.31 #3201, 0.24 #4150), AUS (0.56 #2185, 0.33 #7652, 0.29 #4557), CDN (0.55 #18422, 0.55 #18901, 0.44 #20333), RSA (0.50 #5762, 0.48 #20510, 0.20 #9991), USA (0.48 #8632, 0.37 #9587, 0.33 #10305), RB (0.48 #20510, 0.33 #1155, 0.19 #11189), MW (0.48 #20510, 0.25 #408, 0.20 #9991)
>> best conf = 0.73
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #16767 for best value:
>> intensional similarity = 12
>> extensional distance = 89
>> proper extension:  Kwa; Ruki; Lukuga; Uelle; Busira; Luvua; Fimi; Lualaba; Busira; Lomami; ...
>> query: (?x1048, ZRE) <- ?x1048[ has locatedIn ?x192[ has encompassed ?x213; has religion ?x116; has wasDependentOf ?x1027; is locatedIn of ?x242[ has hasSource ?x1955;]; is neighbor of ?x819[ a Country; has government ?x2064; has religion ?x95;]; is neighbor of ?x820<EAT>;];]

*> Best rule #20510 for first EXPECTED value:
*> intensional similarity = 13
*> extensional distance = 147
*> proper extension:  JoekulsaaFjoellum; JoekulsaaFjoellum; Snaefell; Thjorsa; GreenlandSea; JoekulsaaFjoellum; Thjorsa; Thjorsa;
*> query: (?x1048, ?x819) <- ?x1048[ has locatedIn ?x192[ has ethnicGroup ?x197; has government ?x435; has religion ?x187[ is religion of ?x736<RCA>;]; has wasDependentOf ?x1027; is locatedIn of ?x242[ has locatedIn ?x243<RSA>;]; is locatedIn of ?x2061[ a River; has locatedIn ?x819;];];]
*> conf = 0.48
ranks of expected_values: 10
EVAL	LakeChilwa	locatedIn	MW	CNN-1.+1._MA	0.000	0.000	1.000	0.100	133.000	127.000	226.000	0.725	http://www.semwebtech.org/mondial/10/meta#locatedIn

#304-Amerindian
PRED entity: Amerindian

PRED relation: ethnicGroup!
PRED expected values: RCH PA FGU HCA
=> 26 concepts (18 used for prediction)
PRED predicted values (max 10 best out of 213): HCA (0.50 #1091, 0.50 #713, 0.48 #727), PA (0.50 #1091, 0.50 #678, 0.48 #727), BZ (0.50 #1091, 0.48 #727, 0.42 #909), BR (0.50 #1091, 0.48 #727, 0.42 #909), RCH (0.50 #1091, 0.48 #727, 0.42 #909), BOL (0.50 #1091, 0.48 #727, 0.42 #909), C (0.50 #563, 0.38 #927, 0.38 #745), WL (0.50 #537, 0.33 #173, 0.25 #900), YV (0.50 #1091, 0.14 #2731, 0.10 #363), FGU (0.48 #727, 0.42 #909, 0.19 #2186)
>> best conf = 0.50
=> the first rule below is the first best rule for 7 predicted values
>> Best rule #1091 for best value:
>> intensional similarity = 8
>> extensional distance = 6
>> proper extension:  Maya;
>> query: (?x79, ?x202) <- ?x79[ is ethnicGroup of ?x296[ has language ?x702; has neighbor ?x202;]; is ethnicGroup of ?x351[ has encompassed ?x521<America>; has religion ?x1151<JehovasWitnesses>;];]
>> Best rule #713 for best value:
>> intensional similarity = 10
>> extensional distance = 2
>> proper extension:  European;
>> query: (?x79, HCA) <- ?x79[ is ethnicGroup of ?x296[ has government ?x700; is locatedIn of ?x264; is neighbor of ?x202;]; is ethnicGroup of ?x351[ has encompassed ?x521; has religion ?x1151<JehovasWitnesses>;]; is ethnicGroup of ?x482<MEX>;]
ranks of expected_values: 1, 2, 5, 10
EVAL	Amerindian	ethnicGroup!	HCA	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	26.000	18.000	213.000	0.500	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	Amerindian	ethnicGroup!	FGU	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.143	26.000	18.000	213.000	0.500	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	Amerindian	ethnicGroup!	PA	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	26.000	18.000	213.000	0.500	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	Amerindian	ethnicGroup!	RCH	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.333	26.000	18.000	213.000	0.500	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup!
PRED expected values: RCH PA FGU HCA
=> 61 concepts (61 used for prediction)
PRED predicted values (max 10 best out of 222): HCA (0.70 #1099, 0.68 #736, 0.64 #1648), BZ (0.70 #1099, 0.68 #736, 0.64 #1648), BR (0.68 #736, 0.64 #1648, 0.56 #183), PA (0.68 #736, 0.64 #1648, 0.56 #183), YV (0.68 #736, 0.64 #1648, 0.47 #2569), C (0.67 #1484, 0.60 #935, 0.50 #571), BERM (0.60 #1254, 0.50 #890, 0.27 #1834), BOL (0.56 #183, 0.47 #2569, 0.41 #2016), RCH (0.56 #183, 0.47 #2569, 0.41 #2016), FGU (0.56 #183, 0.41 #2016, 0.40 #551)
>> best conf = 0.70
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #1099 for best value:
>> intensional similarity = 19
>> extensional distance = 3
>> proper extension:  African;
>> query: (?x79, ?x1364) <- ?x79[ a EthnicGroup; is ethnicGroup of ?x80[ has government ?x562;]; is ethnicGroup of ?x215<CO>; is ethnicGroup of ?x654[ has encompassed ?x521; has ethnicGroup ?x197<European>; has neighbor ?x1364; has religion ?x95<Protestant>; has wasDependentOf ?x149; is locatedIn of ?x282;]; is ethnicGroup of ?x899[ is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x1557[ has belongsToIslands ?x1962;];];]
ranks of expected_values: 1, 4, 9, 10
EVAL	Amerindian	ethnicGroup!	HCA	CNN-1.+1._MA	1.000	1.000	1.000	1.000	61.000	61.000	222.000	0.697	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	Amerindian	ethnicGroup!	FGU	CNN-1.+1._MA	0.000	0.000	1.000	0.143	61.000	61.000	222.000	0.697	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	Amerindian	ethnicGroup!	PA	CNN-1.+1._MA	0.000	1.000	1.000	0.333	61.000	61.000	222.000	0.697	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	Amerindian	ethnicGroup!	RCH	CNN-1.+1._MA	0.000	0.000	1.000	0.143	61.000	61.000	222.000	0.697	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#303-E
PRED entity: E

PRED relation: neighbor!
PRED expected values: GBZ
=> 50 concepts (50 used for prediction)
PRED predicted values (max 10 best out of 215): GBZ (0.90 #5249, 0.90 #4932, 0.89 #635), CH (0.40 #44, 0.33 #203, 0.26 #7163), R (0.27 #3186, 0.19 #4617, 0.18 #2551), D (0.26 #7163, 0.21 #1445, 0.20 #14), E (0.26 #7163, 0.20 #19, 0.18 #6523), I (0.26 #7163, 0.20 #37, 0.18 #6523), B (0.26 #7163, 0.20 #94, 0.17 #253), MC (0.26 #7163, 0.20 #155, 0.17 #314), L (0.26 #7163, 0.20 #118, 0.17 #277), TR (0.23 #2546, 0.23 #2417, 0.18 #6523)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5249 for best value:
>> intensional similarity = 5
>> extensional distance = 75
>> proper extension:  FGU;
>> query: (?x149, ?x78) <- ?x149[ a Country; has encompassed ?x195; has language ?x790; has neighbor ?x78; is locatedIn of ?x68;]
ranks of expected_values: 1
EVAL	E	neighbor!	GBZ	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	50.000	50.000	215.000	0.904	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: GBZ
=> 113 concepts (113 used for prediction)
PRED predicted values (max 10 best out of 234): GBZ (0.92 #12650, 0.92 #12651, 0.91 #14439), D (0.67 #5350, 0.55 #6320, 0.50 #1301), E (0.60 #2441, 0.60 #1449, 0.43 #1448), BR (0.60 #1449, 0.43 #1448, 0.36 #6236), HR (0.60 #1449, 0.43 #1448, 0.36 #6467), RCH (0.60 #1449, 0.43 #1448, 0.36 #6467), RA (0.60 #1449, 0.43 #1448, 0.36 #6467), ET (0.60 #1449, 0.43 #1448, 0.36 #6467), GAZA (0.60 #1449, 0.43 #1448, 0.36 #6467), SYR (0.60 #1449, 0.43 #1448, 0.36 #6467)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #12650 for best value:
>> intensional similarity = 12
>> extensional distance = 35
>> proper extension:  NOK;
>> query: (?x149, ?x78) <- ?x149[ a Country; has encompassed ?x195; has neighbor ?x78; has neighbor ?x1027[ is locatedIn of ?x199;]; is locatedIn of ?x275[ has locatedIn ?x185[ a Country; is neighbor of ?x353<GE>;]; has mergesWith ?x1633; is locatedInWater of ?x86;];]
ranks of expected_values: 1
EVAL	E	neighbor!	GBZ	CNN-1.+1._MA	1.000	1.000	1.000	1.000	113.000	113.000	234.000	0.919	http://www.semwebtech.org/mondial/10/meta#neighbor

#302-Romanian
PRED entity: Romanian

PRED relation: ethnicGroup!
PRED expected values: RO UA
=> 22 concepts (20 used for prediction)
PRED predicted values (max 10 best out of 184): CZ (0.57 #97, 0.31 #290, 0.29 #681), UA (0.53 #640, 0.45 #835, 0.33 #1947), A (0.46 #277, 0.33 #1947, 0.30 #863), SRB (0.43 #158, 0.33 #1947, 0.31 #547), SK (0.43 #25, 0.33 #1947, 0.30 #3120), RO (0.43 #28, 0.33 #1947, 0.30 #3120), HR (0.38 #215, 0.33 #1947, 0.31 #411), SLO (0.33 #1947, 0.30 #3120, 0.29 #389), BG (0.33 #1947, 0.30 #3120, 0.21 #1364), MD (0.33 #1947, 0.30 #3120, 0.21 #1364)
>> best conf = 0.57
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #97 for best value:
>> intensional similarity = 3
>> extensional distance = 5
>> proper extension:  Roma; German; Hungarian; Serb; Slovak;
>> query: (?x1531, CZ) <- ?x1531[ a EthnicGroup; is ethnicGroup of ?x236<H>;]

*> Best rule #640 for first EXPECTED value:
*> intensional similarity = 9
*> extensional distance = 15
*> proper extension:  Bosniak; Montenegrin;
*> query: (?x1531, UA) <- ?x1531[ a EthnicGroup; is ethnicGroup of ?x236[ a Country; has neighbor ?x446[ is locatedIn of ?x275;]; is locatedIn of ?x133<Donau>; is locatedIn of ?x708<Theiss>;];]
*> conf = 0.53
ranks of expected_values: 2, 6
EVAL	Romanian	ethnicGroup!	UA	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	22.000	20.000	184.000	0.571	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	Romanian	ethnicGroup!	RO	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.200	22.000	20.000	184.000	0.571	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup!
PRED expected values: RO UA
=> 74 concepts (74 used for prediction)
PRED predicted values (max 10 best out of 219): CZ (0.70 #686, 0.57 #97, 0.55 #884), SK (0.64 #812, 0.54 #1408, 0.50 #2206), RO (0.56 #418, 0.44 #1980, 0.43 #589), A (0.50 #1267, 0.44 #1980, 0.41 #1977), UA (0.45 #3626, 0.43 #589, 0.43 #588), HR (0.44 #388, 0.44 #215, 0.43 #589), PL (0.44 #1980, 0.39 #2774, 0.39 #390), KAZ (0.44 #1980, 0.39 #2774, 0.39 #390), D (0.44 #1980, 0.39 #2774, 0.39 #390), CH (0.44 #1980, 0.39 #2774, 0.39 #390)
>> best conf = 0.70
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #686 for best value:
>> intensional similarity = 20
>> extensional distance = 8
>> proper extension:  Czech; Polish; Moravian;
>> query: (?x1531, CZ) <- ?x1531[ a EthnicGroup; is ethnicGroup of ?x236[ has ethnicGroup ?x164<Roma>; has ethnicGroup ?x237<German>; has ethnicGroup ?x517<Hungarian>; has government ?x254<"parliamentary democracy">; has language ?x684; has neighbor ?x156[ has encompassed ?x195; has religion ?x56; has wasDependentOf ?x1197;]; has neighbor ?x904[ has language ?x1296; is locatedIn of ?x132;]; is locatedIn of ?x133; is neighbor of ?x424;];]

*> Best rule #418 for first EXPECTED value:
*> intensional similarity = 22
*> extensional distance = 7
*> proper extension:  Ukrainian; Russian;
*> query: (?x1531, RO) <- ?x1531[ a EthnicGroup; is ethnicGroup of ?x236[ a Country; has ethnicGroup ?x164<Roma>; has ethnicGroup ?x237<German>; has ethnicGroup ?x517<Hungarian>; has government ?x254; has language ?x684<Hungarian>; has neighbor ?x156[ has neighbor ?x55; has religion ?x56; has wasDependentOf ?x1197;]; has neighbor ?x163[ has ethnicGroup ?x58;]; has neighbor ?x904<SRB>; is locatedIn of ?x133; is neighbor of ?x424;];]
*> conf = 0.56
ranks of expected_values: 3, 5
EVAL	Romanian	ethnicGroup!	UA	CNN-1.+1._MA	0.000	0.000	1.000	0.250	74.000	74.000	219.000	0.700	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	Romanian	ethnicGroup!	RO	CNN-1.+1._MA	0.000	1.000	1.000	0.333	74.000	74.000	219.000	0.700	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#301-GunungBinaiya
PRED entity: GunungBinaiya

PRED relation: locatedOnIsland
PRED expected values: Ceram
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 3): Iceland (0.02 #17), Madagaskar (0.01 #38), NewGuinea (0.01 #30)
>> best conf = 0.02
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #17 for best value:
>> intensional similarity = 1
>> extensional distance = 250
>> proper extension:  JabalKatrina; Tahat; Annapurna; Bjelucha; Schchara; GranSasso; EmiKussi; Zugspitze; Tahan; Demirkazik; ...
>> query: (?x2530, Iceland) <- ?x2530[ a Mountain;]

No rule for expected values
ranks of expected_values: 
EVAL	GunungBinaiya	locatedOnIsland	Ceram	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	2.000	2.000	3.000	0.016	http://www.semwebtech.org/mondial/10/meta#locatedOnIsland

PRED relation: locatedOnIsland
PRED expected values: Ceram
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 3): Iceland (0.02 #17), Madagaskar (0.01 #38), NewGuinea (0.01 #30)
>> best conf = 0.02
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #17 for best value:
>> intensional similarity = 1
>> extensional distance = 250
>> proper extension:  JabalKatrina; Tahat; Annapurna; Bjelucha; Schchara; GranSasso; EmiKussi; Zugspitze; Tahan; Demirkazik; ...
>> query: (?x2530, Iceland) <- ?x2530[ a Mountain;]

No rule for expected values
ranks of expected_values: 
EVAL	GunungBinaiya	locatedOnIsland	Ceram	CNN-1.+1._MA	0.000	0.000	0.000	0.000	2.000	2.000	3.000	0.016	http://www.semwebtech.org/mondial/10/meta#locatedOnIsland

#300-Urubamba
PRED entity: Urubamba

PRED relation: locatedIn
PRED expected values: PE
=> 28 concepts (28 used for prediction)
PRED predicted values (max 10 best out of 68): PE (0.71 #711, 0.71 #474, 0.71 #304), CO (0.36 #5925, 0.33 #5926, 0.30 #4029), BOL (0.36 #5925, 0.33 #5926, 0.30 #4029), RA (0.36 #5925, 0.33 #5926, 0.29 #2843), RCH (0.36 #5925, 0.33 #5926, 0.29 #2843), EC (0.36 #5925, 0.33 #5926, 0.29 #2843), YV (0.36 #5925, 0.33 #5926, 0.29 #2843), USA (0.16 #2202, 0.15 #2677, 0.11 #5760), ZRE (0.14 #2447, 0.12 #3397, 0.11 #1736), I (0.10 #3603, 0.10 #3840, 0.07 #3128)
>> best conf = 0.71
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #711 for best value:
>> intensional similarity = 7
>> extensional distance = 5
>> proper extension:  Ene; RioMamore; Ucayali; Maranon;
>> query: (?x1627, ?x296) <- ?x1627[ a Source; has inMountains ?x431<Andes>; is hasSource of ?x1781[ has flowsInto ?x987[ a River;]; has locatedIn ?x296;];]
>> Best rule #474 for best value:
>> intensional similarity = 7
>> extensional distance = 5
>> proper extension:  Huallaga;
>> query: (?x1627, ?x296) <- ?x1627[ a Source; is hasSource of ?x1781[ has flowsInto ?x987; has hasEstuary ?x2305[ a Estuary; has locatedIn ?x296<PE>;];];]
>> Best rule #304 for best value:
>> intensional similarity = 7
>> extensional distance = 5
>> proper extension:  Huallaga;
>> query: (?x1627, PE) <- ?x1627[ a Source; is hasSource of ?x1781[ has flowsInto ?x987; has hasEstuary ?x2305[ a Estuary; has locatedIn ?x296<PE>;];];]
ranks of expected_values: 1
EVAL	Urubamba	locatedIn	PE	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	28.000	28.000	68.000	0.714	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: PE
=> 112 concepts (112 used for prediction)
PRED predicted values (max 10 best out of 74): PE (0.83 #5005, 0.82 #14558, 0.82 #7631), USA (0.52 #7944, 0.48 #9379, 0.42 #4838), CDN (0.45 #7456, 0.17 #4829, 0.16 #9370), R (0.39 #12652, 0.38 #13368, 0.36 #14804), BOL (0.36 #26040, 0.36 #26039, 0.36 #26037), CO (0.36 #26040, 0.36 #26039, 0.36 #26037), RA (0.36 #26040, 0.36 #26039, 0.36 #26037), RCH (0.36 #26040, 0.36 #26039, 0.36 #26037), EC (0.36 #26040, 0.36 #26039, 0.36 #26037), YV (0.36 #26040, 0.36 #26039, 0.36 #26037)
>> best conf = 0.83
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5005 for best value:
>> intensional similarity = 15
>> extensional distance = 10
>> proper extension:  DetroitRiver;
>> query: (?x1627, ?x296) <- ?x1627[ a Source; is hasSource of ?x1781[ a River; has flowsInto ?x987[ has flowsInto ?x214;]; has locatedIn ?x296[ a Country; has ethnicGroup ?x79; has language ?x796<Spanish>; has religion ?x95; has wasDependentOf ?x149; is locatedIn of ?x282<PacificOcean>; is neighbor of ?x202;];];]
ranks of expected_values: 1
EVAL	Urubamba	locatedIn	PE	CNN-1.+1._MA	1.000	1.000	1.000	1.000	112.000	112.000	74.000	0.833	http://www.semwebtech.org/mondial/10/meta#locatedIn

#299-MACX
PRED entity: MACX

PRED relation: government
PRED expected values: "limited democracy"
=> 50 concepts (50 used for prediction)
PRED predicted values (max 10 best out of 66): "overseas department of France" (0.40 #156, 0.29 #516, 0.25 #588), "British Overseas Territories" (0.36 #1015, 0.33 #799, 0.33 #367), "republic" (0.33 #294, 0.31 #2600, 0.31 #2744), "federal republic" (0.33 #435, 0.17 #219, 0.16 #2811), "parliamentary representative democratic French overseas collectivity" (0.25 #119, 0.25 #47, 0.20 #191), "limited democracy" (0.25 #52, 0.17 #484, 0.17 #412), "parliamentary democracy" (0.18 #1373, 0.18 #1301, 0.18 #1229), "constitutional monarchy" (0.17 #866, 0.17 #218, 0.16 #2811), "Communist state" (0.17 #877, 0.17 #229, 0.16 #2811), "parliamentary government took power in March 2011" (0.17 #259, 0.12 #691, 0.08 #763)
>> best conf = 0.40
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #156 for best value:
>> intensional similarity = 8
>> extensional distance = 3
>> proper extension:  GUAD;
>> query: (?x641, "overseas department of France") <- ?x641[ a Country; has dependentOf ?x232; has ethnicGroup ?x298<Chinese>; has religion ?x352[ is religion of ?x315<USA>;]; is locatedIn of ?x384;]

*> Best rule #52 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 2
*> proper extension:  FPOL; HONX;
*> query: (?x641, "limited democracy") <- ?x641[ has dependentOf ?x232; has ethnicGroup ?x298<Chinese>; has ethnicGroup ?x1673[ a EthnicGroup;]; has language ?x539; has religion ?x95; is locatedIn of ?x384;]
*> conf = 0.25
ranks of expected_values: 6
EVAL	MACX	government	"limited democracy"	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.167	50.000	50.000	66.000	0.400	http://www.semwebtech.org/mondial/10/meta#government

PRED relation: government
PRED expected values: "limited democracy"
=> 89 concepts (89 used for prediction)
PRED predicted values (max 10 best out of 71): "republic" (0.33 #151, 0.32 #2613, 0.32 #3767), "Communist state" (0.33 #13, 0.25 #652, 0.25 #592), "constitutional monarchy and Commonwealth realm" (0.33 #106, 0.14 #830, 0.07 #1771), "British Overseas Territories" (0.32 #2249, 0.15 #2686, 0.14 #2974), "territory of Australia" (0.29 #739, 0.20 #1100, 0.10 #2549), "constitutional monarchy" (0.25 #1377, 0.25 #1305, 0.17 #1160), "limited democracy" (0.25 #557, 0.18 #4050, 0.16 #5931), "republic; authoritarian presidential rule, with little power outside the executive branch" (0.25 #423, 0.18 #4050, 0.16 #5931), "territory of Norway administered by the Ministry of Industry" (0.25 #526, 0.08 #1613, 0.03 #2988), "federal republic" (0.22 #943, 0.18 #4050, 0.17 #655)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #151 for best value:
>> intensional similarity = 18
>> extensional distance = 1
>> proper extension:  RP;
>> query: (?x641, "republic") <- ?x641[ a Country; has encompassed ?x175; has ethnicGroup ?x298<Chinese>; has ethnicGroup ?x1673[ a EthnicGroup; is ethnicGroup of ?x718[ a Country; has encompassed ?x195; has language ?x51; has religion ?x109; has wasDependentOf ?x575; is neighbor of ?x78;];]; has religion ?x352<RomanCatholic>; has religion ?x462<Buddhist>; is locatedIn of ?x384;]

*> Best rule #557 for first EXPECTED value:
*> intensional similarity = 18
*> extensional distance = 2
*> proper extension:  SVAX;
*> query: (?x641, "limited democracy") <- ?x641[ a Country; has dependentOf ?x232[ has encompassed ?x175; has government ?x831; has neighbor ?x83[ is wasDependentOf of ?x943;]; has neighbor ?x334[ has government ?x1979;]; has neighbor ?x381[ a Country;]; has neighbor ?x403[ has ethnicGroup ?x58<Ukrainian>; is locatedIn of ?x127;]; is locatedIn of ?x338[ has hasEstuary ?x1481;];]; has ethnicGroup ?x1673[ a EthnicGroup;];]
*> conf = 0.25
ranks of expected_values: 7
EVAL	MACX	government	"limited democracy"	CNN-1.+1._MA	0.000	0.000	1.000	0.143	89.000	89.000	71.000	0.333	http://www.semwebtech.org/mondial/10/meta#government

#298-NORF
PRED entity: NORF

PRED relation: locatedIn!
PRED expected values: NorfolkIsland
=> 27 concepts (25 used for prediction)
PRED predicted values (max 10 best out of 1089): AtlanticOcean (0.57 #7159, 0.50 #8583, 0.45 #14276), IndianOcean (0.50 #1426, 0.25 #2849, 0.20 #4272), Efate (0.33 #836, 0.04 #8541, 0.04 #31324), CaribbeanSea (0.29 #7222, 0.28 #14339, 0.26 #15763), PulauPanjang (0.25 #2704, 0.10 #5550, 0.09 #6974), Tasmania (0.25 #3012, 0.07 #7283, 0.06 #8707), TeIka-a-Maui-NorthIsland- (0.25 #3294, 0.07 #7565, 0.06 #8989), TeWaka-a-Maui-SouthIsland- (0.25 #3102, 0.07 #7373, 0.06 #8797), Uluru (0.25 #4210, 0.07 #8481, 0.06 #9905), SnowyRiver (0.25 #4205, 0.07 #8476, 0.06 #9900)
>> best conf = 0.57
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #7159 for best value:
>> intensional similarity = 8
>> extensional distance = 12
>> proper extension:  GB; IRL; BS; TT; SY; WL;
>> query: (?x210, AtlanticOcean) <- ?x210[ a Country; has encompassed ?x211; has religion ?x713<Anglican>; has religion ?x1524[ a Religion;]; is locatedIn of ?x282[ is locatedInWater of ?x205;];]

*> Best rule #8541 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 12
*> proper extension:  GB; IRL; BS; TT; SY; WL;
*> query: (?x210, ?x205) <- ?x210[ a Country; has encompassed ?x211; has religion ?x713<Anglican>; has religion ?x1524[ a Religion;]; is locatedIn of ?x282[ is locatedInWater of ?x205;];]
*> conf = 0.04
ranks of expected_values: 194
EVAL	NORF	locatedIn!	NorfolkIsland	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.005	27.000	25.000	1089.000	0.571	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: NorfolkIsland
=> 64 concepts (64 used for prediction)
PRED predicted values (max 10 best out of 1251): AtlanticOcean (0.57 #37159, 0.46 #27166, 0.46 #52881), CaribbeanSea (0.53 #50082, 0.40 #12949, 0.32 #35798), IndianOcean (0.50 #5712, 0.40 #14278, 0.36 #48549), LakeJindabyne (0.33 #5707, 0.25 #10833, 0.24 #11418), EucumbeneRiver (0.33 #5707, 0.25 #10667, 0.24 #11418), MurrumbidgeeRiver (0.33 #5707, 0.25 #10488, 0.24 #11418), Tutuila (0.33 #3108, 0.20 #15960, 0.17 #17390), Efate (0.33 #836, 0.20 #12254, 0.14 #19398), Pitcairn (0.25 #7894, 0.24 #11418, 0.20 #12174), Tasmania (0.25 #10160, 0.24 #11418, 0.14 #5709)
>> best conf = 0.57
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #37159 for best value:
>> intensional similarity = 23
>> extensional distance = 21
>> proper extension:  GROX;
>> query: (?x210, AtlanticOcean) <- ?x210[ has dependentOf ?x196[ a Country; has encompassed ?x211; has religion ?x56; is locatedIn of ?x60;]; has government ?x907; has religion ?x713[ is religion of ?x154[ has encompassed ?x195; has ethnicGroup ?x162; has language ?x247; is locatedIn of ?x153;]; is religion of ?x407<AXA>; is religion of ?x667<TT>; is religion of ?x758[ has government ?x435; is locatedIn of ?x1442;]; is religion of ?x853<PNG>; is religion of ?x1554<WL>;];]

*> Best rule #57125 for first EXPECTED value:
*> intensional similarity = 19
*> extensional distance = 82
*> proper extension:  SK; RO; H; UA; SRB;
*> query: (?x210, ?x205) <- ?x210[ a Country; has encompassed ?x211; has government ?x907; has religion ?x429[ a Religion; is religion of ?x390<SLB>; is religion of ?x428[ a Country; has government ?x562; has language ?x2065; is locatedIn of ?x1372;]; is religion of ?x853<PNG>;]; has religion ?x713[ is religion of ?x667<TT>;]; is locatedIn of ?x282[ is locatedInWater of ?x205;];]
*> conf = 0.03
ranks of expected_values: 927
EVAL	NORF	locatedIn!	NorfolkIsland	CNN-1.+1._MA	0.000	0.000	0.000	0.001	64.000	64.000	1251.000	0.565	http://www.semwebtech.org/mondial/10/meta#locatedIn

#297-Pemba
PRED entity: Pemba

PRED relation: locatedIn
PRED expected values: EAT
=> 18 concepts (18 used for prediction)
PRED predicted values (max 10 best out of 75): RI (0.31 #713, 0.31 #528, 0.25 #52), USA (0.13 #786, 0.13 #1028, 0.08 #1271), AUS (0.12 #45, 0.12 #281, 0.12 #521), RP (0.09 #823, 0.09 #1065, 0.04 #2263), COM (0.08 #211, 0.08 #447, 0.08 #687), GB (0.07 #1448, 0.07 #1685, 0.07 #2163), MS (0.07 #1194, 0.06 #474, 0.06 #475), CL (0.07 #1194, 0.06 #474, 0.06 #475), EAT (0.07 #1194, 0.06 #474, 0.06 #475), IND (0.07 #1194, 0.06 #474, 0.06 #475)
>> best conf = 0.31
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #713 for best value:
>> intensional similarity = 13
>> extensional distance = 24
>> proper extension:  Sulawesi;
>> query: (?x2262, ?x217) <- ?x2262[ a Island; has locatedInWater ?x60[ has locatedIn ?x217<RI>; has mergesWith ?x182; is locatedInWater of ?x1047<Bali>; is locatedInWater of ?x1157<Sumbawa>; is locatedInWater of ?x1666[ has belongsToIslands ?x227; is locatedOnIsland of ?x1247;]; is mergesWith of ?x241;];]
>> Best rule #528 for best value:
>> intensional similarity = 13
>> extensional distance = 24
>> proper extension:  Sulawesi;
>> query: (?x2262, RI) <- ?x2262[ a Island; has locatedInWater ?x60[ has locatedIn ?x217<RI>; has mergesWith ?x182; is locatedInWater of ?x1047<Bali>; is locatedInWater of ?x1157<Sumbawa>; is locatedInWater of ?x1666[ has belongsToIslands ?x227; is locatedOnIsland of ?x1247;]; is mergesWith of ?x241;];]

*> Best rule #1194 for first EXPECTED value:
*> intensional similarity = 13
*> extensional distance = 100
*> proper extension:  Samosir;
*> query: (?x2262, ?x434) <- ?x2262[ a Island; has locatedInWater ?x60[ has locatedIn ?x217<RI>; has locatedIn ?x434[ has religion ?x187<Muslim>;]; has locatedIn ?x758[ has encompassed ?x213; has wasDependentOf ?x81;]; has locatedIn ?x797[ a Country; has religion ?x410;];];]
*> conf = 0.07
ranks of expected_values: 9
EVAL	Pemba	locatedIn	EAT	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.111	18.000	18.000	75.000	0.308	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: EAT
=> 32 concepts (32 used for prediction)
PRED predicted values (max 10 best out of 138): RI (0.31 #782, 0.25 #52, 0.24 #1765), GR (0.17 #2050, 0.10 #4274, 0.08 #4521), USA (0.17 #3021, 0.15 #3267, 0.14 #3514), RP (0.16 #1822, 0.12 #2316, 0.10 #2563), I (0.16 #2008, 0.09 #4232, 0.07 #4479), IND (0.14 #3192, 0.12 #3684, 0.11 #5164), GB (0.13 #3947, 0.10 #4686, 0.09 #1954), AUS (0.12 #45, 0.12 #525, 0.12 #281), EAT (0.10 #1959, 0.10 #1958, 0.10 #4677), CL (0.10 #1959, 0.10 #1958, 0.10 #4677)
>> best conf = 0.31
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #782 for best value:
>> intensional similarity = 32
>> extensional distance = 24
>> proper extension:  Bangka; Sulawesi;
>> query: (?x2262, RI) <- ?x2262[ has locatedInWater ?x60[ has locatedIn ?x434[ a Country;]; has locatedIn ?x474[ has ethnicGroup ?x244;]; has locatedIn ?x508[ has government ?x435; has religion ?x116;]; has locatedIn ?x735<TL>; has locatedIn ?x924[ is neighbor of ?x83;]; is locatedInWater of ?x240<Java>; is locatedInWater of ?x740<Sumatra>; is locatedInWater of ?x1047<Bali>; is locatedInWater of ?x1157<Sumbawa>; is locatedInWater of ?x1555[ is locatedOnIsland of ?x1167;]; is locatedInWater of ?x1611[ has type ?x150;]; is locatedInWater of ?x1768<Lombok>; is mergesWith of ?x182[ a Sea; has locatedIn ?x50; is flowsInto of ?x137; is locatedInWater of ?x112; is mergesWith of ?x121;];];]

*> Best rule #1959 for first EXPECTED value:
*> intensional similarity = 24
*> extensional distance = 43
*> proper extension:  Ambon; Cebu; Borneo; Panay; Samar; Ternate; Negros; Timor; Palawan; Ceram; ...
*> query: (?x2262, ?x474) <- ?x2262[ has locatedInWater ?x60[ a Sea; has locatedIn ?x192[ has encompassed ?x213; has ethnicGroup ?x197; has neighbor ?x193;]; has locatedIn ?x474[ has ethnicGroup ?x244;]; has locatedIn ?x758[ has government ?x435<"republic">; has religion ?x352; has wasDependentOf ?x81;]; has locatedIn ?x820[ a Country; has neighbor ?x348; has religion ?x116;]; has mergesWith ?x1333; is locatedInWater of ?x433[ a Island;]; is locatedInWater of ?x1047[ has belongsToIslands ?x875;]; is mergesWith of ?x282<PacificOcean>;];]
*> conf = 0.10
ranks of expected_values: 9
EVAL	Pemba	locatedIn	EAT	CNN-1.+1._MA	0.000	0.000	1.000	0.111	32.000	32.000	138.000	0.308	http://www.semwebtech.org/mondial/10/meta#locatedIn

#296-Jersey
PRED entity: Jersey

PRED relation: locatedInWater
PRED expected values: TheChannel
=> 55 concepts (55 used for prediction)
PRED predicted values (max 10 best out of 36): TheChannel (0.64 #1476, 0.64 #1345, 0.45 #1521), Donau (0.50 #177, 0.38 #220, 0.07 #656), AtlanticOcean (0.44 #353, 0.43 #310, 0.42 #440), CaribbeanSea (0.40 #279, 0.25 #235, 0.24 #496), MediterraneanSea (0.34 #625, 0.25 #711, 0.23 #668), PacificOcean (0.33 #320, 0.32 #407, 0.29 #450), Jersey (0.33 #477, 0.32 #521, 0.29 #390), NorthSea (0.26 #741, 0.25 #784, 0.22 #698), ArcticOcean (0.25 #100, 0.07 #839, 0.06 #447), NorwegianSea (0.25 #107, 0.06 #759, 0.05 #673)
>> best conf = 0.64
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1476 for best value:
>> intensional similarity = 6
>> extensional distance = 196
>> proper extension:  Fakaofo; Guadalcanal; Bougainville; Streymoy;
>> query: (?x642, ?x1211) <- ?x642[ a Island; has belongsToIslands ?x2310[ a Islands; is belongsToIslands of ?x1210[ a Island; has locatedInWater ?x1211;];];]
ranks of expected_values: 1
EVAL	Jersey	locatedInWater	TheChannel	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	55.000	55.000	36.000	0.641	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater
PRED expected values: TheChannel
=> 126 concepts (126 used for prediction)
PRED predicted values (max 10 best out of 64): TheChannel (0.90 #1017, 0.86 #662, 0.83 #2000), AtlanticOcean (0.64 #580, 0.60 #757, 0.59 #2053), IrishSea (0.60 #349, 0.50 #437, 0.15 #792), Donau (0.60 #267, 0.07 #1692, 0.06 #1424), PacificOcean (0.43 #811, 0.31 #2333, 0.26 #723), CaribbeanSea (0.42 #636, 0.40 #548, 0.40 #502), MediterraneanSea (0.39 #1881, 0.35 #1078, 0.28 #1122), NorthSea (0.37 #1468, 0.29 #1513, 0.28 #2095), Araguaia (0.33 #1, 0.01 #2047, 0.01 #2226), Jersey (0.27 #750, 0.05 #2181, 0.05 #1732)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1017 for best value:
>> intensional similarity = 11
>> extensional distance = 23
>> proper extension:  Reunion;
>> query: (?x642, ?x1211) <- ?x642[ a Island; has locatedIn ?x643[ a Country; has dependentOf ?x81[ is locatedIn of ?x153; is wasDependentOf of ?x63;]; has encompassed ?x195; is locatedIn of ?x1211[ is flowsInto of ?x1383; is locatedInWater of ?x495; is mergesWith of ?x121;];];]
ranks of expected_values: 1
EVAL	Jersey	locatedInWater	TheChannel	CNN-1.+1._MA	1.000	1.000	1.000	1.000	126.000	126.000	64.000	0.897	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#295-RH
PRED entity: RH

PRED relation: language
PRED expected values: French
=> 51 concepts (51 used for prediction)
PRED predicted values (max 10 best out of 97): Spanish (0.64 #410, 0.62 #1478, 0.61 #1672), English (0.56 #295, 0.37 #2042, 0.36 #392), French (0.31 #583, 0.30 #1166, 0.17 #3397), German (0.31 #597, 0.22 #1180, 0.15 #3217), Russian (0.18 #2728, 0.15 #3310, 0.15 #3601), Portuguese (0.17 #106, 0.11 #203, 0.09 #1174), Italian (0.15 #589, 0.09 #1172, 0.05 #3209), Serbian (0.11 #233, 0.10 #2756, 0.09 #3338), Dutch (0.11 #204, 0.09 #398, 0.08 #2533), Hungarian (0.11 #211, 0.09 #3122, 0.08 #2540)
>> best conf = 0.64
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #410 for best value:
>> intensional similarity = 8
>> extensional distance = 9
>> proper extension:  NLSM;
>> query: (?x697, Spanish) <- ?x697[ has government ?x435; has language ?x2186; has religion ?x95<Protestant>; has religion ?x352<RomanCatholic>; is locatedIn of ?x317<CaribbeanSea>;]

*> Best rule #583 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 11
*> proper extension:  F; GB; NAM; RCH; I; CH; PE; RA; MEX; BR; ...
*> query: (?x697, French) <- ?x697[ has government ?x435; has language ?x2186; has neighbor ?x520[ is locatedIn of ?x182<AtlanticOcean>;]; has religion ?x95<Protestant>;]
*> conf = 0.31
ranks of expected_values: 3
EVAL	RH	language	French	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.333	51.000	51.000	97.000	0.636	http://www.semwebtech.org/mondial/10/meta#language

PRED relation: language
PRED expected values: French
=> 68 concepts (68 used for prediction)
PRED predicted values (max 10 best out of 92): Spanish (0.86 #2842, 0.75 #1381, 0.70 #1651), English (0.60 #877, 0.56 #1557, 0.33 #1071), Miskito (0.33 #288, 0.17 #1258, 0.17 #1064), French (0.20 #874, 0.15 #2529, 0.14 #3113), Dutch (0.20 #883, 0.10 #2538, 0.09 #2928), Papiamento (0.20 #887, 0.08 #1944, 0.06 #3321), German (0.18 #3127, 0.15 #2543, 0.14 #2933), Russian (0.16 #4289, 0.14 #4387, 0.10 #4775), Catalan (0.14 #1282, 0.12 #1477, 0.08 #1944), Basque (0.14 #1292, 0.12 #1487, 0.08 #1944)
>> best conf = 0.86
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2842 for best value:
>> intensional similarity = 15
>> extensional distance = 19
>> proper extension:  BZ;
>> query: (?x697, Spanish) <- ?x697[ a Country; has encompassed ?x521; has language ?x2186; has religion ?x95; is neighbor of ?x520[ has ethnicGroup ?x162[ a EthnicGroup; is ethnicGroup of ?x215<CO>; is ethnicGroup of ?x902<EC>; is ethnicGroup of ?x1130<BDS>;]; has language ?x796; is locatedIn of ?x182;];]

*> Best rule #874 for first EXPECTED value:
*> intensional similarity = 16
*> extensional distance = 3
*> proper extension:  NLSM;
*> query: (?x697, French) <- ?x697[ a Country; has government ?x435; has language ?x2186; has religion ?x95<Protestant>; has religion ?x352<RomanCatholic>; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x317<CaribbeanSea>; is locatedIn of ?x2210[ a Island; has locatedIn ?x520[ has encompassed ?x521; has government ?x711;];];]
*> conf = 0.20
ranks of expected_values: 4
EVAL	RH	language	French	CNN-1.+1._MA	0.000	0.000	1.000	0.250	68.000	68.000	92.000	0.857	http://www.semwebtech.org/mondial/10/meta#language

#294-R
PRED entity: R

PRED relation: neighbor
PRED expected values: NOK
=> 37 concepts (37 used for prediction)
PRED predicted values (max 10 best out of 187): IR (0.60 #937, 0.11 #4153, 0.11 #2966), TR (0.40 #918, 0.33 #29, 0.11 #4153), ARM (0.40 #940, 0.04 #1977, 0.03 #3017), R (0.33 #151, 0.33 #3, 0.25 #447), ROK (0.33 #389, 0.25 #685, 0.20 #833), H (0.29 #1078, 0.25 #485, 0.18 #1967), SRB (0.29 #1164, 0.25 #571, 0.14 #2053), SK (0.29 #1059, 0.25 #466, 0.12 #2395), CZ (0.29 #1111, 0.25 #518, 0.10 #2447), BG (0.25 #470, 0.20 #915, 0.14 #1952)
>> best conf = 0.60
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #937 for best value:
>> intensional similarity = 5
>> extensional distance = 3
>> proper extension:  ARM;
>> query: (?x73, IR) <- ?x73[ has ethnicGroup ?x58; has neighbor ?x353<GE>; has neighbor ?x565[ has language ?x247;];]

*> Best rule #644 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 2
*> proper extension:  ROK;
*> query: (?x73, NOK) <- ?x73[ has neighbor ?x1010[ is locatedIn of ?x956;]; has wasDependentOf ?x903; is locatedIn of ?x271<SeaofJapan>;]
*> conf = 0.25
ranks of expected_values: 11
EVAL	R	neighbor	NOK	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.091	37.000	37.000	187.000	0.600	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: NOK
=> 89 concepts (89 used for prediction)
PRED predicted values (max 10 best out of 207): R (0.50 #1342, 0.50 #1045, 0.48 #9294), PK (0.50 #897, 0.33 #2692, 0.32 #4333), BHT (0.50 #958, 0.32 #4333, 0.30 #5540), MYA (0.50 #950, 0.32 #4333, 0.30 #5540), MNE (0.50 #2248, 0.18 #3294, 0.13 #6897), RO (0.45 #1191, 0.44 #1941, 0.36 #2537), TM (0.45 #1191, 0.44 #1941, 0.36 #2537), BG (0.45 #1191, 0.44 #1941, 0.36 #2537), MEX (0.45 #1191, 0.44 #1941, 0.36 #2537), TR (0.45 #1191, 0.44 #1941, 0.36 #2537)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1342 for best value:
>> intensional similarity = 11
>> extensional distance = 2
>> proper extension:  LV;
>> query: (?x73, R) <- ?x73[ has ethnicGroup ?x58<Ukrainian>; has ethnicGroup ?x1193<Russian>; has neighbor ?x194[ has language ?x1314; is locatedIn of ?x146;]; has religion ?x56; has wasDependentOf ?x903; is locatedIn of ?x1457<WesternDwina>;]
>> Best rule #1045 for best value:
>> intensional similarity = 11
>> extensional distance = 2
>> proper extension:  BY;
>> query: (?x73, R) <- ?x73[ has ethnicGroup ?x58<Ukrainian>; has ethnicGroup ?x1193<Russian>; has neighbor ?x194<PL>; has religion ?x56; is locatedIn of ?x1396[ is flowsInto of ?x1395;]; is locatedIn of ?x1794[ a Source;];]

*> Best rule #1191 for first EXPECTED value:
*> intensional similarity = 13
*> extensional distance = 2
*> proper extension:  BY;
*> query: (?x73, ?x117) <- ?x73[ has ethnicGroup ?x58<Ukrainian>; has ethnicGroup ?x1193<Russian>; has neighbor ?x194<PL>; has religion ?x56; is locatedIn of ?x282[ has locatedIn ?x117;]; is locatedIn of ?x1396[ is flowsInto of ?x1395;]; is locatedIn of ?x1794[ a Source;];]
*> conf = 0.45
ranks of expected_values: 11
EVAL	R	neighbor	NOK	CNN-1.+1._MA	0.000	0.000	0.000	0.091	89.000	89.000	207.000	0.500	http://www.semwebtech.org/mondial/10/meta#neighbor

#293-RB
PRED entity: RB

PRED relation: neighbor
PRED expected values: ZW
=> 35 concepts (35 used for prediction)
PRED predicted values (max 10 best out of 217): ANG (0.50 #458, 0.30 #2401, 0.29 #319), MOC (0.40 #190, 0.38 #511, 0.33 #31), ZW (0.40 #313, 0.33 #154, 0.30 #2401), Z (0.38 #568, 0.30 #2401, 0.29 #319), RB (0.33 #149, 0.30 #2401, 0.29 #319), LS (0.33 #6, 0.30 #2401, 0.29 #319), SD (0.26 #3690, 0.25 #641, 0.18 #3528), ZRE (0.25 #378, 0.23 #538, 0.20 #217), EAT (0.25 #449, 0.23 #609, 0.20 #288), RCB (0.25 #409, 0.20 #248, 0.18 #3528)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #458 for best value:
>> intensional similarity = 8
>> extensional distance = 6
>> proper extension:  NAM; ZRE; RCB;
>> query: (?x1239, ANG) <- ?x1239[ has neighbor ?x138[ has neighbor ?x525; has religion ?x95; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x1437<Okavango>;]; is locatedIn of ?x242;]

*> Best rule #313 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 3
*> proper extension:  Z; ANG;
*> query: (?x1239, ZW) <- ?x1239[ has ethnicGroup ?x2322; has neighbor ?x138<NAM>; has neighbor ?x243[ has neighbor ?x89; is locatedIn of ?x182;]; has wasDependentOf ?x81; is locatedIn of ?x242;]
*> conf = 0.40
ranks of expected_values: 3
EVAL	RB	neighbor	ZW	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.333	35.000	35.000	217.000	0.500	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: ZW
=> 90 concepts (90 used for prediction)
PRED predicted values (max 10 best out of 228): ZW (0.95 #3764, 0.92 #8239, 0.92 #8073), EAT (0.67 #1766, 0.57 #1276, 0.50 #1603), ZRE (0.60 #2346, 0.44 #1695, 0.43 #2941), MOC (0.55 #2612, 0.55 #4421, 0.52 #11891), Z (0.55 #2612, 0.55 #4421, 0.52 #11891), RB (0.55 #2612, 0.55 #4421, 0.52 #11891), ANG (0.55 #2612, 0.55 #4421, 0.50 #12225), SD (0.44 #657, 0.43 #2941, 0.40 #4420), MW (0.44 #657, 0.43 #2941, 0.40 #4420), LS (0.43 #2941, 0.40 #4420, 0.40 #7567)
>> best conf = 0.95
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3764 for best value:
>> intensional similarity = 17
>> extensional distance = 14
>> proper extension:  DJI;
>> query: (?x1239, ?x1576) <- ?x1239[ has ethnicGroup ?x2322; has neighbor ?x243[ has ethnicGroup ?x2226; has government ?x435; is locatedIn of ?x182[ has mergesWith ?x60; is flowsInto of ?x214; is locatedInWater of ?x112;]; is locatedIn of ?x1054[ has flowsInto ?x137;]; is locatedIn of ?x1945[ a Estuary;];]; has wasDependentOf ?x81; is locatedIn of ?x2174[ a Lake; has type ?x762;]; is neighbor of ?x1576;]
ranks of expected_values: 1
EVAL	RB	neighbor	ZW	CNN-1.+1._MA	1.000	1.000	1.000	1.000	90.000	90.000	228.000	0.945	http://www.semwebtech.org/mondial/10/meta#neighbor

#292-Schneekoppe
PRED entity: Schneekoppe

PRED relation: locatedIn
PRED expected values: PL
=> 44 concepts (34 used for prediction)
PRED predicted values (max 10 best out of 153): D (0.67 #490, 0.18 #726, 0.14 #4965), SK (0.25 #32, 0.13 #4945, 0.10 #5181), BG (0.25 #38, 0.07 #8016, 0.06 #6835), USA (0.21 #5017, 0.20 #5253, 0.19 #5489), R (0.16 #5186, 0.15 #5657, 0.14 #5893), I (0.13 #989, 0.10 #1696, 0.10 #1224), PL (0.13 #4945, 0.10 #5181, 0.10 #6598), A (0.13 #4945, 0.10 #5181, 0.10 #6598), CN (0.12 #1468, 0.11 #1939, 0.10 #3115), CDN (0.10 #5480, 0.10 #5715, 0.09 #6187)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #490 for best value:
>> intensional similarity = 7
>> extensional distance = 4
>> proper extension:  Zugspitze; Feldberg; GrosserArber; Brocken;
>> query: (?x2173, D) <- ?x2173[ a Mountain; has locatedIn ?x471[ has neighbor ?x120; is locatedIn of ?x1094<Oder>; is locatedIn of ?x1096[ has hasEstuary ?x1097;];];]

*> Best rule #4945 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 486
*> proper extension:  IndianOcean; Dagoe; Ireland; Atacama; Negev; Limpopo; Rhein; GulfofBengal; Karakum; Irawaddy; ...
*> query: (?x2173, ?x120) <- ?x2173[ has locatedIn ?x471[ has ethnicGroup ?x164; has government ?x254; has language ?x1035; is neighbor of ?x120;];]
*> conf = 0.13
ranks of expected_values: 7
EVAL	Schneekoppe	locatedIn	PL	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.143	44.000	34.000	153.000	0.667	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: PL
=> 116 concepts (109 used for prediction)
PRED predicted values (max 10 best out of 230): D (0.83 #10728, 0.69 #2627, 0.67 #727), F (0.75 #1664, 0.35 #11672, 0.15 #17866), CH (0.62 #2425, 0.27 #11722, 0.23 #13634), A (0.51 #4848, 0.42 #7463, 0.25 #11764), CN (0.50 #3614, 0.14 #12438, 0.12 #14352), SK (0.47 #3114, 0.33 #32, 0.29 #975), UA (0.45 #1963, 0.37 #7434, 0.17 #8315), USA (0.43 #5297, 0.41 #5771, 0.41 #6960), R (0.40 #17864, 0.34 #3802, 0.29 #1186), SRB (0.34 #4695, 0.14 #9030, 0.14 #9029)
>> best conf = 0.83
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #10728 for best value:
>> intensional similarity = 11
>> extensional distance = 91
>> proper extension:  Leine; StarnbergerSee; NorthSea; Neckar; Main; Saar; Saar; Neckar; Werra; Würm; ...
>> query: (?x2173, D) <- ?x2173[ has locatedIn ?x471[ a Country; has ethnicGroup ?x164; has neighbor ?x163[ has ethnicGroup ?x58; has religion ?x95<Protestant>;]; has neighbor ?x194<PL>; has neighbor ?x424<A>;];]

*> Best rule #4793 for first EXPECTED value:
*> intensional similarity = 9
*> extensional distance = 51
*> proper extension:  BalticSea; WesternBug; Oder; Narew; Weichsel; Weichsel; Narew; Usedom;
*> query: (?x2173, PL) <- ?x2173[ has locatedIn ?x471[ has encompassed ?x195<Europe>; has ethnicGroup ?x237<German>; has language ?x1035; has neighbor ?x163<SK>; has religion ?x95;];]
*> conf = 0.21
ranks of expected_values: 15
EVAL	Schneekoppe	locatedIn	PL	CNN-1.+1._MA	0.000	0.000	0.000	0.067	116.000	109.000	230.000	0.828	http://www.semwebtech.org/mondial/10/meta#locatedIn

#291-French
PRED entity: French

PRED relation: language!
PRED expected values: GUAD RH
=> 24 concepts (21 used for prediction)
PRED predicted values (max 10 best out of 223): FL (0.54 #543, 0.42 #651, 0.39 #760), NL (0.54 #543, 0.42 #651, 0.39 #760), RSM (0.54 #543, 0.42 #651, 0.39 #760), MK (0.54 #543, 0.42 #651, 0.39 #760), BZ (0.54 #543, 0.42 #651, 0.39 #760), CUR (0.54 #543, 0.42 #651, 0.39 #760), NAM (0.54 #543, 0.42 #651, 0.39 #760), DK (0.54 #543, 0.42 #651, 0.39 #760), SLO (0.54 #543, 0.42 #651, 0.39 #760), MNE (0.54 #543, 0.42 #651, 0.39 #760)
>> best conf = 0.54
=> the first rule below is the first best rule for 12 predicted values
>> Best rule #543 for best value:
>> intensional similarity = 11
>> extensional distance = 15
>> proper extension:  English; Portuguese; Russian; Finnish; Hungarian; Spanish; Pashtu; Serbian; Bosnian; Creole; ...
>> query: (?x51, ?x106) <- ?x51[ a Language; is language of ?x207[ has language ?x1251[ is language of ?x106;]; is locatedIn of ?x86; is wasDependentOf of ?x1165;]; is language of ?x543[ has ethnicGroup ?x1628; has wasDependentOf ?x575;]; is language of ?x651[ has neighbor ?x416;];]

*> Best rule #544 for first EXPECTED value:
*> intensional similarity = 9
*> extensional distance = 15
*> proper extension:  English; Portuguese; Russian; Finnish; Hungarian; Spanish; Pashtu; Serbian; Bosnian; Creole; ...
*> query: (?x51, ?x1165) <- ?x51[ a Language; is language of ?x207[ is locatedIn of ?x86; is wasDependentOf of ?x1165;]; is language of ?x543[ has ethnicGroup ?x1628; has wasDependentOf ?x575;]; is language of ?x651[ has neighbor ?x416;];]
*> conf = 0.30
ranks of expected_values: 31, 133
EVAL	French	language!	RH	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.032	24.000	21.000	223.000	0.541	http://www.semwebtech.org/mondial/10/meta#language
EVAL	French	language!	GUAD	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.008	24.000	21.000	223.000	0.541	http://www.semwebtech.org/mondial/10/meta#language

PRED relation: language!
PRED expected values: GUAD RH
=> 48 concepts (48 used for prediction)
PRED predicted values (max 10 best out of 228): RMM (0.74 #107, 0.72 #109, 0.71 #108), CI (0.74 #107, 0.72 #109, 0.71 #108), SN (0.74 #107, 0.72 #109, 0.71 #108), RIM (0.74 #107, 0.72 #109, 0.71 #108), DZ (0.74 #107, 0.72 #109, 0.71 #108), BF (0.74 #107, 0.72 #109, 0.71 #108), MA (0.74 #107, 0.72 #109, 0.71 #108), RT (0.74 #107, 0.72 #109, 0.71 #108), RCB (0.74 #107, 0.72 #109, 0.71 #108), G (0.74 #107, 0.72 #109, 0.71 #108)
>> best conf = 0.74
=> the first rule below is the first best rule for 39 predicted values
>> Best rule #107 for best value:
>> intensional similarity = 27
>> extensional distance = 1
>> proper extension:  Spanish;
>> query: (?x51, ?x426) <- ?x51[ is language of ?x50<NLSM>; is language of ?x78[ is dependentOf of ?x61; is locatedIn of ?x121; is wasDependentOf of ?x581[ is neighbor of ?x426;];]; is language of ?x651[ has encompassed ?x213; has religion ?x187[ a Religion; is religion of ?x170<N>;]; is locatedIn of ?x182<AtlanticOcean>; is neighbor of ?x1072[ has government ?x180;];]; is language of ?x789<AND>; is language of ?x816[ has neighbor ?x179; has religion ?x352;]; is language of ?x1577[ a Country; has ethnicGroup ?x1672; is locatedIn of ?x275<MediterraneanSea>;];]

*> Best rule #109 for first EXPECTED value:
*> intensional similarity = 28
*> extensional distance = 1
*> proper extension:  Spanish;
*> query: (?x51, ?x94) <- ?x51[ is language of ?x50<NLSM>; is language of ?x78[ is dependentOf of ?x61; is locatedIn of ?x121; is wasDependentOf of ?x94; is wasDependentOf of ?x581[ is neighbor of ?x426;];]; is language of ?x651[ has encompassed ?x213; has religion ?x187[ a Religion; is religion of ?x170<N>;]; is locatedIn of ?x182<AtlanticOcean>; is neighbor of ?x1072[ has government ?x180;];]; is language of ?x789<AND>; is language of ?x816[ has neighbor ?x179; has religion ?x352;]; is language of ?x1577[ a Country; has ethnicGroup ?x1672; is locatedIn of ?x275<MediterraneanSea>;];]
*> conf = 0.72
ranks of expected_values: 41, 121
EVAL	French	language!	RH	CNN-1.+1._MA	0.000	0.000	0.000	0.024	48.000	48.000	228.000	0.739	http://www.semwebtech.org/mondial/10/meta#language
EVAL	French	language!	GUAD	CNN-1.+1._MA	0.000	0.000	0.000	0.008	48.000	48.000	228.000	0.739	http://www.semwebtech.org/mondial/10/meta#language

#290-Pamir
PRED entity: Pamir

PRED relation: inMountains!
PRED expected values: Muztagata PikIsmoilSomoni
=> 17 concepts (17 used for prediction)
PRED predicted values (max 10 best out of 313): Tarim-Yarkend (0.25 #196, 0.20 #447, 0.17 #699), BroadPeak (0.25 #124, 0.20 #375, 0.17 #627), PikChan-Tengri (0.25 #102, 0.20 #353, 0.17 #605), Ili (0.25 #73, 0.20 #324, 0.17 #576), Naryn (0.25 #203, 0.20 #454, 0.17 #706), Irtysch (0.20 #392, 0.14 #897, 0.08 #1904), Katun (0.20 #338, 0.14 #843, 0.08 #1850), Bjelucha (0.20 #256, 0.14 #761, 0.08 #1768), Karasu (0.17 #742, 0.04 #2506, 0.03 #2759), Sabalan (0.17 #664, 0.04 #2428, 0.03 #2681)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #196 for best value:
>> intensional similarity = 13
>> extensional distance = 2
>> proper extension:  Karakorum; TianShan;
>> query: (?x749, Tarim-Yarkend) <- ?x749[ a Mountains; is inMountains of ?x748[ has locatedIn ?x232<CN>;]; is inMountains of ?x932[ a Source; has locatedIn ?x381; is hasSource of ?x300;]; is inMountains of ?x1050[ a Mountain;]; is inMountains of ?x2199[ has locatedIn ?x129[ has ethnicGroup ?x1193;];];]

*> Best rule #3025 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 52
*> proper extension:  Ahaggar; BayrischerWald; Kaukasus; Elburs; Changbai; CordilleradeTalamanca; Harz; CordilleraBlanca; Crete; Azores; ...
*> query: (?x749, ?x231) <- ?x749[ a Mountains; is inMountains of ?x748[ a Mountain; has locatedIn ?x232[ has government ?x831; has neighbor ?x73; is locatedIn of ?x231;];];]
*> conf = 0.08
ranks of expected_values: 151, 172
EVAL	Pamir	inMountains!	PikIsmoilSomoni	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.006	17.000	17.000	313.000	0.250	http://www.semwebtech.org/mondial/10/meta#inMountains
EVAL	Pamir	inMountains!	Muztagata	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.007	17.000	17.000	313.000	0.250	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains!
PRED expected values: Muztagata PikIsmoilSomoni
=> 62 concepts (62 used for prediction)
PRED predicted values (max 10 best out of 313): PikChan-Tengri (0.33 #1114, 0.30 #3040, 0.26 #11914), Ili (0.33 #1085, 0.25 #3620, 0.25 #2860), Naryn (0.33 #1215, 0.25 #3750, 0.25 #2990), Makalu (0.33 #175, 0.25 #2708, 0.25 #1440), MountEverest (0.33 #119, 0.25 #2652, 0.25 #1384), ChoOyu (0.33 #41, 0.25 #2574, 0.25 #1306), NangaParbat (0.33 #133, 0.25 #2666, 0.25 #1398), Dhaulagiri (0.33 #115, 0.25 #2648, 0.25 #1380), NandaDevi (0.33 #77, 0.25 #2610, 0.25 #1342), Kangchendzonga (0.33 #35, 0.25 #2568, 0.25 #1300)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1114 for best value:
>> intensional similarity = 25
>> extensional distance = 1
>> proper extension:  TianShan;
>> query: (?x749, PikChan-Tengri) <- ?x749[ a Mountains; is inMountains of ?x652[ a Source; is hasSource of ?x592[ a River; has hasEstuary ?x593;];]; is inMountains of ?x748[ a Mountain; has locatedIn ?x232<CN>;]; is inMountains of ?x932[ a Source; has locatedIn ?x381[ a Country; has encompassed ?x175; has ethnicGroup ?x2116; has government ?x2442; has neighbor ?x83; has religion ?x187<Muslim>;]; is hasSource of ?x300;]; is inMountains of ?x961[ a Mountain; has locatedIn ?x130<KGZ>;];]

*> Best rule #3040 for first EXPECTED value:
*> intensional similarity = 24
*> extensional distance = 2
*> proper extension:  Andes;
*> query: (?x749, ?x276) <- ?x749[ a Mountains; is inMountains of ?x932[ a Source; has locatedIn ?x381;]; is inMountains of ?x961[ a Mountain; has locatedIn ?x129[ a Country; has ethnicGroup ?x1630; has government ?x435<"republic">; is locatedIn of ?x276;]; has locatedIn ?x130[ a Country; has encompassed ?x175; has ethnicGroup ?x58; has language ?x555; has neighbor ?x232; has religion ?x56; has wasDependentOf ?x903;];]; is inMountains of ?x1106[ a Source; is hasSource of ?x682[ a River;];];]
*> conf = 0.30
ranks of expected_values: 30, 198
EVAL	Pamir	inMountains!	PikIsmoilSomoni	CNN-1.+1._MA	0.000	0.000	0.000	0.033	62.000	62.000	313.000	0.333	http://www.semwebtech.org/mondial/10/meta#inMountains
EVAL	Pamir	inMountains!	Muztagata	CNN-1.+1._MA	0.000	0.000	0.000	0.005	62.000	62.000	313.000	0.333	http://www.semwebtech.org/mondial/10/meta#inMountains

#289-Q
PRED entity: Q

PRED relation: ethnicGroup
PRED expected values: Indian
=> 35 concepts (35 used for prediction)
PRED predicted values (max 10 best out of 213): Jewish (0.38 #553, 0.16 #766, 0.13 #6636), Indian (0.33 #72, 0.12 #838, 0.10 #1093), NorthernIrish (0.33 #230, 0.02 #3572, 0.02 #3828), English (0.33 #227, 0.02 #3572, 0.02 #3828), Welsh (0.33 #155, 0.02 #3572, 0.02 #3828), Scottish (0.33 #114, 0.02 #3572, 0.02 #3828), African (0.27 #1537, 0.22 #4344, 0.21 #4854), European (0.26 #4346, 0.26 #5366, 0.24 #5111), Chinese (0.25 #780, 0.14 #5117, 0.14 #1035), Armenian (0.20 #347, 0.16 #766, 0.13 #6636)
>> best conf = 0.38
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #553 for best value:
>> intensional similarity = 6
>> extensional distance = 14
>> proper extension:  MA;
>> query: (?x174, Jewish) <- ?x174[ has ethnicGroup ?x244[ is ethnicGroup of ?x239<IL>; is ethnicGroup of ?x508[ is locatedIn of ?x60;];]; has religion ?x187;]

*> Best rule #72 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 1
*> proper extension:  GB;
*> query: (?x174, Indian) <- ?x174[ a Country; has encompassed ?x175; has ethnicGroup ?x1686<Pakistani>; has religion ?x187<Muslim>; is locatedIn of ?x918;]
*> conf = 0.33
ranks of expected_values: 2
EVAL	Q	ethnicGroup	Indian	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	35.000	35.000	213.000	0.375	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: Indian
=> 64 concepts (64 used for prediction)
PRED predicted values (max 10 best out of 249): African (0.44 #3083, 0.30 #4369, 0.28 #6673), Afro-Asian (0.33 #768, 0.33 #359, 0.28 #8972), Turkmen (0.33 #768, 0.33 #201, 0.27 #1538), GilakiMazandarani (0.33 #768, 0.33 #167, 0.27 #1538), Azerbaijani (0.33 #768, 0.33 #86, 0.27 #1538), Lur (0.33 #768, 0.33 #83, 0.27 #1538), Baloch (0.33 #768, 0.33 #58, 0.27 #1538), Kurd (0.33 #768, 0.33 #15, 0.27 #1538), European (0.33 #7956, 0.32 #10270, 0.31 #9494), Armenian (0.33 #768, 0.27 #1538, 0.25 #256)
>> best conf = 0.44
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3083 for best value:
>> intensional similarity = 17
>> extensional distance = 14
>> proper extension:  EAT; WAL;
>> query: (?x174, African) <- ?x174[ a Country; has ethnicGroup ?x244[ a EthnicGroup; is ethnicGroup of ?x466[ has government ?x2550;];]; has neighbor ?x751[ is neighbor of ?x803[ a Country; has encompassed ?x175; is locatedIn of ?x419;];]; has religion ?x187<Muslim>; has wasDependentOf ?x81<GB>; is locatedIn of ?x918[ is locatedInWater of ?x1736[ a Island;];];]

*> Best rule #768 for first EXPECTED value:
*> intensional similarity = 15
*> extensional distance = 1
*> proper extension:  KWT;
*> query: (?x174, ?x305) <- ?x174[ a Country; has encompassed ?x175<Asia>; has ethnicGroup ?x244[ is ethnicGroup of ?x304[ a Country; has ethnicGroup ?x305;];]; has ethnicGroup ?x826[ a EthnicGroup;]; has neighbor ?x751; has religion ?x187; has wasDependentOf ?x81<GB>; is locatedIn of ?x918<PersianGulf>;]
*> conf = 0.33
ranks of expected_values: 21
EVAL	Q	ethnicGroup	Indian	CNN-1.+1._MA	0.000	0.000	0.000	0.048	64.000	64.000	249.000	0.438	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#288-Weser
PRED entity: Weser

PRED relation: flowsInto
PRED expected values: NorthSea
=> 39 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 166): Donau (0.30 #8, 0.29 #173, 0.13 #3159), Weser (0.14 #303, 0.10 #138, 0.04 #802), BalticSea (0.13 #3159, 0.06 #1670, 0.06 #2172), NorthSea (0.13 #3159, 0.05 #6, 0.05 #171), AtlanticOcean (0.10 #342, 0.10 #3004, 0.09 #509), Inn (0.10 #80, 0.10 #245, 0.03 #744), Rhein (0.10 #19, 0.05 #184, 0.04 #349), MediterraneanSea (0.06 #353, 0.06 #520, 0.04 #1350), Zaire (0.06 #421, 0.06 #588, 0.04 #1918), Amazonas (0.06 #343, 0.06 #510, 0.03 #842)
>> best conf = 0.30
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #8 for best value:
>> intensional similarity = 5
>> extensional distance = 18
>> proper extension:  Main;
>> query: (?x1533, Donau) <- ?x1533[ a River; has hasEstuary ?x1252[ a Estuary; has locatedIn ?x120<D>;];]

*> Best rule #3159 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 200
*> proper extension:  Sanga; Tobol;
*> query: (?x1533, ?x146) <- ?x1533[ a River; has locatedIn ?x120[ is locatedIn of ?x146[ is flowsInto of ?x590; is locatedInWater of ?x145;];];]
*> conf = 0.13
ranks of expected_values: 4
EVAL	Weser	flowsInto	NorthSea	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.250	39.000	31.000	166.000	0.300	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto
PRED expected values: NorthSea
=> 114 concepts (114 used for prediction)
PRED predicted values (max 10 best out of 180): Donau (0.38 #2007, 0.33 #2173, 0.33 #1677), Weser (0.30 #968, 0.22 #638, 0.20 #803), Inn (0.25 #80, 0.20 #246, 0.11 #1749), Isar (0.25 #105, 0.20 #271, 0.11 #605), Ammer (0.20 #223, 0.04 #2722, 0.04 #4839), Rhein (0.15 #1182, 0.13 #1351, 0.11 #351), MediterraneanSea (0.15 #1186, 0.13 #3355, 0.11 #5201), AtlanticOcean (0.13 #2677, 0.12 #9385, 0.12 #5022), NorthSea (0.11 #338, 0.09 #1001, 0.08 #1169), BlackSea (0.11 #335, 0.09 #998, 0.08 #1166)
>> best conf = 0.38
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2007 for best value:
>> intensional similarity = 10
>> extensional distance = 19
>> proper extension:  Waag;
>> query: (?x1533, Donau) <- ?x1533[ a River; has hasEstuary ?x1252[ has locatedIn ?x120[ a Country; has neighbor ?x194<PL>; has neighbor ?x471<CZ>; has religion ?x95;];]; has hasSource ?x1668;]

*> Best rule #338 for first EXPECTED value:
*> intensional similarity = 9
*> extensional distance = 7
*> proper extension:  Donau; Rhein; Ammer; Mosel; Inn; Isar; Elbe;
*> query: (?x1533, NorthSea) <- ?x1533[ a River; has hasEstuary ?x1252; has hasSource ?x1668; has locatedIn ?x120<D>; is flowsInto of ?x391[ a River; has hasEstuary ?x1218; has hasSource ?x392;];]
*> conf = 0.11
ranks of expected_values: 9
EVAL	Weser	flowsInto	NorthSea	CNN-1.+1._MA	0.000	0.000	1.000	0.111	114.000	114.000	180.000	0.381	http://www.semwebtech.org/mondial/10/meta#flowsInto

#287-Benue
PRED entity: Benue

PRED relation: hasEstuary!
PRED expected values: Benue
=> 37 concepts (27 used for prediction)
PRED predicted values (max 10 best out of 24): Niger (0.33 #56, 0.25 #282, 0.14 #509), Bani (0.25 #265), Schari (0.14 #523, 0.01 #1204, 0.01 #1431), Bomu (0.14 #544), Ubangi (0.14 #483), Niger (0.12 #453, 0.11 #680, 0.02 #3180), Benue (0.12 #453, 0.11 #680, 0.02 #3180), ChadLake (0.12 #453, 0.11 #680, 0.02 #3180), Benue (0.12 #453, 0.11 #680, 0.02 #3180), AsoRock (0.12 #453, 0.11 #680, 0.02 #3180)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #56 for best value:
>> intensional similarity = 3
>> extensional distance = 1
>> proper extension:  Niger;
>> query: (?x2387, Niger) <- ?x2387[ a Estuary; has locatedIn ?x139<WAN>;]

*> Best rule #453 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 2
*> proper extension:  Bani;
*> query: (?x2387, ?x182) <- ?x2387[ a Estuary; has locatedIn ?x139[ has encompassed ?x213; has wasDependentOf ?x81; is locatedIn of ?x182; is neighbor of ?x426<RN>;];]
*> conf = 0.12
ranks of expected_values: 9
EVAL	Benue	hasEstuary!	Benue	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.111	37.000	27.000	24.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasEstuary

PRED relation: hasEstuary!
PRED expected values: Benue
=> 80 concepts (80 used for prediction)
PRED predicted values (max 10 best out of 63): Niger (0.33 #56, 0.25 #455, 0.25 #282), Bomu (0.25 #317, 0.14 #775, 0.04 #2374), Benue (0.25 #455, 0.11 #456, 0.08 #914), Schari (0.20 #526, 0.14 #754, 0.06 #1668), ChadLake (0.17 #454, 0.11 #456, 0.09 #913), AtlanticOcean (0.17 #454, 0.11 #456, 0.09 #913), Bani (0.14 #723, 0.05 #5482, 0.05 #5712), LakeKainji (0.11 #456, 0.08 #914, 0.06 #684), Niger (0.11 #456, 0.08 #914, 0.06 #684), Benue (0.11 #456, 0.08 #914, 0.06 #684)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #56 for best value:
>> intensional similarity = 3
>> extensional distance = 1
>> proper extension:  Niger;
>> query: (?x2387, Niger) <- ?x2387[ a Estuary; has locatedIn ?x139<WAN>;]

*> Best rule #455 for first EXPECTED value:
*> intensional similarity = 13
*> extensional distance = 2
*> proper extension:  Bomu;
*> query: (?x2387, ?x1858) <- ?x2387[ a Estuary; has locatedIn ?x139[ a Country; has encompassed ?x213; has neighbor ?x169<TCH>; has neighbor ?x810[ has wasDependentOf ?x78;]; has religion ?x116; is locatedIn of ?x1858[ a River;]; is locatedIn of ?x2238[ is flowsInto of ?x695;];];]
*> conf = 0.25
ranks of expected_values: 3
EVAL	Benue	hasEstuary!	Benue	CNN-1.+1._MA	0.000	1.000	1.000	0.333	80.000	80.000	63.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasEstuary

#286-KAZ
PRED entity: KAZ

PRED relation: wasDependentOf
PRED expected values: SovietUnion
=> 38 concepts (38 used for prediction)
PRED predicted values (max 10 best out of 33): SovietUnion (0.29 #736, 0.25 #52, 0.21 #112), GB (0.29 #95, 0.23 #156, 0.21 #65), F (0.25 #3, 0.17 #155, 0.14 #707), E (0.21 #370, 0.20 #309, 0.19 #402), NL (0.15 #138, 0.08 #320, 0.08 #290), Yugoslavia (0.15 #266, 0.08 #387, 0.08 #296), R (0.15 #151, 0.06 #150, 0.04 #272), CN (0.15 #151, 0.06 #150, 0.04 #272), UnitedNations (0.13 #167, 0.11 #441, 0.07 #76), OttomanEmpire (0.12 #209, 0.09 #268, 0.07 #359)
>> best conf = 0.29
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #736 for best value:
>> intensional similarity = 5
>> extensional distance = 127
>> proper extension:  CEU;
>> query: (?x403, ?x903) <- ?x403[ is locatedIn of ?x1019[ is flowsInto of ?x2336;]; is neighbor of ?x290[ has ethnicGroup ?x1193; has wasDependentOf ?x903;];]
ranks of expected_values: 1
EVAL	KAZ	wasDependentOf	SovietUnion	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	38.000	38.000	33.000	0.294	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

PRED relation: wasDependentOf
PRED expected values: SovietUnion
=> 85 concepts (85 used for prediction)
PRED predicted values (max 10 best out of 51): SovietUnion (0.50 #685, 0.40 #851, 0.33 #145), GB (0.35 #801, 0.34 #1237, 0.30 #459), E (0.27 #976, 0.26 #1409, 0.25 #1078), Czechoslovakia (0.25 #281, 0.20 #516, 0.15 #655), F (0.25 #159, 0.18 #800, 0.16 #1805), J (0.25 #2351, 0.16 #932, 0.11 #329), OttomanEmpire (0.18 #553, 0.12 #757, 0.12 #282), Yugoslavia (0.14 #1327, 0.12 #1460, 0.12 #1493), S (0.11 #405, 0.06 #744, 0.05 #843), Austria-Hungary (0.10 #519, 0.08 #658, 0.05 #893)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #685 for best value:
>> intensional similarity = 10
>> extensional distance = 12
>> proper extension:  ARM;
>> query: (?x403, SovietUnion) <- ?x403[ has ethnicGroup ?x237[ is ethnicGroup of ?x424[ has neighbor ?x423; is locatedIn of ?x133;];]; has religion ?x56; is neighbor of ?x290[ has government ?x2518; is locatedIn of ?x1337<CaspianSea>; is neighbor of ?x304;];]
ranks of expected_values: 1
EVAL	KAZ	wasDependentOf	SovietUnion	CNN-1.+1._MA	1.000	1.000	1.000	1.000	85.000	85.000	51.000	0.500	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

#285-LittleCayman
PRED entity: LittleCayman

PRED relation: belongsToIslands
PRED expected values: CaymanIslands
=> 50 concepts (50 used for prediction)
PRED predicted values (max 10 best out of 57): LesserAntilles (0.60 #423, 0.58 #559, 0.44 #355), CaymanIslands (0.33 #30, 0.25 #98, 0.17 #166), TurksandCaicosIslands (0.33 #187, 0.05 #663, 0.04 #799), BahamaIslands (0.17 #198), GreaterAntilles (0.15 #319, 0.12 #387, 0.10 #455), Azores (0.09 #752, 0.04 #1296, 0.03 #1500), HawaiiIslands (0.09 #845, 0.09 #913, 0.06 #1049), Canares (0.08 #771, 0.05 #1043, 0.05 #1111), LipariIslands (0.07 #954, 0.05 #1022, 0.05 #1090), InnerHebrides (0.06 #812, 0.04 #1084, 0.04 #1152)
>> best conf = 0.60
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #423 for best value:
>> intensional similarity = 6
>> extensional distance = 18
>> proper extension:  SaintVincent;
>> query: (?x687, LesserAntilles) <- ?x687[ a Island; has locatedIn ?x865[ has encompassed ?x521; has government ?x254;]; has locatedInWater ?x317<CaribbeanSea>;]

*> Best rule #30 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 1
*> proper extension:  GrandCayman;
*> query: (?x687, CaymanIslands) <- ?x687[ a Island; has locatedIn ?x865<CAYM>; has locatedInWater ?x317<CaribbeanSea>;]
*> conf = 0.33
ranks of expected_values: 2
EVAL	LittleCayman	belongsToIslands	CaymanIslands	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	50.000	50.000	57.000	0.600	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

PRED relation: belongsToIslands
PRED expected values: CaymanIslands
=> 125 concepts (125 used for prediction)
PRED predicted values (max 10 best out of 57): CaymanIslands (0.88 #1771, 0.72 #2454, 0.67 #613), LesserAntilles (0.69 #1717, 0.58 #2332, 0.57 #832), ChannelIslands (0.40 #403, 0.08 #2243, 0.07 #1424), HawaiiIslands (0.33 #2072, 0.23 #2551, 0.21 #2688), CanadianArcticIslands (0.29 #1983, 0.21 #2393, 0.11 #3075), BahamaIslands (0.25 #266), CalifornianChannelIslands (0.24 #2102, 0.17 #2581, 0.15 #2718), Canares (0.20 #2545, 0.18 #2682, 0.10 #3226), BermudaIslands (0.20 #442, 0.09 #1123, 0.08 #1191), SamoanIslands (0.18 #965, 0.07 #1509, 0.06 #1783)
>> best conf = 0.88
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1771 for best value:
>> intensional similarity = 13
>> extensional distance = 14
>> proper extension:  SaintVincent;
>> query: (?x687, ?x1357) <- ?x687[ a Island; has locatedIn ?x865[ a Country; has encompassed ?x521<America>; has government ?x254; is locatedIn of ?x317<CaribbeanSea>; is locatedIn of ?x1093[ a Island; has belongsToIslands ?x1357; has locatedInWater ?x317<CaribbeanSea>;];]; has locatedInWater ?x317<CaribbeanSea>;]
ranks of expected_values: 1
EVAL	LittleCayman	belongsToIslands	CaymanIslands	CNN-1.+1._MA	1.000	1.000	1.000	1.000	125.000	125.000	57.000	0.875	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

#284-WEST
PRED entity: WEST

PRED relation: ethnicGroup
PRED expected values: PalestinianArab
=> 42 concepts (42 used for prediction)
PRED predicted values (max 10 best out of 217): Arab (0.50 #525, 0.43 #1297, 0.40 #783), Indian (0.33 #330, 0.20 #1616, 0.13 #2387), Pakistani (0.33 #386, 0.03 #3474, 0.03 #3731), NorthernIrish (0.33 #489, 0.03 #3577, 0.03 #3834), English (0.33 #486, 0.03 #3574, 0.03 #3831), Welsh (0.33 #414, 0.03 #3502, 0.03 #3759), Scottish (0.33 #372, 0.03 #3460, 0.03 #3717), European (0.31 #4381, 0.25 #5924, 0.23 #6438), Russian (0.30 #3674, 0.24 #4188, 0.21 #2901), Armenian (0.29 #1379, 0.19 #3087, 0.19 #7975)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #525 for best value:
>> intensional similarity = 9
>> extensional distance = 2
>> proper extension:  SA;
>> query: (?x568, Arab) <- ?x568[ has encompassed ?x175; has religion ?x187<Muslim>; is locatedIn of ?x419; is neighbor of ?x239[ has ethnicGroup ?x244; is locatedIn of ?x238;]; is neighbor of ?x803<JOR>;]

*> Best rule #1275 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 4
*> proper extension:  AUS;
*> query: (?x568, PalestinianArab) <- ?x568[ a Country; has language ?x1398[ a Language;]; has language ?x1848<Arabic>; has religion ?x187<Muslim>; is locatedIn of ?x419;]
*> conf = 0.17
ranks of expected_values: 18
EVAL	WEST	ethnicGroup	PalestinianArab	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.056	42.000	42.000	217.000	0.500	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: PalestinianArab
=> 103 concepts (103 used for prediction)
PRED predicted values (max 10 best out of 250): Russian (0.71 #5228, 0.67 #4197, 0.46 #7546), Arab (0.60 #3620, 0.40 #3361, 0.35 #10575), Uzbek (0.57 #5311, 0.50 #4280, 0.31 #7629), European (0.50 #2069, 0.39 #18329, 0.37 #15489), Arab-Berber (0.50 #2090, 0.33 #1315, 0.19 #6186), Peuhl (0.50 #2799, 0.25 #5636, 0.22 #5893), Armenian (0.40 #3702, 0.35 #10575, 0.35 #10574), Chinese (0.37 #11879, 0.22 #6201, 0.18 #16788), African (0.36 #6965, 0.26 #14972, 0.25 #13678), Circassian (0.35 #10575, 0.35 #10574, 0.33 #335)
>> best conf = 0.71
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5228 for best value:
>> intensional similarity = 17
>> extensional distance = 5
>> proper extension:  KGZ;
>> query: (?x568, Russian) <- ?x568[ has encompassed ?x175<Asia>; has ethnicGroup ?x852[ a EthnicGroup; is ethnicGroup of ?x303<UA>;]; has language ?x1398; has neighbor ?x239[ has ethnicGroup ?x244; has wasDependentOf ?x485; is locatedIn of ?x238;]; has religion ?x109[ is religion of ?x886<MD>;]; has religion ?x187<Muslim>; is locatedIn of ?x419;]

*> Best rule #2050 for first EXPECTED value:
*> intensional similarity = 21
*> extensional distance = 1
*> proper extension:  GAZA;
*> query: (?x568, PalestinianArab) <- ?x568[ has encompassed ?x175<Asia>; has ethnicGroup ?x852<Jewish>; has language ?x1398<Hebrew>; has language ?x1848<Arabic>; has neighbor ?x239<IL>; has neighbor ?x803[ is locatedIn of ?x1552<RedSea>; is neighbor of ?x466<SYR>;]; has religion ?x116<Christian>; has religion ?x187<Muslim>; is locatedIn of ?x419[ is flowsInto of ?x1999;];]
*> conf = 0.33
ranks of expected_values: 13
EVAL	WEST	ethnicGroup	PalestinianArab	CNN-1.+1._MA	0.000	0.000	0.000	0.077	103.000	103.000	250.000	0.714	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#283-Snaefell
PRED entity: Snaefell

PRED relation: type
PRED expected values: "volcanic"
=> 48 concepts (48 used for prediction)
PRED predicted values (max 10 best out of 7): "volcano" (0.67 #86, 0.61 #230, 0.60 #150), "volcanic" (0.41 #289, 0.38 #242, 0.34 #274), "salt" (0.02 #649, 0.02 #569, 0.02 #488), "granite" (0.02 #142, 0.02 #222), "dam" (0.02 #530, 0.02 #514, 0.02 #627), "monolith" (0.01 #267), "caldera" (0.01 #484)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #86 for best value:
>> intensional similarity = 6
>> extensional distance = 31
>> proper extension:  SoufriereHills;
>> query: (?x1340, "volcano") <- ?x1340[ a Volcano; has locatedIn ?x455[ is locatedIn of ?x373[ has locatedIn ?x81<GB>; is locatedInWater of ?x634;];];]

*> Best rule #289 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 83
*> proper extension:  Tamgak;
*> query: (?x1340, ?x150) <- ?x1340[ a Volcano; has locatedIn ?x455[ has encompassed ?x195; is locatedIn of ?x806[ has type ?x150;];];]
*> conf = 0.41
ranks of expected_values: 2
EVAL	Snaefell	type	"volcanic"	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	48.000	48.000	7.000	0.667	http://www.semwebtech.org/mondial/10/meta#type

PRED relation: type
PRED expected values: "volcanic"
=> 139 concepts (139 used for prediction)
PRED predicted values (max 10 best out of 11): "volcanic" (0.75 #374, 0.71 #178, 0.71 #163), "volcano" (0.75 #374, 0.64 #179, 0.62 #299), "salt" (0.04 #1047, 0.04 #1535, 0.03 #1470), "monolith" (0.03 #239, 0.01 #707), "dam" (0.03 #1347, 0.03 #1464, 0.03 #1529), "granite" (0.02 #404, 0.02 #468, 0.02 #452), "sand" (0.02 #1450, 0.02 #1173, 0.02 #979), "lime" (0.02 #1012, 0.01 #1206), "atoll" (0.02 #1015, 0.01 #1193), "caldera" (0.02 #1076, 0.01 #945, 0.01 #796)
>> best conf = 0.75
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #374 for best value:
>> intensional similarity = 8
>> extensional distance = 44
>> proper extension:  Etna;
>> query: (?x1340, ?x150) <- ?x1340[ a Mountain; has locatedOnIsland ?x807[ a Island; is locatedOnIsland of ?x806[ a Mountain; a Volcano; has locatedIn ?x455; has type ?x150;];];]
ranks of expected_values: 1
EVAL	Snaefell	type	"volcanic"	CNN-1.+1._MA	1.000	1.000	1.000	1.000	139.000	139.000	11.000	0.755	http://www.semwebtech.org/mondial/10/meta#type

#282-IR
PRED entity: IR

PRED relation: neighbor!
PRED expected values: PK
=> 43 concepts (43 used for prediction)
PRED predicted values (max 10 best out of 162): PK (0.90 #4512, 0.89 #6077, 0.89 #2488), SA (0.50 #271, 0.38 #1203, 0.33 #116), CN (0.50 #1598, 0.31 #2374, 0.29 #622), JOR (0.43 #898, 0.29 #622, 0.27 #3265), GE (0.40 #369, 0.33 #680, 0.29 #622), BG (0.33 #494, 0.29 #622, 0.29 #960), GR (0.33 #529, 0.29 #622, 0.29 #995), SRB (0.33 #599, 0.29 #1065, 0.08 #3398), OM (0.33 #101, 0.25 #1188, 0.25 #256), UZB (0.29 #622, 0.27 #3265, 0.27 #5452)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4512 for best value:
>> intensional similarity = 6
>> extensional distance = 96
>> proper extension:  SSD;
>> query: (?x304, ?x83) <- ?x304[ has neighbor ?x83; is locatedIn of ?x1422[ is flowsInto of ?x666;]; is locatedIn of ?x1620[ a River;]; is neighbor of ?x185;]
ranks of expected_values: 1
EVAL	IR	neighbor!	PK	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	43.000	43.000	162.000	0.896	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: PK
=> 115 concepts (115 used for prediction)
PRED predicted values (max 10 best out of 185): PK (0.91 #12890, 0.90 #16425, 0.90 #16101), SA (0.57 #473, 0.57 #472, 0.50 #2970), R (0.57 #473, 0.57 #472, 0.50 #5875), IR (0.57 #473, 0.57 #472, 0.46 #1265), KWT (0.57 #473, 0.57 #472, 0.46 #1265), OM (0.57 #473, 0.57 #472, 0.46 #1265), Q (0.57 #473, 0.57 #472, 0.46 #1265), UAE (0.57 #473, 0.57 #472, 0.46 #1265), BRN (0.57 #473, 0.57 #472, 0.46 #1265), KAZ (0.57 #473, 0.57 #472, 0.46 #1265)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #12890 for best value:
>> intensional similarity = 13
>> extensional distance = 35
>> proper extension:  RSM;
>> query: (?x304, ?x83) <- ?x304[ has language ?x511; has language ?x1104[ a Language;]; has neighbor ?x83; has religion ?x2031; is neighbor of ?x332[ a Country; has government ?x435; has language ?x843; has religion ?x187<Muslim>; is locatedIn of ?x468[ has hasSource ?x469;];];]
ranks of expected_values: 1
EVAL	IR	neighbor!	PK	CNN-1.+1._MA	1.000	1.000	1.000	1.000	115.000	115.000	185.000	0.906	http://www.semwebtech.org/mondial/10/meta#neighbor

#281-SF
PRED entity: SF

PRED relation: neighbor
PRED expected values: R
=> 45 concepts (45 used for prediction)
PRED predicted values (max 10 best out of 206): R (0.91 #2880, 0.90 #2240, 0.90 #6085), IRQ (0.33 #50, 0.05 #369, 0.04 #5974), PL (0.25 #6406, 0.24 #5764, 0.19 #352), AZ (0.25 #6406, 0.24 #5764, 0.17 #56), CN (0.25 #6406, 0.24 #5764, 0.14 #361), UA (0.25 #6406, 0.24 #5764, 0.14 #2451), BY (0.25 #6406, 0.24 #5764, 0.12 #2440), KAZ (0.25 #6406, 0.24 #5764, 0.10 #388), SF (0.25 #6406, 0.24 #5764, 0.10 #414), LV (0.25 #6406, 0.24 #5764, 0.10 #2478)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2880 for best value:
>> intensional similarity = 6
>> extensional distance = 47
>> proper extension:  NAM;
>> query: (?x565, ?x170) <- ?x565[ has language ?x247; has wasDependentOf ?x73; is locatedIn of ?x631; is locatedIn of ?x660[ a River;]; is neighbor of ?x170;]
ranks of expected_values: 1
EVAL	SF	neighbor	R	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	45.000	45.000	206.000	0.906	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: R
=> 125 concepts (125 used for prediction)
PRED predicted values (max 10 best out of 217): R (0.93 #2732, 0.90 #5150, 0.90 #18432), SF (0.54 #10956, 0.51 #13559, 0.50 #898), BR (0.43 #2502, 0.19 #8467, 0.19 #4761), IL (0.33 #1811, 0.25 #160, 0.22 #7240), IRQ (0.33 #1816, 0.25 #690, 0.19 #13231), CR (0.33 #214, 0.20 #1498, 0.20 #1337), CO (0.33 #197, 0.20 #1481, 0.20 #1320), KAZ (0.31 #4090, 0.30 #3286, 0.30 #12578), LV (0.31 #4099, 0.30 #12578, 0.25 #881), PL (0.30 #12578, 0.29 #2605, 0.25 #836)
>> best conf = 0.93
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2732 for best value:
>> intensional similarity = 14
>> extensional distance = 5
>> proper extension:  USA;
>> query: (?x565, ?x73) <- ?x565[ has ethnicGroup ?x1193; has neighbor ?x402[ a Country; is locatedIn of ?x1069[ a River;]; is locatedIn of ?x1870[ is hasSource of ?x1118;]; is locatedIn of ?x2395[ has hasSource ?x1603;];]; is locatedIn of ?x804[ has belongsToIslands ?x944;]; is locatedIn of ?x808[ a Source;]; is neighbor of ?x73;]
ranks of expected_values: 1
EVAL	SF	neighbor	R	CNN-1.+1._MA	1.000	1.000	1.000	1.000	125.000	125.000	217.000	0.929	http://www.semwebtech.org/mondial/10/meta#neighbor

#280-Mississippi
PRED entity: Mississippi

PRED relation: hasSource!
PRED expected values: Mississippi
=> 34 concepts (29 used for prediction)
PRED predicted values (max 10 best out of 147): Missouri (0.06 #157, 0.02 #385, 0.02 #1374), Colorado (0.06 #137, 0.02 #365, 0.02 #1374), Tennessee (0.06 #19, 0.02 #247, 0.02 #1374), AlleghenyRiver (0.06 #217, 0.02 #445, 0.02 #1374), StraitsofMackinac (0.06 #211, 0.02 #439, 0.02 #1374), MerrimackRiver (0.06 #205, 0.02 #433, 0.02 #1374), Arkansas (0.06 #179, 0.02 #407, 0.02 #1374), RioGrande (0.06 #171, 0.02 #399, 0.02 #1374), HudsonRiver (0.06 #155, 0.02 #383, 0.02 #1374), SaintLawrenceRiver (0.06 #150, 0.02 #378, 0.02 #1374)
>> best conf = 0.06
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #157 for best value:
>> intensional similarity = 3
>> extensional distance = 16
>> proper extension:  SaintMarysRiver; SaintLawrenceRiver; OhioRiver; HudsonRiver; Tennessee; NiagaraRiver; MerrimackRiver; AlleghenyRiver; Colorado; RioGrande; ...
>> query: (?x1841, Missouri) <- ?x1841[ a Source; has locatedIn ?x315<USA>;]

*> Best rule #1374 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 161
*> proper extension:  Dalaelv; Selenge; Vaesterdalaelv; Umeaelv; Goetaaelv;
*> query: (?x1841, ?x182) <- ?x1841[ a Source; has locatedIn ?x315[ has language ?x796; has religion ?x95; is locatedIn of ?x182;];]
*> conf = 0.02
ranks of expected_values: 45
EVAL	Mississippi	hasSource!	Mississippi	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.022	34.000	29.000	147.000	0.056	http://www.semwebtech.org/mondial/10/meta#hasSource

PRED relation: hasSource!
PRED expected values: Mississippi
=> 94 concepts (94 used for prediction)
PRED predicted values (max 10 best out of 225): StraitsofMackinac (0.07 #3214, 0.07 #5052, 0.06 #1146), MerrimackRiver (0.07 #3214, 0.07 #5052, 0.06 #1146), Missouri (0.07 #3214, 0.07 #5052, 0.06 #1146), SaintLawrenceRiver (0.07 #3214, 0.07 #5052, 0.06 #1146), TruckeeRiver (0.07 #3214, 0.07 #5052, 0.06 #1146), Colorado (0.07 #3214, 0.07 #5052, 0.06 #1146), SaintMarysRiver (0.07 #3214, 0.07 #5052, 0.06 #1146), Tennessee (0.07 #3214, 0.07 #5052, 0.06 #1146), OhioRiver (0.07 #3214, 0.07 #5052, 0.06 #1146), AlleghenyRiver (0.07 #3214, 0.07 #5052, 0.06 #1146)
>> best conf = 0.07
=> the first rule below is the first best rule for 18 predicted values
>> Best rule #3214 for best value:
>> intensional similarity = 7
>> extensional distance = 135
>> proper extension:  WhiteDrin; Akagera; Narew;
>> query: (?x1841, ?x2018) <- ?x1841[ a Source; has locatedIn ?x315[ has ethnicGroup ?x79; has neighbor ?x482; has wasDependentOf ?x81; is locatedIn of ?x2018[ has hasEstuary ?x2245;];];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 18
EVAL	Mississippi	hasSource!	Mississippi	CNN-1.+1._MA	0.000	0.000	0.000	0.056	94.000	94.000	225.000	0.074	http://www.semwebtech.org/mondial/10/meta#hasSource

#279-Ponape
PRED entity: Ponape

PRED relation: type
PRED expected values: "volcanic"
=> 42 concepts (42 used for prediction)
PRED predicted values (max 10 best out of 9): "volcanic" (0.47 #50, 0.46 #66, 0.44 #18), "atoll" (0.15 #24, 0.13 #40, 0.11 #72), "volcano" (0.08 #150, 0.06 #422, 0.06 #406), "coral" (0.04 #89, 0.04 #137, 0.04 #105), "sand" (0.04 #148), "dam" (0.03 #145, 0.02 #417, 0.02 #513), "salt" (0.02 #487, 0.02 #567, 0.02 #535), "lime" (0.02 #213, 0.02 #229, 0.02 #245), "caldera" (0.02 #147)
>> best conf = 0.47
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #50 for best value:
>> intensional similarity = 6
>> extensional distance = 38
>> proper extension:  Niihau; Oahu; Hawaii; Maui; Kauai; SantaRosaIsland; SantaCruzIsland; Lanai; Paramuschir; Unalaska; ...
>> query: (?x1513, "volcanic") <- ?x1513[ a Island; has belongsToIslands ?x1454; has locatedIn ?x1514[ has encompassed ?x211;]; has locatedInWater ?x282<PacificOcean>;]
ranks of expected_values: 1
EVAL	Ponape	type	"volcanic"	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	42.000	42.000	9.000	0.475	http://www.semwebtech.org/mondial/10/meta#type

PRED relation: type
PRED expected values: "volcanic"
=> 113 concepts (113 used for prediction)
PRED predicted values (max 10 best out of 9): "volcanic" (0.57 #82, 0.50 #178, 0.46 #770), "atoll" (0.33 #8, 0.20 #216, 0.20 #56), "coral" (0.18 #1057, 0.16 #1218, 0.14 #73), "volcano" (0.14 #102, 0.12 #118, 0.09 #1304), "salt" (0.06 #471, 0.02 #1609, 0.02 #1689), "lime" (0.04 #853, 0.04 #709, 0.03 #261), "dam" (0.03 #1283, 0.02 #1379, 0.02 #1459), "sand" (0.02 #468, 0.02 #1222, 0.02 #1238), "caldera" (0.02 #467, 0.01 #1397)
>> best conf = 0.57
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #82 for best value:
>> intensional similarity = 11
>> extensional distance = 5
>> proper extension:  Futuna; Uvea; Rarotonga; Tahiti;
>> query: (?x1513, "volcanic") <- ?x1513[ a Island; has belongsToIslands ?x1454[ a Islands;]; has locatedIn ?x1514[ has encompassed ?x211<Australia-Oceania>; has ethnicGroup ?x1335<Polynesian>; has religion ?x95;]; has locatedInWater ?x282<PacificOcean>;]
ranks of expected_values: 1
EVAL	Ponape	type	"volcanic"	CNN-1.+1._MA	1.000	1.000	1.000	1.000	113.000	113.000	9.000	0.571	http://www.semwebtech.org/mondial/10/meta#type

#278-Ternate
PRED entity: Ternate

PRED relation: locatedIn
PRED expected values: RI
=> 21 concepts (21 used for prediction)
PRED predicted values (max 10 best out of 79): RI (0.87 #1184, 0.82 #1421, 0.71 #525), USA (0.23 #1019, 0.22 #1256, 0.14 #1493), RP (0.18 #818, 0.14 #1056, 0.14 #1293), TL (0.17 #395, 0.15 #947, 0.14 #632), PNG (0.14 #652, 0.07 #3087, 0.05 #4521), J (0.09 #966, 0.08 #1203, 0.04 #1440), GB (0.08 #2382, 0.08 #2619, 0.07 #2856), AUS (0.06 #754, 0.03 #1466, 0.03 #1704), P (0.05 #2095, 0.05 #2333, 0.04 #2570), NMIS (0.05 #1022, 0.05 #1259, 0.02 #1496)
>> best conf = 0.87
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1184 for best value:
>> intensional similarity = 8
>> extensional distance = 55
>> proper extension:  Saipan; Savaii; Guam; Shikoku;
>> query: (?x937, ?x217) <- ?x937[ a Island; has belongsToIslands ?x1099[ a Islands; is belongsToIslands of ?x1098[ a Island; has locatedIn ?x217; has locatedInWater ?x282<PacificOcean>;];];]
ranks of expected_values: 1
EVAL	Ternate	locatedIn	RI	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	21.000	21.000	79.000	0.867	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: RI
=> 46 concepts (46 used for prediction)
PRED predicted values (max 10 best out of 104): RI (0.87 #3337, 0.82 #3574, 0.78 #2856), TL (0.47 #1663, 0.38 #950, 0.17 #633), RP (0.43 #1533, 0.38 #1772, 0.22 #2010), USA (0.27 #2691, 0.23 #3172, 0.22 #3409), MAL (0.14 #797, 0.10 #1749, 0.08 #1272), PNG (0.14 #1129, 0.09 #4770, 0.08 #5734), BRU (0.14 #835, 0.08 #1310, 0.07 #1548), GB (0.14 #4063, 0.09 #5986, 0.09 #5743), NL (0.10 #3097, 0.05 #7895, 0.05 #7655), AUS (0.10 #2185, 0.06 #2904, 0.03 #4339)
>> best conf = 0.87
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3337 for best value:
>> intensional similarity = 8
>> extensional distance = 55
>> proper extension:  Saipan; Savaii; Guam; Shikoku;
>> query: (?x937, ?x217) <- ?x937[ a Island; has belongsToIslands ?x1099[ a Islands; is belongsToIslands of ?x1098[ a Island; has locatedIn ?x217; has locatedInWater ?x282<PacificOcean>;];];]
ranks of expected_values: 1
EVAL	Ternate	locatedIn	RI	CNN-1.+1._MA	1.000	1.000	1.000	1.000	46.000	46.000	104.000	0.867	http://www.semwebtech.org/mondial/10/meta#locatedIn

#277-ArabianSea
PRED entity: ArabianSea

PRED relation: locatedIn
PRED expected values: PK OM
=> 26 concepts (24 used for prediction)
PRED predicted values (max 10 best out of 225): CN (0.68 #1640, 0.67 #4458, 0.67 #4457), IR (0.50 #2414, 0.09 #703, 0.09 #702), SA (0.45 #1332, 0.40 #393, 0.28 #1406), RI (0.40 #520, 0.38 #990, 0.33 #52), CL (0.40 #585, 0.33 #117, 0.25 #1055), MYA (0.40 #552, 0.33 #2815, 0.33 #2814), I (0.37 #3801, 0.05 #1454, 0.05 #4976), EAK (0.33 #113, 0.30 #3284, 0.30 #3283), AUS (0.33 #45, 0.20 #513, 0.20 #278), TL (0.33 #157, 0.20 #625, 0.20 #390)
>> best conf = 0.68
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1640 for best value:
>> intensional similarity = 6
>> extensional distance = 17
>> proper extension:  HudsonBay;
>> query: (?x1333, ?x232) <- ?x1333[ has mergesWith ?x926; is flowsInto of ?x411[ has locatedIn ?x232;]; is locatedInWater of ?x1476[ has belongsToIslands ?x1477;]; is mergesWith of ?x60;]

*> Best rule #2815 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 33
*> proper extension:  SeaofAzov; BlackSea; NorthSea; AtlanticOcean; JavaSea; BarentsSea; ArcticOcean; SeaofJapan; MediterraneanSea; PacificOcean; ...
*> query: (?x1333, ?x83) <- ?x1333[ has locatedIn ?x924[ has wasDependentOf ?x81; is neighbor of ?x83; is neighbor of ?x943[ a Country;];]; is mergesWith of ?x60;]
*> conf = 0.33
ranks of expected_values: 22, 27
EVAL	ArabianSea	locatedIn	OM	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.038	26.000	24.000	225.000	0.681	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	ArabianSea	locatedIn	PK	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.045	26.000	24.000	225.000	0.681	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: PK OM
=> 117 concepts (117 used for prediction)
PRED predicted values (max 10 best out of 235): USA (0.79 #8802, 0.67 #9979, 0.62 #4554), ETH (0.78 #7898, 0.67 #15591, 0.43 #2120), CN (0.67 #7781, 0.67 #15591, 0.66 #6130), BD (0.67 #15591, 0.62 #470, 0.48 #5893), RI (0.62 #12566, 0.62 #470, 0.50 #3121), ZRE (0.62 #470, 0.57 #17085, 0.54 #17562), OM (0.62 #470, 0.52 #18661, 0.50 #469), UAE (0.62 #470, 0.52 #18661, 0.50 #469), SA (0.62 #470, 0.52 #18661, 0.40 #1338), MYA (0.62 #470, 0.50 #5894, 0.48 #5893)
>> best conf = 0.79
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #8802 for best value:
>> intensional similarity = 11
>> extensional distance = 26
>> proper extension:  DetroitRiver; Tennessee; SaintMarysRiver; YukonRiver; NiagaraRiver; TruckeeRiver; Missouri; MerrimackRiver; ColumbiaRiver; StraitsofMackinac; ...
>> query: (?x1333, USA) <- ?x1333[ has locatedIn ?x924[ has encompassed ?x175; has language ?x2392; has religion ?x462<Buddhist>; has wasDependentOf ?x81; is locatedIn of ?x60[ is locatedInWater of ?x226;]; is neighbor of ?x232[ is locatedIn of ?x231;];]; is flowsInto of ?x411;]

*> Best rule #470 for first EXPECTED value:
*> intensional similarity = 13
*> extensional distance = 1
*> proper extension:  PersianGulf;
*> query: (?x1333, ?x63) <- ?x1333[ has locatedIn ?x220; is locatedInWater of ?x1476[ a Island;]; is mergesWith of ?x60[ has locatedIn ?x61; is locatedInWater of ?x226; is mergesWith of ?x182;]; is mergesWith of ?x926<GulfofOman>; is mergesWith of ?x2407[ is mergesWith of ?x1552[ a Sea; has locatedIn ?x63;];];]
*> conf = 0.62
ranks of expected_values: 7, 135
EVAL	ArabianSea	locatedIn	OM	CNN-1.+1._MA	0.000	0.000	1.000	0.143	117.000	117.000	235.000	0.786	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	ArabianSea	locatedIn	PK	CNN-1.+1._MA	0.000	0.000	0.000	0.007	117.000	117.000	235.000	0.786	http://www.semwebtech.org/mondial/10/meta#locatedIn

#276-Mt.Cook
PRED entity: Mt.Cook

PRED relation: locatedOnIsland
PRED expected values: TeWaka-a-Maui-SouthIsland-
=> 59 concepts (55 used for prediction)
PRED predicted values (max 10 best out of 34): TeIka-a-Maui-NorthIsland- (0.33 #21, 0.17 #73, 0.14 #177), NewGuinea (0.18 #238, 0.03 #606, 0.02 #1083), GreatBritain (0.14 #113, 0.08 #270, 0.08 #322), Guadalcanal (0.09 #219, 0.01 #534), Bougainville (0.09 #243), Basse-Terre (0.08 #311), Martinique (0.08 #306), VitiLevu (0.08 #271), Borneo (0.08 #267), EllesmereIsland (0.08 #346, 0.01 #504, 0.01 #556)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #21 for best value:
>> intensional similarity = 3
>> extensional distance = 1
>> proper extension:  Ruapehu;
>> query: (?x1583, TeIka-a-Maui-NorthIsland-) <- ?x1583[ a Mountain; has locatedIn ?x461<NZ>;]

*> Best rule #261 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 9
*> proper extension:  Mt.Giluwe; Mt.Balbi; Mt.Wilhelm;
*> query: (?x1583, ?x282) <- ?x1583[ a Mountain; has locatedIn ?x461[ has encompassed ?x211<Australia-Oceania>; has language ?x51; is locatedIn of ?x282;];]
*> conf = 0.03
ranks of expected_values: 15
EVAL	Mt.Cook	locatedOnIsland	TeWaka-a-Maui-SouthIsland-	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.067	59.000	55.000	34.000	0.333	http://www.semwebtech.org/mondial/10/meta#locatedOnIsland

PRED relation: locatedOnIsland
PRED expected values: TeWaka-a-Maui-SouthIsland-
=> 158 concepts (158 used for prediction)
PRED predicted values (max 10 best out of 51): TeIka-a-Maui-NorthIsland- (0.33 #21, 0.17 #232, 0.17 #179), GreatBritain (0.20 #113, 0.17 #167, 0.06 #590), NewGuinea (0.20 #505, 0.04 #1785, 0.03 #1089), Martinique (0.17 #203, 0.02 #2016, 0.02 #2122), Basse-Terre (0.17 #208, 0.01 #3531, 0.01 #3961), Corse (0.11 #471, 0.05 #737, 0.02 #1591), Guadalcanal (0.10 #486, 0.06 #592, 0.02 #1660), Bougainville (0.10 #510, 0.02 #1790), TeWaka-a-Maui-SouthIsland- (0.08 #421, 0.07 #211, 0.06 #1112), Ruapehu (0.07 #211, 0.04 #316, 0.03 #422)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #21 for best value:
>> intensional similarity = 3
>> extensional distance = 1
>> proper extension:  Ruapehu;
>> query: (?x1583, TeIka-a-Maui-NorthIsland-) <- ?x1583[ a Mountain; has locatedIn ?x461<NZ>;]

*> Best rule #421 for first EXPECTED value:
*> intensional similarity = 15
*> extensional distance = 7
*> proper extension:  Ireland; IrishSea;
*> query: (?x1583, ?x897) <- ?x1583[ has locatedIn ?x461[ a Country; has ethnicGroup ?x197[ a EthnicGroup; is ethnicGroup of ?x318<CR>; is ethnicGroup of ?x450<CV>; is ethnicGroup of ?x520<DOM>;]; has ethnicGroup ?x380<Asian>; has government ?x1947; is locatedIn of ?x897[ has belongsToIslands ?x1523;];];]
*> conf = 0.08
ranks of expected_values: 9
EVAL	Mt.Cook	locatedOnIsland	TeWaka-a-Maui-SouthIsland-	CNN-1.+1._MA	0.000	0.000	1.000	0.111	158.000	158.000	51.000	0.333	http://www.semwebtech.org/mondial/10/meta#locatedOnIsland

#275-NewProvidence
PRED entity: NewProvidence

PRED relation: locatedInWater
PRED expected values: AtlanticOcean
=> 44 concepts (44 used for prediction)
PRED predicted values (max 10 best out of 35): AtlanticOcean (0.65 #520, 0.65 #483, 0.60 #180), CaribbeanSea (0.33 #321, 0.33 #19, 0.25 #105), NewProvidence (0.29 #346, 0.11 #130, 0.06 #390), PacificOcean (0.25 #103, 0.23 #929, 0.21 #712), BandaSea (0.25 #115, 0.05 #462, 0.04 #637), NorthSea (0.14 #611, 0.14 #349, 0.12 #654), MediterraneanSea (0.12 #449, 0.11 #798, 0.11 #841), IndianOcean (0.12 #435, 0.11 #348, 0.08 #1001), JavaSea (0.10 #442, 0.07 #617, 0.07 #660), ArcticOcean (0.08 #490, 0.07 #534, 0.06 #578)
>> best conf = 0.65
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #520 for best value:
>> intensional similarity = 7
>> extensional distance = 84
>> proper extension:  SaintHelena; Ascension; TristanDaCunha;
>> query: (?x2306, ?x182) <- ?x2306[ a Island; has locatedIn ?x279[ a Country; has encompassed ?x521; has government ?x854; is locatedIn of ?x182<AtlanticOcean>;];]
>> Best rule #483 for best value:
>> intensional similarity = 7
>> extensional distance = 84
>> proper extension:  SaintHelena; Ascension; TristanDaCunha;
>> query: (?x2306, AtlanticOcean) <- ?x2306[ a Island; has locatedIn ?x279[ a Country; has encompassed ?x521; has government ?x854; is locatedIn of ?x182<AtlanticOcean>;];]
ranks of expected_values: 1
EVAL	NewProvidence	locatedInWater	AtlanticOcean	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	44.000	44.000	35.000	0.651	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater
PRED expected values: AtlanticOcean
=> 82 concepts (82 used for prediction)
PRED predicted values (max 10 best out of 63): AtlanticOcean (0.70 #1100, 0.70 #1063, 0.69 #1144), PacificOcean (0.47 #766, 0.38 #238, 0.38 #193), CaribbeanSea (0.46 #415, 0.33 #150, 0.21 #283), IndianOcean (0.38 #223, 0.20 #1457, 0.18 #707), ArcticOcean (0.36 #278, 0.23 #322, 0.22 #454), NorthSea (0.22 #355, 0.19 #1414, 0.16 #1770), MediterraneanSea (0.20 #808, 0.19 #1692, 0.12 #2539), JavaSea (0.17 #933, 0.15 #978, 0.14 #1022), LabradorSea (0.14 #275, 0.11 #451, 0.09 #319), IrishSea (0.13 #394, 0.12 #218, 0.10 #526)
>> best conf = 0.70
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1100 for best value:
>> intensional similarity = 10
>> extensional distance = 58
>> proper extension:  Svalbard;
>> query: (?x2306, ?x182) <- ?x2306[ a Island; has belongsToIslands ?x2307[ a Islands;]; has locatedIn ?x279[ a Country; has encompassed ?x521[ a Continent;]; has government ?x854; is locatedIn of ?x182<AtlanticOcean>;];]
>> Best rule #1063 for best value:
>> intensional similarity = 10
>> extensional distance = 58
>> proper extension:  Svalbard;
>> query: (?x2306, AtlanticOcean) <- ?x2306[ a Island; has belongsToIslands ?x2307[ a Islands;]; has locatedIn ?x279[ a Country; has encompassed ?x521[ a Continent;]; has government ?x854; is locatedIn of ?x182<AtlanticOcean>;];]
ranks of expected_values: 1
EVAL	NewProvidence	locatedInWater	AtlanticOcean	CNN-1.+1._MA	1.000	1.000	1.000	1.000	82.000	82.000	63.000	0.700	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#274-English
PRED entity: English

PRED relation: language!
PRED expected values: USA GUAM AMSA MC
=> 21 concepts (21 used for prediction)
PRED predicted values (max 10 best out of 186): B (0.51 #368, 0.40 #338, 0.33 #431), CO (0.51 #368, 0.33 #369, 0.33 #185), R (0.51 #368, 0.33 #369, 0.33 #185), MEX (0.51 #368, 0.33 #58, 0.25 #150), CR (0.51 #368, 0.33 #37, 0.25 #129), RG (0.51 #368, 0.22 #1569, 0.20 #344), F (0.51 #368, 0.22 #1569, 0.20 #280), N (0.51 #368, 0.22 #1569, 0.09 #1292), CI (0.51 #368, 0.22 #1569, 0.09 #1292), WAL (0.51 #368, 0.22 #1569, 0.09 #1292)
>> best conf = 0.51
=> the first rule below is the first best rule for 18 predicted values
>> Best rule #368 for best value:
>> intensional similarity = 12
>> extensional distance = 3
>> proper extension:  French;
>> query: (?x247, ?x215) <- ?x247[ is language of ?x246[ has language ?x611<Papiamento>; is locatedIn of ?x317;]; is language of ?x407[ has religion ?x95;]; is language of ?x783[ a Country; is neighbor of ?x215;]; is language of ?x853[ has wasDependentOf ?x485; is locatedIn of ?x1074;];]

*> Best rule #36 for first EXPECTED value:
*> intensional similarity = 10
*> extensional distance = 1
*> proper extension:  Spanish;
*> query: (?x247, USA) <- ?x247[ is language of ?x161[ a Country;]; is language of ?x246<CUR>; is language of ?x783<PA>; is language of ?x853[ has wasDependentOf ?x485; is locatedIn of ?x1697[ a Volcano;];];]
*> conf = 0.33
ranks of expected_values: 33, 39, 45, 174
EVAL	English	language!	MC	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.026	21.000	21.000	186.000	0.509	http://www.semwebtech.org/mondial/10/meta#language
EVAL	English	language!	AMSA	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.006	21.000	21.000	186.000	0.509	http://www.semwebtech.org/mondial/10/meta#language
EVAL	English	language!	GUAM	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.023	21.000	21.000	186.000	0.509	http://www.semwebtech.org/mondial/10/meta#language
EVAL	English	language!	USA	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.030	21.000	21.000	186.000	0.509	http://www.semwebtech.org/mondial/10/meta#language

PRED relation: language!
PRED expected values: USA GUAM AMSA MC
=> 36 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 212): IR (0.58 #978, 0.15 #2393, 0.15 #2485), B (0.55 #654, 0.53 #655, 0.52 #372), F (0.55 #654, 0.53 #655, 0.52 #372), N (0.55 #654, 0.53 #655, 0.52 #372), S (0.55 #654, 0.53 #655, 0.52 #372), CO (0.55 #654, 0.53 #655, 0.52 #372), CR (0.55 #654, 0.53 #655, 0.52 #372), MEX (0.55 #654, 0.53 #655, 0.52 #372), RG (0.55 #654, 0.53 #655, 0.52 #372), GCA (0.55 #654, 0.53 #655, 0.47 #658)
>> best conf = 0.58
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #978 for best value:
>> intensional similarity = 24
>> extensional distance = 10
>> proper extension:  Kurdish; Luri; Persian; Balochi; Turkic;
>> query: (?x247, IR) <- ?x247[ a Language; is language of ?x138[ has neighbor ?x243;]; is language of ?x161[ a Country;]; is language of ?x196[ has ethnicGroup ?x380; has religion ?x95[ is religion of ?x617<VN>; is religion of ?x667<TT>;]; is locatedIn of ?x60;]; is language of ?x272[ has ethnicGroup ?x197; is locatedIn of ?x182[ is locatedInWater of ?x112;]; is locatedIn of ?x218[ a Lake; is flowsInto of ?x2018;]; is locatedIn of ?x1421[ a Island;];]; is language of ?x671[ has government ?x1947;];]

*> Best rule #465 for first EXPECTED value:
*> intensional similarity = 23
*> extensional distance = 3
*> proper extension:  Portuguese;
*> query: (?x247, MC) <- ?x247[ a Language; is language of ?x81[ is locatedIn of ?x121; is wasDependentOf of ?x63;]; is language of ?x138[ has encompassed ?x213; is locatedIn of ?x137; is neighbor of ?x243;]; is language of ?x272[ has ethnicGroup ?x197; has government ?x2416; is locatedIn of ?x1325[ a River;]; is locatedIn of ?x1574[ a Mountain; has inMountains ?x1722;]; is locatedIn of ?x2359[ a Estuary;];]; is language of ?x407[ has religion ?x95; is locatedIn of ?x317;]; is language of ?x718<L>;]
*> conf = 0.40
ranks of expected_values: 24, 25, 120, 136
EVAL	English	language!	MC	CNN-1.+1._MA	0.000	0.000	0.000	0.042	36.000	36.000	212.000	0.583	http://www.semwebtech.org/mondial/10/meta#language
EVAL	English	language!	AMSA	CNN-1.+1._MA	0.000	0.000	0.000	0.008	36.000	36.000	212.000	0.583	http://www.semwebtech.org/mondial/10/meta#language
EVAL	English	language!	GUAM	CNN-1.+1._MA	0.000	0.000	0.000	0.008	36.000	36.000	212.000	0.583	http://www.semwebtech.org/mondial/10/meta#language
EVAL	English	language!	USA	CNN-1.+1._MA	0.000	0.000	0.000	0.042	36.000	36.000	212.000	0.583	http://www.semwebtech.org/mondial/10/meta#language

#273-PK
PRED entity: PK

PRED relation: encompassed
PRED expected values: Asia
=> 34 concepts (34 used for prediction)
PRED predicted values (max 10 best out of 5): Asia (0.86 #41, 0.82 #36, 0.79 #46), Europe (0.44 #52, 0.42 #104, 0.41 #114), Africa (0.37 #86, 0.33 #76, 0.28 #146), America (0.35 #112, 0.30 #97, 0.27 #132), Australia-Oceania (0.17 #75, 0.12 #150, 0.12 #161)
>> best conf = 0.86
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #41 for best value:
>> intensional similarity = 6
>> extensional distance = 12
>> proper extension:  BD;
>> query: (?x83, Asia) <- ?x83[ has government ?x140; has neighbor ?x232[ has neighbor ?x73; is locatedIn of ?x1950<Brahmaputra>;]; is locatedIn of ?x82;]
ranks of expected_values: 1
EVAL	PK	encompassed	Asia	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	34.000	34.000	5.000	0.857	http://www.semwebtech.org/mondial/10/meta#encompassed

PRED relation: encompassed
PRED expected values: Asia
=> 97 concepts (97 used for prediction)
PRED predicted values (max 10 best out of 5): Asia (0.83 #479, 0.82 #393, 0.78 #473), Europe (0.65 #447, 0.63 #189, 0.60 #235), America (0.57 #164, 0.46 #248, 0.45 #202), Africa (0.51 #350, 0.48 #257, 0.41 #263), Australia-Oceania (0.30 #369, 0.30 #396, 0.20 #54)
>> best conf = 0.83
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #479 for best value:
>> intensional similarity = 11
>> extensional distance = 136
>> proper extension:  BI;
>> query: (?x83, ?x175) <- ?x83[ a Country; has neighbor ?x381[ has encompassed ?x175; has ethnicGroup ?x2116; has government ?x2442; has religion ?x187[ is religion of ?x793<DK>;]; is locatedIn of ?x276;]; is locatedIn of ?x82; is neighbor of ?x304;]
ranks of expected_values: 1
EVAL	PK	encompassed	Asia	CNN-1.+1._MA	1.000	1.000	1.000	1.000	97.000	97.000	5.000	0.832	http://www.semwebtech.org/mondial/10/meta#encompassed

#272-Hispaniola
PRED entity: Hispaniola

PRED relation: belongsToIslands
PRED expected values: GreaterAntilles
=> 44 concepts (44 used for prediction)
PRED predicted values (max 10 best out of 55): LesserAntilles (0.78 #287, 0.58 #355, 0.50 #151), GreaterAntilles (0.25 #251, 0.17 #183, 0.08 #387), SundaIslands (0.19 #422, 0.17 #490, 0.11 #830), Azores (0.10 #548, 0.09 #684, 0.03 #1432), Canares (0.10 #567, 0.08 #703, 0.05 #907), HawaiiIslands (0.09 #777, 0.07 #845, 0.06 #913), CaymanIslands (0.08 #370, 0.03 #710, 0.02 #778), Philipines (0.07 #415, 0.06 #483, 0.04 #1367), InnerHebrides (0.06 #608, 0.06 #744, 0.04 #1152), CalifornianChannelIslands (0.06 #807, 0.05 #875, 0.04 #943)
>> best conf = 0.78
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #287 for best value:
>> intensional similarity = 5
>> extensional distance = 16
>> proper extension:  Grande-Terre; St.Barthelemy;
>> query: (?x2210, LesserAntilles) <- ?x2210[ a Island; has locatedInWater ?x182<AtlanticOcean>; has locatedInWater ?x317<CaribbeanSea>;]

*> Best rule #251 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 6
*> proper extension:  Jamaica; PuertoRico;
*> query: (?x2210, GreaterAntilles) <- ?x2210[ a Island; has locatedIn ?x520; has locatedInWater ?x182[ has locatedIn ?x50;]; has locatedInWater ?x317<CaribbeanSea>; is locatedOnIsland of ?x329;]
*> conf = 0.25
ranks of expected_values: 2
EVAL	Hispaniola	belongsToIslands	GreaterAntilles	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	44.000	44.000	55.000	0.778	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

PRED relation: belongsToIslands
PRED expected values: GreaterAntilles
=> 124 concepts (124 used for prediction)
PRED predicted values (max 10 best out of 62): LesserAntilles (0.78 #1511, 0.64 #695, 0.60 #1647), GreaterAntilles (0.33 #455, 0.33 #47, 0.20 #319), SundaIslands (0.30 #1918, 0.29 #1782, 0.24 #1986), Canares (0.17 #771, 0.15 #907, 0.15 #839), Philipines (0.12 #1775, 0.11 #1911, 0.09 #1979), Azores (0.12 #480, 0.10 #2996, 0.09 #3132), CapeVerdes (0.12 #519, 0.07 #1063, 0.06 #1403), Madeira (0.12 #521, 0.07 #1065, 0.06 #1405), CanadianArcticIslands (0.12 #2592, 0.09 #2864, 0.09 #2932), HawaiiIslands (0.11 #1457, 0.10 #2885, 0.09 #3157)
>> best conf = 0.78
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1511 for best value:
>> intensional similarity = 5
>> extensional distance = 16
>> proper extension:  Grande-Terre; St.Barthelemy;
>> query: (?x2210, LesserAntilles) <- ?x2210[ a Island; has locatedInWater ?x182<AtlanticOcean>; has locatedInWater ?x317<CaribbeanSea>;]

*> Best rule #455 for first EXPECTED value:
*> intensional similarity = 13
*> extensional distance = 4
*> proper extension:  Jamaica; PuertoRico; Martinique;
*> query: (?x2210, GreaterAntilles) <- ?x2210[ a Island; has locatedIn ?x520[ a Country; has ethnicGroup ?x197; has government ?x711;]; has locatedIn ?x697[ a Country; has ethnicGroup ?x162; has religion ?x95;]; has locatedInWater ?x317<CaribbeanSea>; is locatedOnIsland of ?x329[ a Mountain;];]
*> conf = 0.33
ranks of expected_values: 2
EVAL	Hispaniola	belongsToIslands	GreaterAntilles	CNN-1.+1._MA	0.000	1.000	1.000	0.500	124.000	124.000	62.000	0.778	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

#271-Borneo
PRED entity: Borneo

PRED relation: locatedInWater
PRED expected values: JavaSea SouthChinaSea
=> 53 concepts (51 used for prediction)
PRED predicted values (max 10 best out of 78): JavaSea (0.67 #219, 0.64 #1311, 0.54 #261), IndianOcean (0.64 #1311, 0.50 #212, 0.46 #254), SouthChinaSea (0.64 #1311, 0.40 #106, 0.33 #22), BandaSea (0.64 #1311, 0.40 #154, 0.17 #238), MalakkaStrait (0.64 #1311, 0.11 #2001, 0.11 #2002), AndamanSea (0.64 #1311, 0.08 #230, 0.08 #272), AtlanticOcean (0.48 #469, 0.32 #724, 0.28 #1233), PacificOcean (0.29 #860, 0.27 #989, 0.25 #1116), MediterraneanSea (0.20 #775, 0.16 #1030, 0.14 #817), NorthSea (0.19 #465, 0.17 #804, 0.14 #1017)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #219 for best value:
>> intensional similarity = 7
>> extensional distance = 10
>> proper extension:  Bangka; Timor;
>> query: (?x375, JavaSea) <- ?x375[ a Island; has belongsToIslands ?x875<SundaIslands>; has locatedIn ?x376[ a Country; has religion ?x116;]; has locatedInWater ?x625;]
ranks of expected_values: 1, 3
EVAL	Borneo	locatedInWater	SouthChinaSea	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	53.000	51.000	78.000	0.667	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	Borneo	locatedInWater	JavaSea	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	53.000	51.000	78.000	0.667	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater
PRED expected values: JavaSea SouthChinaSea
=> 144 concepts (144 used for prediction)
PRED predicted values (max 10 best out of 89): PacificOcean (0.90 #1728, 0.86 #2393, 0.86 #2565), JavaSea (0.78 #183, 0.64 #4267, 0.64 #4266), SouthChinaSea (0.69 #173, 0.64 #4267, 0.64 #4266), MalakkaStrait (0.69 #173, 0.64 #4267, 0.64 #4266), IndianOcean (0.68 #1934, 0.67 #176, 0.64 #4267), BandaSea (0.64 #4267, 0.64 #4266, 0.64 #4265), AndamanSea (0.64 #4267, 0.64 #4266, 0.64 #4265), AtlanticOcean (0.40 #3922, 0.39 #3217, 0.39 #3127), LakeToba (0.38 #217, 0.05 #1006, 0.02 #2810), Bintan (0.38 #217, 0.05 #1006, 0.02 #2810)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1728 for best value:
>> intensional similarity = 13
>> extensional distance = 27
>> proper extension:  Tongatapu; Niihau; TeWaka-a-Maui-SouthIsland-; Oahu; Kauai; Kiritimati; Tarawa; Halmahera; Koror; SantaRosaIsland; ...
>> query: (?x375, PacificOcean) <- ?x375[ a Island; has belongsToIslands ?x875; has locatedIn ?x376[ a Country; has wasDependentOf ?x81;]; has locatedInWater ?x625[ a Sea; is locatedInWater of ?x1005[ has type ?x150; is locatedOnIsland of ?x1253;]; is locatedInWater of ?x1158<Mindanao>; is mergesWith of ?x241;];]

*> Best rule #183 for first EXPECTED value:
*> intensional similarity = 13
*> extensional distance = 7
*> proper extension:  Java; Krakatau; Sumatra; Sulawesi; Bali; Sumbawa; Lombok;
*> query: (?x375, JavaSea) <- ?x375[ has belongsToIslands ?x875<SundaIslands>; has locatedIn ?x376[ a Country; has encompassed ?x175; has ethnicGroup ?x298; is locatedIn of ?x384<SouthChinaSea>; is neighbor of ?x217[ is locatedIn of ?x1200<Timor>;]; is wasDependentOf of ?x1404;]; is locatedOnIsland of ?x1526;]
*> conf = 0.78
ranks of expected_values: 2, 3
EVAL	Borneo	locatedInWater	SouthChinaSea	CNN-1.+1._MA	0.000	1.000	1.000	0.500	144.000	144.000	89.000	0.897	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	Borneo	locatedInWater	JavaSea	CNN-1.+1._MA	0.000	1.000	1.000	0.500	144.000	144.000	89.000	0.897	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#270-AtlanticOcean
PRED entity: AtlanticOcean

PRED relation: mergesWith
PRED expected values: NorthSea
=> 42 concepts (38 used for prediction)
PRED predicted values (max 10 best out of 506): NorthSea (0.83 #707, 0.82 #902, 0.82 #675), AtlanticOcean (0.33 #582, 0.33 #454, 0.32 #646), HudsonBay (0.33 #456, 0.33 #6, 0.16 #838), ArcticOcean (0.33 #459, 0.26 #684, 0.19 #587), EastSibirianSea (0.33 #16, 0.17 #466, 0.07 #786), BarentsSea (0.33 #7, 0.17 #457, 0.03 #674), KaraSea (0.33 #22, 0.17 #472, 0.03 #792), PacificOcean (0.27 #782, 0.26 #815, 0.24 #882), MarmaraSea (0.17 #445, 0.16 #838, 0.09 #670), BandaSea (0.16 #838, 0.13 #791, 0.13 #824)
>> best conf = 0.83
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #707 for best value:
>> intensional similarity = 5
>> extensional distance = 21
>> proper extension:  HudsonBay; KaraSea;
>> query: (?x182, ?x60) <- ?x182[ is flowsInto of ?x137; is locatedInWater of ?x112; is locatedInWater of ?x727[ a Island;]; is mergesWith of ?x60;]
ranks of expected_values: 1
EVAL	AtlanticOcean	mergesWith	NorthSea	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	42.000	38.000	506.000	0.828	http://www.semwebtech.org/mondial/10/meta#mergesWith

PRED relation: mergesWith
PRED expected values: NorthSea
=> 88 concepts (88 used for prediction)
PRED predicted values (max 10 best out of 505): NorthSea (0.85 #1063, 0.85 #865, 0.85 #703), AtlanticOcean (0.50 #406, 0.44 #1464, 0.43 #674), ArcticOcean (0.44 #1464, 0.29 #1040, 0.24 #1364), BandaSea (0.33 #423, 0.17 #389, 0.16 #1184), PacificOcean (0.31 #1242, 0.29 #845, 0.29 #813), BeringSea (0.24 #1364, 0.17 #425, 0.17 #391), BarentsSea (0.24 #1364, 0.09 #577, 0.04 #1204), LakeHuron (0.24 #1364, 0.04 #166, 0.04 #199), LakeManicouagan (0.24 #1364, 0.03 #269, 0.03 #1196), SuluSea (0.18 #590, 0.17 #422, 0.17 #388)
>> best conf = 0.85
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1063 for best value:
>> intensional similarity = 10
>> extensional distance = 19
>> proper extension:  KaraSea;
>> query: (?x182, ?x121) <- ?x182[ has mergesWith ?x60; is flowsInto of ?x910[ has hasEstuary ?x1796;]; is flowsInto of ?x952[ has hasSource ?x1131; has locatedIn ?x416;]; is locatedInWater of ?x609[ a Island; has locatedIn ?x922;]; is mergesWith of ?x121;]
ranks of expected_values: 1
EVAL	AtlanticOcean	mergesWith	NorthSea	CNN-1.+1._MA	1.000	1.000	1.000	1.000	88.000	88.000	505.000	0.849	http://www.semwebtech.org/mondial/10/meta#mergesWith

#269-Menorca
PRED entity: Menorca

PRED relation: belongsToIslands
PRED expected values: Baleares
=> 52 concepts (52 used for prediction)
PRED predicted values (max 10 best out of 43): LipariIslands (0.46 #70, 0.23 #342, 0.23 #274), Baleares (0.40 #1089, 0.33 #39, 0.10 #379), Canares (0.40 #1089, 0.17 #431, 0.15 #499), Sporades (0.31 #157, 0.29 #225, 0.15 #293), IonicIslands (0.23 #156, 0.21 #224, 0.12 #292), SundaIslands (0.17 #626, 0.15 #558, 0.10 #762), Kyklades (0.15 #186, 0.14 #254, 0.08 #322), WestfriesischeInseln (0.12 #489, 0.08 #557, 0.08 #625), Azores (0.11 #548, 0.07 #820, 0.07 #888), HawaiiIslands (0.08 #709, 0.07 #845, 0.03 #1254)
>> best conf = 0.46
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #70 for best value:
>> intensional similarity = 5
>> extensional distance = 11
>> proper extension:  Stromboli; Salina; Sardegna; Panarea; Filicudi; Lampedusa; Alicudi; Corse; Linosa; Lipari;
>> query: (?x68, LipariIslands) <- ?x68[ a Island; has locatedIn ?x149[ is wasDependentOf of ?x148;]; has locatedInWater ?x275<MediterraneanSea>;]

*> Best rule #1089 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 152
*> proper extension:  VitiLevu; Savaii;
*> query: (?x68, ?x1715) <- ?x68[ a Island; has locatedIn ?x149[ has ethnicGroup ?x2540; has government ?x1657; is locatedIn of ?x2304[ has belongsToIslands ?x1715;];];]
*> conf = 0.40
ranks of expected_values: 2
EVAL	Menorca	belongsToIslands	Baleares	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	52.000	52.000	43.000	0.462	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

PRED relation: belongsToIslands
PRED expected values: Baleares
=> 145 concepts (145 used for prediction)
PRED predicted values (max 10 best out of 51): Canares (0.60 #91, 0.55 #159, 0.48 #273), Baleares (0.48 #273, 0.46 #342, 0.44 #683), LipariIslands (0.32 #206, 0.30 #275, 0.23 #548), Azores (0.29 #346, 0.19 #618, 0.17 #891), SundaIslands (0.22 #1720, 0.18 #1789, 0.16 #2405), LesserAntilles (0.18 #4391, 0.14 #3912, 0.10 #2749), HawaiiIslands (0.17 #1256, 0.16 #1393, 0.16 #1530), Philipines (0.16 #1782, 0.09 #3835, 0.06 #5133), InnerHebrides (0.15 #474, 0.14 #542, 0.13 #747), Sporades (0.13 #567, 0.07 #1932, 0.06 #4445)
>> best conf = 0.60
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #91 for best value:
>> intensional similarity = 8
>> extensional distance = 8
>> proper extension:  GranCanaria; Hierro; Lanzarote; Teneriffa; Gomera; Fuerteventura; LaPalma;
>> query: (?x68, Canares) <- ?x68[ a Island; has locatedIn ?x149<E>; has locatedInWater ?x275[ has locatedIn ?x850[ has encompassed ?x195;]; has mergesWith ?x182; is flowsInto of ?x698;];]

*> Best rule #273 for first EXPECTED value:
*> intensional similarity = 10
*> extensional distance = 17
*> proper extension:  Stromboli; Salina; Sardegna; Panarea; Filicudi; Lampedusa; Alicudi; Linosa; Lipari;
*> query: (?x68, ?x1068) <- ?x68[ a Island; has locatedIn ?x149[ has language ?x790; has neighbor ?x78<F>; has religion ?x352; is locatedIn of ?x1020[ has belongsToIslands ?x1068;]; is wasDependentOf of ?x148;]; has locatedInWater ?x275;]
*> conf = 0.48
ranks of expected_values: 2
EVAL	Menorca	belongsToIslands	Baleares	CNN-1.+1._MA	0.000	1.000	1.000	0.500	145.000	145.000	51.000	0.600	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

#268-RO
PRED entity: RO

PRED relation: ethnicGroup
PRED expected values: Romanian Turkish
=> 44 concepts (44 used for prediction)
PRED predicted values (max 10 best out of 225): Slovak (0.50 #460, 0.43 #966, 0.40 #713), Serb (0.43 #1049, 0.40 #543, 0.29 #796), Belorussian (0.33 #1599, 0.25 #4049, 0.18 #9110), Uzbek (0.33 #1669, 0.07 #1416, 0.06 #3693), European (0.30 #3295, 0.30 #2789, 0.26 #4814), Polish (0.29 #1213, 0.29 #960, 0.27 #1719), Czech (0.29 #788, 0.25 #282, 0.20 #535), Romanian (0.25 #348, 0.25 #4049, 0.20 #601), Bulgarian (0.25 #135, 0.25 #4049, 0.18 #9110), Gagauz (0.25 #173, 0.25 #4049, 0.07 #1691)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #460 for best value:
>> intensional similarity = 9
>> extensional distance = 2
>> proper extension:  H; CZ;
>> query: (?x176, Slovak) <- ?x176[ a Country; has ethnicGroup ?x164<Roma>; has ethnicGroup ?x237<German>; has ethnicGroup ?x517<Hungarian>; has government ?x435; is locatedIn of ?x98;]

*> Best rule #348 for first EXPECTED value:
*> intensional similarity = 9
*> extensional distance = 2
*> proper extension:  H; CZ;
*> query: (?x176, Romanian) <- ?x176[ a Country; has ethnicGroup ?x164<Roma>; has ethnicGroup ?x237<German>; has ethnicGroup ?x517<Hungarian>; has government ?x435; is locatedIn of ?x98;]
*> conf = 0.25
ranks of expected_values: 8, 18
EVAL	RO	ethnicGroup	Turkish	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.059	44.000	44.000	225.000	0.500	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	RO	ethnicGroup	Romanian	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.125	44.000	44.000	225.000	0.500	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: Romanian Turkish
=> 97 concepts (97 used for prediction)
PRED predicted values (max 10 best out of 244): Albanian (0.50 #1359, 0.33 #345, 0.25 #1106), European (0.48 #4324, 0.38 #10161, 0.38 #5845), Serb (0.43 #2319, 0.34 #8376, 0.33 #1812), Polish (0.40 #3246, 0.38 #2737, 0.34 #8376), Turkish (0.40 #1703, 0.34 #8376, 0.33 #1957), Macedonian (0.40 #1535, 0.34 #8376, 0.31 #12442), Bulgarian (0.34 #8376, 0.33 #2164, 0.33 #642), Bosniak (0.34 #8376, 0.33 #448, 0.33 #195), Montenegrin (0.34 #8376, 0.33 #462, 0.33 #209), Belorussian (0.34 #8376, 0.33 #3126, 0.31 #12442)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1359 for best value:
>> intensional similarity = 15
>> extensional distance = 2
>> proper extension:  KOS;
>> query: (?x176, Albanian) <- ?x176[ a Country; has ethnicGroup ?x58; has government ?x435<"republic">; has language ?x684; has neighbor ?x886; has religion ?x56; has wasDependentOf ?x1656; is locatedIn of ?x98; is neighbor of ?x236[ has ethnicGroup ?x775; has government ?x254; is locatedIn of ?x155;]; is neighbor of ?x904<SRB>;]

*> Best rule #1703 for first EXPECTED value:
*> intensional similarity = 15
*> extensional distance = 3
*> proper extension:  BG; MK;
*> query: (?x176, Turkish) <- ?x176[ a Country; has ethnicGroup ?x58; has government ?x435; has language ?x684; has neighbor ?x886; has religion ?x352<RomanCatholic>; has wasDependentOf ?x1656; is locatedIn of ?x98; is neighbor of ?x236[ has ethnicGroup ?x775; is locatedIn of ?x708<Theiss>;]; is neighbor of ?x904<SRB>;]
*> conf = 0.40
ranks of expected_values: 5, 16
EVAL	RO	ethnicGroup	Turkish	CNN-1.+1._MA	0.000	0.000	1.000	0.200	97.000	97.000	244.000	0.500	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	RO	ethnicGroup	Romanian	CNN-1.+1._MA	0.000	0.000	0.000	0.067	97.000	97.000	244.000	0.500	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#267-ANG
PRED entity: ANG

PRED relation: ethnicGroup
PRED expected values: Ovimbundu
=> 32 concepts (32 used for prediction)
PRED predicted values (max 10 best out of 207): African (0.48 #771, 0.38 #1791, 0.33 #516), Mangbetu-Azande (0.33 #181, 0.17 #436, 0.06 #691), Mestizo (0.30 #800, 0.16 #1565, 0.15 #2075), Amerindian (0.26 #767, 0.18 #1532, 0.17 #2042), German (0.23 #1284, 0.22 #1029, 0.16 #1539), Russian (0.18 #1346, 0.16 #2621, 0.15 #2876), Ukrainian (0.18 #1276, 0.14 #1021, 0.12 #2551), Fulani (0.17 #258, 0.11 #513, 0.04 #1788), Kirdi (0.17 #505, 0.06 #760, 0.02 #2035), CameroonHighlanders (0.17 #312, 0.06 #567, 0.02 #1842)
>> best conf = 0.48
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #771 for best value:
>> intensional similarity = 5
>> extensional distance = 21
>> proper extension:  SD;
>> query: (?x934, African) <- ?x934[ has ethnicGroup ?x197<European>; has government ?x1721; has neighbor ?x138; has religion ?x95;]

No rule for expected values
ranks of expected_values: 
EVAL	ANG	ethnicGroup	Ovimbundu	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	32.000	32.000	207.000	0.478	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: Ovimbundu
=> 92 concepts (92 used for prediction)
PRED predicted values (max 10 best out of 246): African (0.71 #6901, 0.60 #5109, 0.57 #3578), Batswana (0.33 #1012, 0.30 #2296, 0.18 #18896), Kgalagadi (0.33 #980, 0.30 #2296, 0.18 #18896), Fulani (0.33 #513, 0.25 #1278, 0.18 #18896), Kirdi (0.33 #760, 0.18 #18896, 0.18 #18897), CameroonHighlanders (0.33 #567, 0.18 #18896, 0.18 #18897), EasternNigritic (0.33 #559, 0.18 #18896, 0.18 #18897), EquatorialBantu (0.33 #535, 0.18 #18896, 0.18 #18897), Mangbetu-Azande (0.33 #436, 0.18 #18896, 0.18 #18897), Bantu (0.33 #75, 0.17 #3392, 0.10 #4413)
>> best conf = 0.71
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #6901 for best value:
>> intensional similarity = 16
>> extensional distance = 19
>> proper extension:  RT;
>> query: (?x934, African) <- ?x934[ has ethnicGroup ?x197[ is ethnicGroup of ?x318<CR>; is ethnicGroup of ?x450<CV>; is ethnicGroup of ?x1364<HCA>;]; has ethnicGroup ?x2098[ a EthnicGroup;]; has religion ?x95; is locatedIn of ?x933[ has locatedIn ?x243<RSA>;]; is neighbor of ?x528[ is locatedIn of ?x265; is neighbor of ?x172;];]

No rule for expected values
ranks of expected_values: 
EVAL	ANG	ethnicGroup	Ovimbundu	CNN-1.+1._MA	0.000	0.000	0.000	0.000	92.000	92.000	246.000	0.714	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#266-Kurdistan
PRED entity: Kurdistan

PRED relation: inMountains!
PRED expected values: Murat
=> 29 concepts (29 used for prediction)
PRED predicted values (max 10 best out of 282): Demirkazik (0.33 #10, 0.20 #521, 0.15 #1535), Mantaro (0.25 #507, 0.07 #2298, 0.06 #2810), PicoBolivar (0.25 #506, 0.07 #2297, 0.06 #2809), Amazonas (0.25 #493, 0.07 #2284, 0.06 #2796), Maranon (0.25 #491, 0.07 #2282, 0.06 #2794), Cotopaxi (0.25 #436, 0.07 #2227, 0.06 #2739), NevadodelRuiz (0.25 #425, 0.07 #2216, 0.06 #2728), Ucayali (0.25 #421, 0.07 #2212, 0.06 #2724), Llullaillaco (0.25 #416, 0.07 #2207, 0.06 #2719), Urubamba (0.25 #414, 0.07 #2205, 0.06 #2717)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #10 for best value:
>> intensional similarity = 9
>> extensional distance = 1
>> proper extension:  Taurus;
>> query: (?x1303, Demirkazik) <- ?x1303[ a Mountains; is inMountains of ?x1693[ a Mountain; has locatedIn ?x304[ has language ?x511; is neighbor of ?x290;];]; is inMountains of ?x2357[ has locatedIn ?x185<TR>;];]

*> Best rule #1535 for first EXPECTED value:
*> intensional similarity = 10
*> extensional distance = 8
*> proper extension:  Changbai;
*> query: (?x1303, ?x98) <- ?x1303[ is inMountains of ?x1693[ a Mountain;]; is inMountains of ?x2357[ has locatedIn ?x185[ a Country; has encompassed ?x175<Asia>; has wasDependentOf ?x1656; is locatedIn of ?x98; is neighbor of ?x177;];];]
*> conf = 0.15
ranks of expected_values: 47
EVAL	Kurdistan	inMountains!	Murat	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.021	29.000	29.000	282.000	0.333	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains!
PRED expected values: Murat
=> 66 concepts (66 used for prediction)
PRED predicted values (max 10 best out of 282): Tigris (0.33 #2563, 0.33 #1189, 0.29 #1027), Demirkazik (0.33 #2563, 0.33 #1293, 0.29 #1027), Karun (0.33 #2949, 0.29 #5387, 0.29 #1027), ZardKuh (0.33 #2858, 0.29 #5387, 0.29 #1027), Damavand (0.33 #2122, 0.29 #5387, 0.29 #1027), Murat (0.33 #2563, 0.29 #1027, 0.28 #3846), Euphrat (0.33 #2563, 0.29 #1027, 0.28 #3846), Euphrat (0.33 #2563, 0.29 #1027, 0.28 #3846), MarmaraSea (0.33 #2563, 0.29 #1027, 0.28 #3846), Murat (0.33 #2563, 0.29 #1027, 0.28 #3846)
>> best conf = 0.33
=> the first rule below is the first best rule for 16 predicted values
>> Best rule #2563 for best value:
>> intensional similarity = 22
>> extensional distance = 1
>> proper extension:  Kaukasus;
>> query: (?x1303, ?x184) <- ?x1303[ a Mountains; is inMountains of ?x1126[ a Mountain; a Volcano; has locatedIn ?x185[ a Country; has encompassed ?x175<Asia>; has ethnicGroup ?x638; has neighbor ?x332<AZ>; has religion ?x187<Muslim>; has wasDependentOf ?x1656; is locatedIn of ?x98<BlackSea>; is locatedIn of ?x184;];]; is inMountains of ?x1693[ a Mountain; a Volcano; has type ?x150<"volcanic">;];]
>> Best rule #1189 for best value:
>> intensional similarity = 20
>> extensional distance = 1
>> proper extension:  Antitaurus;
>> query: (?x1303, Tigris) <- ?x1303[ a Mountains; is inMountains of ?x469[ a Source; is hasSource of ?x468[ a River; has locatedIn ?x353;];]; is inMountains of ?x1126[ has locatedIn ?x185<TR>;]; is inMountains of ?x1693[ has locatedIn ?x304[ has encompassed ?x175; has ethnicGroup ?x244; has language ?x511<Turkish>; has neighbor ?x332<AZ>; has religion ?x187<Muslim>; is neighbor of ?x331;];];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 10
EVAL	Kurdistan	inMountains!	Murat	CNN-1.+1._MA	0.000	0.000	1.000	0.100	66.000	66.000	282.000	0.333	http://www.semwebtech.org/mondial/10/meta#inMountains

#265-Rhein
PRED entity: Rhein

PRED relation: flowsInto!
PRED expected values: Neckar
=> 32 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 366): Saar (0.20 #326, 0.20 #28, 0.05 #924), Elbe (0.20 #534, 0.20 #236, 0.03 #3287), Maas (0.20 #406, 0.20 #108, 0.03 #3287), Thames (0.20 #497, 0.20 #199, 0.02 #1991), Oder (0.20 #153, 0.03 #3287, 0.02 #1945), Maelaren (0.20 #294, 0.02 #2086, 0.01 #2385), Kymijoki (0.20 #277, 0.02 #2069, 0.01 #2368), Umeaelv (0.20 #244, 0.02 #2036, 0.01 #2335), Oulujoki (0.20 #235, 0.02 #2027, 0.01 #2326), Kokemaeenjoki (0.20 #214, 0.02 #2006, 0.01 #2305)
>> best conf = 0.20
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #326 for best value:
>> intensional similarity = 8
>> extensional distance = 3
>> proper extension:  NorthSea;
>> query: (?x256, Saar) <- ?x256[ has locatedIn ?x78<F>; has locatedIn ?x120<D>; has locatedIn ?x424[ has ethnicGroup ?x160; is locatedIn of ?x133<Donau>;];]
>> Best rule #28 for best value:
>> intensional similarity = 7
>> extensional distance = 3
>> proper extension:  BalticSea;
>> query: (?x256, Saar) <- ?x256[ has locatedIn ?x78[ is dependentOf of ?x297[ is locatedIn of ?x282;]; is wasDependentOf of ?x94;]; has locatedIn ?x120<D>; is flowsInto of ?x613;]

*> Best rule #3287 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 90
*> proper extension:  Sobat; Ob;
*> query: (?x256, ?x133) <- ?x256[ has hasEstuary ?x257; has hasSource ?x1695; is flowsInto of ?x1602[ has locatedIn ?x424[ is locatedIn of ?x133;];];]
*> conf = 0.03
ranks of expected_values: 61
EVAL	Rhein	flowsInto!	Neckar	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.016	32.000	31.000	366.000	0.200	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto!
PRED expected values: Neckar
=> 120 concepts (120 used for prediction)
PRED predicted values (max 10 best out of 502): Saar (0.25 #625, 0.20 #1223, 0.20 #1194), Isar (0.25 #459, 0.20 #1653, 0.11 #2986), Inn (0.25 #401, 0.20 #1595, 0.11 #2789), Lech (0.25 #418, 0.20 #1612, 0.11 #2806), Iller (0.25 #355, 0.20 #1549, 0.11 #2743), Breg (0.25 #563, 0.20 #1757, 0.11 #2951), Brigach (0.25 #431, 0.20 #1625, 0.11 #2819), Drau (0.25 #382, 0.20 #1576, 0.11 #2770), March (0.25 #454, 0.20 #1648, 0.11 #2842), Waag (0.25 #411, 0.20 #1605, 0.11 #2799)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #625 for best value:
>> intensional similarity = 13
>> extensional distance = 2
>> proper extension:  Saar;
>> query: (?x256, Saar) <- ?x256[ a River; has hasEstuary ?x257; has hasSource ?x1695[ a Source; has inMountains ?x261; has locatedIn ?x234[ has religion ?x56;];]; has locatedIn ?x78<F>; has locatedIn ?x120<D>; has locatedIn ?x423[ has ethnicGroup ?x2314;];]

*> Best rule #16790 for first EXPECTED value:
*> intensional similarity = 12
*> extensional distance = 83
*> proper extension:  LakeSkutari; DeadSea;
*> query: (?x256, ?x312) <- ?x256[ has locatedIn ?x120[ has neighbor ?x471[ has neighbor ?x163;]; has neighbor ?x575[ has encompassed ?x195; has ethnicGroup ?x734; has religion ?x95; is locatedIn of ?x257; is wasDependentOf of ?x179;]; is locatedIn of ?x312[ a River;];]; is flowsInto of ?x958;]
*> conf = 0.08
ranks of expected_values: 65
EVAL	Rhein	flowsInto!	Neckar	CNN-1.+1._MA	0.000	0.000	0.000	0.015	120.000	120.000	502.000	0.250	http://www.semwebtech.org/mondial/10/meta#flowsInto

#264-IndianOcean
PRED entity: IndianOcean

PRED relation: locatedIn
PRED expected values: RSA
=> 31 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 204): ETH (0.68 #5658, 0.67 #5660, 0.67 #5659), ZW (0.68 #5658, 0.67 #5660, 0.67 #5659), RB (0.68 #5658, 0.67 #5660, 0.67 #5659), RSA (0.68 #5658, 0.67 #5660, 0.67 #5659), Z (0.68 #5658, 0.67 #5660, 0.67 #5659), NAM (0.68 #5658, 0.67 #5660, 0.65 #4785), THA (0.43 #1531, 0.30 #1965, 0.12 #5438), EAU (0.35 #2172, 0.34 #3477, 0.33 #1010), MYA (0.35 #2172, 0.34 #3477, 0.33 #80), BD (0.35 #2172, 0.34 #3477, 0.33 #173)
>> best conf = 0.68
=> the first rule below is the first best rule for 6 predicted values
>> Best rule #5658 for best value:
>> intensional similarity = 5
>> extensional distance = 154
>> proper extension:  Würm; RioLerma; Sanaga;
>> query: (?x60, ?x192) <- ?x60[ is flowsInto of ?x242[ has locatedIn ?x192;]; is flowsInto of ?x750[ has locatedIn ?x476[ has ethnicGroup ?x1179;];];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 4
EVAL	IndianOcean	locatedIn	RSA	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.250	31.000	31.000	204.000	0.676	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: RSA
=> 102 concepts (102 used for prediction)
PRED predicted values (max 10 best out of 226): USA (0.98 #10802, 0.51 #12336, 0.33 #4002), R (0.88 #10519, 0.49 #8315, 0.42 #12709), RSA (0.75 #3334, 0.75 #4593, 0.74 #4592), NAM (0.75 #4593, 0.74 #4592, 0.72 #4154), ZW (0.75 #4593, 0.74 #4592, 0.72 #4154), RB (0.75 #4593, 0.74 #4592, 0.72 #4154), Z (0.75 #4593, 0.74 #4592, 0.72 #4154), ETH (0.75 #4593, 0.74 #4592, 0.72 #4154), MW (0.50 #2182, 0.46 #4372, 0.45 #2184), SD (0.50 #2182, 0.46 #4372, 0.45 #2184)
>> best conf = 0.98
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #10802 for best value:
>> intensional similarity = 9
>> extensional distance = 121
>> proper extension:  ChickamaugaLake; MtAdams; MtElbert; Tennessee; KingsPeak; Mississippi; Franklin.D.RooseveltLake; MtSt.Elias; SaintMarysRiver; Niihau; ...
>> query: (?x60, USA) <- ?x60[ has locatedIn ?x192[ is neighbor of ?x193;]; has locatedIn ?x196[ has encompassed ?x211; has ethnicGroup ?x197<European>; has language ?x247; has wasDependentOf ?x81; is dependentOf of ?x210;];]

*> Best rule #3334 for first EXPECTED value:
*> intensional similarity = 13
*> extensional distance = 10
*> proper extension:  Vaal; Vaal; Vaal; Limpopo; CathkinPeak;
*> query: (?x60, RSA) <- ?x60[ has locatedIn ?x192[ has ethnicGroup ?x1196; has government ?x435; is locatedIn of ?x2061[ a River;]; is neighbor of ?x193<SD>; is neighbor of ?x243[ is neighbor of ?x89;];]; has locatedIn ?x196[ has ethnicGroup ?x197; is locatedIn of ?x1103[ a Mountain;];];]
*> conf = 0.75
ranks of expected_values: 3
EVAL	IndianOcean	locatedIn	RSA	CNN-1.+1._MA	0.000	1.000	1.000	0.333	102.000	102.000	226.000	0.984	http://www.semwebtech.org/mondial/10/meta#locatedIn

#263-BRN
PRED entity: BRN

PRED relation: ethnicGroup
PRED expected values: Arab Iranian
=> 40 concepts (40 used for prediction)
PRED predicted values (max 10 best out of 222): African (0.86 #2310, 0.30 #3078, 0.29 #2822), European (0.55 #2312, 0.34 #5384, 0.31 #4104), Arab (0.43 #267, 0.25 #779, 0.21 #2571), Chinese (0.33 #15, 0.25 #1039, 0.22 #1295), Russian (0.21 #2632, 0.11 #6472, 0.10 #5704), Mestizo (0.17 #2339, 0.12 #4131, 0.12 #4643), Amerindian (0.17 #2306, 0.12 #4098, 0.12 #4610), Uzbek (0.17 #2715, 0.07 #5019, 0.06 #5275), Mulatto (0.17 #2362, 0.06 #3386, 0.05 #5434), Malay (0.17 #98, 0.14 #610, 0.12 #1122)
>> best conf = 0.86
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2310 for best value:
>> intensional similarity = 7
>> extensional distance = 27
>> proper extension:  CR; BR; TUCA;
>> query: (?x1705, African) <- ?x1705[ a Country; has ethnicGroup ?x380[ is ethnicGroup of ?x1072<WAL>;]; has government ?x92; has religion ?x187; is locatedIn of ?x918;]

*> Best rule #267 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 5
*> proper extension:  Q; KWT;
*> query: (?x1705, Arab) <- ?x1705[ a Country; has encompassed ?x175; has ethnicGroup ?x380; has religion ?x187<Muslim>; is locatedIn of ?x918<PersianGulf>;]
*> conf = 0.43
ranks of expected_values: 3, 40
EVAL	BRN	ethnicGroup	Iranian	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.026	40.000	40.000	222.000	0.862	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	BRN	ethnicGroup	Arab	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.333	40.000	40.000	222.000	0.862	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: Arab Iranian
=> 83 concepts (83 used for prediction)
PRED predicted values (max 10 best out of 247): European (0.87 #8755, 0.82 #9270, 0.56 #6695), African (0.80 #6693, 0.75 #4633, 0.67 #3344), Arab (0.46 #4895, 0.43 #5410, 0.40 #1808), Chinese (0.46 #5157, 0.29 #8505, 0.26 #13655), Russian (0.45 #4441, 0.29 #2639, 0.28 #7016), Uzbek (0.45 #4524, 0.29 #2722, 0.22 #3750), PacificIslander (0.44 #770, 0.26 #13897, 0.22 #4884), Maori (0.44 #770, 0.26 #13897, 0.22 #4884), Irish (0.44 #770, 0.22 #4884, 0.17 #2171), Caucasian (0.44 #770, 0.13 #19298, 0.10 #9004)
>> best conf = 0.87
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #8755 for best value:
>> intensional similarity = 13
>> extensional distance = 37
>> proper extension:  SD;
>> query: (?x1705, European) <- ?x1705[ has encompassed ?x175[ is encompassed of ?x568[ has neighbor ?x239;];]; has ethnicGroup ?x380[ is ethnicGroup of ?x154[ has encompassed ?x195; has government ?x2243; is locatedIn of ?x153;]; is ethnicGroup of ?x461<NZ>;]; has ethnicGroup ?x2046[ a EthnicGroup;]; has government ?x92;]

*> Best rule #4895 for first EXPECTED value:
*> intensional similarity = 16
*> extensional distance = 11
*> proper extension:  JOR;
*> query: (?x1705, Arab) <- ?x1705[ has encompassed ?x175<Asia>; has ethnicGroup ?x380; is locatedIn of ?x918[ a Sea; has locatedIn ?x174[ has ethnicGroup ?x244; has religion ?x187<Muslim>;]; has locatedIn ?x302[ has ethnicGroup ?x557; has religion ?x116; has wasDependentOf ?x485; is neighbor of ?x803<JOR>;]; has mergesWith ?x926;];]
*> conf = 0.46
ranks of expected_values: 3, 18
EVAL	BRN	ethnicGroup	Iranian	CNN-1.+1._MA	0.000	0.000	0.000	0.059	83.000	83.000	247.000	0.872	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	BRN	ethnicGroup	Arab	CNN-1.+1._MA	0.000	1.000	1.000	0.333	83.000	83.000	247.000	0.872	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#262-CDN
PRED entity: CDN

PRED relation: religion
PRED expected values: Anglican
=> 34 concepts (34 used for prediction)
PRED predicted values (max 10 best out of 33): Christian (0.36 #237, 0.33 #471, 0.30 #588), Buddhist (0.33 #9, 0.25 #48, 0.21 #243), Jewish (0.33 #2, 0.25 #41, 0.17 #158), Mormon (0.33 #23, 0.25 #62, 0.04 #179), ChristianOrthodox (0.26 #274, 0.26 #625, 0.25 #40), Hindu (0.18 #241, 0.13 #358, 0.13 #514), Anglican (0.17 #171, 0.16 #366, 0.15 #249), Methodist (0.09 #160, 0.05 #316, 0.04 #394), JehovasWitnesses (0.07 #447, 0.07 #135, 0.07 #486), Seventh-DayAdventist (0.05 #242, 0.05 #671, 0.04 #359)
>> best conf = 0.36
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #237 for best value:
>> intensional similarity = 4
>> extensional distance = 37
>> proper extension:  ET; WAN; IRL; FJI; KN; Q; AUS; CY; RSA; JA; ...
>> query: (?x272, Christian) <- ?x272[ has ethnicGroup ?x197; has wasDependentOf ?x81<GB>; is locatedIn of ?x182;]

*> Best rule #171 for first EXPECTED value:
*> intensional similarity = 4
*> extensional distance = 21
*> proper extension:  FALK;
*> query: (?x272, Anglican) <- ?x272[ has ethnicGroup ?x197; has language ?x51; is locatedIn of ?x182<AtlanticOcean>;]
*> conf = 0.17
ranks of expected_values: 7
EVAL	CDN	religion	Anglican	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.143	34.000	34.000	33.000	0.359	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion
PRED expected values: Anglican
=> 115 concepts (115 used for prediction)
PRED predicted values (max 10 best out of 39): ChristianOrthodox (0.70 #1093, 0.63 #1015, 0.43 #1172), Buddhist (0.50 #126, 0.40 #633, 0.36 #3282), Christian (0.48 #1642, 0.43 #1759, 0.37 #2932), Anglican (0.39 #990, 0.36 #3282, 0.34 #2186), Jewish (0.38 #236, 0.36 #3282, 0.34 #2186), Mormon (0.36 #3282, 0.34 #2186, 0.33 #62), Hindu (0.36 #3282, 0.34 #2186, 0.33 #1171), JehovasWitnesses (0.36 #3282, 0.33 #1171, 0.30 #564), Kimbanguist (0.36 #3282, 0.33 #1171, 0.21 #3712), Sikh (0.34 #2186, 0.33 #1171, 0.21 #3712)
>> best conf = 0.70
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1093 for best value:
>> intensional similarity = 9
>> extensional distance = 18
>> proper extension:  TM;
>> query: (?x272, ChristianOrthodox) <- ?x272[ has ethnicGroup ?x197; has language ?x51; has religion ?x187<Muslim>; has religion ?x352[ is religion of ?x55<BIH>;]; is locatedIn of ?x2007[ is flowsInto of ?x406;];]

*> Best rule #990 for first EXPECTED value:
*> intensional similarity = 10
*> extensional distance = 16
*> proper extension:  SVAX;
*> query: (?x272, Anglican) <- ?x272[ a Country; has ethnicGroup ?x197; has government ?x2416; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x866[ a Island; has belongsToIslands ?x479;]; is locatedIn of ?x1891[ has locatedInWater ?x248;];]
*> conf = 0.39
ranks of expected_values: 4
EVAL	CDN	religion	Anglican	CNN-1.+1._MA	0.000	0.000	1.000	0.250	115.000	115.000	39.000	0.700	http://www.semwebtech.org/mondial/10/meta#religion

#261-Douro
PRED entity: Douro

PRED relation: locatedIn
PRED expected values: P
=> 46 concepts (43 used for prediction)
PRED predicted values (max 10 best out of 167): P (0.40 #431, 0.25 #196, 0.13 #1649), USA (0.37 #3842, 0.35 #4551, 0.34 #4787), R (0.30 #4012, 0.29 #4248, 0.27 #4957), I (0.28 #1696, 0.14 #4054, 0.14 #4290), F (0.22 #1656, 0.13 #1649, 0.13 #3771), D (0.21 #4972, 0.13 #6628, 0.12 #1904), ZRE (0.20 #3849, 0.19 #4558, 0.19 #4794), CN (0.15 #1231, 0.12 #5717, 0.08 #8312), AND (0.13 #1649, 0.12 #3299, 0.12 #3298), GBZ (0.13 #1649, 0.12 #3299, 0.12 #3298)
>> best conf = 0.40
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #431 for best value:
>> intensional similarity = 5
>> extensional distance = 3
>> proper extension:  Guadiana; Tajo; Ebro;
>> query: (?x1519, P) <- ?x1519[ a River; has flowsInto ?x182; has hasSource ?x2193; has locatedIn ?x149<E>;]
ranks of expected_values: 1
EVAL	Douro	locatedIn	P	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	46.000	43.000	167.000	0.400	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: P
=> 150 concepts (150 used for prediction)
PRED predicted values (max 10 best out of 233): P (0.89 #10437, 0.88 #7586, 0.60 #471), USA (0.77 #16204, 0.52 #7108, 0.52 #6941), D (0.69 #12592, 0.24 #21379, 0.20 #9983), R (0.53 #4970, 0.38 #20649, 0.31 #8066), F (0.52 #8781, 0.35 #12099, 0.34 #12339), CN (0.50 #1470, 0.21 #3840, 0.17 #21650), CDN (0.40 #16431, 0.37 #17145, 0.33 #18333), GB (0.37 #9495, 0.36 #10446, 0.30 #11628), BR (0.33 #1303, 0.33 #1065, 0.32 #14939), I (0.33 #14986, 0.28 #2410, 0.27 #1701)
>> best conf = 0.89
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #10437 for best value:
>> intensional similarity = 8
>> extensional distance = 75
>> proper extension:  VictoriaNile;
>> query: (?x1519, ?x1027) <- ?x1519[ a River; has hasEstuary ?x1352[ a Estuary; has locatedIn ?x1027;]; has hasSource ?x2193[ a Source; has inMountains ?x1701; has locatedIn ?x149;];]
ranks of expected_values: 1
EVAL	Douro	locatedIn	P	CNN-1.+1._MA	1.000	1.000	1.000	1.000	150.000	150.000	233.000	0.889	http://www.semwebtech.org/mondial/10/meta#locatedIn

#260-CAM
PRED entity: CAM

PRED relation: locatedIn!
PRED expected values: Sanaga
=> 35 concepts (33 used for prediction)
PRED predicted values (max 10 best out of 1369): CaribbeanSea (0.37 #15636, 0.32 #14224, 0.30 #12812), Ubangi (0.33 #2967, 0.33 #1555, 0.29 #5791), Zaire (0.33 #3285, 0.33 #1873, 0.29 #6109), Ubangi (0.33 #2968, 0.33 #1556, 0.29 #5792), MaleboPool (0.33 #2899, 0.33 #1487, 0.29 #5723), WhiteNile (0.33 #506, 0.29 #6154, 0.02 #21686), Bomu (0.33 #1914, 0.14 #6150, 0.10 #42369), Bomu (0.33 #1841, 0.14 #6077, 0.10 #42369), Bomu (0.33 #1813, 0.14 #6049, 0.10 #42369), Ubangi (0.33 #1650, 0.14 #5886, 0.10 #42369)
>> best conf = 0.37
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #15636 for best value:
>> intensional similarity = 6
>> extensional distance = 28
>> proper extension:  WG;
>> query: (?x536, CaribbeanSea) <- ?x536[ a Country; has ethnicGroup ?x162<African>; has ethnicGroup ?x537[ a EthnicGroup;]; is locatedIn of ?x182;]

*> Best rule #25418 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 89
*> proper extension:  GBZ; CEU;
*> query: (?x536, ?x137) <- ?x536[ is locatedIn of ?x182[ is flowsInto of ?x137; is locatedInWater of ?x112; is mergesWith of ?x60;]; is neighbor of ?x139;]
*> conf = 0.06
ranks of expected_values: 207
EVAL	CAM	locatedIn!	Sanaga	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.005	35.000	33.000	1369.000	0.367	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Sanaga
=> 95 concepts (87 used for prediction)
PRED predicted values (max 10 best out of 1429): Sanaga (0.77 #33915, 0.60 #14130, 0.40 #9892), Zaire (0.77 #33915, 0.40 #9892, 0.40 #9891), Niger (0.77 #33915, 0.40 #9889, 0.37 #57971), PacificOcean (0.41 #99098, 0.40 #9889, 0.38 #63716), Oranje (0.40 #9892, 0.40 #9891, 0.40 #9889), Amazonas (0.40 #9892, 0.40 #9891, 0.40 #9889), RioSaoFrancisco (0.40 #9892, 0.40 #9891, 0.40 #9889), Tocantins (0.40 #9892, 0.40 #9891, 0.40 #9889), SaintLawrenceRiver (0.40 #9892, 0.40 #9891, 0.40 #9889), Volta (0.40 #9892, 0.40 #9891, 0.40 #9889)
>> best conf = 0.77
=> the first rule below is the first best rule for 3 predicted values
>> Best rule #33915 for best value:
>> intensional similarity = 13
>> extensional distance = 16
>> proper extension:  N;
>> query: (?x536, ?x929) <- ?x536[ a Country; has government ?x1721; has neighbor ?x528; is locatedIn of ?x182[ has locatedIn ?x667[ has ethnicGroup ?x298;]; is locatedInWater of ?x112; is mergesWith of ?x121<NorthSea>;]; is locatedIn of ?x2087[ has flowsInto ?x929;]; is neighbor of ?x736[ has ethnicGroup ?x992;];]
ranks of expected_values: 1
EVAL	CAM	locatedIn!	Sanaga	CNN-1.+1._MA	1.000	1.000	1.000	1.000	95.000	87.000	1429.000	0.769	http://www.semwebtech.org/mondial/10/meta#locatedIn

#259-Murgab
PRED entity: Murgab

PRED relation: locatedIn
PRED expected values: TAD
=> 43 concepts (43 used for prediction)
PRED predicted values (max 10 best out of 69): TAD (0.69 #2859, 0.67 #978, 0.60 #477), AFG (0.69 #2859, 0.60 #477, 0.60 #476), R (0.55 #2625, 0.16 #6676, 0.16 #1672), TR (0.50 #757, 0.03 #5522, 0.03 #6712), CN (0.42 #2383, 0.42 #2201, 0.38 #2382), KGZ (0.38 #2382, 0.38 #1666, 0.36 #9530), D (0.27 #1212, 0.10 #4071, 0.10 #2403), NEP (0.23 #2162, 0.03 #9309, 0.02 #9547), CH (0.21 #1724, 0.18 #1249, 0.16 #1963), PK (0.17 #715, 0.09 #953, 0.08 #2155)
>> best conf = 0.69
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #2859 for best value:
>> intensional similarity = 7
>> extensional distance = 31
>> proper extension:  NorthernDwina; Chatanga; Lena; Don; Kolyma; Kama;
>> query: (?x1106, ?x381) <- ?x1106[ is hasSource of ?x682[ a River; has flowsInto ?x592; has locatedIn ?x381[ a Country; is neighbor of ?x232<CN>;];];]
ranks of expected_values: 1
EVAL	Murgab	locatedIn	TAD	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	43.000	43.000	69.000	0.686	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: TAD
=> 128 concepts (128 used for prediction)
PRED predicted values (max 10 best out of 69): AFG (0.82 #14647, 0.79 #15847, 0.76 #16086), TAD (0.75 #7445, 0.72 #19452, 0.71 #12968), USA (0.67 #15919, 0.65 #16641, 0.42 #10402), R (0.65 #19218, 0.60 #18736, 0.50 #6492), CH (0.57 #7745, 0.50 #6066, 0.33 #6305), CDN (0.53 #14472, 0.53 #15671, 0.42 #10393), CN (0.50 #9426, 0.50 #2158, 0.50 #2157), KGZ (0.50 #2158, 0.50 #2157, 0.48 #12970), AUS (0.50 #1963, 0.40 #4366, 0.29 #7253), TR (0.50 #8211, 0.22 #8453, 0.17 #5568)
>> best conf = 0.82
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #14647 for best value:
>> intensional similarity = 16
>> extensional distance = 15
>> proper extension:  SaskatchewanRiver; NelsonRiver;
>> query: (?x1106, ?x381) <- ?x1106[ a Source; is hasSource of ?x682[ a River; has flowsInto ?x592; has locatedIn ?x129[ a Country; has ethnicGroup ?x1193;]; has locatedIn ?x381[ a Country; has encompassed ?x175; has government ?x2442; has language ?x1033; has religion ?x187<Muslim>; has wasDependentOf ?x81<GB>;];];]

*> Best rule #7445 for first EXPECTED value:
*> intensional similarity = 18
*> extensional distance = 5
*> proper extension:  RiviereRichelieu; Manicouagan;
*> query: (?x1106, ?x129) <- ?x1106[ a Source; is hasSource of ?x682[ a River; has flowsInto ?x592[ a River; has hasEstuary ?x593;]; has locatedIn ?x129[ a Country; has encompassed ?x175; has ethnicGroup ?x1193; has government ?x435; has religion ?x187;]; has locatedIn ?x381[ a Country; has government ?x2442; has language ?x1033; has wasDependentOf ?x81<GB>;];];]
*> conf = 0.75
ranks of expected_values: 2
EVAL	Murgab	locatedIn	TAD	CNN-1.+1._MA	0.000	1.000	1.000	0.500	128.000	128.000	69.000	0.824	http://www.semwebtech.org/mondial/10/meta#locatedIn

#258-TUCA
PRED entity: TUCA

PRED relation: locatedIn!
PRED expected values: GrandTurk
=> 42 concepts (39 used for prediction)
PRED predicted values (max 10 best out of 1114): CaribbeanSea (0.53 #29949, 0.50 #5789, 0.42 #8631), PacificOcean (0.38 #32771, 0.27 #11453, 0.25 #12875), Anguilla (0.33 #339, 0.11 #4602, 0.10 #6023), NewProvidence (0.33 #4161, 0.11 #5582, 0.07 #12687), GrandCayman (0.33 #1999, 0.07 #42636, 0.07 #41214), LittleCayman (0.33 #1735, 0.07 #42636, 0.07 #41214), IrishSea (0.22 #5310, 0.18 #8152, 0.15 #10994), Ireland (0.22 #4297, 0.18 #7139, 0.15 #9981), SaintLawrenceRiver (0.15 #48326, 0.09 #7831, 0.08 #10673), Oranje (0.15 #48326, 0.08 #9975, 0.07 #42636)
>> best conf = 0.53
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #29949 for best value:
>> intensional similarity = 6
>> extensional distance = 30
>> proper extension:  SBAR;
>> query: (?x561, CaribbeanSea) <- ?x561[ a Country; has encompassed ?x521<America>; has government ?x562; is locatedIn of ?x182<AtlanticOcean>;]

*> Best rule #19897 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 20
*> proper extension:  GBM;
*> query: (?x561, ?x112) <- ?x561[ a Country; has language ?x247<English>; is locatedIn of ?x182[ is locatedInWater of ?x112;]; is locatedIn of ?x1995[ a Island;];]
*> conf = 0.04
ranks of expected_values: 356
EVAL	TUCA	locatedIn!	GrandTurk	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.003	42.000	39.000	1114.000	0.531	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: GrandTurk
=> 95 concepts (95 used for prediction)
PRED predicted values (max 10 best out of 1396): CaribbeanSea (0.67 #32827, 0.56 #22858, 0.53 #62723), PacificOcean (0.48 #74082, 0.40 #75509, 0.37 #76939), IrishSea (0.33 #2472, 0.33 #1425, 0.33 #1424), Ireland (0.33 #1459, 0.33 #1425, 0.33 #1424), NewProvidence (0.33 #4165, 0.33 #1425, 0.33 #1424), Barbados (0.33 #1425, 0.33 #1424, 0.33 #1423), SaintLucia (0.33 #1425, 0.33 #1424, 0.33 #1423), Anguilla (0.33 #1425, 0.33 #1424, 0.33 #1423), MediterraneanSea (0.33 #1425, 0.33 #1424, 0.33 #1423), SaintLawrenceRiver (0.33 #1425, 0.33 #1424, 0.33 #1423)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #32827 for best value:
>> intensional similarity = 12
>> extensional distance = 13
>> proper extension:  WV;
>> query: (?x561, CaribbeanSea) <- ?x561[ a Country; has encompassed ?x521<America>; has government ?x562; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x1995[ a Island; has belongsToIslands ?x2092[ a Islands; is belongsToIslands of ?x1491;]; has locatedInWater ?x182<AtlanticOcean>;];]

*> Best rule #4268 for first EXPECTED value:
*> intensional similarity = 18
*> extensional distance = 1
*> proper extension:  BS;
*> query: (?x561, ?x1491) <- ?x561[ a Country; has encompassed ?x521<America>; has government ?x562; has religion ?x280<Methodist>; has religion ?x713<Anglican>; has religion ?x1082<Baptist>; has religion ?x1667<ChurchofGod>; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x1995[ a Island; has belongsToIslands ?x2092[ is belongsToIslands of ?x1491;];];]
*> conf = 0.33
ranks of expected_values: 487
EVAL	TUCA	locatedIn!	GrandTurk	CNN-1.+1._MA	0.000	0.000	0.000	0.002	95.000	95.000	1396.000	0.667	http://www.semwebtech.org/mondial/10/meta#locatedIn

#257-TM
PRED entity: TM

PRED relation: neighbor!
PRED expected values: KAZ
=> 46 concepts (46 used for prediction)
PRED predicted values (max 10 best out of 175): KAZ (0.92 #1592, 0.92 #1593, 0.91 #1755), R (0.50 #162, 0.47 #639, 0.46 #320), TM (0.40 #49, 0.29 #4635, 0.29 #4634), PL (0.31 #352, 0.25 #194, 0.24 #671), AZ (0.29 #4635, 0.29 #4634, 0.27 #6080), TR (0.29 #4635, 0.29 #4634, 0.27 #6080), IRQ (0.29 #4635, 0.29 #4634, 0.27 #6080), CN (0.29 #4635, 0.29 #4634, 0.27 #6080), TAD (0.29 #4635, 0.29 #4634, 0.27 #6080), KGZ (0.29 #4635, 0.29 #4634, 0.27 #6080)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1592 for best value:
>> intensional similarity = 8
>> extensional distance = 58
>> proper extension:  NOK;
>> query: (?x290, ?x304) <- ?x290[ has language ?x555; has language ?x1430[ a Language;]; has neighbor ?x277[ is locatedIn of ?x883;]; has neighbor ?x304; has wasDependentOf ?x903; is locatedIn of ?x289;]
ranks of expected_values: 1
EVAL	TM	neighbor!	KAZ	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	46.000	46.000	175.000	0.919	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: KAZ
=> 107 concepts (107 used for prediction)
PRED predicted values (max 10 best out of 202): KAZ (0.95 #1613, 0.94 #1614, 0.93 #4225), TAD (0.76 #6509, 0.76 #6835, 0.52 #961), R (0.69 #1941, 0.62 #1293, 0.55 #5202), KGZ (0.60 #983, 0.55 #5202, 0.52 #961), CN (0.55 #5202, 0.52 #961, 0.50 #482), AZ (0.55 #5202, 0.52 #961, 0.50 #482), TM (0.55 #5202, 0.52 #961, 0.50 #482), IRQ (0.55 #5202, 0.52 #961, 0.50 #482), TR (0.55 #5202, 0.52 #961, 0.50 #482), GE (0.55 #5202, 0.52 #961, 0.49 #1775)
>> best conf = 0.95
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1613 for best value:
>> intensional similarity = 15
>> extensional distance = 9
>> proper extension:  CH;
>> query: (?x290, ?x277) <- ?x290[ has encompassed ?x175; has ethnicGroup ?x1193; has government ?x2518; has language ?x555; has neighbor ?x277; has neighbor ?x403[ has ethnicGroup ?x58; is locatedIn of ?x127;]; has religion ?x56<ChristianOrthodox>; has religion ?x187<Muslim>; is locatedIn of ?x301[ a River; has flowsInto ?x1971;];]
ranks of expected_values: 1
EVAL	TM	neighbor!	KAZ	CNN-1.+1._MA	1.000	1.000	1.000	1.000	107.000	107.000	202.000	0.947	http://www.semwebtech.org/mondial/10/meta#neighbor

#256-GUAM
PRED entity: GUAM

PRED relation: language
PRED expected values: English PhilipineLanguage
=> 36 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 88): English (0.93 #586, 0.88 #198, 0.44 #1168), Spanish (0.55 #507, 0.47 #410, 0.34 #895), ChineseLanguage (0.33 #183, 0.13 #2135, 0.06 #280), OtherPacificIslandLanguage (0.33 #162, 0.13 #2135, 0.06 #259), French (0.22 #292, 0.22 #1165, 0.21 #389), Russian (0.12 #1563, 0.11 #1951, 0.11 #1660), German (0.08 #1179, 0.07 #2150, 0.07 #1858), Arabic (0.08 #1222, 0.07 #1707, 0.07 #640), Chinese (0.07 #645, 0.06 #257, 0.06 #354), Turkish (0.07 #1366, 0.06 #1657, 0.06 #1948)
>> best conf = 0.93
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #586 for best value:
>> intensional similarity = 7
>> extensional distance = 26
>> proper extension:  NLSM; GBM;
>> query: (?x1154, English) <- ?x1154[ a Country; has government ?x2344; has language ?x1155[ a Language; is language of ?x322<NMIS>;]; is locatedIn of ?x282;]
ranks of expected_values: 1
EVAL	GUAM	language	PhilipineLanguage	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	36.000	36.000	88.000	0.929	http://www.semwebtech.org/mondial/10/meta#language
EVAL	GUAM	language	English	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	36.000	36.000	88.000	0.929	http://www.semwebtech.org/mondial/10/meta#language

PRED relation: language
PRED expected values: English PhilipineLanguage
=> 58 concepts (58 used for prediction)
PRED predicted values (max 10 best out of 83): English (0.85 #1760, 0.85 #1665, 0.67 #883), Spanish (0.67 #2174, 0.57 #1782, 0.54 #1585), Chinese (0.65 #1759, 0.40 #1858, 0.33 #2151), ChineseLanguage (0.54 #1172, 0.40 #98, 0.33 #2446), OtherPacificIslandLanguage (0.54 #1172, 0.40 #98, 0.33 #2446), French (0.40 #686, 0.33 #880, 0.33 #783), Polynesian (0.33 #97, 0.25 #489, 0.20 #782), Russian (0.22 #2942, 0.22 #3039, 0.11 #3723), Futunian (0.20 #770, 0.17 #867, 0.14 #1257), Pitkern (0.17 #852, 0.14 #1242, 0.14 #1144)
>> best conf = 0.85
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1760 for best value:
>> intensional similarity = 15
>> extensional distance = 11
>> proper extension:  LB;
>> query: (?x1154, ?x247) <- ?x1154[ a Country; has ethnicGroup ?x2149[ a EthnicGroup; is ethnicGroup of ?x773[ a Country; has dependentOf ?x232; has government ?x2356; has language ?x247<English>; has religion ?x116; is locatedIn of ?x384;];]; has language ?x1155[ a Language; is language of ?x322<NMIS>;];]
>> Best rule #1665 for best value:
>> intensional similarity = 15
>> extensional distance = 11
>> proper extension:  LB;
>> query: (?x1154, English) <- ?x1154[ a Country; has ethnicGroup ?x2149[ a EthnicGroup; is ethnicGroup of ?x773[ a Country; has dependentOf ?x232; has government ?x2356; has language ?x247<English>; has religion ?x116; is locatedIn of ?x384;];]; has language ?x1155[ a Language; is language of ?x322<NMIS>;];]
ranks of expected_values: 1
EVAL	GUAM	language	PhilipineLanguage	CNN-1.+1._MA	0.000	0.000	0.000	0.000	58.000	58.000	83.000	0.846	http://www.semwebtech.org/mondial/10/meta#language
EVAL	GUAM	language	English	CNN-1.+1._MA	1.000	1.000	1.000	1.000	58.000	58.000	83.000	0.846	http://www.semwebtech.org/mondial/10/meta#language

#255-THA
PRED entity: THA

PRED relation: religion
PRED expected values: Buddhist
=> 32 concepts (32 used for prediction)
PRED predicted values (max 10 best out of 31): RomanCatholic (0.61 #397, 0.61 #240, 0.60 #279), Protestant (0.56 #393, 0.50 #275, 0.50 #236), Buddhist (0.52 #782, 0.50 #126, 0.50 #87), HoaHao (0.33 #69, 0.25 #108, 0.24 #861), CaoDai (0.33 #50, 0.25 #89, 0.24 #861), JehovasWitnesses (0.24 #861, 0.15 #370, 0.08 #526), Anglican (0.24 #861, 0.09 #993, 0.09 #1032), Seventh-DayAdventist (0.24 #861, 0.06 #516, 0.04 #1181), Taoist (0.24 #861, 0.05 #234, 0.02 #469), Baptist (0.24 #861, 0.02 #1190, 0.02 #995)
>> best conf = 0.61
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #397 for best value:
>> intensional similarity = 8
>> extensional distance = 34
>> proper extension:  F; NAM; I; TL; DK; PNG; P;
>> query: (?x91, RomanCatholic) <- ?x91[ has government ?x92; has religion ?x116[ a Religion;]; is locatedIn of ?x339; is locatedIn of ?x1181[ a Island;]; is neighbor of ?x871[ a Country;];]

*> Best rule #782 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 119
*> proper extension:  DJI; BG; IL; IRQ; AZ; CAM; B; MA; MNG; WAL;
*> query: (?x91, ?x462) <- ?x91[ has ethnicGroup ?x298; has religion ?x116; is locatedIn of ?x339; is neighbor of ?x463[ has ethnicGroup ?x1647; has religion ?x462;]; is neighbor of ?x871[ has wasDependentOf ?x78;];]
*> conf = 0.52
ranks of expected_values: 3
EVAL	THA	religion	Buddhist	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.333	32.000	32.000	31.000	0.611	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion
PRED expected values: Buddhist
=> 93 concepts (93 used for prediction)
PRED predicted values (max 10 best out of 37): Buddhist (0.83 #236, 0.83 #235, 0.73 #670), Protestant (0.83 #236, 0.83 #235, 0.73 #1026), RomanCatholic (0.83 #236, 0.83 #235, 0.64 #1188), JehovasWitnesses (0.83 #236, 0.83 #235, 0.35 #3088), HoaHao (0.83 #236, 0.83 #235, 0.35 #3088), CaoDai (0.83 #236, 0.83 #235, 0.35 #3088), Taoist (0.83 #236, 0.83 #235, 0.33 #156), Anglican (0.83 #236, 0.83 #235, 0.29 #472), Seventh-DayAdventist (0.83 #236, 0.83 #235, 0.23 #3089), Baptist (0.83 #236, 0.83 #235, 0.23 #3089)
>> best conf = 0.83
=> the first rule below is the first best rule for 11 predicted values
>> Best rule #236 for best value:
>> intensional similarity = 15
>> extensional distance = 2
>> proper extension:  RI; IND;
>> query: (?x91, ?x95) <- ?x91[ a Country; has encompassed ?x175; has ethnicGroup ?x298[ is ethnicGroup of ?x667[ has religion ?x95;]; is ethnicGroup of ?x1568[ has government ?x2064; has religion ?x2507; is locatedIn of ?x620;];]; has neighbor ?x366; has religion ?x116; is locatedIn of ?x339<AndamanSea>; is locatedIn of ?x1181[ a Island;];]
>> Best rule #235 for best value:
>> intensional similarity = 13
>> extensional distance = 2
>> proper extension:  RI; IND;
>> query: (?x91, ?x2507) <- ?x91[ a Country; has encompassed ?x175; has ethnicGroup ?x298[ is ethnicGroup of ?x1568[ has government ?x2064; has religion ?x2507; is locatedIn of ?x620;];]; has neighbor ?x366; has religion ?x116; is locatedIn of ?x339<AndamanSea>; is locatedIn of ?x1181[ a Island;];]
ranks of expected_values: 1
EVAL	THA	religion	Buddhist	CNN-1.+1._MA	1.000	1.000	1.000	1.000	93.000	93.000	37.000	0.833	http://www.semwebtech.org/mondial/10/meta#religion

#254-DZ
PRED entity: DZ

PRED relation: neighbor!
PRED expected values: WSA
=> 45 concepts (44 used for prediction)
PRED predicted values (max 10 best out of 191): WSA (0.89 #1094, 0.89 #2343, 0.89 #3599), DZ (0.40 #409, 0.33 #253, 0.27 #3913), RCA (0.40 #744, 0.33 #118, 0.23 #1212), RG (0.40 #419, 0.27 #3913, 0.26 #4700), SSD (0.38 #1136, 0.21 #1606, 0.20 #1762), SN (0.33 #230, 0.27 #3913, 0.26 #4700), WAN (0.33 #18, 0.27 #3913, 0.26 #4700), CAM (0.33 #89, 0.21 #1496, 0.20 #715), EAT (0.31 #1223, 0.17 #1693, 0.16 #1849), BF (0.27 #3913, 0.26 #4700, 0.20 #438)
>> best conf = 0.89
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1094 for best value:
>> intensional similarity = 8
>> extensional distance = 11
>> proper extension:  BIH; MNE; RL; AL; I; GR; SLO;
>> query: (?x581, ?x108) <- ?x581[ a Country; has encompassed ?x213; has neighbor ?x108; has religion ?x109; is locatedIn of ?x84[ has inMountains ?x85;]; is locatedIn of ?x275<MediterraneanSea>;]
ranks of expected_values: 1
EVAL	DZ	neighbor!	WSA	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	45.000	44.000	191.000	0.894	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: WSA
=> 108 concepts (108 used for prediction)
PRED predicted values (max 10 best out of 214): WSA (0.89 #12958, 0.88 #14569, 0.86 #7505), CN (0.82 #3700, 0.65 #6578, 0.31 #10117), TCH (0.67 #6693, 0.64 #15215, 0.57 #7665), WAN (0.64 #15215, 0.57 #7665, 0.50 #10232), CAM (0.64 #15215, 0.57 #7665, 0.50 #10232), DZ (0.60 #1842, 0.55 #3913, 0.41 #5833), BF (0.50 #1237, 0.36 #4771, 0.35 #4772), AND (0.40 #1391, 0.25 #1070, 0.11 #2822), SN (0.38 #6534, 0.36 #4771, 0.35 #1426), RG (0.38 #6534, 0.36 #4771, 0.35 #1426)
>> best conf = 0.89
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #12958 for best value:
>> intensional similarity = 16
>> extensional distance = 62
>> proper extension:  MNG;
>> query: (?x581, ?x646) <- ?x581[ a Country; has neighbor ?x646; has wasDependentOf ?x78[ has neighbor ?x149; is dependentOf of ?x61; is locatedIn of ?x121;]; is neighbor of ?x426[ has ethnicGroup ?x1109; is locatedIn of ?x535; is neighbor of ?x169[ has religion ?x116; is locatedIn of ?x168; is neighbor of ?x536;];]; is neighbor of ?x851[ has religion ?x109; is locatedIn of ?x182;];]
ranks of expected_values: 1
EVAL	DZ	neighbor!	WSA	CNN-1.+1._MA	1.000	1.000	1.000	1.000	108.000	108.000	214.000	0.890	http://www.semwebtech.org/mondial/10/meta#neighbor

#253-LakeAbbe
PRED entity: LakeAbbe

PRED relation: locatedIn
PRED expected values: DJI
=> 36 concepts (29 used for prediction)
PRED predicted values (max 10 best out of 103): SSD (0.51 #5890, 0.48 #1469, 0.21 #760), EAK (0.25 #1292, 0.25 #114, 0.20 #584), SP (0.25 #993, 0.12 #1231, 0.11 #758), R (0.21 #1892, 0.10 #5189, 0.10 #5424), CH (0.18 #1944, 0.07 #2885, 0.06 #3121), RI (0.16 #2645, 0.08 #3823, 0.07 #4058), USA (0.15 #4785, 0.15 #2900, 0.14 #3136), AUS (0.13 #1932, 0.09 #2638, 0.07 #2403), SUD (0.12 #982, 0.11 #747, 0.10 #1650), YE (0.12 #1088, 0.04 #1326)
>> best conf = 0.51
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5890 for best value:
>> intensional similarity = 7
>> extensional distance = 990
>> proper extension:  JoekulsaaFjoellum; JoekulsaaFjoellum; Snaefell; Thjorsa; GreenlandSea; JoekulsaaFjoellum; Thjorsa; Thjorsa;
>> query: (?x2195, ?x229) <- ?x2195[ has locatedIn ?x476[ a Country; is locatedIn of ?x1468[ has type ?x762;]; is locatedIn of ?x2436[ a Estuary; has locatedIn ?x229;];];]

*> Best rule #1414 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 22
*> proper extension:  IndianOcean; MountKenia; Elgon;
*> query: (?x2195, ?x94) <- ?x2195[ has locatedIn ?x476[ a Country; is locatedIn of ?x747[ has flowsInto ?x252;]; is locatedIn of ?x1468<ChewBahir>; is neighbor of ?x94;];]
*> conf = 0.11
ranks of expected_values: 12
EVAL	LakeAbbe	locatedIn	DJI	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.083	36.000	29.000	103.000	0.513	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: DJI
=> 100 concepts (92 used for prediction)
PRED predicted values (max 10 best out of 209): I (0.71 #6676, 0.13 #1464, 0.11 #15228), R (0.62 #11859, 0.62 #12095, 0.42 #13286), EAK (0.57 #12563, 0.25 #2949, 0.25 #114), SSD (0.48 #3840, 0.21 #1947, 0.17 #2890), D (0.45 #12584, 0.43 #12824, 0.15 #15437), RI (0.42 #4785, 0.25 #11193, 0.23 #6443), ZRE (0.40 #12406, 0.18 #14780, 0.17 #15021), STP (0.38 #1133, 0.09 #4689, 0.03 #15416), USA (0.31 #6463, 0.29 #7175, 0.28 #7412), AUS (0.30 #9047, 0.25 #4778, 0.21 #11186)
>> best conf = 0.71
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #6676 for best value:
>> intensional similarity = 12
>> extensional distance = 64
>> proper extension:  Stromboli; GranSasso; Po; MontBlanc; Salina; Mincio; Etna; MonteFalterona; Po; Etsch; ...
>> query: (?x2195, I) <- ?x2195[ has locatedIn ?x476[ a Country; has government ?x140; has religion ?x56<ChristianOrthodox>; has religion ?x95<Protestant>; is locatedIn of ?x228[ a Estuary;]; is locatedIn of ?x655[ a Volcano;]; is neighbor of ?x94;];]

*> Best rule #3073 for first EXPECTED value:
*> intensional similarity = 9
*> extensional distance = 22
*> proper extension:  IndianOcean;
*> query: (?x2195, ?x220) <- ?x2195[ has locatedIn ?x476[ has ethnicGroup ?x1593[ a EthnicGroup; is ethnicGroup of ?x220;]; has neighbor ?x94; has religion ?x95<Protestant>; is locatedIn of ?x1468<ChewBahir>;];]
*> conf = 0.23
ranks of expected_values: 13
EVAL	LakeAbbe	locatedIn	DJI	CNN-1.+1._MA	0.000	0.000	0.000	0.077	100.000	92.000	209.000	0.712	http://www.semwebtech.org/mondial/10/meta#locatedIn

#252-Vaettern
PRED entity: Vaettern

PRED relation: locatedIn
PRED expected values: S
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 37): USA (0.11 #72, 0.05 #308), CDN (0.08 #63, 0.03 #299), R (0.06 #5, 0.04 #241), CH (0.05 #57, 0.01 #293), ZRE (0.04 #79, 0.03 #315), EAT (0.04 #175), D (0.03 #20, 0.03 #256), I (0.03 #48, 0.02 #284), AUS (0.03 #45, 0.01 #281), CN (0.03 #56, 0.02 #292)
>> best conf = 0.11
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #72 for best value:
>> intensional similarity = 1
>> extensional distance = 142
>> proper extension:  LagunaMarChiquita; LakeSkutari; StarnbergerSee; LakeVolta; LakeHuron; MaleboPool; ChickamaugaLake; Poopo; LakeTanganjika; LakeNyos; ...
>> query: (?x2429, USA) <- ?x2429[ a Lake;]

*> Best rule #92 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 142
*> proper extension:  LagunaMarChiquita; LakeSkutari; StarnbergerSee; LakeVolta; LakeHuron; MaleboPool; ChickamaugaLake; Poopo; LakeTanganjika; LakeNyos; ...
*> query: (?x2429, S) <- ?x2429[ a Lake;]
*> conf = 0.03
ranks of expected_values: 11
EVAL	Vaettern	locatedIn	S	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.091	2.000	2.000	37.000	0.111	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: S
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 37): USA (0.11 #72, 0.05 #308), CDN (0.08 #63, 0.03 #299), R (0.06 #5, 0.04 #241), CH (0.05 #57, 0.01 #293), ZRE (0.04 #79, 0.03 #315), EAT (0.04 #175), D (0.03 #20, 0.03 #256), I (0.03 #48, 0.02 #284), AUS (0.03 #45, 0.01 #281), CN (0.03 #56, 0.02 #292)
>> best conf = 0.11
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #72 for best value:
>> intensional similarity = 1
>> extensional distance = 142
>> proper extension:  LagunaMarChiquita; LakeSkutari; StarnbergerSee; LakeVolta; LakeHuron; MaleboPool; ChickamaugaLake; Poopo; LakeTanganjika; LakeNyos; ...
>> query: (?x2429, USA) <- ?x2429[ a Lake;]

*> Best rule #92 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 142
*> proper extension:  LagunaMarChiquita; LakeSkutari; StarnbergerSee; LakeVolta; LakeHuron; MaleboPool; ChickamaugaLake; Poopo; LakeTanganjika; LakeNyos; ...
*> query: (?x2429, S) <- ?x2429[ a Lake;]
*> conf = 0.03
ranks of expected_values: 11
EVAL	Vaettern	locatedIn	S	CNN-1.+1._MA	0.000	0.000	0.000	0.091	2.000	2.000	37.000	0.111	http://www.semwebtech.org/mondial/10/meta#locatedIn

#251-Irtysch
PRED entity: Irtysch

PRED relation: hasSource!
PRED expected values: Irtysch
=> 53 concepts (38 used for prediction)
PRED predicted values (max 10 best out of 123): Ischim (0.33 #197, 0.05 #1797, 0.04 #2254), Katun (0.14 #904, 0.12 #1133, 0.11 #1362), Petschora (0.14 #824, 0.12 #1053, 0.11 #1282), WesternDwina (0.14 #855, 0.12 #1084, 0.11 #1313), Dnepr (0.14 #756, 0.12 #985, 0.11 #1214), Karun (0.12 #1102, 0.04 #2245, 0.02 #2474), Pjandsh (0.11 #1166, 0.05 #1851, 0.02 #2309), Bartang (0.11 #1203, 0.05 #1888, 0.01 #2575), Syrdarja (0.04 #4806, 0.03 #2515, 0.02 #4805), Irtysch (0.04 #4806, 0.03 #2515, 0.02 #2745)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #197 for best value:
>> intensional similarity = 3
>> extensional distance = 1
>> proper extension:  Ischim;
>> query: (?x1512, Ischim) <- ?x1512[ a Source; has locatedIn ?x403<KAZ>;]

*> Best rule #4806 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 209
*> proper extension:  JoekulsaaFjoellum; ColumbiaRiver; YukonRiver; RiviereRichelieu; SaskatchewanRiver; Thames; Thjorsa; MackenzieRiver; Manicouagan; NelsonRiver;
*> query: (?x1512, ?x1337) <- ?x1512[ a Source; has locatedIn ?x403[ has ethnicGroup ?x58; is locatedIn of ?x1337[ is flowsInto of ?x445;];];]
*> conf = 0.04
ranks of expected_values: 10
EVAL	Irtysch	hasSource!	Irtysch	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.100	53.000	38.000	123.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasSource

PRED relation: hasSource!
PRED expected values: Irtysch
=> 116 concepts (116 used for prediction)
PRED predicted values (max 10 best out of 179): Katun (0.40 #1602, 0.33 #219, 0.20 #1592), Ischim (0.33 #426, 0.14 #1799, 0.12 #2029), Ili (0.25 #1143, 0.25 #942, 0.20 #1401), Pjandsh (0.25 #708, 0.14 #1625, 0.03 #3921), Bartang (0.25 #745, 0.14 #1662, 0.03 #3958), Tarim-Yarkend (0.25 #961, 0.08 #2337, 0.02 #6242), Naryn (0.20 #1598, 0.07 #2520, 0.02 #13086), Syrdarja (0.14 #1831, 0.14 #1715, 0.12 #6424), Amudarja (0.14 #1626, 0.07 #2520, 0.03 #4841), Okavango (0.12 #1999, 0.01 #8426)
>> best conf = 0.40
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1602 for best value:
>> intensional similarity = 12
>> extensional distance = 3
>> proper extension:  Naryn;
>> query: (?x1512, ?x2143) <- ?x1512[ a Source; has inMountains ?x1039[ a Mountains; is inMountains of ?x127[ a Mountain; has locatedIn ?x73[ is locatedIn of ?x1038[ a Source; is hasSource of ?x2143;];]; has locatedIn ?x403<KAZ>;]; is inMountains of ?x1038;];]

*> Best rule #6424 for first EXPECTED value:
*> intensional similarity = 10
*> extensional distance = 61
*> proper extension:  DarlingRiver;
*> query: (?x1512, ?x890) <- ?x1512[ a Source; has inMountains ?x1039[ a Mountains;]; has locatedIn ?x403[ has encompassed ?x175; has ethnicGroup ?x58; has government ?x2502; has religion ?x56; is locatedIn of ?x890[ a River;];];]
*> conf = 0.12
ranks of expected_values: 16
EVAL	Irtysch	hasSource!	Irtysch	CNN-1.+1._MA	0.000	0.000	0.000	0.062	116.000	116.000	179.000	0.400	http://www.semwebtech.org/mondial/10/meta#hasSource

#250-GNB
PRED entity: GNB

PRED relation: neighbor!
PRED expected values: SN
=> 42 concepts (42 used for prediction)
PRED predicted values (max 10 best out of 219): SN (0.91 #1136, 0.90 #4543, 0.89 #4217), Z (0.50 #90, 0.40 #252, 0.33 #413), MOC (0.30 #520, 0.17 #681, 0.15 #1656), RMM (0.27 #2754, 0.27 #2917, 0.20 #1594), CI (0.27 #2754, 0.27 #2917, 0.16 #962), LB (0.27 #2754, 0.27 #2917, 0.14 #4706), WAL (0.27 #2754, 0.27 #2917, 0.14 #4706), GNB (0.27 #2754, 0.27 #2917, 0.13 #4707), RIM (0.27 #2917, 0.16 #900, 0.14 #4706), WAG (0.27 #2917, 0.13 #4707, 0.10 #5035)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1136 for best value:
>> intensional similarity = 6
>> extensional distance = 24
>> proper extension:  MEL;
>> query: (?x1755, ?x416) <- ?x1755[ has encompassed ?x213<Africa>; has government ?x435; has neighbor ?x416; is locatedIn of ?x182[ is locatedInWater of ?x112;];]
ranks of expected_values: 1
EVAL	GNB	neighbor!	SN	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	42.000	42.000	219.000	0.913	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: SN
=> 78 concepts (78 used for prediction)
PRED predicted values (max 10 best out of 224): SN (0.95 #2676, 0.94 #2506, 0.93 #2339), RMM (0.56 #2342, 0.56 #2310, 0.50 #2005), CI (0.50 #1491, 0.40 #1505, 0.39 #1504), LAR (0.43 #1656, 0.27 #3830, 0.25 #1823), DZ (0.42 #2613, 0.39 #1504, 0.38 #1774), RIM (0.40 #1505, 0.39 #1504, 0.38 #1762), GNB (0.40 #1505, 0.39 #1504, 0.36 #1506), WAL (0.40 #1505, 0.39 #1504, 0.36 #1506), WAG (0.40 #1505, 0.39 #1504, 0.36 #1506), LB (0.39 #1504, 0.36 #1506, 0.34 #1671)
>> best conf = 0.95
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2676 for best value:
>> intensional similarity = 17
>> extensional distance = 10
>> proper extension:  MA;
>> query: (?x1755, ?x651) <- ?x1755[ has ethnicGroup ?x162[ is ethnicGroup of ?x318[ has religion ?x95;];]; has neighbor ?x416[ a Country; has encompassed ?x213; has ethnicGroup ?x122; has government ?x435<"republic">; has religion ?x116; is neighbor of ?x839<RMM>;]; has neighbor ?x651[ has neighbor ?x621; is locatedIn of ?x580;]; has wasDependentOf ?x1027; is locatedIn of ?x182;]
ranks of expected_values: 1
EVAL	GNB	neighbor!	SN	CNN-1.+1._MA	1.000	1.000	1.000	1.000	78.000	78.000	224.000	0.946	http://www.semwebtech.org/mondial/10/meta#neighbor

#249-FPOL
PRED entity: FPOL

PRED relation: religion
PRED expected values: Protestant
=> 49 concepts (49 used for prediction)
PRED predicted values (max 10 best out of 35): Protestant (0.67 #1069, 0.66 #864, 0.65 #946), Muslim (0.45 #1482, 0.45 #1851, 0.45 #1646), Christian (0.33 #578, 0.29 #455, 0.26 #414), Anglican (0.33 #58, 0.27 #263, 0.27 #222), Buddhist (0.33 #52, 0.19 #914, 0.18 #750), Hindu (0.33 #50, 0.17 #296, 0.16 #1971), ChristianOrthodox (0.29 #781, 0.29 #1232, 0.28 #1191), JehovasWitnesses (0.23 #553, 0.18 #512, 0.16 #1971), Seventh-DayAdventist (0.21 #584, 0.16 #420, 0.16 #1971), ChurchofGod (0.18 #273, 0.18 #232, 0.12 #355)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1069 for best value:
>> intensional similarity = 6
>> extensional distance = 65
>> proper extension:  AND;
>> query: (?x297, Protestant) <- ?x297[ has encompassed ?x211[ is encompassed of ?x564[ a Country;];]; has language ?x51; has religion ?x352<RomanCatholic>;]
ranks of expected_values: 1
EVAL	FPOL	religion	Protestant	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	49.000	49.000	35.000	0.672	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion
PRED expected values: Protestant
=> 86 concepts (86 used for prediction)
PRED predicted values (max 10 best out of 40): Protestant (0.71 #704, 0.70 #2697, 0.67 #829), Muslim (0.52 #3324, 0.46 #3199, 0.46 #3241), Buddhist (0.50 #93, 0.33 #11, 0.30 #548), Anglican (0.50 #99, 0.33 #17, 0.29 #124), Christian (0.42 #748, 0.33 #2156, 0.31 #1578), Hindu (0.40 #133, 0.33 #9, 0.32 #753), ChristianOrthodox (0.29 #2112, 0.29 #2279, 0.29 #2321), JehovasWitnesses (0.29 #722, 0.29 #124, 0.27 #681), Seventh-DayAdventist (0.29 #124, 0.26 #2445, 0.26 #579), Baptist (0.29 #124, 0.26 #2445, 0.26 #579)
>> best conf = 0.71
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #704 for best value:
>> intensional similarity = 11
>> extensional distance = 15
>> proper extension:  USA; ES;
>> query: (?x297, Protestant) <- ?x297[ a Country; has encompassed ?x211; has ethnicGroup ?x1335[ is ethnicGroup of ?x1514[ has government ?x2126; has religion ?x95;];]; has language ?x51; has religion ?x352<RomanCatholic>; is locatedIn of ?x282<PacificOcean>;]
ranks of expected_values: 1
EVAL	FPOL	religion	Protestant	CNN-1.+1._MA	1.000	1.000	1.000	1.000	86.000	86.000	40.000	0.706	http://www.semwebtech.org/mondial/10/meta#religion

#248-YV
PRED entity: YV

PRED relation: neighbor
PRED expected values: CO GUY
=> 38 concepts (38 used for prediction)
PRED predicted values (max 10 best out of 213): CO (0.90 #2909, 0.90 #2423, 0.90 #2098), GUY (0.90 #2909, 0.90 #2423, 0.90 #2098), BOL (0.50 #113, 0.27 #761, 0.26 #4543), PE (0.26 #4543, 0.25 #4707, 0.25 #3236), EC (0.26 #4543, 0.25 #4707, 0.25 #3236), SME (0.26 #4543, 0.25 #4707, 0.25 #3236), YV (0.26 #4543, 0.25 #4707, 0.25 #3236), PY (0.26 #4543, 0.25 #4707, 0.25 #3236), PA (0.26 #4543, 0.25 #4707, 0.25 #3236), RA (0.26 #4543, 0.25 #4707, 0.25 #3236)
>> best conf = 0.90
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #2909 for best value:
>> intensional similarity = 6
>> extensional distance = 87
>> proper extension:  MEL;
>> query: (?x345, ?x215) <- ?x345[ has government ?x140; has neighbor ?x542; is locatedIn of ?x182[ is flowsInto of ?x137; is locatedInWater of ?x112;]; is neighbor of ?x215;]
ranks of expected_values: 1, 2
EVAL	YV	neighbor	GUY	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	38.000	38.000	213.000	0.903	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	YV	neighbor	CO	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	38.000	38.000	213.000	0.903	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: CO GUY
=> 89 concepts (89 used for prediction)
PRED predicted values (max 10 best out of 233): CO (0.94 #3441, 0.94 #3440, 0.93 #3443), GUY (0.94 #3441, 0.94 #3440, 0.92 #10438), BOL (0.60 #1431, 0.54 #2632, 0.54 #494), PY (0.54 #2632, 0.54 #494, 0.43 #1481), PE (0.54 #2632, 0.54 #494, 0.43 #1481), YV (0.54 #2632, 0.54 #494, 0.43 #1481), FGU (0.54 #2632, 0.54 #494, 0.37 #3936), ROU (0.54 #2632, 0.40 #1382, 0.33 #395), SME (0.54 #2632, 0.33 #359, 0.30 #4274), RA (0.54 #494, 0.43 #1481, 0.37 #3936)
>> best conf = 0.94
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #3441 for best value:
>> intensional similarity = 18
>> extensional distance = 9
>> proper extension:  SK; USA; MEX;
>> query: (?x345, ?x351) <- ?x345[ has religion ?x95<Protestant>; has wasDependentOf ?x149; is locatedIn of ?x427[ a Estuary;]; is locatedIn of ?x989[ has inMountains ?x2418;]; is neighbor of ?x351[ has ethnicGroup ?x79; has religion ?x187;]; is neighbor of ?x542[ a Country; has language ?x539; is locatedIn of ?x857[ is flowsInto of ?x47;]; is locatedIn of ?x1578[ has hasSource ?x2164;]; is neighbor of ?x296;];]
>> Best rule #3440 for best value:
>> intensional similarity = 19
>> extensional distance = 9
>> proper extension:  SK; USA; MEX;
>> query: (?x345, ?x215) <- ?x345[ has religion ?x95<Protestant>; has wasDependentOf ?x149; is locatedIn of ?x427[ a Estuary;]; is locatedIn of ?x989[ has inMountains ?x2418;]; is neighbor of ?x215; is neighbor of ?x351[ has ethnicGroup ?x79; has religion ?x187;]; is neighbor of ?x542[ a Country; has language ?x539; is locatedIn of ?x857[ is flowsInto of ?x47;]; is locatedIn of ?x1578[ has hasSource ?x2164;]; is neighbor of ?x296;];]
ranks of expected_values: 1, 2
EVAL	YV	neighbor	GUY	CNN-1.+1._MA	1.000	1.000	1.000	1.000	89.000	89.000	233.000	0.943	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	YV	neighbor	CO	CNN-1.+1._MA	1.000	1.000	1.000	1.000	89.000	89.000	233.000	0.943	http://www.semwebtech.org/mondial/10/meta#neighbor

#247-BIH
PRED entity: BIH

PRED relation: religion
PRED expected values: Muslim
=> 42 concepts (42 used for prediction)
PRED predicted values (max 10 best out of 33): Muslim (0.64 #237, 0.63 #1216, 0.58 #277), Christian (0.30 #1058, 0.28 #1176, 0.28 #1333), Jewish (0.27 #235, 0.14 #79, 0.13 #196), Anglican (0.16 #836, 0.14 #562, 0.13 #719), Buddhist (0.12 #361, 0.10 #1026, 0.10 #673), Hindu (0.09 #1417, 0.09 #124, 0.08 #1181), UkrainianGreekCatholic (0.09 #153, 0.07 #231, 0.07 #192), JehovasWitnesses (0.09 #487, 0.09 #409, 0.09 #526), Catholic (0.07 #191, 0.04 #309, 0.04 #348), Druze (0.05 #266, 0.01 #696, 0.01 #657)
>> best conf = 0.64
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #237 for best value:
>> intensional similarity = 6
>> extensional distance = 20
>> proper extension:  CEU;
>> query: (?x55, Muslim) <- ?x55[ is locatedIn of ?x275<MediterraneanSea>; is neighbor of ?x904[ has ethnicGroup ?x164; has religion ?x56; is locatedIn of ?x132;];]
ranks of expected_values: 1
EVAL	BIH	religion	Muslim	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	42.000	42.000	33.000	0.636	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion
PRED expected values: Muslim
=> 102 concepts (102 used for prediction)
PRED predicted values (max 10 best out of 38): Muslim (0.82 #474, 0.75 #355, 0.73 #630), Christian (0.44 #394, 0.38 #707, 0.35 #1652), Jewish (0.44 #393, 0.36 #980, 0.29 #1768), Buddhist (0.42 #862, 0.28 #1610, 0.26 #1491), Hindu (0.42 #862, 0.28 #1610, 0.26 #1491), Druze (0.29 #1768, 0.29 #1609, 0.28 #1610), CopticChristian (0.29 #1768, 0.29 #1609, 0.26 #1491), JehovasWitnesses (0.26 #235, 0.17 #1312, 0.17 #1115), Anglican (0.26 #235, 0.17 #2018, 0.16 #2806), UkrainianGreekCatholic (0.26 #235, 0.14 #432, 0.11 #392)
>> best conf = 0.82
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #474 for best value:
>> intensional similarity = 13
>> extensional distance = 9
>> proper extension:  ET; RL; SYR; LAR;
>> query: (?x55, Muslim) <- ?x55[ has encompassed ?x195; has ethnicGroup ?x160; has government ?x2074; has neighbor ?x904[ is locatedIn of ?x132;]; has religion ?x95[ is religion of ?x234<CH>; is religion of ?x403<KAZ>;]; has wasDependentOf ?x1197; is locatedIn of ?x275<MediterraneanSea>;]
ranks of expected_values: 1
EVAL	BIH	religion	Muslim	CNN-1.+1._MA	1.000	1.000	1.000	1.000	102.000	102.000	38.000	0.818	http://www.semwebtech.org/mondial/10/meta#religion

#246-MaunaLoa
PRED entity: MaunaLoa

PRED relation: locatedIn
PRED expected values: USA
=> 45 concepts (45 used for prediction)
PRED predicted values (max 10 best out of 97): USA (0.88 #1656, 0.87 #1419, 0.86 #3788), CDN (0.43 #535, 0.21 #4087, 0.19 #4796), E (0.33 #973, 0.25 #736, 0.20 #1209), RI (0.32 #3366, 0.27 #4313, 0.26 #4549), IS (0.14 #2711, 0.11 #2948, 0.06 #6026), RP (0.14 #3423, 0.10 #4370, 0.10 #4606), P (0.12 #906, 0.12 #1853, 0.11 #1143), CV (0.12 #815, 0.11 #1052, 0.07 #1288), PE (0.11 #7642, 0.07 #3144, 0.07 #3855), MEX (0.11 #3193, 0.10 #3904, 0.10 #5323)
>> best conf = 0.88
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1656 for best value:
>> intensional similarity = 8
>> extensional distance = 14
>> proper extension:  Psiloritis;
>> query: (?x588, ?x315) <- ?x588[ a Mountain; has inMountains ?x2053[ a Mountains; is inMountains of ?x722[ a Mountain; has locatedIn ?x315;];]; has locatedOnIsland ?x723[ a Island;];]
ranks of expected_values: 1
EVAL	MaunaLoa	locatedIn	USA	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	45.000	45.000	97.000	0.875	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: USA
=> 71 concepts (71 used for prediction)
PRED predicted values (max 10 best out of 97): USA (0.93 #12621, 0.90 #10489, 0.89 #10015), RI (0.54 #7922, 0.53 #8160, 0.53 #9116), CDN (0.43 #3401, 0.33 #2860, 0.33 #2684), E (0.33 #6229, 0.27 #7183, 0.23 #7659), IS (0.33 #2254, 0.27 #7264, 0.11 #9886), PNG (0.29 #3756, 0.22 #6144, 0.20 #7096), RP (0.21 #9650, 0.15 #10361, 0.14 #3925), NZ (0.20 #822, 0.17 #2971, 0.14 #4644), C (0.20 #1452, 0.17 #1932, 0.14 #3124), I (0.20 #999, 0.06 #15508, 0.06 #15745)
>> best conf = 0.93
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #12621 for best value:
>> intensional similarity = 9
>> extensional distance = 44
>> proper extension:  PicoBasile;
>> query: (?x588, ?x315) <- ?x588[ a Mountain; a Volcano; has locatedOnIsland ?x723[ a Island; is locatedOnIsland of ?x722[ a Mountain; a Volcano; has locatedIn ?x315[ a Country;];];];]
ranks of expected_values: 1
EVAL	MaunaLoa	locatedIn	USA	CNN-1.+1._MA	1.000	1.000	1.000	1.000	71.000	71.000	97.000	0.935	http://www.semwebtech.org/mondial/10/meta#locatedIn

#245-WhiteDrin
PRED entity: WhiteDrin

PRED relation: hasEstuary
PRED expected values: WhiteDrin
=> 51 concepts (44 used for prediction)
PRED predicted values (max 10 best out of 129): SouthernMorava (0.33 #180, 0.25 #407, 0.08 #1087), BlackDrin (0.25 #514, 0.20 #740, 0.08 #967), Buna (0.25 #610, 0.06 #1289, 0.05 #1515), Drin (0.20 #868, 0.06 #1321, 0.05 #1547), Piva (0.08 #1107, 0.02 #1786, 0.01 #2012), Drau (0.08 #1070, 0.01 #1975, 0.01 #2201), Ticino (0.08 #1035, 0.01 #1940), Adda (0.08 #1022, 0.01 #1927), Mincio (0.08 #985), WhiteDrin (0.04 #227, 0.04 #907, 0.03 #454)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #180 for best value:
>> intensional similarity = 6
>> extensional distance = 1
>> proper extension:  SouthernMorava;
>> query: (?x887, SouthernMorava) <- ?x887[ has hasSource ?x784; has locatedIn ?x204[ has ethnicGroup ?x595; has wasDependentOf ?x1656;]; has locatedIn ?x692<KOS>;]

*> Best rule #227 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 1
*> proper extension:  SouthernMorava;
*> query: (?x887, ?x104) <- ?x887[ has hasSource ?x784; has locatedIn ?x204[ has ethnicGroup ?x595; has wasDependentOf ?x1656; is locatedIn of ?x104;]; has locatedIn ?x692<KOS>;]
*> conf = 0.04
ranks of expected_values: 10
EVAL	WhiteDrin	hasEstuary	WhiteDrin	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.100	51.000	44.000	129.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasEstuary

PRED relation: hasEstuary
PRED expected values: WhiteDrin
=> 129 concepts (129 used for prediction)
PRED predicted values (max 10 best out of 231): SouthernMorava (0.33 #180, 0.20 #860, 0.11 #1314), BlackDrin (0.25 #513, 0.25 #286, 0.20 #11140), Drin (0.25 #414, 0.13 #3407, 0.10 #8639), Buna (0.20 #836, 0.13 #3407, 0.11 #1290), WhiteDrin (0.13 #3407, 0.10 #8639, 0.06 #1361), Tiber (0.12 #1111, 0.08 #1565, 0.06 #1792), Etsch (0.12 #1071, 0.08 #1525, 0.06 #1752), Arno (0.12 #1050, 0.08 #1504, 0.06 #1731), Po (0.12 #914, 0.08 #1368, 0.06 #1595), Moraca (0.12 #909, 0.02 #5003, 0.02 #6824)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #180 for best value:
>> intensional similarity = 12
>> extensional distance = 1
>> proper extension:  SouthernMorava;
>> query: (?x887, SouthernMorava) <- ?x887[ a River; has hasSource ?x784[ a Source;]; has locatedIn ?x204[ a Country; has ethnicGroup ?x1472<Albanian>; has religion ?x187<Muslim>; has wasDependentOf ?x1656;]; has locatedIn ?x692<KOS>;]

*> Best rule #3407 for first EXPECTED value:
*> intensional similarity = 16
*> extensional distance = 33
*> proper extension:  Maas; Guadalquivir;
*> query: (?x887, ?x1723) <- ?x887[ a River; has locatedIn ?x204[ has language ?x1251; has religion ?x56[ is religion of ?x196<AUS>; is religion of ?x207<I>; is religion of ?x353<GE>;]; has religion ?x352<RomanCatholic>; is locatedIn of ?x275<MediterraneanSea>; is locatedIn of ?x1723[ a Estuary;];];]
*> conf = 0.13
ranks of expected_values: 5
EVAL	WhiteDrin	hasEstuary	WhiteDrin	CNN-1.+1._MA	0.000	0.000	1.000	0.200	129.000	129.000	231.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasEstuary

#244-NMIS
PRED entity: NMIS

PRED relation: language
PRED expected values: PhilipineLanguage
=> 51 concepts (51 used for prediction)
PRED predicted values (max 10 best out of 90): Spanish (0.53 #2188, 0.33 #190, 0.28 #2846), Samoan (0.33 #190, 0.33 #98, 0.33 #3), French (0.33 #190, 0.33 #96, 0.32 #2544), Chinese (0.33 #190, 0.33 #157, 0.25 #346), Hindi (0.33 #190, 0.33 #181, 0.25 #370), Tongan (0.33 #190, 0.33 #87, 0.25 #95), Arabic (0.33 #190, 0.25 #95, 0.20 #435), Vietnamese (0.33 #190, 0.25 #95, 0.20 #421), Dutch (0.17 #1234, 0.11 #670, 0.04 #3116), Papiamento (0.17 #1238, 0.11 #674, 0.03 #2556)
>> best conf = 0.53
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2188 for best value:
>> intensional similarity = 6
>> extensional distance = 28
>> proper extension:  C; GCA; MOC; RCH; CO; CDN; USA; CR; ROU; MYA; ...
>> query: (?x322, Spanish) <- ?x322[ has encompassed ?x211; has ethnicGroup ?x1232[ a EthnicGroup; is ethnicGroup of ?x1409<NAU>;]; has language ?x247;]

No rule for expected values
ranks of expected_values: 
EVAL	NMIS	language	PhilipineLanguage	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	51.000	51.000	90.000	0.533	http://www.semwebtech.org/mondial/10/meta#language

PRED relation: language
PRED expected values: PhilipineLanguage
=> 104 concepts (104 used for prediction)
PRED predicted values (max 10 best out of 94): Spanish (0.78 #1230, 0.54 #5593, 0.51 #5499), Samoan (0.44 #662, 0.33 #663, 0.33 #570), French (0.44 #662, 0.33 #472, 0.32 #4630), Chinese (0.44 #662, 0.31 #566, 0.27 #852), Hindi (0.44 #662, 0.31 #566, 0.27 #852), Tongan (0.44 #662, 0.20 #275, 0.19 #2270), Arabic (0.27 #852, 0.26 #2553, 0.20 #340), Vietnamese (0.27 #852, 0.26 #2553, 0.20 #326), Portuguese (0.20 #3314, 0.16 #5667, 0.13 #8128), Dutch (0.20 #1333, 0.13 #8696, 0.11 #7655)
>> best conf = 0.78
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1230 for best value:
>> intensional similarity = 17
>> extensional distance = 7
>> proper extension:  CUR;
>> query: (?x322, ?x796) <- ?x322[ has dependentOf ?x315[ a Country; has ethnicGroup ?x79; has language ?x796; has neighbor ?x482[ is locatedIn of ?x288;]; has religion ?x95; is locatedIn of ?x182; is locatedIn of ?x324[ a Estuary;];]; has encompassed ?x211; has language ?x247<English>; has language ?x1980[ a Language;]; is locatedIn of ?x65[ a Island;];]

No rule for expected values
ranks of expected_values: 
EVAL	NMIS	language	PhilipineLanguage	CNN-1.+1._MA	0.000	0.000	0.000	0.000	104.000	104.000	94.000	0.778	http://www.semwebtech.org/mondial/10/meta#language

#243-DJI
PRED entity: DJI

PRED relation: religion
PRED expected values: Muslim
=> 37 concepts (37 used for prediction)
PRED predicted values (max 10 best out of 33): Muslim (0.70 #250, 0.65 #373, 0.65 #455), Protestant (0.52 #1110, 0.50 #1359, 0.45 #330), ChristianOrthodox (0.52 #1110, 0.50 #1359, 0.44 #534), Catholic (0.52 #1110, 0.50 #1359, 0.44 #534), RomanCatholic (0.45 #829, 0.44 #1240, 0.44 #870), Jewish (0.27 #617, 0.19 #413, 0.17 #372), Buddhist (0.25 #216, 0.20 #257, 0.15 #586), Hindu (0.24 #1275, 0.19 #214, 0.17 #1485), CopticChristian (0.17 #1485, 0.17 #1401, 0.16 #1443), Druze (0.17 #1485, 0.17 #1401, 0.16 #1443)
>> best conf = 0.70
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #250 for best value:
>> intensional similarity = 6
>> extensional distance = 18
>> proper extension:  XMAS;
>> query: (?x94, Muslim) <- ?x94[ a Country; has ethnicGroup ?x1593; has government ?x435; is locatedIn of ?x1552[ has locatedIn ?x668<YE>;];]
ranks of expected_values: 1
EVAL	DJI	religion	Muslim	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	37.000	37.000	33.000	0.700	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion
PRED expected values: Muslim
=> 90 concepts (90 used for prediction)
PRED predicted values (max 10 best out of 37): Muslim (0.74 #1337, 0.74 #1299, 0.71 #883), Protestant (0.70 #714, 0.63 #1171, 0.62 #3237), RomanCatholic (0.69 #968, 0.63 #1979, 0.60 #719), ChristianOrthodox (0.63 #3364, 0.63 #1171, 0.60 #2268), Catholic (0.63 #1171, 0.60 #2268, 0.59 #2646), Hindu (0.33 #50, 0.29 #511, 0.26 #796), Jewish (0.33 #2940, 0.33 #3110, 0.31 #3067), Buddhist (0.29 #890, 0.29 #513, 0.17 #1098), CopticChristian (0.26 #796, 0.25 #754, 0.25 #3195), Druze (0.26 #796, 0.19 #2520, 0.18 #1886)
>> best conf = 0.74
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1337 for best value:
>> intensional similarity = 17
>> extensional distance = 25
>> proper extension:  DK;
>> query: (?x94, ?x187) <- ?x94[ has neighbor ?x220[ a Country; has government ?x1766; has neighbor ?x474; has religion ?x187<Muslim>; is locatedIn of ?x2035[ a River;];]; is locatedIn of ?x415[ a Lake;]; is locatedIn of ?x2407[ a Sea; has mergesWith ?x60[ has mergesWith ?x182;]; has mergesWith ?x1333[ a Sea; has locatedIn ?x668;];];]
>> Best rule #1299 for best value:
>> intensional similarity = 17
>> extensional distance = 25
>> proper extension:  DK;
>> query: (?x94, Muslim) <- ?x94[ has neighbor ?x220[ a Country; has government ?x1766; has neighbor ?x474; has religion ?x187<Muslim>; is locatedIn of ?x2035[ a River;];]; is locatedIn of ?x415[ a Lake;]; is locatedIn of ?x2407[ a Sea; has mergesWith ?x60[ has mergesWith ?x182;]; has mergesWith ?x1333[ a Sea; has locatedIn ?x668;];];]
ranks of expected_values: 1
EVAL	DJI	religion	Muslim	CNN-1.+1._MA	1.000	1.000	1.000	1.000	90.000	90.000	37.000	0.741	http://www.semwebtech.org/mondial/10/meta#religion

#242-Murgab
PRED entity: Murgab

PRED relation: locatedIn
PRED expected values: TAD
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 32): R (0.09 #5, 0.04 #241), ZRE (0.08 #79, 0.03 #315), D (0.08 #20, 0.03 #256), USA (0.06 #72, 0.05 #308), CDN (0.04 #63, 0.03 #299), PE (0.04 #67, 0.01 #303), F (0.03 #7, 0.01 #243), I (0.02 #48, 0.02 #284), S (0.02 #92, 0.01 #328), SRB (0.02 #185)
>> best conf = 0.09
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5 for best value:
>> intensional similarity = 1
>> extensional distance = 247
>> proper extension:  Leine; Moraca; Marne; Umeaelv; Lena; Pibor; Po; Hwangho; Aare; Euphrat; ...
>> query: (?x1732, R) <- ?x1732[ a Estuary;]

*> Best rule #22 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 247
*> proper extension:  Leine; Moraca; Marne; Umeaelv; Lena; Pibor; Po; Hwangho; Aare; Euphrat; ...
*> query: (?x1732, TAD) <- ?x1732[ a Estuary;]
*> conf = 0.01
ranks of expected_values: 30
EVAL	Murgab	locatedIn	TAD	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.033	2.000	2.000	32.000	0.088	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: TAD
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 32): R (0.09 #5, 0.04 #241), ZRE (0.08 #79, 0.03 #315), D (0.08 #20, 0.03 #256), USA (0.06 #72, 0.05 #308), CDN (0.04 #63, 0.03 #299), PE (0.04 #67, 0.01 #303), F (0.03 #7, 0.01 #243), I (0.02 #48, 0.02 #284), S (0.02 #92, 0.01 #328), SRB (0.02 #185)
>> best conf = 0.09
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5 for best value:
>> intensional similarity = 1
>> extensional distance = 247
>> proper extension:  Leine; Moraca; Marne; Umeaelv; Lena; Pibor; Po; Hwangho; Aare; Euphrat; ...
>> query: (?x1732, R) <- ?x1732[ a Estuary;]

*> Best rule #22 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 247
*> proper extension:  Leine; Moraca; Marne; Umeaelv; Lena; Pibor; Po; Hwangho; Aare; Euphrat; ...
*> query: (?x1732, TAD) <- ?x1732[ a Estuary;]
*> conf = 0.01
ranks of expected_values: 30
EVAL	Murgab	locatedIn	TAD	CNN-1.+1._MA	0.000	0.000	0.000	0.033	2.000	2.000	32.000	0.088	http://www.semwebtech.org/mondial/10/meta#locatedIn

#241-WhiteNile
PRED entity: WhiteNile

PRED relation: flowsInto!
PRED expected values: Sobat
=> 46 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 315): WhiteNile (0.14 #443, 0.10 #2712, 0.07 #602), Atbara (0.14 #563, 0.10 #2712, 0.07 #602), BlueNile (0.14 #536, 0.10 #2712, 0.07 #602), LakeNasser (0.14 #368, 0.07 #602, 0.07 #669), LakeTana (0.14 #555, 0.07 #856, 0.03 #1158), Zambezi (0.14 #570, 0.03 #1173, 0.01 #2680), MurrayRiver (0.14 #499, 0.03 #1102, 0.01 #2609), Jubba (0.14 #403, 0.03 #1006, 0.01 #2513), Limpopo (0.14 #316, 0.03 #919, 0.01 #2426), Akagera (0.14 #471, 0.02 #1976, 0.01 #4687)
>> best conf = 0.14
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #443 for best value:
>> intensional similarity = 4
>> extensional distance = 5
>> proper extension:  LakeVictoria;
>> query: (?x990, WhiteNile) <- ?x990[ has locatedIn ?x229[ is neighbor of ?x476<ETH>;]; is flowsInto of ?x1170;]

No rule for expected values
ranks of expected_values: 
EVAL	WhiteNile	flowsInto!	Sobat	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	46.000	36.000	315.000	0.143	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto!
PRED expected values: Sobat
=> 111 concepts (111 used for prediction)
PRED predicted values (max 10 best out of 387): WhiteNile (0.27 #2115, 0.25 #1050, 0.25 #142), BlueNile (0.27 #2115, 0.25 #1143, 0.25 #235), Atbara (0.27 #2115, 0.25 #1170, 0.25 #262), Nile (0.27 #2115, 0.12 #5753, 0.11 #302), LakeTana (0.25 #860, 0.25 #254, 0.20 #2067), LakeNasser (0.25 #975, 0.25 #67, 0.20 #1880), Pibor (0.25 #1173, 0.25 #604, 0.19 #605), Baro (0.25 #1009, 0.19 #605, 0.15 #2417), Bomu (0.25 #421, 0.17 #2234, 0.10 #3749), Uelle (0.25 #334, 0.17 #2147, 0.10 #3662)
>> best conf = 0.27
=> the first rule below is the first best rule for 4 predicted values
>> Best rule #2115 for best value:
>> intensional similarity = 12
>> extensional distance = 3
>> proper extension:  Jordan;
>> query: (?x990, ?x1597) <- ?x990[ a River; has hasEstuary ?x1933; has locatedIn ?x186[ has encompassed ?x213; has neighbor ?x1184[ a Country; is locatedIn of ?x275;]; is locatedIn of ?x1552<RedSea>; is locatedIn of ?x1597[ a River;];]; is flowsInto of ?x1170;]

*> Best rule #604 for first EXPECTED value:
*> intensional similarity = 13
*> extensional distance = 2
*> proper extension:  Zaire;
*> query: (?x990, ?x252) <- ?x990[ a River; has flowsInto ?x2124; has locatedIn ?x229[ a Country; is locatedIn of ?x2339[ is hasEstuary of ?x252;]; is neighbor of ?x348<ZRE>; is neighbor of ?x736<RCA>;]; is flowsInto of ?x1170[ a River; has hasSource ?x53;];]
*> conf = 0.25
ranks of expected_values: 19
EVAL	WhiteNile	flowsInto!	Sobat	CNN-1.+1._MA	0.000	0.000	0.000	0.053	111.000	111.000	387.000	0.273	http://www.semwebtech.org/mondial/10/meta#flowsInto

#240-Africa
PRED entity: Africa

PRED relation: encompassed!
PRED expected values: DJI SSD RM CAM EAT MA WAL
=> 4 concepts (4 used for prediction)
PRED predicted values (max 10 best out of 183): WAL (0.93 #173, 0.92 #172, 0.92 #170), CAM (0.93 #173, 0.92 #172, 0.92 #170), GAZA (0.93 #173, 0.92 #172, 0.92 #170), EAT (0.93 #173, 0.92 #172, 0.92 #170), IL (0.93 #173, 0.92 #172, 0.92 #170), DJI (0.93 #173, 0.92 #172, 0.92 #170), SSD (0.93 #173, 0.92 #172, 0.92 #170), MA (0.93 #173, 0.92 #172, 0.92 #170), KAZ (0.50 #423, 0.26 #529, 0.25 #247), TR (0.50 #383, 0.26 #529, 0.25 #207)
>> best conf = 0.93
=> the first rule below is the first best rule for 8 predicted values
>> Best rule #173 for best value:
>> intensional similarity = 52
>> extensional distance = 1
>> proper extension:  America;
>> query: (?x213, ?x820) <- ?x213[ is encompassed of ?x108[ has ethnicGroup ?x197<European>; is locatedIn of ?x275;]; is encompassed of ?x172[ a Country; is neighbor of ?x536;]; is encompassed of ?x348[ is locatedIn of ?x113[ a River;]; is neighbor of ?x229;]; is encompassed of ?x359[ has wasDependentOf ?x485; is locatedIn of ?x284;]; is encompassed of ?x483[ a Country; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x1857[ a Estuary;];]; is encompassed of ?x797[ a Country; has ethnicGroup ?x1728<Creole>; has ethnicGroup ?x2219[ a EthnicGroup;]; has government ?x254; has religion ?x116[ is religion of ?x376<MAL>; is religion of ?x434<RM>; is religion of ?x538<BRU>; is religion of ?x924<IND>; is religion of ?x1010<MNG>;]; has religion ?x187<Muslim>;]; is encompassed of ?x819[ a Country; has neighbor ?x820;]; is encompassed of ?x934[ a Country; has government ?x1721; has religion ?x95; is locatedIn of ?x933;]; is encompassed of ?x1248[ has government ?x435<"republic">; is locatedIn of ?x60;];]
ranks of expected_values: 1, 2, 4, 6, 7, 8, 131
EVAL	Africa	encompassed!	WAL	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	4.000	4.000	183.000	0.926	http://www.semwebtech.org/mondial/10/meta#encompassed
EVAL	Africa	encompassed!	MA	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.333	4.000	4.000	183.000	0.926	http://www.semwebtech.org/mondial/10/meta#encompassed
EVAL	Africa	encompassed!	EAT	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	4.000	4.000	183.000	0.926	http://www.semwebtech.org/mondial/10/meta#encompassed
EVAL	Africa	encompassed!	CAM	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	4.000	4.000	183.000	0.926	http://www.semwebtech.org/mondial/10/meta#encompassed
EVAL	Africa	encompassed!	RM	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.008	4.000	4.000	183.000	0.926	http://www.semwebtech.org/mondial/10/meta#encompassed
EVAL	Africa	encompassed!	SSD	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.333	4.000	4.000	183.000	0.926	http://www.semwebtech.org/mondial/10/meta#encompassed
EVAL	Africa	encompassed!	DJI	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.333	4.000	4.000	183.000	0.926	http://www.semwebtech.org/mondial/10/meta#encompassed

PRED relation: encompassed!
PRED expected values: DJI SSD RM CAM EAT MA WAL
=> 5 concepts (5 used for prediction)
PRED predicted values (max 10 best out of 188): DJI (0.92 #566, 0.92 #551, 0.89 #558), EAT (0.92 #566, 0.92 #551, 0.89 #558), SSD (0.92 #566, 0.92 #551, 0.89 #558), CAM (0.92 #566, 0.92 #551, 0.80 #758), GAZA (0.92 #566, 0.92 #551, 0.80 #758), IL (0.92 #566, 0.92 #551, 0.80 #758), MA (0.92 #566, 0.92 #551, 0.80 #758), WAL (0.92 #566, 0.92 #551, 0.80 #758), GB (0.78 #548, 0.49 #175, 0.46 #552), CDN (0.78 #548, 0.49 #175, 0.46 #552)
>> best conf = 0.92
=> the first rule below is the first best rule for 8 predicted values
>> Best rule #566 for best value:
>> intensional similarity = 138
>> extensional distance = 1
>> proper extension:  America;
>> query: (?x213, ?x851) <- ?x213[ a Continent; is encompassed of ?x63[ has ethnicGroup ?x1582[ a EthnicGroup;]; has government ?x435; has religion ?x1929[ a Religion;]; has wasDependentOf ?x81<GB>; is locatedIn of ?x62; is neighbor of ?x239[ has religion ?x109; is locatedIn of ?x238; is neighbor of ?x466;];]; is encompassed of ?x139[ a Country; is locatedIn of ?x794[ a Mountain;]; is locatedIn of ?x2393[ a Estuary;];]; is encompassed of ?x212[ a Country; has ethnicGroup ?x298<Chinese>; has language ?x247[ is language of ?x671<BZ>; is language of ?x865<CAYM>;]; is locatedIn of ?x283;]; is encompassed of ?x450[ has ethnicGroup ?x1728<Creole>; is locatedIn of ?x449;]; is encompassed of ?x474[ a Country; has ethnicGroup ?x1775[ a EthnicGroup;]; is locatedIn of ?x598[ a Volcano; has inMountains ?x1066;]; is locatedIn of ?x2230[ a Lake;];]; is encompassed of ?x476[ has ethnicGroup ?x1179[ a EthnicGroup;]; has government ?x140; has religion ?x56[ is religion of ?x403<KAZ>;]; has religion ?x95<Protestant>; is locatedIn of ?x655[ a Volcano;]; is locatedIn of ?x1875[ a Source; has inMountains ?x2477;]; is locatedIn of ?x2035[ a River; has hasEstuary ?x2345;];]; is encompassed of ?x483[ a Country; has government ?x180; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x1423[ has type ?x1424;];]; is encompassed of ?x525[ has religion ?x116[ a Religion; is religion of ?x302<IRQ>; is religion of ?x366<MYA>; is religion of ?x508<CL>; is religion of ?x924<IND>;]; is locatedIn of ?x709[ a Source;];]; is encompassed of ?x581[ a Country; is locatedIn of ?x84; is locatedIn of ?x572[ a Desert;];]; is encompassed of ?x621[ a Country; has ethnicGroup ?x162; has neighbor ?x1072[ a Country;];]; is encompassed of ?x688[ a Country; has ethnicGroup ?x529[ a EthnicGroup;]; is locatedIn of ?x600[ a Lake;]; is locatedIn of ?x730[ a Mountain; a Volcano; has type ?x150;]; is neighbor of ?x229[ is locatedIn of ?x53;];]; is encompassed of ?x736[ has ethnicGroup ?x992[ a EthnicGroup;]; is locatedIn of ?x549[ a Source;]; is locatedIn of ?x695[ has hasSource ?x1263;]; is locatedIn of ?x834[ a Estuary;]; is locatedIn of ?x879[ a River;];]; is encompassed of ?x839[ a Country; has ethnicGroup ?x1537[ a EthnicGroup;]; has language ?x1228; is locatedIn of ?x838[ has hasEstuary ?x1801; has hasSource ?x650;];]; is encompassed of ?x1184[ a Country; has ethnicGroup ?x1215[ a EthnicGroup;]; has government ?x1522; is locatedIn of ?x1269;]; is encompassed of ?x1248[ a Country; has religion ?x352<RomanCatholic>; has wasDependentOf ?x78; is locatedIn of ?x1247;]; is encompassed of ?x1588[ a Country; has government ?x2527; is locatedIn of ?x275; is neighbor of ?x851;];]
>> Best rule #551 for best value:
>> intensional similarity = 138
>> extensional distance = 1
>> proper extension:  America;
>> query: (?x213, ?x1072) <- ?x213[ a Continent; is encompassed of ?x63[ has ethnicGroup ?x1582[ a EthnicGroup;]; has government ?x435; has religion ?x1929[ a Religion;]; has wasDependentOf ?x81<GB>; is locatedIn of ?x62; is neighbor of ?x239[ has religion ?x109; is locatedIn of ?x238; is neighbor of ?x466;];]; is encompassed of ?x139[ a Country; is locatedIn of ?x794[ a Mountain;]; is locatedIn of ?x2393[ a Estuary;];]; is encompassed of ?x212[ a Country; has ethnicGroup ?x298<Chinese>; has language ?x247[ is language of ?x671<BZ>; is language of ?x865<CAYM>;]; is locatedIn of ?x283;]; is encompassed of ?x450[ has ethnicGroup ?x1728<Creole>; is locatedIn of ?x449;]; is encompassed of ?x474[ a Country; has ethnicGroup ?x1775[ a EthnicGroup;]; is locatedIn of ?x598[ a Volcano; has inMountains ?x1066;]; is locatedIn of ?x2230[ a Lake;];]; is encompassed of ?x476[ has ethnicGroup ?x1179[ a EthnicGroup;]; has government ?x140; has religion ?x56[ is religion of ?x403<KAZ>;]; has religion ?x95<Protestant>; is locatedIn of ?x655[ a Volcano;]; is locatedIn of ?x1875[ a Source; has inMountains ?x2477;]; is locatedIn of ?x2035[ a River; has hasEstuary ?x2345;];]; is encompassed of ?x483[ a Country; has government ?x180; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x1423[ has type ?x1424;];]; is encompassed of ?x525[ has religion ?x116[ a Religion; is religion of ?x302<IRQ>; is religion of ?x366<MYA>; is religion of ?x508<CL>; is religion of ?x924<IND>;]; is locatedIn of ?x709[ a Source;];]; is encompassed of ?x581[ a Country; is locatedIn of ?x84; is locatedIn of ?x572[ a Desert;];]; is encompassed of ?x621[ a Country; has ethnicGroup ?x162; has neighbor ?x1072[ a Country;];]; is encompassed of ?x688[ a Country; has ethnicGroup ?x529[ a EthnicGroup;]; is locatedIn of ?x600[ a Lake;]; is locatedIn of ?x730[ a Mountain; a Volcano; has type ?x150;]; is neighbor of ?x229[ is locatedIn of ?x53;];]; is encompassed of ?x736[ has ethnicGroup ?x992[ a EthnicGroup;]; is locatedIn of ?x549[ a Source;]; is locatedIn of ?x695[ has hasSource ?x1263;]; is locatedIn of ?x834[ a Estuary;]; is locatedIn of ?x879[ a River;];]; is encompassed of ?x839[ a Country; has ethnicGroup ?x1537[ a EthnicGroup;]; has language ?x1228; is locatedIn of ?x838[ has hasEstuary ?x1801; has hasSource ?x650;];]; is encompassed of ?x1184[ a Country; has ethnicGroup ?x1215[ a EthnicGroup;]; has government ?x1522; is locatedIn of ?x1269;]; is encompassed of ?x1248[ a Country; has religion ?x352<RomanCatholic>; has wasDependentOf ?x78; is locatedIn of ?x1247;]; is encompassed of ?x1588[ a Country; has government ?x2527; is locatedIn of ?x275; is neighbor of ?x851;];]
ranks of expected_values: 1, 2, 3, 4, 7, 8, 89
EVAL	Africa	encompassed!	WAL	CNN-1.+1._MA	0.000	1.000	1.000	0.333	5.000	5.000	188.000	0.923	http://www.semwebtech.org/mondial/10/meta#encompassed
EVAL	Africa	encompassed!	MA	CNN-1.+1._MA	0.000	1.000	1.000	0.333	5.000	5.000	188.000	0.923	http://www.semwebtech.org/mondial/10/meta#encompassed
EVAL	Africa	encompassed!	EAT	CNN-1.+1._MA	1.000	1.000	1.000	1.000	5.000	5.000	188.000	0.923	http://www.semwebtech.org/mondial/10/meta#encompassed
EVAL	Africa	encompassed!	CAM	CNN-1.+1._MA	1.000	1.000	1.000	1.000	5.000	5.000	188.000	0.923	http://www.semwebtech.org/mondial/10/meta#encompassed
EVAL	Africa	encompassed!	RM	CNN-1.+1._MA	0.000	0.000	0.000	0.012	5.000	5.000	188.000	0.923	http://www.semwebtech.org/mondial/10/meta#encompassed
EVAL	Africa	encompassed!	SSD	CNN-1.+1._MA	1.000	1.000	1.000	1.000	5.000	5.000	188.000	0.923	http://www.semwebtech.org/mondial/10/meta#encompassed
EVAL	Africa	encompassed!	DJI	CNN-1.+1._MA	1.000	1.000	1.000	1.000	5.000	5.000	188.000	0.923	http://www.semwebtech.org/mondial/10/meta#encompassed

#239-Saipan
PRED entity: Saipan

PRED relation: locatedInWater
PRED expected values: PacificOcean
=> 56 concepts (56 used for prediction)
PRED predicted values (max 10 best out of 152): PacificOcean (0.64 #1493, 0.53 #458, 0.50 #149), AtlanticOcean (0.50 #184, 0.47 #228, 0.46 #360), Rota (0.38 #177, 0.35 #441, 0.34 #529), Tinian (0.38 #177, 0.35 #441, 0.34 #529), Saipan (0.38 #177, 0.35 #441, 0.34 #529), CaribbeanSea (0.32 #240, 0.25 #372, 0.25 #196), JavaSea (0.21 #450, 0.06 #714, 0.05 #1107), MediterraneanSea (0.20 #545, 0.18 #1070, 0.17 #895), IndianOcean (0.18 #443, 0.17 #134, 0.10 #707), SouthChinaSea (0.11 #463, 0.06 #1341, 0.06 #1428)
>> best conf = 0.64
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1493 for best value:
>> intensional similarity = 6
>> extensional distance = 196
>> proper extension:  Mohilla; Flores; Mallorca; Cebu; Fakaofo; Guadalcanal; CaymanBrac; Ternate; Grande-Terre; Babelthuap; ...
>> query: (?x65, ?x282) <- ?x65[ a Island; has belongsToIslands ?x66[ a Islands; is belongsToIslands of ?x504[ a Island; has locatedInWater ?x282;];];]
ranks of expected_values: 1
EVAL	Saipan	locatedInWater	PacificOcean	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	56.000	56.000	152.000	0.641	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater
PRED expected values: PacificOcean
=> 151 concepts (151 used for prediction)
PRED predicted values (max 10 best out of 156): PacificOcean (0.90 #1856, 0.90 #1829, 0.87 #1461), AtlanticOcean (0.67 #582, 0.47 #936, 0.46 #3675), MediterraneanSea (0.39 #1167, 0.25 #1697, 0.21 #3772), IndianOcean (0.33 #310, 0.30 #399, 0.27 #533), CaribbeanSea (0.33 #948, 0.27 #505, 0.27 #460), Rota (0.27 #974, 0.25 #88, 0.24 #531), Tinian (0.27 #974, 0.25 #88, 0.24 #531), Saipan (0.27 #974, 0.25 #88, 0.24 #531), JavaSea (0.24 #1116, 0.22 #1204, 0.17 #1338), SulawesiSea (0.20 #1973, 0.16 #2550, 0.15 #2239)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1856 for best value:
>> intensional similarity = 11
>> extensional distance = 28
>> proper extension:  TeWaka-a-Maui-SouthIsland-; TeIka-a-Maui-NorthIsland-; SantaRosaIsland; SantaCruzIsland; Paramuschir; SanClementeIsland; SantaCatalinaIsland; SanMiguelIsland;
>> query: (?x65, ?x282) <- ?x65[ a Island; has belongsToIslands ?x66[ a Islands; is belongsToIslands of ?x504[ a Island; has locatedInWater ?x282<PacificOcean>;];]; has locatedIn ?x322[ has encompassed ?x211; has ethnicGroup ?x380; has language ?x247;];]
>> Best rule #1829 for best value:
>> intensional similarity = 11
>> extensional distance = 28
>> proper extension:  TeWaka-a-Maui-SouthIsland-; TeIka-a-Maui-NorthIsland-; SantaRosaIsland; SantaCruzIsland; Paramuschir; SanClementeIsland; SantaCatalinaIsland; SanMiguelIsland;
>> query: (?x65, PacificOcean) <- ?x65[ a Island; has belongsToIslands ?x66[ a Islands; is belongsToIslands of ?x504[ a Island; has locatedInWater ?x282<PacificOcean>;];]; has locatedIn ?x322[ has encompassed ?x211; has ethnicGroup ?x380; has language ?x247;];]
ranks of expected_values: 1
EVAL	Saipan	locatedInWater	PacificOcean	CNN-1.+1._MA	1.000	1.000	1.000	1.000	151.000	151.000	156.000	0.900	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#238-CaymanIslands
PRED entity: CaymanIslands

PRED relation: belongsToIslands!
PRED expected values: LittleCayman
=> 18 concepts (18 used for prediction)
PRED predicted values (max 10 best out of 204): NewProvidence (0.33 #191), LittleCayman (0.27 #396, 0.25 #198, 0.24 #792), CaribbeanSea (0.27 #396, 0.25 #198, 0.11 #594), Cuba (0.25 #361, 0.24 #792, 0.17 #559), PuertoRico (0.25 #329, 0.24 #792, 0.17 #527), Grande-Terre (0.25 #287, 0.24 #792, 0.12 #683), Montserrat (0.25 #254, 0.24 #792, 0.12 #650), SaintVincent (0.25 #201, 0.24 #792, 0.12 #597), St.Barthelemy (0.25 #380, 0.24 #792, 0.12 #776), Basse-Terre (0.25 #378, 0.24 #792, 0.12 #774)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #191 for best value:
>> intensional similarity = 14
>> extensional distance = 1
>> proper extension:  BahamaIslands;
>> query: (?x1357, NewProvidence) <- ?x1357[ a Islands; is belongsToIslands of ?x1093[ a Island; has locatedIn ?x865[ a Country; has encompassed ?x521<America>; has government ?x254; has religion ?x352<RomanCatholic>; has religion ?x713<Anglican>; has religion ?x1667<ChurchofGod>;];];]

*> Best rule #396 for first EXPECTED value:
*> intensional similarity = 15
*> extensional distance = 2
*> proper extension:  LesserAntilles; GreaterAntilles;
*> query: (?x1357, ?x687) <- ?x1357[ a Islands; is belongsToIslands of ?x599[ a Island; has locatedInWater ?x317<CaribbeanSea>;]; is belongsToIslands of ?x1093[ a Island; has locatedIn ?x865[ a Country; has dependentOf ?x81; has encompassed ?x521; has ethnicGroup ?x1147; has religion ?x352<RomanCatholic>; is locatedIn of ?x687;];];]
*> conf = 0.27
ranks of expected_values: 2
EVAL	CaymanIslands	belongsToIslands!	LittleCayman	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	18.000	18.000	204.000	0.333	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

PRED relation: belongsToIslands!
PRED expected values: LittleCayman
=> 14 concepts (14 used for prediction)
PRED predicted values (max 10 best out of 204): St.Barthelemy (0.33 #182, 0.25 #578, 0.16 #1614), Martinique (0.33 #156, 0.25 #552, 0.16 #1614), Antigua (0.33 #152, 0.25 #548, 0.16 #1614), SaintThomas (0.33 #118, 0.25 #514, 0.16 #1614), St.Martin (0.33 #117, 0.25 #513, 0.16 #1614), Grenada (0.33 #99, 0.25 #495, 0.16 #1614), Grande-Terre (0.33 #89, 0.25 #485, 0.16 #1614), Trinidad (0.33 #87, 0.25 #483, 0.16 #1614), Montserrat (0.33 #56, 0.25 #452, 0.16 #1614), Anguilla (0.33 #48, 0.25 #444, 0.16 #1614)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #182 for best value:
>> intensional similarity = 21
>> extensional distance = 1
>> proper extension:  LesserAntilles;
>> query: (?x1357, St.Barthelemy) <- ?x1357[ a Islands; is belongsToIslands of ?x1093[ a Island; has locatedIn ?x865[ a Country; has dependentOf ?x81<GB>; has encompassed ?x521<America>; has ethnicGroup ?x1147<White>; has government ?x254; has language ?x247<English>; has religion ?x352<RomanCatholic>; has religion ?x713<Anglican>; is locatedIn of ?x317<CaribbeanSea>;]; has locatedInWater ?x317<CaribbeanSea>;];]

*> Best rule #594 for first EXPECTED value:
*> intensional similarity = 16
*> extensional distance = 2
*> proper extension:  GreaterAntilles;
*> query: (?x1357, ?x687) <- ?x1357[ a Islands; is belongsToIslands of ?x1093[ a Island; has locatedIn ?x865[ a Country; has dependentOf ?x81; has encompassed ?x521<America>; has ethnicGroup ?x1147; has government ?x254; has religion ?x352<RomanCatholic>; is locatedIn of ?x317<CaribbeanSea>; is locatedIn of ?x687;]; has locatedInWater ?x317<CaribbeanSea>;];]
*> conf = 0.27
ranks of expected_values: 22
EVAL	CaymanIslands	belongsToIslands!	LittleCayman	CNN-1.+1._MA	0.000	0.000	0.000	0.045	14.000	14.000	204.000	0.333	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

#237-NEP
PRED entity: NEP

PRED relation: locatedIn!
PRED expected values: Manaslu
=> 33 concepts (18 used for prediction)
PRED predicted values (max 10 best out of 1313): IndianOcean (0.74 #14176, 0.31 #8507, 0.29 #2839), SouthChinaSea (0.40 #10060, 0.33 #12894, 0.33 #11477), PacificOcean (0.38 #8589, 0.27 #7172, 0.24 #12840), PikChan-Tengri (0.33 #609, 0.25 #4862, 0.14 #17009), Mekong (0.33 #615, 0.20 #10536, 0.14 #13370), Brahmaputra (0.33 #1119, 0.17 #2538, 0.14 #3955), Indus (0.33 #154, 0.17 #1573, 0.14 #2990), Irtysch (0.33 #990, 0.14 #17009, 0.12 #5243), PikPobeda (0.33 #1210, 0.14 #17009, 0.12 #5463), Ili (0.33 #106, 0.14 #17009, 0.12 #4359)
>> best conf = 0.74
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #14176 for best value:
>> intensional similarity = 6
>> extensional distance = 21
>> proper extension:  MV; MAYO;
>> query: (?x111, IndianOcean) <- ?x111[ a Country; has encompassed ?x175; has government ?x1779; is locatedIn of ?x489[ has locatedIn ?x924<IND>;];]

No rule for expected values
ranks of expected_values: 
EVAL	NEP	locatedIn!	Manaslu	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	33.000	18.000	1313.000	0.739	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Manaslu
=> 89 concepts (89 used for prediction)
PRED predicted values (max 10 best out of 1415): IndianOcean (0.89 #4254, 0.87 #1418, 0.50 #2839), GulfofBengal (0.89 #4254, 0.87 #1418, 0.50 #2908), SouthChinaSea (0.89 #4254, 0.87 #1418, 0.48 #63874), AndamanSea (0.89 #4254, 0.87 #1418, 0.48 #63874), ArabianSea (0.89 #4254, 0.87 #1418, 0.48 #63874), EastChinaSea (0.89 #4254, 0.87 #1418, 0.48 #63874), Brahmaputra (0.89 #4254, 0.87 #1418, 0.48 #63874), Thar (0.89 #4254, 0.87 #1418, 0.48 #63874), Ganges (0.89 #4254, 0.87 #1418, 0.48 #63874), Ganges (0.89 #4254, 0.87 #1418, 0.48 #63874)
>> best conf = 0.89
=> the first rule below is the first best rule for 63 predicted values
>> Best rule #4254 for best value:
>> intensional similarity = 15
>> extensional distance = 2
>> proper extension:  CL;
>> query: (?x111, ?x231) <- ?x111[ has encompassed ?x175<Asia>; has government ?x1779; has language ?x2295; has religion ?x187<Muslim>; has religion ?x410<Hindu>; is locatedIn of ?x328[ a Mountain; has locatedIn ?x232[ a Country; has ethnicGroup ?x2285; has government ?x831; is locatedIn of ?x231;];];]

No rule for expected values
ranks of expected_values: 
EVAL	NEP	locatedIn!	Manaslu	CNN-1.+1._MA	0.000	0.000	0.000	0.000	89.000	89.000	1415.000	0.895	http://www.semwebtech.org/mondial/10/meta#locatedIn

#236-Indus
PRED entity: Indus

PRED relation: locatedIn
PRED expected values: PK
=> 26 concepts (26 used for prediction)
PRED predicted values (max 10 best out of 80): IND (0.71 #3110, 0.67 #1913, 0.63 #3823), CN (0.62 #3111, 0.58 #4785, 0.58 #5024), BD (0.50 #429, 0.33 #190, 0.22 #1866), MYA (0.40 #564, 0.33 #1042, 0.25 #1521), R (0.38 #1441, 0.33 #1920, 0.24 #2634), USA (0.33 #2944, 0.16 #2707, 0.12 #3183), SP (0.25 #238, 0.22 #1729, 0.20 #956), AUS (0.25 #1241, 0.20 #763, 0.04 #2917), YE (0.25 #238, 0.09 #5502, 0.05 #955), MOC (0.22 #1719, 0.20 #761, 0.02 #3627)
>> best conf = 0.71
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3110 for best value:
>> intensional similarity = 8
>> extensional distance = 43
>> proper extension:  Manicouagan; MackenzieRiver; RiviereRichelieu; SaskatchewanRiver; NelsonRiver;
>> query: (?x383, ?x924) <- ?x383[ a Estuary; is hasEstuary of ?x411[ has locatedIn ?x924[ a Country; has ethnicGroup ?x1553; has religion ?x116; has wasDependentOf ?x81<GB>;];];]

*> Best rule #477 for first EXPECTED value:
*> intensional similarity = 12
*> extensional distance = 2
*> proper extension:  Ganges;
*> query: (?x383, ?x73) <- ?x383[ a Estuary; is hasEstuary of ?x411[ a River; has locatedIn ?x232[ has ethnicGroup ?x2285; has neighbor ?x334; has religion ?x116; is neighbor of ?x73; is neighbor of ?x409<BHT>;]; has locatedIn ?x924<IND>;];]
*> conf = 0.08
ranks of expected_values: 20
EVAL	Indus	locatedIn	PK	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.050	26.000	26.000	80.000	0.712	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: PK
=> 89 concepts (89 used for prediction)
PRED predicted values (max 10 best out of 89): IND (0.75 #14017, 0.71 #14014, 0.69 #16912), CN (0.71 #14014, 0.64 #16910, 0.63 #17637), USA (0.62 #13847, 0.25 #6596, 0.17 #10708), I (0.55 #5363, 0.43 #2945, 0.17 #2462), D (0.55 #16691, 0.30 #14998, 0.22 #18858), R (0.52 #15708, 0.27 #6288, 0.27 #5805), BD (0.50 #1155, 0.33 #2604, 0.33 #433), SP (0.45 #4100, 0.33 #1982, 0.30 #5794), YE (0.45 #4100, 0.30 #5794, 0.30 #8456), BR (0.38 #3263, 0.27 #4955, 0.27 #9312)
>> best conf = 0.75
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #14017 for best value:
>> intensional similarity = 18
>> extensional distance = 22
>> proper extension:  Tennessee; Mississippi; AlleghenyRiver; Missouri; TruckeeRiver; OhioRiver; MerrimackRiver; ConnecticutRiver; HudsonRiver; StraitsofMackinac; ...
>> query: (?x383, ?x924) <- ?x383[ a Estuary; is hasEstuary of ?x411[ a River; has locatedIn ?x232[ a Country; has encompassed ?x175; has ethnicGroup ?x2285; has neighbor ?x334; has religion ?x116; is neighbor of ?x73;]; has locatedIn ?x924[ a Country; has ethnicGroup ?x1553; has language ?x2392; has religion ?x462<Buddhist>; has wasDependentOf ?x81; is neighbor of ?x943;];];]

*> Best rule #7248 for first EXPECTED value:
*> intensional similarity = 19
*> extensional distance = 10
*> proper extension:  Colorado; Parana;
*> query: (?x383, ?x943) <- ?x383[ a Estuary; is hasEstuary of ?x411[ a River; has flowsInto ?x1333[ has locatedIn ?x220; is locatedInWater of ?x2223; is mergesWith of ?x60<IndianOcean>;]; has locatedIn ?x232[ a Country; has encompassed ?x175; has ethnicGroup ?x2285; has government ?x831; has neighbor ?x334; has religion ?x116; is neighbor of ?x73;]; has locatedIn ?x924[ has wasDependentOf ?x81; is neighbor of ?x943;];];]
*> conf = 0.16
ranks of expected_values: 32
EVAL	Indus	locatedIn	PK	CNN-1.+1._MA	0.000	0.000	0.000	0.031	89.000	89.000	89.000	0.750	http://www.semwebtech.org/mondial/10/meta#locatedIn

#235-GBZ
PRED entity: GBZ

PRED relation: neighbor
PRED expected values: E
=> 41 concepts (39 used for prediction)
PRED predicted values (max 10 best out of 216): E (0.89 #4074, 0.89 #2114, 0.89 #4734), CH (0.33 #370, 0.20 #1019, 0.14 #1182), SRB (0.33 #138, 0.20 #1113, 0.14 #1276), AND (0.33 #450, 0.20 #1099, 0.14 #1262), RO (0.33 #26, 0.20 #837, 0.07 #2959), I (0.33 #363, 0.14 #1175, 0.10 #1012), MK (0.33 #117, 0.14 #1255, 0.10 #928), TR (0.33 #30, 0.12 #5394, 0.10 #841), GR (0.33 #69, 0.12 #5394, 0.10 #880), B (0.33 #421, 0.11 #1721, 0.10 #1070)
>> best conf = 0.89
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4074 for best value:
>> intensional similarity = 9
>> extensional distance = 64
>> proper extension:  AND;
>> query: (?x1826, ?x149) <- ?x1826[ a Country; has religion ?x95[ is religion of ?x408<NIC>; is religion of ?x482<MEX>;]; has religion ?x352<RomanCatholic>; is neighbor of ?x149;]
ranks of expected_values: 1
EVAL	GBZ	neighbor	E	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	41.000	39.000	216.000	0.889	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: E
=> 46 concepts (46 used for prediction)
PRED predicted values (max 10 best out of 224): E (0.91 #3042, 0.88 #5802, 0.88 #5633), MA (0.40 #472, 0.12 #2015, 0.11 #4244), IRL (0.33 #191, 0.08 #524, 0.08 #4074), IL (0.27 #1045, 0.26 #2018, 0.24 #1214), SYR (0.26 #2018, 0.13 #1081, 0.12 #1250), IRQ (0.23 #1903, 0.13 #1050, 0.12 #1219), F (0.21 #842, 0.21 #4079, 0.20 #3045), MNE (0.21 #847, 0.18 #1337, 0.18 #1177), HR (0.21 #859, 0.18 #1189, 0.13 #1162), AND (0.21 #4079, 0.20 #502, 0.20 #463)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3042 for best value:
>> intensional similarity = 28
>> extensional distance = 28
>> proper extension:  AND; RSM; V;
>> query: (?x1826, ?x149) <- ?x1826[ a Country; has encompassed ?x195<Europe>; has religion ?x95[ is religion of ?x78<F>; is religion of ?x176<RO>; is religion of ?x181<GCA>; is religion of ?x196<AUS>; is religion of ?x272<CDN>; is religion of ?x296[ has neighbor ?x215; is locatedIn of ?x264;]; is religion of ?x315<USA>; is religion of ?x407<AXA>; is religion of ?x718<L>; is religion of ?x793<DK>;]; has religion ?x352<RomanCatholic>; is neighbor of ?x149;]
ranks of expected_values: 1
EVAL	GBZ	neighbor	E	CNN-1.+1._MA	1.000	1.000	1.000	1.000	46.000	46.000	224.000	0.908	http://www.semwebtech.org/mondial/10/meta#neighbor

#234-YellowSea
PRED entity: YellowSea

PRED relation: flowsInto!
PRED expected values: Hwangho
=> 25 concepts (22 used for prediction)
PRED predicted values (max 10 best out of 89): ColumbiaRiver (0.33 #276, 0.25 #578, 0.08 #1486), Colorado (0.33 #176, 0.25 #478, 0.08 #1386), SnowyRiver (0.33 #146, 0.25 #448, 0.08 #1356), RioLerma (0.33 #80, 0.25 #382, 0.08 #1290), Jangtse (0.20 #702, 0.14 #1005, 0.08 #1308), Argun (0.14 #961, 0.02 #3636, 0.02 #2476), Schilka (0.14 #1193, 0.02 #3636, 0.02 #2708), Ischim (0.14 #1159, 0.02 #3636, 0.02 #2674), Mekong (0.08 #1375, 0.03 #1678, 0.03 #1980), Amur (0.08 #1444, 0.03 #1747, 0.03 #2049)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #276 for best value:
>> intensional similarity = 7
>> extensional distance = 1
>> proper extension:  PacificOcean;
>> query: (?x270, ColumbiaRiver) <- ?x270[ a Sea; has locatedIn ?x626[ has government ?x435;]; has mergesWith ?x271<SeaofJapan>; has mergesWith ?x620<EastChinaSea>;]

*> Best rule #5760 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 1104
*> proper extension:  Stromboli; Marne; GranSasso; EmiKussi; Pico; Po; MontBlanc; LagodiBolsena; Salina; Orinoco; ...
*> query: (?x270, ?x725) <- ?x270[ has locatedIn ?x232[ has neighbor ?x73; has religion ?x116; is locatedIn of ?x725[ a River;];];]
*> conf = 0.01
ranks of expected_values: 88
EVAL	YellowSea	flowsInto!	Hwangho	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.011	25.000	22.000	89.000	0.333	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto!
PRED expected values: Hwangho
=> 98 concepts (98 used for prediction)
PRED predicted values (max 10 best out of 187): ColumbiaRiver (0.33 #579, 0.25 #1821, 0.20 #2730), Colorado (0.33 #479, 0.25 #1821, 0.20 #2730), SnowyRiver (0.33 #449, 0.25 #1821, 0.20 #2730), RioLerma (0.33 #383, 0.25 #1821, 0.20 #2730), Jangtse (0.33 #98, 0.20 #1007, 0.20 #2730), Amur (0.25 #1821, 0.20 #2730, 0.14 #2427), Mekong (0.17 #1378, 0.09 #6074, 0.08 #1683), Zambezi (0.08 #1789, 0.08 #2395, 0.07 #2698), MurrayRiver (0.08 #1717, 0.08 #2323, 0.07 #2626), Jubba (0.08 #1620, 0.08 #2226, 0.07 #2529)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #579 for best value:
>> intensional similarity = 14
>> extensional distance = 1
>> proper extension:  PacificOcean;
>> query: (?x270, ColumbiaRiver) <- ?x270[ a Sea; has locatedIn ?x334[ a Country; has government ?x1979; has language ?x2244[ a Language;]; has wasDependentOf ?x117; is locatedIn of ?x2111[ a Mountain; has inMountains ?x898;];]; has mergesWith ?x271<SeaofJapan>; has mergesWith ?x620<EastChinaSea>;]

*> Best rule #25871 for first EXPECTED value:
*> intensional similarity = 11
*> extensional distance = 753
*> proper extension:  Gambia;
*> query: (?x270, ?x2428) <- ?x270[ has locatedIn ?x232[ has neighbor ?x381[ has government ?x2442; is locatedIn of ?x82;]; is locatedIn of ?x2428[ a River;];]; has locatedIn ?x626[ a Country; has religion ?x627[ a Religion;]; has wasDependentOf ?x117;];]
*> conf = 0.02
ranks of expected_values: 109
EVAL	YellowSea	flowsInto!	Hwangho	CNN-1.+1._MA	0.000	0.000	0.000	0.009	98.000	98.000	187.000	0.333	http://www.semwebtech.org/mondial/10/meta#flowsInto

#233-Madeira
PRED entity: Madeira

PRED relation: inMountains!
PRED expected values: PicoRuivo
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 259): MtRobson (0.01 #259), BarredesEcrins (0.01 #258), Grossglockner (0.01 #257), PicodeAgulhasNegras (0.01 #256), Mantaro (0.01 #255), PicoBolivar (0.01 #254), Drin (0.01 #253), Vignemale (0.01 #252), Dychtau (0.01 #251), GranitePeak (0.01 #250)
>> best conf = 0.01
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #259 for best value:
>> intensional similarity = 1
>> extensional distance = 91
>> proper extension:  BlackForest; Ahaggar; Apennin; Alps; Troodos; Himalaya; RockyMountains; Karakorum; GreatDividingRange; Kunlun; ...
>> query: (?x1306, MtRobson) <- ?x1306[ a Mountains;]

No rule for expected values
ranks of expected_values: 
EVAL	Madeira	inMountains!	PicoRuivo	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	2.000	2.000	259.000	0.011	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains!
PRED expected values: PicoRuivo
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 259): MtRobson (0.01 #259), BarredesEcrins (0.01 #258), Grossglockner (0.01 #257), PicodeAgulhasNegras (0.01 #256), Mantaro (0.01 #255), PicoBolivar (0.01 #254), Drin (0.01 #253), Vignemale (0.01 #252), Dychtau (0.01 #251), GranitePeak (0.01 #250)
>> best conf = 0.01
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #259 for best value:
>> intensional similarity = 1
>> extensional distance = 91
>> proper extension:  BlackForest; Ahaggar; Apennin; Alps; Troodos; Himalaya; RockyMountains; Karakorum; GreatDividingRange; Kunlun; ...
>> query: (?x1306, MtRobson) <- ?x1306[ a Mountains;]

No rule for expected values
ranks of expected_values: 
EVAL	Madeira	inMountains!	PicoRuivo	CNN-1.+1._MA	0.000	0.000	0.000	0.000	2.000	2.000	259.000	0.011	http://www.semwebtech.org/mondial/10/meta#inMountains

#232-Busira
PRED entity: Busira

PRED relation: locatedIn
PRED expected values: ZRE
=> 42 concepts (42 used for prediction)
PRED predicted values (max 10 best out of 60): ZRE (0.75 #1185, 0.75 #1027, 0.73 #711), PE (0.20 #778, 0.10 #3149, 0.10 #2912), RCA (0.17 #160, 0.12 #1108, 0.12 #1345), CH (0.13 #768, 0.12 #1717, 0.12 #2191), R (0.13 #7591, 0.10 #9016, 0.10 #6167), ANG (0.12 #1137, 0.12 #1374, 0.10 #1611), USA (0.11 #2443, 0.11 #4575, 0.11 #5523), EAU (0.11 #389, 0.09 #626, 0.05 #1574), D (0.11 #5708, 0.09 #1680, 0.09 #2154), RWA (0.10 #1549, 0.09 #601, 0.06 #1312)
>> best conf = 0.75
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1185 for best value:
>> intensional similarity = 8
>> extensional distance = 14
>> proper extension:  Lulua; Cuilo; Lomami; Aruwimi; Cuango;
>> query: (?x2475, ?x348) <- ?x2475[ a Source; is hasSource of ?x563[ a River; has flowsInto ?x281[ has hasSource ?x2361; has locatedIn ?x348<ZRE>;]; has hasEstuary ?x347;];]
>> Best rule #1027 for best value:
>> intensional similarity = 8
>> extensional distance = 14
>> proper extension:  Lulua; Cuilo; Lomami; Aruwimi; Cuango;
>> query: (?x2475, ZRE) <- ?x2475[ a Source; is hasSource of ?x563[ a River; has flowsInto ?x281[ has hasSource ?x2361; has locatedIn ?x348<ZRE>;]; has hasEstuary ?x347;];]
ranks of expected_values: 1
EVAL	Busira	locatedIn	ZRE	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	42.000	42.000	60.000	0.750	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: ZRE
=> 110 concepts (110 used for prediction)
PRED predicted values (max 10 best out of 62): ZRE (0.83 #5245, 0.82 #15746, 0.80 #3887), USA (0.39 #14389, 0.34 #15580, 0.33 #17247), ETH (0.27 #4402, 0.20 #3684, 0.15 #12046), SSD (0.27 #4582, 0.10 #4102, 0.10 #3807), ANG (0.25 #663, 0.17 #8062, 0.17 #7821), R (0.25 #21710, 0.24 #22187, 0.23 #23385), D (0.23 #14098, 0.15 #20058, 0.15 #8848), CDN (0.22 #17000, 0.21 #15571, 0.14 #5308), PE (0.20 #5550, 0.19 #6025, 0.17 #8179), RCA (0.20 #1110, 0.17 #1347, 0.14 #1584)
>> best conf = 0.83
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5245 for best value:
>> intensional similarity = 16
>> extensional distance = 10
>> proper extension:  SaintMarysRiver; StraitsofMackinac;
>> query: (?x2475, ?x348) <- ?x2475[ a Source; is hasSource of ?x563[ a River; has flowsInto ?x281[ has flowsInto ?x929;]; has hasEstuary ?x347[ a Estuary;]; has locatedIn ?x348[ has neighbor ?x934; has religion ?x95<Protestant>; has wasDependentOf ?x543; is locatedIn of ?x182<AtlanticOcean>; is neighbor of ?x229;]; is flowsInto of ?x1749;];]
ranks of expected_values: 1
EVAL	Busira	locatedIn	ZRE	CNN-1.+1._MA	1.000	1.000	1.000	1.000	110.000	110.000	62.000	0.833	http://www.semwebtech.org/mondial/10/meta#locatedIn

#231-MorneDiablotins
PRED entity: MorneDiablotins

PRED relation: locatedIn
PRED expected values: WD
=> 27 concepts (27 used for prediction)
PRED predicted values (max 10 best out of 124): WD (0.86 #2369, 0.85 #3079, 0.85 #2132), RI (0.32 #1236, 0.29 #2657, 0.27 #2894), E (0.18 #738, 0.18 #500, 0.14 #1447), WV (0.17 #21, 0.07 #258, 0.06 #732), MART (0.17 #161, 0.07 #398, 0.06 #872), C (0.17 #26, 0.06 #499, 0.03 #2868), DOM (0.17 #120, 0.06 #593, 0.03 #4503), IS (0.14 #1528, 0.08 #1765, 0.08 #3424), USA (0.13 #309, 0.12 #783, 0.10 #4812), RP (0.13 #2714, 0.12 #2951, 0.10 #2478)
>> best conf = 0.86
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2369 for best value:
>> intensional similarity = 7
>> extensional distance = 26
>> proper extension:  Etna;
>> query: (?x1972, ?x922) <- ?x1972[ a Mountain; a Volcano; has locatedOnIsland ?x609[ a Island; has locatedIn ?x922;]; has type ?x706<"volcano">;]
ranks of expected_values: 1
EVAL	MorneDiablotins	locatedIn	WD	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	27.000	27.000	124.000	0.862	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: WD
=> 42 concepts (35 used for prediction)
PRED predicted values (max 10 best out of 94): WD (0.89 #5262, 0.81 #4308, 0.81 #7639), RI (0.47 #5077, 0.47 #4123, 0.32 #6027), CDN (0.40 #776, 0.18 #4546, 0.18 #4371), C (0.33 #26, 0.20 #978, 0.12 #1453), DOM (0.33 #358, 0.11 #2030, 0.11 #1788), E (0.27 #2657, 0.27 #2418, 0.21 #3859), P (0.25 #1624, 0.22 #2107, 0.22 #1865), WV (0.25 #497, 0.20 #1211, 0.20 #734), MART (0.25 #637, 0.20 #1351, 0.20 #1113), IS (0.21 #3940, 0.15 #3702, 0.10 #5370)
>> best conf = 0.89
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5262 for best value:
>> intensional similarity = 12
>> extensional distance = 17
>> proper extension:  BenNevis;
>> query: (?x1972, ?x922) <- ?x1972[ a Mountain; has locatedOnIsland ?x609[ a Island; has belongsToIslands ?x877[ a Islands;]; has locatedIn ?x922[ a Country; has encompassed ?x521; has government ?x254; has religion ?x95<Protestant>;]; has locatedInWater ?x182;];]
ranks of expected_values: 1
EVAL	MorneDiablotins	locatedIn	WD	CNN-1.+1._MA	1.000	1.000	1.000	1.000	42.000	35.000	94.000	0.895	http://www.semwebtech.org/mondial/10/meta#locatedIn

#230-Indus
PRED entity: Indus

PRED relation: locatedIn
PRED expected values: PK
=> 31 concepts (30 used for prediction)
PRED predicted values (max 10 best out of 158): BD (0.50 #422, 0.33 #188, 0.12 #5645), USA (0.27 #4541, 0.21 #540, 0.15 #1009), IR (0.23 #1477, 0.18 #1714, 0.12 #5645), R (0.22 #4240, 0.22 #4475, 0.17 #1887), SP (0.20 #3999, 0.12 #4000, 0.10 #2353), D (0.16 #1902, 0.11 #2609, 0.10 #2373), J (0.14 #1191, 0.10 #5644, 0.08 #1407), E (0.14 #496, 0.08 #4262, 0.07 #965), MYA (0.14 #469, 0.12 #2824, 0.12 #3059), BHT (0.14 #469, 0.12 #2824, 0.12 #3059)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #422 for best value:
>> intensional similarity = 6
>> extensional distance = 2
>> proper extension:  Ganges;
>> query: (?x411, BD) <- ?x411[ has hasEstuary ?x383[ a Estuary;]; has locatedIn ?x232[ has neighbor ?x73;]; has locatedIn ?x924<IND>;]

*> Best rule #469 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 2
*> proper extension:  Ganges;
*> query: (?x411, ?x73) <- ?x411[ has hasEstuary ?x383[ a Estuary;]; has locatedIn ?x232[ has neighbor ?x73;]; has locatedIn ?x924<IND>;]
*> conf = 0.14
ranks of expected_values: 11
EVAL	Indus	locatedIn	PK	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.091	31.000	30.000	158.000	0.500	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: PK
=> 125 concepts (121 used for prediction)
PRED predicted values (max 10 best out of 230): KAZ (0.69 #17988, 0.61 #23670, 0.60 #22726), PK (0.69 #17988, 0.61 #23670, 0.60 #22726), NEP (0.69 #17988, 0.61 #23670, 0.60 #22726), KGZ (0.69 #17988, 0.61 #23670, 0.60 #22726), AUS (0.64 #1701, 0.27 #9269, 0.12 #15432), SP (0.50 #2362, 0.43 #1655, 0.33 #707), R (0.48 #14446, 0.36 #14676, 0.30 #6384), I (0.45 #1939, 0.18 #15198, 0.15 #15672), USA (0.44 #15458, 0.25 #9295, 0.18 #19486), RI (0.43 #1232, 0.41 #7853, 0.40 #8091)
>> best conf = 0.69
=> the first rule below is the first best rule for 4 predicted values
>> Best rule #17988 for best value:
>> intensional similarity = 9
>> extensional distance = 577
>> proper extension:  QueenMarysPeak; SaintHelena; Ascension; TristanDaCunha;
>> query: (?x411, ?x130) <- ?x411[ has locatedIn ?x232[ a Country; has ethnicGroup ?x2285; has government ?x831; is locatedIn of ?x2122[ a Mountain; has locatedIn ?x130;];]; has locatedIn ?x924[ has language ?x2392;];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 2
EVAL	Indus	locatedIn	PK	CNN-1.+1._MA	0.000	1.000	1.000	0.500	125.000	121.000	230.000	0.690	http://www.semwebtech.org/mondial/10/meta#locatedIn

#229-Aller
PRED entity: Aller

PRED relation: flowsInto!
PRED expected values: Leine
=> 38 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 150): Alz (0.06 #167, 0.05 #470, 0.05 #773), Ammer (0.06 #71, 0.05 #374, 0.05 #677), Salzach (0.06 #213, 0.05 #516, 0.05 #819), Saar (0.06 #28, 0.05 #331, 0.05 #634), Ammersee (0.06 #111, 0.05 #414, 0.05 #717), Chiemsee (0.06 #69, 0.05 #372, 0.05 #675), Würm (0.06 #40, 0.05 #343, 0.05 #646), Inn (0.06 #104, 0.05 #407, 0.05 #1014), Isar (0.06 #163, 0.05 #466, 0.05 #1073), Breg (0.06 #268, 0.05 #571, 0.05 #1178)
>> best conf = 0.06
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #167 for best value:
>> intensional similarity = 6
>> extensional distance = 16
>> proper extension:  Donau; Saar; Werra; Iller; Ammer; Mosel; Inn; Lech; Brigach; Oder; ...
>> query: (?x1543, Alz) <- ?x1543[ a River; has flowsInto ?x1533; has hasEstuary ?x2430; has hasSource ?x1852; has locatedIn ?x120<D>;]

No rule for expected values
ranks of expected_values: 
EVAL	Aller	flowsInto!	Leine	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	38.000	31.000	150.000	0.056	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto!
PRED expected values: Leine
=> 119 concepts (117 used for prediction)
PRED predicted values (max 10 best out of 238): Ammersee (0.11 #1020, 0.10 #1322, 0.07 #1624), Chiemsee (0.11 #978, 0.10 #1280, 0.07 #1582), Würm (0.11 #949, 0.10 #1251, 0.07 #1553), Fulda (0.11 #1144, 0.10 #1446, 0.05 #2356), Aller (0.11 #1139, 0.10 #1441, 0.05 #2351), Werra (0.11 #948, 0.10 #1250, 0.05 #2160), Alz (0.07 #1680, 0.06 #1983, 0.05 #2288), Ammer (0.07 #1584, 0.06 #1887, 0.05 #2192), Saar (0.07 #1541, 0.06 #1844, 0.05 #2149), Salzach (0.07 #1726, 0.06 #2029, 0.05 #2334)
>> best conf = 0.11
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1020 for best value:
>> intensional similarity = 9
>> extensional distance = 7
>> proper extension:  Neckar; Weser;
>> query: (?x1543, Ammersee) <- ?x1543[ a River; has hasEstuary ?x2430[ a Estuary; has locatedIn ?x120<D>;]; has hasSource ?x1852[ a Source; has locatedIn ?x120<D>;]; has locatedIn ?x120<D>;]

*> Best rule #34198 for first EXPECTED value:
*> intensional similarity = 11
*> extensional distance = 954
*> proper extension:  Araguaia; VelikiRatnoOstrvo; Umeaelv; Ene; Poopo; Colorado; MtAdams; Apurimac; MtElbert; CaribbeanSea; ...
*> query: (?x1543, ?x100) <- ?x1543[ has locatedIn ?x120[ has neighbor ?x194[ has ethnicGroup ?x58; is locatedIn of ?x221;]; has religion ?x95; is locatedIn of ?x395[ a Source;]; is locatedIn of ?x1124[ has hasSource ?x1388;]; is locatedIn of ?x2415[ is hasSource of ?x100;];];]
*> conf = 0.01
ranks of expected_values: 211
EVAL	Aller	flowsInto!	Leine	CNN-1.+1._MA	0.000	0.000	0.000	0.005	119.000	117.000	238.000	0.111	http://www.semwebtech.org/mondial/10/meta#flowsInto

#228-BIH
PRED entity: BIH

PRED relation: ethnicGroup
PRED expected values: Bosniak
=> 46 concepts (46 used for prediction)
PRED predicted values (max 10 best out of 236): Serb (0.33 #40, 0.30 #297, 0.19 #553), Albanian (0.33 #96, 0.20 #353, 0.17 #7170), Bosniak (0.33 #198, 0.20 #455, 0.17 #7170), Montenegrin (0.33 #212, 0.17 #7170, 0.16 #10243), Greek (0.30 #286, 0.19 #542, 0.17 #798), European (0.29 #4360, 0.29 #1544, 0.28 #2568), African (0.25 #5382, 0.24 #4358, 0.19 #6662), German (0.22 #1290, 0.21 #1034, 0.19 #1546), Slovene (0.20 #268, 0.17 #7170, 0.16 #10243), Arab (0.19 #523, 0.17 #779, 0.10 #267)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #40 for best value:
>> intensional similarity = 7
>> extensional distance = 1
>> proper extension:  MNE;
>> query: (?x55, Serb) <- ?x55[ has religion ?x352[ is religion of ?x81<GB>; is religion of ?x236[ has ethnicGroup ?x164;];]; is locatedIn of ?x2319<Piva>;]

*> Best rule #198 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 1
*> proper extension:  MNE;
*> query: (?x55, Bosniak) <- ?x55[ has religion ?x352[ is religion of ?x81<GB>; is religion of ?x236[ has ethnicGroup ?x164;];]; is locatedIn of ?x2319<Piva>;]
*> conf = 0.33
ranks of expected_values: 3
EVAL	BIH	ethnicGroup	Bosniak	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.333	46.000	46.000	236.000	0.333	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: Bosniak
=> 100 concepts (100 used for prediction)
PRED predicted values (max 10 best out of 255): German (0.62 #2834, 0.50 #3861, 0.50 #1550), Russian (0.61 #6754, 0.42 #4439, 0.40 #2126), Ukrainian (0.60 #2055, 0.50 #3853, 0.43 #6683), Serb (0.60 #1838, 0.50 #810, 0.43 #2608), Roma (0.50 #1547, 0.42 #4373, 0.40 #2060), Hungarian (0.50 #1564, 0.40 #2077, 0.35 #5419), European (0.44 #10808, 0.40 #11836, 0.39 #6431), Polish (0.40 #4056, 0.40 #2258, 0.25 #3029), Bosniak (0.40 #1996, 0.35 #6681, 0.35 #6940), Albanian (0.35 #6681, 0.35 #6940, 0.33 #3435)
>> best conf = 0.62
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2834 for best value:
>> intensional similarity = 17
>> extensional distance = 6
>> proper extension:  D; CH;
>> query: (?x55, German) <- ?x55[ has neighbor ?x106[ has ethnicGroup ?x1472; has language ?x1251; is locatedIn of ?x224[ a Source;];]; has neighbor ?x156[ has ethnicGroup ?x160<Croat>; has language ?x878; has religion ?x187; is neighbor of ?x236[ is neighbor of ?x163;];]; has religion ?x56; is locatedIn of ?x825[ has inMountains ?x785;]; is locatedIn of ?x1939[ a Estuary;];]

*> Best rule #1996 for first EXPECTED value:
*> intensional similarity = 15
*> extensional distance = 3
*> proper extension:  MNE;
*> query: (?x55, Bosniak) <- ?x55[ has ethnicGroup ?x160; has neighbor ?x156<HR>; has religion ?x95[ is religion of ?x80[ a Country;]; is religion of ?x196<AUS>; is religion of ?x348<ZRE>; is religion of ?x460<RP>;]; has wasDependentOf ?x1197; is locatedIn of ?x152[ has hasSource ?x1363;];]
*> conf = 0.40
ranks of expected_values: 9
EVAL	BIH	ethnicGroup	Bosniak	CNN-1.+1._MA	0.000	0.000	1.000	0.111	100.000	100.000	255.000	0.625	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#227-Luzon
PRED entity: Luzon

PRED relation: locatedInWater
PRED expected values: PacificOcean
=> 45 concepts (27 used for prediction)
PRED predicted values (max 10 best out of 141): IndianOcean (0.67 #214, 0.25 #2, 0.23 #471), PacificOcean (0.64 #898, 0.54 #786, 0.33 #144), SulawesiSea (0.64 #898, 0.45 #452, 0.39 #1159), JavaSea (0.50 #221, 0.25 #9, 0.23 #478), AtlanticOcean (0.30 #603, 0.30 #476, 0.29 #991), EastChinaSea (0.29 #367, 0.20 #110, 0.17 #153), MalakkaStrait (0.25 #22, 0.20 #106, 0.17 #149), AndamanSea (0.25 #20, 0.20 #104, 0.17 #147), CaribbeanSea (0.19 #615, 0.18 #658, 0.17 #701), SeaofJapan (0.17 #142, 0.06 #356, 0.03 #484)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #214 for best value:
>> intensional similarity = 6
>> extensional distance = 10
>> proper extension:  Java; Krakatau; Bali; Sumbawa; Madagaskar; GrandComoro; Lombok;
>> query: (?x824, IndianOcean) <- ?x824[ has locatedInWater ?x384[ has locatedIn ?x232[ has neighbor ?x73; is locatedIn of ?x1950<Brahmaputra>;];]; is locatedOnIsland of ?x524;]

*> Best rule #898 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 192
*> proper extension:  Saipan; Jersey; Ameland; Texel; SaoMiguel; Guam; Samos; Tiree; Spiekeroog; Aruba; ...
*> query: (?x824, ?x282) <- ?x824[ a Island; has belongsToIslands ?x370[ a Islands; is belongsToIslands of ?x880[ has locatedIn ?x460;]; is belongsToIslands of ?x1158[ has locatedInWater ?x282;];];]
*> conf = 0.64
ranks of expected_values: 2
EVAL	Luzon	locatedInWater	PacificOcean	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	45.000	27.000	141.000	0.667	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater
PRED expected values: PacificOcean
=> 99 concepts (73 used for prediction)
PRED predicted values (max 10 best out of 53): PacificOcean (0.83 #1227, 0.80 #1401, 0.78 #1358), SulawesiSea (0.70 #327, 0.67 #170, 0.64 #2082), IndianOcean (0.62 #1080, 0.59 #474, 0.30 #518), AtlanticOcean (0.36 #694, 0.34 #826, 0.34 #739), JavaSea (0.32 #481, 0.30 #525, 0.29 #611), MalakkaStrait (0.22 #169, 0.20 #430, 0.20 #343), SuluSea (0.22 #169, 0.14 #1210, 0.13 #991), BandaSea (0.22 #169, 0.09 #2349, 0.09 #2258), CaribbeanSea (0.21 #706, 0.21 #1140, 0.17 #1184), EastChinaSea (0.20 #430, 0.20 #343, 0.20 #730)
>> best conf = 0.83
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1227 for best value:
>> intensional similarity = 8
>> extensional distance = 52
>> proper extension:  Fakaofo; Babelthuap; VanuaLevu;
>> query: (?x824, PacificOcean) <- ?x824[ a Island; has belongsToIslands ?x370[ a Islands;]; has locatedInWater ?x384[ has locatedIn ?x91; is locatedInWater of ?x716<Taiwan>; is mergesWith of ?x241;];]
ranks of expected_values: 1
EVAL	Luzon	locatedInWater	PacificOcean	CNN-1.+1._MA	1.000	1.000	1.000	1.000	99.000	73.000	53.000	0.833	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#226-Jamaica
PRED entity: Jamaica

PRED relation: belongsToIslands
PRED expected values: GreaterAntilles
=> 54 concepts (54 used for prediction)
PRED predicted values (max 10 best out of 41): LesserAntilles (0.58 #763, 0.38 #151, 0.25 #1579), SundaIslands (0.35 #490, 0.31 #694, 0.29 #558), GreaterAntilles (0.25 #183, 0.17 #115, 0.08 #387), HawaiiIslands (0.18 #301, 0.17 #369, 0.15 #437), CapeVerdes (0.17 #111, 0.08 #383, 0.04 #519), BahamaIslands (0.10 #266, 0.02 #1626), CanadianArcticIslands (0.10 #1572, 0.09 #280, 0.08 #348), NewZealand (0.09 #307, 0.08 #375, 0.08 #443), FijiIslands (0.09 #320, 0.08 #456, 0.04 #728), Azores (0.09 #1636, 0.04 #548, 0.04 #616)
>> best conf = 0.58
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #763 for best value:
>> intensional similarity = 3
>> extensional distance = 24
>> proper extension:  CaymanBrac; Grande-Terre; SanAndres; St.Barthelemy;
>> query: (?x1017, LesserAntilles) <- ?x1017[ a Island; has locatedInWater ?x317<CaribbeanSea>;]

*> Best rule #183 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 6
*> proper extension:  Basse-Terre;
*> query: (?x1017, GreaterAntilles) <- ?x1017[ a Island; has locatedInWater ?x317<CaribbeanSea>; is locatedOnIsland of ?x1410[ a Mountain; has locatedIn ?x321;];]
*> conf = 0.25
ranks of expected_values: 3
EVAL	Jamaica	belongsToIslands	GreaterAntilles	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.333	54.000	54.000	41.000	0.577	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

PRED relation: belongsToIslands
PRED expected values: GreaterAntilles
=> 129 concepts (129 used for prediction)
PRED predicted values (max 10 best out of 68): LesserAntilles (0.67 #559, 0.58 #1307, 0.58 #1783), GreaterAntilles (0.33 #455, 0.25 #795, 0.25 #183), SundaIslands (0.31 #1646, 0.29 #1442, 0.21 #3006), CanadianArcticIslands (0.29 #1368, 0.25 #1504, 0.23 #1708), Philipines (0.21 #3067, 0.19 #3271, 0.12 #1435), HawaiiIslands (0.19 #2885, 0.17 #3157, 0.15 #981), Canares (0.19 #2335, 0.12 #1247, 0.12 #3423), CapeVerdes (0.17 #927, 0.14 #1131, 0.08 #995), TurksandCaicosIslands (0.14 #1139, 0.05 #3179, 0.04 #3451), CalifornianChannelIslands (0.14 #2915, 0.12 #3187, 0.06 #4275)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #559 for best value:
>> intensional similarity = 11
>> extensional distance = 4
>> proper extension:  Barbuda; Antigua;
>> query: (?x1017, LesserAntilles) <- ?x1017[ a Island; has locatedIn ?x321[ a Country; has encompassed ?x521<America>; has government ?x854; has wasDependentOf ?x81<GB>; is locatedIn of ?x317<CaribbeanSea>;]; has locatedInWater ?x317<CaribbeanSea>;]

*> Best rule #455 for first EXPECTED value:
*> intensional similarity = 12
*> extensional distance = 4
*> proper extension:  Martinique;
*> query: (?x1017, GreaterAntilles) <- ?x1017[ a Island; has locatedIn ?x321[ a Country; has ethnicGroup ?x162; has ethnicGroup ?x374[ a EthnicGroup;]; has government ?x854; has religion ?x95;]; has locatedInWater ?x317<CaribbeanSea>; is locatedOnIsland of ?x1410[ a Mountain;];]
*> conf = 0.33
ranks of expected_values: 2
EVAL	Jamaica	belongsToIslands	GreaterAntilles	CNN-1.+1._MA	0.000	1.000	1.000	0.500	129.000	129.000	68.000	0.667	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

#225-TR
PRED entity: TR

PRED relation: language
PRED expected values: Kurdish
=> 38 concepts (38 used for prediction)
PRED predicted values (max 10 best out of 95): Roma (0.40 #143, 0.26 #1361, 0.21 #1751), Armenian (0.40 #219, 0.26 #1361, 0.21 #1751), Serbian (0.40 #135, 0.18 #815, 0.12 #524), Hungarian (0.40 #113, 0.15 #793, 0.08 #1556), Spanish (0.27 #1284, 0.27 #1187, 0.22 #895), Russian (0.26 #1361, 0.21 #1751, 0.21 #787), Arabic (0.26 #1361, 0.21 #1751, 0.20 #252), Azeri (0.26 #1361, 0.21 #1751, 0.20 #266), Georgian (0.26 #1361, 0.21 #1751, 0.20 #254), Lezgi (0.26 #1361, 0.21 #1751, 0.20 #216)
>> best conf = 0.40
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #143 for best value:
>> intensional similarity = 6
>> extensional distance = 3
>> proper extension:  RO; MK; SRB;
>> query: (?x185, Roma) <- ?x185[ has encompassed ?x175; has ethnicGroup ?x638; has neighbor ?x177<BG>; has religion ?x187; is locatedIn of ?x98;]

*> Best rule #1361 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 72
*> proper extension:  ARM;
*> query: (?x185, ?x555) <- ?x185[ has ethnicGroup ?x638; has language ?x511; is neighbor of ?x353[ has language ?x555; has religion ?x56;];]
*> conf = 0.26
ranks of expected_values: 15
EVAL	TR	language	Kurdish	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.067	38.000	38.000	95.000	0.400	http://www.semwebtech.org/mondial/10/meta#language

PRED relation: language
PRED expected values: Kurdish
=> 91 concepts (91 used for prediction)
PRED predicted values (max 10 best out of 95): Albanian (0.57 #1403, 0.50 #1013, 0.40 #1209), Spanish (0.53 #2267, 0.37 #3433, 0.36 #3238), Pashtu (0.50 #904, 0.33 #26, 0.07 #1782), Serbian (0.43 #1405, 0.25 #1015, 0.20 #2187), Greek (0.40 #1222, 0.35 #1075, 0.33 #146), Russian (0.35 #1075, 0.33 #1076, 0.33 #398), Armenian (0.35 #1075, 0.33 #1076, 0.33 #316), Roma (0.35 #1075, 0.33 #1076, 0.28 #1465), Azeri (0.35 #1075, 0.33 #1076, 0.28 #1465), Georgian (0.35 #1075, 0.33 #1076, 0.28 #1465)
>> best conf = 0.57
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1403 for best value:
>> intensional similarity = 16
>> extensional distance = 5
>> proper extension:  MNE; KOS;
>> query: (?x185, Albanian) <- ?x185[ has encompassed ?x175; has ethnicGroup ?x638; is locatedIn of ?x1126[ has inMountains ?x1303;]; is neighbor of ?x304[ has ethnicGroup ?x244; has language ?x511; is locatedIn of ?x573;]; is neighbor of ?x353[ has ethnicGroup ?x908; has religion ?x352<RomanCatholic>; is locatedIn of ?x141;]; is neighbor of ?x399[ is locatedIn of ?x739<LakePrespa>;];]

*> Best rule #1075 for first EXPECTED value:
*> intensional similarity = 18
*> extensional distance = 2
*> proper extension:  AL; MK;
*> query: (?x185, ?x741) <- ?x185[ has ethnicGroup ?x638; is locatedIn of ?x98; is neighbor of ?x302[ has ethnicGroup ?x557; is locatedIn of ?x255; is neighbor of ?x751;]; is neighbor of ?x331[ has language ?x741;]; is neighbor of ?x353[ has ethnicGroup ?x908; has language ?x555; has religion ?x56<ChristianOrthodox>; has religion ?x352<RomanCatholic>; is locatedIn of ?x141;]; is neighbor of ?x399<GR>;]
*> conf = 0.35
ranks of expected_values: 18
EVAL	TR	language	Kurdish	CNN-1.+1._MA	0.000	0.000	0.000	0.056	91.000	91.000	95.000	0.571	http://www.semwebtech.org/mondial/10/meta#language

#224-Moldoveanu
PRED entity: Moldoveanu

PRED relation: locatedIn
PRED expected values: RO
=> 12 concepts (12 used for prediction)
PRED predicted values (max 10 best out of 53): UA (0.57 #308, 0.41 #1426, 0.41 #1425), RO (0.41 #1426, 0.41 #1425, 0.33 #238), USA (0.15 #1260, 0.15 #1735, 0.10 #2446), I (0.13 #522, 0.13 #760, 0.13 #998), CH (0.08 #531, 0.08 #769, 0.08 #1007), TAD (0.07 #496, 0.07 #734, 0.06 #972), RA (0.07 #561, 0.07 #799, 0.06 #1037), CN (0.06 #2430, 0.06 #1244, 0.06 #1719), E (0.06 #1215, 0.06 #1690, 0.05 #977), PE (0.06 #1493, 0.06 #1966, 0.06 #2203)
>> best conf = 0.57
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #308 for best value:
>> intensional similarity = 2
>> extensional distance = 5
>> proper extension:  Theiss; Dnister; Dnjestr; Olt; Pruth;
>> query: (?x974, UA) <- ?x974[ has inMountains ?x1675<Karpaten>;]

*> Best rule #1426 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 153
*> proper extension:  JabalKatrina; Tahat; Annapurna; Schchara; EmiKussi; Demirkazik; Elbrus; Olympos; MtAdams; Manaslu; ...
*> query: (?x974, ?x176) <- ?x974[ a Mountain; has inMountains ?x1675[ a Mountains; is inMountains of ?x2082[ has locatedIn ?x176;];];]
*> conf = 0.41
ranks of expected_values: 2
EVAL	Moldoveanu	locatedIn	RO	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	12.000	12.000	53.000	0.571	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: RO
=> 12 concepts (12 used for prediction)
PRED predicted values (max 10 best out of 53): UA (0.57 #308, 0.41 #1426, 0.41 #1425), RO (0.41 #1426, 0.41 #1425, 0.33 #238), USA (0.15 #1260, 0.15 #1736, 0.10 #2446), I (0.13 #522, 0.13 #760, 0.13 #998), CH (0.08 #531, 0.08 #769, 0.08 #1007), TAD (0.07 #496, 0.07 #734, 0.06 #972), RA (0.07 #561, 0.07 #799, 0.06 #1037), CN (0.06 #2430, 0.06 #1244, 0.06 #1720), E (0.06 #1215, 0.06 #1691, 0.05 #977), PE (0.06 #1493, 0.06 #1967, 0.06 #2203)
>> best conf = 0.57
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #308 for best value:
>> intensional similarity = 2
>> extensional distance = 5
>> proper extension:  Theiss; Dnister; Dnjestr; Olt; Pruth;
>> query: (?x974, UA) <- ?x974[ has inMountains ?x1675<Karpaten>;]

*> Best rule #1426 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 153
*> proper extension:  JabalKatrina; Tahat; Annapurna; Schchara; EmiKussi; Demirkazik; Elbrus; Olympos; MtAdams; Manaslu; ...
*> query: (?x974, ?x176) <- ?x974[ a Mountain; has inMountains ?x1675[ a Mountains; is inMountains of ?x2082[ has locatedIn ?x176;];];]
*> conf = 0.41
ranks of expected_values: 2
EVAL	Moldoveanu	locatedIn	RO	CNN-1.+1._MA	0.000	1.000	1.000	0.500	12.000	12.000	53.000	0.571	http://www.semwebtech.org/mondial/10/meta#locatedIn

#223-Grande-Terre
PRED entity: Grande-Terre

PRED relation: locatedIn
PRED expected values: GUAD
=> 32 concepts (32 used for prediction)
PRED predicted values (max 10 best out of 157): AG (0.35 #4507, 0.35 #4506, 0.34 #4269), BDS (0.35 #4507, 0.35 #4506, 0.34 #4269), MART (0.35 #4507, 0.35 #4506, 0.34 #4269), WD (0.35 #4507, 0.35 #4506, 0.34 #4269), MNTS (0.35 #4507, 0.35 #4506, 0.34 #4269), WV (0.35 #4507, 0.35 #4506, 0.34 #4269), WG (0.35 #4507, 0.35 #4506, 0.34 #4269), WL (0.35 #4507, 0.35 #4506, 0.34 #4269), KN (0.35 #4507, 0.35 #4506, 0.34 #4269), TT (0.35 #4507, 0.35 #4506, 0.34 #4269)
>> best conf = 0.35
=> the first rule below is the first best rule for 15 predicted values
>> Best rule #4507 for best value:
>> intensional similarity = 5
>> extensional distance = 194
>> proper extension:  NewProvidence;
>> query: (?x1132, ?x1554) <- ?x1132[ a Island; has belongsToIslands ?x877[ is belongsToIslands of ?x1984[ a Island; has locatedIn ?x1554;];];]
>> Best rule #4506 for best value:
>> intensional similarity = 5
>> extensional distance = 194
>> proper extension:  NewProvidence;
>> query: (?x1132, ?x1230) <- ?x1132[ a Island; has belongsToIslands ?x877[ is belongsToIslands of ?x1397[ a Island; has locatedIn ?x1230;];];]

*> Best rule #6890 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 270
*> proper extension:  ReneLevasseurIsland; Samosir; IsladaOmetepe;
*> query: (?x1132, ?x810) <- ?x1132[ a Island; has locatedInWater ?x182[ has locatedIn ?x81[ has religion ?x95;]; has locatedIn ?x810[ a Country; has encompassed ?x213;];];]
*> conf = 0.03
ranks of expected_values: 81
EVAL	Grande-Terre	locatedIn	GUAD	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.012	32.000	32.000	157.000	0.346	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: GUAD
=> 54 concepts (54 used for prediction)
PRED predicted values (max 10 best out of 162): MART (0.42 #4312, 0.35 #8440, 0.35 #8687), MNTS (0.42 #4312, 0.35 #8440, 0.35 #8687), WG (0.42 #4312, 0.35 #8440, 0.35 #8687), WD (0.42 #4312, 0.35 #8440, 0.35 #8687), WV (0.42 #4312, 0.35 #8440, 0.35 #8687), KN (0.42 #4312, 0.35 #8440, 0.35 #8687), WL (0.42 #4312, 0.35 #8440, 0.35 #8687), AG (0.35 #8440, 0.35 #8687, 0.35 #8686), BDS (0.35 #8440, 0.35 #8687, 0.35 #8686), TT (0.35 #8440, 0.35 #8687, 0.35 #8686)
>> best conf = 0.42
=> the first rule below is the first best rule for 7 predicted values
>> Best rule #4312 for best value:
>> intensional similarity = 11
>> extensional distance = 66
>> proper extension:  Isabela;
>> query: (?x1132, ?x124) <- ?x1132[ a Island; has belongsToIslands ?x877[ a Islands; is belongsToIslands of ?x123[ has locatedIn ?x124; has type ?x150<"volcanic">;]; is belongsToIslands of ?x1847[ a Island; has locatedIn ?x745;];]; has type ?x704;]

*> Best rule #10387 for first EXPECTED value:
*> intensional similarity = 11
*> extensional distance = 228
*> proper extension:  Cebu; Borneo; Panay; Samar; Negros; Bohol;
*> query: (?x1132, ?x667) <- ?x1132[ a Island; has locatedInWater ?x182[ has locatedIn ?x81[ is wasDependentOf of ?x63;]; has locatedIn ?x667[ has religion ?x410<Hindu>;]; has locatedIn ?x1051[ a Country; has ethnicGroup ?x162;]; is mergesWith of ?x60;];]
*> conf = 0.05
ranks of expected_values: 40
EVAL	Grande-Terre	locatedIn	GUAD	CNN-1.+1._MA	0.000	0.000	0.000	0.025	54.000	54.000	162.000	0.416	http://www.semwebtech.org/mondial/10/meta#locatedIn

#222-SBAR
PRED entity: SBAR

PRED relation: dependentOf
PRED expected values: F
=> 29 concepts (29 used for prediction)
PRED predicted values (max 10 best out of 9): F (0.33 #1, 0.17 #11, 0.16 #21), GB (0.12 #44, 0.12 #55, 0.11 #76), USA (0.06 #49, 0.06 #17, 0.05 #27), NL (0.05 #29, 0.03 #39, 0.02 #83), AUS (0.02 #170, 0.02 #182, 0.02 #194), NZ (0.02 #151, 0.01 #210, 0.01 #235), DK (0.01 #164, 0.01 #175, 0.01 #199), N (0.01 #158), CN (0.01 #208, 0.01 #233, 0.01 #260)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1 for best value:
>> intensional similarity = 9
>> extensional distance = 1
>> proper extension:  SMAR;
>> query: (?x1502, F) <- ?x1502[ a Country; has encompassed ?x521<America>; has government ?x1503<"overseas collectivity of France">; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x317<CaribbeanSea>;]
ranks of expected_values: 1
EVAL	SBAR	dependentOf	F	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	29.000	29.000	9.000	0.333	http://www.semwebtech.org/mondial/10/meta#dependentOf

PRED relation: dependentOf
PRED expected values: F
=> 50 concepts (50 used for prediction)
PRED predicted values (max 10 best out of 16): F (0.33 #1, 0.17 #11, 0.16 #21), GB (0.12 #61, 0.11 #12, 0.11 #22), NL (0.08 #238, 0.06 #125, 0.06 #121), USA (0.06 #66, 0.06 #125, 0.06 #121), DK (0.06 #378, 0.03 #169, 0.03 #196), AUS (0.03 #244, 0.03 #274, 0.02 #292), E (0.02 #199, 0.02 #280, 0.02 #618), P (0.02 #199, 0.02 #280, 0.02 #618), RH (0.02 #199, 0.02 #280, 0.02 #618), BR (0.02 #199, 0.02 #280, 0.02 #618)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1 for best value:
>> intensional similarity = 9
>> extensional distance = 1
>> proper extension:  SMAR;
>> query: (?x1502, F) <- ?x1502[ a Country; has encompassed ?x521<America>; has government ?x1503<"overseas collectivity of France">; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x317<CaribbeanSea>;]
ranks of expected_values: 1
EVAL	SBAR	dependentOf	F	CNN-1.+1._MA	1.000	1.000	1.000	1.000	50.000	50.000	16.000	0.333	http://www.semwebtech.org/mondial/10/meta#dependentOf

#221-USA
PRED entity: USA

PRED relation: government
PRED expected values: "constitution-based federal republic; strong democratic tradition"
=> 31 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 51): "republic" (0.40 #150, 0.35 #1158, 0.34 #1734), "a parliamentary democracy, a federation, and a constitutional monarchy" (0.33 #127, 0.20 #199, 0.10 #343), "parliamentary democracy and a Commonwealth realm" (0.31 #540, 0.22 #684, 0.18 #396), "federal republic" (0.20 #291, 0.14 #939, 0.12 #579), "republic; parliamentary democracy" (0.17 #286, 0.09 #430, 0.08 #502), "parliamentary monarchy" (0.17 #244, 0.09 #388, 0.08 #460), "republic, parliamentary democracy" (0.17 #264, 0.08 #552, 0.06 #2161), "overseas collectivity of France" (0.17 #239, 0.06 #2161, 0.03 #1391), "British Overseas Territories" (0.16 #799, 0.11 #727, 0.10 #871), "parliamentary democracy" (0.11 #1517, 0.11 #1733, 0.10 #1301)
>> best conf = 0.40
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #150 for best value:
>> intensional similarity = 7
>> extensional distance = 3
>> proper extension:  NAM;
>> query: (?x315, "republic") <- ?x315[ has wasDependentOf ?x81; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x267[ a Lake;]; is locatedIn of ?x714[ a Island;];]

No rule for expected values
ranks of expected_values: 
EVAL	USA	government	"constitution-based federal republic; strong democratic tradition"	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	31.000	31.000	51.000	0.400	http://www.semwebtech.org/mondial/10/meta#government

PRED relation: government
PRED expected values: "constitution-based federal republic; strong democratic tradition"
=> 100 concepts (100 used for prediction)
PRED predicted values (max 10 best out of 69): "republic" (0.56 #1591, 0.50 #1879, 0.44 #1519), "federal republic" (0.45 #1946, 0.40 #579, 0.33 #147), "constitutional monarchy" (0.33 #938, 0.25 #1226, 0.17 #2236), "a parliamentary democracy, a federation, and a constitutional monarchy" (0.33 #127, 0.08 #5981, 0.06 #2938), "constitutional democracy" (0.25 #436, 0.17 #796, 0.12 #2743), "parliamentary democracy and a Commonwealth realm" (0.25 #252, 0.12 #1260, 0.12 #2919), "federal parliamentary democracy and a Commonwealth realm" (0.25 #251, 0.12 #1259, 0.11 #1476), "republic; multiparty presidential regime" (0.20 #1758, 0.17 #1037, 0.17 #821), "parliamentary monarchy" (0.20 #532, 0.17 #964, 0.14 #1180), "Communist state" (0.20 #517, 0.12 #1381, 0.08 #2320)
>> best conf = 0.56
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1591 for best value:
>> intensional similarity = 7
>> extensional distance = 7
>> proper extension:  ROK;
>> query: (?x315, "republic") <- ?x315[ has religion ?x95; is locatedIn of ?x809[ is mergesWith of ?x452;]; is locatedIn of ?x1957[ a Island; has type ?x150;]; is neighbor of ?x482;]

No rule for expected values
ranks of expected_values: 
EVAL	USA	government	"constitution-based federal republic; strong democratic tradition"	CNN-1.+1._MA	0.000	0.000	0.000	0.000	100.000	100.000	69.000	0.556	http://www.semwebtech.org/mondial/10/meta#government

#220-Africandescent
PRED entity: Africandescent

PRED relation: ethnicGroup!
PRED expected values: HELX
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 96): EAU (0.03 #132), ETH (0.03 #100), UA (0.03 #57), EAK (0.03 #99), CZ (0.02 #98), SN (0.02 #83), MYA (0.02 #70), IR (0.02 #58), SK (0.02 #25), RCA (0.02 #139)
>> best conf = 0.03
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #132 for best value:
>> intensional similarity = 1
>> extensional distance = 279
>> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
>> query: (?x346, EAU) <- ?x346[ a EthnicGroup;]

No rule for expected values
ranks of expected_values: 
EVAL	Africandescent	ethnicGroup!	HELX	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	2.000	2.000	96.000	0.032	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup!
PRED expected values: HELX
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 96): EAU (0.03 #132), ETH (0.03 #100), UA (0.03 #57), EAK (0.03 #99), CZ (0.02 #98), SN (0.02 #83), MYA (0.02 #70), IR (0.02 #58), SK (0.02 #25), RCA (0.02 #139)
>> best conf = 0.03
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #132 for best value:
>> intensional similarity = 1
>> extensional distance = 279
>> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
>> query: (?x346, EAU) <- ?x346[ a EthnicGroup;]

No rule for expected values
ranks of expected_values: 
EVAL	Africandescent	ethnicGroup!	HELX	CNN-1.+1._MA	0.000	0.000	0.000	0.000	2.000	2.000	96.000	0.032	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#219-Honshu
PRED entity: Honshu

PRED relation: locatedInWater
PRED expected values: SeaofJapan
=> 57 concepts (53 used for prediction)
PRED predicted values (max 10 best out of 45): EastChinaSea (0.63 #1338, 0.56 #642, 0.40 #26), SeaofJapan (0.63 #1338, 0.56 #642, 0.40 #15), SeaofOkhotsk (0.63 #1338, 0.56 #642, 0.39 #861), AtlanticOcean (0.39 #606, 0.38 #650, 0.37 #391), CaribbeanSea (0.31 #705, 0.10 #1398, 0.10 #1572), MediterraneanSea (0.24 #400, 0.13 #921, 0.12 #615), IndianOcean (0.23 #952, 0.12 #775, 0.11 #343), JavaSea (0.14 #350, 0.09 #782, 0.09 #914), SouthChinaSea (0.12 #971, 0.09 #794, 0.06 #926), Shikoku (0.11 #384, 0.08 #643, 0.08 #687)
>> best conf = 0.63
=> the first rule below is the first best rule for 3 predicted values
>> Best rule #1338 for best value:
>> intensional similarity = 6
>> extensional distance = 173
>> proper extension:  Ireland; Tongatapu; Ambon; Java; Borneo; Lefkas; Arran; Tobago; VictoriaIsland; GreatBritain; ...
>> query: (?x967, ?x271) <- ?x967[ a Island; has belongsToIslands ?x1212[ is belongsToIslands of ?x1224[ a Island; has locatedInWater ?x271;];]; has locatedIn ?x117;]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 2
EVAL	Honshu	locatedInWater	SeaofJapan	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	57.000	53.000	45.000	0.634	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater
PRED expected values: SeaofJapan
=> 143 concepts (143 used for prediction)
PRED predicted values (max 10 best out of 46): SeaofOkhotsk (0.84 #958, 0.84 #957, 0.76 #1002), SeaofJapan (0.84 #958, 0.84 #957, 0.76 #1002), EastChinaSea (0.84 #958, 0.84 #957, 0.76 #1002), AtlanticOcean (0.61 #1849, 0.58 #2024, 0.53 #2290), IndianOcean (0.31 #1135, 0.29 #1974, 0.20 #2107), MediterraneanSea (0.27 #886, 0.20 #1236, 0.19 #2560), Shikoku (0.23 #345, 0.18 #695, 0.18 #739), Fujisan (0.23 #345, 0.18 #695, 0.18 #739), Kyushu (0.23 #345, 0.18 #695, 0.18 #739), Honshu (0.23 #345, 0.18 #695, 0.18 #739)
>> best conf = 0.84
=> the first rule below is the first best rule for 3 predicted values
>> Best rule #958 for best value:
>> intensional similarity = 8
>> extensional distance = 25
>> proper extension:  Fakaofo; Babelthuap;
>> query: (?x967, ?x507) <- ?x967[ a Island; has belongsToIslands ?x1212[ a Islands; is belongsToIslands of ?x451[ has locatedInWater ?x507;];]; has locatedInWater ?x282<PacificOcean>; has type ?x150;]
>> Best rule #957 for best value:
>> intensional similarity = 9
>> extensional distance = 25
>> proper extension:  Fakaofo; Babelthuap;
>> query: (?x967, ?x271) <- ?x967[ a Island; has belongsToIslands ?x1212[ a Islands; is belongsToIslands of ?x1224[ a Island; has locatedInWater ?x271;];]; has locatedInWater ?x282<PacificOcean>; has type ?x150;]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 2
EVAL	Honshu	locatedInWater	SeaofJapan	CNN-1.+1._MA	0.000	1.000	1.000	0.500	143.000	143.000	46.000	0.838	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#218-Timor
PRED entity: Timor

PRED relation: locatedInWater
PRED expected values: IndianOcean
=> 38 concepts (34 used for prediction)
PRED predicted values (max 10 best out of 62): JavaSea (0.80 #95, 0.69 #837, 0.69 #836), IndianOcean (0.69 #837, 0.69 #836, 0.64 #923), SouthChinaSea (0.69 #837, 0.69 #836, 0.64 #923), AndamanSea (0.69 #837, 0.69 #836, 0.64 #923), MalakkaStrait (0.69 #837, 0.69 #836, 0.64 #923), SulawesiSea (0.69 #837, 0.69 #836, 0.64 #923), AtlanticOcean (0.50 #533, 0.29 #666, 0.28 #800), PacificOcean (0.46 #792, 0.46 #703, 0.39 #543), MediterraneanSea (0.17 #363, 0.17 #275, 0.12 #982), NorthSea (0.13 #574, 0.10 #840, 0.10 #662)
>> best conf = 0.80
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #95 for best value:
>> intensional similarity = 6
>> extensional distance = 8
>> proper extension:  Java; Krakatau; Sumatra; Bangka; Sulawesi; Bali; Sumbawa; Lombok;
>> query: (?x1200, JavaSea) <- ?x1200[ a Island; has belongsToIslands ?x875<SundaIslands>; has locatedIn ?x735[ has government ?x435<"republic">;];]

*> Best rule #837 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 181
*> proper extension:  Mohilla; Flores; Mallorca; Cebu; Fakaofo; Guadalcanal; CaymanBrac; Ternate; Grande-Terre; Babelthuap; ...
*> query: (?x1200, ?x339) <- ?x1200[ has belongsToIslands ?x875[ is belongsToIslands of ?x740[ a Island; has locatedInWater ?x339;];]; has locatedInWater ?x770;]
*> conf = 0.69
ranks of expected_values: 2
EVAL	Timor	locatedInWater	IndianOcean	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	38.000	34.000	62.000	0.800	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater
PRED expected values: IndianOcean
=> 110 concepts (110 used for prediction)
PRED predicted values (max 10 best out of 71): JavaSea (0.80 #172, 0.80 #138, 0.70 #2061), IndianOcean (0.77 #866, 0.70 #2061, 0.64 #3030), SulawesiSea (0.70 #2061, 0.64 #3030, 0.64 #3029), SouthChinaSea (0.64 #3030, 0.64 #3029, 0.64 #3028), AndamanSea (0.64 #3030, 0.64 #3029, 0.64 #3028), MalakkaStrait (0.64 #3030, 0.64 #3029, 0.64 #3028), AtlanticOcean (0.52 #2767, 0.52 #1385, 0.50 #1477), PacificOcean (0.52 #1833, 0.50 #3123, 0.49 #3172), LakeToba (0.40 #1421, 0.38 #3267, 0.35 #1561), MediterraneanSea (0.23 #1254, 0.22 #1347, 0.21 #2169)
>> best conf = 0.80
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #172 for best value:
>> intensional similarity = 15
>> extensional distance = 8
>> proper extension:  Java; Krakatau; Sumatra; Bangka; Sulawesi; Bali; Sumbawa; Lombok;
>> query: (?x1200, ?x241) <- ?x1200[ a Island; has belongsToIslands ?x875<SundaIslands>; has locatedIn ?x217<RI>; has locatedIn ?x735[ has encompassed ?x175; has religion ?x95<Protestant>; is locatedIn of ?x241<JavaSea>; is locatedIn of ?x770<BandaSea>; is locatedIn of ?x2095[ a Mountain;];];]
>> Best rule #138 for best value:
>> intensional similarity = 15
>> extensional distance = 8
>> proper extension:  Java; Krakatau; Sumatra; Bangka; Sulawesi; Bali; Sumbawa; Lombok;
>> query: (?x1200, JavaSea) <- ?x1200[ a Island; has belongsToIslands ?x875<SundaIslands>; has locatedIn ?x217<RI>; has locatedIn ?x735[ has encompassed ?x175; has religion ?x95<Protestant>; is locatedIn of ?x241<JavaSea>; is locatedIn of ?x770<BandaSea>; is locatedIn of ?x2095[ a Mountain;];];]

*> Best rule #866 for first EXPECTED value:
*> intensional similarity = 13
*> extensional distance = 28
*> proper extension:  ChristmasIsland; Pemba;
*> query: (?x1200, IndianOcean) <- ?x1200[ a Island; has locatedInWater ?x770[ has locatedIn ?x217<RI>; has locatedIn ?x735<TL>; is locatedInWater of ?x216[ a Island; has belongsToIslands ?x1099;]; is mergesWith of ?x241<JavaSea>; is mergesWith of ?x282<PacificOcean>;];]
*> conf = 0.77
ranks of expected_values: 2
EVAL	Timor	locatedInWater	IndianOcean	CNN-1.+1._MA	0.000	1.000	1.000	0.500	110.000	110.000	71.000	0.800	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#217-TR
PRED entity: TR

PRED relation: government
PRED expected values: "republican parliamentary democracy"
=> 34 concepts (34 used for prediction)
PRED predicted values (max 10 best out of 61): "republic" (0.42 #510, 0.40 #366, 0.37 #150), "parliamentary democracy" (0.33 #77, 0.25 #5, 0.15 #581), "republic under an authoritarian regime" (0.25 #69, 0.05 #213, 0.05 #285), "constitutional monarchy" (0.17 #74, 0.08 #1514, 0.08 #650), "federal republic" (0.12 #435, 0.08 #507, 0.07 #1515), "parliamentary republic" (0.11 #163, 0.10 #235, 0.05 #379), "British Overseas Territories" (0.08 #1015, 0.06 #1231, 0.06 #1447), "emerging federal democratic republic" (0.05 #186, 0.05 #258, 0.03 #330), "parliamentary monarchy" (0.05 #172, 0.05 #244, 0.03 #316), "operates under a transitional government" (0.05 #168, 0.05 #240, 0.02 #672)
>> best conf = 0.42
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #510 for best value:
>> intensional similarity = 4
>> extensional distance = 58
>> proper extension:  RM;
>> query: (?x185, "republic") <- ?x185[ has religion ?x187; has wasDependentOf ?x1656; is locatedIn of ?x184[ a Mountain;];]

No rule for expected values
ranks of expected_values: 
EVAL	TR	government	"republican parliamentary democracy"	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	34.000	34.000	61.000	0.417	http://www.semwebtech.org/mondial/10/meta#government

PRED relation: government
PRED expected values: "republican parliamentary democracy"
=> 86 concepts (86 used for prediction)
PRED predicted values (max 10 best out of 68): "republic" (0.50 #512, 0.50 #368, 0.43 #656), "parliamentary democracy" (0.33 #5, 0.32 #2688, 0.30 #2757), "parliamentary republic" (0.33 #236, 0.30 #2757, 0.26 #2756), "Islamic republic" (0.33 #200, 0.11 #3265, 0.08 #1812), "republic under an authoritarian regime" (0.30 #2757, 0.26 #2756, 0.18 #5297), "theocratic republic" (0.26 #2756, 0.18 #5297, 0.15 #3412), "constitutional monarchy" (0.21 #2974, 0.18 #1738, 0.18 #2106), "Communist state" (0.21 #2974, 0.18 #1738, 0.10 #1026), "constitutional monarchy and Commonwealth realm" (0.21 #2974, 0.18 #1738, 0.08 #1812), "a parliamentary government with a constitutional monarchy" (0.21 #2974, 0.18 #1738, 0.08 #217)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #512 for best value:
>> intensional similarity = 17
>> extensional distance = 4
>> proper extension:  ET;
>> query: (?x185, "republic") <- ?x185[ has encompassed ?x195[ is encompassed of ?x446[ has ethnicGroup ?x160; has language ?x738; has religion ?x187;]; is encompassed of ?x575[ a Country; is dependentOf of ?x50; is locatedIn of ?x257; is wasDependentOf of ?x179;];]; is locatedIn of ?x275<MediterraneanSea>; is locatedIn of ?x1126[ has type ?x150;]; is locatedIn of ?x1644[ has flowsInto ?x1422;]; is neighbor of ?x177;]
>> Best rule #368 for best value:
>> intensional similarity = 14
>> extensional distance = 2
>> proper extension:  AZ;
>> query: (?x185, "republic") <- ?x185[ has ethnicGroup ?x638; is locatedIn of ?x275[ has locatedIn ?x156[ has language ?x878; has religion ?x95;]; has locatedIn ?x446[ a Country;]; is flowsInto of ?x698;]; is locatedIn of ?x468<Kura>; is neighbor of ?x177[ has language ?x2511[ a Language;]; has religion ?x56;];]

No rule for expected values
ranks of expected_values: 
EVAL	TR	government	"republican parliamentary democracy"	CNN-1.+1._MA	0.000	0.000	0.000	0.000	86.000	86.000	68.000	0.500	http://www.semwebtech.org/mondial/10/meta#government

#216-Timor
PRED entity: Timor

PRED relation: locatedOnIsland!
PRED expected values: Tatamailau
=> 36 concepts (35 used for prediction)
PRED predicted values (max 10 best out of 53): Rantekombola (0.33 #34, 0.17 #227, 0.17 #163), PuncakJaya (0.17 #249, 0.03 #453, 0.03 #1099), Mt.Wilhelm (0.17 #255, 0.03 #453, 0.03 #1099), Mt.Giluwe (0.17 #244, 0.03 #453, 0.03 #1099), Merapi (0.03 #453, 0.03 #320, 0.02 #65), Krakatau (0.03 #453, 0.03 #302, 0.02 #65), GunungAgung (0.03 #453, 0.03 #300, 0.02 #65), Rinjani (0.03 #453, 0.03 #299, 0.02 #65), Semeru (0.03 #453, 0.03 #297, 0.02 #65), Kerinci (0.03 #453, 0.03 #274, 0.02 #65)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #34 for best value:
>> intensional similarity = 5
>> extensional distance = 1
>> proper extension:  Sulawesi;
>> query: (?x1200, Rantekombola) <- ?x1200[ has belongsToIslands ?x875<SundaIslands>; has locatedIn ?x217; has locatedInWater ?x770<BandaSea>;]

*> Best rule #65 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 1
*> proper extension:  Sulawesi;
*> query: (?x1200, ?x60) <- ?x1200[ has belongsToIslands ?x875<SundaIslands>; has locatedIn ?x735[ is locatedIn of ?x60;]; has locatedInWater ?x770<BandaSea>;]
*> conf = 0.02
ranks of expected_values: 21
EVAL	Timor	locatedOnIsland!	Tatamailau	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.048	36.000	35.000	53.000	0.333	http://www.semwebtech.org/mondial/10/meta#locatedOnIsland

PRED relation: locatedOnIsland!
PRED expected values: Tatamailau
=> 96 concepts (96 used for prediction)
PRED predicted values (max 10 best out of 87): Rantekombola (0.33 #34, 0.20 #100, 0.17 #358), PuncakJaya (0.17 #380, 0.08 #65, 0.08 #777), Mt.Wilhelm (0.17 #386, 0.07 #645, 0.04 #904), Mt.Giluwe (0.17 #375, 0.07 #634, 0.04 #893), Kerinci (0.11 #406, 0.10 #471, 0.08 #65), Leuser (0.11 #397, 0.10 #462, 0.08 #65), Merapi (0.11 #452, 0.10 #517, 0.08 #65), Krakatau (0.11 #434, 0.10 #499, 0.08 #65), GunungAgung (0.11 #432, 0.10 #497, 0.08 #65), Rinjani (0.11 #431, 0.10 #496, 0.08 #65)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #34 for best value:
>> intensional similarity = 13
>> extensional distance = 1
>> proper extension:  Sulawesi;
>> query: (?x1200, Rantekombola) <- ?x1200[ a Island; has belongsToIslands ?x875<SundaIslands>; has locatedIn ?x217<RI>; has locatedIn ?x735[ a Country; has government ?x435; has religion ?x95; is locatedIn of ?x2095[ a Mountain;];]; has locatedInWater ?x770<BandaSea>;]

*> Best rule #65 for first EXPECTED value:
*> intensional similarity = 13
*> extensional distance = 1
*> proper extension:  Sulawesi;
*> query: (?x1200, ?x2095) <- ?x1200[ a Island; has belongsToIslands ?x875<SundaIslands>; has locatedIn ?x217<RI>; has locatedIn ?x735[ a Country; has government ?x435; has religion ?x95; is locatedIn of ?x2095[ a Mountain;];]; has locatedInWater ?x770<BandaSea>;]
*> conf = 0.08
ranks of expected_values: 14
EVAL	Timor	locatedOnIsland!	Tatamailau	CNN-1.+1._MA	0.000	0.000	0.000	0.071	96.000	96.000	87.000	0.333	http://www.semwebtech.org/mondial/10/meta#locatedOnIsland

#215-ZRE
PRED entity: ZRE

PRED relation: locatedIn!
PRED expected values: Kwa Semliki Lulua LakeMaiNdombe Lulua Busira
=> 28 concepts (23 used for prediction)
PRED predicted values (max 10 best out of 1300): Sanga (0.33 #1143, 0.18 #27168, 0.18 #28527), Sanga (0.33 #1228, 0.08 #23093, 0.08 #31245), PacificOcean (0.31 #5516, 0.27 #15025, 0.17 #6874), CaribbeanSea (0.27 #9608, 0.19 #5534, 0.19 #4174), Akagera (0.25 #1968, 0.08 #23093, 0.08 #31245), Akagera (0.25 #2339, 0.03 #29886, 0.02 #6415), MediterraneanSea (0.21 #8230, 0.21 #6872, 0.19 #17739), Senegal (0.18 #27168, 0.18 #28527, 0.17 #14943), SaintLawrenceRiver (0.18 #27168, 0.18 #28527, 0.17 #14943), Tajo (0.18 #27168, 0.18 #28527, 0.17 #14943)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1143 for best value:
>> intensional similarity = 5
>> extensional distance = 1
>> proper extension:  RCB;
>> query: (?x348, Sanga) <- ?x348[ has religion ?x95; has wasDependentOf ?x543; is locatedIn of ?x929<Zaire>; is neighbor of ?x229;]

*> Best rule #27168 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 135
*> proper extension:  F; I; SSD; WSA; DK; FGU; GQ; MEL; GBZ; CEU;
*> query: (?x348, ?x2087) <- ?x348[ is locatedIn of ?x929[ is flowsInto of ?x2087;]; is neighbor of ?x546[ a Country; has religion ?x95;];]
*> conf = 0.18
ranks of expected_values: 27, 28, 30
EVAL	ZRE	locatedIn!	Busira	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	28.000	23.000	1300.000	0.333	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	ZRE	locatedIn!	Lulua	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	28.000	23.000	1300.000	0.333	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	ZRE	locatedIn!	LakeMaiNdombe	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.037	28.000	23.000	1300.000	0.333	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	ZRE	locatedIn!	Lulua	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.036	28.000	23.000	1300.000	0.333	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	ZRE	locatedIn!	Semliki	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.037	28.000	23.000	1300.000	0.333	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	ZRE	locatedIn!	Kwa	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	28.000	23.000	1300.000	0.333	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Kwa Semliki Lulua LakeMaiNdombe Lulua Busira
=> 75 concepts (75 used for prediction)
PRED predicted values (max 10 best out of 1363): Semliki (0.87 #20386, 0.87 #36700, 0.66 #76138), Lulua (0.87 #36700, 0.39 #38060, 0.37 #27183), IndianOcean (0.71 #20389, 0.25 #10874, 0.25 #9516), Luapula (0.68 #20385, 0.67 #38061, 0.66 #32621), Cuango (0.68 #20385, 0.67 #38061, 0.66 #32621), Cuilo (0.68 #20385, 0.67 #38061, 0.66 #32621), Kasai (0.68 #20385, 0.67 #38061, 0.66 #32621), Busira (0.68 #20385, 0.67 #38061, 0.66 #32621), Kwa (0.68 #20385, 0.67 #38061, 0.66 #32621), Bahrel-Djebel-Albert-Nil (0.66 #76138, 0.65 #51666, 0.25 #13166)
>> best conf = 0.87
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #20386 for best value:
>> intensional similarity = 16
>> extensional distance = 11
>> proper extension:  F; D; I; CH; PE; UA; USA; YV; A; ETH;
>> query: (?x348, ?x601) <- ?x348[ has religion ?x95<Protestant>; is locatedIn of ?x265[ a Lake;]; is locatedIn of ?x563[ has hasSource ?x2475;]; is locatedIn of ?x607[ a Estuary;]; is locatedIn of ?x1532[ a Source; has inMountains ?x1066; is hasSource of ?x601;]; is neighbor of ?x359[ has government ?x435;]; is neighbor of ?x528[ a Country;];]
ranks of expected_values: 1, 2, 8, 9, 37
EVAL	ZRE	locatedIn!	Busira	CNN-1.+1._MA	0.000	0.000	1.000	0.167	75.000	75.000	1363.000	0.870	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	ZRE	locatedIn!	Lulua	CNN-1.+1._MA	0.000	0.000	0.000	0.000	75.000	75.000	1363.000	0.870	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	ZRE	locatedIn!	LakeMaiNdombe	CNN-1.+1._MA	0.000	0.000	0.000	0.030	75.000	75.000	1363.000	0.870	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	ZRE	locatedIn!	Lulua	CNN-1.+1._MA	1.000	1.000	1.000	1.000	75.000	75.000	1363.000	0.870	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	ZRE	locatedIn!	Semliki	CNN-1.+1._MA	1.000	1.000	1.000	1.000	75.000	75.000	1363.000	0.870	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	ZRE	locatedIn!	Kwa	CNN-1.+1._MA	0.000	0.000	1.000	0.167	75.000	75.000	1363.000	0.870	http://www.semwebtech.org/mondial/10/meta#locatedIn

#214-AMSA
PRED entity: AMSA

PRED relation: language
PRED expected values: English
=> 45 concepts (45 used for prediction)
PRED predicted values (max 10 best out of 89): English (0.61 #485, 0.60 #388, 0.57 #195), French (0.43 #1156, 0.28 #483, 0.23 #675), Spanish (0.34 #1656, 0.30 #1560, 0.26 #1848), Chamorro (0.33 #32, 0.31 #482, 0.31 #289), ChineseLanguage (0.33 #86, 0.31 #482, 0.31 #289), OtherPacificIslandLanguage (0.33 #65, 0.31 #482, 0.31 #289), German (0.17 #1169, 0.09 #1361, 0.08 #1553), Russian (0.14 #1837, 0.13 #1741, 0.13 #2317), Dutch (0.13 #298, 0.08 #1260, 0.07 #1164), Papiamento (0.13 #302, 0.06 #1264, 0.06 #495)
>> best conf = 0.61
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #485 for best value:
>> intensional similarity = 8
>> extensional distance = 16
>> proper extension:  GBM;
>> query: (?x1276, English) <- ?x1276[ a Country; has dependentOf ?x315[ is locatedIn of ?x1325[ is flowsInto of ?x1288;];]; has government ?x2533; has language ?x189[ a Language;]; is locatedIn of ?x585;]
ranks of expected_values: 1
EVAL	AMSA	language	English	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	45.000	45.000	89.000	0.611	http://www.semwebtech.org/mondial/10/meta#language

PRED relation: language
PRED expected values: English
=> 91 concepts (91 used for prediction)
PRED predicted values (max 10 best out of 94): English (0.75 #2506, 0.69 #2602, 0.60 #773), Spanish (0.71 #2792, 0.61 #3297, 0.57 #1348), French (0.57 #1252, 0.38 #1446, 0.33 #98), Chinese (0.33 #160, 0.25 #545, 0.24 #4923), Hindi (0.33 #186, 0.25 #571, 0.24 #4923), Chamorro (0.33 #225, 0.25 #2502, 0.20 #3178), ChineseLanguage (0.33 #279, 0.25 #2502, 0.20 #3178), OtherPacificIslandLanguage (0.33 #258, 0.25 #2502, 0.20 #3178), Dutch (0.29 #1163, 0.09 #2414, 0.09 #2030), Papiamento (0.29 #1167, 0.09 #2034, 0.04 #6371)
>> best conf = 0.75
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2506 for best value:
>> intensional similarity = 20
>> extensional distance = 10
>> proper extension:  GBM;
>> query: (?x1276, English) <- ?x1276[ a Country; has dependentOf ?x315[ has encompassed ?x521; has religion ?x95; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x766[ a Source;]; is locatedIn of ?x833[ has type ?x150<"volcanic">;]; is locatedIn of ?x1687[ has belongsToIslands ?x2293;]; is locatedIn of ?x2097[ a Mountain; has inMountains ?x337;];]; has government ?x2533; has language ?x189[ a Language;]; is locatedIn of ?x585;]
ranks of expected_values: 1
EVAL	AMSA	language	English	CNN-1.+1._MA	1.000	1.000	1.000	1.000	91.000	91.000	94.000	0.750	http://www.semwebtech.org/mondial/10/meta#language

#213-ZW
PRED entity: ZW

PRED relation: neighbor
PRED expected values: MOC
=> 35 concepts (35 used for prediction)
PRED predicted values (max 10 best out of 195): MOC (0.91 #2409, 0.91 #3221, 0.89 #2408), EAT (0.50 #129, 0.43 #450, 0.38 #610), NAM (0.50 #498, 0.33 #177, 0.29 #4029), ZW (0.38 #634, 0.33 #313, 0.29 #4029), MW (0.33 #288, 0.29 #4029, 0.28 #2410), ZRE (0.33 #219, 0.29 #4029, 0.28 #2410), ANG (0.33 #138, 0.29 #459, 0.26 #4839), SD (0.29 #4029, 0.28 #2410, 0.27 #3222), LS (0.29 #4029, 0.28 #2410, 0.27 #3222), RCB (0.29 #410, 0.25 #570, 0.17 #89)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2409 for best value:
>> intensional similarity = 8
>> extensional distance = 91
>> proper extension:  BIH; ET; R; MNE; TN; RL; KGZ; WAN; HR; SK; ...
>> query: (?x1576, ?x525) <- ?x1576[ has encompassed ?x213; has ethnicGroup ?x162; has neighbor ?x1239; has wasDependentOf ?x81; is neighbor of ?x525[ has government ?x435; is locatedIn of ?x284; is neighbor of ?x138;];]
ranks of expected_values: 1
EVAL	ZW	neighbor	MOC	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	35.000	35.000	195.000	0.912	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: MOC
=> 87 concepts (87 used for prediction)
PRED predicted values (max 10 best out of 226): MOC (0.92 #10054, 0.92 #5903, 0.91 #10219), MW (0.50 #618, 0.38 #2086, 0.36 #650), EAT (0.42 #2610, 0.42 #2575, 0.40 #2250), NAM (0.40 #2301, 0.36 #650, 0.36 #5905), SSD (0.40 #2163, 0.25 #4578, 0.25 #2488), ZRE (0.38 #2609, 0.38 #2017, 0.36 #650), ZW (0.38 #2609, 0.36 #650, 0.36 #5905), ANG (0.38 #2609, 0.36 #650, 0.36 #5905), GR (0.38 #1699, 0.10 #3503, 0.07 #6797), LS (0.36 #650, 0.34 #5906, 0.33 #10224)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #10054 for best value:
>> intensional similarity = 11
>> extensional distance = 101
>> proper extension:  PK;
>> query: (?x1576, ?x243) <- ?x1576[ is locatedIn of ?x242; is neighbor of ?x243[ a Country; has government ?x435; has religion ?x187<Muslim>; is locatedIn of ?x182[ has mergesWith ?x60; is flowsInto of ?x137; is locatedInWater of ?x112;]; is neighbor of ?x193;];]
ranks of expected_values: 1
EVAL	ZW	neighbor	MOC	CNN-1.+1._MA	1.000	1.000	1.000	1.000	87.000	87.000	226.000	0.916	http://www.semwebtech.org/mondial/10/meta#neighbor

#212-MYA
PRED entity: MYA

PRED relation: neighbor
PRED expected values: CN
=> 47 concepts (47 used for prediction)
PRED predicted values (max 10 best out of 188): CN (0.90 #4903, 0.89 #2689, 0.89 #4902), MYA (0.40 #381, 0.26 #3162, 0.26 #6339), AFG (0.40 #384, 0.25 #5703, 0.25 #6022), PK (0.26 #3162, 0.26 #6339, 0.25 #6338), BHT (0.26 #3162, 0.26 #6339, 0.25 #6338), MAL (0.26 #3162, 0.26 #6339, 0.25 #6338), VN (0.26 #3162, 0.26 #6339, 0.25 #6338), K (0.26 #3162, 0.26 #6339, 0.25 #6338), NEP (0.26 #3162, 0.26 #6339, 0.25 #5703), R (0.25 #5703, 0.25 #6022, 0.25 #162)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4903 for best value:
>> intensional similarity = 7
>> extensional distance = 100
>> proper extension:  KGZ; SK; BY; H; UZB; TM; AZ; AFG; PY; FL; ...
>> query: (?x366, ?x924) <- ?x366[ a Country; has ethnicGroup ?x298; has religion ?x116; has wasDependentOf ?x81; is locatedIn of ?x262; is neighbor of ?x924[ is locatedIn of ?x60;];]
ranks of expected_values: 1
EVAL	MYA	neighbor	CN	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	47.000	47.000	188.000	0.897	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: CN
=> 110 concepts (110 used for prediction)
PRED predicted values (max 10 best out of 228): CN (0.94 #9057, 0.93 #8897, 0.91 #4534), R (0.53 #4214, 0.36 #161, 0.36 #160), MYA (0.50 #15920, 0.50 #1127, 0.50 #1032), RI (0.50 #1127, 0.44 #1772, 0.44 #1771), VN (0.50 #1127, 0.36 #161, 0.36 #160), MAL (0.50 #1127, 0.36 #161, 0.36 #160), K (0.50 #1127, 0.36 #161, 0.36 #160), BRU (0.50 #1127, 0.33 #253, 0.27 #4207), EAT (0.50 #1127, 0.27 #4207, 0.25 #773), Z (0.50 #1127, 0.27 #4207, 0.25 #732)
>> best conf = 0.94
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #9057 for best value:
>> intensional similarity = 12
>> extensional distance = 41
>> proper extension:  CH; A;
>> query: (?x366, ?x232) <- ?x366[ a Country; has ethnicGroup ?x298[ a EthnicGroup;]; has language ?x1463; has neighbor ?x91; is locatedIn of ?x338[ a River;]; is neighbor of ?x232[ has government ?x831; is locatedIn of ?x421[ a Mountain; has inMountains ?x422;];];]
ranks of expected_values: 1
EVAL	MYA	neighbor	CN	CNN-1.+1._MA	1.000	1.000	1.000	1.000	110.000	110.000	228.000	0.937	http://www.semwebtech.org/mondial/10/meta#neighbor

#211-CEU
PRED entity: CEU

PRED relation: neighbor
PRED expected values: MA
=> 29 concepts (29 used for prediction)
PRED predicted values (max 10 best out of 164): MA (0.90 #1143, 0.90 #3442, 0.89 #3278), LAR (0.43 #476, 0.25 #149, 0.14 #1130), DZ (0.29 #3443, 0.29 #427, 0.27 #2620), WSA (0.29 #3443, 0.27 #2620, 0.25 #3279), MEL (0.29 #3443, 0.27 #2620, 0.25 #3279), TN (0.29 #338, 0.25 #11, 0.10 #1802), E (0.29 #324, 0.12 #651, 0.11 #979), CN (0.29 #205, 0.12 #532, 0.11 #860), RMM (0.25 #132, 0.15 #2589, 0.14 #459), RN (0.25 #78, 0.14 #405, 0.13 #2535)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1143 for best value:
>> intensional similarity = 7
>> extensional distance = 20
>> proper extension:  IL; MA;
>> query: (?x2084, ?x851) <- ?x2084[ a Country; is locatedIn of ?x275<MediterraneanSea>; is neighbor of ?x851[ has government ?x92; has wasDependentOf ?x78; is locatedIn of ?x182;];]
ranks of expected_values: 1
EVAL	CEU	neighbor	MA	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	29.000	29.000	164.000	0.900	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: MA
=> 70 concepts (70 used for prediction)
PRED predicted values (max 10 best out of 219): MA (0.93 #9597, 0.92 #8751, 0.92 #9936), DZ (0.55 #2626, 0.50 #2120, 0.34 #4211), E (0.50 #330, 0.33 #1865, 0.19 #6909), CN (0.50 #211, 0.26 #6448, 0.26 #6952), LAR (0.43 #4209, 0.43 #1494, 0.36 #3196), RMM (0.43 #4209, 0.41 #3838, 0.37 #4210), RIM (0.43 #4209, 0.40 #924, 0.36 #3196), RN (0.43 #4209, 0.36 #3196, 0.33 #3873), TN (0.43 #4209, 0.36 #3196, 0.33 #3873), WSA (0.43 #4209, 0.36 #3196, 0.32 #2353)
>> best conf = 0.93
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #9597 for best value:
>> intensional similarity = 17
>> extensional distance = 76
>> proper extension:  S;
>> query: (?x2084, ?x851) <- ?x2084[ a Country; is locatedIn of ?x275; is neighbor of ?x851[ has ethnicGroup ?x582; has government ?x92; has neighbor ?x581[ has neighbor ?x426; is locatedIn of ?x84;]; has neighbor ?x1588[ has encompassed ?x213; has government ?x2527;]; has religion ?x109; has wasDependentOf ?x78[ has language ?x51; is locatedIn of ?x121; is neighbor of ?x120;]; is locatedIn of ?x182;];]
ranks of expected_values: 1
EVAL	CEU	neighbor	MA	CNN-1.+1._MA	1.000	1.000	1.000	1.000	70.000	70.000	219.000	0.927	http://www.semwebtech.org/mondial/10/meta#neighbor

#210-CascadeRange
PRED entity: CascadeRange

PRED relation: inMountains!
PRED expected values: MtHood
=> 23 concepts (23 used for prediction)
PRED predicted values (max 10 best out of 353): Haleakala (0.25 #175, 0.20 #432, 0.10 #689), MtBlackburn (0.25 #79, 0.20 #336, 0.10 #593), MaunaKea (0.25 #55, 0.20 #312, 0.10 #569), MaunaLoa (0.25 #41, 0.20 #298, 0.10 #555), BorahPeak (0.20 #478, 0.10 #735, 0.08 #992), GuadalupePeak (0.20 #477, 0.10 #734, 0.08 #991), WheelerPeak (0.20 #461, 0.10 #718, 0.08 #975), PikesPeak (0.20 #453, 0.10 #710, 0.08 #967), HarneyPeak (0.20 #443, 0.10 #700, 0.08 #957), HumphreysPeak (0.20 #318, 0.10 #575, 0.08 #832)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #175 for best value:
>> intensional similarity = 8
>> extensional distance = 2
>> proper extension:  WrangellMountains; Hawaii;
>> query: (?x1405, Haleakala) <- ?x1405[ a Mountains; is inMountains of ?x823[ a Mountain; a Volcano; has locatedIn ?x315<USA>; has type ?x706<"volcano">;];]

*> Best rule #2576 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 18
*> proper extension:  Azbine;
*> query: (?x1405, ?x182) <- ?x1405[ a Mountains; is inMountains of ?x823[ a Mountain; a Volcano; has locatedIn ?x315[ has ethnicGroup ?x79; is locatedIn of ?x182;]; has type ?x706;];]
*> conf = 0.08
ranks of expected_values: 155
EVAL	CascadeRange	inMountains!	MtHood	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.006	23.000	23.000	353.000	0.250	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains!
PRED expected values: MtHood
=> 60 concepts (60 used for prediction)
PRED predicted values (max 10 best out of 353): Haleakala (0.25 #175, 0.20 #432, 0.17 #1721), MtBlackburn (0.25 #79, 0.20 #336, 0.17 #1625), MaunaKea (0.25 #55, 0.20 #312, 0.17 #1601), MaunaLoa (0.25 #41, 0.20 #298, 0.17 #1587), BorahPeak (0.20 #478, 0.17 #1767, 0.17 #1509), GuadalupePeak (0.20 #477, 0.17 #1766, 0.17 #1508), WheelerPeak (0.20 #461, 0.17 #1750, 0.17 #1492), PikesPeak (0.20 #453, 0.17 #1742, 0.17 #1484), HarneyPeak (0.20 #443, 0.17 #1732, 0.17 #1474), HumphreysPeak (0.20 #318, 0.17 #1607, 0.17 #1349)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #175 for best value:
>> intensional similarity = 8
>> extensional distance = 2
>> proper extension:  WrangellMountains; Hawaii;
>> query: (?x1405, Haleakala) <- ?x1405[ a Mountains; is inMountains of ?x294[ a Mountain; a Volcano; has locatedIn ?x315<USA>; has type ?x706<"volcano">;];]

*> Best rule #3867 for first EXPECTED value:
*> intensional similarity = 13
*> extensional distance = 9
*> proper extension:  Amhara;
*> query: (?x1405, ?x182) <- ?x1405[ a Mountains; is inMountains of ?x294[ a Mountain; a Volcano; has locatedIn ?x315[ a Country; has encompassed ?x521; has ethnicGroup ?x79; has neighbor ?x482; has religion ?x95<Protestant>; is locatedIn of ?x182;]; has type ?x706;];]
*> conf = 0.08
ranks of expected_values: 229
EVAL	CascadeRange	inMountains!	MtHood	CNN-1.+1._MA	0.000	0.000	0.000	0.004	60.000	60.000	353.000	0.250	http://www.semwebtech.org/mondial/10/meta#inMountains

#209-A
PRED entity: A

PRED relation: wasDependentOf
PRED expected values: Austria-Hungary
=> 35 concepts (35 used for prediction)
PRED predicted values (max 10 best out of 46): OttomanEmpire (0.25 #149, 0.10 #180, 0.09 #209), HolyRomanEmpire (0.25 #90, 0.01 #365, 0.01 #334), GB (0.20 #902, 0.20 #278, 0.20 #873), Yugoslavia (0.20 #116, 0.12 #147, 0.10 #178), Austria-Hungary (0.20 #120, 0.02 #271, 0.01 #364), E (0.19 #250, 0.18 #343, 0.18 #312), SovietUnion (0.15 #357, 0.15 #326, 0.14 #510), F (0.14 #649, 0.13 #807, 0.13 #618), UnitedNations (0.09 #568, 0.09 #598, 0.07 #974), Czechoslovakia (0.07 #179, 0.06 #208, 0.04 #237)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #149 for best value:
>> intensional similarity = 4
>> extensional distance = 6
>> proper extension:  CY;
>> query: (?x424, OttomanEmpire) <- ?x424[ a Country; has ethnicGroup ?x2136<Turkish>; is locatedIn of ?x133;]

*> Best rule #120 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 3
*> proper extension:  HR; I; H;
*> query: (?x424, Austria-Hungary) <- ?x424[ a Country; has neighbor ?x236; is locatedIn of ?x133; is locatedIn of ?x256[ a River;]; is neighbor of ?x446<SLO>;]
*> conf = 0.20
ranks of expected_values: 5
EVAL	A	wasDependentOf	Austria-Hungary	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.200	35.000	35.000	46.000	0.250	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

PRED relation: wasDependentOf
PRED expected values: Austria-Hungary
=> 87 concepts (87 used for prediction)
PRED predicted values (max 10 best out of 61): OttomanEmpire (0.40 #783, 0.40 #248, 0.33 #347), Yugoslavia (0.40 #783, 0.37 #1462, 0.37 #855), Czechoslovakia (0.40 #783, 0.37 #1462, 0.37 #855), HolyRomanEmpire (0.40 #783, 0.37 #1462, 0.37 #855), Austria-Hungary (0.40 #783, 0.33 #154, 0.20 #283), SovietUnion (0.40 #783, 0.25 #474, 0.24 #1044), GB (0.23 #2696, 0.23 #1804, 0.22 #2765), E (0.23 #626, 0.23 #1098, 0.21 #829), F (0.18 #1697, 0.18 #1668, 0.14 #2659), NL (0.15 #403, 0.08 #471, 0.07 #603)
>> best conf = 0.40
=> the first rule below is the first best rule for 6 predicted values
>> Best rule #783 for best value:
>> intensional similarity = 11
>> extensional distance = 34
>> proper extension:  CDN;
>> query: (?x424, ?x2516) <- ?x424[ a Country; has ethnicGroup ?x160; has language ?x511[ a Language;]; is locatedIn of ?x256[ a River; has hasSource ?x1695; has locatedIn ?x423[ has wasDependentOf ?x2516;]; is flowsInto of ?x742[ has hasEstuary ?x988;];];]
>> Best rule #248 for best value:
>> intensional similarity = 14
>> extensional distance = 3
>> proper extension:  BG; TR;
>> query: (?x424, OttomanEmpire) <- ?x424[ has ethnicGroup ?x2136<Turkish>; has language ?x511<Turkish>; is locatedIn of ?x1838[ has hasEstuary ?x1265;]; is neighbor of ?x120[ is locatedIn of ?x1594[ a River;];]; is neighbor of ?x423[ has encompassed ?x195;]; is neighbor of ?x471[ has religion ?x95; is locatedIn of ?x442;];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 5
EVAL	A	wasDependentOf	Austria-Hungary	CNN-1.+1._MA	0.000	0.000	1.000	0.200	87.000	87.000	61.000	0.400	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

#208-Euphrat
PRED entity: Euphrat

PRED relation: hasSource!
PRED expected values: Euphrat
=> 31 concepts (27 used for prediction)
PRED predicted values (max 10 best out of 27): Karasu (0.17 #161, 0.14 #389, 0.09 #618), Murat (0.17 #144, 0.14 #372, 0.09 #601), Tigris (0.17 #70, 0.14 #298, 0.09 #527), Kura (0.17 #41, 0.14 #269, 0.09 #498), SchattalArab (0.14 #393, 0.04 #2978), SouthernMorava (0.09 #632, 0.03 #1089), BlackDrin (0.09 #524, 0.03 #981), Olt (0.09 #633), WesternMorava (0.09 #488), Morava (0.09 #463)
>> best conf = 0.17
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #161 for best value:
>> intensional similarity = 3
>> extensional distance = 4
>> proper extension:  Kura; Murat; Tigris; Karasu;
>> query: (?x1665, Karasu) <- ?x1665[ a Source; has locatedIn ?x185<TR>;]

*> Best rule #686 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 9
*> proper extension:  WesternMorava; BlackDrin; Olt; SouthernMorava; Morava;
*> query: (?x1665, ?x98) <- ?x1665[ a Source; has locatedIn ?x185[ has language ?x511; has neighbor ?x177<BG>; is locatedIn of ?x98;];]
*> conf = 0.07
ranks of expected_values: 11
EVAL	Euphrat	hasSource!	Euphrat	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.091	31.000	27.000	27.000	0.167	http://www.semwebtech.org/mondial/10/meta#hasSource

PRED relation: hasSource!
PRED expected values: Euphrat
=> 98 concepts (98 used for prediction)
PRED predicted values (max 10 best out of 157): Tigris (0.25 #1377, 0.17 #916, 0.17 #1147), Kura (0.25 #1377, 0.17 #916, 0.17 #1147), Karasu (0.25 #1377, 0.17 #916, 0.17 #1147), Murat (0.25 #1377, 0.17 #916, 0.17 #1147), Euphrat (0.25 #1377, 0.17 #916, 0.17 #1147), SchattalArab (0.14 #622, 0.04 #7595, 0.04 #6667), Karun (0.14 #416, 0.01 #3866, 0.01 #1839), Buna (0.12 #930, 0.09 #1391, 0.06 #1621), Olt (0.11 #1324, 0.02 #2936, 0.02 #3165), SouthernMorava (0.11 #1323)
>> best conf = 0.25
=> the first rule below is the first best rule for 5 predicted values
>> Best rule #1377 for best value:
>> intensional similarity = 15
>> extensional distance = 7
>> proper extension:  BlackDrin; SouthernMorava;
>> query: (?x1665, ?x666) <- ?x1665[ a Source; has locatedIn ?x185[ has language ?x511; is locatedIn of ?x184[ has inMountains ?x1024;]; is locatedIn of ?x666[ a River;]; is neighbor of ?x177<BG>; is neighbor of ?x304[ has ethnicGroup ?x244; is locatedIn of ?x573;]; is neighbor of ?x331[ has ethnicGroup ?x1193; has language ?x555;];];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 5
EVAL	Euphrat	hasSource!	Euphrat	CNN-1.+1._MA	0.000	0.000	1.000	0.200	98.000	98.000	157.000	0.250	http://www.semwebtech.org/mondial/10/meta#hasSource

#207-Volga
PRED entity: Volga

PRED relation: inMountains
PRED expected values: WaldaiHills
=> 35 concepts (35 used for prediction)
PRED predicted values (max 10 best out of 28): WaldaiHills (0.25 #51, 0.10 #138, 0.09 #225), Karpaten (0.15 #748, 0.03 #1270, 0.03 #1357), Alps (0.12 #787, 0.12 #613, 0.10 #1309), SnowyMountains (0.09 #630, 0.04 #1152, 0.02 #1326), Ural (0.08 #505, 0.05 #244, 0.04 #331), Andes (0.07 #1316, 0.07 #794, 0.05 #1751), EastAfricanRift (0.06 #1159, 0.04 #1768, 0.04 #1507), Kurdistan (0.06 #731, 0.01 #1340, 0.01 #1514), Altai (0.05 #201, 0.04 #288, 0.04 #462), Beskides (0.04 #378, 0.02 #813, 0.01 #1509)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #51 for best value:
>> intensional similarity = 5
>> extensional distance = 2
>> proper extension:  Angara; Dnepr;
>> query: (?x492, WaldaiHills) <- ?x492[ a Source; has locatedIn ?x73<R>; is hasSource of ?x445[ has flowsThrough ?x444;];]
ranks of expected_values: 1
EVAL	Volga	inMountains	WaldaiHills	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	35.000	35.000	28.000	0.250	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains
PRED expected values: WaldaiHills
=> 100 concepts (100 used for prediction)
PRED predicted values (max 10 best out of 36): WaldaiHills (0.25 #225, 0.20 #312, 0.17 #486), Alps (0.24 #961, 0.16 #1657, 0.14 #2179), Pamir (0.18 #1061, 0.07 #1844, 0.05 #2192), Andes (0.15 #2447, 0.11 #2969, 0.08 #2186), Karpaten (0.14 #2053, 0.14 #2140, 0.08 #2662), Ural (0.14 #4618, 0.13 #3744, 0.09 #3046), Altai (0.14 #4618, 0.13 #3744, 0.09 #3046), Kaukasus (0.14 #4618, 0.13 #3744, 0.09 #3046), Kamchatka (0.14 #4618, 0.13 #3744, 0.09 #3046), EastAfricanRift (0.08 #2986, 0.07 #2812, 0.05 #2203)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #225 for best value:
>> intensional similarity = 11
>> extensional distance = 2
>> proper extension:  Angara; Dnepr;
>> query: (?x492, WaldaiHills) <- ?x492[ a Source; has locatedIn ?x73<R>; is hasSource of ?x445[ a River; has flowsInto ?x1337; has flowsThrough ?x444[ a Lake; is flowsInto of ?x1800;]; has hasEstuary ?x2249; is flowsInto of ?x1544;];]
ranks of expected_values: 1
EVAL	Volga	inMountains	WaldaiHills	CNN-1.+1._MA	1.000	1.000	1.000	1.000	100.000	100.000	36.000	0.250	http://www.semwebtech.org/mondial/10/meta#inMountains

#206-ArcticOcean
PRED entity: ArcticOcean

PRED relation: locatedIn
PRED expected values: SVAX
=> 26 concepts (26 used for prediction)
PRED predicted values (max 10 best out of 233): SVAX (0.91 #3252, 0.90 #2322, 0.89 #3718), N (0.50 #497, 0.33 #265, 0.12 #3019), RI (0.35 #1212, 0.28 #1676, 0.26 #2140), GB (0.33 #937, 0.33 #240, 0.25 #472), IS (0.33 #337, 0.25 #569, 0.13 #1266), FARX (0.33 #311, 0.25 #543, 0.12 #3019), F (0.33 #935, 0.17 #1167, 0.14 #1631), MEX (0.25 #1042, 0.13 #1274, 0.12 #3019), C (0.25 #954, 0.13 #1186, 0.12 #3019), J (0.17 #1179, 0.14 #1643, 0.13 #2107)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3252 for best value:
>> intensional similarity = 4
>> extensional distance = 38
>> proper extension:  Donau; Save; LakeHuron; LakeNicaragua; LakeManicouagan; Waag; LakeToba; Zambezi;
>> query: (?x263, ?x272) <- ?x263[ has locatedIn ?x73; is locatedInWater of ?x1238[ a Island; has locatedIn ?x272;];]
ranks of expected_values: 1
EVAL	ArcticOcean	locatedIn	SVAX	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	26.000	26.000	233.000	0.906	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: SVAX
=> 101 concepts (101 used for prediction)
PRED predicted values (max 10 best out of 236): SVAX (0.89 #5595, 0.89 #5594, 0.67 #1861), RCH (0.67 #1861, 0.40 #1673, 0.33 #741), N (0.67 #1861, 0.33 #265, 0.33 #33), GB (0.67 #1861, 0.33 #472, 0.33 #240), IS (0.67 #1861, 0.33 #569, 0.33 #337), FARX (0.67 #1861, 0.33 #543, 0.33 #311), C (0.67 #1861, 0.33 #489, 0.30 #1886), F (0.67 #1861, 0.33 #470, 0.22 #3032), E (0.67 #1861, 0.33 #490, 0.22 #5854), MA (0.67 #1861, 0.33 #638, 0.20 #2035)
>> best conf = 0.89
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5595 for best value:
>> intensional similarity = 3
>> extensional distance = 41
>> proper extension:  Araguaia;
>> query: (?x263, ?x272) <- ?x263[ is locatedInWater of ?x869[ a Island; has locatedIn ?x272;];]
>> Best rule #5594 for best value:
>> intensional similarity = 5
>> extensional distance = 41
>> proper extension:  Araguaia;
>> query: (?x263, ?x73) <- ?x263[ is locatedInWater of ?x869[ a Island; has locatedIn ?x272;]; is locatedInWater of ?x931[ has locatedIn ?x73;];]
ranks of expected_values: 1
EVAL	ArcticOcean	locatedIn	SVAX	CNN-1.+1._MA	1.000	1.000	1.000	1.000	101.000	101.000	236.000	0.890	http://www.semwebtech.org/mondial/10/meta#locatedIn

#205-WAG
PRED entity: WAG

PRED relation: religion
PRED expected values: Christian
=> 39 concepts (39 used for prediction)
PRED predicted values (max 10 best out of 31): RomanCatholic (0.60 #417, 0.58 #89, 0.58 #663), Christian (0.56 #291, 0.54 #373, 0.50 #1150), Protestant (0.51 #412, 0.47 #535, 0.47 #658), Anglican (0.27 #739, 0.16 #427, 0.14 #509), Jewish (0.27 #739, 0.14 #126, 0.12 #536), Hindu (0.27 #739, 0.14 #419, 0.12 #501), Buddhist (0.27 #739, 0.12 #708, 0.12 #1202), Sikh (0.27 #739, 0.02 #443, 0.02 #525), ChristianOrthodox (0.26 #740, 0.20 #986, 0.19 #1109), JehovasWitnesses (0.08 #102, 0.08 #676, 0.08 #717)
>> best conf = 0.60
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #417 for best value:
>> intensional similarity = 6
>> extensional distance = 41
>> proper extension:  SPMI; BVIR; GB; C; FARX; AXA; CV; TUCA; GUAD; TT; ...
>> query: (?x1051, RomanCatholic) <- ?x1051[ a Country; has encompassed ?x213; has ethnicGroup ?x162; has religion ?x187; is locatedIn of ?x182<AtlanticOcean>;]

*> Best rule #291 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 34
*> proper extension:  NAM; TCH; G; BI; RCB; MW;
*> query: (?x1051, Christian) <- ?x1051[ a Country; has encompassed ?x213<Africa>; has wasDependentOf ?x81; is locatedIn of ?x182; is neighbor of ?x416;]
*> conf = 0.56
ranks of expected_values: 2
EVAL	WAG	religion	Christian	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	39.000	39.000	31.000	0.605	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion
PRED expected values: Christian
=> 82 concepts (82 used for prediction)
PRED predicted values (max 10 best out of 36): Christian (0.66 #1161, 0.64 #745, 0.64 #538), RomanCatholic (0.66 #1993, 0.65 #1622, 0.63 #1663), Protestant (0.65 #1245, 0.54 #1988, 0.51 #1409), Jewish (0.30 #455, 0.30 #454, 0.28 #2941), Hindu (0.30 #455, 0.30 #454, 0.28 #2941), Buddhist (0.30 #455, 0.30 #454, 0.28 #2941), Sikh (0.30 #455, 0.30 #454, 0.28 #2941), Anglican (0.30 #455, 0.30 #454, 0.28 #2317), JehovasWitnesses (0.28 #2941, 0.28 #2857, 0.28 #2942), Mormon (0.28 #2941, 0.28 #2857, 0.28 #2942)
>> best conf = 0.66
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1161 for best value:
>> intensional similarity = 11
>> extensional distance = 34
>> proper extension:  LB; AND;
>> query: (?x1051, ?x116) <- ?x1051[ a Country; has ethnicGroup ?x162; is neighbor of ?x416[ a Country; has ethnicGroup ?x122[ a EthnicGroup;]; has government ?x435; has religion ?x116; is locatedIn of ?x182<AtlanticOcean>; is neighbor of ?x515;];]
ranks of expected_values: 1
EVAL	WAG	religion	Christian	CNN-1.+1._MA	1.000	1.000	1.000	1.000	82.000	82.000	36.000	0.662	http://www.semwebtech.org/mondial/10/meta#religion

#204-EastAfricanRift
PRED entity: EastAfricanRift

PRED relation: inMountains!
PRED expected values: Luapula Mawenzi
=> 25 concepts (25 used for prediction)
PRED predicted values (max 10 best out of 318): Mantaro (0.25 #240, 0.06 #484, 0.05 #729), PicoBolivar (0.25 #239, 0.06 #483, 0.05 #728), Amazonas (0.25 #225, 0.06 #469, 0.05 #714), Maranon (0.25 #223, 0.06 #467, 0.05 #712), Cotopaxi (0.25 #174, 0.06 #418, 0.05 #663), NevadodelRuiz (0.25 #163, 0.06 #407, 0.05 #652), Ucayali (0.25 #158, 0.06 #402, 0.05 #647), Llullaillaco (0.25 #153, 0.06 #397, 0.05 #642), Urubamba (0.25 #151, 0.06 #395, 0.05 #640), Licancabur (0.25 #150, 0.06 #394, 0.05 #639)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #240 for best value:
>> intensional similarity = 12
>> extensional distance = 2
>> proper extension:  Andes; Kurdistan;
>> query: (?x1066, Mantaro) <- ?x1066[ a Mountains; is inMountains of ?x545[ a Mountain; a Volcano; has locatedIn ?x348;]; is inMountains of ?x1532[ is hasSource of ?x601;]; is inMountains of ?x1572[ has locatedIn ?x820; has type ?x706;]; is inMountains of ?x1803[ a Source;];]

*> Best rule #1224 for first EXPECTED value:
*> intensional similarity = 11
*> extensional distance = 21
*> proper extension:  SnowyMountains;
*> query: (?x1066, ?x113) <- ?x1066[ a Mountains; is inMountains of ?x545[ has locatedIn ?x348[ is locatedIn of ?x113;]; has locatedIn ?x546[ has ethnicGroup ?x1946; has religion ?x95;];]; is inMountains of ?x1434[ a Source;]; is inMountains of ?x1538[ a Mountain;];]
*> conf = 0.09
ranks of expected_values: 45
EVAL	EastAfricanRift	inMountains!	Mawenzi	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.022	25.000	25.000	318.000	0.250	http://www.semwebtech.org/mondial/10/meta#inMountains
EVAL	EastAfricanRift	inMountains!	Luapula	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	25.000	25.000	318.000	0.250	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains!
PRED expected values: Luapula Mawenzi
=> 59 concepts (59 used for prediction)
PRED predicted values (max 10 best out of 318): JabalMarra (0.33 #641, 0.25 #2118, 0.25 #1870), Mantaro (0.33 #979, 0.25 #3194, 0.20 #3934), PicoBolivar (0.33 #978, 0.25 #3193, 0.20 #3933), Amazonas (0.33 #964, 0.25 #3179, 0.20 #3919), Maranon (0.33 #962, 0.25 #3177, 0.20 #3917), Cotopaxi (0.33 #913, 0.25 #3128, 0.20 #3868), NevadodelRuiz (0.33 #902, 0.25 #3117, 0.20 #3857), Ucayali (0.33 #897, 0.25 #3112, 0.20 #3852), Llullaillaco (0.33 #892, 0.25 #3107, 0.20 #3847), Urubamba (0.33 #890, 0.25 #3105, 0.20 #3845)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #641 for best value:
>> intensional similarity = 27
>> extensional distance = 1
>> proper extension:  Darfur;
>> query: (?x1066, JabalMarra) <- ?x1066[ a Mountains; is inMountains of ?x545[ a Mountain; a Volcano; has locatedIn ?x348[ a Country; has religion ?x187; is neighbor of ?x229;]; has locatedIn ?x546[ a Country; has encompassed ?x213; has ethnicGroup ?x1946; has neighbor ?x359; has religion ?x352;]; has type ?x706;]; is inMountains of ?x598[ a Mountain; a Volcano; has locatedIn ?x474[ a Country; has ethnicGroup ?x244<Arab>; has neighbor ?x220; has religion ?x95; has wasDependentOf ?x81;]; has type ?x150<"volcanic">;];]

*> Best rule #4188 for first EXPECTED value:
*> intensional similarity = 21
*> extensional distance = 3
*> proper extension:  Troodos;
*> query: (?x1066, ?x1468) <- ?x1066[ a Mountains; is inMountains of ?x598[ a Mountain; has locatedIn ?x474[ a Country; has ethnicGroup ?x244; is locatedIn of ?x1468;];]; is inMountains of ?x1532[ has locatedIn ?x348[ has encompassed ?x213; has religion ?x95; has wasDependentOf ?x543;];]; is inMountains of ?x1551[ a Mountain; has locatedIn ?x820[ has ethnicGroup ?x1233; has government ?x435<"republic">; has religion ?x116; has wasDependentOf ?x81<GB>;];];]
*> conf = 0.24
ranks of expected_values: 162
EVAL	EastAfricanRift	inMountains!	Mawenzi	CNN-1.+1._MA	0.000	0.000	0.000	0.006	59.000	59.000	318.000	0.333	http://www.semwebtech.org/mondial/10/meta#inMountains
EVAL	EastAfricanRift	inMountains!	Luapula	CNN-1.+1._MA	0.000	0.000	0.000	0.000	59.000	59.000	318.000	0.333	http://www.semwebtech.org/mondial/10/meta#inMountains

#203-Z
PRED entity: Z

PRED relation: neighbor!
PRED expected values: ANG
=> 36 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 188): ANG (0.89 #2658, 0.89 #2501, 0.89 #3125), Z (0.40 #555, 0.33 #87, 0.27 #1875), SD (0.33 #188, 0.33 #32, 0.27 #1875), RSA (0.33 #46, 0.27 #1875, 0.27 #4375), RB (0.33 #303, 0.27 #1875, 0.27 #4375), LS (0.33 #163, 0.25 #319, 0.20 #475), BI (0.27 #1875, 0.27 #4375, 0.26 #2657), EAK (0.27 #1875, 0.27 #4375, 0.26 #2657), EAU (0.27 #1875, 0.27 #4375, 0.26 #2657), RWA (0.27 #1875, 0.27 #4375, 0.26 #2657)
>> best conf = 0.89
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2658 for best value:
>> intensional similarity = 6
>> extensional distance = 118
>> proper extension:  ROK; SMAR;
>> query: (?x525, ?x934) <- ?x525[ has encompassed ?x213; has government ?x435; has neighbor ?x934; is locatedIn of ?x284; is neighbor of ?x138[ is neighbor of ?x243;];]
ranks of expected_values: 1
EVAL	Z	neighbor!	ANG	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	36.000	36.000	188.000	0.894	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: ANG
=> 70 concepts (70 used for prediction)
PRED predicted values (max 10 best out of 237): ANG (0.91 #5927, 0.91 #4807, 0.90 #5770), Z (0.57 #1434, 0.54 #2389, 0.53 #3348), BI (0.57 #1434, 0.54 #2389, 0.53 #3348), RWA (0.50 #1371, 0.40 #1529, 0.36 #631), RB (0.44 #4640, 0.38 #788, 0.38 #156), RSA (0.44 #4640, 0.38 #156, 0.36 #631), SD (0.36 #631, 0.33 #664, 0.33 #506), EAU (0.36 #631, 0.33 #426, 0.33 #267), EAK (0.36 #631, 0.33 #396, 0.32 #8017), RCB (0.36 #631, 0.33 #244, 0.32 #4805)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5927 for best value:
>> intensional similarity = 17
>> extensional distance = 41
>> proper extension:  THA;
>> query: (?x525, ?x934) <- ?x525[ a Country; has encompassed ?x213; has ethnicGroup ?x162; has government ?x435; has neighbor ?x934; has religion ?x187<Muslim>; is locatedIn of ?x1977[ is flowsInto of ?x387;]; is locatedIn of ?x2185[ a River;]; is neighbor of ?x348[ is locatedIn of ?x113;]; is neighbor of ?x819[ a Country; has government ?x2064; has religion ?x95;];]
ranks of expected_values: 1
EVAL	Z	neighbor!	ANG	CNN-1.+1._MA	1.000	1.000	1.000	1.000	70.000	70.000	237.000	0.913	http://www.semwebtech.org/mondial/10/meta#neighbor

#202-BandaSea
PRED entity: BandaSea

PRED relation: mergesWith
PRED expected values: SulawesiSea
=> 34 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 69): SulawesiSea (0.83 #223, 0.45 #487, 0.33 #21), SuluSea (0.50 #59, 0.33 #22, 0.12 #133), BandaSea (0.45 #487, 0.40 #97, 0.33 #23), SeaofJapan (0.33 #11, 0.12 #122, 0.09 #308), EastChinaSea (0.33 #20, 0.12 #131, 0.09 #317), BeringSea (0.33 #25, 0.12 #136, 0.07 #173), SeaofOkhotsk (0.33 #19, 0.12 #130, 0.07 #167), AtlanticOcean (0.27 #152, 0.26 #227, 0.26 #189), MalakkaStrait (0.25 #54, 0.12 #128, 0.07 #165), ArcticOcean (0.23 #194, 0.17 #420, 0.17 #157)
>> best conf = 0.83
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #223 for best value:
>> intensional similarity = 4
>> extensional distance = 29
>> proper extension:  HudsonBay; KaraSea;
>> query: (?x770, ?x625) <- ?x770[ has mergesWith ?x60[ is locatedInWater of ?x226;]; is locatedInWater of ?x216; is mergesWith of ?x625;]
ranks of expected_values: 1
EVAL	BandaSea	mergesWith	SulawesiSea	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	34.000	31.000	69.000	0.831	http://www.semwebtech.org/mondial/10/meta#mergesWith

PRED relation: mergesWith
PRED expected values: SulawesiSea
=> 99 concepts (99 used for prediction)
PRED predicted values (max 10 best out of 141): SulawesiSea (0.80 #779, 0.79 #780, 0.75 #156), BandaSea (0.55 #620, 0.53 #860, 0.51 #658), SuluSea (0.50 #178, 0.33 #61, 0.31 #77), GulfofBengal (0.33 #86, 0.31 #77, 0.26 #741), MalakkaStrait (0.33 #95, 0.26 #741, 0.26 #939), AtlanticOcean (0.32 #664, 0.32 #586, 0.31 #77), EastChinaSea (0.31 #77, 0.26 #741, 0.26 #350), SeaofJapan (0.31 #77, 0.26 #741, 0.26 #350), SouthChinaSea (0.31 #77, 0.26 #741, 0.26 #350), SeaofOkhotsk (0.31 #77, 0.26 #741, 0.26 #350)
>> best conf = 0.80
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #779 for best value:
>> intensional similarity = 14
>> extensional distance = 25
>> proper extension:  IrishSea;
>> query: (?x770, ?x60) <- ?x770[ a Sea; is locatedInWater of ?x937[ has belongsToIslands ?x1099;]; is locatedInWater of ?x1074[ a Island; has locatedIn ?x217;]; is mergesWith of ?x60; is mergesWith of ?x241[ has locatedIn ?x735[ a Country; has government ?x435;]; is locatedInWater of ?x240; is mergesWith of ?x384[ is mergesWith of ?x385;];];]
ranks of expected_values: 1
EVAL	BandaSea	mergesWith	SulawesiSea	CNN-1.+1._MA	1.000	1.000	1.000	1.000	99.000	99.000	141.000	0.802	http://www.semwebtech.org/mondial/10/meta#mergesWith

#201-SeaofJapan
PRED entity: SeaofJapan

PRED relation: locatedInWater!
PRED expected values: Honshu Sachalin
=> 27 concepts (24 used for prediction)
PRED predicted values (max 10 best out of 374): Taiwan (0.33 #329, 0.33 #57, 0.11 #600), Okinawa (0.33 #321, 0.33 #49, 0.11 #592), Paramuschir (0.33 #426, 0.22 #697, 0.20 #969), Unalaska (0.33 #460, 0.22 #731, 0.20 #1003), Honshu (0.33 #360, 0.11 #631, 0.10 #903), Tasmania (0.33 #297, 0.11 #568, 0.10 #840), Mindanao (0.33 #389, 0.11 #660, 0.10 #932), NewGuinea (0.33 #377, 0.11 #648, 0.10 #920), Leyte (0.33 #335, 0.11 #606, 0.10 #878), SanMiguelIsland (0.33 #537, 0.11 #808, 0.10 #1080)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #329 for best value:
>> intensional similarity = 5
>> extensional distance = 1
>> proper extension:  PacificOcean;
>> query: (?x271, Taiwan) <- ?x271[ a Sea; has locatedIn ?x73; has mergesWith ?x507<SeaofOkhotsk>; is locatedInWater of ?x451;]
>> Best rule #57 for best value:
>> intensional similarity = 6
>> extensional distance = 1
>> proper extension:  EastChinaSea;
>> query: (?x271, Taiwan) <- ?x271[ has locatedIn ?x73; has mergesWith ?x270<YellowSea>; has mergesWith ?x507[ is locatedInWater of ?x1411;]; is locatedInWater of ?x451;]

*> Best rule #360 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 1
*> proper extension:  PacificOcean;
*> query: (?x271, Honshu) <- ?x271[ a Sea; has locatedIn ?x73; has mergesWith ?x507<SeaofOkhotsk>; is locatedInWater of ?x451;]
*> conf = 0.33
ranks of expected_values: 5, 65
EVAL	SeaofJapan	locatedInWater!	Sachalin	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.016	27.000	24.000	374.000	0.333	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	SeaofJapan	locatedInWater!	Honshu	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.200	27.000	24.000	374.000	0.333	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater!
PRED expected values: Honshu Sachalin
=> 71 concepts (70 used for prediction)
PRED predicted values (max 10 best out of 768): Okinawa (0.38 #1364, 0.30 #4106, 0.25 #321), Honshu (0.38 #1364, 0.30 #4106, 0.13 #1363), Shikoku (0.38 #1364, 0.13 #1363, 0.11 #1365), Paramuschir (0.33 #154, 0.30 #4106, 0.25 #698), Sachalin (0.33 #203, 0.30 #4106, 0.25 #747), Unalaska (0.30 #4106, 0.25 #732, 0.22 #1553), Taiwan (0.30 #4106, 0.25 #329, 0.21 #2736), Jeju (0.30 #4106, 0.25 #319, 0.20 #864), Zhoushan (0.30 #4106, 0.25 #289, 0.20 #834), Tasmania (0.30 #4106, 0.18 #3282, 0.14 #2485)
>> best conf = 0.38
=> the first rule below is the first best rule for 3 predicted values
>> Best rule #1364 for best value:
>> intensional similarity = 9
>> extensional distance = 6
>> proper extension:  IndianOcean; SulawesiSea; SuluSea; BandaSea;
>> query: (?x271, ?x967) <- ?x271[ a Sea; has locatedIn ?x117[ has ethnicGroup ?x2391; has government ?x2476; has religion ?x462; is locatedIn of ?x967[ a Island;];]; is mergesWith of ?x282<PacificOcean>;]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 2, 5
EVAL	SeaofJapan	locatedInWater!	Sachalin	CNN-1.+1._MA	0.000	0.000	1.000	0.250	71.000	70.000	768.000	0.376	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	SeaofJapan	locatedInWater!	Honshu	CNN-1.+1._MA	0.000	1.000	1.000	0.500	71.000	70.000	768.000	0.376	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#200-Zambezi
PRED entity: Zambezi

PRED relation: flowsInto!
PRED expected values: LakeKariba
=> 43 concepts (32 used for prediction)
PRED predicted values (max 10 best out of 295): LakeKariba (0.95 #3910, 0.03 #5418, 0.03 #6621), LakeHume (0.33 #873, 0.03 #4788, 0.01 #5692), MurrumbidgeeRiver (0.33 #736, 0.03 #4651, 0.01 #5555), DarlingRiver (0.33 #642, 0.03 #4557, 0.01 #5461), Ruzizi (0.33 #450, 0.02 #5267, 0.01 #6170), Breg (0.25 #1167, 0.10 #2070, 0.05 #2672), Olt (0.25 #1123, 0.10 #2026, 0.05 #2628), Pruth (0.25 #1090, 0.10 #1993, 0.05 #2595), Isar (0.25 #1062, 0.10 #1965, 0.05 #2567), March (0.25 #1057, 0.10 #1960, 0.05 #2562)
>> best conf = 0.95
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3910 for best value:
>> intensional similarity = 5
>> extensional distance = 30
>> proper extension:  Würm; RioLerma; SnowyRiver; Sanaga;
>> query: (?x1977, ?x1676) <- ?x1977[ a River; has flowsInto ?x60; has flowsThrough ?x1676; has hasSource ?x1596; is flowsInto of ?x387;]
ranks of expected_values: 1
EVAL	Zambezi	flowsInto!	LakeKariba	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	43.000	32.000	295.000	0.950	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto!
PRED expected values: LakeKariba
=> 138 concepts (130 used for prediction)
PRED predicted values (max 10 best out of 466): LakeKariba (0.95 #10879, 0.95 #11180, 0.87 #6652), Shabelle (0.36 #12088, 0.14 #3605, 0.05 #8439), LakeHume (0.36 #12088, 0.07 #6926, 0.05 #8433), MurrumbidgeeRiver (0.36 #12088, 0.07 #6789, 0.05 #8296), DarlingRiver (0.36 #12088, 0.07 #6695, 0.05 #8202), Oranje (0.33 #1218, 0.21 #10271, 0.21 #2417), Zaire (0.33 #1338, 0.17 #3153, 0.14 #2416), MerrimackRiver (0.33 #1474, 0.17 #3289, 0.14 #2416), RioSaoFrancisco (0.33 #1455, 0.17 #3270, 0.14 #2416), Sanaga (0.33 #1436, 0.17 #3251, 0.14 #2416)
>> best conf = 0.95
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #10879 for best value:
>> intensional similarity = 9
>> extensional distance = 28
>> proper extension:  Adda;
>> query: (?x1977, ?x387) <- ?x1977[ a River; has flowsThrough ?x387; has locatedIn ?x525[ a Country; has neighbor ?x820[ has ethnicGroup ?x1233; is locatedIn of ?x60;];]; has locatedIn ?x1239[ has religion ?x116;];]
ranks of expected_values: 1
EVAL	Zambezi	flowsInto!	LakeKariba	CNN-1.+1._MA	1.000	1.000	1.000	1.000	138.000	130.000	466.000	0.947	http://www.semwebtech.org/mondial/10/meta#flowsInto

#199-Anglican
PRED entity: Anglican

PRED relation: religion!
PRED expected values: CDN
=> 21 concepts (18 used for prediction)
PRED predicted values (max 10 best out of 223): I (0.57 #1452, 0.56 #1653, 0.50 #2265), GUY (0.57 #1479, 0.50 #472, 0.44 #1680), SME (0.57 #1443, 0.50 #436, 0.44 #1644), GBZ (0.57 #1608, 0.50 #601, 0.44 #1809), USA (0.50 #465, 0.44 #1673, 0.43 #1472), RI (0.50 #447, 0.44 #1655, 0.43 #1454), RP (0.50 #498, 0.44 #1706, 0.43 #1505), IND (0.50 #1976, 0.44 #1775, 0.42 #2387), CH (0.50 #451, 0.43 #1458, 0.40 #652), UA (0.50 #463, 0.43 #1470, 0.40 #664)
>> best conf = 0.57
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1452 for best value:
>> intensional similarity = 11
>> extensional distance = 5
>> proper extension:  Jewish; Muslim; Hindu;
>> query: (?x713, I) <- ?x713[ is religion of ?x81<GB>; is religion of ?x390[ has language ?x247;]; is religion of ?x439[ has ethnicGroup ?x1672; has government ?x1174;]; is religion of ?x865[ a Country; is locatedIn of ?x317<CaribbeanSea>;];]

*> Best rule #457 for first EXPECTED value:
*> intensional similarity = 14
*> extensional distance = 2
*> proper extension:  Protestant;
*> query: (?x713, CDN) <- ?x713[ is religion of ?x81<GB>; is religion of ?x390<SLB>; is religion of ?x407<AXA>; is religion of ?x865[ a Country; has encompassed ?x521; has ethnicGroup ?x1147; has language ?x247; is locatedIn of ?x317;]; is religion of ?x1008<BERM>;]
*> conf = 0.50
ranks of expected_values: 12
EVAL	Anglican	religion!	CDN	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.083	21.000	18.000	223.000	0.571	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion!
PRED expected values: CDN
=> 36 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 226): F (0.75 #1621, 0.50 #1012, 0.50 #817), I (0.67 #1870, 0.67 #1666, 0.67 #1460), GUY (0.67 #1693, 0.60 #1286, 0.50 #2914), SME (0.67 #1657, 0.60 #1250, 0.50 #1861), CH (0.67 #1876, 0.60 #1265, 0.50 #1672), HR (0.67 #1852, 0.60 #1241, 0.50 #1648), RI (0.60 #1261, 0.52 #4274, 0.50 #1872), RP (0.60 #1312, 0.50 #1923, 0.50 #1719), CDN (0.60 #1271, 0.50 #1882, 0.50 #1827), GBZ (0.60 #1415, 0.50 #2026, 0.50 #1822)
>> best conf = 0.75
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1621 for best value:
>> intensional similarity = 22
>> extensional distance = 4
>> proper extension:  Buddhist;
>> query: (?x713, ?x78) <- ?x713[ a Religion; is religion of ?x81<GB>; is religion of ?x407[ has ethnicGroup ?x1009; has language ?x247;]; is religion of ?x439[ has ethnicGroup ?x1672[ is ethnicGroup of ?x272<CDN>;]; has government ?x1174; has wasDependentOf ?x78; is locatedIn of ?x1488[ has belongsToIslands ?x2250;];]; is religion of ?x461<NZ>; is religion of ?x561[ has encompassed ?x521; has government ?x562; is locatedIn of ?x182;]; is religion of ?x865[ a Country;];]

*> Best rule #1271 for first EXPECTED value:
*> intensional similarity = 26
*> extensional distance = 3
*> proper extension:  Muslim;
*> query: (?x713, CDN) <- ?x713[ is religion of ?x154[ has ethnicGroup ?x162<African>; has government ?x2243; is locatedIn of ?x153;]; is religion of ?x390[ has ethnicGroup ?x298<Chinese>; has ethnicGroup ?x1129[ a EthnicGroup;]; has government ?x1947;]; is religion of ?x407[ has ethnicGroup ?x1009; has language ?x247<English>; has religion ?x352<RomanCatholic>; is locatedIn of ?x317;]; is religion of ?x439[ has ethnicGroup ?x1672<French>;]; is religion of ?x561[ has encompassed ?x521; is locatedIn of ?x1995;]; is religion of ?x667<TT>;]
*> conf = 0.60
ranks of expected_values: 9
EVAL	Anglican	religion!	CDN	CNN-1.+1._MA	0.000	0.000	1.000	0.111	36.000	36.000	226.000	0.750	http://www.semwebtech.org/mondial/10/meta#religion

#198-Vulcano
PRED entity: Vulcano

PRED relation: locatedIn
PRED expected values: I
=> 31 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 139): I (0.81 #945, 0.78 #284, 0.75 #48), GR (0.43 #798, 0.33 #1035, 0.14 #1745), M (0.18 #649, 0.07 #1122, 0.05 #6884), GB (0.14 #2613, 0.09 #4036, 0.09 #4273), P (0.13 #1378, 0.12 #1615, 0.11 #3514), USA (0.11 #1253, 0.11 #1490, 0.10 #1964), E (0.10 #2841, 0.10 #2631, 0.08 #2393), RI (0.09 #1707, 0.08 #4791, 0.07 #5029), J (0.06 #1200, 0.06 #1437, 0.06 #1911), D (0.05 #5710, 0.05 #5471, 0.05 #6426)
>> best conf = 0.81
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #945 for best value:
>> intensional similarity = 8
>> extensional distance = 19
>> proper extension:  Mallorca; Lefkas; Rhodos; Zakynthos; Ikaria; Syros; Korfu; Mykonos; Formentera; Kos; ...
>> query: (?x1654, ?x207) <- ?x1654[ has belongsToIslands ?x87[ a Islands; is belongsToIslands of ?x86[ a Island;]; is belongsToIslands of ?x341[ has locatedIn ?x207;];]; has locatedInWater ?x275<MediterraneanSea>;]
ranks of expected_values: 1
EVAL	Vulcano	locatedIn	I	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	31.000	31.000	139.000	0.810	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: I
=> 43 concepts (43 used for prediction)
PRED predicted values (max 10 best out of 138): I (0.82 #1419, 0.81 #1182, 0.78 #521), GR (0.45 #1272, 0.43 #1035, 0.33 #1509), M (0.18 #886, 0.11 #413, 0.07 #1596), GB (0.14 #2612, 0.13 #4049, 0.13 #4293), P (0.13 #1852, 0.13 #2089, 0.13 #2326), USA (0.12 #1727, 0.11 #1964, 0.11 #2201), RI (0.10 #3373, 0.07 #6051, 0.07 #6288), E (0.10 #2840, 0.10 #2630, 0.09 #3795), D (0.09 #4060, 0.09 #4304, 0.07 #6493), F (0.07 #6959, 0.07 #8194, 0.06 #7699)
>> best conf = 0.82
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1419 for best value:
>> intensional similarity = 8
>> extensional distance = 20
>> proper extension:  Samos;
>> query: (?x1654, ?x207) <- ?x1654[ a Island; has belongsToIslands ?x87[ a Islands; is belongsToIslands of ?x86[ a Island; has locatedIn ?x207; has locatedInWater ?x275<MediterraneanSea>;];];]
ranks of expected_values: 1
EVAL	Vulcano	locatedIn	I	CNN-1.+1._MA	1.000	1.000	1.000	1.000	43.000	43.000	138.000	0.818	http://www.semwebtech.org/mondial/10/meta#locatedIn

#197-Dutch
PRED entity: Dutch

PRED relation: language!
PRED expected values: MC
=> 27 concepts (22 used for prediction)
PRED predicted values (max 10 best out of 212): BG (0.60 #257, 0.18 #851, 0.16 #948), L (0.50 #443, 0.49 #236, 0.40 #207), BZ (0.50 #438, 0.40 #202, 0.33 #558), F (0.49 #236, 0.33 #118, 0.33 #4), D (0.49 #236, 0.33 #118, 0.29 #949), SF (0.47 #668, 0.24 #786, 0.21 #1266), ARU (0.44 #472, 0.05 #1309, 0.05 #1069), NZ (0.40 #186, 0.33 #660, 0.33 #542), AND (0.40 #211, 0.33 #567, 0.33 #447), CDN (0.40 #155, 0.33 #511, 0.33 #391)
>> best conf = 0.60
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #257 for best value:
>> intensional similarity = 9
>> extensional distance = 3
>> proper extension:  Turkish; Roma; Bulgarian;
>> query: (?x544, BG) <- ?x544[ is language of ?x50[ a Country; has religion ?x109<Jewish>;]; is language of ?x575[ a Country; has ethnicGroup ?x2136<Turkish>; is neighbor of ?x120;];]

*> Best rule #471 for first EXPECTED value:
*> intensional similarity = 10
*> extensional distance = 4
*> proper extension:  German;
*> query: (?x544, MC) <- ?x544[ is language of ?x50[ has religion ?x95<Protestant>; is locatedIn of ?x182<AtlanticOcean>;]; is language of ?x575[ is dependentOf of ?x1171; is locatedIn of ?x121; is wasDependentOf of ?x179[ has ethnicGroup ?x79;];];]
*> conf = 0.33
ranks of expected_values: 16
EVAL	Dutch	language!	MC	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.062	27.000	22.000	212.000	0.600	http://www.semwebtech.org/mondial/10/meta#language

PRED relation: language!
PRED expected values: MC
=> 55 concepts (55 used for prediction)
PRED predicted values (max 10 best out of 218): L (0.71 #1788, 0.62 #1940, 0.58 #1938), MK (0.67 #1543, 0.62 #1332, 0.56 #2274), BZ (0.67 #2148, 0.51 #2670, 0.50 #562), D (0.62 #1332, 0.58 #1938, 0.57 #1451), NZ (0.62 #2014, 0.51 #2670, 0.50 #2376), A (0.62 #1332, 0.49 #1450, 0.40 #910), HONX (0.62 #1332, 0.43 #966, 0.33 #91), BG (0.62 #1332, 0.35 #846, 0.33 #2207), CY (0.62 #1332, 0.35 #846, 0.32 #3534), TR (0.62 #1332, 0.35 #846, 0.32 #3534)
>> best conf = 0.71
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1788 for best value:
>> intensional similarity = 22
>> extensional distance = 5
>> proper extension:  Luxembourgish;
>> query: (?x544, L) <- ?x544[ is language of ?x246[ a Country; has religion ?x352<RomanCatholic>;]; is language of ?x543[ a Country; has ethnicGroup ?x1628; has language ?x51<French>; is neighbor of ?x78<F>; is neighbor of ?x120<D>; is neighbor of ?x718[ has encompassed ?x195; has language ?x539;];]; is language of ?x575[ has ethnicGroup ?x734; has wasDependentOf ?x149[ has government ?x1657; is locatedIn of ?x68;]; is locatedIn of ?x121;];]

*> Best rule #2670 for first EXPECTED value:
*> intensional similarity = 20
*> extensional distance = 8
*> proper extension:  Czech; Polish; Slovak;
*> query: (?x544, ?x77) <- ?x544[ is language of ?x246[ a Country; has religion ?x352<RomanCatholic>;]; is language of ?x543[ a Country; has ethnicGroup ?x1628; has language ?x51[ is language of ?x77;]; is neighbor of ?x120<D>; is neighbor of ?x718[ has encompassed ?x195; has ethnicGroup ?x237; has language ?x539; has religion ?x109;];]; is language of ?x575[ has ethnicGroup ?x734; has wasDependentOf ?x149; is locatedIn of ?x121;];]
*> conf = 0.51
ranks of expected_values: 35
EVAL	Dutch	language!	MC	CNN-1.+1._MA	0.000	0.000	0.000	0.029	55.000	55.000	218.000	0.714	http://www.semwebtech.org/mondial/10/meta#language

#196-Sanga
PRED entity: Sanga

PRED relation: hasSource!
PRED expected values: Sanga
=> 39 concepts (27 used for prediction)
PRED predicted values (max 10 best out of 28): Bomu (0.25 #92, 0.11 #320, 0.07 #3437), Ubangi (0.25 #32, 0.11 #260, 0.07 #3437), Sanga (0.07 #3437, 0.03 #2748, 0.03 #2290), Schari (0.07 #3437, 0.03 #2748, 0.03 #2290), Lomami (0.06 #682, 0.01 #1599, 0.01 #1828), Aruwimi (0.06 #655, 0.01 #1572, 0.01 #1801), Tshuapa (0.06 #652, 0.01 #1569, 0.01 #1798), Lukenie (0.06 #646, 0.01 #1563, 0.01 #1792), Fimi (0.06 #597, 0.01 #1514, 0.01 #1743), Ruzizi (0.06 #577, 0.01 #1494, 0.01 #1723)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #92 for best value:
>> intensional similarity = 3
>> extensional distance = 2
>> proper extension:  Ubangi; Bomu;
>> query: (?x2328, Bomu) <- ?x2328[ a Source; has locatedIn ?x736<RCA>;]

*> Best rule #3437 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 217
*> proper extension:  NorthernDwina; Kura; Volga; Chatanga; Tobol; OhioRiver; HudsonRiver; Schilka; Tennessee; WesternDwina; ...
*> query: (?x2328, ?x695) <- ?x2328[ a Source; has locatedIn ?x736[ is locatedIn of ?x695[ a River;]; is neighbor of ?x229[ is locatedIn of ?x53;];];]
*> conf = 0.07
ranks of expected_values: 3
EVAL	Sanga	hasSource!	Sanga	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.333	39.000	27.000	28.000	0.250	http://www.semwebtech.org/mondial/10/meta#hasSource

PRED relation: hasSource!
PRED expected values: Sanga
=> 82 concepts (82 used for prediction)
PRED predicted values (max 10 best out of 144): Ubangi (0.25 #32, 0.17 #260, 0.11 #491), Bomu (0.25 #92, 0.17 #320, 0.11 #551), Benue (0.17 #458, 0.17 #431, 0.05 #2048), Sanaga (0.17 #406, 0.05 #2023, 0.02 #3182), Schari (0.08 #4621, 0.08 #3003, 0.07 #1151), Sanga (0.08 #4621, 0.07 #459, 0.04 #1613), Bomu (0.07 #459, 0.04 #4390, 0.03 #5085), Bomu (0.07 #459, 0.04 #4390, 0.03 #5085), Ubangi (0.07 #459, 0.04 #4390, 0.03 #5085), Sanga (0.07 #459, 0.04 #4390, 0.03 #5085)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #32 for best value:
>> intensional similarity = 3
>> extensional distance = 2
>> proper extension:  Ubangi; Bomu;
>> query: (?x2328, Ubangi) <- ?x2328[ a Source; has locatedIn ?x736<RCA>;]

*> Best rule #4621 for first EXPECTED value:
*> intensional similarity = 13
*> extensional distance = 81
*> proper extension:  Kasai; Cuilo; Okavango; Cuango;
*> query: (?x2328, ?x695) <- ?x2328[ a Source; has locatedIn ?x736[ has ethnicGroup ?x992; has neighbor ?x169[ has neighbor ?x139; is locatedIn of ?x168;]; has neighbor ?x536[ has government ?x1721;]; has religion ?x187; has wasDependentOf ?x78[ is locatedIn of ?x121;]; is locatedIn of ?x695[ a River;];];]
*> conf = 0.08
ranks of expected_values: 6
EVAL	Sanga	hasSource!	Sanga	CNN-1.+1._MA	0.000	0.000	1.000	0.167	82.000	82.000	144.000	0.250	http://www.semwebtech.org/mondial/10/meta#hasSource

#195-Raab
PRED entity: Raab

PRED relation: hasSource
PRED expected values: Raab
=> 35 concepts (29 used for prediction)
PRED predicted values (max 10 best out of 74): Iller (0.08 #151, 0.04 #379, 0.03 #1601), Mur (0.08 #136, 0.04 #364, 0.03 #1601), Enns (0.08 #107, 0.04 #335, 0.03 #1601), Salzach (0.08 #106, 0.04 #334, 0.03 #1601), Isar (0.08 #100, 0.04 #328, 0.03 #1601), Lech (0.08 #72, 0.04 #300, 0.03 #1601), March (0.08 #169, 0.04 #397, 0.03 #1601), Rhein (0.08 #128, 0.04 #356, 0.03 #1601), Drau (0.08 #87, 0.04 #315, 0.03 #1601), Donau (0.08 #80, 0.04 #308, 0.03 #1601)
>> best conf = 0.08
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #151 for best value:
>> intensional similarity = 6
>> extensional distance = 11
>> proper extension:  Donau; Mur; Rhein; Iller; Enns; Drau; Inn; Lech; March; Isar; ...
>> query: (?x1838, Iller) <- ?x1838[ a River; has hasEstuary ?x1265[ a Estuary; has locatedIn ?x236;]; has locatedIn ?x424<A>;]

*> Best rule #3428 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 202
*> proper extension:  DarlingRiver; JoekulsaaFjoellum; NelsonRiver; EucumbeneRiver; Manicouagan; SaskatchewanRiver; MackenzieRiver; Thjorsa; RiviereRichelieu;
*> query: (?x1838, ?x133) <- ?x1838[ a River; has hasEstuary ?x1265[ a Estuary; has locatedIn ?x236;]; has locatedIn ?x424[ is locatedIn of ?x133;];]
*> conf = 0.01
ranks of expected_values: 46
EVAL	Raab	hasSource	Raab	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.022	35.000	29.000	74.000	0.077	http://www.semwebtech.org/mondial/10/meta#hasSource

PRED relation: hasSource
PRED expected values: Raab
=> 105 concepts (105 used for prediction)
PRED predicted values (max 10 best out of 195): Mur (0.33 #136, 0.20 #364, 0.14 #822), March (0.20 #397, 0.14 #626, 0.12 #1084), Waag (0.20 #240, 0.05 #1844, 0.03 #3453), Enns (0.14 #564, 0.12 #1022, 0.11 #2292), Salzach (0.14 #563, 0.12 #1021, 0.11 #2292), Inn (0.14 #493, 0.12 #951, 0.11 #2292), WesternBug (0.14 #865, 0.05 #2011, 0.02 #4537), Oder (0.14 #707, 0.04 #2314, 0.03 #3921), Narew (0.14 #907, 0.02 #4579, 0.01 #6187), Weichsel (0.14 #853, 0.02 #4525, 0.01 #6133)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #136 for best value:
>> intensional similarity = 7
>> extensional distance = 1
>> proper extension:  Mur;
>> query: (?x1838, Mur) <- ?x1838[ a River; has hasEstuary ?x1265[ a Estuary; has locatedIn ?x236<H>;]; has locatedIn ?x424<A>;]

*> Best rule #2062 for first EXPECTED value:
*> intensional similarity = 12
*> extensional distance = 20
*> proper extension:  Dnjestr;
*> query: (?x1838, ?x1111) <- ?x1838[ a River; has hasEstuary ?x1265[ a Estuary;]; has locatedIn ?x424[ has ethnicGroup ?x160; has language ?x511; has neighbor ?x234[ is locatedIn of ?x233;]; has neighbor ?x236<H>; is locatedIn of ?x1111[ a Source;];];]
*> conf = 0.10
ranks of expected_values: 17
EVAL	Raab	hasSource	Raab	CNN-1.+1._MA	0.000	0.000	0.000	0.059	105.000	105.000	195.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasSource

#194-RioMagdalena
PRED entity: RioMagdalena

PRED relation: flowsInto
PRED expected values: CaribbeanSea
=> 39 concepts (33 used for prediction)
PRED predicted values (max 10 best out of 80): AtlanticOcean (0.40 #12, 0.15 #177, 0.12 #509), Donau (0.16 #2182, 0.15 #2512, 0.14 #2347), CaribbeanSea (0.07 #664, 0.06 #496, 0.06 #362), PacificOcean (0.07 #664, 0.03 #2173, 0.03 #2003), MediterraneanSea (0.05 #2857, 0.05 #2362, 0.05 #2527), RioSanJuan (0.05 #697, 0.05 #864, 0.04 #1199), BlackSea (0.05 #2177, 0.05 #2342, 0.05 #2507), BalticSea (0.04 #3837, 0.04 #3175, 0.04 #3341), Amazonas (0.03 #2173, 0.03 #665, 0.03 #510), RioSaoFrancisco (0.03 #645, 0.02 #1483)
>> best conf = 0.40
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #12 for best value:
>> intensional similarity = 6
>> extensional distance = 3
>> proper extension:  Amazonas; Orinoco; RioNegro;
>> query: (?x2258, AtlanticOcean) <- ?x2258[ a River; has hasEstuary ?x1511; has hasSource ?x1153[ a Source;]; has locatedIn ?x215<CO>;]

*> Best rule #664 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 30
*> proper extension:  Araguaia; Paraguay; Tocantins; IlhadoBananal; Tocantins; Uruguay; RioMadeira; Araguaia; RioSaoFrancisco; LagodeSobradinho; ...
*> query: (?x2258, ?x317) <- ?x2258[ has locatedIn ?x215[ is locatedIn of ?x214<Amazonas>; is locatedIn of ?x317[ a Sea; is locatedInWater of ?x123; is mergesWith of ?x182;];];]
*> conf = 0.07
ranks of expected_values: 3
EVAL	RioMagdalena	flowsInto	CaribbeanSea	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.333	39.000	33.000	80.000	0.400	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto
PRED expected values: CaribbeanSea
=> 115 concepts (115 used for prediction)
PRED predicted values (max 10 best out of 126): AtlanticOcean (0.40 #1007, 0.40 #342, 0.38 #840), PacificOcean (0.33 #520, 0.18 #6704, 0.12 #2168), CaribbeanSea (0.25 #5528, 0.18 #6704, 0.17 #662), Donau (0.18 #5038, 0.10 #4872, 0.09 #7049), GulfofMexico (0.17 #623, 0.04 #2129, 0.03 #4485), Amazonas (0.12 #995, 0.12 #841, 0.10 #1162), IndianOcean (0.12 #663, 0.04 #2002, 0.03 #6703), Niger (0.12 #722, 0.03 #4080, 0.03 #4757), Tocantins (0.12 #917, 0.01 #5119), Parana (0.10 #1046, 0.08 #1214, 0.02 #4071)
>> best conf = 0.40
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1007 for best value:
>> intensional similarity = 10
>> extensional distance = 8
>> proper extension:  Paraguay; Tocantins; Uruguay; RioMadeira; RioSaoFrancisco;
>> query: (?x2258, AtlanticOcean) <- ?x2258[ a River; has locatedIn ?x215[ a Country; has encompassed ?x521; has ethnicGroup ?x79; has government ?x1377; is locatedIn of ?x214<Amazonas>; is neighbor of ?x296<PE>;];]
>> Best rule #342 for best value:
>> intensional similarity = 10
>> extensional distance = 3
>> proper extension:  Orinoco;
>> query: (?x2258, AtlanticOcean) <- ?x2258[ a River; has hasEstuary ?x1511[ a Estuary;]; has hasSource ?x1153[ a Source; has inMountains ?x431[ a Mountains;];]; has locatedIn ?x215<CO>;]

*> Best rule #5528 for first EXPECTED value:
*> intensional similarity = 16
*> extensional distance = 78
*> proper extension:  Kwa; Tennessee; Ruki; Lukuga; Uelle; Mississippi; Luvua; Lualaba; Busira; OhioRiver; ...
*> query: (?x2258, ?x317) <- ?x2258[ a River; has locatedIn ?x215[ a Country; has neighbor ?x296; is locatedIn of ?x214[ is flowsInto of ?x949;]; is locatedIn of ?x317[ has locatedIn ?x161<KN>; has locatedIn ?x628<SMAR>; has locatedIn ?x633<GUAD>; has locatedIn ?x1502<SBAR>; is locatedInWater of ?x123;];];]
*> conf = 0.25
ranks of expected_values: 3
EVAL	RioMagdalena	flowsInto	CaribbeanSea	CNN-1.+1._MA	0.000	1.000	1.000	0.333	115.000	115.000	126.000	0.400	http://www.semwebtech.org/mondial/10/meta#flowsInto

#193-Guadalquivir
PRED entity: Guadalquivir

PRED relation: hasSource!
PRED expected values: Guadalquivir
=> 45 concepts (37 used for prediction)
PRED predicted values (max 10 best out of 162): Douro (0.20 #405, 0.17 #633, 0.07 #861), Tajo (0.20 #401, 0.17 #629, 0.07 #857), Guadiana (0.20 #362, 0.17 #590, 0.07 #818), Ebro (0.17 #649, 0.03 #1105, 0.03 #2056), Guadalquivir (0.03 #2056, 0.02 #2742, 0.02 #8245), LagunadeGallocanta (0.03 #2056, 0.02 #2742, 0.02 #4346), Vignemale (0.03 #2056, 0.02 #2742, 0.02 #4346), Ibiza (0.03 #2056, 0.02 #2742, 0.02 #4346), Guadalquivir (0.03 #2056, 0.02 #2742, 0.02 #4346), Douro (0.03 #2056, 0.02 #2742, 0.02 #4346)
>> best conf = 0.20
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #405 for best value:
>> intensional similarity = 4
>> extensional distance = 3
>> proper extension:  Tajo; Guadiana; Douro;
>> query: (?x2246, Douro) <- ?x2246[ a Source; has inMountains ?x744; has locatedIn ?x149<E>;]

*> Best rule #2056 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 74
*> proper extension:  DarlingRiver; EucumbeneRiver; MurrumbidgeeRiver; MurrayRiver;
*> query: (?x2246, ?x68) <- ?x2246[ a Source; has inMountains ?x744; has locatedIn ?x149[ has government ?x1657; is locatedIn of ?x68;];]
*> conf = 0.03
ranks of expected_values: 5
EVAL	Guadalquivir	hasSource!	Guadalquivir	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.200	45.000	37.000	162.000	0.200	http://www.semwebtech.org/mondial/10/meta#hasSource

PRED relation: hasSource!
PRED expected values: Guadalquivir
=> 135 concepts (135 used for prediction)
PRED predicted values (max 10 best out of 244): Tajo (0.20 #401, 0.17 #857, 0.17 #629), Douro (0.20 #405, 0.17 #861, 0.17 #633), Guadiana (0.20 #362, 0.17 #818, 0.17 #590), Ebro (0.17 #877, 0.12 #1105, 0.09 #1336), Garonne (0.17 #547, 0.03 #11720, 0.02 #8038), Thames (0.12 #1070, 0.09 #1301, 0.06 #2220), Asahan (0.12 #964, 0.02 #5105), Umeaelv (0.09 #1337, 0.02 #4786, 0.02 #5247), Dalaelv (0.09 #1295, 0.02 #4744, 0.02 #5205), Vaesterdalaelv (0.09 #1269, 0.02 #4718, 0.02 #5179)
>> best conf = 0.20
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #401 for best value:
>> intensional similarity = 8
>> extensional distance = 3
>> proper extension:  Tajo; Guadiana; Douro;
>> query: (?x2246, Tajo) <- ?x2246[ a Source; has inMountains ?x744[ a Mountains; is inMountains of ?x1924[ a Mountain; has locatedIn ?x149<E>;];]; has locatedIn ?x149<E>;]

*> Best rule #14022 for first EXPECTED value:
*> intensional similarity = 12
*> extensional distance = 178
*> proper extension:  RioDesaguadero; Ischim; Syrdarja; RioMadeira; Nile; Amudarja;
*> query: (?x2246, ?x1145) <- ?x2246[ a Source; has locatedIn ?x149[ has neighbor ?x1826[ a Country; has encompassed ?x195; has religion ?x95;]; is locatedIn of ?x275[ is flowsInto of ?x698;]; is locatedIn of ?x1145[ a River;]; is locatedIn of ?x2440[ has inMountains ?x1864;];];]
*> conf = 0.06
ranks of expected_values: 26
EVAL	Guadalquivir	hasSource!	Guadalquivir	CNN-1.+1._MA	0.000	0.000	0.000	0.038	135.000	135.000	244.000	0.200	http://www.semwebtech.org/mondial/10/meta#hasSource

#192-Shishapangma
PRED entity: Shishapangma

PRED relation: inMountains
PRED expected values: Himalaya
=> 29 concepts (29 used for prediction)
PRED predicted values (max 10 best out of 39): Himalaya (0.30 #93, 0.18 #6, 0.07 #180), Kunlun (0.12 #10, 0.10 #97, 0.05 #184), Pamir (0.08 #539, 0.06 #17, 0.05 #104), Alps (0.08 #700, 0.06 #874, 0.06 #961), CanaryIslands (0.07 #230, 0.02 #752, 0.02 #926), RockyMountains (0.06 #703, 0.05 #877, 0.05 #1225), TianShan (0.06 #31, 0.05 #118, 0.03 #292), Karakorum (0.06 #8, 0.05 #95, 0.03 #269), Transhimalaya (0.06 #24, 0.05 #111, 0.02 #198), Andes (0.05 #881, 0.05 #968, 0.05 #1055)
>> best conf = 0.30
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #93 for best value:
>> intensional similarity = 8
>> extensional distance = 18
>> proper extension:  Annapurna; Kangchendzonga; Dhaulagiri;
>> query: (?x2255, Himalaya) <- ?x2255[ a Mountain; has locatedIn ?x232[ has neighbor ?x617[ has religion ?x95;]; is locatedIn of ?x576<ChoOyu>; is locatedIn of ?x1320<MountEverest>;];]
ranks of expected_values: 1
EVAL	Shishapangma	inMountains	Himalaya	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	29.000	29.000	39.000	0.300	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains
PRED expected values: Himalaya
=> 84 concepts (84 used for prediction)
PRED predicted values (max 10 best out of 57): Himalaya (0.18 #6, 0.17 #93, 0.15 #262), RockyMountains (0.16 #356, 0.12 #1581, 0.10 #1757), Kunlun (0.15 #262, 0.15 #1662, 0.12 #10), Pamir (0.15 #262, 0.15 #1662, 0.11 #104), TianShan (0.15 #262, 0.15 #1662, 0.06 #3757), Karakorum (0.15 #262, 0.15 #1662, 0.06 #3757), Transhimalaya (0.15 #262, 0.15 #1662, 0.06 #3757), Kaukasus (0.12 #193, 0.04 #1593, 0.04 #1769), Andes (0.06 #2198, 0.06 #2285, 0.06 #3331), Kurdistan (0.06 #209, 0.06 #297, 0.03 #1960)
>> best conf = 0.18
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #6 for best value:
>> intensional similarity = 3
>> extensional distance = 15
>> proper extension:  Lhotse; UlugMuztag; GasherbrumII; ChoOyu; Muztagata; Kongur; Kailash; LiushiShan; K2; PikChan-Tengri; ...
>> query: (?x2255, Himalaya) <- ?x2255[ a Mountain; has locatedIn ?x232<CN>;]
ranks of expected_values: 1
EVAL	Shishapangma	inMountains	Himalaya	CNN-1.+1._MA	1.000	1.000	1.000	1.000	84.000	84.000	57.000	0.176	http://www.semwebtech.org/mondial/10/meta#inMountains

#191-SP
PRED entity: SP

PRED relation: neighbor!
PRED expected values: ETH
=> 29 concepts (29 used for prediction)
PRED predicted values (max 10 best out of 228): SA (0.44 #918, 0.03 #2039, 0.03 #2360), SSD (0.33 #42, 0.30 #479, 0.26 #2079), ER (0.33 #104, 0.30 #479, 0.26 #2079), EAT (0.30 #479, 0.29 #160, 0.26 #2079), SP (0.30 #479, 0.29 #160, 0.26 #2079), ETH (0.30 #479, 0.29 #160, 0.26 #2079), EAU (0.30 #479, 0.26 #2079, 0.26 #2884), Z (0.29 #248, 0.25 #407, 0.17 #1045), MW (0.29 #289, 0.25 #448, 0.17 #1086), YE (0.29 #160, 0.22 #908, 0.13 #3368)
>> best conf = 0.44
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #918 for best value:
>> intensional similarity = 8
>> extensional distance = 7
>> proper extension:  KWT;
>> query: (?x220, SA) <- ?x220[ is locatedIn of ?x1333[ a Sea; is locatedInWater of ?x1476; is mergesWith of ?x926<GulfofOman>; is mergesWith of ?x2407[ is mergesWith of ?x1552;];]; is neighbor of ?x94;]

*> Best rule #479 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 6
*> proper extension:  MOC; RI; EAK; TL; EAT; IND;
*> query: (?x220, ?x476) <- ?x220[ has government ?x1766; is locatedIn of ?x60<IndianOcean>; is neighbor of ?x94[ has ethnicGroup ?x1593; is locatedIn of ?x415; is neighbor of ?x476;];]
*> conf = 0.30
ranks of expected_values: 6
EVAL	SP	neighbor!	ETH	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.167	29.000	29.000	228.000	0.444	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: ETH
=> 77 concepts (77 used for prediction)
PRED predicted values (max 10 best out of 234): SSD (0.50 #1796, 0.50 #1674, 0.50 #373), SYR (0.45 #2201, 0.12 #2691, 0.11 #6362), SP (0.42 #3425, 0.40 #164, 0.40 #163), ETH (0.42 #3425, 0.40 #164, 0.35 #2772), MOC (0.40 #164, 0.40 #163, 0.35 #2772), EAT (0.40 #163, 0.35 #2772, 0.33 #1273), RI (0.40 #163, 0.35 #2772, 0.33 #491), IND (0.40 #163, 0.35 #2772, 0.31 #2773), CL (0.40 #163, 0.35 #2772, 0.31 #2773), AUS (0.40 #163, 0.35 #2772, 0.31 #2773)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1796 for best value:
>> intensional similarity = 15
>> extensional distance = 6
>> proper extension:  SUD; RCA;
>> query: (?x220, ?x229) <- ?x220[ a Country; has ethnicGroup ?x1593; has government ?x1766; has religion ?x187; is locatedIn of ?x60[ has locatedIn ?x906[ has encompassed ?x211;]; is flowsInto of ?x242;]; is locatedIn of ?x510[ a Estuary;]; is neighbor of ?x474[ a Country; has government ?x435; has neighbor ?x229<SSD>;];]
>> Best rule #1674 for best value:
>> intensional similarity = 15
>> extensional distance = 6
>> proper extension:  SUD; RCA;
>> query: (?x220, SSD) <- ?x220[ a Country; has ethnicGroup ?x1593; has government ?x1766; has religion ?x187; is locatedIn of ?x60[ has locatedIn ?x906[ has encompassed ?x211;]; is flowsInto of ?x242;]; is locatedIn of ?x510[ a Estuary;]; is neighbor of ?x474[ a Country; has government ?x435; has neighbor ?x229<SSD>;];]
>> Best rule #373 for best value:
>> intensional similarity = 13
>> extensional distance = 2
>> proper extension:  ETH;
>> query: (?x220, SSD) <- ?x220[ a Country; has ethnicGroup ?x1593; has religion ?x187; is locatedIn of ?x60[ has locatedIn ?x735[ a Country; has government ?x435; has religion ?x95;]; is flowsInto of ?x242;]; is locatedIn of ?x510[ a Estuary;]; is neighbor of ?x474<EAK>;]

*> Best rule #3425 for first EXPECTED value:
*> intensional similarity = 16
*> extensional distance = 24
*> proper extension:  CDN;
*> query: (?x220, ?x476) <- ?x220[ a Country; has encompassed ?x213; has ethnicGroup ?x1593; has government ?x1766; has religion ?x187<Muslim>; is locatedIn of ?x60[ a Sea; has locatedIn ?x474[ has neighbor ?x229;]; is locatedInWater of ?x226;]; is locatedIn of ?x750[ a River; has locatedIn ?x476;]; is locatedIn of ?x2035[ has hasSource ?x1917;];]
*> conf = 0.42
ranks of expected_values: 4
EVAL	SP	neighbor!	ETH	CNN-1.+1._MA	0.000	0.000	1.000	0.250	77.000	77.000	234.000	0.500	http://www.semwebtech.org/mondial/10/meta#neighbor

#190-SuluSea
PRED entity: SuluSea

PRED relation: mergesWith
PRED expected values: SouthChinaSea
=> 34 concepts (28 used for prediction)
PRED predicted values (max 10 best out of 42): SouthChinaSea (0.83 #352, 0.80 #232, 0.79 #78), BandaSea (0.50 #63, 0.33 #102, 0.25 #24), IndianOcean (0.50 #40, 0.33 #79, 0.23 #235), JavaSea (0.50 #6, 0.30 #161, 0.22 #123), SuluSea (0.50 #23, 0.25 #62, 0.22 #140), SeaofJapan (0.33 #91, 0.25 #52, 0.22 #39), AtlanticOcean (0.26 #239, 0.25 #198, 0.23 #278), EastChinaSea (0.25 #61, 0.22 #39, 0.17 #273), SeaofOkhotsk (0.25 #60, 0.22 #39, 0.17 #273), BeringSea (0.25 #65, 0.22 #39, 0.17 #273)
>> best conf = 0.83
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #352 for best value:
>> intensional similarity = 6
>> extensional distance = 34
>> proper extension:  HudsonBay; KaraSea;
>> query: (?x677, ?x384) <- ?x677[ a Sea; has mergesWith ?x625[ is locatedInWater of ?x369;]; is mergesWith of ?x384[ has locatedIn ?x91; is locatedInWater of ?x518;];]
ranks of expected_values: 1
EVAL	SuluSea	mergesWith	SouthChinaSea	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	34.000	28.000	42.000	0.830	http://www.semwebtech.org/mondial/10/meta#mergesWith

PRED relation: mergesWith
PRED expected values: SouthChinaSea
=> 105 concepts (105 used for prediction)
PRED predicted values (max 10 best out of 498): SouthChinaSea (0.84 #123, 0.79 #568, 0.78 #569), JavaSea (0.60 #47, 0.36 #120, 0.30 #210), IndianOcean (0.50 #1, 0.44 #367, 0.37 #527), BandaSea (0.50 #24, 0.36 #120, 0.33 #105), SuluSea (0.40 #64, 0.36 #120, 0.25 #267), SeaofJapan (0.36 #120, 0.33 #94, 0.25 #13), EastChinaSea (0.36 #120, 0.25 #22, 0.19 #363), SeaofOkhotsk (0.36 #120, 0.25 #21, 0.18 #979), BeringSea (0.36 #120, 0.25 #26, 0.18 #979), AtlanticOcean (0.28 #655, 0.27 #696, 0.25 #780)
>> best conf = 0.84
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #123 for best value:
>> intensional similarity = 17
>> extensional distance = 4
>> proper extension:  MalakkaStrait;
>> query: (?x677, ?x384) <- ?x677[ a Sea; has mergesWith ?x625[ has locatedIn ?x376<MAL>; has locatedIn ?x460<RP>; has mergesWith ?x241<JavaSea>; has mergesWith ?x770[ a Sea; is locatedInWater of ?x216;]; is locatedInWater of ?x375[ has belongsToIslands ?x875; is locatedOnIsland of ?x1526;]; is locatedInWater of ?x624[ a Island;];]; is mergesWith of ?x384;]
ranks of expected_values: 1
EVAL	SuluSea	mergesWith	SouthChinaSea	CNN-1.+1._MA	1.000	1.000	1.000	1.000	105.000	105.000	498.000	0.842	http://www.semwebtech.org/mondial/10/meta#mergesWith

#189-Katla
PRED entity: Katla

PRED relation: locatedIn
PRED expected values: IS
=> 23 concepts (23 used for prediction)
PRED predicted values (max 10 best out of 60): IS (0.93 #4517, 0.85 #2614, 0.85 #713), CDN (0.29 #299, 0.13 #1965, 0.04 #5054), RI (0.28 #2666, 0.25 #3142, 0.25 #2429), E (0.23 #503, 0.20 #740, 0.17 #979), SVAX (0.23 #474, 0.18 #476, 0.02 #3328), GROX (0.23 #474, 0.18 #476, 0.02 #3328), GB (0.18 #476, 0.08 #485, 0.05 #1436), N (0.18 #476, 0.02 #3328, 0.02 #3804), FARX (0.18 #476, 0.02 #3328, 0.02 #3804), P (0.13 #910, 0.09 #1862, 0.09 #2099)
>> best conf = 0.93
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4517 for best value:
>> intensional similarity = 8
>> extensional distance = 44
>> proper extension:  PicoBasile;
>> query: (?x1236, ?x455) <- ?x1236[ a Mountain; a Volcano; has locatedOnIsland ?x807[ a Island; is locatedOnIsland of ?x806[ a Mountain; a Volcano; has locatedIn ?x455;];];]
ranks of expected_values: 1
EVAL	Katla	locatedIn	IS	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	23.000	23.000	60.000	0.935	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: IS
=> 41 concepts (26 used for prediction)
PRED predicted values (max 10 best out of 26): IS (0.83 #5738, 0.82 #5501, 0.82 #5264), RI (0.40 #4837, 0.38 #3401, 0.33 #4356), E (0.38 #3137, 0.33 #3853, 0.21 #5767), RM (0.38 #2973, 0.25 #5603, 0.20 #6083), SVAX (0.30 #2390, 0.20 #2393, 0.17 #1434), RP (0.29 #1545, 0.25 #5610, 0.20 #6090), REUN (0.29 #1919, 0.18 #5267), CDN (0.29 #2216), USA (0.25 #2703), P (0.22 #4741, 0.17 #914, 0.09 #5461)
>> best conf = 0.83
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5738 for best value:
>> intensional similarity = 16
>> extensional distance = 10
>> proper extension:  Mantalingajan;
>> query: (?x1236, ?x455) <- ?x1236[ a Mountain; a Volcano; has locatedOnIsland ?x807[ a Island; has locatedInWater ?x1419[ a Sea; has locatedIn ?x455[ is locatedIn of ?x806[ a Mountain; a Volcano; has type ?x150<"volcanic">;];]; has mergesWith ?x263; is flowsInto of ?x534; is mergesWith of ?x263;]; is locatedOnIsland of ?x806;];]
ranks of expected_values: 1
EVAL	Katla	locatedIn	IS	CNN-1.+1._MA	1.000	1.000	1.000	1.000	41.000	26.000	26.000	0.833	http://www.semwebtech.org/mondial/10/meta#locatedIn

#188-CaribIndians
PRED entity: CaribIndians

PRED relation: ethnicGroup!
PRED expected values: WD
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 96): EAU (0.03 #132), ETH (0.03 #100), UA (0.03 #57), EAK (0.03 #99), CZ (0.02 #98), SN (0.02 #83), MYA (0.02 #70), IR (0.02 #58), SK (0.02 #25), RCA (0.02 #139)
>> best conf = 0.03
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #132 for best value:
>> intensional similarity = 1
>> extensional distance = 279
>> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
>> query: (?x2539, EAU) <- ?x2539[ a EthnicGroup;]

No rule for expected values
ranks of expected_values: 
EVAL	CaribIndians	ethnicGroup!	WD	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	2.000	2.000	96.000	0.032	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup!
PRED expected values: WD
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 96): EAU (0.03 #132), ETH (0.03 #100), UA (0.03 #57), EAK (0.03 #99), CZ (0.02 #98), SN (0.02 #83), MYA (0.02 #70), IR (0.02 #58), SK (0.02 #25), RCA (0.02 #139)
>> best conf = 0.03
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #132 for best value:
>> intensional similarity = 1
>> extensional distance = 279
>> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
>> query: (?x2539, EAU) <- ?x2539[ a EthnicGroup;]

No rule for expected values
ranks of expected_values: 
EVAL	CaribIndians	ethnicGroup!	WD	CNN-1.+1._MA	0.000	0.000	0.000	0.000	2.000	2.000	96.000	0.032	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#187-Lampedusa
PRED entity: Lampedusa

PRED relation: type
PRED expected values: "volcanic"
=> 36 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 9): "volcanic" (0.70 #2, 0.64 #18, 0.40 #162), "lime" (0.08 #69, 0.07 #85, 0.05 #197), "volcano" (0.05 #422, 0.04 #118, 0.04 #454), "caldera" (0.04 #115, 0.03 #147, 0.02 #419), "atoll" (0.03 #248, 0.03 #280, 0.03 #296), "salt" (0.03 #455, 0.03 #519, 0.02 #535), "dam" (0.03 #353, 0.01 #449, 0.01 #545), "coral" (0.02 #409, 0.01 #377, 0.01 #393), "sand" (0.01 #356)
>> best conf = 0.70
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2 for best value:
>> intensional similarity = 5
>> extensional distance = 8
>> proper extension:  Stromboli; Salina; Sardegna; Panarea; Filicudi; Alicudi; Linosa; Lipari;
>> query: (?x1767, "volcanic") <- ?x1767[ a Island; has locatedIn ?x207<I>; has locatedInWater ?x275<MediterraneanSea>;]
ranks of expected_values: 1
EVAL	Lampedusa	type	"volcanic"	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	36.000	36.000	9.000	0.700	http://www.semwebtech.org/mondial/10/meta#type

PRED relation: type
PRED expected values: "volcanic"
=> 114 concepts (114 used for prediction)
PRED predicted values (max 10 best out of 9): "volcanic" (0.70 #2, 0.64 #18, 0.62 #571), "volcano" (0.27 #82, 0.27 #81, 0.25 #260), "caldera" (0.27 #82, 0.27 #81, 0.25 #260), "lime" (0.16 #521, 0.16 #830, 0.16 #749), "sand" (0.05 #460, 0.02 #979), "atoll" (0.05 #562, 0.04 #301, 0.03 #821), "dam" (0.04 #457, 0.03 #976, 0.02 #1352), "coral" (0.03 #563, 0.02 #822, 0.02 #920), "salt" (0.03 #1633, 0.03 #1502, 0.02 #1551)
>> best conf = 0.70
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2 for best value:
>> intensional similarity = 5
>> extensional distance = 8
>> proper extension:  Stromboli; Salina; Sardegna; Panarea; Filicudi; Alicudi; Linosa; Lipari;
>> query: (?x1767, "volcanic") <- ?x1767[ a Island; has locatedIn ?x207<I>; has locatedInWater ?x275<MediterraneanSea>;]
ranks of expected_values: 1
EVAL	Lampedusa	type	"volcanic"	CNN-1.+1._MA	1.000	1.000	1.000	1.000	114.000	114.000	9.000	0.700	http://www.semwebtech.org/mondial/10/meta#type

#186-SME
PRED entity: SME

PRED relation: neighbor
PRED expected values: BR
=> 47 concepts (44 used for prediction)
PRED predicted values (max 10 best out of 225): BR (0.89 #4346, 0.89 #4023, 0.89 #5475), YV (0.33 #59, 0.25 #5150, 0.18 #864), SME (0.33 #28, 0.25 #5150, 0.17 #188), THA (0.30 #651, 0.27 #971, 0.17 #1453), LAO (0.30 #724, 0.27 #1044, 0.17 #1526), PE (0.25 #5150, 0.23 #1984, 0.14 #1176), CO (0.25 #5150, 0.18 #1972, 0.14 #1164), BOL (0.25 #5150, 0.18 #2047, 0.13 #2853), PY (0.25 #5150, 0.14 #2005, 0.08 #2327), RA (0.25 #5150, 0.09 #2000, 0.07 #6120)
>> best conf = 0.89
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4346 for best value:
>> intensional similarity = 6
>> extensional distance = 80
>> proper extension:  ROK;
>> query: (?x179, ?x542) <- ?x179[ has government ?x180; has neighbor ?x351; has religion ?x187[ is religion of ?x416<SN>;]; is neighbor of ?x542;]
ranks of expected_values: 1
EVAL	SME	neighbor	BR	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	47.000	44.000	225.000	0.893	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: BR
=> 98 concepts (98 used for prediction)
PRED predicted values (max 10 best out of 242): BR (0.93 #11526, 0.91 #12869, 0.91 #8888), MYA (0.43 #1541, 0.33 #64, 0.27 #162), PE (0.35 #2629, 0.35 #2627, 0.28 #3672), CO (0.35 #2629, 0.35 #2627, 0.27 #162), EC (0.35 #2629, 0.35 #2627, 0.27 #162), BOL (0.35 #2629, 0.35 #2627, 0.25 #14200), PY (0.35 #2629, 0.35 #2627, 0.25 #14200), RA (0.35 #2629, 0.35 #2627, 0.23 #11527), RCH (0.35 #2629, 0.35 #2627, 0.22 #161), PA (0.35 #2629, 0.35 #2627, 0.22 #161)
>> best conf = 0.93
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #11526 for best value:
>> intensional similarity = 13
>> extensional distance = 86
>> proper extension:  SD; ARM; BHT; BZ;
>> query: (?x179, ?x542) <- ?x179[ has ethnicGroup ?x79; has government ?x180; has neighbor ?x351; has religion ?x95; has wasDependentOf ?x575; is neighbor of ?x542[ has ethnicGroup ?x162; has neighbor ?x404[ a Country;]; is locatedIn of ?x182[ is locatedInWater of ?x112; is mergesWith of ?x60;]; is neighbor of ?x379;];]
ranks of expected_values: 1
EVAL	SME	neighbor	BR	CNN-1.+1._MA	1.000	1.000	1.000	1.000	98.000	98.000	242.000	0.932	http://www.semwebtech.org/mondial/10/meta#neighbor

#185-NorthUist
PRED entity: NorthUist

PRED relation: locatedIn
PRED expected values: GB
=> 11 concepts (11 used for prediction)
PRED predicted values (max 10 best out of 54): GB (0.73 #710, 0.73 #482, 0.71 #473), P (0.13 #907, 0.13 #1144, 0.04 #1380), E (0.10 #737, 0.10 #974, 0.04 #1210), RI (0.07 #1235, 0.07 #1472, 0.07 #1709), USA (0.07 #1255, 0.07 #1492, 0.07 #1729), GBM (0.06 #434, 0.05 #671, 0.02 #908), IRL (0.06 #264, 0.05 #501, 0.02 #738), GR (0.05 #1273, 0.05 #1510, 0.05 #1747), D (0.05 #2150, 0.04 #1203, 0.04 #1440), RP (0.04 #1292, 0.04 #1529, 0.04 #1766)
>> best conf = 0.73
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #710 for best value:
>> intensional similarity = 8
>> extensional distance = 20
>> proper extension:  ShetlandMainland; OrkneyMainland; Westray; SouthRonaldsay; Hoy;
>> query: (?x2388, ?x81) <- ?x2388[ a Island; has belongsToIslands ?x1415[ a Islands; is belongsToIslands of ?x526[ a Island; has locatedIn ?x81<GB>; has locatedInWater ?x182;];];]
>> Best rule #482 for best value:
>> intensional similarity = 8
>> extensional distance = 20
>> proper extension:  ShetlandMainland; OrkneyMainland; Westray; SouthRonaldsay; Hoy;
>> query: (?x2388, GB) <- ?x2388[ a Island; has belongsToIslands ?x1415[ a Islands; is belongsToIslands of ?x526[ a Island; has locatedIn ?x81<GB>; has locatedInWater ?x182;];];]
ranks of expected_values: 1
EVAL	NorthUist	locatedIn	GB	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	11.000	11.000	54.000	0.727	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: GB
=> 11 concepts (11 used for prediction)
PRED predicted values (max 10 best out of 54): GB (0.73 #710, 0.73 #482, 0.71 #473), P (0.13 #907, 0.13 #1144, 0.04 #1380), E (0.10 #737, 0.10 #974, 0.04 #1210), RI (0.07 #1235, 0.07 #1472, 0.07 #1709), USA (0.07 #1255, 0.07 #1492, 0.07 #1729), GBM (0.06 #434, 0.05 #671, 0.02 #908), IRL (0.06 #264, 0.05 #501, 0.02 #738), GR (0.05 #1273, 0.05 #1510, 0.05 #1747), D (0.05 #2150, 0.04 #1203, 0.04 #1440), RP (0.04 #1292, 0.04 #1529, 0.04 #1766)
>> best conf = 0.73
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #710 for best value:
>> intensional similarity = 8
>> extensional distance = 20
>> proper extension:  ShetlandMainland; OrkneyMainland; Westray; SouthRonaldsay; Hoy;
>> query: (?x2388, ?x81) <- ?x2388[ a Island; has belongsToIslands ?x1415[ a Islands; is belongsToIslands of ?x526[ a Island; has locatedIn ?x81<GB>; has locatedInWater ?x182;];];]
>> Best rule #482 for best value:
>> intensional similarity = 8
>> extensional distance = 20
>> proper extension:  ShetlandMainland; OrkneyMainland; Westray; SouthRonaldsay; Hoy;
>> query: (?x2388, GB) <- ?x2388[ a Island; has belongsToIslands ?x1415[ a Islands; is belongsToIslands of ?x526[ a Island; has locatedIn ?x81<GB>; has locatedInWater ?x182;];];]
ranks of expected_values: 1
EVAL	NorthUist	locatedIn	GB	CNN-1.+1._MA	1.000	1.000	1.000	1.000	11.000	11.000	54.000	0.727	http://www.semwebtech.org/mondial/10/meta#locatedIn

#184-CAYM
PRED entity: CAYM

PRED relation: ethnicGroup
PRED expected values: Black
=> 51 concepts (51 used for prediction)
PRED predicted values (max 10 best out of 185): European (0.64 #2824, 0.48 #5896, 0.43 #2568), Black (0.60 #569, 0.50 #1081, 0.33 #1337), African (0.50 #4614, 0.50 #2822, 0.38 #5638), Mestizo (0.50 #2852, 0.32 #4644, 0.30 #5924), Amerindian (0.43 #2818, 0.32 #4610, 0.30 #5890), Chinese (0.33 #15, 0.27 #5391, 0.27 #5135), EastIndian (0.33 #136, 0.17 #2440, 0.17 #904), Roma (0.33 #2055, 0.17 #775, 0.11 #6919), Mulatto (0.29 #2875, 0.18 #4667, 0.14 #5691), Asian (0.25 #2323, 0.21 #2579, 0.20 #4115)
>> best conf = 0.64
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2824 for best value:
>> intensional similarity = 7
>> extensional distance = 12
>> proper extension:  C; GCA; CO; CR; NIC; MEX; DOM; RH; PA; HCA;
>> query: (?x865, European) <- ?x865[ has ethnicGroup ?x1147; has government ?x254; has language ?x247; has religion ?x352<RomanCatholic>; is locatedIn of ?x317<CaribbeanSea>;]

*> Best rule #569 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 3
*> proper extension:  BVIR; AXA; BERM;
*> query: (?x865, Black) <- ?x865[ a Country; has dependentOf ?x81<GB>; has government ?x254; has religion ?x352<RomanCatholic>; is locatedIn of ?x1093[ a Island;];]
*> conf = 0.60
ranks of expected_values: 2
EVAL	CAYM	ethnicGroup	Black	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	51.000	51.000	185.000	0.643	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: Black
=> 80 concepts (80 used for prediction)
PRED predicted values (max 10 best out of 233): European (0.80 #6947, 0.73 #7715, 0.63 #8740), African (0.61 #6431, 0.45 #7201, 0.42 #10790), Black (0.53 #6682, 0.40 #1853, 0.40 #1597), Amerindian (0.53 #6682, 0.40 #6941, 0.39 #6427), Mestizo (0.50 #7743, 0.50 #6975, 0.38 #7999), Chinese (0.35 #2053, 0.26 #2568, 0.20 #7210), EastIndian (0.33 #2447, 0.20 #1932, 0.14 #3989), Mulatto (0.22 #6484, 0.20 #7254, 0.17 #8022), Asian (0.21 #4130, 0.21 #3872, 0.21 #3614), African-white-Indian (0.20 #1091, 0.11 #3339, 0.09 #11298)
>> best conf = 0.80
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #6947 for best value:
>> intensional similarity = 24
>> extensional distance = 18
>> proper extension:  USA; PY; ES;
>> query: (?x865, European) <- ?x865[ has encompassed ?x521; has ethnicGroup ?x1147; has language ?x247[ is language of ?x718[ is locatedIn of ?x742; is neighbor of ?x78;]; is language of ?x783<PA>; is language of ?x1444[ has government ?x562;];]; has language ?x796<Spanish>; has religion ?x713[ is religion of ?x196<AUS>; is religion of ?x407<AXA>; is religion of ?x667<TT>; is religion of ?x758<SY>; is religion of ?x1554<WL>;]; is locatedIn of ?x317;]

*> Best rule #6682 for first EXPECTED value:
*> intensional similarity = 16
*> extensional distance = 16
*> proper extension:  WG;
*> query: (?x865, ?x1009) <- ?x865[ a Country; has encompassed ?x521; has ethnicGroup ?x1147[ a EthnicGroup; is ethnicGroup of ?x1008[ a Country; has dependentOf ?x81<GB>; has ethnicGroup ?x1009; has government ?x2483; has religion ?x95; is locatedIn of ?x182<AtlanticOcean>;];]; has government ?x254; is locatedIn of ?x317<CaribbeanSea>;]
*> conf = 0.53
ranks of expected_values: 3
EVAL	CAYM	ethnicGroup	Black	CNN-1.+1._MA	0.000	1.000	1.000	0.333	80.000	80.000	233.000	0.800	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#183-CaribbeanSea
PRED entity: CaribbeanSea

PRED relation: flowsInto!
PRED expected values: RioMagdalena
=> 31 concepts (30 used for prediction)
PRED predicted values (max 10 best out of 266): Orinoco (0.33 #31, 0.20 #1832, 0.12 #2133), Amazonas (0.33 #11, 0.20 #1812, 0.12 #2113), MerrimackRiver (0.33 #264, 0.20 #2065, 0.12 #2366), RioSaoFrancisco (0.33 #245, 0.20 #2046, 0.12 #2347), Sanaga (0.33 #226, 0.20 #2027, 0.12 #2328), Douro (0.33 #225, 0.20 #2026, 0.12 #2327), Tajo (0.33 #220, 0.20 #2021, 0.12 #2322), HudsonRiver (0.33 #197, 0.20 #1998, 0.12 #2299), SaintLawrenceRiver (0.33 #191, 0.20 #1992, 0.12 #2293), Loire (0.33 #182, 0.20 #1983, 0.12 #2284)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #31 for best value:
>> intensional similarity = 8
>> extensional distance = 1
>> proper extension:  AtlanticOcean;
>> query: (?x317, Orinoco) <- ?x317[ has locatedIn ?x50; is locatedInWater of ?x609<Dominica>; is locatedInWater of ?x1219<Grenada>; is locatedInWater of ?x1380<St.Martin>; is mergesWith of ?x182;]

*> Best rule #2403 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 6
*> proper extension:  Orinoco; Orinoco; LakeMaracaibo; PicoBolivar;
*> query: (?x317, ?x214) <- ?x317[ has locatedIn ?x215[ has religion ?x352; is locatedIn of ?x214; is neighbor of ?x296;]; has locatedIn ?x345<YV>;]
*> conf = 0.05
ranks of expected_values: 70
EVAL	CaribbeanSea	flowsInto!	RioMagdalena	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.014	31.000	30.000	266.000	0.333	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto!
PRED expected values: RioMagdalena
=> 90 concepts (90 used for prediction)
PRED predicted values (max 10 best out of 348): Orinoco (0.33 #332, 0.25 #2439, 0.25 #1837), Amazonas (0.33 #312, 0.25 #2419, 0.25 #1817), MerrimackRiver (0.33 #565, 0.25 #2672, 0.25 #2070), RioSaoFrancisco (0.33 #546, 0.25 #2653, 0.25 #2051), Sanaga (0.33 #527, 0.25 #2634, 0.25 #2032), Douro (0.33 #526, 0.25 #2633, 0.25 #2031), Tajo (0.33 #521, 0.25 #2628, 0.25 #2026), HudsonRiver (0.33 #498, 0.25 #2605, 0.25 #2003), SaintLawrenceRiver (0.33 #492, 0.25 #2599, 0.25 #1997), Loire (0.33 #483, 0.25 #2590, 0.25 #1988)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #332 for best value:
>> intensional similarity = 17
>> extensional distance = 1
>> proper extension:  AtlanticOcean;
>> query: (?x317, Orinoco) <- ?x317[ a Sea; has locatedIn ?x899<PR>; has locatedIn ?x922[ has wasDependentOf ?x81;]; is locatedInWater of ?x477<Tobago>; is locatedInWater of ?x1397<SaintThomas>; is locatedInWater of ?x1557[ has belongsToIslands ?x1962;]; is locatedInWater of ?x1829<Antigua>; is locatedInWater of ?x1847<Martinique>; is locatedInWater of ?x2161<St.Barthelemy>;]

*> Best rule #1504 for first EXPECTED value:
*> intensional similarity = 12
*> extensional distance = 2
*> proper extension:  St.Martin;
*> query: (?x317, ?x282) <- ?x317[ has locatedIn ?x50<NLSM>; has locatedIn ?x80[ a Country;]; has locatedIn ?x318[ has government ?x711; has religion ?x1151;]; has locatedIn ?x321[ has encompassed ?x521;]; has locatedIn ?x482[ is locatedIn of ?x282; is neighbor of ?x315;];]
*> conf = 0.05
ranks of expected_values: 106
EVAL	CaribbeanSea	flowsInto!	RioMagdalena	CNN-1.+1._MA	0.000	0.000	0.000	0.009	90.000	90.000	348.000	0.333	http://www.semwebtech.org/mondial/10/meta#flowsInto

#182-RA
PRED entity: RA

PRED relation: locatedIn!
PRED expected values: LagunaMarChiquita
=> 42 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 1406): MediterraneanSea (0.40 #1494, 0.19 #39627, 0.17 #9966), PacificOcean (0.37 #21268, 0.33 #85, 0.32 #49437), CaribbeanSea (0.35 #5753, 0.31 #11401, 0.29 #19876), LicancaburCraterLake (0.33 #1285, 0.17 #4109, 0.12 #40959), Licancabur (0.33 #914, 0.17 #3738, 0.12 #40959), EasterIsland (0.33 #923, 0.12 #40959, 0.08 #3747), Atacama (0.33 #47, 0.12 #40959, 0.08 #2871), Donau (0.30 #1438, 0.12 #22621, 0.11 #25445), BlackSea (0.20 #1425, 0.06 #22608, 0.06 #36733), HamadaduDraa (0.20 #2357, 0.04 #10829, 0.04 #17892)
>> best conf = 0.40
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1494 for best value:
>> intensional similarity = 6
>> extensional distance = 8
>> proper extension:  TN; BG; IL; UA; ROU; DZ; MA; MD;
>> query: (?x379, MediterraneanSea) <- ?x379[ has government ?x435; has religion ?x109<Jewish>; has wasDependentOf ?x149; is locatedIn of ?x182; is neighbor of ?x202;]

No rule for expected values
ranks of expected_values: 
EVAL	RA	locatedIn!	LagunaMarChiquita	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	42.000	36.000	1406.000	0.400	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: LagunaMarChiquita
=> 99 concepts (98 used for prediction)
PRED predicted values (max 10 best out of 1416): Paraguay (0.72 #25445, 0.64 #11313, 0.41 #4247), Uruguay (0.72 #25445, 0.64 #11313, 0.41 #4247), Parana (0.72 #25445, 0.64 #11313, 0.06 #53717), PacificOcean (0.71 #32597, 0.64 #29769, 0.64 #26943), CaribbeanSea (0.62 #35442, 0.55 #42508, 0.55 #26963), Amazonas (0.60 #4246, 0.41 #4247, 0.33 #5711), RioSaoFrancisco (0.60 #4246, 0.41 #4247, 0.33 #6619), Tocantins (0.60 #4246, 0.41 #4247, 0.33 #6078), SaintLawrenceRiver (0.60 #4246, 0.41 #4247, 0.33 #2831), MerrimackRiver (0.60 #4246, 0.41 #4247, 0.33 #2831)
>> best conf = 0.72
=> the first rule below is the first best rule for 3 predicted values
>> Best rule #25445 for best value:
>> intensional similarity = 16
>> extensional distance = 7
>> proper extension:  IS;
>> query: (?x379, ?x938) <- ?x379[ has ethnicGroup ?x197; has language ?x796; has religion ?x109[ is religion of ?x78<F>; is religion of ?x363<ROU>; is religion of ?x568[ has neighbor ?x803;];]; has religion ?x352<RomanCatholic>; has wasDependentOf ?x149; is locatedIn of ?x512[ has hasSource ?x938;]; is locatedIn of ?x2452[ a Volcano;];]

No rule for expected values
ranks of expected_values: 
EVAL	RA	locatedIn!	LagunaMarChiquita	CNN-1.+1._MA	0.000	0.000	0.000	0.000	99.000	98.000	1416.000	0.719	http://www.semwebtech.org/mondial/10/meta#locatedIn

#181-SLO
PRED entity: SLO

PRED relation: neighbor!
PRED expected values: I
=> 36 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 208): I (0.92 #799, 0.91 #1917, 0.91 #479), D (0.58 #655, 0.50 #335, 0.30 #1279), PL (0.43 #195, 0.33 #34, 0.25 #674), CZ (0.33 #720, 0.33 #80, 0.30 #1279), UA (0.33 #51, 0.30 #1279, 0.30 #1280), CH (0.30 #1279, 0.30 #1280, 0.29 #2240), SK (0.30 #1279, 0.30 #1280, 0.29 #2240), SLO (0.30 #1279, 0.30 #1280, 0.29 #2240), FL (0.30 #1279, 0.30 #1280, 0.29 #2240), MNE (0.30 #1279, 0.30 #1280, 0.29 #2240)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #799 for best value:
>> intensional similarity = 8
>> extensional distance = 10
>> proper extension:  PL; A;
>> query: (?x446, ?x156) <- ?x446[ has ethnicGroup ?x160; has neighbor ?x156; has neighbor ?x424[ has encompassed ?x195; has neighbor ?x120; is locatedIn of ?x889<Lech>;]; is locatedIn of ?x155;]
ranks of expected_values: 1
EVAL	SLO	neighbor!	I	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	36.000	36.000	208.000	0.920	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: I
=> 99 concepts (99 used for prediction)
PRED predicted values (max 10 best out of 229): I (0.95 #3405, 0.94 #3729, 0.93 #3730), F (0.57 #1302, 0.34 #3407, 0.33 #330), SLO (0.49 #8629, 0.49 #8630, 0.49 #8627), RO (0.45 #2463, 0.37 #8626, 0.34 #3407), D (0.40 #989, 0.40 #825, 0.34 #3407), FL (0.40 #1048, 0.34 #3407, 0.33 #400), CH (0.40 #1018, 0.34 #3407, 0.33 #209), CN (0.40 #2153, 0.25 #1665, 0.24 #3289), UA (0.37 #8626, 0.36 #2488, 0.34 #3407), MNE (0.37 #8626, 0.34 #3407, 0.33 #496)
>> best conf = 0.95
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3405 for best value:
>> intensional similarity = 14
>> extensional distance = 19
>> proper extension:  TR; AL; UZB; TM; MYA; GR; RG; KOS; MK; RMM;
>> query: (?x446, ?x156) <- ?x446[ has encompassed ?x195; has ethnicGroup ?x160; has language ?x738; has neighbor ?x156; has neighbor ?x207[ has language ?x51; is locatedIn of ?x86;]; has neighbor ?x236[ has ethnicGroup ?x164; is locatedIn of ?x133;]; has religion ?x187<Muslim>; has wasDependentOf ?x1197; is locatedIn of ?x155;]
ranks of expected_values: 1
EVAL	SLO	neighbor!	I	CNN-1.+1._MA	1.000	1.000	1.000	1.000	99.000	99.000	229.000	0.945	http://www.semwebtech.org/mondial/10/meta#neighbor

#180-Javanese
PRED entity: Javanese

PRED relation: ethnicGroup!
PRED expected values: SME
=> 31 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 228): GB (0.32 #395, 0.12 #1362, 0.12 #1168), VN (0.28 #312, 0.10 #4072, 0.08 #5044), MAL (0.22 #263, 0.21 #3101, 0.21 #459), TL (0.21 #3101, 0.20 #2326, 0.20 #2521), PNG (0.21 #3101, 0.20 #2326, 0.20 #2521), NZ (0.21 #483, 0.14 #676, 0.13 #869), CR (0.17 #641, 0.17 #834, 0.16 #448), R (0.17 #585, 0.17 #778, 0.16 #971), SGP (0.17 #374, 0.16 #570, 0.10 #4072), HONX (0.17 #335, 0.10 #4072, 0.08 #5044)
>> best conf = 0.32
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #395 for best value:
>> intensional similarity = 10
>> extensional distance = 17
>> proper extension:  Amerindian; European; Chinese; Asian; Borneoindigenous; Indian; PacificIslander; Malay; Maori; Scottish; ...
>> query: (?x778, GB) <- ?x778[ a EthnicGroup; is ethnicGroup of ?x217[ has religion ?x462<Buddhist>; is locatedIn of ?x333[ a Island;]; is locatedIn of ?x584[ a Mountain;]; is locatedIn of ?x1253[ has type ?x150;];];]

*> Best rule #419 for first EXPECTED value:
*> intensional similarity = 10
*> extensional distance = 17
*> proper extension:  Amerindian; European; Chinese; Asian; Borneoindigenous; Indian; PacificIslander; Malay; Maori; Scottish; ...
*> query: (?x778, SME) <- ?x778[ a EthnicGroup; is ethnicGroup of ?x217[ has religion ?x462<Buddhist>; is locatedIn of ?x333[ a Island;]; is locatedIn of ?x584[ a Mountain;]; is locatedIn of ?x1253[ has type ?x150;];];]
*> conf = 0.11
ranks of expected_values: 36
EVAL	Javanese	ethnicGroup!	SME	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.028	31.000	31.000	228.000	0.316	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup!
PRED expected values: SME
=> 75 concepts (75 used for prediction)
PRED predicted values (max 10 best out of 230): GB (0.60 #1578, 0.50 #2367, 0.43 #2561), MAL (0.54 #1571, 0.36 #1178, 0.34 #7486), PNG (0.54 #1571, 0.36 #1178, 0.33 #3535), TL (0.54 #1571, 0.36 #1178, 0.33 #3535), CR (0.50 #1433, 0.43 #1043, 0.26 #3988), CDN (0.50 #1230, 0.27 #2944, 0.27 #2799), VN (0.45 #1885, 0.28 #3850, 0.23 #4837), ETH (0.45 #4423, 0.41 #5212, 0.28 #6991), CO (0.43 #1025, 0.40 #1415, 0.24 #3382), EC (0.43 #1141, 0.40 #1531, 0.21 #4086)
>> best conf = 0.60
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1578 for best value:
>> intensional similarity = 22
>> extensional distance = 8
>> proper extension:  Indian; Scottish; Pakistani; Welsh; English; NorthernIrish;
>> query: (?x778, GB) <- ?x778[ a EthnicGroup; is ethnicGroup of ?x217[ a Country; has government ?x435; has neighbor ?x376; has religion ?x95<Protestant>; has religion ?x187<Muslim>; has religion ?x462<Buddhist>; is locatedIn of ?x216[ a Island;]; is locatedIn of ?x625[ a Sea; is locatedInWater of ?x369;]; is locatedIn of ?x1183[ has type ?x287;]; is locatedIn of ?x1330[ a Source;]; is locatedIn of ?x1768[ has belongsToIslands ?x875;];];]

*> Best rule #1404 for first EXPECTED value:
*> intensional similarity = 21
*> extensional distance = 8
*> proper extension:  Mestizo;
*> query: (?x778, SME) <- ?x778[ a EthnicGroup; is ethnicGroup of ?x217[ has neighbor ?x376; has religion ?x95<Protestant>; has religion ?x187[ is religion of ?x120<D>; is religion of ?x170<N>;]; is locatedIn of ?x282<PacificOcean>; is locatedIn of ?x339[ a Sea;]; is locatedIn of ?x584[ a Mountain; a Volcano;]; is locatedIn of ?x625[ is locatedInWater of ?x369;]; is locatedIn of ?x1183[ has type ?x287;];];]
*> conf = 0.20
ranks of expected_values: 46
EVAL	Javanese	ethnicGroup!	SME	CNN-1.+1._MA	0.000	0.000	0.000	0.022	75.000	75.000	230.000	0.600	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#179-Hwangho
PRED entity: Hwangho

PRED relation: locatedIn
PRED expected values: CN
=> 21 concepts (21 used for prediction)
PRED predicted values (max 10 best out of 58): CN (0.80 #1670, 0.79 #2148, 0.70 #530), R (0.65 #1913, 0.11 #242, 0.11 #3584), N (0.20 #1466, 0.02 #2659, 0.02 #2897), AUS (0.20 #1477, 0.02 #3147, 0.02 #4100), TAD (0.13 #1214, 0.09 #975, 0.08 #2624), AFG (0.13 #1280, 0.08 #2624, 0.07 #3576), RG (0.10 #1816, 0.10 #2294, 0.09 #2532), KAZ (0.09 #1046, 0.08 #2624, 0.07 #3576), ZRE (0.09 #2465, 0.07 #3658, 0.07 #4134), USA (0.08 #3651, 0.07 #4599, 0.07 #4127)
>> best conf = 0.80
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1670 for best value:
>> intensional similarity = 9
>> extensional distance = 13
>> proper extension:  DarlingRiver; EucumbeneRiver; MurrumbidgeeRiver;
>> query: (?x2525, ?x232) <- ?x2525[ a Source; is hasSource of ?x1022[ a River; has locatedIn ?x232[ has encompassed ?x175; has ethnicGroup ?x2285; has religion ?x187<Muslim>; is dependentOf of ?x641;];];]
ranks of expected_values: 1
EVAL	Hwangho	locatedIn	CN	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	21.000	21.000	58.000	0.800	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: CN
=> 48 concepts (48 used for prediction)
PRED predicted values (max 10 best out of 65): CN (0.83 #5995, 0.80 #3351, 0.79 #6476), R (0.67 #5759, 0.65 #6241, 0.17 #8645), I (0.27 #3880, 0.12 #6524, 0.12 #7248), ETH (0.25 #3466, 0.20 #2748, 0.14 #3947), AUS (0.25 #282, 0.20 #3158, 0.03 #8448), TAD (0.25 #3614, 0.17 #4334, 0.13 #2895), N (0.23 #2427, 0.20 #3147, 0.12 #271), UA (0.23 #5343, 0.05 #9430, 0.04 #8951), TR (0.17 #4353, 0.10 #993, 0.08 #2434), RG (0.14 #4219, 0.12 #4938, 0.12 #4698)
>> best conf = 0.83
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5995 for best value:
>> intensional similarity = 17
>> extensional distance = 25
>> proper extension:  Volga; Chatanga; Schilka; Paatsjoki; WesternDwina; Lena; Katun; Petschora; Angara; Narva; ...
>> query: (?x2525, ?x232) <- ?x2525[ a Source; is hasSource of ?x1022[ a River; has hasEstuary ?x231[ a Estuary;]; has locatedIn ?x232[ has encompassed ?x175; has ethnicGroup ?x2285; has neighbor ?x403<KAZ>; has religion ?x116; is locatedIn of ?x472<Argun>; is locatedIn of ?x1748<Irtysch>; is neighbor of ?x463; is wasDependentOf of ?x1010;];];]
ranks of expected_values: 1
EVAL	Hwangho	locatedIn	CN	CNN-1.+1._MA	1.000	1.000	1.000	1.000	48.000	48.000	65.000	0.828	http://www.semwebtech.org/mondial/10/meta#locatedIn

#178-Russian
PRED entity: Russian

PRED relation: language!
PRED expected values: UA AZ
=> 24 concepts (24 used for prediction)
PRED predicted values (max 10 best out of 142): IR (0.59 #1660, 0.33 #1262, 0.30 #110), KAZ (0.59 #1660, 0.30 #110, 0.25 #550), AZ (0.59 #1660, 0.30 #110, 0.25 #550), NZ (0.33 #172, 0.20 #837, 0.12 #1393), AUS (0.33 #135, 0.14 #1247, 0.08 #1356), L (0.33 #193, 0.13 #858, 0.08 #1414), HONX (0.33 #195, 0.13 #860, 0.08 #1416), GBJ (0.33 #185, 0.13 #850, 0.08 #1406), BZ (0.33 #188, 0.10 #1300, 0.08 #1961), NLSM (0.33 #111, 0.10 #1223, 0.07 #1441)
>> best conf = 0.59
=> the first rule below is the first best rule for 3 predicted values
>> Best rule #1660 for best value:
>> intensional similarity = 13
>> extensional distance = 44
>> proper extension:  Chamorro; OtherPacificIslandLanguage; ChineseLanguage;
>> query: (?x555, ?x304) <- ?x555[ a Language; is language of ?x130[ has encompassed ?x175; is locatedIn of ?x662;]; is language of ?x290[ has ethnicGroup ?x1193; has government ?x2518; is locatedIn of ?x1337[ has locatedIn ?x304; has type ?x762;];]; is language of ?x962[ has encompassed ?x195; has ethnicGroup ?x963;];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 3, 34
EVAL	Russian	language!	AZ	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.333	24.000	24.000	142.000	0.590	http://www.semwebtech.org/mondial/10/meta#language
EVAL	Russian	language!	UA	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.030	24.000	24.000	142.000	0.590	http://www.semwebtech.org/mondial/10/meta#language

PRED relation: language!
PRED expected values: UA AZ
=> 57 concepts (57 used for prediction)
PRED predicted values (max 10 best out of 214): KAZ (0.86 #4870, 0.85 #4301, 0.71 #1584), SK (0.67 #1602, 0.50 #2168, 0.45 #1244), AUS (0.50 #1499, 0.24 #1361, 0.24 #1360), IR (0.47 #2865, 0.45 #1244, 0.25 #4120), RO (0.45 #1244, 0.43 #1947, 0.38 #2057), UA (0.45 #1244, 0.41 #1246, 0.41 #1245), N (0.45 #1244, 0.41 #1246, 0.41 #1245), PL (0.45 #1244, 0.41 #1246, 0.41 #1245), AZ (0.45 #1244, 0.41 #1246, 0.41 #1245), CN (0.45 #1244, 0.41 #1246, 0.41 #1245)
>> best conf = 0.86
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4870 for best value:
>> intensional similarity = 14
>> extensional distance = 35
>> proper extension:  Japanese; Sinhala; Tamil; Polynesian;
>> query: (?x555, ?x403) <- ?x555[ is language of ?x73[ has ethnicGroup ?x58; has religion ?x187; is locatedIn of ?x127[ a Mountain; has locatedIn ?x403;]; is locatedIn of ?x464[ is locatedInWater of ?x2277;];]; is language of ?x962[ has encompassed ?x195; has ethnicGroup ?x963[ a EthnicGroup;]; has religion ?x56; is locatedIn of ?x146;];]

*> Best rule #1244 for first EXPECTED value:
*> intensional similarity = 18
*> extensional distance = 3
*> proper extension:  Creole;
*> query: (?x555, ?x163) <- ?x555[ is language of ?x73[ has ethnicGroup ?x58; has neighbor ?x303[ a Country; has religion ?x95; is locatedIn of ?x221; is neighbor of ?x163;]; has religion ?x56; has wasDependentOf ?x903; is locatedIn of ?x98[ is flowsInto of ?x133;]; is locatedIn of ?x263[ is locatedInWater of ?x1075<Greenland>; is mergesWith of ?x248;];]; is language of ?x962[ a Country; has ethnicGroup ?x963;];]
*> conf = 0.45
ranks of expected_values: 6, 9
EVAL	Russian	language!	AZ	CNN-1.+1._MA	0.000	0.000	1.000	0.125	57.000	57.000	214.000	0.855	http://www.semwebtech.org/mondial/10/meta#language
EVAL	Russian	language!	UA	CNN-1.+1._MA	0.000	0.000	1.000	0.167	57.000	57.000	214.000	0.855	http://www.semwebtech.org/mondial/10/meta#language

#177-Mallorca
PRED entity: Mallorca

PRED relation: locatedIn
PRED expected values: E
=> 23 concepts (23 used for prediction)
PRED predicted values (max 10 best out of 101): E (0.82 #710, 0.81 #473, 0.35 #1895), GR (0.45 #563, 0.43 #326, 0.33 #800), I (0.30 #758, 0.29 #284, 0.27 #521), GB (0.12 #955, 0.08 #1667, 0.08 #1904), P (0.09 #1143, 0.05 #1381, 0.05 #1618), USA (0.09 #1256, 0.08 #1493, 0.07 #2679), F (0.08 #4277, 0.08 #3559, 0.07 #2606), ET (0.08 #4277, 0.08 #3559, 0.07 #2606), RI (0.07 #1710, 0.07 #1947, 0.07 #2184), M (0.07 #887, 0.05 #5001, 0.05 #5000)
>> best conf = 0.82
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #710 for best value:
>> intensional similarity = 8
>> extensional distance = 20
>> proper extension:  Samos;
>> query: (?x327, ?x149) <- ?x327[ a Island; has belongsToIslands ?x1715[ a Islands; is belongsToIslands of ?x2304[ a Island; has locatedIn ?x149; has locatedInWater ?x275<MediterraneanSea>;];];]
ranks of expected_values: 1
EVAL	Mallorca	locatedIn	E	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	23.000	23.000	101.000	0.818	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: E
=> 33 concepts (33 used for prediction)
PRED predicted values (max 10 best out of 138): E (0.82 #947, 0.81 #710, 0.70 #473), GR (0.45 #800, 0.43 #563, 0.33 #1037), I (0.30 #995, 0.29 #521, 0.27 #758), USA (0.15 #2483, 0.11 #3216, 0.08 #3460), GB (0.14 #1929, 0.13 #2908, 0.09 #3397), RI (0.13 #1484, 0.13 #1727, 0.08 #3686), F (0.11 #1422, 0.07 #7017, 0.05 #7515), ET (0.11 #1422, 0.07 #7017, 0.05 #1919), D (0.11 #2675, 0.09 #2919, 0.06 #5328), P (0.10 #2117, 0.08 #3096, 0.05 #5016)
>> best conf = 0.82
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #947 for best value:
>> intensional similarity = 8
>> extensional distance = 20
>> proper extension:  Samos;
>> query: (?x327, ?x149) <- ?x327[ a Island; has belongsToIslands ?x1715[ a Islands; is belongsToIslands of ?x2304[ a Island; has locatedIn ?x149; has locatedInWater ?x275<MediterraneanSea>;];];]
ranks of expected_values: 1
EVAL	Mallorca	locatedIn	E	CNN-1.+1._MA	1.000	1.000	1.000	1.000	33.000	33.000	138.000	0.818	http://www.semwebtech.org/mondial/10/meta#locatedIn

#176-GranitePeak
PRED entity: GranitePeak

PRED relation: locatedIn
PRED expected values: USA
=> 17 concepts (17 used for prediction)
PRED predicted values (max 10 best out of 54): USA (0.81 #1185, 0.81 #1020, 0.79 #948), CDN (0.59 #473, 0.45 #2369, 0.42 #474), PE (0.11 #1962, 0.06 #2911, 0.05 #3149), ZRE (0.08 #1974, 0.03 #1501, 0.03 #1264), R (0.07 #1427, 0.07 #1190, 0.07 #1663), I (0.06 #2892, 0.06 #3130, 0.06 #3603), CN (0.06 #3375, 0.06 #2188, 0.06 #2426), MEX (0.06 #1538, 0.06 #1301, 0.06 #1774), RA (0.06 #1509, 0.06 #1272, 0.06 #1745), E (0.06 #2159, 0.06 #2397, 0.06 #2635)
>> best conf = 0.81
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1185 for best value:
>> intensional similarity = 6
>> extensional distance = 29
>> proper extension:  HudsonRiver; AlleghenyRiver;
>> query: (?x2400, ?x315) <- ?x2400[ has inMountains ?x337[ a Mountains; is inMountains of ?x336[ a Mountain; has locatedIn ?x315<USA>;];];]
>> Best rule #1020 for best value:
>> intensional similarity = 6
>> extensional distance = 29
>> proper extension:  HudsonRiver; AlleghenyRiver;
>> query: (?x2400, USA) <- ?x2400[ has inMountains ?x337[ a Mountains; is inMountains of ?x336[ a Mountain; has locatedIn ?x315<USA>;];];]
ranks of expected_values: 1
EVAL	GranitePeak	locatedIn	USA	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	17.000	17.000	54.000	0.806	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: USA
=> 20 concepts (20 used for prediction)
PRED predicted values (max 10 best out of 54): USA (0.81 #1425, 0.81 #1260, 0.79 #1188), CDN (0.67 #1426, 0.66 #1187, 0.59 #949), PE (0.13 #2681, 0.06 #3630, 0.05 #3868), ZRE (0.09 #2693, 0.04 #1505, 0.03 #1743), R (0.08 #1669, 0.08 #1431, 0.07 #2145), MEX (0.08 #1542, 0.06 #1780, 0.06 #2256), RA (0.08 #1513, 0.06 #1751, 0.06 #2227), I (0.06 #3611, 0.06 #3849, 0.06 #4323), CN (0.06 #4095, 0.06 #2908, 0.06 #3146), E (0.06 #2879, 0.06 #3117, 0.06 #3354)
>> best conf = 0.81
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1425 for best value:
>> intensional similarity = 6
>> extensional distance = 29
>> proper extension:  HudsonRiver; AlleghenyRiver;
>> query: (?x2400, ?x315) <- ?x2400[ has inMountains ?x337[ a Mountains; is inMountains of ?x314[ a Mountain; has locatedIn ?x315<USA>;];];]
>> Best rule #1260 for best value:
>> intensional similarity = 6
>> extensional distance = 29
>> proper extension:  HudsonRiver; AlleghenyRiver;
>> query: (?x2400, USA) <- ?x2400[ has inMountains ?x337[ a Mountains; is inMountains of ?x314[ a Mountain; has locatedIn ?x315<USA>;];];]
ranks of expected_values: 1
EVAL	GranitePeak	locatedIn	USA	CNN-1.+1._MA	1.000	1.000	1.000	1.000	20.000	20.000	54.000	0.806	http://www.semwebtech.org/mondial/10/meta#locatedIn

#175-Brahmaputra
PRED entity: Brahmaputra

PRED relation: hasSource
PRED expected values: Brahmaputra
=> 47 concepts (39 used for prediction)
PRED predicted values (max 10 best out of 164): Ganges (0.33 #109, 0.20 #337, 0.07 #565), Argun (0.02 #3659, 0.02 #3658, 0.02 #2744), Brahmaputra (0.02 #3659, 0.02 #3658, 0.02 #2744), Brahmaputra (0.02 #3659, 0.02 #3658, 0.02 #2744), Ganges (0.02 #3659, 0.02 #3658, 0.02 #2744), Ganges (0.02 #3659, 0.02 #3658, 0.02 #2744), GulfofBengal (0.02 #3659, 0.02 #3658, 0.02 #2744), Mekong (0.02 #3659, 0.02 #3658, 0.02 #2744), OzeroChanka (0.02 #3659, 0.02 #3658, 0.02 #2744), GasherbrumI (0.02 #3659, 0.02 #3658, 0.02 #2744)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #109 for best value:
>> intensional similarity = 7
>> extensional distance = 1
>> proper extension:  Ganges;
>> query: (?x1950, Ganges) <- ?x1950[ a River; has flowsInto ?x1258; has hasEstuary ?x1951; has locatedIn ?x232[ is neighbor of ?x73;]; has locatedIn ?x943<BD>;]

*> Best rule #3659 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 172
*> proper extension:  Bahrel-Ghasal; Bahrel-Djebel-Albert-Nil;
*> query: (?x1950, ?x942) <- ?x1950[ a River; has hasEstuary ?x1951; has locatedIn ?x943[ has government ?x254; is locatedIn of ?x942;];]
*> conf = 0.02
ranks of expected_values: 12
EVAL	Brahmaputra	hasSource	Brahmaputra	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.083	47.000	39.000	164.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasSource

PRED relation: hasSource
PRED expected values: Brahmaputra
=> 124 concepts (124 used for prediction)
PRED predicted values (max 10 best out of 252): Ganges (0.33 #109, 0.17 #1253, 0.07 #3090), MurrumbidgeeRiver (0.20 #810, 0.03 #4714, 0.02 #6551), DarlingRiver (0.20 #698, 0.03 #4602, 0.02 #6439), Limpopo (0.17 #1308, 0.07 #3604, 0.02 #5902), MurrayRiver (0.17 #1289, 0.07 #3585), Manicouagan (0.17 #1122, 0.01 #8008, 0.01 #8468), RiviereRichelieu (0.17 #1030, 0.01 #7916, 0.01 #8376), Mekong (0.09 #1830, 0.08 #2060, 0.07 #3209), Argun (0.09 #1720, 0.08 #1950, 0.07 #3099), Tarim-Yarkend (0.09 #1764, 0.08 #1994, 0.07 #3143)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #109 for best value:
>> intensional similarity = 13
>> extensional distance = 1
>> proper extension:  Ganges;
>> query: (?x1950, Ganges) <- ?x1950[ a River; has flowsInto ?x1258; has locatedIn ?x232[ has encompassed ?x175; has religion ?x116<Christian>; is neighbor of ?x641[ has language ?x539; has religion ?x95;];]; has locatedIn ?x924<IND>; has locatedIn ?x943<BD>;]

*> Best rule #16077 for first EXPECTED value:
*> intensional similarity = 10
*> extensional distance = 161
*> proper extension:  Goetaaelv; Oesterdalaelv; Umeaelv;
*> query: (?x1950, ?x2454) <- ?x1950[ a River; has flowsInto ?x1258; has hasEstuary ?x1951; has locatedIn ?x232[ a Country; has ethnicGroup ?x2285; has neighbor ?x334; is locatedIn of ?x2454[ a Source;]; is neighbor of ?x73;];]
*> conf = 0.07
ranks of expected_values: 29
EVAL	Brahmaputra	hasSource	Brahmaputra	CNN-1.+1._MA	0.000	0.000	0.000	0.034	124.000	124.000	252.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasSource

#174-LV
PRED entity: LV

PRED relation: language
PRED expected values: Latvian
=> 44 concepts (44 used for prediction)
PRED predicted values (max 10 best out of 88): Belorussian (0.25 #164, 0.25 #67, 0.19 #1359), Polish (0.25 #40, 0.19 #1359, 0.19 #1554), Spanish (0.24 #797, 0.24 #1088, 0.23 #991), English (0.21 #295, 0.20 #1558, 0.20 #2043), Roma (0.21 #337, 0.15 #434, 0.09 #725), Serbian (0.20 #426, 0.16 #523, 0.16 #620), Estonian (0.19 #1359, 0.19 #1554, 0.17 #251), Lithuanian (0.19 #1359, 0.19 #1554, 0.17 #225), Ukrainian (0.17 #223, 0.15 #417, 0.08 #514), Hungarian (0.15 #404, 0.14 #307, 0.14 #501)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #164 for best value:
>> intensional similarity = 7
>> extensional distance = 2
>> proper extension:  R;
>> query: (?x448, Belorussian) <- ?x448[ has encompassed ?x195; has ethnicGroup ?x58; has neighbor ?x73; has wasDependentOf ?x903; is locatedIn of ?x885; is neighbor of ?x962<LT>;]
>> Best rule #67 for best value:
>> intensional similarity = 7
>> extensional distance = 2
>> proper extension:  PL;
>> query: (?x448, Belorussian) <- ?x448[ has ethnicGroup ?x58; has government ?x254; has neighbor ?x73; has religion ?x56; is locatedIn of ?x885; is neighbor of ?x962<LT>;]

No rule for expected values
ranks of expected_values: 
EVAL	LV	language	Latvian	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	44.000	44.000	88.000	0.250	http://www.semwebtech.org/mondial/10/meta#language

PRED relation: language
PRED expected values: Latvian
=> 67 concepts (67 used for prediction)
PRED predicted values (max 10 best out of 97): English (0.71 #2152, 0.48 #2055, 0.23 #4100), Spanish (0.56 #1288, 0.26 #3436, 0.24 #3630), Belorussian (0.33 #164, 0.31 #390, 0.27 #1170), Polish (0.33 #40, 0.31 #390, 0.27 #1170), Lithuanian (0.33 #31, 0.31 #390, 0.27 #1170), Ukrainian (0.33 #711, 0.25 #419, 0.20 #614), Romanian (0.33 #730, 0.16 #4882, 0.13 #1022), Estonian (0.31 #390, 0.25 #2829, 0.25 #545), Kazakh (0.27 #1952, 0.19 #1365, 0.17 #814), Roma (0.25 #338, 0.18 #1216, 0.17 #728)
>> best conf = 0.71
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2152 for best value:
>> intensional similarity = 14
>> extensional distance = 33
>> proper extension:  GBM;
>> query: (?x448, English) <- ?x448[ a Country; has language ?x555[ is language of ?x353[ has neighbor ?x185; is locatedIn of ?x98;]; is language of ?x565<SF>;]; is locatedIn of ?x1457[ has locatedIn ?x222[ has encompassed ?x195; has ethnicGroup ?x58; has neighbor ?x194; has religion ?x352<RomanCatholic>;];];]

No rule for expected values
ranks of expected_values: 
EVAL	LV	language	Latvian	CNN-1.+1._MA	0.000	0.000	0.000	0.000	67.000	67.000	97.000	0.714	http://www.semwebtech.org/mondial/10/meta#language

#173-LakeBangweulu
PRED entity: LakeBangweulu

PRED relation: flowsInto
PRED expected values: Luapula
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 9): Missouri (0.02 #127), Dnepr (0.02 #72), Vuoksi (0.01 #131), Colorado (0.01 #112), Aare (0.01 #93), VictoriaNile (0.01 #82), Asahan (0.01 #53), RioSanJuan (0.01 #31), Tennessee (0.01 #21)
>> best conf = 0.02
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #127 for best value:
>> intensional similarity = 1
>> extensional distance = 142
>> proper extension:  LagunaMarChiquita; LakeSkutari; StarnbergerSee; LakeVolta; LakeHuron; MaleboPool; ChickamaugaLake; Poopo; LakeTanganjika; LakeNyos; ...
>> query: (?x2008, Missouri) <- ?x2008[ a Lake;]

No rule for expected values
ranks of expected_values: 
EVAL	LakeBangweulu	flowsInto	Luapula	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	2.000	2.000	9.000	0.021	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto
PRED expected values: Luapula
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 9): Missouri (0.02 #127), Dnepr (0.02 #72), Vuoksi (0.01 #131), Colorado (0.01 #112), Aare (0.01 #93), VictoriaNile (0.01 #82), Asahan (0.01 #53), RioSanJuan (0.01 #31), Tennessee (0.01 #21)
>> best conf = 0.02
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #127 for best value:
>> intensional similarity = 1
>> extensional distance = 142
>> proper extension:  LagunaMarChiquita; LakeSkutari; StarnbergerSee; LakeVolta; LakeHuron; MaleboPool; ChickamaugaLake; Poopo; LakeTanganjika; LakeNyos; ...
>> query: (?x2008, Missouri) <- ?x2008[ a Lake;]

No rule for expected values
ranks of expected_values: 
EVAL	LakeBangweulu	flowsInto	Luapula	CNN-1.+1._MA	0.000	0.000	0.000	0.000	2.000	2.000	9.000	0.021	http://www.semwebtech.org/mondial/10/meta#flowsInto

#172-GQ
PRED entity: GQ

PRED relation: locatedIn!
PRED expected values: Bioko
=> 41 concepts (37 used for prediction)
PRED predicted values (max 10 best out of 1315): Bioko (0.86 #5689), MediterraneanSea (0.38 #21419, 0.23 #15725, 0.22 #18571), PacificOcean (0.35 #35648, 0.23 #48454, 0.23 #15728), CaribbeanSea (0.35 #4371, 0.31 #12904, 0.30 #25709), Ubangi (0.33 #144, 0.29 #1566, 0.10 #5833), Sanga (0.33 #1198, 0.29 #2620, 0.08 #42678), Zaire (0.33 #463, 0.19 #44101, 0.18 #28449), Ubangi (0.33 #145, 0.14 #1567, 0.10 #5834), MaleboPool (0.33 #75, 0.14 #1497, 0.10 #5764), Sanga (0.33 #1287, 0.14 #2709, 0.05 #6976)
>> best conf = 0.86
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5689 for best value:
>> intensional similarity = 6
>> extensional distance = 15
>> proper extension:  WV; HELX;
>> query: (?x1408, ?x772) <- ?x1408[ has encompassed ?x213; has government ?x435; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x771[ has locatedOnIsland ?x772;];]
ranks of expected_values: 1
EVAL	GQ	locatedIn!	Bioko	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	41.000	37.000	1315.000	0.864	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Bioko
=> 73 concepts (72 used for prediction)
PRED predicted values (max 10 best out of 1420): Bioko (0.91 #25635, 0.33 #5694, 0.33 #4271), Sanga (0.50 #6892, 0.40 #8316, 0.33 #2622), MediterraneanSea (0.50 #14318, 0.29 #74184, 0.28 #69895), CaribbeanSea (0.46 #25740, 0.42 #51388, 0.41 #71348), PacificOcean (0.46 #25720, 0.41 #32844, 0.40 #82740), ChadLake (0.43 #21354, 0.33 #1282, 0.32 #59839), Benue (0.43 #21354, 0.27 #12812, 0.27 #21356), Schari (0.40 #7437, 0.33 #319, 0.29 #11708), Ubangi (0.40 #7262, 0.33 #1568, 0.29 #11533), Akagera (0.40 #9187, 0.12 #43382, 0.12 #17729)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #25635 for best value:
>> intensional similarity = 14
>> extensional distance = 11
>> proper extension:  RM;
>> query: (?x1408, ?x772) <- ?x1408[ a Country; has government ?x435<"republic">; is locatedIn of ?x182[ has locatedIn ?x50[ has religion ?x95;]; has mergesWith ?x60[ has mergesWith ?x262;]; is flowsInto of ?x137; is locatedInWater of ?x112;]; is locatedIn of ?x771[ a Mountain; has locatedOnIsland ?x772[ a Island;];];]
ranks of expected_values: 1
EVAL	GQ	locatedIn!	Bioko	CNN-1.+1._MA	1.000	1.000	1.000	1.000	73.000	72.000	1420.000	0.909	http://www.semwebtech.org/mondial/10/meta#locatedIn

#171-IND
PRED entity: IND

PRED relation: locatedIn!
PRED expected values: Dodabetta
=> 32 concepts (27 used for prediction)
PRED predicted values (max 10 best out of 1235): PacificOcean (0.69 #15608, 0.40 #84, 0.26 #22671), SouthChinaSea (0.40 #10016, 0.40 #5783, 0.27 #11427), AtlanticOcean (0.38 #31099, 0.38 #36750, 0.37 #22628), CaribbeanSea (0.23 #18454, 0.21 #22691, 0.17 #24103), Mekong (0.20 #11901, 0.20 #10490, 0.17 #9079), MalakkaStrait (0.20 #5784, 0.20 #139, 0.14 #23999), SulawesiSea (0.20 #5922, 0.20 #277, 0.13 #10155), LakeMalawi (0.20 #924, 0.17 #2335, 0.14 #3746), LakeVictoria (0.20 #641, 0.17 #2052, 0.11 #14754), PikChan-Tengri (0.20 #11895, 0.17 #9073, 0.09 #22587)
>> best conf = 0.69
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #15608 for best value:
>> intensional similarity = 6
>> extensional distance = 49
>> proper extension:  TOK;
>> query: (?x924, PacificOcean) <- ?x924[ a Country; has religion ?x116; is locatedIn of ?x60[ is locatedInWater of ?x433<Tasmania>; is mergesWith of ?x182;];]

No rule for expected values
ranks of expected_values: 
EVAL	IND	locatedIn!	Dodabetta	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	32.000	27.000	1235.000	0.686	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Dodabetta
=> 96 concepts (93 used for prediction)
PRED predicted values (max 10 best out of 1420): PacificOcean (0.82 #43878, 0.74 #50943, 0.64 #32576), Brahmaputra (0.61 #118764, 0.25 #11005, 0.24 #5651), Indus (0.61 #118764), GulfofOman (0.60 #2825, 0.60 #2824, 0.53 #81987), Irawaddy (0.60 #2825, 0.60 #2824, 0.33 #5769), Saluen (0.60 #2825, 0.60 #2824, 0.33 #7022), Saluen (0.60 #2825, 0.60 #2824, 0.33 #7026), Irawaddy (0.60 #2825, 0.60 #2824, 0.33 #6475), BroadPeak (0.60 #2825, 0.60 #2824, 0.33 #3575), K2 (0.60 #2825, 0.60 #2824, 0.33 #3363)
>> best conf = 0.82
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #43878 for best value:
>> intensional similarity = 14
>> extensional distance = 20
>> proper extension:  GUAM;
>> query: (?x924, PacificOcean) <- ?x924[ has ethnicGroup ?x1553; has government ?x140; has language ?x2392; is locatedIn of ?x60[ has locatedIn ?x196<AUS>; has locatedIn ?x217<RI>; has locatedIn ?x797[ has ethnicGroup ?x1728; has religion ?x116; has wasDependentOf ?x81;]; is flowsInto of ?x242; is locatedInWater of ?x226;];]

No rule for expected values
ranks of expected_values: 
EVAL	IND	locatedIn!	Dodabetta	CNN-1.+1._MA	0.000	0.000	0.000	0.000	96.000	93.000	1420.000	0.818	http://www.semwebtech.org/mondial/10/meta#locatedIn

#170-DJI
PRED entity: DJI

PRED relation: encompassed
PRED expected values: Africa
=> 41 concepts (41 used for prediction)
PRED predicted values (max 10 best out of 5): Africa (0.57 #19, 0.56 #24, 0.50 #9), America (0.41 #35, 0.39 #81, 0.36 #76), Europe (0.37 #63, 0.30 #113, 0.28 #128), Australia-Oceania (0.27 #33, 0.18 #74, 0.18 #79), Asia (0.24 #157, 0.24 #142, 0.24 #137)
>> best conf = 0.57
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #19 for best value:
>> intensional similarity = 6
>> extensional distance = 5
>> proper extension:  RM; COM;
>> query: (?x94, Africa) <- ?x94[ a Country; has government ?x435; has wasDependentOf ?x78<F>; is locatedIn of ?x415[ has type ?x762;];]
ranks of expected_values: 1
EVAL	DJI	encompassed	Africa	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	41.000	41.000	5.000	0.571	http://www.semwebtech.org/mondial/10/meta#encompassed

PRED relation: encompassed
PRED expected values: Africa
=> 78 concepts (78 used for prediction)
PRED predicted values (max 10 best out of 5): Africa (0.85 #178, 0.84 #287, 0.83 #205), Europe (0.70 #146, 0.60 #73, 0.60 #45), Asia (0.44 #137, 0.42 #150, 0.39 #431), America (0.41 #236, 0.31 #177, 0.30 #241), Australia-Oceania (0.20 #340, 0.18 #112, 0.15 #134)
>> best conf = 0.85
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #178 for best value:
>> intensional similarity = 15
>> extensional distance = 50
>> proper extension:  BIH; MNE; KGZ; HR; SK; N; RO; BG; SME; GCA; ...
>> query: (?x94, ?x213) <- ?x94[ a Country; has ethnicGroup ?x1593; has government ?x435; has religion ?x116; has wasDependentOf ?x78; is neighbor of ?x476[ a Country; has encompassed ?x213; has ethnicGroup ?x1179; has religion ?x95<Protestant>; is locatedIn of ?x228; is neighbor of ?x474[ has ethnicGroup ?x244; is locatedIn of ?x60;];];]
ranks of expected_values: 1
EVAL	DJI	encompassed	Africa	CNN-1.+1._MA	1.000	1.000	1.000	1.000	78.000	78.000	5.000	0.852	http://www.semwebtech.org/mondial/10/meta#encompassed

#169-FGU
PRED entity: FGU

PRED relation: ethnicGroup
PRED expected values: Amerindian
=> 47 concepts (47 used for prediction)
PRED predicted values (max 10 best out of 181): European (0.47 #4396, 0.42 #4654, 0.36 #5428), Chinese (0.40 #273, 0.29 #2080, 0.20 #8006), African (0.39 #4394, 0.38 #5426, 0.38 #4652), French (0.33 #1415, 0.27 #1673, 0.25 #2964), Italian (0.33 #1505, 0.25 #1247, 0.23 #2021), Polynesian (0.33 #863, 0.18 #1637, 0.17 #6972), Amerindian (0.28 #4390, 0.28 #4648, 0.24 #5422), Portuguese (0.23 #1932, 0.22 #1416, 0.15 #2707), Mestizo (0.23 #4682, 0.22 #4424, 0.20 #5456), White (0.22 #2390, 0.19 #3164, 0.15 #3681)
>> best conf = 0.47
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4396 for best value:
>> intensional similarity = 7
>> extensional distance = 34
>> proper extension:  BS; JA; TT; WL;
>> query: (?x816, European) <- ?x816[ a Country; has encompassed ?x521<America>; has government ?x828; has religion ?x352<RomanCatholic>; is locatedIn of ?x182;]

*> Best rule #4390 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 34
*> proper extension:  BS; JA; TT; WL;
*> query: (?x816, Amerindian) <- ?x816[ a Country; has encompassed ?x521<America>; has government ?x828; has religion ?x352<RomanCatholic>; is locatedIn of ?x182;]
*> conf = 0.28
ranks of expected_values: 7
EVAL	FGU	ethnicGroup	Amerindian	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.143	47.000	47.000	181.000	0.472	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: Amerindian
=> 93 concepts (93 used for prediction)
PRED predicted values (max 10 best out of 243): European (0.75 #7779, 0.69 #8039, 0.62 #6477), Chinese (0.63 #8030, 0.55 #8291, 0.54 #6729), Amerindian (0.56 #8290, 0.56 #8033, 0.55 #8291), Creole (0.55 #8291, 0.54 #6729, 0.54 #8031), Europeans (0.55 #8291, 0.54 #6729, 0.54 #8031), Hindustani (0.55 #8291, 0.54 #6729, 0.54 #8031), Mestizo (0.54 #6505, 0.50 #7807, 0.44 #8067), White (0.50 #5243, 0.43 #4208, 0.43 #3430), African (0.46 #6475, 0.44 #7777, 0.39 #9340), Black (0.38 #5233, 0.29 #4717, 0.29 #4198)
>> best conf = 0.75
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #7779 for best value:
>> intensional similarity = 16
>> extensional distance = 14
>> proper extension:  BZ;
>> query: (?x816, European) <- ?x816[ a Country; has encompassed ?x521; has government ?x828; has language ?x51[ a Language;]; has neighbor ?x179[ has ethnicGroup ?x298[ is ethnicGroup of ?x318<CR>; is ethnicGroup of ?x1731<XMAS>;]; has religion ?x187[ is religion of ?x120<D>;];]; has religion ?x352<RomanCatholic>;]

*> Best rule #8290 for first EXPECTED value:
*> intensional similarity = 21
*> extensional distance = 14
*> proper extension:  USA; ES;
*> query: (?x816, ?x79) <- ?x816[ a Country; has religion ?x352; is locatedIn of ?x182; is neighbor of ?x179[ a Country; has encompassed ?x521; has ethnicGroup ?x79<Amerindian>; has government ?x180; has religion ?x95<Protestant>; has religion ?x187[ is religion of ?x81<GB>; is religion of ?x156<HR>; is religion of ?x460<RP>; is religion of ?x667<TT>;]; has wasDependentOf ?x575;];]
*> conf = 0.56
ranks of expected_values: 3
EVAL	FGU	ethnicGroup	Amerindian	CNN-1.+1._MA	0.000	1.000	1.000	0.333	93.000	93.000	243.000	0.750	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#168-ChristianOrthodox
PRED entity: ChristianOrthodox

PRED relation: religion!
PRED expected values: GR
=> 15 concepts (15 used for prediction)
PRED predicted values (max 10 best out of 195): KOS (0.61 #382, 0.56 #380, 0.54 #764), GR (0.61 #382, 0.56 #380, 0.54 #764), N (0.61 #382, 0.54 #764, 0.50 #1335), D (0.61 #382, 0.54 #764, 0.50 #1335), F (0.61 #382, 0.54 #764, 0.50 #1335), CZ (0.61 #382, 0.54 #764, 0.50 #1335), S (0.61 #382, 0.54 #764, 0.50 #1335), H (0.61 #382, 0.54 #764, 0.50 #1335), SLO (0.61 #382, 0.54 #764, 0.50 #1335), MNG (0.61 #382, 0.54 #764, 0.50 #1335)
>> best conf = 0.61
=> the first rule below is the first best rule for 25 predicted values
>> Best rule #382 for best value:
>> intensional similarity = 11
>> extensional distance = 2
>> proper extension:  Protestant;
>> query: (?x56, ?x129) <- ?x56[ a Religion; is religion of ?x130[ is locatedIn of ?x662; is neighbor of ?x129;]; is religion of ?x177[ has wasDependentOf ?x1656;]; is religion of ?x204[ has neighbor ?x692<KOS>;]; is religion of ?x403<KAZ>;]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 2
EVAL	ChristianOrthodox	religion!	GR	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	15.000	15.000	195.000	0.606	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion!
PRED expected values: GR
=> 25 concepts (25 used for prediction)
PRED predicted values (max 10 best out of 210): IR (0.75 #190, 0.70 #768, 0.69 #769), TR (0.75 #190, 0.70 #768, 0.69 #769), GR (0.75 #190, 0.70 #768, 0.69 #769), N (0.75 #190, 0.70 #768, 0.69 #769), CN (0.75 #190, 0.70 #768, 0.69 #769), PL (0.75 #190, 0.70 #768, 0.69 #769), TAD (0.75 #190, 0.70 #768, 0.69 #769), H (0.75 #190, 0.70 #768, 0.69 #769), AFG (0.75 #190, 0.70 #768, 0.69 #769), A (0.75 #190, 0.70 #768, 0.69 #769)
>> best conf = 0.75
=> the first rule below is the first best rule for 21 predicted values
>> Best rule #190 for best value:
>> intensional similarity = 32
>> extensional distance = 1
>> proper extension:  RomanCatholic;
>> query: (?x56, ?x170) <- ?x56[ is religion of ?x55<BIH>; is religion of ?x177<BG>; is religion of ?x196<AUS>; is religion of ?x207<I>; is religion of ?x222<BY>; is religion of ?x234<CH>; is religion of ?x353<GE>; is religion of ?x403[ has encompassed ?x195<Europe>; has ethnicGroup ?x58<Ukrainian>; has language ?x1245; is locatedIn of ?x319[ has hasSource ?x874;]; is locatedIn of ?x320[ a Lake;];]; is religion of ?x565[ has language ?x247; is locatedIn of ?x631; is neighbor of ?x170;]; is religion of ?x701<MK>; is religion of ?x904<SRB>;]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 3
EVAL	ChristianOrthodox	religion!	GR	CNN-1.+1._MA	0.000	1.000	1.000	0.333	25.000	25.000	210.000	0.746	http://www.semwebtech.org/mondial/10/meta#religion

#167-Cayambe
PRED entity: Cayambe

PRED relation: locatedIn
PRED expected values: EC
=> 25 concepts (25 used for prediction)
PRED predicted values (max 10 best out of 76): PE (0.45 #4503, 0.42 #4504, 0.41 #1895), RA (0.45 #4503, 0.42 #4504, 0.41 #1895), RCH (0.45 #4503, 0.42 #4504, 0.41 #1895), EC (0.45 #4503, 0.42 #4504, 0.41 #1895), BOL (0.45 #4503, 0.42 #4504, 0.41 #1895), CO (0.45 #4503, 0.42 #4504, 0.41 #1895), YV (0.45 #4503, 0.42 #4504, 0.41 #1895), USA (0.21 #2439, 0.16 #2677, 0.16 #2916), ZRE (0.16 #1259, 0.08 #2684, 0.08 #2923), RI (0.12 #2183, 0.09 #3607, 0.09 #3135)
>> best conf = 0.45
=> the first rule below is the first best rule for 7 predicted values
>> Best rule #4503 for best value:
>> intensional similarity = 6
>> extensional distance = 152
>> proper extension:  JabalKatrina; Annapurna; Demirkazik; Olympos; Manaslu; MtSt.Elias; UlugMuztag; Kangchendzonga; TorredeCerredo; ChoOyu; ...
>> query: (?x209, ?x902) <- ?x209[ a Mountain; has inMountains ?x431[ a Mountains; is inMountains of ?x1362[ a Mountain; has locatedIn ?x902;];];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 4
EVAL	Cayambe	locatedIn	EC	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.250	25.000	25.000	76.000	0.453	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: EC
=> 42 concepts (42 used for prediction)
PRED predicted values (max 10 best out of 77): RA (0.58 #5522, 0.45 #8639, 0.45 #8636), PE (0.45 #8639, 0.45 #8636, 0.45 #8635), BOL (0.45 #8639, 0.45 #8636, 0.45 #8635), CO (0.45 #8639, 0.45 #8636, 0.45 #8635), RCH (0.45 #8639, 0.45 #8636, 0.45 #8635), EC (0.45 #8639, 0.45 #8636, 0.45 #8635), YV (0.45 #8639, 0.45 #8636, 0.45 #8635), MEX (0.29 #1786, 0.25 #1307, 0.20 #2511), USA (0.18 #3911, 0.17 #4154, 0.17 #5355), ZRE (0.17 #4397, 0.16 #4637, 0.11 #3200)
>> best conf = 0.58
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5522 for best value:
>> intensional similarity = 16
>> extensional distance = 51
>> proper extension:  Schchara; MtElbert; KingsPeak; MtColumbia; GannettPeak; MontGreboun; HarneyPeak; PikesPeak; WheelerPeak; Zachwoa; ...
>> query: (?x209, ?x379) <- ?x209[ a Mountain; has inMountains ?x431[ a Mountains; is inMountains of ?x914[ a Mountain; a Volcano; has type ?x706;]; is inMountains of ?x1161[ a Volcano; has locatedIn ?x379; has type ?x150<"volcanic">;]; is inMountains of ?x1717[ a Mountain; a Volcano; has locatedIn ?x215;];];]

*> Best rule #8639 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 152
*> proper extension:  JabalKatrina; Annapurna; Demirkazik; Olympos; Manaslu; MtSt.Elias; UlugMuztag; Kangchendzonga; TorredeCerredo; ChoOyu; ...
*> query: (?x209, ?x345) <- ?x209[ a Mountain; has inMountains ?x431[ a Mountains; is inMountains of ?x2451[ a Mountain; has locatedIn ?x345;];];]
*> conf = 0.45
ranks of expected_values: 6
EVAL	Cayambe	locatedIn	EC	CNN-1.+1._MA	0.000	0.000	1.000	0.167	42.000	42.000	77.000	0.576	http://www.semwebtech.org/mondial/10/meta#locatedIn

#166-WAFU
PRED entity: WAFU

PRED relation: language
PRED expected values: Wallisian
=> 46 concepts (46 used for prediction)
PRED predicted values (max 10 best out of 89): English (0.76 #1155, 0.35 #1923, 0.33 #1827), German (0.44 #494, 0.40 #206, 0.38 #398), Spanish (0.35 #1941, 0.22 #885, 0.22 #3096), Dutch (0.17 #9, 0.15 #393, 0.15 #2017), Polynesian (0.17 #96, 0.13 #3075, 0.11 #192), Papiamento (0.17 #13, 0.13 #3075, 0.11 #109), Italian (0.15 #2017, 0.13 #3075, 0.12 #486), Albanian (0.15 #2017, 0.13 #3075, 0.11 #132), Slovenian (0.15 #2017, 0.13 #3075, 0.11 #114), Portuguese (0.15 #2017, 0.13 #3075, 0.10 #200)
>> best conf = 0.76
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1155 for best value:
>> intensional similarity = 7
>> extensional distance = 19
>> proper extension:  IRL; AUS; AXA; BR; TUCA; SF; GBJ; CAYM; FALK;
>> query: (?x564, English) <- ?x564[ a Country; has ethnicGroup ?x1335; has language ?x51[ is language of ?x718<L>;]; is locatedIn of ?x1279[ a Island;];]

No rule for expected values
ranks of expected_values: 
EVAL	WAFU	language	Wallisian	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	46.000	46.000	89.000	0.762	http://www.semwebtech.org/mondial/10/meta#language

PRED relation: language
PRED expected values: Wallisian
=> 105 concepts (105 used for prediction)
PRED predicted values (max 10 best out of 95): Spanish (0.71 #3478, 0.62 #3286, 0.60 #2997), English (0.62 #4038, 0.57 #2883, 0.57 #1155), German (0.43 #4145, 0.33 #1838, 0.33 #1742), Polynesian (0.33 #96, 0.25 #576, 0.25 #480), Chinese (0.25 #351, 0.20 #831, 0.17 #1023), Samoan (0.25 #290, 0.20 #770, 0.15 #7979), Hindi (0.25 #376, 0.20 #856, 0.15 #7979), Pitkern (0.25 #166, 0.15 #7979, 0.11 #5095), Portuguese (0.21 #4228, 0.20 #3265, 0.20 #2120), Dutch (0.21 #4228, 0.20 #3265, 0.17 #2601)
>> best conf = 0.71
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3478 for best value:
>> intensional similarity = 14
>> extensional distance = 15
>> proper extension:  ES;
>> query: (?x564, Spanish) <- ?x564[ has encompassed ?x211; has ethnicGroup ?x1335; has language ?x51[ is language of ?x272[ has religion ?x95; is locatedIn of ?x2007<ColumbiaRiver>;]; is language of ?x297[ has government ?x2145;]; is language of ?x789<AND>;]; has religion ?x352; is locatedIn of ?x282<PacificOcean>;]

No rule for expected values
ranks of expected_values: 
EVAL	WAFU	language	Wallisian	CNN-1.+1._MA	0.000	0.000	0.000	0.000	105.000	105.000	95.000	0.706	http://www.semwebtech.org/mondial/10/meta#language

#165-MNG
PRED entity: MNG

PRED relation: locatedIn!
PRED expected values: Gobi
=> 37 concepts (30 used for prediction)
PRED predicted values (max 10 best out of 1389): PacificOcean (0.61 #11465, 0.32 #41263, 0.29 #35567), GulfofBengal (0.43 #1494, 0.10 #7184, 0.08 #10030), AtlanticOcean (0.33 #41, 0.28 #7153, 0.26 #32762), CaribbeanSea (0.33 #105, 0.17 #11485, 0.16 #15751), St.Martin (0.33 #761, 0.01 #26372), BarentsSea (0.32 #41263, 0.29 #35567, 0.14 #5754), SeaofJapan (0.32 #41263, 0.29 #35567, 0.08 #19916), ArcticOcean (0.32 #41263, 0.29 #35567, 0.08 #19916), BeringSea (0.32 #41263, 0.29 #35567, 0.08 #19916), SeaofOkhotsk (0.32 #41263, 0.29 #35567, 0.08 #19916)
>> best conf = 0.61
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #11465 for best value:
>> intensional similarity = 6
>> extensional distance = 34
>> proper extension:  PAL;
>> query: (?x1010, PacificOcean) <- ?x1010[ a Country; has government ?x2058; has wasDependentOf ?x232; is locatedIn of ?x72[ has locatedIn ?x73<R>;];]

*> Best rule #4643 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 10
*> proper extension:  CN;
*> query: (?x1010, Gobi) <- ?x1010[ a Country; has government ?x2058; has neighbor ?x73<R>; has religion ?x116; is locatedIn of ?x72;]
*> conf = 0.08
ranks of expected_values: 172
EVAL	MNG	locatedIn!	Gobi	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.006	37.000	30.000	1389.000	0.611	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Gobi
=> 106 concepts (106 used for prediction)
PRED predicted values (max 10 best out of 1406): PacificOcean (0.61 #65603, 0.50 #54209, 0.45 #112615), AtlanticOcean (0.56 #132505, 0.47 #95476, 0.44 #34220), IndianOcean (0.50 #9974, 0.43 #11397, 0.33 #22780), GulfofBengal (0.50 #10043, 0.43 #11466, 0.33 #14311), SouthChinaSea (0.45 #21493, 0.42 #22917, 0.33 #8689), Donau (0.38 #27076, 0.18 #74084, 0.18 #69813), CaribbeanSea (0.35 #45682, 0.33 #7232, 0.30 #38559), Brahmaputra (0.33 #9673, 0.33 #5399, 0.33 #2550), Argun (0.33 #8739, 0.33 #4274, 0.33 #3041), EastChinaSea (0.33 #8827, 0.33 #7125, 0.33 #5976)
>> best conf = 0.61
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #65603 for best value:
>> intensional similarity = 6
>> extensional distance = 34
>> proper extension:  PAL;
>> query: (?x1010, PacificOcean) <- ?x1010[ a Country; has government ?x2058; has wasDependentOf ?x232; is locatedIn of ?x72[ has locatedIn ?x73<R>;];]

*> Best rule #8926 for first EXPECTED value:
*> intensional similarity = 18
*> extensional distance = 1
*> proper extension:  CN;
*> query: (?x1010, Gobi) <- ?x1010[ a Country; has ethnicGroup ?x1553; has government ?x2058; has neighbor ?x73<R>; has religion ?x116<Christian>; has religion ?x187<Muslim>; has religion ?x462[ is religion of ?x196<AUS>; is religion of ?x460<RP>; is religion of ?x538<BRU>; is religion of ?x924<IND>;];]
*> conf = 0.33
ranks of expected_values: 161
EVAL	MNG	locatedIn!	Gobi	CNN-1.+1._MA	0.000	0.000	0.000	0.006	106.000	106.000	1406.000	0.611	http://www.semwebtech.org/mondial/10/meta#locatedIn

#164-NorthernDwina
PRED entity: NorthernDwina

PRED relation: hasEstuary!
PRED expected values: NorthernDwina
=> 26 concepts (22 used for prediction)
PRED predicted values (max 10 best out of 123): Katun (0.04 #217, 0.02 #443, 0.02 #1133), Schilka (0.04 #214, 0.02 #440, 0.02 #1133), Kama (0.04 #199, 0.02 #425, 0.02 #1133), Irtysch (0.04 #194, 0.02 #420, 0.02 #1133), Amur (0.04 #181, 0.02 #407, 0.02 #1133), Oka (0.04 #179, 0.02 #405, 0.02 #1133), Kolyma (0.04 #163, 0.02 #389, 0.02 #1133), Don (0.04 #147, 0.02 #373, 0.02 #1133), Lena (0.04 #107, 0.02 #333, 0.02 #1133), Chatanga (0.04 #97, 0.02 #323, 0.02 #1133)
>> best conf = 0.04
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #217 for best value:
>> intensional similarity = 3
>> extensional distance = 21
>> proper extension:  Lena; Swir; Kama; Paatsjoki; Suchona; Chatanga; Vuoksi; Irtysch; Jenissej; Argun; ...
>> query: (?x2138, Katun) <- ?x2138[ a Estuary; has locatedIn ?x73<R>;]

*> Best rule #1133 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 146
*> proper extension:  Thames;
*> query: (?x2138, ?x72) <- ?x2138[ a Estuary; has locatedIn ?x73[ has language ?x555; has neighbor ?x170; is locatedIn of ?x72;];]
*> conf = 0.02
ranks of expected_values: 45
EVAL	NorthernDwina	hasEstuary!	NorthernDwina	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.022	26.000	22.000	123.000	0.043	http://www.semwebtech.org/mondial/10/meta#hasEstuary

PRED relation: hasEstuary!
PRED expected values: NorthernDwina
=> 81 concepts (81 used for prediction)
PRED predicted values (max 10 best out of 224): Amur (0.08 #3895, 0.08 #3437, 0.07 #3896), Swir (0.08 #3895, 0.08 #3437, 0.07 #3896), Jenissej (0.08 #3895, 0.08 #3437, 0.07 #3896), Newa (0.08 #3895, 0.08 #3437, 0.07 #3896), Paatsjoki (0.08 #3895, 0.08 #3437, 0.07 #3896), Irtysch (0.08 #3895, 0.08 #3437, 0.07 #3896), Volga (0.08 #3895, 0.08 #3437, 0.07 #3896), Angara (0.08 #3895, 0.08 #3437, 0.07 #3896), Dnepr (0.08 #3895, 0.08 #3437, 0.07 #3896), Kolyma (0.08 #3895, 0.08 #3437, 0.07 #3896)
>> best conf = 0.08
=> the first rule below is the first best rule for 22 predicted values
>> Best rule #3895 for best value:
>> intensional similarity = 11
>> extensional distance = 98
>> proper extension:  Karun;
>> query: (?x2138, ?x1457) <- ?x2138[ a Estuary; has locatedIn ?x73[ has language ?x555; is locatedIn of ?x282[ is locatedInWater of ?x205; is mergesWith of ?x60;]; is locatedIn of ?x1457[ a River; has hasEstuary ?x885;]; is neighbor of ?x962[ has ethnicGroup ?x963;];];]

*> Best rule #3896 for first EXPECTED value:
*> intensional similarity = 13
*> extensional distance = 98
*> proper extension:  Karun;
*> query: (?x2138, ?x103) <- ?x2138[ a Estuary; has locatedIn ?x73[ has language ?x555; is locatedIn of ?x103[ a River;]; is locatedIn of ?x282[ is locatedInWater of ?x205; is mergesWith of ?x60;]; is locatedIn of ?x1457[ a River; has hasEstuary ?x885;]; is neighbor of ?x962[ has ethnicGroup ?x963;];];]
*> conf = 0.07
ranks of expected_values: 24
EVAL	NorthernDwina	hasEstuary!	NorthernDwina	CNN-1.+1._MA	0.000	0.000	0.000	0.042	81.000	81.000	224.000	0.077	http://www.semwebtech.org/mondial/10/meta#hasEstuary

#163-RCA
PRED entity: RCA

PRED relation: religion
PRED expected values: Protestant
=> 31 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 34): Protestant (0.68 #446, 0.66 #486, 0.60 #82), Christian (0.60 #82, 0.60 #45, 0.53 #404), Kimbanguist (0.60 #82, 0.50 #727, 0.50 #726), ChristianOrthodox (0.27 #405, 0.20 #525, 0.20 #565), Catholic (0.17 #118, 0.14 #198, 0.14 #158), Buddhist (0.15 #372, 0.10 #654, 0.10 #937), Anglican (0.14 #460, 0.12 #500, 0.08 #1063), Jewish (0.10 #487, 0.09 #607, 0.09 #688), Hindu (0.09 #1055, 0.09 #855, 0.09 #895), JehovasWitnesses (0.08 #463, 0.07 #503, 0.05 #543)
>> best conf = 0.68
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #446 for best value:
>> intensional similarity = 5
>> extensional distance = 77
>> proper extension:  SLB; AMSA;
>> query: (?x736, Protestant) <- ?x736[ has encompassed ?x213; has ethnicGroup ?x992; has religion ?x352<RomanCatholic>; is locatedIn of ?x388;]
ranks of expected_values: 1
EVAL	RCA	religion	Protestant	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	31.000	31.000	34.000	0.684	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion
PRED expected values: Protestant
=> 96 concepts (96 used for prediction)
PRED predicted values (max 10 best out of 39): Protestant (0.76 #1088, 0.73 #2991, 0.72 #3394), Christian (0.70 #1167, 0.70 #1130, 0.64 #727), Hindu (0.57 #571, 0.20 #974, 0.16 #3678), Kimbanguist (0.55 #2425, 0.54 #2628, 0.54 #3554), CopticChristian (0.40 #2142, 0.31 #2101, 0.16 #3678), ChristianOrthodox (0.38 #1168, 0.35 #805, 0.33 #1087), Catholic (0.33 #116, 0.31 #2101, 0.20 #358), Anglican (0.29 #579, 0.22 #3271, 0.21 #1546), Jewish (0.26 #1449, 0.18 #726, 0.17 #1370), Seventh-DayAdventist (0.22 #3271, 0.13 #3840, 0.12 #2467)
>> best conf = 0.76
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1088 for best value:
>> intensional similarity = 15
>> extensional distance = 19
>> proper extension:  ROU;
>> query: (?x736, Protestant) <- ?x736[ a Country; has encompassed ?x213; has ethnicGroup ?x992; has government ?x435; has religion ?x352<RomanCatholic>; has wasDependentOf ?x78; is locatedIn of ?x388[ has hasEstuary ?x389;]; is locatedIn of ?x834[ a Estuary;]; is locatedIn of ?x879[ a River;]; is neighbor of ?x229[ is locatedIn of ?x53;];]
ranks of expected_values: 1
EVAL	RCA	religion	Protestant	CNN-1.+1._MA	1.000	1.000	1.000	1.000	96.000	96.000	39.000	0.762	http://www.semwebtech.org/mondial/10/meta#religion

#162-EAU
PRED entity: EAU

PRED relation: neighbor!
PRED expected values: ZRE
=> 35 concepts (34 used for prediction)
PRED predicted values (max 10 best out of 213): ZRE (0.90 #2211, 0.90 #3321, 0.89 #3320), DJI (0.33 #9, 0.04 #324, 0.03 #3014), ER (0.33 #103, 0.04 #1051, 0.04 #2472), EAU (0.25 #1738, 0.17 #426, 0.11 #5064), Z (0.25 #1738, 0.17 #403, 0.09 #878), MOC (0.25 #1738, 0.12 #348, 0.08 #507), BI (0.25 #1738, 0.12 #378, 0.07 #5384), MW (0.25 #1738, 0.12 #443, 0.07 #5384), RCA (0.25 #1738, 0.07 #5384, 0.06 #5063), ETH (0.25 #1738, 0.07 #5384, 0.06 #5063)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2211 for best value:
>> intensional similarity = 6
>> extensional distance = 70
>> proper extension:  LB;
>> query: (?x688, ?x348) <- ?x688[ a Country; has ethnicGroup ?x529; has neighbor ?x348; has religion ?x187<Muslim>; is neighbor of ?x229;]
ranks of expected_values: 1
EVAL	EAU	neighbor!	ZRE	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	35.000	34.000	213.000	0.902	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: ZRE
=> 93 concepts (93 used for prediction)
PRED predicted values (max 10 best out of 230): ZRE (0.96 #9214, 0.94 #10844, 0.92 #8072), EAU (0.50 #1394, 0.50 #752, 0.43 #1713), BI (0.50 #704, 0.40 #484, 0.39 #1121), MW (0.50 #1090, 0.40 #484, 0.39 #1121), MOC (0.50 #995, 0.40 #484, 0.39 #1121), DJI (0.50 #812, 0.33 #9, 0.28 #3862), Z (0.40 #484, 0.39 #1121, 0.33 #572), ETH (0.40 #484, 0.39 #1121, 0.33 #246), SP (0.40 #484, 0.39 #1121, 0.33 #204), RCA (0.40 #484, 0.33 #2204, 0.33 #602)
>> best conf = 0.96
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #9214 for best value:
>> intensional similarity = 14
>> extensional distance = 56
>> proper extension:  BRU;
>> query: (?x688, ?x348) <- ?x688[ a Country; has ethnicGroup ?x529; has neighbor ?x348[ has neighbor ?x525; has wasDependentOf ?x543; is locatedIn of ?x545[ a Mountain; has type ?x706;];]; has neighbor ?x474[ a Country; has ethnicGroup ?x244; has neighbor ?x220; has wasDependentOf ?x81;]; is locatedIn of ?x600;]
ranks of expected_values: 1
EVAL	EAU	neighbor!	ZRE	CNN-1.+1._MA	1.000	1.000	1.000	1.000	93.000	93.000	230.000	0.955	http://www.semwebtech.org/mondial/10/meta#neighbor

#161-GB
PRED entity: GB

PRED relation: ethnicGroup
PRED expected values: African
=> 42 concepts (42 used for prediction)
PRED predicted values (max 10 best out of 236): European (0.45 #3792, 0.38 #1268, 0.31 #2024), German (0.40 #1018, 0.33 #514, 0.18 #3794), Asian (0.38 #1279, 0.33 #19, 0.25 #1783), African (0.33 #6, 0.31 #4798, 0.25 #762), Irish (0.33 #117, 0.25 #873, 0.13 #3025), Turkish (0.33 #687, 0.20 #1191, 0.13 #3025), Arab (0.33 #263, 0.13 #3025, 0.11 #3279), Iranian (0.33 #296, 0.13 #3025, 0.11 #3279), White (0.25 #1831, 0.23 #2335, 0.07 #4355), Ukrainian (0.20 #1009, 0.12 #3785, 0.12 #5045)
>> best conf = 0.45
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3792 for best value:
>> intensional similarity = 5
>> extensional distance = 31
>> proper extension:  C; CDN; CV;
>> query: (?x81, European) <- ?x81[ has ethnicGroup ?x1196; has religion ?x352<RomanCatholic>; is locatedIn of ?x1509[ has inMountains ?x2469;];]

*> Best rule #6 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 1
*> proper extension:  IRL;
*> query: (?x81, African) <- ?x81[ has ethnicGroup ?x1196; has religion ?x95; is locatedIn of ?x373[ is locatedInWater of ?x807; is mergesWith of ?x251;]; is locatedIn of ?x1833<IrishSea>;]
*> conf = 0.33
ranks of expected_values: 4
EVAL	GB	ethnicGroup	African	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.250	42.000	42.000	236.000	0.455	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: African
=> 117 concepts (117 used for prediction)
PRED predicted values (max 10 best out of 249): European (0.60 #4822, 0.60 #510, 0.60 #509), Asian (0.60 #4833, 0.60 #510, 0.60 #509), Chinese (0.60 #510, 0.60 #509, 0.60 #508), Amerindian (0.60 #510, 0.60 #509, 0.60 #508), Malay (0.60 #510, 0.60 #509, 0.60 #508), African (0.60 #510, 0.60 #509, 0.60 #508), Borneoindigenous (0.60 #510, 0.60 #509, 0.60 #508), Creole (0.60 #510, 0.60 #509, 0.60 #508), Arab (0.60 #510, 0.60 #509, 0.60 #508), Shan (0.60 #510, 0.60 #509, 0.60 #508)
>> best conf = 0.60
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4822 for best value:
>> intensional similarity = 13
>> extensional distance = 3
>> proper extension:  NZ;
>> query: (?x81, European) <- ?x81[ has encompassed ?x195; has ethnicGroup ?x1196[ is ethnicGroup of ?x376[ a Country; has religion ?x116; is neighbor of ?x91;];]; has religion ?x95<Protestant>; is dependentOf of ?x561[ has ethnicGroup ?x162; has religion ?x280; is locatedIn of ?x1995;]; is locatedIn of ?x121;]
>> Best rule #510 for best value:
>> intensional similarity = 16
>> extensional distance = 1
>> proper extension:  MAL;
>> query: (?x81, ?x2169) <- ?x81[ has encompassed ?x195; has ethnicGroup ?x1196; has religion ?x352[ is religion of ?x962[ has language ?x555;]; is religion of ?x1248<COM>;]; has religion ?x462<Buddhist>; is locatedIn of ?x495[ has belongsToIslands ?x945;]; is wasDependentOf of ?x1554[ has ethnicGroup ?x162; is locatedIn of ?x317;]; is wasDependentOf of ?x1963[ has ethnicGroup ?x2169;];]
>> Best rule #509 for best value:
>> intensional similarity = 14
>> extensional distance = 1
>> proper extension:  MAL;
>> query: (?x81, ?x162) <- ?x81[ has encompassed ?x195; has ethnicGroup ?x1196; has religion ?x352[ is religion of ?x962[ has language ?x555;]; is religion of ?x1248<COM>;]; has religion ?x462<Buddhist>; is locatedIn of ?x495[ has belongsToIslands ?x945;]; is wasDependentOf of ?x1554[ has ethnicGroup ?x162; is locatedIn of ?x317;];]
>> Best rule #508 for best value:
>> intensional similarity = 16
>> extensional distance = 1
>> proper extension:  MAL;
>> query: (?x81, ?x380) <- ?x81[ has encompassed ?x195; has ethnicGroup ?x1196; has religion ?x352[ is religion of ?x962[ has language ?x555;]; is religion of ?x1248<COM>;]; has religion ?x462<Buddhist>; is locatedIn of ?x495[ has belongsToIslands ?x945;]; is wasDependentOf of ?x1554[ has ethnicGroup ?x162; is locatedIn of ?x317;]; is wasDependentOf of ?x1705[ has ethnicGroup ?x380;];]

*> Best rule #510 for first EXPECTED value:
*> intensional similarity = 16
*> extensional distance = 1
*> proper extension:  MAL;
*> query: (?x81, ?x2169) <- ?x81[ has encompassed ?x195; has ethnicGroup ?x1196; has religion ?x352[ is religion of ?x962[ has language ?x555;]; is religion of ?x1248<COM>;]; has religion ?x462<Buddhist>; is locatedIn of ?x495[ has belongsToIslands ?x945;]; is wasDependentOf of ?x1554[ has ethnicGroup ?x162; is locatedIn of ?x317;]; is wasDependentOf of ?x1963[ has ethnicGroup ?x2169;];]
*> conf = 0.60
ranks of expected_values: 6
EVAL	GB	ethnicGroup	African	CNN-1.+1._MA	0.000	0.000	1.000	0.167	117.000	117.000	249.000	0.600	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#160-Dodabetta
PRED entity: Dodabetta

PRED relation: locatedIn
PRED expected values: IND
=> 4 concepts (4 used for prediction)
PRED predicted values (max 10 best out of 46): USA (0.15 #72, 0.10 #308, 0.10 #544), CN (0.06 #292, 0.06 #56, 0.04 #528), I (0.06 #520, 0.05 #48, 0.04 #284), E (0.06 #27, 0.05 #499, 0.04 #263), CDN (0.05 #63, 0.04 #299, 0.03 #535), PE (0.05 #539, 0.03 #67, 0.02 #303), R (0.05 #477, 0.04 #5, 0.04 #713), D (0.04 #492, 0.03 #728, 0.03 #20), RI (0.04 #288, 0.02 #760), F (0.04 #479, 0.03 #7, 0.02 #243)
>> best conf = 0.15
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #72 for best value:
>> intensional similarity = 3
>> extensional distance = 156
>> proper extension:  JabalKatrina; Tahat; Annapurna; Bjelucha; Schchara; GranSasso; EmiKussi; Zugspitze; Demirkazik; Cayambe; ...
>> query: (?x1961, USA) <- ?x1961[ a Mountain; has inMountains ?x2349[ a Mountains;];]

*> Best rule #188 for first EXPECTED value:
*> intensional similarity = 3
*> extensional distance = 156
*> proper extension:  JabalKatrina; Tahat; Annapurna; Bjelucha; Schchara; GranSasso; EmiKussi; Zugspitze; Demirkazik; Cayambe; ...
*> query: (?x1961, IND) <- ?x1961[ a Mountain; has inMountains ?x2349[ a Mountains;];]
*> conf = 0.01
ranks of expected_values: 34
EVAL	Dodabetta	locatedIn	IND	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.029	4.000	4.000	46.000	0.146	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: IND
=> 4 concepts (4 used for prediction)
PRED predicted values (max 10 best out of 46): USA (0.15 #72, 0.10 #308, 0.10 #544), CN (0.06 #292, 0.06 #56, 0.04 #528), I (0.06 #520, 0.05 #48, 0.04 #284), E (0.06 #27, 0.05 #499, 0.04 #263), CDN (0.05 #63, 0.04 #299, 0.03 #535), PE (0.05 #539, 0.03 #67, 0.02 #303), R (0.05 #477, 0.04 #5, 0.04 #713), D (0.04 #492, 0.03 #728, 0.03 #20), RI (0.04 #288, 0.02 #760), F (0.04 #479, 0.03 #7, 0.02 #243)
>> best conf = 0.15
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #72 for best value:
>> intensional similarity = 3
>> extensional distance = 156
>> proper extension:  JabalKatrina; Tahat; Annapurna; Bjelucha; Schchara; GranSasso; EmiKussi; Zugspitze; Demirkazik; Cayambe; ...
>> query: (?x1961, USA) <- ?x1961[ a Mountain; has inMountains ?x2349[ a Mountains;];]

*> Best rule #188 for first EXPECTED value:
*> intensional similarity = 3
*> extensional distance = 156
*> proper extension:  JabalKatrina; Tahat; Annapurna; Bjelucha; Schchara; GranSasso; EmiKussi; Zugspitze; Demirkazik; Cayambe; ...
*> query: (?x1961, IND) <- ?x1961[ a Mountain; has inMountains ?x2349[ a Mountains;];]
*> conf = 0.01
ranks of expected_values: 34
EVAL	Dodabetta	locatedIn	IND	CNN-1.+1._MA	0.000	0.000	0.000	0.029	4.000	4.000	46.000	0.146	http://www.semwebtech.org/mondial/10/meta#locatedIn

#159-TAD
PRED entity: TAD

PRED relation: locatedIn!
PRED expected values: Amudarja Murgab Murgab
=> 32 concepts (32 used for prediction)
PRED predicted values (max 10 best out of 1322): OzeroAral (0.62 #21111, 0.50 #5346, 0.20 #6753), Amudarja (0.62 #21111, 0.33 #7132, 0.33 #2911), UstUrt (0.50 #5258, 0.20 #6665, 0.17 #8072), Kysylkum (0.50 #4650, 0.20 #6057, 0.17 #7464), PikChan-Tengri (0.40 #6229, 0.33 #7636, 0.33 #2008), SouthChinaSea (0.36 #11398, 0.24 #12806, 0.23 #9990), Rigestan (0.33 #2824, 0.33 #9, 0.20 #5638), CaspianSea (0.33 #7756, 0.25 #9164, 0.25 #4942), Naryn (0.33 #2731, 0.25 #5545, 0.20 #6952), Thar (0.33 #1061, 0.25 #9505, 0.15 #10912)
>> best conf = 0.62
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #21111 for best value:
>> intensional similarity = 5
>> extensional distance = 74
>> proper extension:  AUS; CDN; IS;
>> query: (?x129, ?x301) <- ?x129[ has encompassed ?x175; has ethnicGroup ?x1193; has government ?x435; is locatedIn of ?x300[ has flowsInto ?x301;];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 2
EVAL	TAD	locatedIn!	Murgab	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	32.000	32.000	1322.000	0.615	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	TAD	locatedIn!	Murgab	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	32.000	32.000	1322.000	0.615	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	TAD	locatedIn!	Amudarja	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	32.000	32.000	1322.000	0.615	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Amudarja Murgab Murgab
=> 100 concepts (99 used for prediction)
PRED predicted values (max 10 best out of 1384): Amudarja (0.94 #53521, 0.92 #40843, 0.92 #53520), Rigestan (0.94 #53521, 0.33 #4234, 0.33 #2826), Dascht-e-Margoh (0.94 #53521, 0.33 #5381, 0.18 #26756), Naryn (0.91 #80289, 0.55 #49293, 0.47 #63381), OzeroAral (0.72 #80288, 0.51 #76063, 0.47 #63380), IndianOcean (0.71 #30985, 0.23 #70428, 0.21 #59158), Murgab (0.69 #53519, 0.56 #19713, 0.54 #105652), Syrdarja (0.66 #80287, 0.54 #39433, 0.51 #64789), PacificOcean (0.55 #70510, 0.29 #107146, 0.28 #81782), BlackSea (0.50 #8461, 0.43 #14092, 0.38 #18317)
>> best conf = 0.94
=> the first rule below is the first best rule for 3 predicted values
>> Best rule #53521 for best value:
>> intensional similarity = 14
>> extensional distance = 28
>> proper extension:  F; I;
>> query: (?x129, ?x82) <- ?x129[ has encompassed ?x175; has neighbor ?x130; has religion ?x187; is locatedIn of ?x276[ a Estuary;]; is locatedIn of ?x652[ a Source;]; is locatedIn of ?x682[ has hasSource ?x1106;]; is locatedIn of ?x2401[ has locatedIn ?x381[ has language ?x1033; is locatedIn of ?x82;]; is hasSource of ?x301;];]
ranks of expected_values: 1, 7
EVAL	TAD	locatedIn!	Murgab	CNN-1.+1._MA	0.000	0.000	0.000	0.000	100.000	99.000	1384.000	0.943	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	TAD	locatedIn!	Murgab	CNN-1.+1._MA	0.000	0.000	1.000	0.167	100.000	99.000	1384.000	0.943	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	TAD	locatedIn!	Amudarja	CNN-1.+1._MA	1.000	1.000	1.000	1.000	100.000	99.000	1384.000	0.943	http://www.semwebtech.org/mondial/10/meta#locatedIn

#158-AndamanSea
PRED entity: AndamanSea

PRED relation: flowsInto!
PRED expected values: Saluen
=> 35 concepts (30 used for prediction)
PRED predicted values (max 10 best out of 202): Mekong (0.33 #466, 0.10 #1068, 0.09 #1371), Zambezi (0.33 #873, 0.10 #1174, 0.03 #2079), MurrayRiver (0.33 #801, 0.10 #1102, 0.03 #2007), Jubba (0.33 #704, 0.10 #1005, 0.03 #1910), Limpopo (0.33 #618, 0.10 #919, 0.03 #1824), Ganges (0.33 #183, 0.03 #1992, 0.03 #1691), Asahan (0.10 #968, 0.03 #1873, 0.03 #1572), ColumbiaRiver (0.10 #1179, 0.03 #2084, 0.03 #1783), Colorado (0.10 #1079, 0.03 #1984, 0.03 #1683), SnowyRiver (0.10 #1049, 0.03 #1954, 0.03 #1653)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #466 for best value:
>> intensional similarity = 5
>> extensional distance = 1
>> proper extension:  SouthChinaSea;
>> query: (?x339, Mekong) <- ?x339[ has locatedIn ?x366[ has neighbor ?x463; has wasDependentOf ?x81;]; is mergesWith of ?x385<MalakkaStrait>;]

*> Best rule #5133 for first EXPECTED value:
*> intensional similarity = 4
*> extensional distance = 133
*> proper extension:  Würm; RioLerma; SnowyRiver; Sanaga;
*> query: (?x339, ?x231) <- ?x339[ is flowsInto of ?x338[ has locatedIn ?x232[ has government ?x831; is locatedIn of ?x231;];];]
*> conf = 0.04
ranks of expected_values: 27
EVAL	AndamanSea	flowsInto!	Saluen	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.037	35.000	30.000	202.000	0.333	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto!
PRED expected values: Saluen
=> 106 concepts (106 used for prediction)
PRED predicted values (max 10 best out of 323): Zambezi (0.33 #270, 0.25 #1176, 0.25 #13021), MurrayRiver (0.33 #198, 0.25 #1104, 0.25 #13021), Jubba (0.33 #101, 0.25 #1007, 0.25 #13021), Limpopo (0.33 #15, 0.25 #921, 0.25 #13021), Mekong (0.33 #466, 0.25 #13021, 0.17 #2585), Ganges (0.33 #787, 0.25 #13021, 0.17 #1694), Asahan (0.25 #13021, 0.17 #2485, 0.17 #1879), ColumbiaRiver (0.20 #1484, 0.12 #3604, 0.10 #4210), Colorado (0.20 #1384, 0.12 #3504, 0.10 #4110), SnowyRiver (0.20 #1354, 0.12 #3474, 0.10 #4080)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #270 for best value:
>> intensional similarity = 14
>> extensional distance = 1
>> proper extension:  IndianOcean;
>> query: (?x339, Zambezi) <- ?x339[ has locatedIn ?x217<RI>; has locatedIn ?x366[ a Country; has encompassed ?x175; has ethnicGroup ?x2461[ a EthnicGroup;]; has religion ?x116; is neighbor of ?x943<BD>;]; is locatedInWater of ?x740<Sumatra>; is mergesWith of ?x262<GulfofBengal>;]

*> Best rule #11808 for first EXPECTED value:
*> intensional similarity = 13
*> extensional distance = 20
*> proper extension:  Sanaga;
*> query: (?x339, ?x262) <- ?x339[ is flowsInto of ?x338[ has locatedIn ?x232[ a Country; has government ?x831; has neighbor ?x73;]; has locatedIn ?x366[ has ethnicGroup ?x298; has government ?x2096; has neighbor ?x91; has religion ?x116<Christian>; has wasDependentOf ?x81; is locatedIn of ?x262;];];]
*> conf = 0.12
ranks of expected_values: 53
EVAL	AndamanSea	flowsInto!	Saluen	CNN-1.+1._MA	0.000	0.000	0.000	0.019	106.000	106.000	323.000	0.333	http://www.semwebtech.org/mondial/10/meta#flowsInto

#157-CordilleraCentral
PRED entity: CordilleraCentral

PRED relation: inMountains!
PRED expected values: TorredeEstrela
=> 35 concepts (35 used for prediction)
PRED predicted values (max 10 best out of 279): Vignemale (0.14 #251, 0.12 #509, 0.12 #7252), Guadalquivir (0.14 #238, 0.12 #496, 0.12 #7252), Douro (0.14 #231, 0.12 #489, 0.12 #7252), Mulhacen (0.14 #201, 0.12 #459, 0.12 #7252), RoquedelosMuchachos (0.14 #179, 0.12 #437, 0.12 #7252), Moncayo (0.14 #174, 0.12 #432, 0.12 #7252), Guadiana (0.14 #173, 0.12 #431, 0.12 #7252), Tajo (0.14 #170, 0.12 #428, 0.12 #7252), PicodeAneto (0.14 #124, 0.12 #382, 0.12 #7252), PicodeTeide (0.14 #113, 0.12 #371, 0.12 #7252)
>> best conf = 0.14
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #251 for best value:
>> intensional similarity = 5
>> extensional distance = 5
>> proper extension:  CordilleraBetica; CordilleraIberica; CanaryIslands; Pyrenees; CordilleraCantabrica;
>> query: (?x2492, Vignemale) <- ?x2492[ a Mountains; is inMountains of ?x1744[ a Mountain; has locatedIn ?x149<E>;];]

No rule for expected values
ranks of expected_values: 
EVAL	CordilleraCentral	inMountains!	TorredeEstrela	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	35.000	35.000	279.000	0.143	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains!
PRED expected values: TorredeEstrela
=> 48 concepts (48 used for prediction)
PRED predicted values (max 10 best out of 279): Vignemale (0.14 #251, 0.14 #10102, 0.12 #1804), Guadalquivir (0.14 #238, 0.14 #10102, 0.12 #1791), Douro (0.14 #231, 0.14 #10102, 0.12 #1784), Mulhacen (0.14 #201, 0.14 #10102, 0.12 #1754), RoquedelosMuchachos (0.14 #179, 0.14 #10102, 0.12 #1732), Moncayo (0.14 #174, 0.14 #10102, 0.12 #1727), Guadiana (0.14 #173, 0.14 #10102, 0.12 #1726), Tajo (0.14 #170, 0.14 #10102, 0.12 #1723), PicodeAneto (0.14 #124, 0.14 #10102, 0.12 #1677), PicodeTeide (0.14 #113, 0.14 #10102, 0.12 #1666)
>> best conf = 0.14
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #251 for best value:
>> intensional similarity = 5
>> extensional distance = 5
>> proper extension:  CordilleraBetica; CordilleraIberica; CanaryIslands; Pyrenees; CordilleraCantabrica;
>> query: (?x2492, Vignemale) <- ?x2492[ a Mountains; is inMountains of ?x1744[ a Mountain; has locatedIn ?x149<E>;];]

No rule for expected values
ranks of expected_values: 
EVAL	CordilleraCentral	inMountains!	TorredeEstrela	CNN-1.+1._MA	0.000	0.000	0.000	0.000	48.000	48.000	279.000	0.143	http://www.semwebtech.org/mondial/10/meta#inMountains

#156-MontGreboun
PRED entity: MontGreboun

PRED relation: locatedIn
PRED expected values: RN
=> 15 concepts (15 used for prediction)
PRED predicted values (max 10 best out of 54): RN (0.58 #473, 0.45 #1894, 0.43 #1420), USA (0.21 #545, 0.21 #1018, 0.21 #782), PE (0.13 #1487, 0.06 #2434, 0.05 #2671), ZRE (0.09 #1499, 0.04 #315, 0.03 #552), R (0.08 #478, 0.08 #241, 0.07 #951), MEX (0.08 #352, 0.06 #589, 0.06 #1062), RA (0.08 #323, 0.06 #560, 0.06 #1033), I (0.06 #2415, 0.06 #2652, 0.06 #3124), CN (0.06 #2896, 0.06 #1713, 0.06 #1950), E (0.06 #1684, 0.06 #1921, 0.06 #2158)
>> best conf = 0.58
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #473 for best value:
>> intensional similarity = 9
>> extensional distance = 51
>> proper extension:  Tahat; Schchara; Cayambe; Elbrus; MtElbert; KingsPeak; PuyDeDome; Irazu; Karisimbi; MountKenia; ...
>> query: (?x1133, ?x426) <- ?x1133[ a Mountain; has inMountains ?x1501[ a Mountains; is inMountains of ?x535[ a Mountain; a Volcano; has locatedIn ?x426; has type ?x150<"volcanic">;];];]
ranks of expected_values: 1
EVAL	MontGreboun	locatedIn	RN	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	15.000	15.000	54.000	0.576	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: RN
=> 14 concepts (14 used for prediction)
PRED predicted values (max 10 best out of 54): RN (0.58 #473, 0.45 #1894, 0.43 #1420), USA (0.21 #545, 0.21 #1019, 0.21 #782), PE (0.13 #1487, 0.06 #2197, 0.05 #2434), ZRE (0.09 #1499, 0.04 #315, 0.03 #552), R (0.08 #478, 0.08 #241, 0.07 #952), MEX (0.08 #352, 0.06 #589, 0.06 #1063), RA (0.08 #323, 0.06 #560, 0.06 #1034), I (0.06 #2178, 0.06 #2415, 0.06 #2887), CN (0.06 #2659, 0.06 #1713, 0.06 #1950), E (0.06 #1684, 0.06 #1921, 0.06 #2157)
>> best conf = 0.58
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #473 for best value:
>> intensional similarity = 9
>> extensional distance = 51
>> proper extension:  Tahat; Schchara; Cayambe; Elbrus; MtElbert; KingsPeak; PuyDeDome; Irazu; Karisimbi; MountKenia; ...
>> query: (?x1133, ?x426) <- ?x1133[ a Mountain; has inMountains ?x1501[ a Mountains; is inMountains of ?x535[ a Mountain; a Volcano; has locatedIn ?x426; has type ?x150<"volcanic">;];];]
ranks of expected_values: 1
EVAL	MontGreboun	locatedIn	RN	CNN-1.+1._MA	1.000	1.000	1.000	1.000	14.000	14.000	54.000	0.576	http://www.semwebtech.org/mondial/10/meta#locatedIn

#155-SovietUnion
PRED entity: SovietUnion

PRED relation: wasDependentOf!
PRED expected values: TAD KAZ EW
=> 13 concepts (13 used for prediction)
PRED predicted values (max 10 best out of 181): RO (0.34 #585, 0.33 #20, 0.33 #1176), AFG (0.34 #585, 0.33 #1176, 0.33 #1028), IR (0.34 #585, 0.33 #1176, 0.33 #1028), KAZ (0.34 #585, 0.33 #1176, 0.33 #1028), H (0.34 #585, 0.33 #1176, 0.33 #1028), SK (0.34 #585, 0.33 #1176, 0.33 #1028), PL (0.34 #585, 0.33 #1176, 0.33 #1028), EW (0.34 #585, 0.33 #1176, 0.33 #1028), N (0.34 #585, 0.33 #1176, 0.33 #1028), MNG (0.34 #585, 0.33 #1028, 0.32 #1174)
>> best conf = 0.34
=> the first rule below is the first best rule for 10 predicted values
>> Best rule #585 for best value:
>> intensional similarity = 13
>> extensional distance = 15
>> proper extension:  R; F; E; CO; S; BR; NL; RH; SRB; Yugoslavia; ...
>> query: (?x903, ?x304) <- ?x903[ is wasDependentOf of ?x290[ a Country; has language ?x555; is locatedIn of ?x289; is neighbor of ?x304[ has ethnicGroup ?x244; has government ?x2318; has language ?x511; is locatedIn of ?x573;];]; is wasDependentOf of ?x331[ has ethnicGroup ?x1193; has language ?x741; is neighbor of ?x185;];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 4, 8, 15
EVAL	SovietUnion	wasDependentOf!	EW	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.143	13.000	13.000	181.000	0.342	http://www.semwebtech.org/mondial/10/meta#wasDependentOf
EVAL	SovietUnion	wasDependentOf!	KAZ	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.250	13.000	13.000	181.000	0.342	http://www.semwebtech.org/mondial/10/meta#wasDependentOf
EVAL	SovietUnion	wasDependentOf!	TAD	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.077	13.000	13.000	181.000	0.342	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

PRED relation: wasDependentOf!
PRED expected values: TAD KAZ EW
=> 23 concepts (23 used for prediction)
PRED predicted values (max 10 best out of 219): MNG (0.58 #3051, 0.50 #1521, 0.44 #449), SF (0.58 #3051, 0.50 #1521, 0.35 #3055), CN (0.50 #1521, 0.50 #1365, 0.47 #904), EW (0.50 #1521, 0.50 #1365, 0.44 #449), RO (0.50 #1365, 0.47 #904, 0.44 #449), SK (0.50 #1365, 0.47 #904, 0.44 #449), H (0.50 #1365, 0.47 #904, 0.44 #449), PL (0.50 #1365, 0.47 #904, 0.44 #449), IR (0.50 #1365, 0.47 #904, 0.44 #449), KAZ (0.50 #1365, 0.47 #904, 0.44 #449)
>> best conf = 0.58
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #3051 for best value:
>> intensional similarity = 35
>> extensional distance = 13
>> proper extension:  I;
>> query: (?x903, ?x1010) <- ?x903[ is wasDependentOf of ?x130[ a Country; has ethnicGroup ?x1802; has religion ?x56[ is religion of ?x207<I>;]; is locatedIn of ?x662; is neighbor of ?x232[ is locatedIn of ?x231; is wasDependentOf of ?x1010;];]; is wasDependentOf of ?x331[ has ethnicGroup ?x1193; has religion ?x1805[ a Religion;];]; is wasDependentOf of ?x332[ is neighbor of ?x304[ has encompassed ?x175; has government ?x2318; has language ?x511; has religion ?x2031; is locatedIn of ?x573;];]; is wasDependentOf of ?x886[ has ethnicGroup ?x58; has religion ?x109; is neighbor of ?x176[ has encompassed ?x195; has ethnicGroup ?x164; is locatedIn of ?x98; is neighbor of ?x236;];]; is wasDependentOf of ?x962[ has ethnicGroup ?x963; has religion ?x352[ is religion of ?x1826<GBZ>;]; is neighbor of ?x194;];]

*> Best rule #1521 for first EXPECTED value:
*> intensional similarity = 37
*> extensional distance = 4
*> proper extension:  DK;
*> query: (?x903, ?x565) <- ?x903[ is wasDependentOf of ?x73[ has ethnicGroup ?x58; has language ?x555; has religion ?x56; is locatedIn of ?x97[ a Sea;]; is locatedIn of ?x103[ a River;]; is locatedIn of ?x263[ is mergesWith of ?x248;]; is locatedIn of ?x397[ a Estuary;]; is locatedIn of ?x445[ has hasEstuary ?x2249;]; is locatedIn of ?x492[ a Source;]; is locatedIn of ?x976[ is hasSource of ?x1845;]; is locatedIn of ?x1396[ has hasSource ?x2282; has locatedIn ?x565;]; is locatedIn of ?x1938[ a Volcano; has type ?x706;];]; is wasDependentOf of ?x130[ has encompassed ?x175; has language ?x986; is locatedIn of ?x662;]; is wasDependentOf of ?x290[ a Country; has ethnicGroup ?x1948; is locatedIn of ?x289;]; is wasDependentOf of ?x332[ has ethnicGroup ?x908; has government ?x435; has language ?x1031[ a Language;];];]
*> conf = 0.50
ranks of expected_values: 4, 10, 11
EVAL	SovietUnion	wasDependentOf!	EW	CNN-1.+1._MA	0.000	0.000	1.000	0.250	23.000	23.000	219.000	0.583	http://www.semwebtech.org/mondial/10/meta#wasDependentOf
EVAL	SovietUnion	wasDependentOf!	KAZ	CNN-1.+1._MA	0.000	0.000	1.000	0.111	23.000	23.000	219.000	0.583	http://www.semwebtech.org/mondial/10/meta#wasDependentOf
EVAL	SovietUnion	wasDependentOf!	TAD	CNN-1.+1._MA	0.000	0.000	1.000	0.111	23.000	23.000	219.000	0.583	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

#154-Leyte
PRED entity: Leyte

PRED relation: belongsToIslands
PRED expected values: Philipines
=> 38 concepts (38 used for prediction)
PRED predicted values (max 10 best out of 51): Philipines (0.44 #75, 0.33 #7, 0.16 #273), SundaIslands (0.16 #218, 0.06 #355, 0.06 #423), HawaiiIslands (0.14 #165, 0.12 #302, 0.03 #506), CalifornianChannelIslands (0.10 #195, 0.09 #332, 0.02 #536), Japan (0.08 #162, 0.07 #299, 0.03 #367), LesserAntilles (0.08 #1104, 0.07 #492, 0.07 #560), MoluccanIslands (0.05 #228, 0.02 #160, 0.02 #297), MarianaIslands (0.04 #137, 0.04 #274, 0.02 #342), SamoanIslands (0.04 #148, 0.04 #285, 0.02 #353), NewZealand (0.04 #171, 0.04 #308, 0.01 #376)
>> best conf = 0.44
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #75 for best value:
>> intensional similarity = 2
>> extensional distance = 16
>> proper extension:  SouthChinaSea; LagunadeBay; Pulog; SulawesiSea; MountApo; Mantalingajan; Kanlaon; Pinatubo;
>> query: (?x765, Philipines) <- ?x765[ has locatedIn ?x460<RP>;]
ranks of expected_values: 1
EVAL	Leyte	belongsToIslands	Philipines	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	38.000	38.000	51.000	0.444	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

PRED relation: belongsToIslands
PRED expected values: Philipines
=> 51 concepts (51 used for prediction)
PRED predicted values (max 10 best out of 59): Philipines (0.64 #75, 0.44 #143, 0.41 #211), SundaIslands (0.23 #218, 0.18 #82, 0.13 #286), Azores (0.10 #276, 0.06 #616, 0.05 #684), RiauIslands (0.09 #262, 0.03 #330, 0.02 #398), Canares (0.09 #295, 0.07 #363, 0.05 #635), LipariIslands (0.09 #274, 0.06 #478, 0.06 #546), LesserAntilles (0.08 #1308, 0.08 #1445, 0.08 #1513), HawaiiIslands (0.07 #437, 0.06 #1771, 0.06 #1430), CanadianArcticIslands (0.06 #484, 0.06 #416, 0.05 #620), Japan (0.06 #1771, 0.06 #1430, 0.05 #366)
>> best conf = 0.64
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #75 for best value:
>> intensional similarity = 11
>> extensional distance = 9
>> proper extension:  Cebu; Borneo; Panay; Samar; Sulawesi; Negros; Palawan; Bohol;
>> query: (?x765, Philipines) <- ?x765[ has locatedInWater ?x282[ a Sea; has locatedIn ?x783[ has encompassed ?x521;]; has locatedIn ?x1002[ has religion ?x95;]; has mergesWith ?x60; is locatedInWater of ?x1158<Mindanao>;]; has locatedInWater ?x625<SulawesiSea>;]
ranks of expected_values: 1
EVAL	Leyte	belongsToIslands	Philipines	CNN-1.+1._MA	1.000	1.000	1.000	1.000	51.000	51.000	59.000	0.636	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

#153-Loire
PRED entity: Loire

PRED relation: inMountains
PRED expected values: Cevennes
=> 35 concepts (35 used for prediction)
PRED predicted values (max 10 best out of 29): Alps (0.43 #1135, 0.29 #1048, 0.27 #613), Pyrenees (0.33 #62, 0.20 #149, 0.11 #236), Vogesen (0.30 #309, 0.22 #222, 0.20 #570), CordilleraIberica (0.20 #577, 0.14 #838, 0.09 #490), Balkan (0.14 #1064, 0.04 #2108, 0.04 #1934), Andes (0.09 #2186, 0.08 #2012, 0.08 #2273), EastAfricanRift (0.08 #1681, 0.05 #1942, 0.05 #2029), SudetyMountains (0.08 #1189, 0.04 #1798, 0.03 #1972), BlackForest (0.08 #1132, 0.04 #1045, 0.03 #1915), SnowyMountains (0.07 #978, 0.04 #1413, 0.03 #1761)
>> best conf = 0.43
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1135 for best value:
>> intensional similarity = 7
>> extensional distance = 35
>> proper extension:  Enns;
>> query: (?x2420, Alps) <- ?x2420[ a Source; is hasSource of ?x1257[ has hasEstuary ?x2424; has locatedIn ?x78[ has neighbor ?x234<CH>; has religion ?x95;];];]

No rule for expected values
ranks of expected_values: 
EVAL	Loire	inMountains	Cevennes	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	35.000	35.000	29.000	0.432	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains
PRED expected values: Cevennes
=> 76 concepts (76 used for prediction)
PRED predicted values (max 10 best out of 29): CordilleraIberica (0.50 #142, 0.30 #664, 0.30 #577), Alps (0.43 #4267, 0.33 #3745, 0.29 #3832), Pyrenees (0.33 #62, 0.17 #149, 0.14 #236), Vogesen (0.30 #483, 0.22 #396, 0.20 #744), Andes (0.17 #2969, 0.11 #1316, 0.10 #533), Balkan (0.14 #3848, 0.03 #6197, 0.03 #5762), Jura (0.12 #293, 0.03 #4121), EastAfricanRift (0.12 #4030, 0.11 #4378, 0.05 #1159), BlackForest (0.11 #3742, 0.08 #4264, 0.04 #3829), SierraParima (0.10 #602, 0.07 #863, 0.07 #950)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #142 for best value:
>> intensional similarity = 11
>> extensional distance = 4
>> proper extension:  Tajo; Guadiana; Douro;
>> query: (?x2420, CordilleraIberica) <- ?x2420[ a Source; is hasSource of ?x1257[ a River; has flowsInto ?x182<AtlanticOcean>; has locatedIn ?x78[ has language ?x51; has religion ?x95; is dependentOf of ?x61; is neighbor of ?x120; is wasDependentOf of ?x94;];];]

No rule for expected values
ranks of expected_values: 
EVAL	Loire	inMountains	Cevennes	CNN-1.+1._MA	0.000	0.000	0.000	0.000	76.000	76.000	29.000	0.500	http://www.semwebtech.org/mondial/10/meta#inMountains

#152-European
PRED entity: European

PRED relation: ethnicGroup!
PRED expected values: PE RH
=> 25 concepts (25 used for prediction)
PRED predicted values (max 10 best out of 193): PE (0.48 #317, 0.46 #636, 0.40 #202), RH (0.48 #317, 0.46 #636, 0.36 #796), ZW (0.48 #317, 0.46 #636, 0.36 #796), BZ (0.48 #317, 0.46 #636, 0.36 #796), RT (0.48 #317, 0.46 #636, 0.36 #796), SME (0.48 #317, 0.46 #636, 0.36 #796), PY (0.48 #317, 0.46 #636, 0.36 #796), EAT (0.48 #317, 0.46 #636, 0.36 #796), RG (0.48 #317, 0.46 #636, 0.36 #796), NAM (0.48 #317, 0.46 #636, 0.36 #796)
>> best conf = 0.48
=> the first rule below is the first best rule for 25 predicted values
>> Best rule #317 for best value:
>> intensional similarity = 10
>> extensional distance = 3
>> proper extension:  Amerindian; Mestizo;
>> query: (?x197, ?x138) <- ?x197[ is ethnicGroup of ?x215<CO>; is ethnicGroup of ?x318<CR>; is ethnicGroup of ?x379[ has encompassed ?x521; has wasDependentOf ?x149; is locatedIn of ?x182;]; is ethnicGroup of ?x525[ has neighbor ?x138;];]
ranks of expected_values: 1, 2
EVAL	European	ethnicGroup!	RH	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	25.000	25.000	193.000	0.481	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	European	ethnicGroup!	PE	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	25.000	25.000	193.000	0.481	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup!
PRED expected values: PE RH
=> 66 concepts (66 used for prediction)
PRED predicted values (max 10 best out of 204): PE (0.55 #477, 0.52 #476, 0.50 #999), BZ (0.55 #477, 0.52 #476, 0.50 #1437), RH (0.55 #477, 0.52 #476, 0.49 #1434), PY (0.55 #477, 0.52 #476, 0.49 #1434), GAZA (0.55 #477, 0.52 #476, 0.49 #1434), IL (0.55 #477, 0.52 #476, 0.49 #1434), RG (0.55 #477, 0.52 #476, 0.49 #1434), RMM (0.55 #477, 0.52 #476, 0.49 #1434), NAM (0.55 #477, 0.52 #476, 0.49 #1434), RSA (0.55 #477, 0.52 #476, 0.49 #1434)
>> best conf = 0.55
=> the first rule below is the first best rule for 25 predicted values
>> Best rule #477 for best value:
>> intensional similarity = 22
>> extensional distance = 1
>> proper extension:  Amerindian;
>> query: (?x197, ?x348) <- ?x197[ is ethnicGroup of ?x272[ has encompassed ?x521; is locatedIn of ?x219<DetroitRiver>; is locatedIn of ?x263<ArcticOcean>;]; is ethnicGroup of ?x390[ a Country; has language ?x247; has religion ?x429[ a Religion;];]; is ethnicGroup of ?x408<NIC>; is ethnicGroup of ?x482<MEX>; is ethnicGroup of ?x525[ is locatedIn of ?x284; is neighbor of ?x348;]; is ethnicGroup of ?x902<EC>; is ethnicGroup of ?x934[ has neighbor ?x138;];]
ranks of expected_values: 1, 3
EVAL	European	ethnicGroup!	RH	CNN-1.+1._MA	0.000	1.000	1.000	0.500	66.000	66.000	204.000	0.550	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	European	ethnicGroup!	PE	CNN-1.+1._MA	1.000	1.000	1.000	1.000	66.000	66.000	204.000	0.550	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#151-DetroitRiver
PRED entity: DetroitRiver

PRED relation: hasEstuary!
PRED expected values: DetroitRiver
=> 23 concepts (21 used for prediction)
PRED predicted values (max 10 best out of 163): NiagaraRiver (0.25 #122, 0.10 #348, 0.04 #576), SaintMarysRiver (0.25 #49, 0.10 #275, 0.04 #503), SaintLawrenceRiver (0.10 #453, 0.10 #376, 0.04 #604), RiviereRichelieu (0.10 #445, 0.04 #673, 0.02 #1361), MackenzieRiver (0.10 #439, 0.04 #667, 0.02 #1361), SaskatchewanRiver (0.10 #415, 0.04 #643, 0.02 #1361), Manicouagan (0.10 #374, 0.04 #602, 0.02 #1361), NelsonRiver (0.10 #340, 0.04 #568, 0.02 #1361), AlleghenyRiver (0.04 #669, 0.02 #1361, 0.02 #1588), ColumbiaRiver (0.04 #663, 0.02 #1361, 0.02 #1588)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #122 for best value:
>> intensional similarity = 5
>> extensional distance = 2
>> proper extension:  SaintMarysRiver; NiagaraRiver;
>> query: (?x691, NiagaraRiver) <- ?x691[ a Estuary; has locatedIn ?x272<CDN>; has locatedIn ?x315<USA>;]

*> Best rule #1361 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 144
*> proper extension:  Umeaelv; Goetaaelv; Vaesterdalaelv; Klaraelv; Dalaelv; Kura; Oesterdalaelv;
*> query: (?x691, ?x182) <- ?x691[ a Estuary; has locatedIn ?x272[ has ethnicGroup ?x197; has language ?x51; is locatedIn of ?x182;];]
*> conf = 0.02
ranks of expected_values: 161
EVAL	DetroitRiver	hasEstuary!	DetroitRiver	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.006	23.000	21.000	163.000	0.250	http://www.semwebtech.org/mondial/10/meta#hasEstuary

PRED relation: hasEstuary!
PRED expected values: DetroitRiver
=> 87 concepts (87 used for prediction)
PRED predicted values (max 10 best out of 223): SaintMarysRiver (0.25 #49, 0.08 #7293, 0.08 #7292), NiagaraRiver (0.25 #122, 0.08 #7293, 0.08 #7292), YukonRiver (0.08 #7293, 0.08 #7292, 0.08 #6607), ColumbiaRiver (0.08 #7293, 0.08 #7292, 0.08 #6607), MerrimackRiver (0.08 #7293, 0.08 #7292, 0.08 #6607), Mississippi (0.08 #7293, 0.08 #7292, 0.08 #6607), Missouri (0.08 #7293, 0.08 #7292, 0.08 #6607), TruckeeRiver (0.08 #7293, 0.08 #7292, 0.08 #6607), Tennessee (0.08 #7293, 0.08 #7292, 0.08 #6607), OhioRiver (0.08 #7293, 0.08 #7292, 0.08 #6607)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #49 for best value:
>> intensional similarity = 5
>> extensional distance = 2
>> proper extension:  SaintMarysRiver; NiagaraRiver;
>> query: (?x691, SaintMarysRiver) <- ?x691[ a Estuary; has locatedIn ?x272<CDN>; has locatedIn ?x315<USA>;]

*> Best rule #7293 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 229
*> proper extension:  Bahrel-Ghasal; Bahrel-Djebel-Albert-Nil; Sobat;
*> query: (?x691, ?x268) <- ?x691[ a Estuary; has locatedIn ?x315[ is locatedIn of ?x182[ is flowsInto of ?x137;]; is locatedIn of ?x268[ a River;];];]
*> conf = 0.08
ranks of expected_values: 19
EVAL	DetroitRiver	hasEstuary!	DetroitRiver	CNN-1.+1._MA	0.000	0.000	0.000	0.053	87.000	87.000	223.000	0.250	http://www.semwebtech.org/mondial/10/meta#hasEstuary

#150-RWA
PRED entity: RWA

PRED relation: religion
PRED expected values: Muslim
=> 40 concepts (40 used for prediction)
PRED predicted values (max 10 best out of 34): Muslim (0.74 #724, 0.73 #846, 0.67 #124), Christian (0.52 #762, 0.51 #884, 0.50 #1125), Kimbanguist (0.49 #1286, 0.33 #74, 0.11 #154), ChristianOrthodox (0.32 #481, 0.26 #401, 0.26 #601), Buddhist (0.19 #250, 0.18 #290, 0.17 #210), Hindu (0.17 #208, 0.14 #288, 0.13 #528), JehovasWitnesses (0.15 #259, 0.15 #179, 0.14 #299), Anglican (0.15 #696, 0.12 #1020, 0.11 #778), Jewish (0.13 #682, 0.12 #322, 0.12 #642), Seventh-DayAdventist (0.05 #933, 0.05 #569, 0.05 #893)
>> best conf = 0.74
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #724 for best value:
>> intensional similarity = 6
>> extensional distance = 67
>> proper extension:  THA; TN; NAM; G; SP; SSD; IL; IRQ; MYA; SN; ...
>> query: (?x546, Muslim) <- ?x546[ a Country; has neighbor ?x820[ has religion ?x116<Christian>; has wasDependentOf ?x81;]; is locatedIn of ?x545;]
ranks of expected_values: 1
EVAL	RWA	religion	Muslim	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	40.000	40.000	34.000	0.739	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion
PRED expected values: Muslim
=> 104 concepts (104 used for prediction)
PRED predicted values (max 10 best out of 39): Muslim (0.92 #1372, 0.77 #773, 0.77 #732), Kimbanguist (0.92 #1372, 0.64 #1864, 0.63 #2067), Christian (0.58 #3490, 0.56 #368, 0.55 #2923), ChristianOrthodox (0.41 #2435, 0.40 #1092, 0.36 #1539), Anglican (0.33 #422, 0.24 #4064, 0.24 #1147), JehovasWitnesses (0.31 #829, 0.17 #667, 0.15 #1310), Buddhist (0.24 #4064, 0.23 #820, 0.22 #416), Jewish (0.24 #4064, 0.23 #1985, 0.20 #2312), Hindu (0.24 #4064, 0.22 #454, 0.20 #938), Sikh (0.24 #4064, 0.12 #1454, 0.11 #3736)
>> best conf = 0.92
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #1372 for best value:
>> intensional similarity = 12
>> extensional distance = 26
>> proper extension:  REUN;
>> query: (?x546, ?x187) <- ?x546[ has government ?x2266; has religion ?x95[ is religion of ?x783<PA>;]; has religion ?x352<RomanCatholic>; is locatedIn of ?x545[ a Mountain; a Volcano; has locatedIn ?x348[ has religion ?x187;]; has type ?x706;];]
ranks of expected_values: 1
EVAL	RWA	religion	Muslim	CNN-1.+1._MA	1.000	1.000	1.000	1.000	104.000	104.000	39.000	0.921	http://www.semwebtech.org/mondial/10/meta#religion

#149-LakeGenezareth
PRED entity: LakeGenezareth

PRED relation: locatedIn
PRED expected values: IL
=> 46 concepts (44 used for prediction)
PRED predicted values (max 10 best out of 142): JOR (0.60 #240, 0.55 #2609, 0.54 #1661), RL (0.60 #240, 0.55 #2609, 0.54 #1661), IL (0.60 #240, 0.55 #2609, 0.54 #1662), WEST (0.60 #240, 0.55 #2609, 0.54 #1662), IR (0.52 #2206, 0.40 #3632, 0.36 #1021), TR (0.50 #237, 0.45 #3602, 0.40 #950), IRQ (0.50 #237, 0.40 #950, 0.37 #1424), SA (0.45 #875, 0.19 #2371, 0.17 #1348), I (0.40 #523, 0.24 #6690, 0.17 #288), EAK (0.27 #1063, 0.06 #5456, 0.06 #5220)
>> best conf = 0.60
=> the first rule below is the first best rule for 4 predicted values
>> Best rule #240 for best value:
>> intensional similarity = 10
>> extensional distance = 1
>> proper extension:  Jordan;
>> query: (?x1999, ?x115) <- ?x1999[ has flowsInto ?x419[ has locatedIn ?x115; has locatedIn ?x239<IL>; has locatedIn ?x466[ has encompassed ?x175; has wasDependentOf ?x485; is neighbor of ?x185;]; has locatedIn ?x803<JOR>;];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 3
EVAL	LakeGenezareth	locatedIn	IL	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.333	46.000	44.000	142.000	0.600	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: IL
=> 122 concepts (118 used for prediction)
PRED predicted values (max 10 best out of 228): IR (0.74 #5085, 0.67 #1263, 0.57 #1973), JOR (0.73 #9565, 0.72 #9567, 0.72 #9566), RL (0.73 #9565, 0.72 #9567, 0.72 #9566), IL (0.73 #9565, 0.72 #9567, 0.72 #9566), WEST (0.73 #9565, 0.72 #9567, 0.72 #9566), IRQ (0.67 #1428, 0.50 #474, 0.47 #3336), SA (0.67 #1428, 0.50 #2782, 0.47 #3336), TR (0.50 #756, 0.50 #474, 0.47 #3336), ET (0.50 #4062, 0.50 #474, 0.35 #3335), GAZA (0.50 #474, 0.35 #3335, 0.32 #2860)
>> best conf = 0.74
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5085 for best value:
>> intensional similarity = 13
>> extensional distance = 21
>> proper extension:  ZardKuh; Damavand; Dascht-e-Kavir; Karun; Karun; Khark; Karun; Sabalan; Lavan; Dascht-e-Lut;
>> query: (?x1999, IR) <- ?x1999[ has locatedIn ?x466[ a Country; has ethnicGroup ?x244<Arab>; has government ?x2550; has neighbor ?x185<TR>; has neighbor ?x302<IRQ>; is locatedIn of ?x275[ is flowsInto of ?x698;]; is locatedIn of ?x953[ a Desert;];];]

*> Best rule #9565 for first EXPECTED value:
*> intensional similarity = 12
*> extensional distance = 50
*> proper extension:  Vaenern; Zurichsee;
*> query: (?x1999, ?x803) <- ?x1999[ a Lake; has flowsInto ?x419[ a River; has hasEstuary ?x420; has locatedIn ?x115[ a Country;]; has locatedIn ?x803[ has ethnicGroup ?x244; has neighbor ?x302;];]; has locatedIn ?x466[ is neighbor of ?x185[ has wasDependentOf ?x1656;];];]
*> conf = 0.73
ranks of expected_values: 4
EVAL	LakeGenezareth	locatedIn	IL	CNN-1.+1._MA	0.000	0.000	1.000	0.250	122.000	118.000	228.000	0.739	http://www.semwebtech.org/mondial/10/meta#locatedIn

#148-SouthUist
PRED entity: SouthUist

PRED relation: belongsToIslands
PRED expected values: OuterHebrides
=> 52 concepts (52 used for prediction)
PRED predicted values (max 10 best out of 46): InnerHebrides (0.40 #1634, 0.39 #1293, 0.31 #64), OuterHebrides (0.40 #1634, 0.39 #1293, 0.23 #31), BritishIsles (0.40 #1634, 0.39 #1293, 0.17 #87), OrkneyIslands (0.40 #1634, 0.39 #1293, 0.17 #85), ScillyIslands (0.40 #1634, 0.39 #1293, 0.12 #2723), ShetlandIslands (0.40 #1634, 0.39 #1293, 0.12 #2723), LesserAntilles (0.25 #899, 0.24 #967, 0.18 #491), Canares (0.19 #295, 0.16 #363, 0.13 #499), SundaIslands (0.18 #626, 0.12 #1034, 0.08 #1238), HawaiiIslands (0.13 #573, 0.06 #1117, 0.05 #1322)
>> best conf = 0.40
=> the first rule below is the first best rule for 6 predicted values
>> Best rule #1634 for best value:
>> intensional similarity = 5
>> extensional distance = 192
>> proper extension:  IsleofMan;
>> query: (?x2151, ?x945) <- ?x2151[ a Island; has locatedIn ?x81[ is locatedIn of ?x495[ has belongsToIslands ?x945;];]; has locatedInWater ?x182;]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 2
EVAL	SouthUist	belongsToIslands	OuterHebrides	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	52.000	52.000	46.000	0.404	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

PRED relation: belongsToIslands
PRED expected values: OuterHebrides
=> 98 concepts (98 used for prediction)
PRED predicted values (max 10 best out of 56): InnerHebrides (0.33 #3004, 0.33 #3003, 0.33 #3002), OuterHebrides (0.33 #3004, 0.33 #3003, 0.33 #3002), BritishIsles (0.33 #3004, 0.33 #3003, 0.33 #3002), OrkneyIslands (0.33 #3004, 0.33 #3003, 0.33 #3002), ScillyIslands (0.33 #3004, 0.33 #3003, 0.33 #3002), ShetlandIslands (0.33 #3004, 0.33 #3003, 0.33 #3002), LesserAntilles (0.27 #492, 0.25 #2336, 0.24 #2472), Canares (0.25 #363, 0.20 #773, 0.15 #1183), Azores (0.20 #754, 0.13 #1438, 0.12 #1574), WestfriesischeInseln (0.14 #1037, 0.07 #2266, 0.04 #2946)
>> best conf = 0.33
=> the first rule below is the first best rule for 6 predicted values
>> Best rule #3004 for best value:
>> intensional similarity = 9
>> extensional distance = 115
>> proper extension:  Aruba;
>> query: (?x2151, ?x2364) <- ?x2151[ a Island; has locatedIn ?x81[ has religion ?x95<Protestant>; has religion ?x352<RomanCatholic>; is locatedIn of ?x674[ a Island; has belongsToIslands ?x2364;];];]
>> Best rule #3003 for best value:
>> intensional similarity = 11
>> extensional distance = 115
>> proper extension:  Aruba;
>> query: (?x2151, ?x503) <- ?x2151[ a Island; has locatedIn ?x81[ has religion ?x95<Protestant>; has religion ?x352<RomanCatholic>; is locatedIn of ?x502[ has belongsToIslands ?x503;]; is locatedIn of ?x674[ a Island; has belongsToIslands ?x2364;];];]
>> Best rule #3002 for best value:
>> intensional similarity = 11
>> extensional distance = 115
>> proper extension:  Aruba;
>> query: (?x2151, ?x945) <- ?x2151[ a Island; has locatedIn ?x81[ has religion ?x95<Protestant>; has religion ?x352<RomanCatholic>; is locatedIn of ?x674[ a Island; has belongsToIslands ?x2364;]; is locatedIn of ?x2257[ has belongsToIslands ?x945;];];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 2
EVAL	SouthUist	belongsToIslands	OuterHebrides	CNN-1.+1._MA	0.000	1.000	1.000	0.500	98.000	98.000	56.000	0.329	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

#147-InnerHebrides
PRED entity: InnerHebrides

PRED relation: belongsToIslands!
PRED expected values: Islay
=> 20 concepts (20 used for prediction)
PRED predicted values (max 10 best out of 221): LewisandHarris (0.25 #581, 0.20 #172, 0.18 #1162), Barra (0.25 #581, 0.20 #45, 0.18 #1162), Benbecula (0.25 #581, 0.20 #29, 0.18 #1162), BishopRock (0.25 #581, 0.20 #25, 0.18 #1162), GreatBritain (0.25 #581, 0.20 #24, 0.18 #1162), Ireland (0.25 #581, 0.20 #4, 0.18 #1162), Anglesey (0.25 #581, 0.20 #185, 0.18 #1162), SouthUist (0.25 #581, 0.18 #1162, 0.13 #387), Islay (0.25 #581, 0.18 #1162, 0.13 #387), Hoy (0.25 #581, 0.18 #1162, 0.13 #387)
>> best conf = 0.25
=> the first rule below is the first best rule for 23 predicted values
>> Best rule #581 for best value:
>> intensional similarity = 10
>> extensional distance = 6
>> proper extension:  LesserAntilles; BermudaIslands; CapeVerdes;
>> query: (?x2364, ?x121) <- ?x2364[ a Islands; is belongsToIslands of ?x674[ a Island; has locatedIn ?x81[ has religion ?x95; is locatedIn of ?x121;]; has type ?x150;]; is belongsToIslands of ?x2413[ has locatedInWater ?x182<AtlanticOcean>;];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 9
EVAL	InnerHebrides	belongsToIslands!	Islay	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.111	20.000	20.000	221.000	0.247	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

PRED relation: belongsToIslands!
PRED expected values: Islay
=> 49 concepts (49 used for prediction)
PRED predicted values (max 10 best out of 221): GreatBritain (0.60 #971, 0.50 #583, 0.43 #777), LewisandHarris (0.60 #971, 0.50 #583, 0.43 #777), Barra (0.60 #971, 0.50 #583, 0.43 #777), Benbecula (0.60 #971, 0.50 #583, 0.43 #777), BishopRock (0.60 #971, 0.50 #583, 0.43 #777), Ireland (0.60 #971, 0.50 #583, 0.43 #777), Anglesey (0.60 #971, 0.50 #583, 0.43 #777), ShetlandMainland (0.60 #971, 0.50 #583, 0.43 #777), Hoy (0.60 #971, 0.50 #583, 0.43 #777), Westray (0.60 #971, 0.50 #583, 0.43 #777)
>> best conf = 0.60
=> the first rule below is the first best rule for 23 predicted values
>> Best rule #971 for best value:
>> intensional similarity = 18
>> extensional distance = 2
>> proper extension:  MarianaIslands;
>> query: (?x2364, ?x121) <- ?x2364[ a Islands; is belongsToIslands of ?x467[ a Island; has locatedIn ?x81[ a Country; has encompassed ?x195; has ethnicGroup ?x1196; has government ?x1854; has language ?x247<English>; is locatedIn of ?x121;];]; is belongsToIslands of ?x674[ a Island; has type ?x150<"volcanic">;]; is belongsToIslands of ?x2113[ a Island; has locatedInWater ?x182;];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 13
EVAL	InnerHebrides	belongsToIslands!	Islay	CNN-1.+1._MA	0.000	0.000	0.000	0.077	49.000	49.000	221.000	0.600	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

#146-Moraca
PRED entity: Moraca

PRED relation: locatedIn
PRED expected values: MNE
=> 37 concepts (37 used for prediction)
PRED predicted values (max 10 best out of 102): MNE (0.93 #4998, 0.91 #2617, 0.91 #5714), AL (0.57 #1189, 0.57 #1190, 0.55 #475), BIH (0.50 #2, 0.43 #239, 0.13 #1188), I (0.31 #760, 0.14 #2190, 0.14 #1951), R (0.27 #2385, 0.24 #2622, 0.16 #2861), D (0.20 #1684, 0.20 #1210, 0.19 #1447), TR (0.20 #516, 0.14 #992, 0.05 #2421), CN (0.20 #531, 0.10 #1007, 0.05 #4816), EAU (0.20 #627, 0.10 #1103, 0.03 #5388), RWA (0.20 #602, 0.10 #1078, 0.03 #1317)
>> best conf = 0.93
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4998 for best value:
>> intensional similarity = 6
>> extensional distance = 174
>> proper extension:  Irawaddy; Uelle; Mississippi; Jordan; Main; Drin; Jubba; Maas; Ural; Mincio; ...
>> query: (?x2296, ?x106) <- ?x2296[ a River; has flowsInto ?x104; has hasEstuary ?x105[ a Estuary; has locatedIn ?x106[ is neighbor of ?x55;];];]
ranks of expected_values: 1
EVAL	Moraca	locatedIn	MNE	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	37.000	37.000	102.000	0.925	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: MNE
=> 128 concepts (128 used for prediction)
PRED predicted values (max 10 best out of 110): MNE (0.93 #13856, 0.93 #18894, 0.92 #23214), AL (0.71 #3824, 0.60 #3825, 0.53 #6210), BIH (0.60 #1674, 0.60 #957, 0.50 #719), I (0.60 #5544, 0.47 #9365, 0.40 #9843), TR (0.60 #1475, 0.38 #4106, 0.27 #6013), R (0.57 #13861, 0.57 #14581, 0.56 #15780), USA (0.57 #3657, 0.31 #8671, 0.22 #28578), D (0.50 #10292, 0.35 #15315, 0.33 #8141), A (0.50 #8220, 0.33 #3444, 0.21 #16113), UZB (0.50 #542, 0.20 #1258, 0.18 #6275)
>> best conf = 0.93
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #13856 for best value:
>> intensional similarity = 15
>> extensional distance = 25
>> proper extension:  HudsonRiver; AlleghenyRiver;
>> query: (?x2296, ?x106) <- ?x2296[ a River; has flowsInto ?x104[ has locatedIn ?x106[ a Country; has encompassed ?x195; has ethnicGroup ?x775; has language ?x1251; has neighbor ?x156; has religion ?x56; is locatedIn of ?x224[ a Source; has inMountains ?x785[ a Mountains;];]; is neighbor of ?x156;];]; has hasSource ?x224;]
ranks of expected_values: 1
EVAL	Moraca	locatedIn	MNE	CNN-1.+1._MA	1.000	1.000	1.000	1.000	128.000	128.000	110.000	0.926	http://www.semwebtech.org/mondial/10/meta#locatedIn

#145-Mississippi
PRED entity: Mississippi

PRED relation: flowsInto!
PRED expected values: OhioRiver
=> 56 concepts (48 used for prediction)
PRED predicted values (max 10 best out of 366): AlleghenyRiver (0.33 #286, 0.08 #887, 0.03 #1189), Tennessee (0.33 #18, 0.08 #619, 0.03 #921), Angara (0.08 #653, 0.03 #955, 0.03 #1256), Ticino (0.08 #775, 0.03 #1077, 0.03 #1378), Adda (0.08 #744, 0.03 #1046, 0.03 #1347), Mincio (0.08 #741, 0.03 #1043, 0.03 #1344), Lagen (0.08 #671, 0.03 #973, 0.03 #1274), Salzach (0.08 #813, 0.03 #1115, 0.03 #1416), Alz (0.08 #768, 0.03 #1070, 0.03 #1371), Ammer (0.08 #672, 0.03 #974, 0.03 #1275)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #286 for best value:
>> intensional similarity = 5
>> extensional distance = 1
>> proper extension:  OhioRiver;
>> query: (?x361, AlleghenyRiver) <- ?x361[ a River; has locatedIn ?x315<USA>; is flowsInto of ?x1366[ has flowsThrough ?x1113;];]

*> Best rule #903 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 11
*> proper extension:  Rhein; Ammer; Bartang; Po; Inn; Jenissej; Aare; Isar; Dalaelv; Glomma;
*> query: (?x361, ?x182) <- ?x361[ a River; has locatedIn ?x315[ is locatedIn of ?x182;]; is flowsInto of ?x1366[ has flowsThrough ?x1113;];]
*> conf = 0.03
ranks of expected_values: 127
EVAL	Mississippi	flowsInto!	OhioRiver	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.008	56.000	48.000	366.000	0.333	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto!
PRED expected values: OhioRiver
=> 159 concepts (159 used for prediction)
PRED predicted values (max 10 best out of 391): LakeOntario (0.33 #155, 0.25 #2568, 0.25 #2268), RiviereRichelieu (0.33 #293, 0.25 #2706, 0.25 #2406), Manicouagan (0.33 #190, 0.25 #2603, 0.25 #2303), Tennessee (0.33 #924, 0.11 #9960, 0.11 #4242), AlleghenyRiver (0.33 #1192, 0.11 #4510, 0.09 #5717), Murgab (0.33 #393, 0.10 #4617, 0.03 #9749), LakeHume (0.33 #876, 0.07 #6609, 0.04 #7817), MurrumbidgeeRiver (0.33 #741, 0.07 #6474, 0.04 #7682), DarlingRiver (0.33 #647, 0.07 #6380, 0.04 #7588), StraitsofMackinac (0.25 #2690, 0.20 #3293, 0.20 #2991)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #155 for best value:
>> intensional similarity = 9
>> extensional distance = 1
>> proper extension:  SaintLawrenceRiver;
>> query: (?x361, LakeOntario) <- ?x361[ a River; has flowsInto ?x1371[ has locatedIn ?x148; is mergesWith of ?x182;]; has locatedIn ?x315<USA>; is flowsInto of ?x1366[ has hasEstuary ?x1254; is flowsInto of ?x1113;];]

*> Best rule #9960 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 36
*> proper extension:  Bahrel-Djebel-Albert-Nil;
*> query: (?x361, ?x1887) <- ?x361[ a River; has flowsInto ?x1371; has locatedIn ?x315[ is locatedIn of ?x1887[ is flowsInto of ?x2042;]; is neighbor of ?x482;]; is flowsInto of ?x1366[ is flowsInto of ?x1989;];]
*> conf = 0.11
ranks of expected_values: 39
EVAL	Mississippi	flowsInto!	OhioRiver	CNN-1.+1._MA	0.000	0.000	0.000	0.026	159.000	159.000	391.000	0.333	http://www.semwebtech.org/mondial/10/meta#flowsInto

#144-SME
PRED entity: SME

PRED relation: ethnicGroup
PRED expected values: African Javanese
=> 38 concepts (38 used for prediction)
PRED predicted values (max 10 best out of 245): European (0.65 #2790, 0.33 #3296, 0.32 #2537), African (0.54 #2788, 0.41 #2535, 0.31 #2282), EastIndian (0.25 #387, 0.19 #6076, 0.17 #640), Indian (0.25 #324, 0.17 #577, 0.14 #8610), Pakistani (0.25 #380, 0.05 #1645, 0.04 #1898), NorthernIrish (0.25 #481, 0.05 #1746, 0.04 #1999), English (0.25 #478, 0.05 #1743, 0.04 #1996), Welsh (0.25 #407, 0.05 #1672, 0.04 #1925), Scottish (0.25 #366, 0.05 #1631, 0.04 #1884), Russian (0.21 #4879, 0.21 #2094, 0.15 #3106)
>> best conf = 0.65
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2790 for best value:
>> intensional similarity = 7
>> extensional distance = 50
>> proper extension:  KN; WG;
>> query: (?x179, European) <- ?x179[ a Country; has ethnicGroup ?x79[ is ethnicGroup of ?x80[ has dependentOf ?x81;]; is ethnicGroup of ?x902<EC>;]; has government ?x180;]

*> Best rule #2788 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 50
*> proper extension:  KN; WG;
*> query: (?x179, African) <- ?x179[ a Country; has ethnicGroup ?x79[ is ethnicGroup of ?x80[ has dependentOf ?x81;]; is ethnicGroup of ?x902<EC>;]; has government ?x180;]
*> conf = 0.54
ranks of expected_values: 2, 64
EVAL	SME	ethnicGroup	Javanese	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.016	38.000	38.000	245.000	0.654	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	SME	ethnicGroup	African	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	38.000	38.000	245.000	0.654	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: African Javanese
=> 84 concepts (84 used for prediction)
PRED predicted values (max 10 best out of 241): European (0.89 #4829, 0.63 #6351, 0.58 #3810), African (0.80 #7616, 0.54 #4058, 0.44 #4318), EastIndian (0.54 #4058, 0.29 #6851, 0.28 #8883), Mestizo (0.50 #3836, 0.40 #3076, 0.33 #5362), Indian (0.43 #2353, 0.40 #1592, 0.33 #2099), Malay (0.40 #1617, 0.33 #2124, 0.33 #1870), Black (0.38 #2843, 0.20 #7865, 0.20 #3097), White (0.38 #2853, 0.20 #7865, 0.18 #14968), Russian (0.33 #5905, 0.17 #8698, 0.16 #11235), Mulatto (0.29 #6851, 0.23 #6090, 0.20 #7865)
>> best conf = 0.89
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4829 for best value:
>> intensional similarity = 18
>> extensional distance = 17
>> proper extension:  AUS; CDN; ROU; NZ; NCA; BDS;
>> query: (?x179, European) <- ?x179[ has ethnicGroup ?x79[ a EthnicGroup; is ethnicGroup of ?x181<GCA>; is ethnicGroup of ?x654<ES>; is ethnicGroup of ?x902<EC>;]; has ethnicGroup ?x298[ a EthnicGroup; is ethnicGroup of ?x1731<XMAS>;]; has ethnicGroup ?x1728[ is ethnicGroup of ?x450<CV>;]; has government ?x180; has religion ?x95<Protestant>;]

*> Best rule #7616 for first EXPECTED value:
*> intensional similarity = 15
*> extensional distance = 33
*> proper extension:  KN; WG;
*> query: (?x179, African) <- ?x179[ a Country; has ethnicGroup ?x79[ a EthnicGroup; is ethnicGroup of ?x181[ is locatedIn of ?x282; is neighbor of ?x671;]; is ethnicGroup of ?x215<CO>; is ethnicGroup of ?x351<GUY>; is ethnicGroup of ?x899[ has encompassed ?x521<America>; is locatedIn of ?x182;];]; has government ?x180;]
*> conf = 0.80
ranks of expected_values: 2, 79
EVAL	SME	ethnicGroup	Javanese	CNN-1.+1._MA	0.000	0.000	0.000	0.013	84.000	84.000	241.000	0.895	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	SME	ethnicGroup	African	CNN-1.+1._MA	0.000	1.000	1.000	0.500	84.000	84.000	241.000	0.895	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#143-PikIsmoilSomoni
PRED entity: PikIsmoilSomoni

PRED relation: inMountains
PRED expected values: Pamir
=> 36 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 38): Pamir (0.67 #17, 0.50 #104, 0.33 #191), TianShan (0.12 #118, 0.05 #292, 0.03 #379), Andes (0.09 #534, 0.06 #796, 0.06 #621), Alps (0.08 #527, 0.07 #614, 0.07 #963), EastAfricanRift (0.07 #551, 0.04 #813, 0.04 #900), RockyMountains (0.06 #879, 0.05 #966, 0.05 #1140), Altai (0.05 #375, 0.05 #463, 0.02 #724), Himalaya (0.05 #791, 0.04 #1052, 0.04 #965), CordilleraVolcanica (0.04 #675, 0.03 #850, 0.03 #937), EliasRange (0.03 #625, 0.02 #1061, 0.02 #887)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #17 for best value:
>> intensional similarity = 3
>> extensional distance = 4
>> proper extension:  PikLenina; PikRevoluzija; PikMoskva; PikKarl-Marx;
>> query: (?x1601, Pamir) <- ?x1601[ a Mountain; has locatedIn ?x129<TAD>;]
ranks of expected_values: 1
EVAL	PikIsmoilSomoni	inMountains	Pamir	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	36.000	36.000	38.000	0.667	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains
PRED expected values: Pamir
=> 92 concepts (92 used for prediction)
PRED predicted values (max 10 best out of 61): Pamir (0.67 #1048, 0.67 #17, 0.50 #104), Himalaya (0.28 #1054, 0.18 #1403, 0.14 #703), Alps (0.25 #1139, 0.24 #877, 0.10 #1662), Kaukasus (0.21 #541, 0.14 #1067, 0.12 #1154), EastAfricanRift (0.20 #988, 0.14 #1338, 0.06 #2122), Andes (0.16 #971, 0.12 #1669, 0.11 #1321), Altai (0.12 #114, 0.11 #288, 0.06 #1249), TianShan (0.12 #118, 0.11 #292, 0.05 #553), Kurdistan (0.11 #209, 0.05 #1432, 0.05 #1606), Zagros (0.11 #187, 0.03 #1410, 0.02 #1584)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1048 for best value:
>> intensional similarity = 11
>> extensional distance = 23
>> proper extension:  MountKenia;
>> query: (?x1601, ?x749) <- ?x1601[ a Mountain; has locatedIn ?x129[ has ethnicGroup ?x1193; has government ?x435<"republic">; has neighbor ?x232; has religion ?x187; is locatedIn of ?x300[ has flowsInto ?x301;]; is locatedIn of ?x932[ has inMountains ?x749;];];]
>> Best rule #17 for best value:
>> intensional similarity = 3
>> extensional distance = 4
>> proper extension:  PikLenina; PikRevoluzija; PikMoskva; PikKarl-Marx;
>> query: (?x1601, Pamir) <- ?x1601[ a Mountain; has locatedIn ?x129<TAD>;]
ranks of expected_values: 1
EVAL	PikIsmoilSomoni	inMountains	Pamir	CNN-1.+1._MA	1.000	1.000	1.000	1.000	92.000	92.000	61.000	0.667	http://www.semwebtech.org/mondial/10/meta#inMountains

#142-TL
PRED entity: TL

PRED relation: religion
PRED expected values: Muslim RomanCatholic
=> 32 concepts (32 used for prediction)
PRED predicted values (max 10 best out of 35): RomanCatholic (0.86 #748, 0.53 #417, 0.53 #994), Muslim (0.73 #250, 0.71 #291, 0.67 #332), Christian (0.44 #331, 0.43 #290, 0.37 #577), Buddhist (0.36 #257, 0.33 #11, 0.29 #298), Hindu (0.29 #296, 0.27 #255, 0.22 #337), ChristianOrthodox (0.20 #411, 0.19 #783, 0.18 #742), Anglican (0.17 #699, 0.16 #741, 0.16 #1275), Sikh (0.17 #699, 0.16 #741, 0.16 #1275), Jains (0.17 #699, 0.16 #741, 0.16 #1275), Jewish (0.11 #701, 0.10 #659, 0.10 #412)
>> best conf = 0.86
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #748 for best value:
>> intensional similarity = 7
>> extensional distance = 104
>> proper extension:  RSM;
>> query: (?x735, RomanCatholic) <- ?x735[ has encompassed ?x175; has government ?x435; has religion ?x95[ is religion of ?x81<GB>; is religion of ?x1276<AMSA>;];]
ranks of expected_values: 1, 2
EVAL	TL	religion	RomanCatholic	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	32.000	32.000	35.000	0.858	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	TL	religion	Muslim	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	32.000	32.000	35.000	0.858	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion
PRED expected values: Muslim RomanCatholic
=> 87 concepts (87 used for prediction)
PRED predicted values (max 10 best out of 40): RomanCatholic (0.85 #3160, 0.82 #1912, 0.79 #1376), Muslim (0.78 #709, 0.73 #1578, 0.68 #1825), Christian (0.67 #914, 0.67 #708, 0.55 #1413), Buddhist (0.60 #798, 0.60 #343, 0.57 #2319), Hindu (0.50 #177, 0.48 #2650, 0.47 #1905), ChristianOrthodox (0.50 #953, 0.31 #1864, 0.30 #1534), Anglican (0.47 #1905, 0.46 #2361, 0.45 #1821), Seventh-DayAdventist (0.47 #1905, 0.46 #2361, 0.45 #1821), Presbyterian (0.47 #1905, 0.46 #2361, 0.45 #1821), JehovasWitnesses (0.31 #1266, 0.31 #1140, 0.29 #518)
>> best conf = 0.85
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3160 for best value:
>> intensional similarity = 17
>> extensional distance = 114
>> proper extension:  AND;
>> query: (?x735, RomanCatholic) <- ?x735[ a Country; has encompassed ?x175[ a Continent; is encompassed of ?x130[ is locatedIn of ?x662;]; is encompassed of ?x290[ has ethnicGroup ?x1193;];]; has religion ?x95[ a Religion; is religion of ?x279<BS>; is religion of ?x345<YV>; is religion of ?x455<IS>; is religion of ?x853<PNG>;];]
ranks of expected_values: 1, 2
EVAL	TL	religion	RomanCatholic	CNN-1.+1._MA	1.000	1.000	1.000	1.000	87.000	87.000	40.000	0.853	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	TL	religion	Muslim	CNN-1.+1._MA	1.000	1.000	1.000	1.000	87.000	87.000	40.000	0.853	http://www.semwebtech.org/mondial/10/meta#religion

#141-LB
PRED entity: LB

PRED relation: government
PRED expected values: "republic"
=> 50 concepts (50 used for prediction)
PRED predicted values (max 10 best out of 59): "republic" (0.48 #1663, 0.45 #1446, 0.44 #1807), "parliamentary democracy and a Commonwealth realm" (0.33 #324, 0.26 #540, 0.22 #468), "constitutional democracy" (0.33 #4, 0.25 #148, 0.22 #1513), "constitutional monarchy and Commonwealth realm" (0.33 #106, 0.06 #466, 0.05 #538), "republic; multiparty presidential regime established 1960" (0.22 #1513, 0.16 #3029, 0.02 #1505), "British Overseas Territories" (0.22 #799, 0.20 #223, 0.19 #1015), "republic, parliamentary democracy" (0.20 #264, 0.17 #336, 0.09 #3246), "democratic republic" (0.14 #2667, 0.14 #2740, 0.14 #2594), "parliamentary democracy" (0.14 #2667, 0.14 #2740, 0.14 #2594), "federal republic" (0.14 #2667, 0.14 #2740, 0.14 #2594)
>> best conf = 0.48
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1663 for best value:
>> intensional similarity = 6
>> extensional distance = 46
>> proper extension:  REUN;
>> query: (?x621, "republic") <- ?x621[ has encompassed ?x213<Africa>; has religion ?x116[ a Religion; is religion of ?x525[ is locatedIn of ?x284;];];]
ranks of expected_values: 1
EVAL	LB	government	"republic"	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	50.000	50.000	59.000	0.479	http://www.semwebtech.org/mondial/10/meta#government

PRED relation: government
PRED expected values: "republic"
=> 96 concepts (96 used for prediction)
PRED predicted values (max 10 best out of 66): "republic" (0.50 #1608, 0.50 #1244, 0.50 #444), "republic; multiparty presidential regime established 1960" (0.35 #1311, 0.33 #511, 0.33 #220), "constitutional democracy" (0.34 #2984, 0.33 #220, 0.23 #1312), "British Overseas Territories" (0.33 #7, 0.31 #1385, 0.31 #1319), "parliamentary democracy and a Commonwealth realm" (0.23 #2001, 0.21 #1782, 0.19 #1348), "parliamentary democracy" (0.18 #3931, 0.17 #369, 0.14 #516), "federal republic" (0.17 #367, 0.14 #514, 0.12 #587), "republic, parliamentary democracy" (0.12 #5380, 0.12 #5381, 0.10 #3275), "limited democracy" (0.12 #5380, 0.12 #5381, 0.10 #855), "a parliamentary democracy, a federation, and a constitutional monarchy" (0.12 #5380, 0.12 #5381, 0.06 #1367)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1608 for best value:
>> intensional similarity = 13
>> extensional distance = 16
>> proper extension:  TCH; RCB;
>> query: (?x621, "republic") <- ?x621[ a Country; has encompassed ?x213; has religion ?x116<Christian>; is neighbor of ?x1206[ a Country; has ethnicGroup ?x2201; has neighbor ?x483[ has ethnicGroup ?x162; is locatedIn of ?x135;]; has wasDependentOf ?x78<F>; is locatedIn of ?x182;];]
>> Best rule #1244 for best value:
>> intensional similarity = 18
>> extensional distance = 12
>> proper extension:  MA;
>> query: (?x621, "republic") <- ?x621[ a Country; has ethnicGroup ?x622[ a EthnicGroup;]; has neighbor ?x1072[ has government ?x180; has wasDependentOf ?x81;]; has neighbor ?x1206[ has encompassed ?x213<Africa>; has ethnicGroup ?x2201; has government ?x2531; has neighbor ?x811[ is locatedIn of ?x610;]; has neighbor ?x839<RMM>; is locatedIn of ?x182;]; has religion ?x116<Christian>;]
>> Best rule #444 for best value:
>> intensional similarity = 19
>> extensional distance = 4
>> proper extension:  WAL;
>> query: (?x621, "republic") <- ?x621[ a Country; has ethnicGroup ?x622[ a EthnicGroup;]; has neighbor ?x651<RG>; has neighbor ?x1072[ has wasDependentOf ?x81;]; has neighbor ?x1206[ has encompassed ?x213<Africa>; has ethnicGroup ?x2201; has government ?x2531; has neighbor ?x811[ is locatedIn of ?x610;]; has neighbor ?x839<RMM>; is locatedIn of ?x182;]; has religion ?x116<Christian>;]
>> Best rule #298 for best value:
>> intensional similarity = 17
>> extensional distance = 2
>> proper extension:  MOC;
>> query: (?x621, "republic") <- ?x621[ a Country; has encompassed ?x213<Africa>; has ethnicGroup ?x622[ a EthnicGroup;]; has language ?x247; has religion ?x187<Muslim>; is neighbor of ?x1072[ has ethnicGroup ?x380; has wasDependentOf ?x81<GB>; is locatedIn of ?x182;]; is neighbor of ?x1206[ a Country; has ethnicGroup ?x2201; is locatedIn of ?x350;];]
>> Best rule #226 for best value:
>> intensional similarity = 17
>> extensional distance = 2
>> proper extension:  RMM;
>> query: (?x621, "republic") <- ?x621[ a Country; has encompassed ?x213<Africa>; has ethnicGroup ?x622[ a EthnicGroup;]; has language ?x247; has religion ?x187<Muslim>; is neighbor of ?x1072[ has ethnicGroup ?x380; has wasDependentOf ?x81[ has religion ?x95; is dependentOf of ?x80; is locatedIn of ?x121<NorthSea>;];]; is neighbor of ?x1206<CI>;]
ranks of expected_values: 1
EVAL	LB	government	"republic"	CNN-1.+1._MA	1.000	1.000	1.000	1.000	96.000	96.000	66.000	0.500	http://www.semwebtech.org/mondial/10/meta#government

#140-RomanCatholic
PRED entity: RomanCatholic

PRED relation: religion!
PRED expected values: MNE PE ZRE FL A VU EAK VN GUAD TL M WD GUAM
=> 17 concepts (17 used for prediction)
PRED predicted values (max 10 best out of 130): SF (0.55 #574, 0.50 #410, 0.46 #1146), LV (0.55 #574, 0.50 #396, 0.46 #1146), MAL (0.55 #574, 0.46 #1146, 0.44 #1490), ZRE (0.55 #574, 0.46 #1146, 0.44 #1490), TL (0.55 #574, 0.46 #1146, 0.44 #1490), EAK (0.55 #574, 0.46 #1146, 0.44 #1490), PE (0.55 #574, 0.46 #1146, 0.44 #1490), A (0.55 #574, 0.46 #1146, 0.44 #1490), FL (0.55 #574, 0.46 #1146, 0.44 #1490), NAM (0.55 #574, 0.46 #1146, 0.44 #1490)
>> best conf = 0.55
=> the first rule below is the first best rule for 28 predicted values
>> Best rule #574 for best value:
>> intensional similarity = 10
>> extensional distance = 2
>> proper extension:  JehovasWitnesses;
>> query: (?x352, ?x106) <- ?x352[ is religion of ?x78[ is locatedIn of ?x121;]; is religion of ?x471[ has ethnicGroup ?x164; has government ?x254; has language ?x1035;]; is religion of ?x482<MEX>; is religion of ?x904[ has neighbor ?x106;];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 4, 5, 6, 7, 8, 9, 11, 29, 35, 45, 109, 118
EVAL	RomanCatholic	religion!	GUAM	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	17.000	17.000	130.000	0.548	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	RomanCatholic	religion!	WD	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.028	17.000	17.000	130.000	0.548	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	RomanCatholic	religion!	M	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.009	17.000	17.000	130.000	0.548	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	RomanCatholic	religion!	TL	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.250	17.000	17.000	130.000	0.548	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	RomanCatholic	religion!	GUAD	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.037	17.000	17.000	130.000	0.548	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	RomanCatholic	religion!	VN	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.045	17.000	17.000	130.000	0.548	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	RomanCatholic	religion!	EAK	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.250	17.000	17.000	130.000	0.548	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	RomanCatholic	religion!	VU	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.010	17.000	17.000	130.000	0.548	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	RomanCatholic	religion!	A	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.250	17.000	17.000	130.000	0.548	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	RomanCatholic	religion!	FL	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.250	17.000	17.000	130.000	0.548	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	RomanCatholic	religion!	ZRE	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.250	17.000	17.000	130.000	0.548	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	RomanCatholic	religion!	PE	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.250	17.000	17.000	130.000	0.548	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	RomanCatholic	religion!	MNE	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.200	17.000	17.000	130.000	0.548	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion!
PRED expected values: MNE PE ZRE FL A VU EAK VN GUAD TL M WD GUAM
=> 30 concepts (30 used for prediction)
PRED predicted values (max 10 best out of 143): R (0.59 #929, 0.59 #1047, 0.58 #1045), MD (0.59 #929, 0.59 #1047, 0.58 #1045), A (0.59 #929, 0.59 #1047, 0.58 #1045), TR (0.59 #929, 0.59 #1047, 0.58 #1045), MNE (0.59 #929, 0.59 #1047, 0.58 #1045), AZ (0.59 #929, 0.59 #1047, 0.58 #1045), PE (0.59 #929, 0.59 #1047, 0.58 #1045), LV (0.59 #929, 0.59 #1047, 0.58 #1045), SF (0.59 #929, 0.59 #1047, 0.58 #1045), GR (0.59 #929, 0.59 #1047, 0.58 #1045)
>> best conf = 0.59
=> the first rule below is the first best rule for 28 predicted values
>> Best rule #929 for best value:
>> intensional similarity = 27
>> extensional distance = 2
>> proper extension:  Muslim;
>> query: (?x352, ?x886) <- ?x352[ is religion of ?x78[ a Country; is locatedIn of ?x121;]; is religion of ?x176[ has neighbor ?x886;]; is religion of ?x196<AUS>; is religion of ?x279[ has government ?x854; is locatedIn of ?x182;]; is religion of ?x671[ has government ?x1947; has language ?x796;]; is religion of ?x718[ has encompassed ?x195; has language ?x539<Portuguese>;]; is religion of ?x904[ has ethnicGroup ?x164; is locatedIn of ?x132;]; is religion of ?x1577[ a Country; has ethnicGroup ?x2314<Italian>; has government ?x92;]; is religion of ?x1826<GBZ>;]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 3, 5, 7, 12, 13, 15, 16, 33, 35, 38, 47, 124, 140
EVAL	RomanCatholic	religion!	GUAM	CNN-1.+1._MA	0.000	0.000	0.000	0.008	30.000	30.000	143.000	0.595	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	RomanCatholic	religion!	WD	CNN-1.+1._MA	0.000	0.000	0.000	0.027	30.000	30.000	143.000	0.595	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	RomanCatholic	religion!	M	CNN-1.+1._MA	0.000	0.000	0.000	0.009	30.000	30.000	143.000	0.595	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	RomanCatholic	religion!	TL	CNN-1.+1._MA	0.000	0.000	1.000	0.100	30.000	30.000	143.000	0.595	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	RomanCatholic	religion!	GUAD	CNN-1.+1._MA	0.000	0.000	0.000	0.034	30.000	30.000	143.000	0.595	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	RomanCatholic	religion!	VN	CNN-1.+1._MA	0.000	0.000	0.000	0.038	30.000	30.000	143.000	0.595	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	RomanCatholic	religion!	EAK	CNN-1.+1._MA	0.000	0.000	1.000	0.100	30.000	30.000	143.000	0.595	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	RomanCatholic	religion!	VU	CNN-1.+1._MA	0.000	0.000	0.000	0.037	30.000	30.000	143.000	0.595	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	RomanCatholic	religion!	A	CNN-1.+1._MA	0.000	1.000	1.000	0.333	30.000	30.000	143.000	0.595	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	RomanCatholic	religion!	FL	CNN-1.+1._MA	0.000	0.000	1.000	0.111	30.000	30.000	143.000	0.595	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	RomanCatholic	religion!	ZRE	CNN-1.+1._MA	0.000	0.000	1.000	0.111	30.000	30.000	143.000	0.595	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	RomanCatholic	religion!	PE	CNN-1.+1._MA	0.000	0.000	1.000	0.200	30.000	30.000	143.000	0.595	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	RomanCatholic	religion!	MNE	CNN-1.+1._MA	0.000	0.000	1.000	0.250	30.000	30.000	143.000	0.595	http://www.semwebtech.org/mondial/10/meta#religion

#139-Bete
PRED entity: Bete

PRED relation: ethnicGroup!
PRED expected values: CI
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 96): EAU (0.03 #132), ETH (0.03 #100), UA (0.03 #57), EAK (0.03 #99), CZ (0.02 #98), SN (0.02 #83), MYA (0.02 #70), IR (0.02 #58), SK (0.02 #25), RCA (0.02 #139)
>> best conf = 0.03
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #132 for best value:
>> intensional similarity = 1
>> extensional distance = 279
>> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
>> query: (?x870, EAU) <- ?x870[ a EthnicGroup;]

*> Best rule #176 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 279
*> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
*> query: (?x870, CI) <- ?x870[ a EthnicGroup;]
*> conf = 0.01
ranks of expected_values: 60
EVAL	Bete	ethnicGroup!	CI	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.017	2.000	2.000	96.000	0.032	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup!
PRED expected values: CI
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 96): EAU (0.03 #132), ETH (0.03 #100), UA (0.03 #57), EAK (0.03 #99), CZ (0.02 #98), SN (0.02 #83), MYA (0.02 #70), IR (0.02 #58), SK (0.02 #25), RCA (0.02 #139)
>> best conf = 0.03
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #132 for best value:
>> intensional similarity = 1
>> extensional distance = 279
>> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
>> query: (?x870, EAU) <- ?x870[ a EthnicGroup;]

*> Best rule #176 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 279
*> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
*> query: (?x870, CI) <- ?x870[ a EthnicGroup;]
*> conf = 0.01
ranks of expected_values: 60
EVAL	Bete	ethnicGroup!	CI	CNN-1.+1._MA	0.000	0.000	0.000	0.017	2.000	2.000	96.000	0.032	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#138-KWT
PRED entity: KWT

PRED relation: government
PRED expected values: "constitutional emirate"
=> 39 concepts (39 used for prediction)
PRED predicted values (max 10 best out of 60): "republic" (0.36 #942, 0.36 #798, 0.34 #1158), "federation with specified powers delegated to the UAE federal government and other powers reserved to member emirates" (0.25 #90, 0.14 #162, 0.03 #522), "parliamentary democracy" (0.25 #77, 0.13 #1157, 0.13 #1301), "constitutional monarchy" (0.18 #218, 0.14 #146, 0.13 #290), "parliamentary democracy and a Commonwealth realm" (0.15 #684, 0.05 #2124, 0.05 #1692), "monarchy" (0.14 #152, 0.04 #368, 0.04 #440), "federal republic" (0.09 #219, 0.08 #363, 0.08 #435), "parliamentary government took power in March 2011" (0.09 #259, 0.07 #331, 0.04 #403), "constitutional sultanate (locally known as Malay Islamic Monarchy)" (0.09 #242, 0.07 #314, 0.04 #386), "Islamic republic" (0.09 #272, 0.07 #344, 0.04 #416)
>> best conf = 0.36
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #942 for best value:
>> intensional similarity = 6
>> extensional distance = 81
>> proper extension:  NOK;
>> query: (?x1963, "republic") <- ?x1963[ a Country; has neighbor ?x302; has wasDependentOf ?x81[ a Country; is wasDependentOf of ?x63[ has religion ?x187;];];]

No rule for expected values
ranks of expected_values: 
EVAL	KWT	government	"constitutional emirate"	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	39.000	39.000	60.000	0.361	http://www.semwebtech.org/mondial/10/meta#government

PRED relation: government
PRED expected values: "constitutional emirate"
=> 72 concepts (72 used for prediction)
PRED predicted values (max 10 best out of 72): "republic" (0.44 #1901, 0.43 #804, 0.40 #3078), "constitutional monarchy" (0.33 #2, 0.27 #726, 0.26 #3295), "parliamentary democracy" (0.33 #1601, 0.21 #1681, 0.21 #1969), "federation with specified powers delegated to the UAE federal government and other powers reserved to member emirates" (0.33 #90, 0.21 #1969, 0.18 #4182), "republic under an authoritarian regime" (0.27 #726, 0.26 #3295, 0.25 #2925), "monarchy" (0.27 #726, 0.25 #2117, 0.25 #225), "theocratic republic" (0.27 #726, 0.25 #2117, 0.25 #267), "parliamentary government took power in March 2011" (0.17 #695, 0.14 #916, 0.12 #1135), "federal republic" (0.17 #655, 0.12 #1095, 0.12 #1022), "Islamic republic" (0.17 #708, 0.12 #1148, 0.12 #1075)
>> best conf = 0.44
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1901 for best value:
>> intensional similarity = 20
>> extensional distance = 14
>> proper extension:  RM;
>> query: (?x1963, "republic") <- ?x1963[ has religion ?x187<Muslim>; has wasDependentOf ?x81; is locatedIn of ?x918[ a Sea; has locatedIn ?x302[ a Country; has ethnicGroup ?x557; has government ?x254; has neighbor ?x185;]; has locatedIn ?x639[ a Country; has encompassed ?x175; is locatedIn of ?x637<RubAlChali>;]; has locatedIn ?x1705[ has ethnicGroup ?x380<Asian>; has government ?x92;]; is locatedInWater of ?x1443;];]

No rule for expected values
ranks of expected_values: 
EVAL	KWT	government	"constitutional emirate"	CNN-1.+1._MA	0.000	0.000	0.000	0.000	72.000	72.000	72.000	0.438	http://www.semwebtech.org/mondial/10/meta#government

#137-Doubs
PRED entity: Doubs

PRED relation: locatedIn
PRED expected values: F
=> 41 concepts (41 used for prediction)
PRED predicted values (max 10 best out of 164): F (0.90 #3769, 0.90 #4005, 0.55 #714), I (0.64 #992, 0.14 #284, 0.13 #520), D (0.20 #1435, 0.17 #1670, 0.15 #1905), ZRE (0.18 #1258, 0.17 #1728, 0.15 #1963), USA (0.15 #4311, 0.14 #5726, 0.13 #5962), R (0.15 #4245, 0.12 #4481, 0.12 #3068), A (0.14 #334, 0.13 #1513, 0.13 #4476), FL (0.13 #4476, 0.13 #4712, 0.12 #4948), PE (0.09 #1246, 0.08 #1481, 0.08 #1716), CDN (0.08 #5245, 0.07 #5717, 0.07 #5953)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3769 for best value:
>> intensional similarity = 3
>> extensional distance = 186
>> proper extension:  Sanaga;
>> query: (?x1114, ?x78) <- ?x1114[ has flowsInto ?x1385; has hasSource ?x1115[ has locatedIn ?x78;];]
ranks of expected_values: 1
EVAL	Doubs	locatedIn	F	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	41.000	41.000	164.000	0.898	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: F
=> 136 concepts (129 used for prediction)
PRED predicted values (max 10 best out of 220): F (0.95 #13271, 0.95 #14932, 0.95 #7822), I (0.68 #19921, 0.64 #9054, 0.33 #3601), D (0.62 #4046, 0.61 #2859, 0.58 #4995), CDN (0.52 #3139, 0.24 #17547, 0.24 #17371), R (0.50 #16125, 0.42 #5693, 0.36 #6641), USA (0.37 #4809, 0.35 #4335, 0.32 #3148), ZRE (0.31 #8373, 0.18 #9558, 0.17 #8848), A (0.29 #4263, 0.29 #4124, 0.29 #3075), CN (0.28 #2184, 0.19 #18318, 0.15 #5268), RSM (0.27 #3550, 0.11 #16118, 0.11 #14694)
>> best conf = 0.95
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #13271 for best value:
>> intensional similarity = 9
>> extensional distance = 146
>> proper extension:  MackenzieRiver; Thjorsa;
>> query: (?x1114, ?x78) <- ?x1114[ a River; has hasSource ?x1115[ a Source; has locatedIn ?x78;]; has locatedIn ?x234[ has language ?x51; has religion ?x56; is locatedIn of ?x1707[ a Source;];];]
ranks of expected_values: 1
EVAL	Doubs	locatedIn	F	CNN-1.+1._MA	1.000	1.000	1.000	1.000	136.000	129.000	220.000	0.954	http://www.semwebtech.org/mondial/10/meta#locatedIn

#136-Suchona
PRED entity: Suchona

PRED relation: flowsInto
PRED expected values: NorthernDwina
=> 27 concepts (21 used for prediction)
PRED predicted values (max 10 best out of 147): BarentsSea (0.12 #17, 0.11 #182, 0.08 #1326), EastSibirianSea (0.12 #45, 0.11 #210, 0.08 #1326), Ob (0.11 #318, 0.04 #153, 0.03 #483), AtlanticOcean (0.09 #1339, 0.09 #1505, 0.09 #1672), Amur (0.08 #140, 0.08 #1326, 0.07 #305), OzeroLadoga (0.08 #61, 0.08 #1326, 0.07 #226), BalticSea (0.08 #10, 0.07 #175, 0.04 #838), CaspianSea (0.08 #1326, 0.07 #289, 0.04 #124), BlackSea (0.08 #1326, 0.04 #3, 0.04 #168), Volga (0.08 #1326, 0.04 #44, 0.04 #209)
>> best conf = 0.12
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #17 for best value:
>> intensional similarity = 5
>> extensional distance = 23
>> proper extension:  Selenge; Swir; Volga; Angara; Argun; Newa; Paatsjoki; NorthernDwina; Dnepr; Jenissej; ...
>> query: (?x103, BarentsSea) <- ?x103[ a River; has hasSource ?x1180[ a Source;]; has locatedIn ?x73<R>;]

*> Best rule #828 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 165
*> proper extension:  Kwa; Morava; Donau; Oranje; Save; Mur; Buna; Amazonas; DetroitRiver; WesternBug; ...
*> query: (?x103, ?x72) <- ?x103[ a River; has locatedIn ?x73[ has neighbor ?x170; has wasDependentOf ?x903; is locatedIn of ?x72;];]
*> conf = 0.02
ranks of expected_values: 45
EVAL	Suchona	flowsInto	NorthernDwina	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.022	27.000	21.000	147.000	0.120	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto
PRED expected values: NorthernDwina
=> 113 concepts (110 used for prediction)
PRED predicted values (max 10 best out of 188): EastSibirianSea (0.14 #45, 0.14 #210, 0.12 #375), AtlanticOcean (0.13 #2021, 0.12 #1513, 0.10 #4043), BarentsSea (0.12 #347, 0.11 #1837, 0.11 #1668), PacificOcean (0.11 #1837, 0.11 #1668, 0.08 #5552), SeaofOkhotsk (0.11 #1837, 0.11 #1668, 0.08 #5552), BeringSea (0.11 #1837, 0.11 #1668, 0.08 #5552), OzeroBaikal (0.11 #1837, 0.11 #1668, 0.08 #6567), Ob (0.11 #815, 0.08 #1149, 0.06 #1487), Donau (0.10 #3024, 0.10 #4039, 0.09 #3365), BalticSea (0.10 #10, 0.09 #175, 0.09 #1850)
>> best conf = 0.14
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #45 for best value:
>> intensional similarity = 6
>> extensional distance = 19
>> proper extension:  Swir; Volga; Angara; Newa; NorthernDwina; Dnepr; Jenissej; Narva; Chatanga; Lena; ...
>> query: (?x103, EastSibirianSea) <- ?x103[ a River; has hasSource ?x1180[ a Source; has locatedIn ?x73<R>;]; has locatedIn ?x73<R>;]

*> Best rule #5381 for first EXPECTED value:
*> intensional similarity = 10
*> extensional distance = 164
*> proper extension:  Jordan; Drin; Raab;
*> query: (?x103, ?x72) <- ?x103[ a River; has locatedIn ?x73[ has language ?x555; has neighbor ?x403; has religion ?x56; is locatedIn of ?x72[ a River;]; is neighbor of ?x222[ has language ?x1987; is locatedIn of ?x221;];];]
*> conf = 0.03
ranks of expected_values: 58
EVAL	Suchona	flowsInto	NorthernDwina	CNN-1.+1._MA	0.000	0.000	0.000	0.017	113.000	110.000	188.000	0.143	http://www.semwebtech.org/mondial/10/meta#flowsInto

#135-Kymijoki
PRED entity: Kymijoki

PRED relation: hasEstuary!
PRED expected values: Kymijoki
=> 43 concepts (29 used for prediction)
PRED predicted values (max 10 best out of 78): Oulujoki (0.17 #184, 0.11 #1363, 0.08 #410), Kemijoki (0.17 #67, 0.11 #1363, 0.08 #293), Kokemaeenjoki (0.17 #171, 0.11 #1363, 0.08 #397), Ounasjoki (0.17 #95, 0.11 #1363, 0.08 #321), Vuoksi (0.13 #907, 0.11 #1363, 0.08 #2729), Paatsjoki (0.11 #1363, 0.08 #2729, 0.07 #1362), Kymijoki (0.11 #1363, 0.08 #2729, 0.07 #1362), Paeijaenne (0.11 #1363, 0.04 #1135, 0.03 #453), Oulujaervi (0.11 #1363, 0.04 #1135, 0.03 #453), Kallavesi (0.11 #1363, 0.04 #1135, 0.03 #453)
>> best conf = 0.17
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #184 for best value:
>> intensional similarity = 3
>> extensional distance = 4
>> proper extension:  Kemijoki; Oulujoki; Ounasjoki; Kokemaeenjoki;
>> query: (?x1107, Oulujoki) <- ?x1107[ a Estuary; has locatedIn ?x565<SF>;]

*> Best rule #1363 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 117
*> proper extension:  Thjorsa; JoekulsaaFjoellum;
*> query: (?x1107, ?x1396) <- ?x1107[ a Estuary; has locatedIn ?x565[ has government ?x435; has language ?x247; is locatedIn of ?x1396[ has flowsInto ?x589;];];]
*> conf = 0.11
ranks of expected_values: 7
EVAL	Kymijoki	hasEstuary!	Kymijoki	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.143	43.000	29.000	78.000	0.167	http://www.semwebtech.org/mondial/10/meta#hasEstuary

PRED relation: hasEstuary!
PRED expected values: Kymijoki
=> 110 concepts (110 used for prediction)
PRED predicted values (max 10 best out of 205): Kemijoki (0.20 #4116, 0.17 #67, 0.16 #683), Oulujoki (0.20 #4116, 0.17 #184, 0.16 #683), Ounasjoki (0.20 #4116, 0.17 #95, 0.16 #683), Kokemaeenjoki (0.20 #4116, 0.17 #171, 0.16 #683), Paatsjoki (0.20 #4116, 0.16 #683, 0.15 #5028), Vuoksi (0.20 #4116, 0.16 #683, 0.15 #5028), Kymijoki (0.20 #4116, 0.16 #683, 0.15 #5028), Paeijaenne (0.11 #915, 0.07 #912, 0.05 #4117), Oulujaervi (0.11 #915, 0.07 #912, 0.05 #4117), Kallavesi (0.11 #915, 0.07 #912, 0.05 #4117)
>> best conf = 0.20
=> the first rule below is the first best rule for 7 predicted values
>> Best rule #4116 for best value:
>> intensional similarity = 11
>> extensional distance = 81
>> proper extension:  Mekong; Senegal; Vaal;
>> query: (?x1107, ?x631) <- ?x1107[ a Estuary; has locatedIn ?x565[ a Country; has encompassed ?x195; has ethnicGroup ?x1193; has government ?x435; has religion ?x56; has wasDependentOf ?x73; is locatedIn of ?x631[ a River;]; is neighbor of ?x170;];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 7
EVAL	Kymijoki	hasEstuary!	Kymijoki	CNN-1.+1._MA	0.000	0.000	1.000	0.143	110.000	110.000	205.000	0.199	http://www.semwebtech.org/mondial/10/meta#hasEstuary

#134-CI
PRED entity: CI

PRED relation: ethnicGroup
PRED expected values: Bete
=> 31 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 201): African (0.46 #773, 0.43 #6, 0.40 #1283), European (0.33 #2815, 0.30 #1285, 0.29 #775), Peuhl (0.17 #6378, 0.16 #6635, 0.16 #6634), Tuareg (0.17 #6378, 0.16 #6635, 0.16 #6634), Mande (0.17 #6378, 0.16 #6635, 0.16 #6634), Voltaic (0.17 #6378, 0.16 #6635, 0.16 #6634), Songhai (0.17 #6378, 0.16 #6635, 0.16 #6634), Soussou (0.17 #6378, 0.16 #6635, 0.16 #6634), Mossi (0.17 #6378, 0.16 #6635, 0.16 #6634), Americo-Liberians (0.17 #6378, 0.16 #6634, 0.14 #31)
>> best conf = 0.46
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #773 for best value:
>> intensional similarity = 6
>> extensional distance = 22
>> proper extension:  WAN; SME; RCH; RSA; USA; ZRE; GUY; RA; SN; GH; ...
>> query: (?x1206, African) <- ?x1206[ a Country; has ethnicGroup ?x2201; has neighbor ?x483; has wasDependentOf ?x78; is locatedIn of ?x182<AtlanticOcean>;]

No rule for expected values
ranks of expected_values: 
EVAL	CI	ethnicGroup	Bete	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	31.000	31.000	201.000	0.458	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: Bete
=> 86 concepts (86 used for prediction)
PRED predicted values (max 10 best out of 243): European (0.50 #5385, 0.38 #8202, 0.36 #2570), African (0.43 #2824, 0.43 #1800, 0.42 #8200), Tuareg (0.33 #1446, 0.33 #679, 0.31 #770), Peuhl (0.33 #736, 0.31 #770, 0.25 #2527), Fulani (0.33 #3, 0.31 #770, 0.20 #1028), Wolof (0.33 #225, 0.31 #770, 0.20 #1250), EuropeanLebanese (0.33 #219, 0.31 #770, 0.20 #1244), Diola (0.33 #48, 0.31 #770, 0.20 #1073), Toucouleur (0.33 #35, 0.31 #770, 0.20 #1060), Mandingo (0.33 #34, 0.31 #770, 0.20 #1059)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5385 for best value:
>> intensional similarity = 11
>> extensional distance = 34
>> proper extension:  SGP;
>> query: (?x1206, European) <- ?x1206[ has encompassed ?x213; has ethnicGroup ?x2201; has government ?x2531; has wasDependentOf ?x78[ has government ?x435; has religion ?x95; is locatedIn of ?x323[ a Mountain;]; is neighbor of ?x789[ has religion ?x352;];]; is locatedIn of ?x182;]

No rule for expected values
ranks of expected_values: 
EVAL	CI	ethnicGroup	Bete	CNN-1.+1._MA	0.000	0.000	0.000	0.000	86.000	86.000	243.000	0.500	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#133-Paatsjoki
PRED entity: Paatsjoki

PRED relation: locatedIn
PRED expected values: N
=> 42 concepts (41 used for prediction)
PRED predicted values (max 10 best out of 166): N (0.91 #5865, 0.23 #937, 0.19 #5161), S (0.50 #559, 0.19 #5161, 0.16 #5396), SVAX (0.23 #937, 0.14 #7975, 0.12 #8212), CN (0.20 #55, 0.19 #5161, 0.14 #992), UA (0.19 #5161, 0.18 #4521, 0.13 #3984), BY (0.19 #5161, 0.13 #3984, 0.13 #7270), EW (0.19 #5161, 0.13 #3984, 0.13 #7270), PL (0.19 #5161, 0.13 #3984, 0.13 #7270), LV (0.19 #5161, 0.13 #3984, 0.13 #7270), GE (0.19 #5161, 0.13 #3984, 0.13 #7270)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5865 for best value:
>> intensional similarity = 5
>> extensional distance = 169
>> proper extension:  Araguaia; Vaesterdalaelv; Thames; Moraca;
>> query: (?x631, ?x73) <- ?x631[ a River; has flowsInto ?x251; has hasEstuary ?x632[ has locatedIn ?x73;]; has hasSource ?x808;]
ranks of expected_values: 1
EVAL	Paatsjoki	locatedIn	N	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	42.000	41.000	166.000	0.908	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: N
=> 146 concepts (146 used for prediction)
PRED predicted values (max 10 best out of 240): N (0.93 #17912, 0.93 #17676, 0.92 #13191), D (0.78 #5666, 0.38 #9205, 0.35 #21953), USA (0.72 #19636, 0.54 #5955, 0.53 #7371), RI (0.54 #11591, 0.40 #941, 0.17 #22458), GB (0.52 #10136, 0.40 #11310, 0.36 #12488), S (0.51 #9983, 0.50 #1502, 0.33 #1738), SVAX (0.45 #4940, 0.40 #941, 0.38 #2586), CDN (0.42 #7833, 0.41 #18918, 0.40 #941), UA (0.40 #941, 0.35 #4774, 0.25 #304), AUS (0.40 #941, 0.33 #1927, 0.20 #18900)
>> best conf = 0.93
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #17912 for best value:
>> intensional similarity = 7
>> extensional distance = 131
>> proper extension:  Cuango; Tarim-Yarkend; Schari; Bomu; Cuilo; Vaal; Bahrel-Ghasal; Atbara; Pibor; Shabelle;
>> query: (?x631, ?x170) <- ?x631[ a River; has flowsInto ?x251; has hasEstuary ?x632[ has locatedIn ?x170;]; has hasSource ?x808; has locatedIn ?x565[ has government ?x435;];]
ranks of expected_values: 1
EVAL	Paatsjoki	locatedIn	N	CNN-1.+1._MA	1.000	1.000	1.000	1.000	146.000	146.000	240.000	0.930	http://www.semwebtech.org/mondial/10/meta#locatedIn

#132-YV
PRED entity: YV

PRED relation: language
PRED expected values: Spanish
=> 32 concepts (32 used for prediction)
PRED predicted values (max 10 best out of 82): Spanish (0.34 #512, 0.33 #610, 0.33 #218), Portuguese (0.33 #9, 0.21 #1962, 0.21 #1765), English (0.26 #396, 0.24 #494, 0.20 #1082), French (0.13 #393, 0.12 #1373, 0.12 #1079), Russian (0.12 #1677, 0.11 #1874, 0.09 #2071), German (0.12 #799, 0.11 #897, 0.11 #995), Hungarian (0.11 #703, 0.10 #801, 0.09 #899), Creole (0.09 #178, 0.08 #276, 0.07 #374), Dutch (0.08 #1088, 0.06 #598, 0.06 #696), Icelandic (0.07 #354, 0.04 #452, 0.02 #2551)
>> best conf = 0.34
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #512 for best value:
>> intensional similarity = 7
>> extensional distance = 27
>> proper extension:  WV; KN; WG; AG; MNTS; SBAR;
>> query: (?x345, Spanish) <- ?x345[ has encompassed ?x521; has government ?x140; is locatedIn of ?x317<CaribbeanSea>; is locatedIn of ?x344[ has locatedIn ?x215<CO>;];]
ranks of expected_values: 1
EVAL	YV	language	Spanish	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	32.000	32.000	82.000	0.345	http://www.semwebtech.org/mondial/10/meta#language

PRED relation: language
PRED expected values: Spanish
=> 108 concepts (108 used for prediction)
PRED predicted values (max 10 best out of 95): Spanish (0.71 #1788, 0.68 #2670, 0.67 #806), English (0.33 #1180, 0.29 #1672, 0.28 #2064), Portuguese (0.31 #4718, 0.27 #3931, 0.22 #3634), Russian (0.22 #2758, 0.16 #4630, 0.12 #1580), Catalan (0.22 #7472, 0.20 #3733, 0.19 #5013), Basque (0.22 #7472, 0.20 #3733, 0.19 #5013), Hungarian (0.19 #2469, 0.14 #3552, 0.14 #3452), Quechua (0.17 #936, 0.16 #4325, 0.14 #3534), Aymara (0.17 #900, 0.16 #4325, 0.14 #3534), Miskito (0.17 #879, 0.13 #5799, 0.13 #5112)
>> best conf = 0.71
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1788 for best value:
>> intensional similarity = 13
>> extensional distance = 15
>> proper extension:  R; ES;
>> query: (?x345, Spanish) <- ?x345[ a Country; has neighbor ?x542; has religion ?x95; has wasDependentOf ?x149; is locatedIn of ?x317[ has locatedIn ?x783<PA>; has locatedIn ?x899[ has encompassed ?x521; has ethnicGroup ?x79;]; is locatedInWater of ?x1928[ a Island; has belongsToIslands ?x1962;];];]
ranks of expected_values: 1
EVAL	YV	language	Spanish	CNN-1.+1._MA	1.000	1.000	1.000	1.000	108.000	108.000	95.000	0.706	http://www.semwebtech.org/mondial/10/meta#language

#131-E
PRED entity: E

PRED relation: locatedIn!
PRED expected values: Mallorca Garonne Formentera Garonne
=> 46 concepts (45 used for prediction)
PRED predicted values (max 10 best out of 1390): PacificOcean (0.59 #12594, 0.53 #15374, 0.42 #9814), CaribbeanSea (0.53 #15394, 0.50 #5663, 0.50 #1493), PortoSanto (0.33 #1230, 0.06 #37534, 0.04 #9731), Faial (0.33 #618, 0.06 #37534, 0.04 #9731), SaoJorge (0.33 #609, 0.06 #37534, 0.04 #9731), Madeira (0.33 #536, 0.06 #37534, 0.04 #9731), SantaMaria (0.33 #512, 0.06 #37534, 0.04 #9731), Graciosa (0.33 #396, 0.06 #37534, 0.04 #9731), Terceira (0.33 #368, 0.06 #37534, 0.04 #9731), Corvo (0.33 #322, 0.06 #37534, 0.04 #9731)
>> best conf = 0.59
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #12594 for best value:
>> intensional similarity = 5
>> extensional distance = 15
>> proper extension:  GCA; RCH; PE; CR; ROU; RA; NIC; MEX; DOM; BOL; ...
>> query: (?x149, PacificOcean) <- ?x149[ has government ?x1657; has language ?x796<Spanish>; is locatedIn of ?x68; is neighbor of ?x78;]

*> Best rule #26413 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 56
*> proper extension:  LS; TAD; SUD; RN; ETH; RWA; EAU;
*> query: (?x149, ?x698) <- ?x149[ has ethnicGroup ?x2540; is locatedIn of ?x275[ is flowsInto of ?x698;]; is locatedIn of ?x1726[ has inMountains ?x1701;];]
*> conf = 0.27
ranks of expected_values: 29, 1159, 1164
EVAL	E	locatedIn!	Garonne	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	46.000	45.000	1390.000	0.588	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	E	locatedIn!	Formentera	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.001	46.000	45.000	1390.000	0.588	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	E	locatedIn!	Garonne	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.034	46.000	45.000	1390.000	0.588	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	E	locatedIn!	Mallorca	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.001	46.000	45.000	1390.000	0.588	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Mallorca Garonne Formentera Garonne
=> 125 concepts (125 used for prediction)
PRED predicted values (max 10 best out of 1417): Douro (0.77 #16700, 0.76 #25053, 0.75 #18091), PacificOcean (0.69 #57136, 0.64 #58527, 0.62 #39056), NorthSea (0.67 #27857, 0.50 #9764, 0.50 #8371), RedSea (0.60 #50963, 0.33 #864, 0.25 #14781), TheChannel (0.50 #8998, 0.45 #8348, 0.45 #6955), Maas (0.50 #10140, 0.33 #40760, 0.33 #28233), CaribbeanSea (0.45 #8348, 0.45 #6955, 0.40 #20979), GulfofMexico (0.45 #8348, 0.45 #6955, 0.40 #21620), NorwegianSea (0.45 #8348, 0.45 #6955, 0.25 #15442), IndianOcean (0.45 #8348, 0.45 #6955, 0.25 #6960)
>> best conf = 0.77
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #16700 for best value:
>> intensional similarity = 13
>> extensional distance = 2
>> proper extension:  IS;
>> query: (?x149, ?x1352) <- ?x149[ a Country; has encompassed ?x195; has ethnicGroup ?x2540; has language ?x790; has religion ?x352; is locatedIn of ?x500[ a Mountain;]; is locatedIn of ?x1519[ a River; has hasEstuary ?x1352;]; is locatedIn of ?x1661[ a Island; has type ?x150;];]

*> Best rule #6956 for first EXPECTED value:
*> intensional similarity = 12
*> extensional distance = 2
*> proper extension:  MAL;
*> query: (?x149, ?x698) <- ?x149[ has ethnicGroup ?x2540; is locatedIn of ?x275[ has mergesWith ?x1633; is flowsInto of ?x698; is locatedInWater of ?x86;]; is locatedIn of ?x1007[ a Mountain;]; is locatedIn of ?x1661[ has belongsToIslands ?x1068;]; is neighbor of ?x78; is wasDependentOf of ?x575[ has ethnicGroup ?x734;];]
*> conf = 0.44
ranks of expected_values: 25, 610, 611, 907
EVAL	E	locatedIn!	Garonne	CNN-1.+1._MA	0.000	0.000	0.000	0.001	125.000	125.000	1417.000	0.773	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	E	locatedIn!	Formentera	CNN-1.+1._MA	0.000	0.000	0.000	0.002	125.000	125.000	1417.000	0.773	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	E	locatedIn!	Garonne	CNN-1.+1._MA	0.000	0.000	0.000	0.040	125.000	125.000	1417.000	0.773	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	E	locatedIn!	Mallorca	CNN-1.+1._MA	0.000	0.000	0.000	0.002	125.000	125.000	1417.000	0.773	http://www.semwebtech.org/mondial/10/meta#locatedIn

#130-PK
PRED entity: PK

PRED relation: religion
PRED expected values: Muslim
=> 34 concepts (34 used for prediction)
PRED predicted values (max 10 best out of 28): RomanCatholic (0.79 #260, 0.70 #724, 0.69 #682), Muslim (0.71 #511, 0.68 #383, 0.64 #215), Protestant (0.64 #254, 0.53 #886, 0.50 #802), Buddhist (0.52 #1011, 0.50 #96, 0.49 #1266), Hindu (0.52 #1011, 0.50 #94, 0.49 #1266), Christian (0.52 #1011, 0.49 #1266, 0.49 #1223), Sikh (0.52 #1011, 0.49 #1266, 0.49 #1223), Jains (0.52 #1011, 0.49 #1266, 0.49 #1223), ChristianOrthodox (0.36 #591, 0.34 #549, 0.32 #801), Bahai (0.33 #74, 0.20 #158, 0.16 #1351)
>> best conf = 0.79
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #260 for best value:
>> intensional similarity = 5
>> extensional distance = 12
>> proper extension:  J;
>> query: (?x83, RomanCatholic) <- ?x83[ has government ?x140; has language ?x559; is locatedIn of ?x82; is wasDependentOf of ?x943[ is neighbor of ?x366;];]

*> Best rule #511 for first EXPECTED value:
*> intensional similarity = 4
*> extensional distance = 32
*> proper extension:  TCH; RN; Z; RB;
*> query: (?x83, Muslim) <- ?x83[ has government ?x140; has neighbor ?x232; is locatedIn of ?x82[ a Desert;];]
*> conf = 0.71
ranks of expected_values: 2
EVAL	PK	religion	Muslim	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	34.000	34.000	28.000	0.786	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion
PRED expected values: Muslim
=> 96 concepts (96 used for prediction)
PRED predicted values (max 10 best out of 38): RomanCatholic (0.83 #1595, 0.82 #1466, 0.75 #2413), Muslim (0.80 #1420, 0.75 #1079, 0.72 #3299), Protestant (0.68 #2364, 0.67 #1589, 0.64 #1460), Hindu (0.66 #3934, 0.60 #169, 0.60 #2745), Buddhist (0.60 #169, 0.60 #2745, 0.60 #2405), Sikh (0.60 #169, 0.60 #2745, 0.60 #2405), Christian (0.60 #169, 0.60 #2745, 0.56 #3935), Jains (0.60 #169, 0.60 #2745, 0.56 #3935), Jewish (0.60 #2405, 0.51 #2191, 0.40 #518), Anglican (0.60 #2405, 0.51 #2191, 0.33 #1975)
>> best conf = 0.83
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1595 for best value:
>> intensional similarity = 13
>> extensional distance = 10
>> proper extension:  SRB;
>> query: (?x83, RomanCatholic) <- ?x83[ has government ?x140; has language ?x559; has language ?x649[ a Language;]; is locatedIn of ?x926[ has locatedIn ?x107[ has encompassed ?x175; has ethnicGroup ?x1595; has government ?x1136;];]; is neighbor of ?x381[ has wasDependentOf ?x81;]; is wasDependentOf of ?x943[ a Country;];]

*> Best rule #1420 for first EXPECTED value:
*> intensional similarity = 15
*> extensional distance = 8
*> proper extension:  Q;
*> query: (?x83, Muslim) <- ?x83[ a Country; is locatedIn of ?x926[ a Sea; has locatedIn ?x304<IR>; is locatedInWater of ?x2355; is mergesWith of ?x918;]; is locatedIn of ?x1877[ has locatedIn ?x924[ a Country; has encompassed ?x175; has ethnicGroup ?x1553; has government ?x140; has religion ?x116; is neighbor of ?x111;];];]
*> conf = 0.80
ranks of expected_values: 2
EVAL	PK	religion	Muslim	CNN-1.+1._MA	0.000	1.000	1.000	0.500	96.000	96.000	38.000	0.833	http://www.semwebtech.org/mondial/10/meta#religion

#129-Kasai
PRED entity: Kasai

PRED relation: hasEstuary!
PRED expected values: Kasai
=> 26 concepts (22 used for prediction)
PRED predicted values (max 10 best out of 71): Cuilo (0.08 #2498, 0.07 #2043, 0.05 #98), Lomami (0.08 #2498, 0.07 #2043, 0.05 #224), Luapula (0.08 #2498, 0.07 #2043, 0.05 #220), Aruwimi (0.08 #2498, 0.07 #2043, 0.05 #198), Tshuapa (0.08 #2498, 0.07 #2043, 0.05 #195), Lukenie (0.08 #2498, 0.07 #2043, 0.05 #188), Fimi (0.08 #2498, 0.07 #2043, 0.05 #141), Ruzizi (0.08 #2498, 0.07 #2043, 0.05 #120), Bomu (0.08 #2498, 0.07 #2043, 0.05 #91), Busira (0.08 #2498, 0.07 #2043, 0.05 #55)
>> best conf = 0.08
=> the first rule below is the first best rule for 20 predicted values
>> Best rule #2498 for best value:
>> intensional similarity = 5
>> extensional distance = 230
>> proper extension:  EucumbeneRiver; SaintLawrenceRiver; Manicouagan; Thjorsa; DarlingRiver; JoekulsaaFjoellum; MurrayRiver; MackenzieRiver; RiviereRichelieu; SnowyRiver; ...
>> query: (?x1785, ?x879) <- ?x1785[ a Estuary; has locatedIn ?x348[ a Country; is locatedIn of ?x879[ a River;];];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 18
EVAL	Kasai	hasEstuary!	Kasai	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.056	26.000	22.000	71.000	0.077	http://www.semwebtech.org/mondial/10/meta#hasEstuary

PRED relation: hasEstuary!
PRED expected values: Kasai
=> 76 concepts (76 used for prediction)
PRED predicted values (max 10 best out of 77): Ubangi (0.06 #1820, 0.05 #682, 0.05 #30), Fimi (0.06 #1820, 0.05 #682, 0.05 #141), Ruzizi (0.06 #1820, 0.05 #682, 0.05 #120), Lualaba (0.06 #1820, 0.05 #682, 0.05 #51), Luvua (0.06 #1820, 0.05 #682, 0.05 #28), Lukuga (0.06 #1820, 0.05 #682, 0.05 #16), Ruki (0.06 #1820, 0.05 #682, 0.05 #15), Lomami (0.06 #1820, 0.05 #682, 0.05 #224), Aruwimi (0.06 #1820, 0.05 #682, 0.05 #198), Tshuapa (0.06 #1820, 0.05 #682, 0.05 #195)
>> best conf = 0.06
=> the first rule below is the first best rule for 15 predicted values
>> Best rule #1820 for best value:
>> intensional similarity = 6
>> extensional distance = 129
>> proper extension:  EucumbeneRiver; SaintLawrenceRiver; Manicouagan; Thjorsa; DarlingRiver; JoekulsaaFjoellum; MurrayRiver; MackenzieRiver; RiviereRichelieu; SnowyRiver; ...
>> query: (?x1785, ?x601) <- ?x1785[ a Estuary; has locatedIn ?x348[ a Country; has wasDependentOf ?x543; is locatedIn of ?x1532[ is hasSource of ?x601;];];]

*> Best rule #682 for first EXPECTED value:
*> intensional similarity = 10
*> extensional distance = 31
*> proper extension:  Pibor; Bahrel-Ghasal; BlueNile; Sanaga; Schari; Atbara; WhiteNile; Bahrel-Djebel-Albert-Nil; Sanga; Sobat; ...
*> query: (?x1785, ?x281) <- ?x1785[ a Estuary; has locatedIn ?x348[ is locatedIn of ?x281[ a River;]; is locatedIn of ?x1770[ has flowsInto ?x1727;]; is neighbor of ?x359[ has religion ?x116;]; is neighbor of ?x736<RCA>;];]
*> conf = 0.05
ranks of expected_values: 21
EVAL	Kasai	hasEstuary!	Kasai	CNN-1.+1._MA	0.000	0.000	0.000	0.048	76.000	76.000	77.000	0.064	http://www.semwebtech.org/mondial/10/meta#hasEstuary

#128-IsleofWight
PRED entity: IsleofWight

PRED relation: locatedIn
PRED expected values: GB
=> 30 concepts (30 used for prediction)
PRED predicted values (max 10 best out of 80): GB (0.71 #711, 0.71 #483, 0.35 #4053), IRL (0.35 #4053, 0.34 #4292, 0.34 #4291), GBM (0.35 #4053, 0.34 #4292, 0.34 #4291), GBG (0.25 #387, 0.05 #6677, 0.05 #6676), GR (0.16 #1039, 0.10 #1754, 0.08 #2232), P (0.13 #908, 0.12 #1384, 0.10 #1621), D (0.12 #1444, 0.11 #1684, 0.09 #2162), E (0.11 #738, 0.10 #1214, 0.08 #1451), I (0.11 #997, 0.09 #1712, 0.07 #2190), RI (0.11 #1001, 0.08 #3150, 0.07 #3868)
>> best conf = 0.71
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #711 for best value:
>> intensional similarity = 10
>> extensional distance = 15
>> proper extension:  Tiree; NorthUist;
>> query: (?x2158, ?x81) <- ?x2158[ a Island; has belongsToIslands ?x945[ is belongsToIslands of ?x153[ has locatedInWater ?x182<AtlanticOcean>;]; is belongsToIslands of ?x2257[ a Island; has locatedIn ?x81<GB>; has locatedInWater ?x1833;];];]
>> Best rule #483 for best value:
>> intensional similarity = 10
>> extensional distance = 15
>> proper extension:  Tiree; NorthUist;
>> query: (?x2158, GB) <- ?x2158[ a Island; has belongsToIslands ?x945[ is belongsToIslands of ?x153[ has locatedInWater ?x182<AtlanticOcean>;]; is belongsToIslands of ?x2257[ a Island; has locatedIn ?x81<GB>; has locatedInWater ?x1833;];];]
ranks of expected_values: 1
EVAL	IsleofWight	locatedIn	GB	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	30.000	30.000	80.000	0.706	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: GB
=> 49 concepts (49 used for prediction)
PRED predicted values (max 10 best out of 122): GB (0.83 #1913, 0.75 #1914, 0.71 #1435), IRL (0.75 #1914, 0.69 #1915, 0.62 #1436), GBM (0.69 #1915, 0.56 #1437, 0.38 #1198), F (0.33 #957, 0.29 #958, 0.21 #2875), D (0.33 #1696, 0.12 #4575, 0.12 #4817), GBG (0.29 #958, 0.25 #629, 0.14 #868), GBJ (0.29 #958, 0.12 #1103, 0.06 #7951), GR (0.24 #2005, 0.21 #2246, 0.20 #2726), NL (0.21 #1810, 0.04 #3250, 0.04 #3489), I (0.18 #2684, 0.16 #1963, 0.14 #2204)
>> best conf = 0.83
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1913 for best value:
>> intensional similarity = 8
>> extensional distance = 22
>> proper extension:  Ameland; Texel; Spiekeroog;
>> query: (?x2158, ?x81) <- ?x2158[ a Island; has belongsToIslands ?x945[ a Islands; is belongsToIslands of ?x495[ a Island; has locatedIn ?x81; has locatedInWater ?x121<NorthSea>;];];]
ranks of expected_values: 1
EVAL	IsleofWight	locatedIn	GB	CNN-1.+1._MA	1.000	1.000	1.000	1.000	49.000	49.000	122.000	0.833	http://www.semwebtech.org/mondial/10/meta#locatedIn

#127-EAU
PRED entity: EAU

PRED relation: ethnicGroup
PRED expected values: Arab Karamojong Rwanda
=> 38 concepts (38 used for prediction)
PRED predicted values (max 10 best out of 227): Arab (0.33 #11, 0.19 #5728, 0.11 #1256), Kikuyu (0.33 #224, 0.19 #5728, 0.03 #1220), Kamba (0.33 #156, 0.19 #5728, 0.03 #1152), Luo (0.33 #143, 0.19 #5728, 0.03 #1139), Kalenjin (0.33 #132, 0.19 #5728, 0.03 #1128), Meru (0.33 #115, 0.19 #5728, 0.03 #1111), Kisii (0.33 #91, 0.19 #5728, 0.03 #1087), Luhya (0.33 #13, 0.19 #5728, 0.03 #1009), European (0.32 #2498, 0.30 #2249, 0.26 #2996), Amerindian (0.21 #1496, 0.19 #2243, 0.18 #1745)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #11 for best value:
>> intensional similarity = 6
>> extensional distance = 1
>> proper extension:  EAK;
>> query: (?x688, Arab) <- ?x688[ has ethnicGroup ?x529; has neighbor ?x229; has religion ?x95<Protestant>; is locatedIn of ?x1195<LakeVictoria>;]
ranks of expected_values: 1
EVAL	EAU	ethnicGroup	Rwanda	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	38.000	38.000	227.000	0.333	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	EAU	ethnicGroup	Karamojong	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	38.000	38.000	227.000	0.333	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	EAU	ethnicGroup	Arab	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	38.000	38.000	227.000	0.333	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: Arab Karamojong Rwanda
=> 111 concepts (111 used for prediction)
PRED predicted values (max 10 best out of 246): European (0.67 #3751, 0.62 #5745, 0.55 #8241), Arab (0.63 #7484, 0.33 #2756, 0.33 #1009), Kikuyu (0.63 #7484, 0.33 #1222, 0.27 #13225), Kamba (0.63 #7484, 0.33 #1154, 0.27 #13225), Luo (0.63 #7484, 0.33 #1141, 0.27 #13225), Kalenjin (0.63 #7484, 0.33 #1130, 0.27 #13225), Meru (0.63 #7484, 0.33 #1113, 0.27 #13225), Kisii (0.63 #7484, 0.33 #1089, 0.27 #13225), Luhya (0.63 #7484, 0.33 #1011, 0.27 #13225), Mangbetu-Azande (0.63 #7484, 0.33 #177, 0.27 #13225)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3751 for best value:
>> intensional similarity = 12
>> extensional distance = 7
>> proper extension:  NZ;
>> query: (?x688, European) <- ?x688[ has ethnicGroup ?x529[ a EthnicGroup;]; has religion ?x95<Protestant>; has religion ?x352<RomanCatholic>; has wasDependentOf ?x81; is locatedIn of ?x730[ a Mountain; has type ?x150;]; is locatedIn of ?x1880[ has inMountains ?x1066;];]

*> Best rule #7484 for first EXPECTED value:
*> intensional similarity = 11
*> extensional distance = 14
*> proper extension:  HELX;
*> query: (?x688, ?x2121) <- ?x688[ a Country; has encompassed ?x213<Africa>; has ethnicGroup ?x529; has ethnicGroup ?x743[ a EthnicGroup;]; is locatedIn of ?x1538[ a Mountain; has locatedIn ?x348[ a Country; has ethnicGroup ?x2121;];];]
*> conf = 0.63
ranks of expected_values: 2
EVAL	EAU	ethnicGroup	Rwanda	CNN-1.+1._MA	0.000	0.000	0.000	0.000	111.000	111.000	246.000	0.667	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	EAU	ethnicGroup	Karamojong	CNN-1.+1._MA	0.000	0.000	0.000	0.000	111.000	111.000	246.000	0.667	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	EAU	ethnicGroup	Arab	CNN-1.+1._MA	0.000	1.000	1.000	0.500	111.000	111.000	246.000	0.667	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#126-Zugspitze
PRED entity: Zugspitze

PRED relation: locatedIn
PRED expected values: A
=> 27 concepts (27 used for prediction)
PRED predicted values (max 10 best out of 75): A (0.67 #333, 0.45 #3066, 0.42 #3539), I (0.46 #518, 0.45 #3066, 0.43 #754), CH (0.45 #3066, 0.42 #3539, 0.42 #3538), F (0.45 #3066, 0.42 #3539, 0.42 #3538), SLO (0.45 #3066, 0.42 #3539, 0.42 #3538), USA (0.32 #4554, 0.16 #2901, 0.15 #3373), R (0.27 #4488, 0.11 #4723, 0.10 #4960), CDN (0.17 #4545, 0.07 #4780, 0.06 #5017), RI (0.10 #4534, 0.04 #4769, 0.04 #4299), PL (0.09 #5900, 0.08 #5901, 0.08 #707)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #333 for best value:
>> intensional similarity = 6
>> extensional distance = 10
>> proper extension:  Lech; Isar; Salzach; Enns; Mur; Iller;
>> query: (?x171, A) <- ?x171[ has inMountains ?x261<Alps>; has locatedIn ?x120[ has neighbor ?x78; is locatedIn of ?x1439<Salzach>;];]
ranks of expected_values: 1
EVAL	Zugspitze	locatedIn	A	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	27.000	27.000	75.000	0.667	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: A
=> 88 concepts (88 used for prediction)
PRED predicted values (max 10 best out of 209): I (0.67 #2176, 0.60 #710, 0.60 #709), F (0.60 #710, 0.60 #709, 0.60 #708), CH (0.60 #710, 0.60 #709, 0.60 #708), A (0.60 #710, 0.60 #709, 0.60 #708), SLO (0.60 #710, 0.60 #709, 0.60 #708), R (0.47 #8079, 0.32 #8790, 0.25 #9979), E (0.39 #2393, 0.30 #4769, 0.25 #2631), USA (0.33 #9808, 0.24 #11473, 0.21 #12191), UA (0.23 #4337, 0.21 #19264, 0.21 #9974), BG (0.21 #19264, 0.21 #9974, 0.21 #14257)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2176 for best value:
>> intensional similarity = 15
>> extensional distance = 13
>> proper extension:  GranSasso; Etna; MonteFalterona; MonteRosa; PizBernina; Vesuv; Matterhorn; Marmolata; GranParadiso;
>> query: (?x171, I) <- ?x171[ a Mountain; has locatedIn ?x120[ a Country; has neighbor ?x78<F>; has neighbor ?x234<CH>; is locatedIn of ?x256[ has hasEstuary ?x257;]; is locatedIn of ?x269[ a Estuary;]; is locatedIn of ?x1100[ a Island;]; is locatedIn of ?x1589[ has belongsToIslands ?x1590;];];]

*> Best rule #710 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 3
*> proper extension:  GrosserArber; Brocken;
*> query: (?x171, ?x424) <- ?x171[ a Mountain; has inMountains ?x261[ a Mountains; is inMountains of ?x1869[ has locatedIn ?x424;];]; has locatedIn ?x120<D>;]
*> conf = 0.60
ranks of expected_values: 4
EVAL	Zugspitze	locatedIn	A	CNN-1.+1._MA	0.000	0.000	1.000	0.250	88.000	88.000	209.000	0.667	http://www.semwebtech.org/mondial/10/meta#locatedIn

#125-MontOrohena
PRED entity: MontOrohena

PRED relation: locatedOnIsland
PRED expected values: Tahiti
=> 61 concepts (54 used for prediction)
PRED predicted values (max 10 best out of 46): TeIka-a-Maui-NorthIsland- (0.14 #177, 0.03 #751, 0.02 #803), Sicilia (0.14 #202, 0.03 #776, 0.02 #828), Luzon (0.11 #382, 0.10 #435, 0.04 #540), Madagaskar (0.09 #664, 0.09 #716, 0.04 #1399), Reunion (0.09 #491, 0.04 #960, 0.03 #648), Iceland (0.08 #277, 0.07 #799, 0.07 #903), Guadalcanal (0.08 #271, 0.06 #323, 0.05 #375), Bougainville (0.06 #347, 0.03 #661, 0.03 #713), MontOrohena (0.06 #522, 0.04 #417, 0.03 #1414), Tahiti (0.06 #522, 0.04 #417, 0.03 #1414)
>> best conf = 0.14
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #177 for best value:
>> intensional similarity = 6
>> extensional distance = 5
>> proper extension:  Etna; Vesuv;
>> query: (?x2130, TeIka-a-Maui-NorthIsland-) <- ?x2130[ a Volcano; has locatedIn ?x297[ has encompassed ?x211; has language ?x51<French>;]; has type ?x150;]

*> Best rule #522 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 20
*> proper extension:  Mayotte; Reunion; PulauPanjang;
*> query: (?x2130, ?x282) <- ?x2130[ has locatedIn ?x297[ has dependentOf ?x78; has encompassed ?x211; has government ?x2145; is locatedIn of ?x282;]; has type ?x150;]
*> conf = 0.06
ranks of expected_values: 10
EVAL	MontOrohena	locatedOnIsland	Tahiti	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.100	61.000	54.000	46.000	0.143	http://www.semwebtech.org/mondial/10/meta#locatedOnIsland

PRED relation: locatedOnIsland
PRED expected values: Tahiti
=> 153 concepts (153 used for prediction)
PRED predicted values (max 10 best out of 54): Tahiti (0.43 #263, 0.20 #264, 0.18 #422), Reunion (0.33 #390, 0.33 #22, 0.12 #1713), PacificOcean (0.20 #264, 0.18 #422, 0.17 #53), MontOrohena (0.20 #264, 0.18 #422, 0.17 #53), Guadalcanal (0.20 #327, 0.17 #540, 0.14 #697), Martinique (0.20 #255, 0.17 #413, 0.10 #1100), TristanDaCunha (0.20 #244, 0.17 #563, 0.10 #1142), PuertoRico (0.20 #249, 0.06 #1997, 0.03 #2740), Luzon (0.18 #1179, 0.14 #861, 0.12 #1817), Basse-Terre (0.17 #418, 0.10 #1105, 0.07 #1582)
>> best conf = 0.43
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #263 for best value:
>> intensional similarity = 10
>> extensional distance = 3
>> proper extension:  CerrodePunta;
>> query: (?x2130, ?x2009) <- ?x2130[ a Mountain; has locatedIn ?x297[ a Country; has dependentOf ?x78; has encompassed ?x211[ a Continent;]; has ethnicGroup ?x298; has government ?x2145; is locatedIn of ?x2009[ a Island;];];]
ranks of expected_values: 1
EVAL	MontOrohena	locatedOnIsland	Tahiti	CNN-1.+1._MA	1.000	1.000	1.000	1.000	153.000	153.000	54.000	0.429	http://www.semwebtech.org/mondial/10/meta#locatedOnIsland

#124-Mincio
PRED entity: Mincio

PRED relation: hasSource
PRED expected values: Mincio
=> 54 concepts (41 used for prediction)
PRED predicted values (max 10 best out of 220): Adda (0.25 #170, 0.20 #398, 0.17 #626), Ticino (0.25 #81, 0.20 #309, 0.17 #537), Drau (0.20 #315, 0.14 #771, 0.12 #1000), Po (0.17 #508, 0.11 #1193, 0.07 #1421), Arno (0.11 #1288, 0.02 #5256, 0.02 #1745), Etsch (0.11 #1208, 0.02 #5256, 0.02 #1665), Tiber (0.11 #1190, 0.02 #5256, 0.02 #1647), Rhone (0.07 #1500, 0.02 #1957, 0.02 #2185), Rhein (0.07 #1497, 0.02 #1954, 0.02 #2182), Mosel (0.07 #1529, 0.01 #3129, 0.01 #3357)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #170 for best value:
>> intensional similarity = 6
>> extensional distance = 2
>> proper extension:  Adda; Ticino;
>> query: (?x983, Adda) <- ?x983[ a River; has flowsInto ?x699<Po>; has locatedIn ?x207<I>; is flowsInto of ?x1471;]

*> Best rule #5256 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 126
*> proper extension:  MackenzieRiver; Thjorsa;
*> query: (?x983, ?x86) <- ?x983[ has hasEstuary ?x1156; has locatedIn ?x207[ has government ?x435; has language ?x51; is locatedIn of ?x86;];]
*> conf = 0.02
ranks of expected_values: 19
EVAL	Mincio	hasSource	Mincio	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.053	54.000	41.000	220.000	0.250	http://www.semwebtech.org/mondial/10/meta#hasSource

PRED relation: hasSource
PRED expected values: Mincio
=> 173 concepts (173 used for prediction)
PRED predicted values (max 10 best out of 260): Adda (0.29 #2059, 0.26 #8477, 0.25 #170), Ticino (0.29 #2059, 0.26 #8477, 0.25 #81), Drau (0.20 #315, 0.17 #544, 0.12 #1000), Po (0.17 #737, 0.17 #509, 0.11 #21784), Etsch (0.11 #21784, 0.11 #21783, 0.11 #1208), Tiber (0.11 #21784, 0.11 #21783, 0.11 #1190), Arno (0.11 #21784, 0.11 #21783, 0.11 #1288), Mincio (0.11 #21784, 0.11 #21783, 0.08 #1599), BlackDrin (0.10 #1509, 0.03 #5177, 0.03 #5406), Saone (0.09 #1928, 0.06 #3074, 0.05 #3532)
>> best conf = 0.29
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #2059 for best value:
>> intensional similarity = 10
>> extensional distance = 9
>> proper extension:  LakeNasser; LacLeman;
>> query: (?x983, ?x1202) <- ?x983[ has flowsInto ?x699[ a River; has flowsInto ?x275<MediterraneanSea>; has hasEstuary ?x230; has hasSource ?x911; is flowsInto of ?x1201[ a River; has hasSource ?x1202;];]; has locatedIn ?x207;]

*> Best rule #21784 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 150
*> proper extension:  Bahrel-Ghasal; Bahrel-Djebel-Albert-Nil;
*> query: (?x983, ?x863) <- ?x983[ a River; has hasEstuary ?x1156; has locatedIn ?x207[ has government ?x435; is locatedIn of ?x863[ a Source;]; is neighbor of ?x78;];]
*> conf = 0.11
ranks of expected_values: 8
EVAL	Mincio	hasSource	Mincio	CNN-1.+1._MA	0.000	0.000	1.000	0.125	173.000	173.000	260.000	0.292	http://www.semwebtech.org/mondial/10/meta#hasSource

#123-Raab
PRED entity: Raab

PRED relation: inMountains
PRED expected values: Alps
=> 26 concepts (26 used for prediction)
PRED predicted values (max 10 best out of 28): Alps (0.75 #4, 0.29 #91, 0.15 #178), Andes (0.07 #533, 0.07 #446, 0.05 #707), Balkan (0.05 #542, 0.05 #455, 0.03 #803), Vogesen (0.05 #222, 0.02 #396, 0.02 #570), EastAfricanRift (0.04 #811, 0.04 #898, 0.04 #985), Karpaten (0.03 #574, 0.03 #487, 0.02 #748), SudetyMountains (0.03 #406, 0.02 #580, 0.02 #493), BlackForest (0.03 #262, 0.02 #610, 0.01 #958), Cevennes (0.03 #251, 0.02 #425, 0.01 #686), Apennin (0.02 #612, 0.01 #525)
>> best conf = 0.75
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4 for best value:
>> intensional similarity = 3
>> extensional distance = 6
>> proper extension:  Lech; Isar; Salzach; Enns; Mur; Iller;
>> query: (?x1837, Alps) <- ?x1837[ a Source; has locatedIn ?x424<A>;]
ranks of expected_values: 1
EVAL	Raab	inMountains	Alps	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	26.000	26.000	28.000	0.750	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains
PRED expected values: Alps
=> 72 concepts (72 used for prediction)
PRED predicted values (max 10 best out of 31): Alps (0.75 #4, 0.60 #91, 0.41 #265), Beskides (0.20 #117, 0.02 #1597, 0.02 #1161), Vogesen (0.11 #396, 0.09 #657, 0.02 #1528), BlackForest (0.10 #175, 0.07 #262, 0.07 #349), Andes (0.09 #1142, 0.08 #1230, 0.08 #1317), Apennin (0.06 #612, 0.01 #1483), Kurdistan (0.05 #992, 0.02 #1166, 0.02 #1254), Balkan (0.05 #1239, 0.05 #1326, 0.05 #1151), Rhön (0.05 #228, 0.04 #315, 0.04 #402), ThueringerWald (0.05 #219, 0.04 #306, 0.04 #393)
>> best conf = 0.75
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4 for best value:
>> intensional similarity = 3
>> extensional distance = 6
>> proper extension:  Lech; Isar; Salzach; Enns; Mur; Iller;
>> query: (?x1837, Alps) <- ?x1837[ a Source; has locatedIn ?x424<A>;]
ranks of expected_values: 1
EVAL	Raab	inMountains	Alps	CNN-1.+1._MA	1.000	1.000	1.000	1.000	72.000	72.000	31.000	0.750	http://www.semwebtech.org/mondial/10/meta#inMountains

#122-RCA
PRED entity: RCA

PRED relation: locatedIn!
PRED expected values: Schari
=> 47 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 1376): AtlanticOcean (0.94 #19875, 0.89 #14208, 0.47 #4292), Zaire (0.66 #16999, 0.58 #19833, 0.58 #24084), ChadLake (0.66 #16999, 0.58 #19833, 0.27 #1417), Ubangi (0.64 #17000, 0.63 #22666, 0.61 #8500), Schari (0.64 #17000, 0.63 #22666, 0.61 #8500), Akagera (0.50 #3474, 0.09 #14166, 0.07 #7724), Uelle (0.45 #24083, 0.33 #123, 0.27 #1417), LakeTanganjika (0.33 #2921, 0.33 #88, 0.27 #1417), MaleboPool (0.33 #1493, 0.33 #76, 0.27 #1417), Ruzizi (0.33 #551, 0.27 #1417, 0.17 #3384)
>> best conf = 0.94
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #19875 for best value:
>> intensional similarity = 5
>> extensional distance = 63
>> proper extension:  WV; AG; MNTS; SBAR;
>> query: (?x736, AtlanticOcean) <- ?x736[ a Country; has encompassed ?x213; is locatedIn of ?x2087[ has locatedIn ?x536<CAM>;];]

No rule for expected values
ranks of expected_values: 
EVAL	RCA	locatedIn!	Schari	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	47.000	36.000	1376.000	0.938	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Schari
=> 93 concepts (88 used for prediction)
PRED predicted values (max 10 best out of 1407): AtlanticOcean (0.97 #65296, 0.96 #62460, 0.95 #51110), Ubangi (0.67 #17025, 0.67 #35465, 0.35 #8509), Schari (0.67 #17025, 0.67 #35465, 0.33 #5011), Zaire (0.60 #89371, 0.59 #73763, 0.50 #8511), ChadLake (0.60 #89371, 0.59 #73763, 0.47 #11351), Schari (0.57 #14187, 0.48 #48228, 0.27 #19862), LakeTanganjika (0.50 #8511, 0.43 #11441, 0.35 #8509), Kasai (0.50 #8511, 0.40 #7310, 0.35 #8509), Uelle (0.50 #9932, 0.35 #8509, 0.35 #8515), Ruzizi (0.50 #8511, 0.35 #8509, 0.35 #8515)
>> best conf = 0.97
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #65296 for best value:
>> intensional similarity = 12
>> extensional distance = 56
>> proper extension:  MNTS; SBAR;
>> query: (?x736, AtlanticOcean) <- ?x736[ a Country; has encompassed ?x213; has government ?x435; is locatedIn of ?x388[ has locatedIn ?x528<RCB>; is flowsInto of ?x343[ a River; has hasEstuary ?x1901;];]; is locatedIn of ?x834[ has locatedIn ?x348<ZRE>;];]

*> Best rule #14187 for first EXPECTED value:
*> intensional similarity = 11
*> extensional distance = 6
*> proper extension:  SSD;
*> query: (?x736, ?x1263) <- ?x736[ has government ?x435; is locatedIn of ?x549[ a Source;]; is locatedIn of ?x695[ a River; has hasSource ?x1263;]; is neighbor of ?x229[ a Country; is locatedIn of ?x53; is neighbor of ?x474<EAK>;];]
*> conf = 0.57
ranks of expected_values: 6
EVAL	RCA	locatedIn!	Schari	CNN-1.+1._MA	0.000	0.000	1.000	0.167	93.000	88.000	1407.000	0.966	http://www.semwebtech.org/mondial/10/meta#locatedIn

#121-BarredesEcrins
PRED entity: BarredesEcrins

PRED relation: locatedIn
PRED expected values: F
=> 12 concepts (12 used for prediction)
PRED predicted values (max 10 best out of 53): I (0.45 #1660, 0.43 #48, 0.42 #1661), CH (0.45 #1660, 0.42 #1661, 0.36 #57), A (0.45 #1660, 0.42 #1661, 0.32 #2372), D (0.45 #1660, 0.42 #1661, 0.32 #2372), F (0.45 #1660, 0.42 #1661, 0.32 #2372), SLO (0.42 #1661, 0.32 #2372, 0.32 #2135), USA (0.15 #1495, 0.15 #1733, 0.10 #2447), TAD (0.06 #731, 0.06 #494, 0.04 #970), RA (0.06 #796, 0.06 #559, 0.03 #1035), E (0.06 #736, 0.06 #1450, 0.06 #1688)
>> best conf = 0.45
=> the first rule below is the first best rule for 5 predicted values
>> Best rule #1660 for best value:
>> intensional similarity = 5
>> extensional distance = 152
>> proper extension:  JabalKatrina; Tahat; Annapurna; Schchara; EmiKussi; Demirkazik; Elbrus; Olympos; MtAdams; Manaslu; ...
>> query: (?x2524, ?x78) <- ?x2524[ a Mountain; has inMountains ?x261[ is inMountains of ?x323[ a Mountain; has locatedIn ?x78;];];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 5
EVAL	BarredesEcrins	locatedIn	F	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.200	12.000	12.000	53.000	0.450	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: F
=> 21 concepts (21 used for prediction)
PRED predicted values (max 10 best out of 53): I (0.45 #3846, 0.45 #3845, 0.45 #3844), CH (0.45 #3846, 0.45 #3845, 0.45 #3844), F (0.45 #3846, 0.45 #3845, 0.45 #3844), A (0.45 #3846, 0.45 #3845, 0.45 #3844), D (0.45 #3846, 0.45 #3845, 0.45 #3844), SLO (0.42 #3847, 0.32 #4566, 0.32 #4565), USA (0.15 #3677, 0.15 #4160, 0.10 #4638), E (0.12 #503, 0.11 #985, 0.06 #3632), TAD (0.07 #1940, 0.07 #2422, 0.07 #2180), RA (0.07 #2005, 0.07 #2487, 0.07 #2245)
>> best conf = 0.45
=> the first rule below is the first best rule for 5 predicted values
>> Best rule #3846 for best value:
>> intensional similarity = 6
>> extensional distance = 152
>> proper extension:  JabalKatrina; Tahat; Annapurna; Schchara; EmiKussi; Demirkazik; Elbrus; Olympos; MtAdams; Manaslu; ...
>> query: (?x2524, ?x78) <- ?x2524[ a Mountain; has inMountains ?x261[ a Mountains; is inMountains of ?x323[ a Mountain; has locatedIn ?x78;];];]
>> Best rule #3845 for best value:
>> intensional similarity = 6
>> extensional distance = 152
>> proper extension:  JabalKatrina; Tahat; Annapurna; Schchara; EmiKussi; Demirkazik; Elbrus; Olympos; MtAdams; Manaslu; ...
>> query: (?x2524, ?x424) <- ?x2524[ a Mountain; has inMountains ?x261[ a Mountains; is inMountains of ?x260[ a Mountain; has locatedIn ?x424;];];]
>> Best rule #3844 for best value:
>> intensional similarity = 10
>> extensional distance = 152
>> proper extension:  JabalKatrina; Tahat; Annapurna; Schchara; EmiKussi; Demirkazik; Elbrus; Olympos; MtAdams; Manaslu; ...
>> query: (?x2524, ?x234) <- ?x2524[ a Mountain; has inMountains ?x261[ a Mountains; is inMountains of ?x171[ a Mountain;]; is inMountains of ?x757[ has locatedIn ?x234[ is locatedIn of ?x1182[ a Mountain;];];]; is inMountains of ?x1182;];]
>> Best rule #3843 for best value:
>> intensional similarity = 6
>> extensional distance = 152
>> proper extension:  JabalKatrina; Tahat; Annapurna; Schchara; EmiKussi; Demirkazik; Elbrus; Olympos; MtAdams; Manaslu; ...
>> query: (?x2524, ?x120) <- ?x2524[ a Mountain; has inMountains ?x261[ a Mountains; is inMountains of ?x171[ a Mountain; has locatedIn ?x120;];];]
>> Best rule #3842 for best value:
>> intensional similarity = 6
>> extensional distance = 152
>> proper extension:  JabalKatrina; Tahat; Annapurna; Schchara; EmiKussi; Demirkazik; Elbrus; Olympos; MtAdams; Manaslu; ...
>> query: (?x2524, ?x207) <- ?x2524[ a Mountain; has inMountains ?x261[ a Mountains; is inMountains of ?x847[ a Mountain; has locatedIn ?x207;];];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 3
EVAL	BarredesEcrins	locatedIn	F	CNN-1.+1._MA	0.000	1.000	1.000	0.333	21.000	21.000	53.000	0.450	http://www.semwebtech.org/mondial/10/meta#locatedIn

#120-MurrayRiver
PRED entity: MurrayRiver

PRED relation: locatedIn
PRED expected values: AUS
=> 45 concepts (45 used for prediction)
PRED predicted values (max 10 best out of 225): AUS (0.91 #7347, 0.91 #9247, 0.91 #8534), D (0.50 #731, 0.19 #4994, 0.18 #1204), USA (0.40 #2439, 0.16 #6708, 0.16 #3624), ZW (0.35 #10440, 0.35 #10439, 0.35 #10438), RB (0.35 #10440, 0.35 #10439, 0.35 #10438), MOC (0.35 #10440, 0.35 #10439, 0.35 #10438), RSA (0.35 #10440, 0.35 #10439, 0.35 #10438), Z (0.35 #10440, 0.35 #10439, 0.35 #10438), NAM (0.35 #10440, 0.35 #10439, 0.35 #10438), SP (0.35 #10440, 0.35 #10439, 0.35 #10438)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #7347 for best value:
>> intensional similarity = 5
>> extensional distance = 149
>> proper extension:  Suchona;
>> query: (?x1356, ?x196) <- ?x1356[ a River; has hasSource ?x1820[ a Source; has locatedIn ?x196[ has language ?x247;];];]
ranks of expected_values: 1
EVAL	MurrayRiver	locatedIn	AUS	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	45.000	45.000	225.000	0.909	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: AUS
=> 147 concepts (147 used for prediction)
PRED predicted values (max 10 best out of 226): AUS (0.91 #33296, 0.91 #32567, 0.90 #27276), R (0.77 #16490, 0.50 #5729, 0.29 #13860), MOC (0.72 #7627, 0.35 #34996, 0.35 #34995), ZW (0.72 #7627, 0.35 #34996, 0.35 #34995), RB (0.72 #7627, 0.35 #34996, 0.35 #34995), RSA (0.72 #7627, 0.35 #34996, 0.35 #34995), ETH (0.61 #7628, 0.50 #7629, 0.50 #7505), SP (0.61 #7628, 0.35 #34996, 0.35 #34995), IND (0.57 #5196, 0.54 #8105, 0.50 #4007), PE (0.54 #9364, 0.21 #19428, 0.15 #26147)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #33296 for best value:
>> intensional similarity = 11
>> extensional distance = 166
>> proper extension:  Pibor;
>> query: (?x1356, ?x196) <- ?x1356[ a River; has flowsInto ?x60[ is flowsInto of ?x750[ has hasEstuary ?x510; has locatedIn ?x220;];]; has hasEstuary ?x2049[ a Estuary;]; has hasSource ?x1820[ a Source; has locatedIn ?x196[ a Country;];];]
ranks of expected_values: 1
EVAL	MurrayRiver	locatedIn	AUS	CNN-1.+1._MA	1.000	1.000	1.000	1.000	147.000	147.000	226.000	0.914	http://www.semwebtech.org/mondial/10/meta#locatedIn

#119-RSA
PRED entity: RSA

PRED relation: locatedIn!
PRED expected values: IndianOcean Oranje
=> 37 concepts (33 used for prediction)
PRED predicted values (max 10 best out of 1367): Oranje (0.68 #25478, 0.67 #26895, 0.67 #29729), IndianOcean (0.68 #25478, 0.67 #26895, 0.67 #29729), Limpopo (0.61 #26894, 0.61 #25477, 0.20 #2272), Zambezi (0.40 #2549, 0.33 #1133, 0.10 #42469), Okavango (0.33 #797, 0.20 #2213, 0.11 #7874), MakarikariSaltPan (0.33 #1233, 0.20 #2649, 0.10 #42469), LakeNgami (0.33 #1087, 0.20 #2503, 0.10 #42469), Okavango (0.33 #996, 0.20 #2412, 0.10 #42469), CaribbeanSea (0.30 #12841, 0.29 #15671, 0.18 #42573), Tajo (0.27 #24061, 0.19 #38223, 0.15 #5074)
>> best conf = 0.68
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #25478 for best value:
>> intensional similarity = 6
>> extensional distance = 97
>> proper extension:  BIH; ET; R; F; LS; THA; MNE; RL; D; TAD; ...
>> query: (?x243, ?x137) <- ?x243[ has neighbor ?x89; has religion ?x187; is locatedIn of ?x242[ has hasEstuary ?x1520;]; is locatedIn of ?x1054[ has flowsInto ?x137;];]
ranks of expected_values: 1, 2
EVAL	RSA	locatedIn!	Oranje	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	37.000	33.000	1367.000	0.677	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	RSA	locatedIn!	IndianOcean	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	37.000	33.000	1367.000	0.677	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: IndianOcean Oranje
=> 84 concepts (81 used for prediction)
PRED predicted values (max 10 best out of 1411): IndianOcean (0.89 #15592, 0.83 #5673, 0.80 #65209), Oranje (0.89 #15592, 0.83 #5673, 0.79 #69466), Limpopo (0.80 #5672, 0.78 #12760, 0.74 #11343), Zambezi (0.77 #43938, 0.49 #12759, 0.43 #26929), Chire (0.55 #11342, 0.49 #12759, 0.33 #1179), LakeMalawi (0.55 #11342, 0.49 #12759, 0.33 #929), Zambezi (0.55 #11342, 0.49 #12759, 0.33 #1090), Chire (0.55 #11342, 0.49 #12759, 0.33 #896), LakeChilwa (0.55 #11342, 0.49 #12759, 0.33 #546), LakeCabora-Bassa (0.55 #11342, 0.49 #12759, 0.33 #142)
>> best conf = 0.89
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #15592 for best value:
>> intensional similarity = 16
>> extensional distance = 8
>> proper extension:  RG; CI;
>> query: (?x243, ?x60) <- ?x243[ a Country; has encompassed ?x213; has neighbor ?x1239[ has government ?x1174; has religion ?x116; is locatedIn of ?x1437;]; has neighbor ?x1576[ has ethnicGroup ?x162; has religion ?x187;]; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x242[ a River; has flowsInto ?x60;]; is locatedIn of ?x1015[ a Source;];]
ranks of expected_values: 1, 2
EVAL	RSA	locatedIn!	Oranje	CNN-1.+1._MA	1.000	1.000	1.000	1.000	84.000	81.000	1411.000	0.895	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	RSA	locatedIn!	IndianOcean	CNN-1.+1._MA	1.000	1.000	1.000	1.000	84.000	81.000	1411.000	0.895	http://www.semwebtech.org/mondial/10/meta#locatedIn

#118-RI
PRED entity: RI

PRED relation: encompassed
PRED expected values: Asia
=> 41 concepts (41 used for prediction)
PRED predicted values (max 10 best out of 4): Asia (0.80 #25, 0.60 #13, 0.60 #9), Europe (0.57 #22, 0.54 #30, 0.41 #70), America (0.39 #72, 0.38 #84, 0.37 #44), Africa (0.31 #135, 0.28 #139, 0.28 #59)
>> best conf = 0.80
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #25 for best value:
>> intensional similarity = 6
>> extensional distance = 8
>> proper extension:  MACX;
>> query: (?x217, Asia) <- ?x217[ has ethnicGroup ?x425; has neighbor ?x376; is locatedIn of ?x282[ is locatedInWater of ?x205;]; is locatedIn of ?x384<SouthChinaSea>;]
ranks of expected_values: 1
EVAL	RI	encompassed	Asia	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	41.000	41.000	4.000	0.800	http://www.semwebtech.org/mondial/10/meta#encompassed

PRED relation: encompassed
PRED expected values: Asia
=> 112 concepts (112 used for prediction)
PRED predicted values (max 10 best out of 4): Asia (0.91 #337, 0.80 #101, 0.67 #42), America (0.64 #142, 0.61 #180, 0.53 #159), Europe (0.57 #72, 0.56 #152, 0.47 #291), Africa (0.50 #83, 0.50 #78, 0.50 #40)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #337 for best value:
>> intensional similarity = 10
>> extensional distance = 54
>> proper extension:  MW;
>> query: (?x217, ?x211) <- ?x217[ has neighbor ?x853[ has encompassed ?x211;]; has religion ?x95<Protestant>; is locatedIn of ?x60[ has locatedIn ?x787[ a Country;]; has locatedIn ?x820[ is neighbor of ?x348;]; is flowsInto of ?x242;];]
ranks of expected_values: 1
EVAL	RI	encompassed	Asia	CNN-1.+1._MA	1.000	1.000	1.000	1.000	112.000	112.000	4.000	0.911	http://www.semwebtech.org/mondial/10/meta#encompassed

#117-NorthUist
PRED entity: NorthUist

PRED relation: locatedInWater
PRED expected values: AtlanticOcean
=> 11 concepts (11 used for prediction)
PRED predicted values (max 10 best out of 23): AtlanticOcean (0.83 #219, 0.83 #182, 0.82 #175), CaribbeanSea (0.27 #150, 0.27 #194, 0.10 #238), NorthSea (0.27 #90, 0.08 #222, 0.08 #266), IrishSea (0.24 #85, 0.18 #129, 0.06 #173), PacificOcean (0.23 #367, 0.22 #280, 0.21 #324), TheChannel (0.12 #80, 0.09 #124, 0.03 #168), MediterraneanSea (0.10 #235, 0.10 #279, 0.10 #323), IndianOcean (0.08 #396, 0.07 #221, 0.07 #265), SulawesiSea (0.05 #247, 0.05 #291, 0.05 #335), NorwegianSea (0.05 #108, 0.02 #371, 0.02 #415)
>> best conf = 0.83
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #219 for best value:
>> intensional similarity = 7
>> extensional distance = 61
>> proper extension:  Streymoy;
>> query: (?x2388, ?x182) <- ?x2388[ a Island; has belongsToIslands ?x1415[ a Islands; is belongsToIslands of ?x526[ a Island; has locatedInWater ?x182<AtlanticOcean>;];];]
>> Best rule #182 for best value:
>> intensional similarity = 7
>> extensional distance = 61
>> proper extension:  Streymoy;
>> query: (?x2388, AtlanticOcean) <- ?x2388[ a Island; has belongsToIslands ?x1415[ a Islands; is belongsToIslands of ?x526[ a Island; has locatedInWater ?x182<AtlanticOcean>;];];]
ranks of expected_values: 1
EVAL	NorthUist	locatedInWater	AtlanticOcean	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	11.000	11.000	23.000	0.825	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater
PRED expected values: AtlanticOcean
=> 11 concepts (11 used for prediction)
PRED predicted values (max 10 best out of 23): AtlanticOcean (0.83 #219, 0.83 #182, 0.82 #175), CaribbeanSea (0.27 #150, 0.27 #194, 0.10 #238), NorthSea (0.27 #90, 0.08 #222, 0.08 #266), IrishSea (0.24 #85, 0.18 #129, 0.06 #173), PacificOcean (0.23 #367, 0.22 #280, 0.21 #324), TheChannel (0.12 #80, 0.09 #124, 0.03 #168), MediterraneanSea (0.10 #235, 0.10 #279, 0.10 #323), IndianOcean (0.08 #396, 0.07 #221, 0.07 #265), SulawesiSea (0.05 #247, 0.05 #291, 0.05 #335), NorwegianSea (0.05 #108, 0.02 #371, 0.02 #415)
>> best conf = 0.83
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #219 for best value:
>> intensional similarity = 7
>> extensional distance = 61
>> proper extension:  Streymoy;
>> query: (?x2388, ?x182) <- ?x2388[ a Island; has belongsToIslands ?x1415[ a Islands; is belongsToIslands of ?x526[ a Island; has locatedInWater ?x182<AtlanticOcean>;];];]
>> Best rule #182 for best value:
>> intensional similarity = 7
>> extensional distance = 61
>> proper extension:  Streymoy;
>> query: (?x2388, AtlanticOcean) <- ?x2388[ a Island; has belongsToIslands ?x1415[ a Islands; is belongsToIslands of ?x526[ a Island; has locatedInWater ?x182<AtlanticOcean>;];];]
ranks of expected_values: 1
EVAL	NorthUist	locatedInWater	AtlanticOcean	CNN-1.+1._MA	1.000	1.000	1.000	1.000	11.000	11.000	23.000	0.825	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#116-Protestant
PRED entity: Protestant

PRED relation: religion!
PRED expected values: FPOL BOL KIR RCA STP P
=> 18 concepts (18 used for prediction)
PRED predicted values (max 10 best out of 146): BG (0.66 #794, 0.62 #661, 0.50 #420), IRL (0.66 #794, 0.62 #661, 0.50 #680), MK (0.66 #794, 0.62 #661, 0.50 #480), SLO (0.66 #794, 0.62 #661, 0.50 #453), CO (0.66 #794, 0.62 #661, 0.47 #1456), E (0.66 #794, 0.62 #661, 0.47 #1456), R (0.66 #794, 0.62 #661, 0.47 #1456), DOM (0.66 #794, 0.62 #661, 0.47 #1456), V (0.66 #794, 0.62 #661, 0.47 #1456), RSM (0.66 #794, 0.62 #661, 0.47 #1456)
>> best conf = 0.66
=> the first rule below is the first best rule for 35 predicted values
>> Best rule #794 for best value:
>> intensional similarity = 13
>> extensional distance = 2
>> proper extension:  Anglican;
>> query: (?x95, ?x177) <- ?x95[ a Religion; is religion of ?x176[ has neighbor ?x177;]; is religion of ?x379[ is locatedIn of ?x182;]; is religion of ?x407<AXA>; is religion of ?x575[ has ethnicGroup ?x734;]; is religion of ?x899[ has encompassed ?x521;]; is religion of ?x1554<WL>;]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 14, 35, 45, 46, 54, 135
EVAL	Protestant	religion!	P	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.023	18.000	18.000	146.000	0.659	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Protestant	religion!	STP	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.008	18.000	18.000	146.000	0.659	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Protestant	religion!	RCA	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.029	18.000	18.000	146.000	0.659	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Protestant	religion!	KIR	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.023	18.000	18.000	146.000	0.659	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Protestant	religion!	BOL	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.071	18.000	18.000	146.000	0.659	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Protestant	religion!	FPOL	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.020	18.000	18.000	146.000	0.659	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion!
PRED expected values: FPOL BOL KIR RCA STP P
=> 28 concepts (28 used for prediction)
PRED predicted values (max 10 best out of 165): CO (0.65 #940, 0.64 #1211, 0.64 #941), R (0.65 #940, 0.64 #1211, 0.64 #941), BG (0.65 #940, 0.64 #1211, 0.64 #941), SLO (0.65 #940, 0.64 #1211, 0.64 #941), Z (0.65 #940, 0.64 #1211, 0.64 #941), EC (0.65 #940, 0.64 #1211, 0.64 #941), BOL (0.65 #940, 0.64 #1211, 0.64 #941), MNE (0.65 #940, 0.64 #1211, 0.64 #941), DOM (0.65 #940, 0.64 #1211, 0.64 #941), UZB (0.65 #940, 0.64 #1211, 0.64 #941)
>> best conf = 0.65
=> the first rule below is the first best rule for 35 predicted values
>> Best rule #940 for best value:
>> intensional similarity = 18
>> extensional distance = 3
>> proper extension:  Seventh-DayAdventist;
>> query: (?x95, ?x73) <- ?x95[ is religion of ?x78[ has encompassed ?x195; is locatedIn of ?x165;]; is religion of ?x156[ is neighbor of ?x106;]; is religion of ?x170[ has neighbor ?x73; is locatedIn of ?x121;]; is religion of ?x565[ has ethnicGroup ?x1193; has government ?x435; has language ?x247; is locatedIn of ?x631;]; is religion of ?x853<PNG>; is religion of ?x1364[ a Country; has ethnicGroup ?x162;];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 7, 16, 47, 72, 90, 132
EVAL	Protestant	religion!	P	CNN-1.+1._MA	0.000	0.000	0.000	0.022	28.000	28.000	165.000	0.652	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Protestant	religion!	STP	CNN-1.+1._MA	0.000	0.000	0.000	0.008	28.000	28.000	165.000	0.652	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Protestant	religion!	RCA	CNN-1.+1._MA	0.000	0.000	0.000	0.067	28.000	28.000	165.000	0.652	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Protestant	religion!	KIR	CNN-1.+1._MA	0.000	0.000	0.000	0.014	28.000	28.000	165.000	0.652	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Protestant	religion!	BOL	CNN-1.+1._MA	0.000	0.000	1.000	0.143	28.000	28.000	165.000	0.652	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	Protestant	religion!	FPOL	CNN-1.+1._MA	0.000	0.000	0.000	0.012	28.000	28.000	165.000	0.652	http://www.semwebtech.org/mondial/10/meta#religion

#115-MC
PRED entity: MC

PRED relation: language
PRED expected values: English Italian Dutch
=> 37 concepts (37 used for prediction)
PRED predicted values (max 10 best out of 95): Italian (0.40 #196, 0.25 #6, 0.22 #766), Spanish (0.32 #1350, 0.25 #20, 0.22 #2111), English (0.27 #1714, 0.25 #573, 0.22 #2094), Romansch (0.25 #50, 0.20 #240, 0.20 #145), Catalan (0.25 #19, 0.20 #209, 0.20 #114), Basque (0.25 #29, 0.20 #219, 0.20 #124), Albanian (0.23 #1080, 0.23 #985, 0.20 #225), Greek (0.23 #1093, 0.23 #998, 0.12 #1188), Dutch (0.22 #674, 0.17 #484, 0.17 #389), Slovenian (0.20 #207, 0.15 #1062, 0.15 #967)
>> best conf = 0.40
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #196 for best value:
>> intensional similarity = 8
>> extensional distance = 3
>> proper extension:  I;
>> query: (?x1577, Italian) <- ?x1577[ a Country; has encompassed ?x195<Europe>; has government ?x92; has language ?x51; has neighbor ?x78<F>; is locatedIn of ?x275;]
ranks of expected_values: 1, 3, 9
EVAL	MC	language	Dutch	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.143	37.000	37.000	95.000	0.400	http://www.semwebtech.org/mondial/10/meta#language
EVAL	MC	language	Italian	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	37.000	37.000	95.000	0.400	http://www.semwebtech.org/mondial/10/meta#language
EVAL	MC	language	English	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	37.000	37.000	95.000	0.400	http://www.semwebtech.org/mondial/10/meta#language

PRED relation: language
PRED expected values: English Italian Dutch
=> 86 concepts (86 used for prediction)
PRED predicted values (max 10 best out of 95): English (0.84 #2672, 0.71 #3823, 0.63 #3920), Spanish (0.41 #4511, 0.39 #3552, 0.36 #763), Italian (0.40 #476, 0.36 #1145, 0.33 #101), Albanian (0.40 #476, 0.33 #130, 0.20 #1752), Slovenian (0.40 #476, 0.33 #112, 0.20 #1734), Hungarian (0.38 #1255, 0.33 #2018, 0.30 #1637), Romansch (0.36 #1145, 0.33 #50, 0.25 #572), Portuguese (0.36 #763, 0.29 #772, 0.25 #572), Luxembourgish (0.36 #763, 0.25 #572, 0.23 #1526), Catalan (0.36 #763, 0.20 #1622, 0.20 #5257)
>> best conf = 0.84
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2672 for best value:
>> intensional similarity = 9
>> extensional distance = 17
>> proper extension:  FALK;
>> query: (?x1577, English) <- ?x1577[ has ethnicGroup ?x2314[ a EthnicGroup;]; has government ?x92; has language ?x635[ is language of ?x138<NAM>;]; is locatedIn of ?x275[ has mergesWith ?x182; is locatedInWater of ?x68;];]
ranks of expected_values: 1, 3, 18
EVAL	MC	language	Dutch	CNN-1.+1._MA	0.000	0.000	0.000	0.062	86.000	86.000	95.000	0.842	http://www.semwebtech.org/mondial/10/meta#language
EVAL	MC	language	Italian	CNN-1.+1._MA	0.000	1.000	1.000	0.500	86.000	86.000	95.000	0.842	http://www.semwebtech.org/mondial/10/meta#language
EVAL	MC	language	English	CNN-1.+1._MA	1.000	1.000	1.000	1.000	86.000	86.000	95.000	0.842	http://www.semwebtech.org/mondial/10/meta#language

#114-GR
PRED entity: GR

PRED relation: religion
PRED expected values: ChristianOrthodox
=> 26 concepts (26 used for prediction)
PRED predicted values (max 10 best out of 21): RomanCatholic (0.57 #171, 0.53 #212, 0.45 #787), Protestant (0.47 #207, 0.39 #577, 0.38 #659), ChristianOrthodox (0.45 #288, 0.43 #165, 0.38 #83), Jewish (0.45 #288, 0.26 #249, 0.20 #904), Buddhist (0.40 #216, 0.16 #988, 0.10 #668), Christian (0.33 #292, 0.30 #333, 0.29 #456), Bahai (0.33 #31, 0.20 #72, 0.12 #113), Druze (0.20 #904, 0.20 #946, 0.16 #988), CopticChristian (0.20 #946, 0.16 #988, 0.04 #276), Hindu (0.20 #214, 0.16 #988, 0.09 #789)
>> best conf = 0.57
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #171 for best value:
>> intensional similarity = 7
>> extensional distance = 12
>> proper extension:  CY; M;
>> query: (?x399, RomanCatholic) <- ?x399[ has government ?x1174; has language ?x1567; is locatedIn of ?x275<MediterraneanSea>; is locatedIn of ?x739[ has locatedIn ?x701[ is neighbor of ?x177;];];]

*> Best rule #288 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 21
*> proper extension:  GBZ; CEU;
*> query: (?x399, ?x56) <- ?x399[ a Country; is locatedIn of ?x275<MediterraneanSea>; is neighbor of ?x701[ has government ?x254; has religion ?x56;];]
*> conf = 0.45
ranks of expected_values: 3
EVAL	GR	religion	ChristianOrthodox	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.333	26.000	26.000	21.000	0.571	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion
PRED expected values: ChristianOrthodox
=> 110 concepts (110 used for prediction)
PRED predicted values (max 10 best out of 39): RomanCatholic (0.85 #1798, 0.79 #3635, 0.75 #2757), Protestant (0.76 #2794, 0.67 #1502, 0.66 #2463), ChristianOrthodox (0.73 #1501, 0.70 #706, 0.67 #3379), Jewish (0.63 #3127, 0.62 #3002, 0.59 #3754), Bahai (0.49 #2126, 0.46 #2210, 0.46 #1500), Christian (0.47 #2838, 0.46 #2210, 0.46 #1500), Buddhist (0.46 #4258, 0.46 #1345, 0.45 #1179), Hindu (0.46 #4258, 0.39 #1585, 0.38 #2219), ArmenianApostolic (0.40 #1042, 0.02 #3572, 0.01 #3740), Yezidi (0.40 #1042, 0.02 #3559, 0.01 #3727)
>> best conf = 0.85
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1798 for best value:
>> intensional similarity = 12
>> extensional distance = 18
>> proper extension:  NLSM;
>> query: (?x399, RomanCatholic) <- ?x399[ a Country; has government ?x1174; has language ?x1567; has religion ?x187[ is religion of ?x177<BG>; is religion of ?x736<RCA>;]; is locatedIn of ?x2006[ a Island; has belongsToIslands ?x1053[ a Islands;];];]

*> Best rule #1501 for first EXPECTED value:
*> intensional similarity = 11
*> extensional distance = 13
*> proper extension:  BY; LV; MD; LT;
*> query: (?x399, ChristianOrthodox) <- ?x399[ a Country; has encompassed ?x195; has government ?x1174; has language ?x1567; has wasDependentOf ?x1656; is locatedIn of ?x275; is neighbor of ?x185[ has ethnicGroup ?x638; has religion ?x187; is locatedIn of ?x98<BlackSea>;];]
*> conf = 0.73
ranks of expected_values: 3
EVAL	GR	religion	ChristianOrthodox	CNN-1.+1._MA	0.000	1.000	1.000	0.333	110.000	110.000	39.000	0.850	http://www.semwebtech.org/mondial/10/meta#religion

#113-VU
PRED entity: VU

PRED relation: religion
PRED expected values: RomanCatholic ChurchChrist
=> 26 concepts (26 used for prediction)
PRED predicted values (max 10 best out of 27): RomanCatholic (0.84 #437, 0.71 #86, 0.64 #164), Protestant (0.69 #431, 0.55 #158, 0.47 #197), Muslim (0.49 #395, 0.46 #747, 0.45 #786), Christian (0.30 #394, 0.27 #472, 0.27 #277), Bahai (0.25 #29, 0.20 #68, 0.16 #977), ChurchTuvalu (0.25 #15, 0.20 #54, 0.16 #977), ChristianOrthodox (0.23 #313, 0.19 #391, 0.16 #430), Buddhist (0.20 #283, 0.16 #977, 0.15 #127), UnitingChurchAustralia (0.20 #61, 0.16 #977, 0.13 #703), Hindu (0.16 #977, 0.15 #126, 0.13 #703)
>> best conf = 0.84
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #437 for best value:
>> intensional similarity = 5
>> extensional distance = 121
>> proper extension:  RSM; V;
>> query: (?x439, RomanCatholic) <- ?x439[ a Country; has religion ?x713[ a Religion; is religion of ?x407<AXA>;];]
ranks of expected_values: 1
EVAL	VU	religion	ChurchChrist	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	26.000	26.000	27.000	0.837	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	VU	religion	RomanCatholic	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	26.000	26.000	27.000	0.837	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion
PRED expected values: RomanCatholic ChurchChrist
=> 71 concepts (71 used for prediction)
PRED predicted values (max 10 best out of 37): RomanCatholic (0.96 #2057, 0.92 #928, 0.86 #1009), Protestant (0.70 #2091, 0.70 #1609, 0.69 #2251), Muslim (0.65 #765, 0.64 #884, 0.64 #522), Christian (0.55 #764, 0.55 #521, 0.55 #481), Jewish (0.50 #79, 0.45 #80, 0.41 #1240), Buddhist (0.45 #80, 0.35 #358, 0.34 #1200), HoaHao (0.45 #80, 0.34 #1200, 0.33 #1485), CaoDai (0.45 #80, 0.34 #1200, 0.33 #1485), Baptist (0.35 #358, 0.34 #2411, 0.29 #279), United (0.35 #358, 0.34 #2411, 0.29 #279)
>> best conf = 0.96
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2057 for best value:
>> intensional similarity = 19
>> extensional distance = 97
>> proper extension:  RSM;
>> query: (?x439, RomanCatholic) <- ?x439[ has government ?x1174; has religion ?x429[ is religion of ?x210<NORF>; is religion of ?x390<SLB>; is religion of ?x428[ a Country; has dependentOf ?x81; has ethnicGroup ?x125; has language ?x2065; is locatedIn of ?x282<PacificOcean>;]; is religion of ?x672[ has encompassed ?x211; has government ?x1947; is locatedIn of ?x1730;]; is religion of ?x853<PNG>;];]
ranks of expected_values: 1
EVAL	VU	religion	ChurchChrist	CNN-1.+1._MA	0.000	0.000	0.000	0.000	71.000	71.000	37.000	0.960	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	VU	religion	RomanCatholic	CNN-1.+1._MA	1.000	1.000	1.000	1.000	71.000	71.000	37.000	0.960	http://www.semwebtech.org/mondial/10/meta#religion

#112-AFG
PRED entity: AFG

PRED relation: neighbor
PRED expected values: TAD
=> 35 concepts (35 used for prediction)
PRED predicted values (max 10 best out of 189): TAD (0.90 #3797, 0.89 #2689, 0.89 #3795), KAZ (0.60 #700, 0.50 #383, 0.33 #66), BHT (0.50 #545, 0.31 #793, 0.25 #475), MYA (0.50 #536, 0.31 #793, 0.25 #475), AFG (0.40 #697, 0.33 #222, 0.31 #793), KGZ (0.40 #649, 0.31 #793, 0.25 #491), IND (0.33 #293, 0.31 #793, 0.25 #610), R (0.31 #793, 0.25 #479, 0.25 #475), VN (0.31 #793, 0.25 #573, 0.25 #475), NEP (0.31 #793, 0.25 #487, 0.25 #475)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3797 for best value:
>> intensional similarity = 7
>> extensional distance = 132
>> proper extension:  ZRE; BF; WAG; RT; KWT;
>> query: (?x381, ?x129) <- ?x381[ a Country; has ethnicGroup ?x2116; is neighbor of ?x129[ is locatedIn of ?x276;]; is neighbor of ?x277[ has encompassed ?x175; is locatedIn of ?x289;];]
ranks of expected_values: 1
EVAL	AFG	neighbor	TAD	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	35.000	35.000	189.000	0.898	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: TAD
=> 100 concepts (100 used for prediction)
PRED predicted values (max 10 best out of 194): TAD (0.93 #6265, 0.91 #9488, 0.91 #6906), TR (0.58 #3072, 0.38 #1113, 0.38 #1112), BHT (0.50 #1023, 0.35 #1114, 0.33 #1432), MYA (0.50 #1014, 0.35 #1114, 0.33 #1432), ARM (0.50 #1167, 0.35 #1114, 0.33 #3362), GE (0.50 #1173, 0.35 #1114, 0.33 #3362), KAZ (0.49 #6428, 0.35 #1114, 0.34 #11099), AZ (0.49 #6428, 0.35 #1114, 0.34 #11099), R (0.49 #6428, 0.35 #1114, 0.33 #1432), IL (0.46 #3247, 0.38 #2123, 0.38 #1113)
>> best conf = 0.93
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #6265 for best value:
>> intensional similarity = 14
>> extensional distance = 34
>> proper extension:  BIH; MNE; HR; TR; MOC; PL; AL; CH; H; GE; ...
>> query: (?x381, ?x232) <- ?x381[ a Country; has encompassed ?x175; has ethnicGroup ?x2116; has language ?x1033; is locatedIn of ?x82; is neighbor of ?x232[ has government ?x831; has religion ?x187<Muslim>; is locatedIn of ?x498[ a Estuary;]; is neighbor of ?x617[ has ethnicGroup ?x872; has religion ?x95;];];]
ranks of expected_values: 1
EVAL	AFG	neighbor	TAD	CNN-1.+1._MA	1.000	1.000	1.000	1.000	100.000	100.000	194.000	0.934	http://www.semwebtech.org/mondial/10/meta#neighbor

#111-RIM
PRED entity: RIM

PRED relation: ethnicGroup
PRED expected values: BlackMaur
=> 32 concepts (32 used for prediction)
PRED predicted values (max 10 best out of 216): Arab-Berber (0.50 #801, 0.33 #29, 0.20 #1316), African (0.38 #3092, 0.33 #2321, 0.31 #3349), European (0.33 #8, 0.28 #1552, 0.28 #3094), Peuhl (0.33 #480, 0.20 #1509, 0.20 #1252), Tuareg (0.33 #422, 0.20 #1451, 0.20 #1194), Mande (0.33 #371, 0.20 #1400, 0.20 #1143), Voltaic (0.33 #369, 0.20 #1398, 0.20 #1141), Songhai (0.33 #358, 0.20 #1387, 0.20 #1130), Fulani (0.33 #518, 0.18 #5914, 0.17 #1030), Wolof (0.33 #740, 0.18 #5914, 0.17 #1030)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #801 for best value:
>> intensional similarity = 8
>> extensional distance = 2
>> proper extension:  MA;
>> query: (?x515, Arab-Berber) <- ?x515[ a Country; has ethnicGroup ?x1406; has neighbor ?x416[ has encompassed ?x213; has ethnicGroup ?x122;]; has neighbor ?x646<WSA>; is locatedIn of ?x182;]

No rule for expected values
ranks of expected_values: 
EVAL	RIM	ethnicGroup	BlackMaur	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	32.000	32.000	216.000	0.500	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: BlackMaur
=> 90 concepts (90 used for prediction)
PRED predicted values (max 10 best out of 252): African (0.43 #8021, 0.43 #9055, 0.40 #6469), European (0.40 #6471, 0.37 #11379, 0.36 #6213), Peuhl (0.33 #479, 0.33 #222, 0.25 #2812), Arab-Berber (0.33 #546, 0.30 #3909, 0.29 #2101), Malinke (0.33 #196, 0.29 #2526, 0.25 #2786), Tuareg (0.33 #421, 0.25 #1814, 0.25 #1202), Mande (0.33 #370, 0.25 #1814, 0.25 #1151), Voltaic (0.33 #368, 0.25 #1814, 0.25 #1149), Songhai (0.33 #357, 0.25 #1814, 0.25 #1138), Soussou (0.33 #128, 0.25 #1814, 0.21 #14730)
>> best conf = 0.43
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #8021 for best value:
>> intensional similarity = 13
>> extensional distance = 21
>> proper extension:  BR; FGU;
>> query: (?x515, African) <- ?x515[ a Country; has neighbor ?x416[ has ethnicGroup ?x122; has neighbor ?x651; has religion ?x187<Muslim>; has wasDependentOf ?x78; is locatedIn of ?x838;]; has neighbor ?x581[ is locatedIn of ?x275[ is locatedInWater of ?x68;];]; is locatedIn of ?x182<AtlanticOcean>;]

No rule for expected values
ranks of expected_values: 
EVAL	RIM	ethnicGroup	BlackMaur	CNN-1.+1._MA	0.000	0.000	0.000	0.000	90.000	90.000	252.000	0.435	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#110-IR
PRED entity: IR

PRED relation: locatedIn!
PRED expected values: Gheschm
=> 36 concepts (33 used for prediction)
PRED predicted values (max 10 best out of 1322): RubAlChali (0.43 #1694, 0.33 #287, 0.10 #14362), RedSea (0.40 #5090, 0.33 #868, 0.14 #2275), Jordan (0.40 #4383, 0.13 #8607, 0.11 #2975), MediterraneanSea (0.39 #7037, 0.39 #5712, 0.32 #35185), SouthChinaSea (0.35 #8587, 0.27 #12809, 0.26 #14216), AtlanticOcean (0.35 #40859, 0.31 #7080, 0.31 #22560), SyrianDesert (0.33 #478, 0.30 #4700, 0.29 #1885), Nefud (0.33 #910, 0.14 #2317, 0.10 #5132), PacificOcean (0.26 #22604, 0.21 #40903, 0.20 #26826), Euphrat (0.24 #9854, 0.22 #3733, 0.15 #36594)
>> best conf = 0.43
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1694 for best value:
>> intensional similarity = 6
>> extensional distance = 5
>> proper extension:  UAE; OM; KWT;
>> query: (?x304, RubAlChali) <- ?x304[ a Country; has neighbor ?x83; has religion ?x187; is locatedIn of ?x918<PersianGulf>; is neighbor of ?x331;]

*> Best rule #23926 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 76
*> proper extension:  NLSM; CUR;
*> query: (?x304, ?x1736) <- ?x304[ a Country; has language ?x511; has religion ?x187; is locatedIn of ?x918[ is locatedInWater of ?x1736;];]
*> conf = 0.03
ranks of expected_values: 654
EVAL	IR	locatedIn!	Gheschm	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.002	36.000	33.000	1322.000	0.429	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Gheschm
=> 115 concepts (115 used for prediction)
PRED predicted values (max 10 best out of 1404): IndianOcean (0.85 #46478, 0.33 #12679, 0.24 #33800), SchattalArab (0.78 #2819, 0.33 #11530, 0.29 #9861), SchattalArab (0.70 #76057, 0.58 #101411, 0.33 #12254), MediterraneanSea (0.70 #77467, 0.38 #42332, 0.34 #76140), PacificOcean (0.62 #47970, 0.53 #55008, 0.52 #90144), BlackSea (0.60 #25364, 0.52 #90144, 0.50 #32405), Kura (0.52 #90144, 0.40 #25539, 0.33 #32580), Kasbek (0.52 #90144, 0.40 #25781, 0.33 #32822), Schchara (0.52 #90144, 0.40 #25380, 0.33 #32421), Amudarja (0.52 #90144, 0.40 #26858, 0.33 #4326)
>> best conf = 0.85
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #46478 for best value:
>> intensional similarity = 13
>> extensional distance = 11
>> proper extension:  REUN; RM; COM;
>> query: (?x304, IndianOcean) <- ?x304[ has government ?x2318; has religion ?x187; is locatedIn of ?x573[ a Mountain;]; is locatedIn of ?x918[ has locatedIn ?x1963[ a Country; has ethnicGroup ?x2169; has wasDependentOf ?x81;]; is locatedInWater of ?x1736;]; is locatedIn of ?x926[ has mergesWith ?x1333<ArabianSea>;];]

*> Best rule #40841 for first EXPECTED value:
*> intensional similarity = 14
*> extensional distance = 5
*> proper extension:  RP;
*> query: (?x304, ?x1736) <- ?x304[ has encompassed ?x175; has ethnicGroup ?x244; has government ?x2318; has religion ?x187; is locatedIn of ?x859[ has type ?x706;]; is locatedIn of ?x918[ is locatedInWater of ?x1736;]; is locatedIn of ?x1092[ a Lake;]; is locatedIn of ?x1693[ a Volcano;]; is locatedIn of ?x2355[ a Island;];]
*> conf = 0.08
ranks of expected_values: 812
EVAL	IR	locatedIn!	Gheschm	CNN-1.+1._MA	0.000	0.000	0.000	0.001	115.000	115.000	1404.000	0.846	http://www.semwebtech.org/mondial/10/meta#locatedIn

#109-Maas
PRED entity: Maas

PRED relation: hasSource
PRED expected values: Maas
=> 41 concepts (33 used for prediction)
PRED predicted values (max 10 best out of 180): Elbe (0.25 #365, 0.04 #1277, 0.03 #1505), Thames (0.25 #362, 0.04 #1274, 0.01 #2875), Mosel (0.09 #844, 0.09 #616, 0.08 #1072), Rhone (0.09 #815, 0.09 #587, 0.08 #1043), Rhein (0.09 #812, 0.09 #584, 0.03 #1496), Seine (0.09 #827, 0.08 #1055, 0.04 #1283), Loire (0.09 #902, 0.08 #1130, 0.04 #1358), Garonne (0.09 #833, 0.08 #1061, 0.04 #1289), Isere (0.09 #849, 0.08 #1077, 0.03 #1533), Saar (0.09 #773, 0.08 #1001, 0.03 #1457)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #365 for best value:
>> intensional similarity = 5
>> extensional distance = 2
>> proper extension:  Thames;
>> query: (?x829, Elbe) <- ?x829[ a River; has flowsInto ?x121<NorthSea>; has hasEstuary ?x935[ a Estuary;];]

*> Best rule #4799 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 203
*> proper extension:  Oranje; Mur; Buna; WesternBug; Limpopo; Tennessee; Ruki; Lukuga; Pjandsh; Amudarja; ...
*> query: (?x829, ?x121) <- ?x829[ a River; has hasEstuary ?x935; has locatedIn ?x78[ has neighbor ?x149; is locatedIn of ?x121;];]
*> conf = 0.01
ranks of expected_values: 90
EVAL	Maas	hasSource	Maas	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.011	41.000	33.000	180.000	0.250	http://www.semwebtech.org/mondial/10/meta#hasSource

PRED relation: hasSource
PRED expected values: Maas
=> 127 concepts (127 used for prediction)
PRED predicted values (max 10 best out of 207): Rhein (0.33 #128, 0.17 #1273, 0.14 #1503), Thames (0.33 #363, 0.04 #4260, 0.04 #4719), Mosel (0.25 #846, 0.17 #1305, 0.14 #1535), Saar (0.25 #775, 0.17 #1234, 0.14 #1464), Glomma (0.20 #1019, 0.05 #3772, 0.04 #4230), Klaraelv (0.20 #1032, 0.05 #3785, 0.03 #5622), Lagen (0.20 #1095, 0.05 #3848, 0.03 #5685), Rhone (0.17 #1276, 0.14 #1506, 0.09 #2654), Guadiana (0.14 #1508, 0.11 #1738, 0.08 #3113), Douro (0.11 #1793, 0.08 #3168, 0.05 #3856)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #128 for best value:
>> intensional similarity = 13
>> extensional distance = 1
>> proper extension:  Rhein;
>> query: (?x829, Rhein) <- ?x829[ has hasEstuary ?x935; has locatedIn ?x78<F>; has locatedIn ?x575[ a Country; has ethnicGroup ?x734[ a EthnicGroup;]; has ethnicGroup ?x2136<Turkish>; has language ?x544; is locatedIn of ?x121[ is locatedInWater of ?x495;]; is neighbor of ?x120;];]

*> Best rule #16300 for first EXPECTED value:
*> intensional similarity = 13
*> extensional distance = 151
*> proper extension:  Bahrel-Ghasal; Bahrel-Djebel-Albert-Nil;
*> query: (?x829, ?x1923) <- ?x829[ has hasEstuary ?x935; has locatedIn ?x78[ has neighbor ?x718[ has ethnicGroup ?x237;]; is locatedIn of ?x256[ a River;]; is locatedIn of ?x1923[ a Source;];]; has locatedIn ?x575[ has government ?x92; is locatedIn of ?x121[ is flowsInto of ?x1381;]; is neighbor of ?x120;];]
*> conf = 0.09
ranks of expected_values: 31
EVAL	Maas	hasSource	Maas	CNN-1.+1._MA	0.000	0.000	0.000	0.032	127.000	127.000	207.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasSource

#108-RI
PRED entity: RI

PRED relation: religion
PRED expected values: Hindu
=> 36 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 36): Christian (0.44 #877, 0.33 #231, 0.32 #839), Hindu (0.35 #197, 0.35 #311, 0.29 #121), ChristianOrthodox (0.33 #39, 0.23 #647, 0.22 #685), Anglican (0.33 #52, 0.18 #204, 0.17 #242), Jewish (0.20 #78, 0.14 #116, 0.12 #192), Mormon (0.20 #98, 0.14 #136, 0.09 #592), JehovasWitnesses (0.15 #587, 0.12 #549, 0.12 #663), Seventh-DayAdventist (0.14 #388, 0.13 #160, 0.12 #540), Sikh (0.12 #220, 0.11 #258, 0.10 #334), Bahai (0.07 #180, 0.05 #408, 0.03 #636)
>> best conf = 0.44
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #877 for best value:
>> intensional similarity = 4
>> extensional distance = 118
>> proper extension:  KGZ; KOS;
>> query: (?x217, Christian) <- ?x217[ has religion ?x462[ is religion of ?x538<BRU>;]; is locatedIn of ?x60;]

*> Best rule #197 for first EXPECTED value:
*> intensional similarity = 4
*> extensional distance = 15
*> proper extension:  GB; I; MYA; MAL; RP; CL; BRU; VN; MACX; K; ...
*> query: (?x217, Hindu) <- ?x217[ has religion ?x462<Buddhist>; is locatedIn of ?x282[ is locatedInWater of ?x205;];]
*> conf = 0.35
ranks of expected_values: 2
EVAL	RI	religion	Hindu	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	36.000	36.000	36.000	0.442	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion
PRED expected values: Hindu
=> 136 concepts (136 used for prediction)
PRED predicted values (max 10 best out of 36): Hindu (0.72 #959, 0.60 #576, 0.59 #806), Christian (0.72 #959, 0.60 #576, 0.59 #806), Anglican (0.60 #576, 0.59 #806, 0.56 #4473), Seventh-DayAdventist (0.60 #576, 0.59 #806, 0.56 #4473), Presbyterian (0.60 #576, 0.59 #806, 0.56 #4473), Jewish (0.40 #463, 0.33 #116, 0.29 #617), Mormon (0.33 #136, 0.24 #3597, 0.23 #4206), ChristianOrthodox (0.32 #3750, 0.32 #2527, 0.32 #3253), JehovasWitnesses (0.27 #1665, 0.24 #2008, 0.24 #1970), Sikh (0.25 #412, 0.25 #297, 0.24 #3597)
>> best conf = 0.72
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #959 for best value:
>> intensional similarity = 13
>> extensional distance = 7
>> proper extension:  P;
>> query: (?x217, ?x116) <- ?x217[ a Country; has neighbor ?x376[ has ethnicGroup ?x298; has religion ?x116;]; has religion ?x352<RomanCatholic>; is locatedIn of ?x625[ is locatedInWater of ?x369;]; is locatedIn of ?x788[ a Mountain;]; is locatedIn of ?x1047[ a Island; has type ?x150;];]
ranks of expected_values: 1
EVAL	RI	religion	Hindu	CNN-1.+1._MA	1.000	1.000	1.000	1.000	136.000	136.000	36.000	0.722	http://www.semwebtech.org/mondial/10/meta#religion

#107-IndianOcean
PRED entity: IndianOcean

PRED relation: locatedInWater!
PRED expected values: Reunion Timor
=> 31 concepts (26 used for prediction)
PRED predicted values (max 10 best out of 383): Sulawesi (0.33 #338, 0.27 #1344, 0.25 #841), Hokkaido (0.27 #1280, 0.25 #777, 0.10 #2262), Kyushu (0.27 #1375, 0.25 #872, 0.10 #2262), Taiwan (0.27 #1309, 0.15 #1560, 0.12 #806), NewGuinea (0.18 #1355, 0.17 #349, 0.12 #852), Bangka (0.18 #1323, 0.17 #317, 0.10 #502), Paramuschir (0.18 #1403, 0.12 #900, 0.10 #2262), Okinawa (0.18 #1301, 0.12 #798, 0.10 #2262), Unalaska (0.18 #1431, 0.12 #928, 0.10 #2262), Mindanao (0.18 #1366, 0.12 #863, 0.10 #2262)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #338 for best value:
>> intensional similarity = 6
>> extensional distance = 4
>> proper extension:  Timor; Tatamailau;
>> query: (?x60, Sulawesi) <- ?x60[ has locatedIn ?x474[ is neighbor of ?x229;]; has locatedIn ?x735<TL>; has locatedIn ?x1248[ has religion ?x187;];]

*> Best rule #365 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 4
*> proper extension:  Timor; Tatamailau;
*> query: (?x60, Timor) <- ?x60[ has locatedIn ?x474[ is neighbor of ?x229;]; has locatedIn ?x735<TL>; has locatedIn ?x1248[ has religion ?x187;];]
*> conf = 0.17
ranks of expected_values: 14, 261
EVAL	IndianOcean	locatedInWater!	Timor	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.071	31.000	26.000	383.000	0.333	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	IndianOcean	locatedInWater!	Reunion	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.004	31.000	26.000	383.000	0.333	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater!
PRED expected values: Reunion Timor
=> 112 concepts (109 used for prediction)
PRED predicted values (max 10 best out of 1009): Cuba (0.38 #4735, 0.33 #3730, 0.29 #4484), Sulawesi (0.38 #5377, 0.33 #595, 0.29 #4120), GreatBritain (0.38 #4564, 0.29 #4313, 0.23 #6580), SouthAndamanIsland (0.33 #891, 0.25 #5171, 0.20 #2656), Kavaratti (0.33 #411, 0.25 #1922, 0.20 #2932), Bangka (0.33 #574, 0.25 #5356, 0.19 #10336), Singapore (0.29 #3911, 0.25 #5419, 0.25 #5168), Taiwan (0.29 #3834, 0.25 #1568, 0.22 #5846), Hokkaido (0.29 #4056, 0.25 #1539, 0.22 #5817), Hispaniola (0.25 #5022, 0.25 #4771, 0.22 #6029)
>> best conf = 0.38
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4735 for best value:
>> intensional similarity = 13
>> extensional distance = 6
>> proper extension:  AtlanticOcean;
>> query: (?x60, Cuba) <- ?x60[ has locatedIn ?x434[ has religion ?x116;]; has locatedIn ?x906[ has encompassed ?x211;]; has mergesWith ?x182[ has locatedIn ?x628<SMAR>; is flowsInto of ?x137; is locatedInWater of ?x112;]; is flowsInto of ?x242; is locatedInWater of ?x333[ a Island; has belongsToIslands ?x875;];]

*> Best rule #759 for first EXPECTED value:
*> intensional similarity = 17
*> extensional distance = 1
*> proper extension:  JavaSea;
*> query: (?x60, ?x371) <- ?x60[ has locatedIn ?x61[ has government ?x828;]; has locatedIn ?x196[ is locatedIn of ?x371;]; has locatedIn ?x508[ has ethnicGroup ?x244; has religion ?x116;]; has mergesWith ?x1333; is locatedInWater of ?x226[ has belongsToIslands ?x227;]; is locatedInWater of ?x333<Krakatau>; is locatedInWater of ?x1157<Sumbawa>; is locatedInWater of ?x2050[ a Island;]; is mergesWith of ?x182;]
*> conf = 0.19
ranks of expected_values: 95, 181
EVAL	IndianOcean	locatedInWater!	Timor	CNN-1.+1._MA	0.000	0.000	0.000	0.011	112.000	109.000	1009.000	0.375	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	IndianOcean	locatedInWater!	Reunion	CNN-1.+1._MA	0.000	0.000	0.000	0.006	112.000	109.000	1009.000	0.375	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#106-MNG
PRED entity: MNG

PRED relation: neighbor!
PRED expected values: CN
=> 45 concepts (45 used for prediction)
PRED predicted values (max 10 best out of 184): CN (0.40 #523, 0.33 #43, 0.26 #5801), BD (0.33 #141, 0.20 #621, 0.11 #782), PK (0.33 #5, 0.11 #325, 0.10 #485), NEP (0.33 #12, 0.11 #332, 0.10 #492), BHT (0.33 #73, 0.11 #393, 0.10 #553), MYA (0.33 #64, 0.11 #705, 0.10 #544), THA (0.30 #487, 0.22 #648, 0.06 #809), PL (0.26 #5801, 0.26 #6450, 0.25 #194), UA (0.26 #5801, 0.26 #6450, 0.25 #212), LT (0.26 #5801, 0.26 #6450, 0.25 #302)
>> best conf = 0.40
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #523 for best value:
>> intensional similarity = 6
>> extensional distance = 8
>> proper extension:  BHT;
>> query: (?x1010, CN) <- ?x1010[ has ethnicGroup ?x1553; has government ?x2058; has religion ?x462<Buddhist>; has wasDependentOf ?x232; is neighbor of ?x73;]
ranks of expected_values: 1
EVAL	MNG	neighbor!	CN	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	45.000	45.000	184.000	0.400	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: CN
=> 80 concepts (80 used for prediction)
PRED predicted values (max 10 best out of 200): CN (0.50 #690, 0.50 #529, 0.48 #2327), MNG (0.46 #809, 0.40 #322, 0.28 #11633), BD (0.40 #464, 0.33 #788, 0.33 #627), PK (0.33 #166, 0.21 #6862, 0.21 #8015), BHT (0.33 #234, 0.21 #6862, 0.21 #8015), NEP (0.33 #173, 0.21 #6862, 0.21 #8015), LAO (0.33 #727, 0.21 #6862, 0.21 #8015), MYA (0.33 #225, 0.21 #8014, 0.20 #387), UA (0.30 #2009, 0.28 #11633, 0.27 #6699), D (0.30 #1971, 0.21 #6862, 0.21 #8015)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #690 for best value:
>> intensional similarity = 12
>> extensional distance = 4
>> proper extension:  RI; MYA; VN;
>> query: (?x1010, CN) <- ?x1010[ a Country; has ethnicGroup ?x1553[ a EthnicGroup;]; has government ?x2058; has religion ?x187<Muslim>; has religion ?x462<Buddhist>; is locatedIn of ?x72[ a River; has flowsInto ?x464[ is locatedInWater of ?x2277;];];]
>> Best rule #529 for best value:
>> intensional similarity = 14
>> extensional distance = 4
>> proper extension:  I;
>> query: (?x1010, CN) <- ?x1010[ a Country; has government ?x2058; has language ?x335; has religion ?x462<Buddhist>; is neighbor of ?x73[ has ethnicGroup ?x58; has religion ?x56; is locatedIn of ?x397[ a Estuary;]; is locatedIn of ?x1134[ a Mountain;]; is locatedIn of ?x1284[ a River;];];]
ranks of expected_values: 1
EVAL	MNG	neighbor!	CN	CNN-1.+1._MA	1.000	1.000	1.000	1.000	80.000	80.000	200.000	0.500	http://www.semwebtech.org/mondial/10/meta#neighbor

#105-IL
PRED entity: IL

PRED relation: locatedIn!
PRED expected values: LakeGenezareth
=> 36 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 1340): AtlanticOcean (0.35 #12816, 0.34 #35530, 0.34 #31270), Donau (0.33 #5703, 0.29 #7122, 0.27 #8541), Jordan (0.33 #159, 0.25 #4417, 0.08 #34068), Nile (0.33 #4052, 0.22 #19873, 0.08 #34068), SouthChinaSea (0.33 #10074, 0.12 #14332, 0.12 #21431), LibyanDesert (0.33 #3527, 0.08 #34068, 0.08 #41167), LakeNasser (0.33 #3067, 0.08 #34068, 0.08 #41167), ArabianDesert (0.33 #3977, 0.08 #34068, 0.08 #41167), Nile (0.33 #3607, 0.08 #34068, 0.08 #41167), JabalKatrina (0.33 #2843, 0.08 #34068, 0.08 #41167)
>> best conf = 0.35
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #12816 for best value:
>> intensional similarity = 6
>> extensional distance = 38
>> proper extension:  MNTS;
>> query: (?x239, AtlanticOcean) <- ?x239[ a Country; has government ?x254; has language ?x1848[ is language of ?x565<SF>;]; is locatedIn of ?x238;]

*> Best rule #19873 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 73
*> proper extension:  BIH; R; F; MNE; NAM; E; IRL; HR; SK; N; ...
*> query: (?x239, ?x1999) <- ?x239[ has language ?x1398; is locatedIn of ?x419[ is flowsInto of ?x1999;]; is neighbor of ?x63[ is locatedIn of ?x62;];]
*> conf = 0.22
ranks of expected_values: 17
EVAL	IL	locatedIn!	LakeGenezareth	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.059	36.000	31.000	1340.000	0.350	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: LakeGenezareth
=> 95 concepts (95 used for prediction)
PRED predicted values (max 10 best out of 1398): LakeGenezareth (0.67 #24154, 0.55 #46893, 0.33 #2568), AtlanticOcean (0.65 #76779, 0.56 #24196, 0.55 #58306), Jordan (0.60 #24153, 0.52 #66790, 0.46 #46892), SyrianDesert (0.50 #9003, 0.38 #21791, 0.33 #14689), Euphrat (0.50 #9452, 0.38 #22240, 0.33 #2347), HamadaduDraa (0.40 #13740, 0.33 #6631, 0.12 #40735), Donau (0.39 #55447, 0.37 #48341, 0.25 #63974), IndianOcean (0.38 #39788, 0.36 #75320, 0.23 #34104), PacificOcean (0.38 #22817, 0.34 #59771, 0.34 #83926), Nile (0.33 #15424, 0.29 #19685, 0.29 #16844)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #24154 for best value:
>> intensional similarity = 15
>> extensional distance = 6
>> proper extension:  AUS; CDN;
>> query: (?x239, ?x1999) <- ?x239[ a Country; has government ?x254; has language ?x1398; has religion ?x109[ is religion of ?x363<ROU>;]; is locatedIn of ?x419[ has flowsThrough ?x1999; has hasEstuary ?x420;]; is locatedIn of ?x567[ a Lake; has locatedIn ?x803[ has encompassed ?x175; has government ?x92;]; has type ?x762;];]
ranks of expected_values: 1
EVAL	IL	locatedIn!	LakeGenezareth	CNN-1.+1._MA	1.000	1.000	1.000	1.000	95.000	95.000	1398.000	0.667	http://www.semwebtech.org/mondial/10/meta#locatedIn

#104-IND
PRED entity: IND

PRED relation: neighbor
PRED expected values: NEP BD
=> 41 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 197): BD (0.89 #5571, 0.89 #4448, 0.89 #3968), NEP (0.89 #5571, 0.89 #4448, 0.89 #3968), THA (0.36 #1275, 0.29 #319, 0.29 #318), IND (0.33 #135, 0.29 #319, 0.29 #318), VN (0.33 #98, 0.29 #319, 0.29 #318), AFG (0.33 #65, 0.29 #319, 0.29 #318), KAZ (0.33 #68, 0.29 #319, 0.29 #318), R (0.33 #3, 0.29 #319, 0.29 #318), KGZ (0.33 #15, 0.29 #319, 0.29 #318), TAD (0.33 #14, 0.29 #319, 0.29 #318)
>> best conf = 0.89
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #5571 for best value:
>> intensional similarity = 9
>> extensional distance = 148
>> proper extension:  SMAR;
>> query: (?x924, ?x83) <- ?x924[ is locatedIn of ?x60; is neighbor of ?x83[ has government ?x140; is neighbor of ?x304[ is neighbor of ?x185;];]; is neighbor of ?x111[ is locatedIn of ?x110;]; is neighbor of ?x366[ has encompassed ?x175;];]
ranks of expected_values: 1, 2
EVAL	IND	neighbor	BD	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	41.000	36.000	197.000	0.894	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	IND	neighbor	NEP	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	41.000	36.000	197.000	0.894	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: NEP BD
=> 114 concepts (114 used for prediction)
PRED predicted values (max 10 best out of 205): BD (0.94 #9229, 0.93 #6144, 0.93 #9066), NEP (0.94 #9229, 0.93 #6144, 0.93 #9066), R (0.60 #1129, 0.35 #163, 0.33 #3), THA (0.43 #2426, 0.42 #158, 0.35 #163), LAO (0.43 #2497, 0.40 #1525, 0.40 #1363), IND (0.42 #158, 0.40 #1099, 0.35 #163), EAT (0.42 #158, 0.33 #2224, 0.29 #1933), EAK (0.42 #158, 0.33 #245, 0.29 #1933), MOC (0.42 #158, 0.29 #1933, 0.29 #1930), TL (0.42 #158, 0.29 #1933, 0.29 #1930)
>> best conf = 0.94
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #9229 for best value:
>> intensional similarity = 11
>> extensional distance = 27
>> proper extension:  GCA; RCH; MACX; ES; HONX; PA; HCA;
>> query: (?x924, ?x111) <- ?x924[ has ethnicGroup ?x1553; has neighbor ?x83; has religion ?x116; is locatedIn of ?x60[ has locatedIn ?x217<RI>; is flowsInto of ?x242; is locatedInWater of ?x1159[ a Island; has belongsToIslands ?x1724;];]; is neighbor of ?x111;]
ranks of expected_values: 1, 2
EVAL	IND	neighbor	BD	CNN-1.+1._MA	1.000	1.000	1.000	1.000	114.000	114.000	205.000	0.941	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	IND	neighbor	NEP	CNN-1.+1._MA	1.000	1.000	1.000	1.000	114.000	114.000	205.000	0.941	http://www.semwebtech.org/mondial/10/meta#neighbor

#103-DaryachehyeNamak
PRED entity: DaryachehyeNamak

PRED relation: type
PRED expected values: "salt"
=> 30 concepts (30 used for prediction)
PRED predicted values (max 10 best out of 10): "salt" (0.60 #7, 0.57 #23, 0.24 #135), "dam" (0.16 #81, 0.12 #145, 0.12 #129), "volcanic" (0.09 #289, 0.08 #419, 0.08 #403), "volcano" (0.09 #289, 0.06 #38, 0.05 #278), "caldera" (0.06 #115, 0.05 #99, 0.05 #163), "sand" (0.03 #68, 0.01 #196, 0.01 #228), "impact" (0.02 #186, 0.02 #138, 0.02 #154), "acid" (0.01 #95, 0.01 #111, 0.01 #127), "crater" (0.01 #93, 0.01 #109, 0.01 #125), "naturaldam" (0.01 #112, 0.01 #128)
>> best conf = 0.60
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #7 for best value:
>> intensional similarity = 3
>> extensional distance = 3
>> proper extension:  LakeUrmia; CaspianSea; HamuneJazMurian;
>> query: (?x1494, "salt") <- ?x1494[ a Lake; has locatedIn ?x304<IR>;]
ranks of expected_values: 1
EVAL	DaryachehyeNamak	type	"salt"	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	30.000	30.000	10.000	0.600	http://www.semwebtech.org/mondial/10/meta#type

PRED relation: type
PRED expected values: "salt"
=> 108 concepts (108 used for prediction)
PRED predicted values (max 10 best out of 12): "salt" (0.71 #55, 0.67 #23, 0.60 #87), "volcanic" (0.23 #582, 0.23 #567, 0.19 #356), "volcano" (0.19 #356, 0.15 #437, 0.15 #555), "dam" (0.18 #421, 0.13 #292, 0.12 #534), "caldera" (0.10 #294, 0.09 #310, 0.06 #456), "atoll" (0.05 #168, 0.02 #672, 0.01 #638), "sand" (0.05 #181, 0.04 #634, 0.04 #279), "impact" (0.03 #301, 0.03 #317, 0.03 #349), "lime" (0.03 #720, 0.02 #816, 0.01 #635), "crater" (0.02 #417, 0.02 #401, 0.02 #433)
>> best conf = 0.71
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #55 for best value:
>> intensional similarity = 13
>> extensional distance = 5
>> proper extension:  OzeroBalchash;
>> query: (?x1494, "salt") <- ?x1494[ a Lake; has locatedIn ?x304[ has encompassed ?x175; has ethnicGroup ?x244; has government ?x2318; has language ?x511; has religion ?x187; is locatedIn of ?x1337<CaspianSea>; is neighbor of ?x290<TM>; is neighbor of ?x381[ a Country;];];]
ranks of expected_values: 1
EVAL	DaryachehyeNamak	type	"salt"	CNN-1.+1._MA	1.000	1.000	1.000	1.000	108.000	108.000	12.000	0.714	http://www.semwebtech.org/mondial/10/meta#type

#102-NorthernDwina
PRED entity: NorthernDwina

PRED relation: hasEstuary
PRED expected values: NorthernDwina
=> 33 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 178): Paatsjoki (0.19 #4533, 0.05 #41, 0.04 #267), Petschora (0.19 #4533, 0.05 #201, 0.04 #427), Kolyma (0.05 #190, 0.04 #416, 0.04 #642), Amur (0.05 #175, 0.04 #401, 0.04 #627), Chatanga (0.05 #57, 0.04 #283, 0.04 #509), Lena (0.05 #5, 0.04 #231, 0.04 #457), Schilka (0.05 #225, 0.04 #451, 0.04 #677), Oka (0.05 #172, 0.04 #398, 0.04 #624), Don (0.05 #119, 0.04 #345, 0.04 #571), Argun (0.05 #112, 0.04 #338, 0.04 #564)
>> best conf = 0.19
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #4533 for best value:
>> intensional similarity = 4
>> extensional distance = 220
>> proper extension:  LakeMaiNdombe;
>> query: (?x648, ?x632) <- ?x648[ has flowsInto ?x251[ has locatedIn ?x73; is flowsInto of ?x631[ has hasEstuary ?x632;];];]

*> Best rule #6123 for first EXPECTED value:
*> intensional similarity = 4
*> extensional distance = 1089
*> proper extension:  ReneLevasseurIsland; DarlingRiver; GreatSandyDesert; Tasmania; VictoriaIsland; EucumbeneRiver; MtColumbia; JoekulsaaFjoellum; LakeWinnipeg; GreatSlaveLake; ...
*> query: (?x648, ?x632) <- ?x648[ has locatedIn ?x73[ has ethnicGroup ?x58; is locatedIn of ?x632[ a Estuary;];];]
*> conf = 0.02
ranks of expected_values: 34
EVAL	NorthernDwina	hasEstuary	NorthernDwina	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.029	33.000	31.000	178.000	0.186	http://www.semwebtech.org/mondial/10/meta#hasEstuary

PRED relation: hasEstuary
PRED expected values: NorthernDwina
=> 114 concepts (114 used for prediction)
PRED predicted values (max 10 best out of 239): Paatsjoki (0.35 #2720, 0.35 #2266, 0.25 #267), Petschora (0.35 #2720, 0.33 #201, 0.25 #427), Elbe (0.12 #661, 0.09 #1114, 0.04 #3609), Glomma (0.12 #551, 0.09 #1004, 0.03 #3726), Thames (0.12 #565, 0.09 #1018, 0.02 #4422), Maas (0.12 #511, 0.04 #3459, 0.03 #3686), Amur (0.10 #854, 0.09 #1533, 0.09 #1307), Newa (0.10 #804, 0.09 #1483, 0.09 #1257), Kolyma (0.10 #869, 0.09 #1548, 0.09 #1322), Chatanga (0.10 #736, 0.09 #1415, 0.09 #1189)
>> best conf = 0.35
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #2720 for best value:
>> intensional similarity = 8
>> extensional distance = 18
>> proper extension:  Swir; Angara; Vuoksi; Oka; Kama;
>> query: (?x648, ?x632) <- ?x648[ has flowsInto ?x251[ has locatedIn ?x973[ has ethnicGroup ?x798;]; is flowsInto of ?x631[ has hasEstuary ?x632;];]; has hasSource ?x418; has locatedIn ?x73<R>;]

*> Best rule #15464 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 209
*> proper extension:  Raab;
*> query: (?x648, ?x720) <- ?x648[ a River; has locatedIn ?x73[ has ethnicGroup ?x58; has religion ?x56; is locatedIn of ?x720[ a Estuary;]; is neighbor of ?x1010[ is locatedIn of ?x956;];];]
*> conf = 0.07
ranks of expected_values: 33
EVAL	NorthernDwina	hasEstuary	NorthernDwina	CNN-1.+1._MA	0.000	0.000	0.000	0.030	114.000	114.000	239.000	0.347	http://www.semwebtech.org/mondial/10/meta#hasEstuary

#101-Isabela
PRED entity: Isabela

PRED relation: locatedInWater
PRED expected values: PacificOcean
=> 71 concepts (71 used for prediction)
PRED predicted values (max 10 best out of 37): AtlanticOcean (0.69 #93, 0.43 #444, 0.38 #312), PacificOcean (0.58 #743, 0.58 #716, 0.43 #190), CaribbeanSea (0.34 #456, 0.25 #105, 0.10 #1067), Cotopaxi (0.24 #130, 0.09 #612, 0.05 #2316), Chimborazo (0.24 #130, 0.09 #612, 0.05 #2316), Isabela (0.24 #130, 0.09 #612, 0.05 #2316), MediterraneanSea (0.24 #890, 0.20 #1629, 0.19 #189), JavaSea (0.24 #139, 0.13 #708, 0.12 #883), IndianOcean (0.18 #132, 0.12 #1136, 0.12 #1571), NorthSea (0.16 #1703, 0.15 #877, 0.14 #1485)
>> best conf = 0.69
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #93 for best value:
>> intensional similarity = 5
>> extensional distance = 14
>> proper extension:  Jamaica;
>> query: (?x901, AtlanticOcean) <- ?x901[ a Island; has locatedIn ?x902[ a Country; has ethnicGroup ?x162<African>;];]

*> Best rule #743 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 58
*> proper extension:  Efate;
*> query: (?x901, ?x282) <- ?x901[ a Island; has belongsToIslands ?x1640[ a Islands;]; has locatedIn ?x902[ is locatedIn of ?x282<PacificOcean>;];]
*> conf = 0.58
ranks of expected_values: 2
EVAL	Isabela	locatedInWater	PacificOcean	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	71.000	71.000	37.000	0.688	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater
PRED expected values: PacificOcean
=> 188 concepts (188 used for prediction)
PRED predicted values (max 10 best out of 121): PacificOcean (0.88 #1568, 0.87 #1342, 0.81 #1522), AtlanticOcean (0.71 #579, 0.71 #535, 0.70 #712), MediterraneanSea (0.64 #1252, 0.46 #853, 0.41 #1700), JavaSea (0.50 #625, 0.40 #669, 0.31 #979), Cotopaxi (0.47 #483, 0.43 #44, 0.40 #572), Chimborazo (0.47 #483, 0.43 #44, 0.40 #572), Isabela (0.47 #483, 0.43 #44, 0.40 #572), CaribbeanSea (0.38 #1793, 0.34 #2735, 0.33 #19), IndianOcean (0.38 #618, 0.30 #662, 0.23 #972), GulfofMexico (0.33 #39, 0.17 #478, 0.07 #1054)
>> best conf = 0.88
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1568 for best value:
>> intensional similarity = 11
>> extensional distance = 14
>> proper extension:  Paramuschir;
>> query: (?x901, PacificOcean) <- ?x901[ a Island; has belongsToIslands ?x1640; has locatedIn ?x902[ has ethnicGroup ?x79; has neighbor ?x215[ has neighbor ?x345; is locatedIn of ?x214;]; has wasDependentOf ?x149; is locatedIn of ?x1774[ has inMountains ?x431; has type ?x706;];];]
ranks of expected_values: 1
EVAL	Isabela	locatedInWater	PacificOcean	CNN-1.+1._MA	1.000	1.000	1.000	1.000	188.000	188.000	121.000	0.875	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#100-StraitsofMackinac
PRED entity: StraitsofMackinac

PRED relation: locatedIn
PRED expected values: USA
=> 24 concepts (24 used for prediction)
PRED predicted values (max 10 best out of 70): USA (0.79 #1898, 0.77 #1661, 0.76 #2611), CDN (0.40 #63, 0.32 #1487, 0.22 #537), R (0.36 #2379, 0.12 #3802, 0.09 #4752), ZRE (0.27 #2928, 0.11 #3402, 0.11 #3165), PE (0.27 #1728, 0.06 #3864, 0.05 #4814), MEX (0.18 #1064, 0.12 #1302, 0.11 #590), I (0.18 #2184, 0.04 #3608, 0.04 #3134), D (0.15 #3580, 0.09 #4292, 0.09 #4767), F (0.14 #2618, 0.06 #3567, 0.04 #4516), CN (0.12 #1954, 0.09 #2667, 0.02 #2430)
>> best conf = 0.79
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1898 for best value:
>> intensional similarity = 8
>> extensional distance = 28
>> proper extension:  Orinoco; RioSanJuan; Tambo; RioMagdalena; Ene; Apurimac; Maranon; Perene; Ucayali; Huallaga; ...
>> query: (?x2245, ?x315) <- ?x2245[ a Estuary; is hasEstuary of ?x2018[ has hasSource ?x2200; has locatedIn ?x315[ has encompassed ?x521; has ethnicGroup ?x79<Amerindian>; has religion ?x95;];];]
ranks of expected_values: 1
EVAL	StraitsofMackinac	locatedIn	USA	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	24.000	24.000	70.000	0.788	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: USA
=> 77 concepts (77 used for prediction)
PRED predicted values (max 10 best out of 76): USA (0.86 #7861, 0.83 #11199, 0.82 #3805), CDN (0.67 #1189, 0.62 #3806, 0.61 #6667), ZRE (0.42 #11039, 0.41 #10323, 0.38 #11517), R (0.41 #10488, 0.39 #11204, 0.15 #15253), PE (0.31 #7690, 0.27 #9835, 0.27 #9119), MEX (0.25 #2257, 0.22 #2970, 0.18 #3208), D (0.23 #13603, 0.20 #14554, 0.16 #15030), I (0.22 #7909, 0.09 #6477, 0.07 #13869), F (0.21 #8345, 0.10 #13828, 0.07 #8822), BR (0.20 #7510, 0.14 #10132, 0.12 #11563)
>> best conf = 0.86
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #7861 for best value:
>> intensional similarity = 14
>> extensional distance = 24
>> proper extension:  RioSanJuan; Tambo; Ene; Apurimac; Maranon; Perene; Ucayali; Huallaga; Urubamba;
>> query: (?x2245, ?x315) <- ?x2245[ a Estuary; is hasEstuary of ?x2018[ a River; has hasSource ?x2200[ a Source;]; has locatedIn ?x315[ has encompassed ?x521; has ethnicGroup ?x79; has language ?x796; has religion ?x95<Protestant>; is locatedIn of ?x282<PacificOcean>; is neighbor of ?x482;];];]
ranks of expected_values: 1
EVAL	StraitsofMackinac	locatedIn	USA	CNN-1.+1._MA	1.000	1.000	1.000	1.000	77.000	77.000	76.000	0.862	http://www.semwebtech.org/mondial/10/meta#locatedIn

#99-Save
PRED entity: Save

PRED relation: locatedIn
PRED expected values: SLO
=> 55 concepts (53 used for prediction)
PRED predicted values (max 10 best out of 234): SLO (0.89 #9102, 0.89 #6766, 0.87 #7233), H (0.60 #756, 0.50 #523, 0.34 #1866), A (0.60 #796, 0.50 #563, 0.25 #330), SK (0.40 #962, 0.33 #1196, 0.25 #496), MNE (0.34 #1866, 0.34 #1166, 0.33 #1645), RO (0.34 #1866, 0.34 #1166, 0.30 #4667), BG (0.34 #1866, 0.34 #1166, 0.30 #4667), MK (0.34 #1866, 0.30 #4667, 0.30 #4666), KOS (0.34 #1866, 0.30 #4667, 0.30 #4666), F (0.26 #5140, 0.25 #239, 0.23 #5373)
>> best conf = 0.89
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #9102 for best value:
>> intensional similarity = 5
>> extensional distance = 198
>> proper extension:  Leine;
>> query: (?x152, ?x446) <- ?x152[ a River; has hasSource ?x1363[ a Source; has locatedIn ?x446[ has neighbor ?x156;];];]
ranks of expected_values: 1
EVAL	Save	locatedIn	SLO	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	55.000	53.000	234.000	0.893	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: SLO
=> 127 concepts (109 used for prediction)
PRED predicted values (max 10 best out of 237): SLO (0.93 #12717, 0.90 #15318, 0.88 #9180), A (0.80 #3622, 0.50 #5264, 0.46 #13193), H (0.71 #3346, 0.50 #1698, 0.46 #13193), R (0.57 #3058, 0.55 #2586, 0.40 #9184), I (0.50 #2394, 0.34 #10358, 0.33 #46), D (0.47 #3544, 0.46 #13193, 0.43 #12957), UA (0.46 #13193, 0.44 #13192, 0.43 #12957), RO (0.46 #13193, 0.44 #13192, 0.43 #12957), BG (0.46 #13193, 0.44 #13192, 0.43 #12957), SK (0.46 #13193, 0.44 #13192, 0.43 #12957)
>> best conf = 0.93
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #12717 for best value:
>> intensional similarity = 10
>> extensional distance = 114
>> proper extension:  Guadiana; Tajo;
>> query: (?x152, ?x446) <- ?x152[ has hasSource ?x1363[ has locatedIn ?x446;]; has locatedIn ?x904[ a Country; has wasDependentOf ?x1197; is neighbor of ?x236[ has ethnicGroup ?x164; has language ?x684; has religion ?x95; is locatedIn of ?x155;];];]
ranks of expected_values: 1
EVAL	Save	locatedIn	SLO	CNN-1.+1._MA	1.000	1.000	1.000	1.000	127.000	109.000	237.000	0.934	http://www.semwebtech.org/mondial/10/meta#locatedIn

#98-PortoSanto
PRED entity: PortoSanto

PRED relation: belongsToIslands
PRED expected values: Madeira
=> 38 concepts (38 used for prediction)
PRED predicted values (max 10 best out of 36): Azores (0.64 #72, 0.60 #4, 0.37 #140), LesserAntilles (0.25 #219, 0.07 #831, 0.07 #899), SundaIslands (0.13 #286, 0.12 #354, 0.08 #490), Madeira (0.10 #45, 0.09 #113, 0.05 #181), Canares (0.08 #227, 0.07 #295, 0.06 #363), LipariIslands (0.07 #274, 0.06 #342, 0.05 #410), InnerHebrides (0.07 #268, 0.05 #404, 0.05 #336), HawaiiIslands (0.06 #437, 0.05 #505, 0.03 #641), Japan (0.05 #366, 0.05 #298, 0.02 #706), WestfriesischeInseln (0.05 #353, 0.04 #489, 0.04 #285)
>> best conf = 0.64
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #72 for best value:
>> intensional similarity = 3
>> extensional distance = 9
>> proper extension:  SaoMiguel;
>> query: (?x2198, Azores) <- ?x2198[ a Island; has locatedIn ?x1027<P>;]

*> Best rule #45 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 8
*> proper extension:  Pico; Corvo; Terceira; Graciosa; SantaMaria; Madeira; SaoJorge; Faial;
*> query: (?x2198, Madeira) <- ?x2198[ a Island; has locatedIn ?x1027<P>; has locatedInWater ?x182<AtlanticOcean>;]
*> conf = 0.10
ranks of expected_values: 4
EVAL	PortoSanto	belongsToIslands	Madeira	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.250	38.000	38.000	36.000	0.636	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

PRED relation: belongsToIslands
PRED expected values: Madeira
=> 116 concepts (116 used for prediction)
PRED predicted values (max 10 best out of 58): Azores (0.64 #72, 0.60 #4, 0.59 #1095), Madeira (0.59 #1095, 0.43 #3489, 0.43 #4449), Canares (0.32 #159, 0.21 #433, 0.16 #501), LesserAntilles (0.28 #1728, 0.25 #2616, 0.24 #2820), HawaiiIslands (0.27 #370, 0.16 #781, 0.15 #1124), SundaIslands (0.20 #835, 0.20 #1040, 0.20 #903), CalifornianChannelIslands (0.19 #400, 0.12 #811, 0.11 #1154), WestfriesischeInseln (0.15 #286, 0.08 #1932, 0.08 #2068), CanadianArcticIslands (0.14 #760, 0.14 #829, 0.13 #897), InnerHebrides (0.13 #611, 0.11 #542, 0.11 #679)
>> best conf = 0.64
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #72 for best value:
>> intensional similarity = 3
>> extensional distance = 9
>> proper extension:  SaoMiguel;
>> query: (?x2198, Azores) <- ?x2198[ a Island; has locatedIn ?x1027<P>;]

*> Best rule #1095 for first EXPECTED value:
*> intensional similarity = 10
*> extensional distance = 43
*> proper extension:  SaintVincent; Barbuda; Gozo; Malta; Male; Koror; Nevis; Antigua;
*> query: (?x2198, ?x200) <- ?x2198[ a Island; has locatedIn ?x1027[ a Country; has encompassed ?x195; has government ?x2551; has wasDependentOf ?x149; is locatedIn of ?x1338[ has belongsToIslands ?x200; has type ?x150;];]; has locatedInWater ?x182;]
*> conf = 0.59
ranks of expected_values: 2
EVAL	PortoSanto	belongsToIslands	Madeira	CNN-1.+1._MA	0.000	1.000	1.000	0.500	116.000	116.000	58.000	0.636	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

#97-SA
PRED entity: SA

PRED relation: neighbor!
PRED expected values: IRQ
=> 35 concepts (32 used for prediction)
PRED predicted values (max 10 best out of 171): IRQ (0.91 #2329, 0.89 #4524, 0.89 #3742), SA (0.50 #428, 0.43 #583, 0.33 #118), TR (0.33 #339, 0.20 #650, 0.19 #805), MOC (0.33 #186, 0.05 #1894, 0.05 #2832), SYR (0.30 #701, 0.26 #4054, 0.17 #390), IL (0.30 #666, 0.26 #4054, 0.12 #2331), CN (0.30 #818, 0.29 #973, 0.16 #1905), WEST (0.26 #4054, 0.20 #717, 0.12 #2331), RL (0.20 #634, 0.09 #3898, 0.09 #2643), SSD (0.20 #662, 0.07 #3270, 0.05 #3783)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2329 for best value:
>> intensional similarity = 4
>> extensional distance = 82
>> proper extension:  GB; MC;
>> query: (?x751, ?x107) <- ?x751[ a Country; has language ?x1848; has neighbor ?x107; is locatedIn of ?x637;]
ranks of expected_values: 1
EVAL	SA	neighbor!	IRQ	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	35.000	32.000	171.000	0.909	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: IRQ
=> 92 concepts (92 used for prediction)
PRED predicted values (max 10 best out of 205): IRQ (0.92 #7649, 0.91 #5717, 0.91 #6359), SYR (0.50 #2527, 0.50 #2451, 0.50 #938), SA (0.50 #2331, 0.50 #938, 0.33 #3127), IL (0.50 #2416, 0.40 #1304, 0.35 #2528), TR (0.50 #3515, 0.33 #3994, 0.33 #2242), ET (0.43 #2209, 0.33 #472, 0.32 #2211), ARM (0.40 #3222, 0.30 #3382, 0.25 #993), LAR (0.40 #1404, 0.29 #2673, 0.25 #2995), WEST (0.35 #2528, 0.33 #3324, 0.33 #2467), IR (0.33 #3166, 0.33 #3061, 0.33 #52)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #7649 for best value:
>> intensional similarity = 16
>> extensional distance = 43
>> proper extension:  NEP; PNG;
>> query: (?x751, ?x302) <- ?x751[ has government ?x640; has language ?x1848; has neighbor ?x302; is locatedIn of ?x637; is neighbor of ?x107[ a Country; has encompassed ?x175; has ethnicGroup ?x1595; has government ?x1136; has religion ?x187<Muslim>;]; is neighbor of ?x174[ has ethnicGroup ?x244[ a EthnicGroup;];]; is neighbor of ?x803[ is locatedIn of ?x419;];]
ranks of expected_values: 1
EVAL	SA	neighbor!	IRQ	CNN-1.+1._MA	1.000	1.000	1.000	1.000	92.000	92.000	205.000	0.918	http://www.semwebtech.org/mondial/10/meta#neighbor

#96-Azores
PRED entity: Azores

PRED relation: belongsToIslands!
PRED expected values: Faial
=> 22 concepts (22 used for prediction)
PRED predicted values (max 10 best out of 217): Madeira (0.33 #74, 0.25 #265, 0.23 #1533), Fuerteventura (0.25 #342, 0.14 #725, 0.12 #916), Gomera (0.25 #335, 0.14 #718, 0.12 #909), Teneriffa (0.25 #324, 0.14 #707, 0.12 #898), Lanzarote (0.25 #314, 0.14 #697, 0.12 #888), Hierro (0.25 #270, 0.14 #653, 0.12 #844), GranCanaria (0.25 #262, 0.14 #645, 0.12 #836), PortoSanto (0.23 #1533, 0.16 #4026, 0.16 #3067), Faial (0.23 #1533, 0.16 #4026, 0.16 #3067), Tajo (0.23 #1533, 0.16 #4026, 0.16 #3067)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #74 for best value:
>> intensional similarity = 11
>> extensional distance = 1
>> proper extension:  Madeira;
>> query: (?x200, Madeira) <- ?x200[ is belongsToIslands of ?x199[ a Island; has locatedIn ?x1027<P>; has locatedInWater ?x182<AtlanticOcean>; is locatedOnIsland of ?x1026[ has type ?x706;];]; is belongsToIslands of ?x299[ has type ?x150<"volcanic">;];]

*> Best rule #1533 for first EXPECTED value:
*> intensional similarity = 11
*> extensional distance = 8
*> proper extension:  Galapagos;
*> query: (?x200, ?x182) <- ?x200[ is belongsToIslands of ?x199[ a Island;]; is belongsToIslands of ?x299[ has type ?x150;]; is belongsToIslands of ?x1000[ has locatedIn ?x1027[ has government ?x2551; has religion ?x352<RomanCatholic>; has wasDependentOf ?x149; is locatedIn of ?x182;];];]
*> conf = 0.23
ranks of expected_values: 9
EVAL	Azores	belongsToIslands!	Faial	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.111	22.000	22.000	217.000	0.333	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

PRED relation: belongsToIslands!
PRED expected values: Faial
=> 41 concepts (41 used for prediction)
PRED predicted values (max 10 best out of 217): Madeira (0.33 #74, 0.30 #3655, 0.26 #6355), PortoSanto (0.30 #3655, 0.26 #6355, 0.25 #7319), Faial (0.30 #3655, 0.26 #6355, 0.25 #7319), Tajo (0.30 #3655, 0.26 #6355, 0.25 #7319), Guadiana (0.30 #3655, 0.26 #6355, 0.25 #7319), Tajo (0.30 #3655, 0.26 #6355, 0.25 #7319), Douro (0.30 #3655, 0.26 #6355, 0.25 #7319), Guadiana (0.30 #3655, 0.26 #6355, 0.25 #7319), PicoRuivo (0.30 #3655, 0.26 #6355, 0.25 #7319), Pico (0.30 #3655, 0.26 #6355, 0.25 #7319)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #74 for best value:
>> intensional similarity = 18
>> extensional distance = 1
>> proper extension:  Madeira;
>> query: (?x200, Madeira) <- ?x200[ a Islands; is belongsToIslands of ?x199[ a Island; is locatedOnIsland of ?x1026[ a Volcano; has type ?x706;];]; is belongsToIslands of ?x707[ a Island; has locatedInWater ?x182<AtlanticOcean>;]; is belongsToIslands of ?x779[ a Island; has locatedIn ?x1027<P>;]; is belongsToIslands of ?x1338[ a Island; has type ?x150<"volcanic">;];]

*> Best rule #3655 for first EXPECTED value:
*> intensional similarity = 18
*> extensional distance = 11
*> proper extension:  Philipines; NewZealand;
*> query: (?x200, ?x1036) <- ?x200[ is belongsToIslands of ?x199[ is locatedOnIsland of ?x1026;]; is belongsToIslands of ?x707[ a Island; has locatedInWater ?x182[ a Sea; has locatedIn ?x272<CDN>; has mergesWith ?x60<IndianOcean>; is flowsInto of ?x137;];]; is belongsToIslands of ?x1149[ a Island; has locatedIn ?x1027[ a Country; has government ?x2551; has religion ?x352; is locatedIn of ?x1036;];];]
*> conf = 0.30
ranks of expected_values: 3
EVAL	Azores	belongsToIslands!	Faial	CNN-1.+1._MA	0.000	1.000	1.000	0.333	41.000	41.000	217.000	0.333	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

#95-Luxembourgish
PRED entity: Luxembourgish

PRED relation: ethnicGroup!
PRED expected values: L
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 96): EAU (0.03 #132), ETH (0.03 #100), UA (0.03 #57), EAK (0.03 #99), CZ (0.02 #98), SN (0.02 #83), MYA (0.02 #70), IR (0.02 #58), SK (0.02 #25), RCA (0.02 #139)
>> best conf = 0.03
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #132 for best value:
>> intensional similarity = 1
>> extensional distance = 279
>> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
>> query: (?x2521, EAU) <- ?x2521[ a EthnicGroup;]

*> Best rule #137 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 279
*> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
*> query: (?x2521, L) <- ?x2521[ a EthnicGroup;]
*> conf = 0.01
ranks of expected_values: 67
EVAL	Luxembourgish	ethnicGroup!	L	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.015	2.000	2.000	96.000	0.032	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup!
PRED expected values: L
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 96): EAU (0.03 #132), ETH (0.03 #100), UA (0.03 #57), EAK (0.03 #99), CZ (0.02 #98), SN (0.02 #83), MYA (0.02 #70), IR (0.02 #58), SK (0.02 #25), RCA (0.02 #139)
>> best conf = 0.03
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #132 for best value:
>> intensional similarity = 1
>> extensional distance = 279
>> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
>> query: (?x2521, EAU) <- ?x2521[ a EthnicGroup;]

*> Best rule #137 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 279
*> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
*> query: (?x2521, L) <- ?x2521[ a EthnicGroup;]
*> conf = 0.01
ranks of expected_values: 67
EVAL	Luxembourgish	ethnicGroup!	L	CNN-1.+1._MA	0.000	0.000	0.000	0.015	2.000	2.000	96.000	0.032	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#94-COM
PRED entity: COM

PRED relation: locatedIn!
PRED expected values: Mohilla
=> 40 concepts (28 used for prediction)
PRED predicted values (max 10 best out of 1393): PacificOcean (0.57 #18550, 0.36 #8606, 0.34 #11446), AtlanticOcean (0.54 #14243, 0.50 #9982, 0.47 #17085), CaribbeanSea (0.34 #11466, 0.26 #22834, 0.25 #17149), Akagera (0.29 #4907, 0.18 #6327, 0.09 #7747), MediterraneanSea (0.21 #19969, 0.15 #14284, 0.15 #29914), Santiago (0.20 #2501, 0.20 #1081, 0.14 #5342), Fogo (0.20 #1619, 0.20 #199, 0.14 #4460), Fogo (0.20 #1597, 0.20 #177, 0.14 #4438), Mahe (0.20 #2224, 0.20 #804, 0.14 #3644), PitondelaFournaise (0.20 #606, 0.14 #4867, 0.14 #3446)
>> best conf = 0.57
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #18550 for best value:
>> intensional similarity = 6
>> extensional distance = 59
>> proper extension:  VU;
>> query: (?x1248, PacificOcean) <- ?x1248[ has religion ?x187; is locatedIn of ?x60[ has locatedIn ?x217<RI>; is locatedInWater of ?x226; is mergesWith of ?x182;];]

*> Best rule #18465 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 51
*> proper extension:  CUR;
*> query: (?x1248, ?x226) <- ?x1248[ a Country; has encompassed ?x213; has religion ?x352<RomanCatholic>; is locatedIn of ?x60[ is locatedInWater of ?x226;]; is locatedIn of ?x1619[ a Island;];]
*> conf = 0.05
ranks of expected_values: 193
EVAL	COM	locatedIn!	Mohilla	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.005	40.000	28.000	1393.000	0.574	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Mohilla
=> 74 concepts (70 used for prediction)
PRED predicted values (max 10 best out of 1417): Mayotte (0.76 #35613, 0.61 #17086, 0.50 #11380), Mohilla (0.76 #35613, 0.61 #17086, 0.50 #11380), PacificOcean (0.75 #34274, 0.56 #42810, 0.56 #15750), AtlanticOcean (0.62 #49893, 0.56 #14276, 0.50 #12849), SouthChinaSea (0.50 #4404, 0.33 #15663, 0.33 #140), SulawesiSea (0.50 #4545, 0.33 #15663, 0.33 #281), CaribbeanSea (0.45 #45681, 0.40 #31443, 0.38 #49957), NorthSea (0.38 #25661, 0.26 #29931, 0.20 #24214), BandaSea (0.33 #15663, 0.33 #367, 0.32 #5685), Lombok (0.33 #1006, 0.32 #5685, 0.31 #5687)
>> best conf = 0.76
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #35613 for best value:
>> intensional similarity = 20
>> extensional distance = 18
>> proper extension:  GUAM;
>> query: (?x1248, ?x226) <- ?x1248[ has government ?x435; is locatedIn of ?x60[ has locatedIn ?x192[ is neighbor of ?x193;]; has locatedIn ?x196<AUS>; has locatedIn ?x735[ a Country;]; has locatedIn ?x906[ has encompassed ?x211;]; is locatedInWater of ?x1159[ a Island;]; is mergesWith of ?x182;]; is locatedIn of ?x1247[ has type ?x706;]; is locatedIn of ?x1666[ a Island; has belongsToIslands ?x227[ a Islands; is belongsToIslands of ?x226;];];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 2
EVAL	COM	locatedIn!	Mohilla	CNN-1.+1._MA	0.000	1.000	1.000	0.500	74.000	70.000	1417.000	0.759	http://www.semwebtech.org/mondial/10/meta#locatedIn

#93-NIC
PRED entity: NIC

PRED relation: ethnicGroup
PRED expected values: Mestizo
=> 42 concepts (42 used for prediction)
PRED predicted values (max 10 best out of 224): Mestizo (0.53 #288, 0.37 #543, 0.33 #798), Russian (0.23 #3640, 0.19 #2365, 0.19 #2875), Polynesian (0.21 #1616, 0.08 #851, 0.08 #7396), Mulatto (0.21 #566, 0.20 #56, 0.13 #1841), German (0.19 #2302, 0.19 #2812, 0.13 #4342), Ukrainian (0.17 #2296, 0.16 #2806, 0.14 #5101), White (0.16 #574, 0.10 #1849, 0.08 #7396), Hungarian (0.15 #2061, 0.14 #2316, 0.14 #2826), Polish (0.15 #2243, 0.14 #2498, 0.14 #3008), Chinese (0.14 #8683, 0.14 #4092, 0.12 #9703)
>> best conf = 0.53
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #288 for best value:
>> intensional similarity = 6
>> extensional distance = 13
>> proper extension:  CUR;
>> query: (?x408, Mestizo) <- ?x408[ has encompassed ?x521; has language ?x796<Spanish>; has religion ?x95<Protestant>; is locatedIn of ?x282;]
ranks of expected_values: 1
EVAL	NIC	ethnicGroup	Mestizo	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	42.000	42.000	224.000	0.533	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: Mestizo
=> 108 concepts (108 used for prediction)
PRED predicted values (max 10 best out of 237): Mestizo (0.60 #1308, 0.56 #1531, 0.56 #8458), Chinese (0.56 #1531, 0.36 #4340, 0.33 #522), Mulatto (0.33 #6950, 0.33 #4651, 0.24 #7460), Hungarian (0.29 #7170, 0.29 #9977, 0.21 #6405), Roma (0.24 #9961, 0.24 #7154, 0.13 #14557), Asian (0.21 #5633, 0.19 #13532, 0.18 #3845), Russian (0.20 #13346, 0.19 #19476, 0.19 #10026), Ukrainian (0.19 #9957, 0.18 #7150, 0.15 #18128), German (0.19 #9963, 0.18 #7156, 0.14 #18389), Polish (0.19 #10159, 0.13 #16286, 0.13 #18330)
>> best conf = 0.60
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1308 for best value:
>> intensional similarity = 15
>> extensional distance = 3
>> proper extension:  HCA;
>> query: (?x408, Mestizo) <- ?x408[ has ethnicGroup ?x79; has neighbor ?x318[ is locatedIn of ?x496;]; has neighbor ?x1364[ a Country; has ethnicGroup ?x676; has language ?x796;]; has religion ?x95<Protestant>; has religion ?x352<RomanCatholic>; has wasDependentOf ?x149<E>; is locatedIn of ?x317<CaribbeanSea>;]
ranks of expected_values: 1
EVAL	NIC	ethnicGroup	Mestizo	CNN-1.+1._MA	1.000	1.000	1.000	1.000	108.000	108.000	237.000	0.600	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#92-WD
PRED entity: WD

PRED relation: locatedIn!
PRED expected values: AtlanticOcean MorneDiablotins
=> 42 concepts (33 used for prediction)
PRED predicted values (max 10 best out of 1348): AtlanticOcean (0.92 #7112, 0.89 #31302, 0.84 #18491), MorneDiablotins (0.73 #2845), PacificOcean (0.61 #21423, 0.36 #1508, 0.33 #10043), Donau (0.21 #8560, 0.18 #26, 0.10 #17094), IndianOcean (0.19 #9960, 0.18 #12804, 0.17 #15648), TheChannel (0.18 #656, 0.12 #32726, 0.11 #9190), MalyZitnyOstrov (0.18 #1403, 0.11 #9937, 0.05 #18471), ArcticOcean (0.18 #1496, 0.10 #10031, 0.07 #2919), NiagaraRiver (0.18 #2820, 0.10 #11355, 0.07 #4243), DetroitRiver (0.18 #2737, 0.10 #11272, 0.07 #4160)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #7112 for best value:
>> intensional similarity = 7
>> extensional distance = 14
>> proper extension:  CUR;
>> query: (?x922, ?x182) <- ?x922[ a Country; has religion ?x95; is locatedIn of ?x317<CaribbeanSea>; is locatedIn of ?x609[ a Island; has locatedInWater ?x182;];]
ranks of expected_values: 1, 2
EVAL	WD	locatedIn!	MorneDiablotins	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	42.000	33.000	1348.000	0.920	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	WD	locatedIn!	AtlanticOcean	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	42.000	33.000	1348.000	0.920	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: AtlanticOcean MorneDiablotins
=> 121 concepts (114 used for prediction)
PRED predicted values (max 10 best out of 1399): AtlanticOcean (0.94 #108271, 0.93 #132515, 0.91 #74065), MorneDiablotins (0.82 #34180, 0.81 #74066, 0.78 #21360), PacificOcean (0.67 #105506, 0.66 #88398, 0.63 #79854), SaintLucia (0.33 #122532, 0.33 #81193, 0.33 #13961), SaintVincent (0.33 #122532, 0.33 #81193, 0.33 #8572), Tobago (0.33 #122532, 0.33 #81193, 0.33 #10166), Trinidad (0.33 #122532, 0.33 #81193, 0.33 #10566), Martinique (0.33 #122532, 0.33 #81193, 0.33 #3906), Barbados (0.33 #122532, 0.33 #81193, 0.32 #56958), Anguilla (0.33 #122532, 0.33 #81193, 0.32 #56958)
>> best conf = 0.94
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #108271 for best value:
>> intensional similarity = 12
>> extensional distance = 47
>> proper extension:  SK; H; SRB;
>> query: (?x922, ?x182) <- ?x922[ a Country; has encompassed ?x521; has government ?x254; has wasDependentOf ?x81; is locatedIn of ?x317[ is flowsInto of ?x311; is locatedInWater of ?x123; is locatedInWater of ?x506[ a Island;];]; is locatedIn of ?x609[ a Island; has locatedInWater ?x182;];]
ranks of expected_values: 1, 2
EVAL	WD	locatedIn!	MorneDiablotins	CNN-1.+1._MA	1.000	1.000	1.000	1.000	121.000	114.000	1399.000	0.938	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	WD	locatedIn!	AtlanticOcean	CNN-1.+1._MA	1.000	1.000	1.000	1.000	121.000	114.000	1399.000	0.938	http://www.semwebtech.org/mondial/10/meta#locatedIn

#91-SSD
PRED entity: SSD

PRED relation: locatedIn!
PRED expected values: Sobat
=> 24 concepts (22 used for prediction)
PRED predicted values (max 10 best out of 1023): Sobat (0.89 #12704, 0.50 #2822), MediterraneanSea (0.50 #8548, 0.33 #4313, 0.26 #9961), AtlanticOcean (0.43 #5685, 0.36 #7096, 0.34 #1411), Ubangi (0.34 #1411, 0.33 #1552, 0.33 #141), RedSea (0.34 #1411, 0.33 #9341, 0.33 #5106), Bomu (0.34 #1411, 0.33 #1839, 0.33 #428), Bomu (0.34 #1411, 0.33 #1912, 0.33 #501), Bomu (0.34 #1411, 0.33 #1811, 0.33 #400), Ubangi (0.34 #1411, 0.33 #1647, 0.33 #236), Schari (0.34 #1411, 0.33 #1727, 0.29 #5960)
>> best conf = 0.89
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #12704 for best value:
>> intensional similarity = 8
>> extensional distance = 41
>> proper extension:  AUS; CDN; IS;
>> query: (?x229, ?x252) <- ?x229[ a Country; has government ?x435; is locatedIn of ?x747[ a River;]; is locatedIn of ?x2101[ a Source;]; is locatedIn of ?x2339[ is hasEstuary of ?x252;];]
ranks of expected_values: 1
EVAL	SSD	locatedIn!	Sobat	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	24.000	22.000	1023.000	0.886	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Sobat
=> 70 concepts (69 used for prediction)
PRED predicted values (max 10 best out of 1378): Sobat (0.90 #53710, 0.88 #21201, 0.83 #2822), AtlanticOcean (0.89 #62233, 0.50 #29687, 0.50 #28313), Nile (0.83 #2822, 0.75 #42405, 0.73 #9889), IndianOcean (0.83 #25447, 0.34 #2823, 0.33 #5652), WhiteNile (0.62 #59363, 0.34 #2823, 0.33 #3929), LakeSeseSeko-Albertsee (0.56 #66431, 0.52 #56536, 0.34 #2823), MediterraneanSea (0.55 #39662, 0.18 #18457, 0.17 #73585), Bahrel-Djebel-Albert-Nil (0.50 #53709, 0.34 #2823, 0.30 #2824), Baro (0.50 #53709, 0.33 #485, 0.30 #2824), CaribbeanSea (0.42 #55228, 0.39 #65123, 0.26 #62295)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #53710 for best value:
>> intensional similarity = 15
>> extensional distance = 52
>> proper extension:  ROU;
>> query: (?x229, ?x252) <- ?x229[ a Country; has government ?x435; is locatedIn of ?x1727[ has hasSource ?x1880;]; is locatedIn of ?x1895[ a River;]; is locatedIn of ?x2339[ a Estuary; is hasEstuary of ?x252;]; is neighbor of ?x476[ has ethnicGroup ?x1418[ a EthnicGroup;]; is locatedIn of ?x1635[ has type ?x762;]; is neighbor of ?x94;];]
ranks of expected_values: 1
EVAL	SSD	locatedIn!	Sobat	CNN-1.+1._MA	1.000	1.000	1.000	1.000	70.000	69.000	1378.000	0.899	http://www.semwebtech.org/mondial/10/meta#locatedIn

#90-ER
PRED entity: ER

PRED relation: neighbor!
PRED expected values: ETH
=> 25 concepts (24 used for prediction)
PRED predicted values (max 10 best out of 206): ETH (0.89 #1613, 0.89 #2427, 0.89 #647), SP (0.50 #39, 0.29 #3070, 0.27 #2428), SSD (0.40 #204, 0.29 #3070, 0.27 #2428), TCH (0.40 #185, 0.29 #3070, 0.27 #2428), MOC (0.36 #356, 0.12 #678, 0.09 #998), ER (0.29 #3070, 0.27 #2428, 0.26 #3391), LAR (0.29 #3070, 0.27 #2428, 0.26 #3391), RCA (0.29 #3070, 0.27 #2428, 0.26 #3391), EAT (0.29 #455, 0.25 #130, 0.22 #777), EAK (0.27 #2428, 0.20 #245, 0.09 #890)
>> best conf = 0.89
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1613 for best value:
>> intensional similarity = 7
>> extensional distance = 95
>> proper extension:  OM; YE; PNG; V;
>> query: (?x629, ?x94) <- ?x629[ has encompassed ?x213[ a Continent; is encompassed of ?x243[ is locatedIn of ?x182;];]; has government ?x1090; has neighbor ?x94; has wasDependentOf ?x476;]
ranks of expected_values: 1
EVAL	ER	neighbor!	ETH	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	25.000	24.000	206.000	0.890	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: ETH
=> 74 concepts (74 used for prediction)
PRED predicted values (max 10 best out of 210): ETH (0.92 #3802, 0.91 #8279, 0.91 #7271), TCH (0.55 #2336, 0.50 #2146, 0.50 #2006), SSD (0.50 #490, 0.43 #1697, 0.31 #1817), ER (0.50 #490, 0.34 #3135, 0.33 #1980), SP (0.50 #490, 0.34 #3135, 0.33 #1980), LAR (0.50 #490, 0.33 #2627, 0.33 #148), ET (0.50 #490, 0.33 #1495, 0.33 #166), GAZA (0.50 #490, 0.33 #1647, 0.33 #154), RCA (0.50 #490, 0.33 #447, 0.31 #1817), EAK (0.50 #741, 0.33 #410, 0.31 #1817)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3802 for best value:
>> intensional similarity = 14
>> extensional distance = 18
>> proper extension:  NAM;
>> query: (?x629, ?x94) <- ?x629[ a Country; has encompassed ?x213<Africa>; has government ?x1090; has neighbor ?x94; has wasDependentOf ?x476; is locatedIn of ?x1552[ has locatedIn ?x239[ has ethnicGroup ?x244; has religion ?x109; is locatedIn of ?x275<MediterraneanSea>; is neighbor of ?x115;]; is mergesWith of ?x2407;];]
ranks of expected_values: 1
EVAL	ER	neighbor!	ETH	CNN-1.+1._MA	1.000	1.000	1.000	1.000	74.000	74.000	210.000	0.917	http://www.semwebtech.org/mondial/10/meta#neighbor

#89-Volga
PRED entity: Volga

PRED relation: locatedIn
PRED expected values: R
=> 46 concepts (46 used for prediction)
PRED predicted values (max 10 best out of 72): R (0.80 #4986, 0.76 #6883, 0.76 #4272), KAZ (0.60 #2372, 0.56 #4033, 0.50 #949), AZ (0.60 #2372, 0.56 #4033, 0.50 #949), IR (0.60 #2372, 0.56 #4033, 0.50 #949), TM (0.60 #2372, 0.56 #4033, 0.50 #949), UZB (0.50 #1013, 0.33 #64, 0.09 #3860), IRQ (0.33 #306, 0.08 #2916, 0.02 #6241), USA (0.25 #784, 0.23 #6718, 0.22 #2207), AUS (0.25 #757, 0.20 #1469, 0.11 #2180), UA (0.25 #1256, 0.12 #1968, 0.11 #4748)
>> best conf = 0.80
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4986 for best value:
>> intensional similarity = 9
>> extensional distance = 37
>> proper extension:  Mincio;
>> query: (?x2249, ?x73) <- ?x2249[ a Estuary; is hasEstuary of ?x445[ a River; has flowsInto ?x1337[ has locatedIn ?x73[ is locatedIn of ?x1544;];]; is flowsInto of ?x444[ a Lake;]; is flowsInto of ?x1544;];]
ranks of expected_values: 1
EVAL	Volga	locatedIn	R	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	46.000	46.000	72.000	0.800	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: R
=> 101 concepts (101 used for prediction)
PRED predicted values (max 10 best out of 81): R (0.82 #9792, 0.78 #18152, 0.77 #18630), KAZ (0.70 #9793, 0.60 #6918, 0.50 #2859), AZ (0.70 #9793, 0.60 #6918, 0.50 #2859), IR (0.70 #9793, 0.60 #6918, 0.50 #2859), TM (0.70 #9793, 0.60 #6918, 0.50 #2859), UZB (0.50 #1971, 0.33 #3876, 0.33 #303), USA (0.42 #10818, 0.34 #17033, 0.33 #552), D (0.41 #17933, 0.22 #6219, 0.19 #19845), CDN (0.33 #63, 0.25 #6024, 0.25 #5545), S (0.27 #7730, 0.27 #7490, 0.21 #8450)
>> best conf = 0.82
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #9792 for best value:
>> intensional similarity = 9
>> extensional distance = 14
>> proper extension:  Moraca;
>> query: (?x2249, ?x73) <- ?x2249[ a Estuary; is hasEstuary of ?x445[ a River; has flowsInto ?x1337[ a Lake; has locatedIn ?x73[ is locatedIn of ?x492[ a Source;];];]; has hasSource ?x492;];]
ranks of expected_values: 1
EVAL	Volga	locatedIn	R	CNN-1.+1._MA	1.000	1.000	1.000	1.000	101.000	101.000	81.000	0.824	http://www.semwebtech.org/mondial/10/meta#locatedIn

#88-Ural
PRED entity: Ural

PRED relation: inMountains
PRED expected values: Ural
=> 27 concepts (27 used for prediction)
PRED predicted values (max 10 best out of 24): Alps (0.10 #439, 0.08 #613, 0.08 #700), Ural (0.08 #70, 0.06 #1132, 0.04 #157), WaldaiHills (0.08 #51, 0.06 #1132, 0.04 #138), Andes (0.07 #446, 0.06 #533, 0.05 #620), Altai (0.06 #1132, 0.04 #27, 0.02 #114), Kaukasus (0.06 #1132, 0.04 #193, 0.04 #280), Kamchatka (0.06 #1132), Balkan (0.04 #455, 0.04 #542, 0.03 #629), EastAfricanRift (0.04 #550, 0.04 #637, 0.04 #724), Karpaten (0.03 #487, 0.03 #574, 0.02 #661)
>> best conf = 0.10
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #439 for best value:
>> intensional similarity = 4
>> extensional distance = 161
>> proper extension:  DarlingRiver; JoekulsaaFjoellum; EucumbeneRiver; MurrumbidgeeRiver; MurrayRiver; Thjorsa;
>> query: (?x1507, Alps) <- ?x1507[ a Source; has locatedIn ?x73[ has language ?x555; has religion ?x56;];]

*> Best rule #70 for first EXPECTED value:
*> intensional similarity = 3
*> extensional distance = 22
*> proper extension:  NorthernDwina; Volga; Chatanga; Tobol; Schilka; WesternDwina; Lena; Ob; Katun; Suchona; ...
*> query: (?x1507, Ural) <- ?x1507[ a Source; has locatedIn ?x73<R>;]
*> conf = 0.08
ranks of expected_values: 2
EVAL	Ural	inMountains	Ural	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	27.000	27.000	24.000	0.098	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains
PRED expected values: Ural
=> 84 concepts (84 used for prediction)
PRED predicted values (max 10 best out of 35): Ural (0.16 #785, 0.14 #3054, 0.13 #1659), WaldaiHills (0.16 #785, 0.14 #3054, 0.13 #1659), Altai (0.16 #785, 0.14 #3054, 0.13 #1659), Kaukasus (0.16 #785, 0.14 #3054, 0.13 #1659), Kamchatka (0.16 #785, 0.14 #3054, 0.13 #1659), Andes (0.13 #796, 0.11 #884, 0.11 #1146), Balkan (0.13 #717, 0.06 #1591, 0.05 #893), Alps (0.10 #2970, 0.08 #3145, 0.08 #1314), Karpaten (0.09 #749, 0.05 #925, 0.05 #1187), EastAfricanRift (0.07 #1599, 0.07 #1163, 0.07 #813)
>> best conf = 0.16
=> the first rule below is the first best rule for 5 predicted values
>> Best rule #785 for best value:
>> intensional similarity = 10
>> extensional distance = 52
>> proper extension:  Buna; Moraca; Piva; Waag; Oder; Kura; WhiteDrin; Tara; Drina; Theiss; ...
>> query: (?x1507, ?x2187) <- ?x1507[ a Source; has locatedIn ?x73[ has ethnicGroup ?x58; has wasDependentOf ?x903; is locatedIn of ?x1416[ has inMountains ?x2187;]; is neighbor of ?x222[ has religion ?x56<ChristianOrthodox>; is locatedIn of ?x221;];];]
ranks of expected_values: 1
EVAL	Ural	inMountains	Ural	CNN-1.+1._MA	1.000	1.000	1.000	1.000	84.000	84.000	35.000	0.165	http://www.semwebtech.org/mondial/10/meta#inMountains

#87-Tobol
PRED entity: Tobol

PRED relation: hasSource
PRED expected values: Tobol
=> 34 concepts (28 used for prediction)
PRED predicted values (max 10 best out of 114): Katun (0.19 #3660, 0.04 #522, 0.03 #751), Oka (0.04 #671, 0.03 #900, 0.02 #5954), Angara (0.04 #564, 0.03 #793, 0.02 #5954), Schilka (0.04 #497, 0.03 #726, 0.02 #5954), Swir (0.04 #665, 0.03 #894, 0.02 #5954), Newa (0.04 #656, 0.03 #885, 0.02 #5954), Kama (0.04 #653, 0.03 #882, 0.02 #5954), Jenissej (0.04 #650, 0.03 #879, 0.02 #5954), Amur (0.04 #638, 0.03 #867, 0.02 #5954), Kolyma (0.04 #629, 0.03 #858, 0.02 #5954)
>> best conf = 0.19
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3660 for best value:
>> intensional similarity = 4
>> extensional distance = 215
>> proper extension:  RioLerma; SnowyRiver; MurrayRiver; Thames; Sanaga; Ob; Moraca;
>> query: (?x2102, ?x1038) <- ?x2102[ a River; has flowsInto ?x1845[ is flowsInto of ?x2143[ has hasSource ?x1038;];];]

*> Best rule #5954 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 1002
*> proper extension:  Bahrel-Ghasal; Umeaelv; Sobat; Bahrel-Ghasal; Dalaelv; Portefjaellen; Vaenern; Gotland; Kebnekaise; Goetaaelv; ...
*> query: (?x2102, ?x976) <- ?x2102[ has locatedIn ?x73[ has neighbor ?x170; is locatedIn of ?x976[ a Source;]; is locatedIn of ?x1457[ a River;];];]
*> conf = 0.02
ranks of expected_values: 29
EVAL	Tobol	hasSource	Tobol	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.034	34.000	28.000	114.000	0.187	http://www.semwebtech.org/mondial/10/meta#hasSource

PRED relation: hasSource
PRED expected values: Tobol
=> 94 concepts (93 used for prediction)
PRED predicted values (max 10 best out of 257): Katun (0.49 #4358, 0.26 #6886, 0.25 #294), Angara (0.17 #794, 0.12 #1251, 0.11 #1480), Oka (0.17 #901, 0.12 #1358, 0.11 #1587), Schilka (0.12 #1184, 0.08 #1642, 0.08 #1872), Argun (0.12 #1261, 0.08 #1719, 0.08 #1949), Swir (0.07 #11940, 0.07 #11939, 0.06 #2270), Kama (0.07 #11940, 0.07 #11939, 0.06 #2258), Dnepr (0.07 #11940, 0.07 #11939, 0.05 #10329), WesternDwina (0.07 #11940, 0.07 #11939, 0.05 #10329), Narva (0.07 #11940, 0.07 #11939, 0.05 #10329)
>> best conf = 0.49
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4358 for best value:
>> intensional similarity = 10
>> extensional distance = 32
>> proper extension:  Araguaia;
>> query: (?x2102, ?x1038) <- ?x2102[ a River; has flowsInto ?x1845[ has flowsInto ?x801[ a Sea; has mergesWith ?x263; is locatedInWater of ?x931;]; has hasEstuary ?x1765; is flowsInto of ?x2143[ a River; has hasSource ?x1038;];];]

*> Best rule #11940 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 226
*> proper extension:  MackenzieRiver; Thjorsa;
*> query: (?x2102, ?x492) <- ?x2102[ a River; has locatedIn ?x73[ a Country; has religion ?x56; is locatedIn of ?x492[ a Source;]; is locatedIn of ?x1457[ has hasEstuary ?x885;];];]
*> conf = 0.07
ranks of expected_values: 22
EVAL	Tobol	hasSource	Tobol	CNN-1.+1._MA	0.000	0.000	0.000	0.045	94.000	93.000	257.000	0.492	http://www.semwebtech.org/mondial/10/meta#hasSource

#86-Paramuschir
PRED entity: Paramuschir

PRED relation: type
PRED expected values: "volcanic"
=> 30 concepts (30 used for prediction)
PRED predicted values (max 10 best out of 7): "volcanic" (0.46 #18, 0.43 #34, 0.38 #50), "atoll" (0.11 #24, 0.09 #40, 0.06 #56), "volcano" (0.07 #289, 0.07 #262, 0.05 #278), "salt" (0.07 #289, 0.02 #279, 0.02 #360), "coral" (0.04 #41, 0.02 #57, 0.02 #233), "lime" (0.02 #53, 0.02 #149, 0.02 #165), "dam" (0.02 #97, 0.02 #290, 0.02 #257)
>> best conf = 0.46
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #18 for best value:
>> intensional similarity = 4
>> extensional distance = 44
>> proper extension:  Fakaofo; Guadalcanal; Babelthuap; Bougainville; VanuaLevu;
>> query: (?x1411, "volcanic") <- ?x1411[ a Island; has belongsToIslands ?x530; has locatedInWater ?x282<PacificOcean>;]
ranks of expected_values: 1
EVAL	Paramuschir	type	"volcanic"	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	30.000	30.000	7.000	0.457	http://www.semwebtech.org/mondial/10/meta#type

PRED relation: type
PRED expected values: "volcanic"
=> 102 concepts (102 used for prediction)
PRED predicted values (max 10 best out of 8): "volcanic" (0.50 #18, 0.46 #130, 0.46 #66), "volcano" (0.23 #274, 0.07 #1389, 0.07 #1372), "salt" (0.23 #274, 0.07 #1389, 0.07 #1372), "atoll" (0.19 #824, 0.18 #661, 0.18 #307), "coral" (0.18 #661, 0.18 #307, 0.04 #332), "lime" (0.03 #393, 0.03 #457, 0.03 #812), "dam" (0.02 #549, 0.02 #711, 0.02 #760), "sand" (0.02 #763, 0.01 #681, 0.01 #1036)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #18 for best value:
>> intensional similarity = 11
>> extensional distance = 2
>> proper extension:  Sachalin;
>> query: (?x1411, "volcanic") <- ?x1411[ a Island; has locatedInWater ?x282[ has locatedIn ?x73<R>; has locatedIn ?x482[ has language ?x796; is neighbor of ?x671;]; has mergesWith ?x60; is flowsInto of ?x602;]; has locatedInWater ?x507<SeaofOkhotsk>;]
ranks of expected_values: 1
EVAL	Paramuschir	type	"volcanic"	CNN-1.+1._MA	1.000	1.000	1.000	1.000	102.000	102.000	8.000	0.500	http://www.semwebtech.org/mondial/10/meta#type

#85-Gheschm
PRED entity: Gheschm

PRED relation: locatedIn
PRED expected values: IR
=> 15 concepts (15 used for prediction)
PRED predicted values (max 10 best out of 61): IR (0.40 #71, 0.06 #307, 0.06 #718), GR (0.29 #326, 0.12 #569, 0.04 #809), I (0.26 #284, 0.11 #527, 0.04 #767), BRN (0.20 #228, 0.06 #718, 0.05 #719), RI (0.12 #531, 0.06 #1012, 0.06 #2698), RP (0.11 #588, 0.04 #1069, 0.03 #2033), GB (0.07 #728, 0.07 #1210, 0.07 #1450), USA (0.07 #791, 0.06 #1032, 0.06 #1273), E (0.06 #263, 0.04 #746, 0.04 #987), M (0.06 #413, 0.02 #656)
>> best conf = 0.40
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #71 for best value:
>> intensional similarity = 3
>> extensional distance = 3
>> proper extension:  Khark; Bahrain; Lavan;
>> query: (?x1736, IR) <- ?x1736[ a Island; has locatedInWater ?x918<PersianGulf>;]
ranks of expected_values: 1
EVAL	Gheschm	locatedIn	IR	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	15.000	15.000	61.000	0.400	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: IR
=> 31 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 111): IR (0.43 #484, 0.40 #71, 0.30 #732), SA (0.30 #732, 0.19 #481, 0.17 #480), GR (0.29 #1321, 0.28 #1570, 0.14 #2569), BRN (0.29 #723, 0.20 #228, 0.19 #481), I (0.26 #1279, 0.25 #1528, 0.13 #2527), H (0.22 #792), SK (0.22 #765), USA (0.22 #1802, 0.21 #2052, 0.19 #2301), RI (0.21 #1035, 0.13 #2531, 0.12 #4023), UAE (0.19 #481, 0.17 #480, 0.17 #477)
>> best conf = 0.43
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #484 for best value:
>> intensional similarity = 30
>> extensional distance = 5
>> proper extension:  NowajaSemlja;
>> query: (?x1736, ?x304) <- ?x1736[ a Island; has locatedInWater ?x918[ a Sea; has locatedIn ?x302[ has ethnicGroup ?x557; has government ?x254; has religion ?x187<Muslim>; is neighbor of ?x185;]; has locatedIn ?x304[ a Country; has ethnicGroup ?x305; has language ?x511; has neighbor ?x83; is locatedIn of ?x1337<CaspianSea>; is neighbor of ?x331;]; has locatedIn ?x639[ a Country; has encompassed ?x175; has government ?x640; has wasDependentOf ?x1027;]; has locatedIn ?x1705[ has ethnicGroup ?x380; has government ?x92<"constitutional monarchy">; has wasDependentOf ?x81;]; has mergesWith ?x926[ a Sea; has mergesWith ?x1333;];];]
ranks of expected_values: 1
EVAL	Gheschm	locatedIn	IR	CNN-1.+1._MA	1.000	1.000	1.000	1.000	31.000	31.000	111.000	0.429	http://www.semwebtech.org/mondial/10/meta#locatedIn

#84-SSD
PRED entity: SSD

PRED relation: encompassed
PRED expected values: Africa
=> 24 concepts (24 used for prediction)
PRED predicted values (max 10 best out of 5): Africa (0.81 #31, 0.79 #87, 0.41 #40), Europe (0.45 #22, 0.45 #33, 0.44 #17), America (0.39 #56, 0.23 #107, 0.23 #112), Asia (0.24 #98, 0.24 #93, 0.23 #82), Australia-Oceania (0.12 #115, 0.10 #105, 0.10 #110)
>> best conf = 0.81
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #31 for best value:
>> intensional similarity = 7
>> extensional distance = 50
>> proper extension:  ANG;
>> query: (?x229, ?x213) <- ?x229[ has government ?x435; has neighbor ?x348[ is locatedIn of ?x113;]; has neighbor ?x476[ has encompassed ?x213;]; is locatedIn of ?x2101[ a Source;];]
ranks of expected_values: 1
EVAL	SSD	encompassed	Africa	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	24.000	24.000	5.000	0.807	http://www.semwebtech.org/mondial/10/meta#encompassed

PRED relation: encompassed
PRED expected values: Africa
=> 71 concepts (71 used for prediction)
PRED predicted values (max 10 best out of 5): Africa (0.89 #187, 0.87 #210, 0.87 #198), Europe (0.54 #153, 0.52 #178, 0.48 #189), America (0.52 #96, 0.45 #84, 0.37 #308), Asia (0.47 #75, 0.29 #69, 0.28 #103), Australia-Oceania (0.17 #88, 0.16 #326, 0.15 #321)
>> best conf = 0.89
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #187 for best value:
>> intensional similarity = 15
>> extensional distance = 59
>> proper extension:  SMAR;
>> query: (?x229, ?x213) <- ?x229[ a Country; is locatedIn of ?x53; is neighbor of ?x348[ is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x1244[ is flowsInto of ?x1604;]; is neighbor of ?x525[ a Country; has government ?x435; is locatedIn of ?x709; is neighbor of ?x138;]; is neighbor of ?x528[ has encompassed ?x213; is neighbor of ?x172;];];]
ranks of expected_values: 1
EVAL	SSD	encompassed	Africa	CNN-1.+1._MA	1.000	1.000	1.000	1.000	71.000	71.000	5.000	0.885	http://www.semwebtech.org/mondial/10/meta#encompassed

#83-LT
PRED entity: LT

PRED relation: neighbor!
PRED expected values: LV
=> 43 concepts (43 used for prediction)
PRED predicted values (max 10 best out of 199): LV (0.91 #2882, 0.91 #3203, 0.91 #2400), LT (0.40 #458, 0.40 #299, 0.33 #140), EW (0.40 #257, 0.33 #98, 0.27 #5135), SK (0.40 #339, 0.28 #796, 0.27 #5135), H (0.33 #520, 0.25 #680, 0.20 #361), CZ (0.28 #796, 0.27 #5135, 0.26 #798), UA (0.27 #5135, 0.26 #798, 0.26 #5459), D (0.27 #5135, 0.26 #5459, 0.25 #3684), CN (0.27 #5135, 0.26 #5459, 0.25 #3684), GE (0.27 #5135, 0.26 #5459, 0.25 #3684)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2882 for best value:
>> intensional similarity = 6
>> extensional distance = 70
>> proper extension:  NEP;
>> query: (?x962, ?x73) <- ?x962[ a Country; has government ?x254; has language ?x555; has neighbor ?x73; has religion ?x56; is locatedIn of ?x146;]
ranks of expected_values: 1
EVAL	LT	neighbor!	LV	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	43.000	43.000	199.000	0.909	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: LV
=> 94 concepts (94 used for prediction)
PRED predicted values (max 10 best out of 223): LV (0.92 #7203, 0.92 #7202, 0.92 #7531), LT (0.50 #952, 0.50 #301, 0.44 #488), UA (0.50 #211, 0.45 #1353, 0.44 #488), EW (0.44 #488, 0.42 #1141, 0.40 #749), SK (0.44 #488, 0.42 #1141, 0.37 #1465), CZ (0.44 #488, 0.42 #1141, 0.37 #1465), D (0.44 #488, 0.42 #1141, 0.37 #1465), CN (0.43 #1669, 0.28 #9505, 0.28 #3427), H (0.36 #1346, 0.32 #2491, 0.29 #1835), RO (0.36 #1329, 0.29 #1982, 0.25 #1954)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #7203 for best value:
>> intensional similarity = 16
>> extensional distance = 61
>> proper extension:  PK;
>> query: (?x962, ?x448) <- ?x962[ a Country; has language ?x555; has language ?x1116[ a Language;]; has neighbor ?x73[ has encompassed ?x195; has ethnicGroup ?x58; has neighbor ?x403; is locatedIn of ?x127[ a Mountain;]; is locatedIn of ?x632[ a Estuary;];]; has neighbor ?x448[ has religion ?x56; has wasDependentOf ?x903;]; is locatedIn of ?x146;]
ranks of expected_values: 1
EVAL	LT	neighbor!	LV	CNN-1.+1._MA	1.000	1.000	1.000	1.000	94.000	94.000	223.000	0.923	http://www.semwebtech.org/mondial/10/meta#neighbor

#82-KIR
PRED entity: KIR

PRED relation: religion
PRED expected values: Protestant Mormon
=> 41 concepts (41 used for prediction)
PRED predicted values (max 10 best out of 36): Protestant (0.63 #207, 0.55 #289, 0.50 #84), Muslim (0.57 #497, 0.52 #538, 0.51 #702), Christian (0.37 #496, 0.36 #537, 0.31 #701), ChristianOrthodox (0.24 #534, 0.20 #1150, 0.20 #1149), Buddhist (0.24 #11, 0.23 #503, 0.22 #134), Hindu (0.23 #501, 0.20 #1150, 0.20 #1149), JehovasWitnesses (0.21 #225, 0.20 #1150, 0.20 #1149), Anglican (0.20 #755, 0.20 #1150, 0.20 #1149), Seventh-DayAdventist (0.20 #1150, 0.20 #1149, 0.17 #174), Mormon (0.20 #1150, 0.20 #1149, 0.15 #1521)
>> best conf = 0.63
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #207 for best value:
>> intensional similarity = 7
>> extensional distance = 17
>> proper extension:  ES;
>> query: (?x728, Protestant) <- ?x728[ a Country; has encompassed ?x211; has wasDependentOf ?x81[ is dependentOf of ?x80; is locatedIn of ?x121;]; is locatedIn of ?x282<PacificOcean>;]
ranks of expected_values: 1, 10
EVAL	KIR	religion	Mormon	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.111	41.000	41.000	36.000	0.632	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	KIR	religion	Protestant	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	41.000	41.000	36.000	0.632	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion
PRED expected values: Protestant Mormon
=> 83 concepts (83 used for prediction)
PRED predicted values (max 10 best out of 39): Protestant (0.73 #958, 0.71 #1916, 0.62 #998), Muslim (0.62 #998, 0.59 #1418, 0.52 #2549), Anglican (0.62 #998, 0.47 #124, 0.40 #622), Buddhist (0.62 #998, 0.43 #2128, 0.39 #2754), Jewish (0.62 #998, 0.33 #3, 0.23 #1540), Hindu (0.62 #998, 0.25 #423, 0.25 #217), Sikh (0.62 #998, 0.18 #456, 0.12 #1249), Christian (0.50 #294, 0.50 #128, 0.47 #124), Seventh-DayAdventist (0.47 #124, 0.40 #622, 0.27 #1040), Baptist (0.47 #124, 0.40 #622, 0.27 #1040)
>> best conf = 0.73
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #958 for best value:
>> intensional similarity = 14
>> extensional distance = 13
>> proper extension:  ES;
>> query: (?x728, Protestant) <- ?x728[ a Country; has encompassed ?x211; has ethnicGroup ?x1129; has religion ?x352<RomanCatholic>; has wasDependentOf ?x81[ a Country; has religion ?x95; is dependentOf of ?x80; is locatedIn of ?x121; is locatedIn of ?x467[ a Island;];]; is locatedIn of ?x282<PacificOcean>;]
ranks of expected_values: 1, 12
EVAL	KIR	religion	Mormon	CNN-1.+1._MA	0.000	0.000	0.000	0.091	83.000	83.000	39.000	0.733	http://www.semwebtech.org/mondial/10/meta#religion
EVAL	KIR	religion	Protestant	CNN-1.+1._MA	1.000	1.000	1.000	1.000	83.000	83.000	39.000	0.733	http://www.semwebtech.org/mondial/10/meta#religion

#81-Mawenzi
PRED entity: Mawenzi

PRED relation: type
PRED expected values: "volcano"
=> 51 concepts (51 used for prediction)
PRED predicted values (max 10 best out of 9): "volcano" (0.58 #262, 0.57 #214, 0.56 #230), "volcanic" (0.38 #34, 0.35 #274, 0.33 #290), "salt" (0.23 #55, 0.20 #71, 0.15 #87), "dam" (0.05 #65, 0.03 #369, 0.02 #561), "monolith" (0.03 #155, 0.03 #171, 0.02 #187), "granite" (0.03 #158, 0.03 #174, 0.02 #318), "sand" (0.01 #452, 0.01 #372, 0.01 #532), "lime" (0.01 #453), "atoll" (0.01 #456)
>> best conf = 0.58
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #262 for best value:
>> intensional similarity = 5
>> extensional distance = 65
>> proper extension:  PicoBasile; PicodelosNieves; PicodeTeide; RoquedelosMuchachos;
>> query: (?x2194, "volcano") <- ?x2194[ a Mountain; a Volcano; has locatedIn ?x820[ has neighbor ?x688[ is locatedIn of ?x600;];];]
ranks of expected_values: 1
EVAL	Mawenzi	type	"volcano"	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	51.000	51.000	9.000	0.582	http://www.semwebtech.org/mondial/10/meta#type

PRED relation: type
PRED expected values: "volcano"
=> 118 concepts (118 used for prediction)
PRED predicted values (max 10 best out of 12): "volcano" (0.64 #377, 0.62 #491, 0.61 #426), "salt" (0.36 #130, 0.29 #185, 0.25 #453), "volcanic" (0.33 #907, 0.33 #308, 0.33 #260), "atoll" (0.07 #444, 0.02 #1121, 0.02 #1204), "dam" (0.05 #856, 0.04 #1114, 0.04 #791), "sand" (0.05 #794, 0.03 #989, 0.03 #1101), "lime" (0.05 #441, 0.03 #1118, 0.02 #1201), "caldera" (0.03 #680, 0.03 #697, 0.02 #793), "monolith" (0.03 #333, 0.03 #350, 0.02 #512), "granite" (0.03 #353, 0.02 #467, 0.02 #515)
>> best conf = 0.64
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #377 for best value:
>> intensional similarity = 12
>> extensional distance = 40
>> proper extension:  Tahat; Irazu; NevadodelHuila; Ampato; Coropuna; Damavand; OjosdelSalado; PicodelosNieves; MontePissis; PicodeTeide; ...
>> query: (?x2194, "volcano") <- ?x2194[ a Mountain; a Volcano; has locatedIn ?x820[ a Country; has ethnicGroup ?x1233; has government ?x435; has neighbor ?x192; has neighbor ?x688[ a Country;]; is locatedIn of ?x60[ has mergesWith ?x182; is locatedInWater of ?x226;];];]
ranks of expected_values: 1
EVAL	Mawenzi	type	"volcano"	CNN-1.+1._MA	1.000	1.000	1.000	1.000	118.000	118.000	12.000	0.643	http://www.semwebtech.org/mondial/10/meta#type

#80-Asahi-Dake
PRED entity: Asahi-Dake

PRED relation: locatedIn
PRED expected values: J
=> 29 concepts (29 used for prediction)
PRED predicted values (max 10 best out of 94): J (0.87 #4270, 0.86 #3085, 0.83 #4508), RI (0.47 #1475, 0.36 #1950, 0.32 #2900), USA (0.25 #309, 0.22 #546, 0.20 #783), RP (0.25 #109, 0.14 #2957, 0.14 #1295), PNG (0.23 #1127, 0.22 #653, 0.20 #890), RM (0.21 #1288, 0.14 #2000, 0.09 #3187), E (0.18 #1450, 0.14 #1688, 0.13 #2400), IS (0.14 #1769, 0.13 #2481, 0.11 #2719), P (0.12 #197, 0.12 #1620, 0.10 #1858), NZ (0.12 #347, 0.11 #584, 0.10 #821)
>> best conf = 0.87
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4270 for best value:
>> intensional similarity = 8
>> extensional distance = 36
>> proper extension:  PitondelaFournaise;
>> query: (?x1456, ?x117) <- ?x1456[ a Mountain; a Volcano; has locatedOnIsland ?x451[ a Island; has locatedIn ?x117[ a Country; has government ?x2476;];]; has type ?x150;]
ranks of expected_values: 1
EVAL	Asahi-Dake	locatedIn	J	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	29.000	29.000	94.000	0.872	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: J
=> 53 concepts (53 used for prediction)
PRED predicted values (max 10 best out of 94): J (0.85 #10575, 0.82 #7202, 0.80 #10338), RI (0.70 #6533, 0.64 #7015, 0.47 #9858), RP (0.50 #2027, 0.43 #4673, 0.22 #5871), E (0.43 #4350, 0.40 #2905, 0.38 #5312), IS (0.33 #5632, 0.33 #108, 0.25 #5393), NZ (0.33 #1551, 0.25 #2510, 0.25 #2268), PNG (0.33 #4024, 0.25 #2818, 0.25 #2579), USA (0.33 #4156, 0.25 #963, 0.22 #6242), RC (0.33 #1426, 0.25 #963, 0.20 #238), P (0.33 #436, 0.18 #7399, 0.11 #10056)
>> best conf = 0.85
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #10575 for best value:
>> intensional similarity = 11
>> extensional distance = 31
>> proper extension:  PitondelaFournaise;
>> query: (?x1456, ?x117) <- ?x1456[ a Mountain; a Volcano; has locatedOnIsland ?x451[ a Island; has locatedIn ?x117[ a Country; has encompassed ?x175; has government ?x2476; has religion ?x462;];]; has type ?x150[ a string;];]
ranks of expected_values: 1
EVAL	Asahi-Dake	locatedIn	J	CNN-1.+1._MA	1.000	1.000	1.000	1.000	53.000	53.000	94.000	0.853	http://www.semwebtech.org/mondial/10/meta#locatedIn

#79-RioDesaguadero
PRED entity: RioDesaguadero

PRED relation: hasEstuary
PRED expected values: RioDesaguadero
=> 55 concepts (38 used for prediction)
PRED predicted values (max 10 best out of 160): RioMadeira (0.33 #122, 0.25 #575, 0.25 #348), RioMamore (0.25 #853, 0.25 #627, 0.09 #1758), Volga (0.14 #1545, 0.14 #1319, 0.09 #1998), TruckeeRiver (0.14 #1452, 0.14 #1226, 0.09 #1905), Syrdarja (0.14 #1556, 0.14 #1330, 0.09 #2009), Amudarja (0.14 #1421, 0.14 #1195, 0.09 #1874), EucumbeneRiver (0.14 #1166, 0.09 #1845, 0.08 #2071), Jordan (0.14 #1384, 0.08 #2289), Kura (0.09 #2016, 0.08 #2468, 0.08 #2242), Ili (0.09 #1912, 0.08 #2364, 0.08 #2138)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #122 for best value:
>> intensional similarity = 6
>> extensional distance = 1
>> proper extension:  RioMadeira;
>> query: (?x481, RioMadeira) <- ?x481[ a River; has flowsInto ?x274; has hasSource ?x780; has locatedIn ?x690<BOL>; is flowsInto of ?x480;]

*> Best rule #7019 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 148
*> proper extension:  Thjorsa;
*> query: (?x481, ?x274) <- ?x481[ a River; has hasSource ?x780; has locatedIn ?x690[ has ethnicGroup ?x197; has language ?x702; is locatedIn of ?x274;];]
*> conf = 0.01
ranks of expected_values: 90
EVAL	RioDesaguadero	hasEstuary	RioDesaguadero	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.011	55.000	38.000	160.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasEstuary

PRED relation: hasEstuary
PRED expected values: RioDesaguadero
=> 183 concepts (182 used for prediction)
PRED predicted values (max 10 best out of 222): RioMamore (0.33 #174, 0.25 #400, 0.14 #1307), Amazonas (0.20 #675, 0.14 #1129, 0.14 #902), RioMadeira (0.20 #574, 0.14 #1255, 0.10 #3071), Tambo (0.14 #990, 0.14 #763, 0.12 #1444), Ucayali (0.14 #1074, 0.14 #847, 0.10 #3117), Maranon (0.14 #1045, 0.14 #818, 0.10 #3088), Tocantins (0.14 #1231, 0.10 #2820, 0.10 #2593), Paraguay (0.14 #1260, 0.10 #2849, 0.10 #2622), Araguaia (0.14 #1283, 0.01 #15598), TruckeeRiver (0.12 #1908, 0.12 #1681, 0.11 #2362)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #174 for best value:
>> intensional similarity = 8
>> extensional distance = 1
>> proper extension:  RioMamore;
>> query: (?x481, RioMamore) <- ?x481[ a River; has flowsInto ?x274[ has locatedIn ?x690<BOL>;]; has hasSource ?x780[ a Source; has locatedIn ?x690<BOL>;]; has locatedIn ?x690<BOL>;]

*> Best rule #19541 for first EXPECTED value:
*> intensional similarity = 12
*> extensional distance = 122
*> proper extension:  Raab;
*> query: (?x481, ?x947) <- ?x481[ a River; has locatedIn ?x690[ has ethnicGroup ?x2045[ a EthnicGroup;]; has language ?x702; is locatedIn of ?x947[ a Estuary;]; is locatedIn of ?x1120[ a Source;]; is locatedIn of ?x2274[ has inMountains ?x1862;]; is neighbor of ?x202;];]
*> conf = 0.06
ranks of expected_values: 47
EVAL	RioDesaguadero	hasEstuary	RioDesaguadero	CNN-1.+1._MA	0.000	0.000	0.000	0.021	183.000	182.000	222.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasEstuary

#78-MV
PRED entity: MV

PRED relation: religion
PRED expected values: Muslim
=> 31 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 36): Muslim (0.67 #89, 0.64 #257, 0.64 #215), Christian (0.50 #46, 0.45 #256, 0.42 #88), RomanCatholic (0.47 #429, 0.46 #976, 0.45 #892), Protestant (0.40 #339, 0.38 #676, 0.37 #718), Buddhist (0.38 #54, 0.38 #12, 0.33 #96), Hindu (0.38 #52, 0.38 #10, 0.31 #136), Anglican (0.25 #60, 0.25 #18, 0.24 #295), ChristianOrthodox (0.24 #295, 0.20 #1180, 0.19 #380), Sikh (0.24 #295, 0.20 #1180, 0.16 #1266), Jains (0.24 #295, 0.20 #1180, 0.16 #1266)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #89 for best value:
>> intensional similarity = 7
>> extensional distance = 10
>> proper extension:  RM;
>> query: (?x547, Muslim) <- ?x547[ a Country; has wasDependentOf ?x81[ is locatedIn of ?x121;]; is locatedIn of ?x60<IndianOcean>; is locatedIn of ?x1159[ a Island;];]
ranks of expected_values: 1
EVAL	MV	religion	Muslim	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	31.000	31.000	36.000	0.667	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion
PRED expected values: Muslim
=> 76 concepts (76 used for prediction)
PRED predicted values (max 10 best out of 40): Muslim (0.78 #772, 0.73 #1764, 0.73 #901), Buddhist (0.60 #435, 0.60 #349, 0.57 #693), Christian (0.60 #427, 0.60 #341, 0.57 #685), RomanCatholic (0.52 #2288, 0.51 #2453, 0.51 #2418), Protestant (0.49 #724, 0.44 #2541, 0.43 #380), Hindu (0.49 #724, 0.43 #380, 0.43 #691), Sikh (0.49 #724, 0.43 #380, 0.34 #894), Jains (0.49 #724, 0.43 #380, 0.34 #894), ChristianOrthodox (0.43 #380, 0.40 #1282, 0.34 #2586), Anglican (0.43 #380, 0.34 #2586, 0.27 #2455)
>> best conf = 0.78
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #772 for best value:
>> intensional similarity = 13
>> extensional distance = 7
>> proper extension:  AFG;
>> query: (?x547, Muslim) <- ?x547[ has encompassed ?x175<Asia>; has government ?x435; has wasDependentOf ?x81<GB>; is locatedIn of ?x60[ has locatedIn ?x758[ a Country; has encompassed ?x213;]; has locatedIn ?x820[ has neighbor ?x348;]; is flowsInto of ?x242[ has locatedIn ?x243;];];]
ranks of expected_values: 1
EVAL	MV	religion	Muslim	CNN-1.+1._MA	1.000	1.000	1.000	1.000	76.000	76.000	40.000	0.778	http://www.semwebtech.org/mondial/10/meta#religion

#77-Malta
PRED entity: Malta

PRED relation: belongsToIslands
PRED expected values: Malta
=> 61 concepts (61 used for prediction)
PRED predicted values (max 10 best out of 52): LipariIslands (0.64 #274, 0.60 #206, 0.23 #478), Malta (0.41 #2586, 0.33 #28, 0.31 #953), LesserAntilles (0.33 #151, 0.18 #1376, 0.13 #1308), Sporades (0.31 #361, 0.15 #429, 0.14 #565), IonicIslands (0.19 #360, 0.12 #428, 0.10 #496), Canares (0.17 #567, 0.12 #635, 0.11 #703), Azores (0.15 #820, 0.12 #888, 0.08 #1161), HawaiiIslands (0.14 #641, 0.13 #709, 0.12 #845), Kyklades (0.12 #390, 0.08 #458, 0.07 #526), SundaIslands (0.10 #1511, 0.09 #1715, 0.08 #830)
>> best conf = 0.64
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #274 for best value:
>> intensional similarity = 4
>> extensional distance = 9
>> proper extension:  Vulcano;
>> query: (?x849, LipariIslands) <- ?x849[ a Island; has locatedInWater ?x275<MediterraneanSea>; has type ?x704;]

*> Best rule #2586 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 210
*> proper extension:  Aruba;
*> query: (?x849, ?x1302) <- ?x849[ a Island; has locatedIn ?x850[ a Country; is locatedIn of ?x777[ has belongsToIslands ?x1302;];];]
*> conf = 0.41
ranks of expected_values: 2
EVAL	Malta	belongsToIslands	Malta	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	61.000	61.000	52.000	0.636	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

PRED relation: belongsToIslands
PRED expected values: Malta
=> 156 concepts (156 used for prediction)
PRED predicted values (max 10 best out of 64): LipariIslands (0.64 #684, 0.38 #1161, 0.35 #1297), Malta (0.61 #3822, 0.54 #2866, 0.53 #2592), LesserAntilles (0.44 #628, 0.38 #1242, 0.33 #355), HawaiiIslands (0.40 #983, 0.15 #3167, 0.15 #2826), Azores (0.33 #1435, 0.20 #2458, 0.19 #2527), Sporades (0.29 #839, 0.19 #1793, 0.17 #1656), SundaIslands (0.25 #1104, 0.21 #1377, 0.16 #3699), WestfriesischeInseln (0.24 #1444, 0.19 #1785, 0.11 #3151), IonicIslands (0.21 #838, 0.12 #1655, 0.12 #1792), Canares (0.19 #1932, 0.16 #2615, 0.12 #2820)
>> best conf = 0.64
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #684 for best value:
>> intensional similarity = 4
>> extensional distance = 9
>> proper extension:  Vulcano;
>> query: (?x849, LipariIslands) <- ?x849[ a Island; has locatedInWater ?x275<MediterraneanSea>; has type ?x704;]

*> Best rule #3822 for first EXPECTED value:
*> intensional similarity = 9
*> extensional distance = 58
*> proper extension:  Shikoku;
*> query: (?x849, ?x1302) <- ?x849[ a Island; has locatedIn ?x850[ a Country; has government ?x435; has language ?x247; is locatedIn of ?x777[ a Island; has belongsToIslands ?x1302; has type ?x704;];];]
*> conf = 0.61
ranks of expected_values: 2
EVAL	Malta	belongsToIslands	Malta	CNN-1.+1._MA	0.000	1.000	1.000	0.500	156.000	156.000	64.000	0.636	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

#76-Bioko
PRED entity: Bioko

PRED relation: locatedIn
PRED expected values: GQ
=> 35 concepts (35 used for prediction)
PRED predicted values (max 10 best out of 51): GQ (0.89 #7372, 0.89 #6896, 0.89 #7845), RI (0.64 #4335, 0.44 #6000, 0.39 #6237), E (0.60 #1692, 0.50 #2407, 0.50 #2168), CV (0.33 #584, 0.25 #1534, 0.25 #1297), NIC (0.33 #96, 0.25 #1048, 0.17 #2713), F (0.33 #246, 0.17 #2624, 0.12 #3576), HELX (0.33 #765, 0.07 #5522, 0.03 #7422), P (0.29 #3051, 0.20 #4243, 0.17 #5195), USA (0.29 #3165, 0.13 #5781, 0.05 #7917), COM (0.25 #1639, 0.20 #2115, 0.06 #6159)
>> best conf = 0.89
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #7372 for best value:
>> intensional similarity = 9
>> extensional distance = 35
>> proper extension:  Jamaica;
>> query: (?x772, ?x1408) <- ?x772[ a Island; is locatedOnIsland of ?x771[ a Mountain; has locatedIn ?x1408[ a Country; has encompassed ?x213; has government ?x435; has religion ?x352<RomanCatholic>;];];]
>> Best rule #6896 for best value:
>> intensional similarity = 9
>> extensional distance = 17
>> proper extension:  Madagaskar;
>> query: (?x772, ?x1408) <- ?x772[ a Island; is locatedOnIsland of ?x771[ a Mountain; a Volcano; has locatedIn ?x1408[ a Country; has government ?x435<"republic">; has religion ?x352;];];]
ranks of expected_values: 1
EVAL	Bioko	locatedIn	GQ	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	35.000	35.000	51.000	0.895	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: GQ
=> 35 concepts (35 used for prediction)
PRED predicted values (max 10 best out of 51): GQ (0.89 #7372, 0.89 #6896, 0.89 #7845), RI (0.64 #4335, 0.44 #6000, 0.39 #6237), E (0.60 #1692, 0.50 #2407, 0.50 #2168), CV (0.33 #821, 0.25 #1534, 0.25 #1297), NIC (0.33 #96, 0.25 #1048, 0.17 #2713), F (0.33 #246, 0.17 #2624, 0.12 #3576), HELX (0.33 #528, 0.07 #5522, 0.03 #7422), P (0.29 #3290, 0.20 #4243, 0.17 #5195), USA (0.29 #2926, 0.13 #5781, 0.05 #7917), COM (0.25 #1639, 0.20 #2115, 0.06 #6159)
>> best conf = 0.89
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #7372 for best value:
>> intensional similarity = 9
>> extensional distance = 35
>> proper extension:  Jamaica;
>> query: (?x772, ?x1408) <- ?x772[ a Island; is locatedOnIsland of ?x771[ a Mountain; has locatedIn ?x1408[ a Country; has encompassed ?x213; has government ?x435; has religion ?x352<RomanCatholic>;];];]
>> Best rule #6896 for best value:
>> intensional similarity = 9
>> extensional distance = 17
>> proper extension:  Madagaskar;
>> query: (?x772, ?x1408) <- ?x772[ a Island; is locatedOnIsland of ?x771[ a Mountain; a Volcano; has locatedIn ?x1408[ a Country; has government ?x435<"republic">; has religion ?x352;];];]
ranks of expected_values: 1
EVAL	Bioko	locatedIn	GQ	CNN-1.+1._MA	1.000	1.000	1.000	1.000	35.000	35.000	51.000	0.895	http://www.semwebtech.org/mondial/10/meta#locatedIn

#75-B
PRED entity: B

PRED relation: encompassed
PRED expected values: Europe
=> 39 concepts (39 used for prediction)
PRED predicted values (max 10 best out of 5): Europe (0.83 #82, 0.81 #67, 0.80 #166), America (0.60 #75, 0.59 #80, 0.54 #90), Africa (0.28 #175, 0.27 #170, 0.27 #164), Asia (0.23 #151, 0.22 #172, 0.22 #161), Australia-Oceania (0.17 #43, 0.15 #123, 0.14 #138)
>> best conf = 0.83
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #82 for best value:
>> intensional similarity = 7
>> extensional distance = 33
>> proper extension:  BIH; MNE; TR; AL; GR; KOS; MK;
>> query: (?x543, Europe) <- ?x543[ a Country; is locatedIn of ?x121; is neighbor of ?x718[ has ethnicGroup ?x237[ a EthnicGroup; is ethnicGroup of ?x236<H>;];];]
ranks of expected_values: 1
EVAL	B	encompassed	Europe	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	39.000	39.000	5.000	0.829	http://www.semwebtech.org/mondial/10/meta#encompassed

PRED relation: encompassed
PRED expected values: Europe
=> 90 concepts (90 used for prediction)
PRED predicted values (max 10 best out of 5): Europe (0.91 #222, 0.89 #160, 0.88 #150), America (0.62 #58, 0.62 #158, 0.50 #305), Africa (0.35 #215, 0.30 #351, 0.29 #259), Asia (0.33 #32, 0.28 #343, 0.28 #271), Australia-Oceania (0.25 #126, 0.19 #187, 0.18 #308)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #222 for best value:
>> intensional similarity = 12
>> extensional distance = 42
>> proper extension:  IRL;
>> query: (?x543, ?x195) <- ?x543[ a Country; has ethnicGroup ?x1628; has religion ?x352<RomanCatholic>; is locatedIn of ?x121; is neighbor of ?x120[ has encompassed ?x195; has government ?x140; is locatedIn of ?x133[ is flowsInto of ?x132;]; is locatedIn of ?x737[ a Estuary;];];]
ranks of expected_values: 1
EVAL	B	encompassed	Europe	CNN-1.+1._MA	1.000	1.000	1.000	1.000	90.000	90.000	5.000	0.909	http://www.semwebtech.org/mondial/10/meta#encompassed

#74-Samos
PRED entity: Samos

PRED relation: locatedInWater
PRED expected values: MediterraneanSea
=> 37 concepts (34 used for prediction)
PRED predicted values (max 10 best out of 94): MediterraneanSea (0.82 #87, 0.82 #59, 0.80 #174), AtlanticOcean (0.27 #846, 0.26 #756, 0.26 #1021), PacificOcean (0.24 #413, 0.24 #589, 0.24 #722), IndianOcean (0.14 #442, 0.13 #574, 0.13 #398), NorthSea (0.13 #664, 0.12 #311, 0.12 #266), CaribbeanSea (0.10 #858, 0.10 #990, 0.10 #1033), JavaSea (0.10 #361, 0.10 #317, 0.10 #272), BalticSea (0.09 #533, 0.08 #489, 0.07 #666), ArcticOcean (0.08 #410, 0.07 #454, 0.05 #586), SouthChinaSea (0.07 #462, 0.06 #374, 0.06 #330)
>> best conf = 0.82
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #87 for best value:
>> intensional similarity = 6
>> extensional distance = 9
>> proper extension:  Lefkas; Zakynthos; Syros; Korfu; Mykonos;
>> query: (?x1445, ?x275) <- ?x1445[ a Island; has belongsToIslands ?x1053[ is belongsToIslands of ?x1831[ has locatedInWater ?x275;];]; has locatedIn ?x399<GR>;]
>> Best rule #59 for best value:
>> intensional similarity = 6
>> extensional distance = 9
>> proper extension:  Lefkas; Zakynthos; Syros; Korfu; Mykonos;
>> query: (?x1445, MediterraneanSea) <- ?x1445[ a Island; has belongsToIslands ?x1053[ is belongsToIslands of ?x1831[ has locatedInWater ?x275;];]; has locatedIn ?x399<GR>;]
ranks of expected_values: 1
EVAL	Samos	locatedInWater	MediterraneanSea	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	37.000	34.000	94.000	0.818	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater
PRED expected values: MediterraneanSea
=> 133 concepts (133 used for prediction)
PRED predicted values (max 10 best out of 93): MediterraneanSea (0.91 #308, 0.91 #280, 0.88 #625), AtlanticOcean (0.31 #1490, 0.31 #588, 0.30 #724), PacificOcean (0.29 #462, 0.27 #598, 0.23 #2764), IndianOcean (0.19 #1259, 0.13 #1621, 0.13 #1214), NorthSea (0.18 #629, 0.18 #1080, 0.18 #765), JavaSea (0.13 #771, 0.11 #997, 0.10 #1176), BalticSea (0.10 #1082, 0.04 #1579, 0.04 #1713), CaribbeanSea (0.10 #2633, 0.10 #2677, 0.10 #2766), ArcticOcean (0.08 #731, 0.07 #1271, 0.07 #1633), SouthChinaSea (0.08 #784, 0.07 #1010, 0.07 #1551)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #308 for best value:
>> intensional similarity = 8
>> extensional distance = 20
>> proper extension:  Mallorca; Vulcano; Formentera;
>> query: (?x1445, ?x275) <- ?x1445[ a Island; has belongsToIslands ?x1053[ a Islands; is belongsToIslands of ?x1831[ a Island; has locatedIn ?x399; has locatedInWater ?x275<MediterraneanSea>;];];]
>> Best rule #280 for best value:
>> intensional similarity = 8
>> extensional distance = 20
>> proper extension:  Mallorca; Vulcano; Formentera;
>> query: (?x1445, MediterraneanSea) <- ?x1445[ a Island; has belongsToIslands ?x1053[ a Islands; is belongsToIslands of ?x1831[ a Island; has locatedIn ?x399; has locatedInWater ?x275<MediterraneanSea>;];];]
ranks of expected_values: 1
EVAL	Samos	locatedInWater	MediterraneanSea	CNN-1.+1._MA	1.000	1.000	1.000	1.000	133.000	133.000	93.000	0.909	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#73-Swede
PRED entity: Swede

PRED relation: ethnicGroup!
PRED expected values: S
=> 32 concepts (22 used for prediction)
PRED predicted values (max 10 best out of 198): R (0.62 #196, 0.32 #2911, 0.30 #3495), EW (0.50 #118, 0.38 #311, 0.23 #970), KAZ (0.38 #271, 0.25 #78, 0.23 #970), S (0.30 #582, 0.27 #1745, 0.25 #77), N (0.30 #582, 0.27 #1745, 0.22 #3885), LV (0.25 #283, 0.25 #90, 0.24 #478), KGZ (0.25 #210, 0.25 #17, 0.24 #405), UZB (0.25 #245, 0.25 #52, 0.23 #970), MD (0.25 #348, 0.25 #155, 0.23 #970), BY (0.25 #237, 0.25 #44, 0.23 #970)
>> best conf = 0.62
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #196 for best value:
>> intensional similarity = 9
>> extensional distance = 6
>> proper extension:  Ukrainian; Tatar; Bashkir; Chuvash;
>> query: (?x2382, R) <- ?x2382[ a EthnicGroup; is ethnicGroup of ?x565[ has language ?x247; is locatedIn of ?x631<Paatsjoki>; is locatedIn of ?x804[ has locatedInWater ?x146;]; is neighbor of ?x402[ is locatedIn of ?x191;];];]

*> Best rule #582 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 23
*> proper extension:  Latvian; Azeri; Lithuanian; Georgian; Belorussian; Moldavian-Romanian; Karakalpak; Tajik; Bulgarian; Dungan; ...
*> query: (?x2382, ?x73) <- ?x2382[ a EthnicGroup; is ethnicGroup of ?x565[ has language ?x555<Russian>; has religion ?x56; is locatedIn of ?x631[ has flowsInto ?x251;]; is neighbor of ?x73;];]
*> conf = 0.30
ranks of expected_values: 4
EVAL	Swede	ethnicGroup!	S	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.250	32.000	22.000	198.000	0.625	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup!
PRED expected values: S
=> 92 concepts (92 used for prediction)
PRED predicted values (max 10 best out of 209): EW (0.71 #704, 0.50 #118, 0.44 #1292), LV (0.67 #1264, 0.43 #676, 0.39 #3539), R (0.62 #981, 0.39 #3539, 0.39 #3538), GB (0.60 #1375, 0.40 #2563, 0.35 #3350), IR (0.54 #1626, 0.50 #2218, 0.30 #3990), GE (0.50 #260, 0.39 #3539, 0.39 #3538), UA (0.44 #1231, 0.43 #643, 0.39 #3539), BY (0.44 #1218, 0.43 #630, 0.39 #3539), LT (0.44 #1336, 0.39 #3539, 0.39 #3538), L (0.43 #918, 0.30 #9640, 0.30 #9639)
>> best conf = 0.71
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #704 for best value:
>> intensional similarity = 12
>> extensional distance = 5
>> proper extension:  Estonian;
>> query: (?x2382, EW) <- ?x2382[ a EthnicGroup; is ethnicGroup of ?x565[ a Country; has language ?x555<Russian>; has language ?x1983[ a Language;]; has religion ?x95<Protestant>; is locatedIn of ?x1395[ a Lake;]; is neighbor of ?x73;];]

*> Best rule #1368 for first EXPECTED value:
*> intensional similarity = 14
*> extensional distance = 7
*> proper extension:  Latvian; Lithuanian;
*> query: (?x2382, ?x170) <- ?x2382[ a EthnicGroup; is ethnicGroup of ?x565[ a Country; has government ?x435; has language ?x555<Russian>; has neighbor ?x170; has wasDependentOf ?x73; is locatedIn of ?x1608[ a River; has flowsInto ?x146<BalticSea>;]; is locatedIn of ?x1959[ a Estuary;];];]
*> conf = 0.42
ranks of expected_values: 11
EVAL	Swede	ethnicGroup!	S	CNN-1.+1._MA	0.000	0.000	0.000	0.091	92.000	92.000	209.000	0.714	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#72-Shikoku
PRED entity: Shikoku

PRED relation: locatedInWater
PRED expected values: PacificOcean
=> 43 concepts (37 used for prediction)
PRED predicted values (max 10 best out of 39): PacificOcean (0.73 #177, 0.73 #149, 0.71 #311), EastChinaSea (0.71 #311, 0.69 #176, 0.65 #573), SeaofJapan (0.71 #311, 0.69 #176, 0.65 #573), SeaofOkhotsk (0.71 #311, 0.69 #176, 0.65 #573), AtlanticOcean (0.39 #274, 0.38 #361, 0.38 #229), MediterraneanSea (0.15 #238, 0.10 #545, 0.10 #413), CaribbeanSea (0.14 #373, 0.13 #286, 0.13 #330), IndianOcean (0.12 #269, 0.12 #313, 0.11 #356), SulawesiSea (0.11 #160, 0.05 #425, 0.05 #557), JavaSea (0.11 #231, 0.09 #276, 0.09 #320)
>> best conf = 0.73
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #177 for best value:
>> intensional similarity = 7
>> extensional distance = 53
>> proper extension:  Saipan; Tongatapu; Ambon; Tinian; Mindoro; TeWaka-a-Maui-SouthIsland-; Panay; Luzon; Samar; TeIka-a-Maui-NorthIsland-; ...
>> query: (?x2398, ?x282) <- ?x2398[ has belongsToIslands ?x1212[ is belongsToIslands of ?x451[ has locatedInWater ?x282<PacificOcean>;]; is belongsToIslands of ?x630[ a Island;];]; has locatedIn ?x117;]
>> Best rule #149 for best value:
>> intensional similarity = 7
>> extensional distance = 53
>> proper extension:  Saipan; Tongatapu; Ambon; Tinian; Mindoro; TeWaka-a-Maui-SouthIsland-; Panay; Luzon; Samar; TeIka-a-Maui-NorthIsland-; ...
>> query: (?x2398, PacificOcean) <- ?x2398[ has belongsToIslands ?x1212[ is belongsToIslands of ?x451[ has locatedInWater ?x282<PacificOcean>;]; is belongsToIslands of ?x630[ a Island;];]; has locatedIn ?x117;]
ranks of expected_values: 1
EVAL	Shikoku	locatedInWater	PacificOcean	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	43.000	37.000	39.000	0.727	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater
PRED expected values: PacificOcean
=> 100 concepts (99 used for prediction)
PRED predicted values (max 10 best out of 49): PacificOcean (0.88 #499, 0.88 #472, 0.88 #684), SeaofOkhotsk (0.78 #1006, 0.75 #915, 0.75 #914), SeaofJapan (0.78 #1006, 0.75 #915, 0.75 #914), EastChinaSea (0.78 #1006, 0.75 #915, 0.75 #914), AtlanticOcean (0.53 #373, 0.52 #1846, 0.49 #969), JavaSea (0.33 #601, 0.33 #554, 0.32 #694), SouthChinaSea (0.31 #1077, 0.29 #1029, 0.27 #846), SulawesiSea (0.27 #852, 0.24 #1035, 0.23 #1083), MediterraneanSea (0.26 #517, 0.20 #2179, 0.20 #1436), Shikoku (0.26 #137, 0.26 #136, 0.22 #90)
>> best conf = 0.88
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #499 for best value:
>> intensional similarity = 8
>> extensional distance = 15
>> proper extension:  Guadalcanal; Bougainville;
>> query: (?x2398, ?x282) <- ?x2398[ a Island; has belongsToIslands ?x1212[ a Islands; is belongsToIslands of ?x451[ a Island; has locatedInWater ?x282<PacificOcean>; is locatedOnIsland of ?x1456;];];]
>> Best rule #472 for best value:
>> intensional similarity = 8
>> extensional distance = 15
>> proper extension:  Guadalcanal; Bougainville;
>> query: (?x2398, PacificOcean) <- ?x2398[ a Island; has belongsToIslands ?x1212[ a Islands; is belongsToIslands of ?x451[ a Island; has locatedInWater ?x282<PacificOcean>; is locatedOnIsland of ?x1456;];];]
ranks of expected_values: 1
EVAL	Shikoku	locatedInWater	PacificOcean	CNN-1.+1._MA	1.000	1.000	1.000	1.000	100.000	99.000	49.000	0.882	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#71-BOL
PRED entity: BOL

PRED relation: locatedIn!
PRED expected values: Sajama
=> 45 concepts (41 used for prediction)
PRED predicted values (max 10 best out of 1379): PacificOcean (0.67 #2903, 0.57 #85, 0.56 #4312), CaribbeanSea (0.50 #4332, 0.44 #2923, 0.25 #9968), AtlanticOcean (0.39 #9905, 0.39 #4269, 0.32 #26815), Llullaillaco (0.35 #22546, 0.29 #926, 0.11 #5153), OjosdelSalado (0.35 #22546, 0.29 #506, 0.11 #4733), Mantaro (0.35 #22546, 0.14 #1393, 0.09 #21136), Amazonas (0.35 #22546, 0.14 #1279, 0.09 #21136), Maranon (0.35 #22546, 0.14 #1273, 0.09 #21136), Ucayali (0.35 #22546, 0.14 #951, 0.09 #21136), Perene (0.35 #22546, 0.14 #735, 0.09 #21136)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2903 for best value:
>> intensional similarity = 6
>> extensional distance = 16
>> proper extension:  PY; ES;
>> query: (?x690, PacificOcean) <- ?x690[ has encompassed ?x521; has ethnicGroup ?x197; has language ?x796<Spanish>; has neighbor ?x202; is locatedIn of ?x274;]

*> Best rule #22546 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 52
*> proper extension:  BIH; ET; R; GB; LS; MNE; RL; D; TAD; KGZ; ...
*> query: (?x690, ?x209) <- ?x690[ has ethnicGroup ?x2333[ a EthnicGroup;]; has neighbor ?x202; is locatedIn of ?x1120[ has inMountains ?x431[ is inMountains of ?x209;];];]
*> conf = 0.35
ranks of expected_values: 24
EVAL	BOL	locatedIn!	Sajama	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.042	45.000	41.000	1379.000	0.667	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Sajama
=> 128 concepts (128 used for prediction)
PRED predicted values (max 10 best out of 1420): PacificOcean (0.91 #60732, 0.71 #8545, 0.65 #35348), AtlanticOcean (0.89 #93147, 0.71 #29621, 0.71 #28253), Llullaillaco (0.77 #11280, 0.41 #31032, 0.33 #5158), OjosdelSalado (0.77 #11280, 0.41 #31032, 0.33 #4738), Ampato (0.77 #11280, 0.41 #31032, 0.33 #1734), Amazonas (0.77 #11280, 0.41 #31032, 0.33 #2693), Maranon (0.77 #11280, 0.41 #31032, 0.33 #2687), Ucayali (0.77 #11280, 0.41 #31032, 0.33 #2365), Perene (0.77 #11280, 0.41 #31032, 0.33 #2149), Tambo (0.77 #11280, 0.41 #31032, 0.33 #1578)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #60732 for best value:
>> intensional similarity = 8
>> extensional distance = 20
>> proper extension:  NORF;
>> query: (?x690, PacificOcean) <- ?x690[ a Country; has encompassed ?x521; has government ?x2135; has religion ?x352<RomanCatholic>; is locatedIn of ?x480[ has locatedIn ?x296<PE>;];]

*> Best rule #11280 for first EXPECTED value:
*> intensional similarity = 12
*> extensional distance = 6
*> proper extension:  R; TR;
*> query: (?x690, ?x209) <- ?x690[ has encompassed ?x521; has ethnicGroup ?x197; has language ?x702; is locatedIn of ?x274[ a Lake; has type ?x762;]; is locatedIn of ?x1120[ a Source; has inMountains ?x431[ is inMountains of ?x209;];]; is neighbor of ?x379[ has government ?x435;];]
*> conf = 0.77
ranks of expected_values: 23
EVAL	BOL	locatedIn!	Sajama	CNN-1.+1._MA	0.000	0.000	0.000	0.043	128.000	128.000	1420.000	0.909	http://www.semwebtech.org/mondial/10/meta#locatedIn

#70-Sporades
PRED entity: Sporades

PRED relation: belongsToIslands!
PRED expected values: Lesbos
=> 16 concepts (16 used for prediction)
PRED predicted values (max 10 best out of 209): Mykonos (0.25 #127, 0.20 #516, 0.20 #321), Korfu (0.25 #116, 0.20 #505, 0.20 #310), Syros (0.25 #108, 0.20 #497, 0.20 #302), Zakynthos (0.25 #72, 0.20 #461, 0.20 #266), Lefkas (0.25 #16, 0.20 #405, 0.20 #210), Ibiza (0.20 #381, 0.17 #771, 0.12 #1365), Formentera (0.20 #336, 0.17 #726, 0.12 #1365), Mallorca (0.20 #205, 0.17 #595, 0.12 #1365), Gozo (0.20 #442, 0.12 #1365, 0.11 #1169), Lesbos (0.18 #2540, 0.17 #2736, 0.17 #2148)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #127 for best value:
>> intensional similarity = 7
>> extensional distance = 2
>> proper extension:  IonicIslands; Kyklades;
>> query: (?x1053, Mykonos) <- ?x1053[ a Islands; is belongsToIslands of ?x1063[ a Island; has locatedIn ?x399<GR>; has locatedInWater ?x275<MediterraneanSea>;];]

*> Best rule #2540 for first EXPECTED value:
*> intensional similarity = 12
*> extensional distance = 55
*> proper extension:  TongaIslands; Comores; Carolines; Maldives; CocosIslands;
*> query: (?x1053, ?x398) <- ?x1053[ a Islands; is belongsToIslands of ?x505[ a Island; has locatedIn ?x399[ has government ?x1174; is locatedIn of ?x398;];]; is belongsToIslands of ?x1831[ a Island; has locatedInWater ?x275[ has locatedIn ?x55; is flowsInto of ?x698; is mergesWith of ?x182;];];]
*> conf = 0.18
ranks of expected_values: 10
EVAL	Sporades	belongsToIslands!	Lesbos	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.100	16.000	16.000	209.000	0.250	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

PRED relation: belongsToIslands!
PRED expected values: Lesbos
=> 34 concepts (34 used for prediction)
PRED predicted values (max 10 best out of 209): Mykonos (0.29 #2738, 0.27 #1759, 0.25 #127), Korfu (0.29 #2738, 0.27 #1759, 0.25 #116), Syros (0.29 #2738, 0.27 #1759, 0.25 #108), Zakynthos (0.29 #2738, 0.27 #1759, 0.25 #72), Lefkas (0.29 #2738, 0.27 #1759, 0.25 #16), Lesbos (0.29 #2738, 0.27 #1759, 0.22 #1367), Athos (0.29 #2738, 0.27 #1759, 0.22 #1367), Psiloritis (0.29 #2738, 0.27 #1759, 0.22 #1367), Kreta (0.29 #2738, 0.27 #1759, 0.22 #1367), Olymp (0.29 #2738, 0.27 #1759, 0.22 #1367)
>> best conf = 0.29
=> the first rule below is the first best rule for 12 predicted values
>> Best rule #2738 for best value:
>> intensional similarity = 18
>> extensional distance = 11
>> proper extension:  Maldives;
>> query: (?x1053, ?x398) <- ?x1053[ a Islands; is belongsToIslands of ?x505[ a Island; has locatedInWater ?x275[ a Sea; has locatedIn ?x204[ a Country;]; has mergesWith ?x182<AtlanticOcean>; is flowsInto of ?x698;];]; is belongsToIslands of ?x1063[ a Island; has locatedIn ?x399[ a Country; has encompassed ?x195; has government ?x1174; has wasDependentOf ?x1656; is locatedIn of ?x398;];];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 6
EVAL	Sporades	belongsToIslands!	Lesbos	CNN-1.+1._MA	0.000	0.000	1.000	0.167	34.000	34.000	209.000	0.294	http://www.semwebtech.org/mondial/10/meta#belongsToIslands

#69-NevadodelHuila
PRED entity: NevadodelHuila

PRED relation: inMountains
PRED expected values: Andes
=> 39 concepts (39 used for prediction)
PRED predicted values (max 10 best out of 42): Andes (0.33 #11, 0.27 #272, 0.26 #533), CordilleraVolcanica (0.33 #326, 0.09 #674, 0.07 #761), SierraNevadadeSantaMarta (0.25 #162, 0.08 #249, 0.06 #423), CordilleradeTalamanca (0.13 #286, 0.11 #460, 0.04 #634), RockyMountains (0.11 #1486, 0.06 #1660, 0.05 #2008), EastAfricanRift (0.07 #985, 0.06 #1072, 0.05 #1246), SierraMadre (0.07 #311, 0.02 #659, 0.02 #746), Alps (0.06 #1744, 0.05 #2005, 0.05 #1918), SierraParima (0.06 #428, 0.02 #776, 0.01 #950), CanaryIslands (0.05 #839, 0.04 #1013, 0.03 #1100)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #11 for best value:
>> intensional similarity = 4
>> extensional distance = 1
>> proper extension:  NevadodelRuiz;
>> query: (?x663, Andes) <- ?x663[ a Mountain; a Volcano; has locatedIn ?x215<CO>;]
ranks of expected_values: 1
EVAL	NevadodelHuila	inMountains	Andes	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	39.000	39.000	42.000	0.333	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains
PRED expected values: Andes
=> 148 concepts (148 used for prediction)
PRED predicted values (max 10 best out of 69): Andes (0.67 #784, 0.43 #2091, 0.35 #1753), SierraNevadadeSantaMarta (0.67 #784, 0.43 #2091, 0.25 #162), CordilleraVolcanica (0.40 #239, 0.36 #326, 0.33 #587), RockyMountains (0.24 #2794, 0.18 #4273, 0.12 #6712), CordilleradeTalamanca (0.18 #373, 0.17 #547, 0.15 #721), EastAfricanRift (0.11 #3250, 0.10 #2031, 0.08 #3772), SierraMadre (0.10 #224, 0.08 #572, 0.08 #485), CanaryIslands (0.09 #2321, 0.06 #3278, 0.06 #3365), Alps (0.09 #7405, 0.07 #7927, 0.07 #8101), EliasRange (0.09 #2802, 0.08 #4455, 0.06 #4281)
>> best conf = 0.67
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #784 for best value:
>> intensional similarity = 11
>> extensional distance = 11
>> proper extension:  PicoTurquino;
>> query: (?x663, ?x431) <- ?x663[ a Mountain; has locatedIn ?x215[ has ethnicGroup ?x676<Mestizo>; has language ?x796; has religion ?x352; is locatedIn of ?x344[ has locatedIn ?x345<YV>;]; is locatedIn of ?x1717[ has inMountains ?x431;];];]
ranks of expected_values: 1
EVAL	NevadodelHuila	inMountains	Andes	CNN-1.+1._MA	1.000	1.000	1.000	1.000	148.000	148.000	69.000	0.667	http://www.semwebtech.org/mondial/10/meta#inMountains

#68-JOR
PRED entity: JOR

PRED relation: neighbor!
PRED expected values: SA
=> 44 concepts (44 used for prediction)
PRED predicted values (max 10 best out of 181): SA (0.90 #5231, 0.90 #2843, 0.90 #4435), JOR (0.50 #437, 0.40 #594, 0.33 #123), TR (0.33 #1261, 0.33 #1134, 0.32 #1262), IR (0.32 #1262, 0.28 #1155, 0.27 #2528), RL (0.32 #1262, 0.27 #2528, 0.26 #3163), CN (0.29 #1306, 0.11 #1147, 0.11 #2210), BG (0.28 #1131, 0.09 #3955, 0.09 #3640), ET (0.27 #2528, 0.26 #3163, 0.26 #5072), GAZA (0.27 #2528, 0.26 #3163, 0.26 #5072), KWT (0.27 #2528, 0.26 #3163, 0.26 #5232)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5231 for best value:
>> intensional similarity = 4
>> extensional distance = 146
>> proper extension:  PK; SSD; MEL;
>> query: (?x803, ?x751) <- ?x803[ has neighbor ?x751[ a Country; has ethnicGroup ?x244;]; is locatedIn of ?x419;]
ranks of expected_values: 1
EVAL	JOR	neighbor!	SA	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	44.000	44.000	181.000	0.898	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: SA
=> 82 concepts (82 used for prediction)
PRED predicted values (max 10 best out of 187): SA (0.95 #5425, 0.94 #5427, 0.94 #4593), TR (0.62 #3479, 0.40 #1505, 0.40 #1178), JOR (0.60 #1270, 0.56 #6410, 0.51 #8554), RL (0.56 #6410, 0.51 #8554, 0.50 #8719), OM (0.50 #1147, 0.48 #2459, 0.46 #2624), YE (0.50 #1147, 0.48 #2459, 0.46 #2624), UAE (0.50 #1147, 0.48 #2459, 0.46 #2624), IR (0.45 #3169, 0.43 #2182, 0.39 #1308), ET (0.41 #2288, 0.39 #1308, 0.38 #1310), SUD (0.41 #2288, 0.33 #3110, 0.30 #3275)
>> best conf = 0.95
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5425 for best value:
>> intensional similarity = 13
>> extensional distance = 22
>> proper extension:  E;
>> query: (?x803, ?x568) <- ?x803[ has neighbor ?x466[ is locatedIn of ?x275<MediterraneanSea>;]; has neighbor ?x568[ has ethnicGroup ?x852;]; has neighbor ?x751[ has ethnicGroup ?x244; has language ?x1848;]; has religion ?x187[ is religion of ?x204<AL>;]; is locatedIn of ?x1552[ a Sea;];]
ranks of expected_values: 1
EVAL	JOR	neighbor!	SA	CNN-1.+1._MA	1.000	1.000	1.000	1.000	82.000	82.000	187.000	0.951	http://www.semwebtech.org/mondial/10/meta#neighbor

#67-RB
PRED entity: RB

PRED relation: neighbor!
PRED expected values: NAM
=> 43 concepts (39 used for prediction)
PRED predicted values (max 10 best out of 208): NAM (0.90 #2734, 0.90 #2735, 0.90 #3382), Z (0.50 #411, 0.50 #250, 0.33 #89), ZRE (0.33 #60, 0.25 #542, 0.10 #702), RCB (0.33 #90, 0.07 #3702, 0.06 #1055), ANG (0.29 #1768, 0.29 #1930, 0.27 #5632), RB (0.29 #1768, 0.29 #1930, 0.27 #5632), MOC (0.29 #1768, 0.29 #1930, 0.27 #5632), LS (0.29 #1768, 0.29 #1930, 0.27 #5632), SD (0.29 #1768, 0.29 #1930, 0.27 #5632), EAT (0.25 #613, 0.25 #453, 0.16 #1096)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2734 for best value:
>> intensional similarity = 6
>> extensional distance = 83
>> proper extension:  ROK;
>> query: (?x1239, ?x243) <- ?x1239[ has neighbor ?x138[ has language ?x247;]; has neighbor ?x243; has wasDependentOf ?x81; is locatedIn of ?x242; is neighbor of ?x1576;]
ranks of expected_values: 1
EVAL	RB	neighbor!	NAM	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	43.000	39.000	208.000	0.905	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: NAM
=> 101 concepts (101 used for prediction)
PRED predicted values (max 10 best out of 231): NAM (0.93 #5113, 0.93 #9929, 0.93 #7267), RG (0.57 #2749, 0.25 #4567, 0.19 #5225), MOC (0.54 #5773, 0.50 #1512, 0.50 #1479), Z (0.54 #5773, 0.50 #1477, 0.50 #1403), RB (0.54 #5773, 0.46 #5775, 0.46 #9933), EAT (0.54 #5773, 0.43 #2474, 0.40 #1776), SD (0.54 #5773, 0.43 #2474, 0.40 #1679), ZRE (0.54 #5773, 0.43 #2474, 0.40 #1872), LS (0.54 #5773, 0.43 #2474, 0.36 #3959), MW (0.54 #5773, 0.43 #2474, 0.33 #2441)
>> best conf = 0.93
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5113 for best value:
>> intensional similarity = 17
>> extensional distance = 14
>> proper extension:  GCA; RCH; PA; EC; HCA;
>> query: (?x1239, ?x138) <- ?x1239[ a Country; has government ?x1174; has neighbor ?x138[ a Country; has language ?x247; has neighbor ?x525; has religion ?x95<Protestant>; is locatedIn of ?x137;]; has religion ?x116; is locatedIn of ?x242[ has locatedIn ?x192[ has ethnicGroup ?x197; has neighbor ?x193; is locatedIn of ?x60<IndianOcean>;];]; is neighbor of ?x1576;]
ranks of expected_values: 1
EVAL	RB	neighbor!	NAM	CNN-1.+1._MA	1.000	1.000	1.000	1.000	101.000	101.000	231.000	0.926	http://www.semwebtech.org/mondial/10/meta#neighbor

#66-Montserrat
PRED entity: Montserrat

PRED relation: locatedOnIsland!
PRED expected values: SoufriereHills
=> 39 concepts (39 used for prediction)
PRED predicted values (max 10 best out of 37): MorneDiablotins (0.12 #189, 0.11 #253, 0.07 #317), Pelee (0.12 #185, 0.11 #249, 0.07 #313), Soufriere (0.12 #183, 0.11 #247, 0.07 #311), PicoTurquino (0.07 #315, 0.06 #379, 0.05 #443), PicoDuarte (0.06 #323, 0.05 #451, 0.04 #580), CerrodePunta (0.05 #394, 0.05 #458, 0.04 #587), LaSoufriere (0.05 #421, 0.04 #614, 0.03 #742), BlueMountainPeak (0.05 #484, 0.04 #613, 0.01 #805), RoquedelosMuchachos (0.03 #695, 0.03 #759, 0.01 #823), PicodeTeide (0.03 #668, 0.03 #732, 0.01 #796)
>> best conf = 0.12
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #189 for best value:
>> intensional similarity = 6
>> extensional distance = 6
>> proper extension:  Tortola;
>> query: (?x817, MorneDiablotins) <- ?x817[ has locatedIn ?x1444; has locatedInWater ?x182<AtlanticOcean>; has locatedInWater ?x317<CaribbeanSea>; has type ?x150;]

*> Best rule #577 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 23
*> proper extension:  Irazu; NevadodelHuila; LakeIrazu; Citlaltepetl-PicodeOrizaba-; LaSoufriere; CerroChirripo; Concepcion; Iztaccihuatl; Soufriere; Pelee; ...
*> query: (?x817, ?x182) <- ?x817[ has locatedIn ?x1444[ a Country; is locatedIn of ?x182; is locatedIn of ?x317<CaribbeanSea>;]; has type ?x150;]
*> conf = 0.01
ranks of expected_values: 34
EVAL	Montserrat	locatedOnIsland!	SoufriereHills	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.029	39.000	39.000	37.000	0.125	http://www.semwebtech.org/mondial/10/meta#locatedOnIsland

PRED relation: locatedOnIsland!
PRED expected values: SoufriereHills
=> 108 concepts (108 used for prediction)
PRED predicted values (max 10 best out of 56): PicoTurquino (0.17 #379, 0.12 #636, 0.10 #831), SoufriereHills (0.14 #837, 0.14 #1550, 0.12 #967), Pelee (0.14 #570, 0.12 #699, 0.08 #959), Soufriere (0.14 #568, 0.12 #697, 0.08 #957), MorneDiablotins (0.14 #574, 0.12 #703, 0.08 #1028), PicoDuarte (0.12 #580, 0.10 #775, 0.09 #840), Montserrat (0.12 #967, 0.10 #1356, 0.08 #642), CaribbeanSea (0.10 #1356, 0.08 #642, 0.06 #902), AtlanticOcean (0.10 #1356, 0.08 #642, 0.06 #902), CerrodePunta (0.06 #1105, 0.05 #1366, 0.05 #1301)
>> best conf = 0.17
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #379 for best value:
>> intensional similarity = 10
>> extensional distance = 4
>> proper extension:  Cuba;
>> query: (?x817, PicoTurquino) <- ?x817[ a Island; has belongsToIslands ?x877; has locatedIn ?x1444[ a Country; has encompassed ?x521; has language ?x247;]; has locatedInWater ?x182<AtlanticOcean>; has locatedInWater ?x317<CaribbeanSea>;]

*> Best rule #837 for first EXPECTED value:
*> intensional similarity = 10
*> extensional distance = 8
*> proper extension:  PicoDuarte; GulfofMexico; PicoTurquino;
*> query: (?x817, ?x2234) <- ?x817[ has locatedIn ?x1444[ a Country; has government ?x562; has language ?x247; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x317<CaribbeanSea>; is locatedIn of ?x2234[ a Mountain;];];]
*> conf = 0.14
ranks of expected_values: 2
EVAL	Montserrat	locatedOnIsland!	SoufriereHills	CNN-1.+1._MA	0.000	1.000	1.000	0.500	108.000	108.000	56.000	0.167	http://www.semwebtech.org/mondial/10/meta#locatedOnIsland

#65-S
PRED entity: S

PRED relation: locatedIn!
PRED expected values: BalticSea Vaesterdalaelv Kattegat Oesterdalaelv Vaettern
=> 29 concepts (28 used for prediction)
PRED predicted values (max 10 best out of 1389): Klaraelv (0.69 #2796, 0.09 #12577, 0.08 #32145), Oesterdalaelv (0.69 #2796), AtlanticOcean (0.41 #5632, 0.38 #36380, 0.36 #27992), PacificOcean (0.31 #12662, 0.28 #22445, 0.28 #2881), Glomma (0.26 #9782, 0.21 #20963, 0.14 #23758), Vaesterdalaelv (0.26 #9782, 0.21 #20963, 0.14 #23758), CaribbeanSea (0.21 #18273, 0.20 #5695, 0.19 #25260), NorthSea (0.17 #22, 0.11 #39135, 0.11 #37736), MediterraneanSea (0.16 #8466, 0.16 #14057, 0.15 #15454), Donau (0.15 #4219, 0.14 #7013, 0.12 #1424)
>> best conf = 0.69
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #2796 for best value:
>> intensional similarity = 6
>> extensional distance = 46
>> proper extension:  AUS; CDN; IS;
>> query: (?x402, ?x1603) <- ?x402[ is locatedIn of ?x191[ a Estuary;]; is locatedIn of ?x401[ a Source;]; is locatedIn of ?x2395[ has hasSource ?x1603;];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 2, 6, 12, 82
EVAL	S	locatedIn!	Vaettern	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	29.000	28.000	1389.000	0.687	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	S	locatedIn!	Oesterdalaelv	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	29.000	28.000	1389.000	0.687	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	S	locatedIn!	Kattegat	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.100	29.000	28.000	1389.000	0.687	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	S	locatedIn!	Vaesterdalaelv	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.200	29.000	28.000	1389.000	0.687	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	S	locatedIn!	BalticSea	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.013	29.000	28.000	1389.000	0.687	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: BalticSea Vaesterdalaelv Kattegat Oesterdalaelv Vaettern
=> 111 concepts (111 used for prediction)
PRED predicted values (max 10 best out of 1402): Vaesterdalaelv (0.94 #82599, 0.93 #53196, 0.93 #77001), BalticSea (0.93 #99412, 0.93 #130221, 0.93 #96612), AtlanticOcean (0.91 #117655, 0.89 #84046, 0.87 #70040), Kattegat (0.82 #26601, 0.74 #43400, 0.70 #102214), Klaraelv (0.65 #6999, 0.61 #68597, 0.61 #68596), Oesterdalaelv (0.65 #6999, 0.61 #68597, 0.61 #68596), NorthSea (0.50 #4222, 0.40 #5623, 0.38 #12599), PacificOcean (0.46 #50483, 0.43 #7085, 0.39 #47686), Glomma (0.40 #43401, 0.36 #102215, 0.35 #77002), NorwegianSea (0.40 #5736, 0.33 #2936, 0.31 #93814)
>> best conf = 0.94
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #82599 for best value:
>> intensional similarity = 15
>> extensional distance = 33
>> proper extension:  RB;
>> query: (?x402, ?x1118) <- ?x402[ a Country; has ethnicGroup ?x1473; has neighbor ?x170[ has government ?x92; is locatedIn of ?x121;]; has religion ?x95; is locatedIn of ?x1119[ a Estuary; is hasEstuary of ?x1118;]; is locatedIn of ?x1328[ a River;]; is locatedIn of ?x1664[ is flowsInto of ?x1446;]; is locatedIn of ?x1992[ has type ?x1424;];]
ranks of expected_values: 1, 2, 4, 6
EVAL	S	locatedIn!	Vaettern	CNN-1.+1._MA	0.000	0.000	0.000	0.000	111.000	111.000	1402.000	0.940	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	S	locatedIn!	Oesterdalaelv	CNN-1.+1._MA	0.000	1.000	1.000	0.333	111.000	111.000	1402.000	0.940	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	S	locatedIn!	Kattegat	CNN-1.+1._MA	0.000	1.000	1.000	0.500	111.000	111.000	1402.000	0.940	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	S	locatedIn!	Vaesterdalaelv	CNN-1.+1._MA	1.000	1.000	1.000	1.000	111.000	111.000	1402.000	0.940	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	S	locatedIn!	BalticSea	CNN-1.+1._MA	1.000	1.000	1.000	1.000	111.000	111.000	1402.000	0.940	http://www.semwebtech.org/mondial/10/meta#locatedIn

#64-MOC
PRED entity: MOC

PRED relation: locatedIn!
PRED expected values: Zambezi
=> 44 concepts (40 used for prediction)
PRED predicted values (max 10 best out of 1384): Zambezi (0.85 #18400, 0.80 #16983, 0.67 #38216), AtlanticOcean (0.80 #15608, 0.42 #14193, 0.40 #22688), PacificOcean (0.36 #12821, 0.33 #4329, 0.31 #34053), Kalahari (0.33 #7542, 0.33 #1881, 0.33 #466), Okavango (0.33 #7874, 0.33 #2213, 0.20 #6459), AndamanSea (0.33 #8609, 0.29 #11439, 0.11 #56620), LakeVictoria (0.33 #9136, 0.29 #11966, 0.10 #18399), Cuilo (0.33 #1875, 0.20 #6121, 0.08 #16027), Kasai (0.33 #1631, 0.20 #5877, 0.08 #15783), Cuango (0.33 #1580, 0.20 #5826, 0.08 #15732)
>> best conf = 0.85
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #18400 for best value:
>> intensional similarity = 5
>> extensional distance = 26
>> proper extension:  F; D; TAD; I; CH; S; A; BR;
>> query: (?x192, ?x1977) <- ?x192[ is locatedIn of ?x387[ is flowsThrough of ?x1977;]; is neighbor of ?x820[ has ethnicGroup ?x1233; is locatedIn of ?x284;];]
ranks of expected_values: 1
EVAL	MOC	locatedIn!	Zambezi	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	44.000	40.000	1384.000	0.854	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Zambezi
=> 86 concepts (86 used for prediction)
PRED predicted values (max 10 best out of 1413): AtlanticOcean (0.93 #69507, 0.90 #48240, 0.75 #29814), PacificOcean (0.73 #51119, 0.66 #55372, 0.42 #65297), SouthChinaSea (0.56 #15598, 0.47 #42668, 0.45 #35580), MalakkaStrait (0.56 #15598, 0.40 #18580, 0.38 #18437), SulawesiSea (0.56 #15598, 0.23 #22688, 0.22 #56708), SuluSea (0.56 #15598, 0.23 #22688, 0.22 #56708), Borneo (0.56 #15598, 0.23 #22688, 0.20 #18575), Kinabalu (0.56 #15598, 0.23 #22688, 0.20 #19297), Labuan (0.56 #15598, 0.23 #22688, 0.20 #19181), Tahan (0.56 #15598, 0.23 #22688, 0.20 #18480)
>> best conf = 0.93
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #69507 for best value:
>> intensional similarity = 13
>> extensional distance = 65
>> proper extension:  SBAR;
>> query: (?x192, AtlanticOcean) <- ?x192[ a Country; has government ?x435; is locatedIn of ?x242[ has locatedIn ?x243<RSA>; has locatedIn ?x1239[ has encompassed ?x213; has ethnicGroup ?x2322; has government ?x1174; has neighbor ?x138<NAM>; is locatedIn of ?x1437<Okavango>;];];]

*> Best rule #32322 for first EXPECTED value:
*> intensional similarity = 15
*> extensional distance = 7
*> proper extension:  RB;
*> query: (?x192, Zambezi) <- ?x192[ a Country; has neighbor ?x1576; is locatedIn of ?x1048[ a Lake;]; is neighbor of ?x525[ has encompassed ?x213; has ethnicGroup ?x162; has government ?x435; has religion ?x116; is locatedIn of ?x1676<LakeKariba>;]; is neighbor of ?x820[ is locatedIn of ?x1194[ a River;]; is neighbor of ?x348;];]
*> conf = 0.44
ranks of expected_values: 38
EVAL	MOC	locatedIn!	Zambezi	CNN-1.+1._MA	0.000	0.000	0.000	0.026	86.000	86.000	1413.000	0.925	http://www.semwebtech.org/mondial/10/meta#locatedIn

#63-Lulua
PRED entity: Lulua

PRED relation: locatedIn
PRED expected values: ZRE
=> 34 concepts (34 used for prediction)
PRED predicted values (max 10 best out of 62): ZRE (0.88 #1664, 0.88 #1505, 0.88 #1188), ANG (0.65 #950, 0.64 #1187, 0.61 #1663), USA (0.21 #546, 0.19 #1260, 0.14 #1736), D (0.19 #2398, 0.16 #4061, 0.15 #2160), BR (0.14 #1789, 0.14 #2027, 0.12 #1313), P (0.14 #671, 0.12 #1385, 0.10 #1861), F (0.10 #1671, 0.09 #1909, 0.05 #2147), R (0.09 #5706, 0.09 #7602, 0.09 #6182), E (0.09 #1929, 0.07 #501, 0.06 #1215), PE (0.09 #3871, 0.08 #4346, 0.08 #3396)
>> best conf = 0.88
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1664 for best value:
>> intensional similarity = 9
>> extensional distance = 15
>> proper extension:  Uelle;
>> query: (?x2406, ?x348) <- ?x2406[ a Estuary; is hasEstuary of ?x1057[ a River; has flowsInto ?x509[ a River; has flowsInto ?x113; has hasSource ?x1076; has locatedIn ?x348<ZRE>;];];]
>> Best rule #1505 for best value:
>> intensional similarity = 9
>> extensional distance = 15
>> proper extension:  Uelle;
>> query: (?x2406, ZRE) <- ?x2406[ a Estuary; is hasEstuary of ?x1057[ a River; has flowsInto ?x509[ a River; has flowsInto ?x113; has hasSource ?x1076; has locatedIn ?x348<ZRE>;];];]
ranks of expected_values: 1
EVAL	Lulua	locatedIn	ZRE	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	34.000	34.000	62.000	0.882	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: ZRE
=> 31 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 62): ZRE (0.88 #1427, 0.88 #1268, 0.88 #1189), ANG (0.65 #951, 0.65 #713, 0.64 #1188), D (0.19 #2399, 0.16 #1923, 0.15 #2161), USA (0.14 #1499, 0.14 #1737, 0.09 #5300), BR (0.14 #1552, 0.14 #1790, 0.03 #6540), F (0.10 #1434, 0.09 #1672, 0.05 #1910), P (0.10 #1624, 0.09 #1862, 0.02 #5425), R (0.09 #5233, 0.09 #6892, 0.09 #5471), E (0.09 #1692, 0.05 #1454, 0.02 #5255), PE (0.09 #3397, 0.08 #3872, 0.08 #3159)
>> best conf = 0.88
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1427 for best value:
>> intensional similarity = 9
>> extensional distance = 15
>> proper extension:  Uelle;
>> query: (?x2406, ?x348) <- ?x2406[ a Estuary; is hasEstuary of ?x1057[ a River; has flowsInto ?x509[ a River; has flowsInto ?x113; has hasSource ?x1076; has locatedIn ?x348<ZRE>;];];]
>> Best rule #1268 for best value:
>> intensional similarity = 9
>> extensional distance = 15
>> proper extension:  Uelle;
>> query: (?x2406, ZRE) <- ?x2406[ a Estuary; is hasEstuary of ?x1057[ a River; has flowsInto ?x509[ a River; has flowsInto ?x113; has hasSource ?x1076; has locatedIn ?x348<ZRE>;];];]
ranks of expected_values: 1
EVAL	Lulua	locatedIn	ZRE	CNN-1.+1._MA	1.000	1.000	1.000	1.000	31.000	31.000	62.000	0.882	http://www.semwebtech.org/mondial/10/meta#locatedIn

#62-CN
PRED entity: CN

PRED relation: neighbor
PRED expected values: LAO MNG
=> 33 concepts (33 used for prediction)
PRED predicted values (max 10 best out of 191): LAO (0.89 #3998, 0.87 #3554, 0.87 #3553), BY (0.44 #1220, 0.40 #776, 0.40 #628), SK (0.40 #757, 0.29 #905, 0.22 #1201), CN (0.35 #592, 0.33 #335, 0.33 #186), MNG (0.35 #592, 0.33 #297, 0.33 #278), UA (0.35 #592, 0.33 #195, 0.31 #2666), AZ (0.35 #592, 0.33 #200, 0.31 #2666), PL (0.35 #592, 0.33 #177, 0.31 #2666), LT (0.35 #592, 0.33 #276, 0.31 #2666), LV (0.35 #592, 0.33 #218, 0.31 #2666)
>> best conf = 0.89
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3998 for best value:
>> intensional similarity = 4
>> extensional distance = 138
>> proper extension:  RSM; V;
>> query: (?x232, ?x463) <- ?x232[ has government ?x831; has neighbor ?x73[ is locatedIn of ?x72;]; is neighbor of ?x463;]
ranks of expected_values: 1, 5
EVAL	CN	neighbor	MNG	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.250	33.000	33.000	191.000	0.886	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	CN	neighbor	LAO	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	33.000	33.000	191.000	0.886	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: LAO MNG
=> 80 concepts (80 used for prediction)
PRED predicted values (max 10 best out of 230): LAO (0.90 #10204, 0.90 #7481, 0.90 #6725), CN (0.60 #1225, 0.49 #8235, 0.35 #3767), K (0.49 #8235, 0.33 #1457, 0.32 #444), BD (0.49 #8235, 0.32 #444, 0.31 #4479), THA (0.49 #8235, 0.32 #444, 0.31 #4479), UZB (0.40 #1081, 0.33 #43, 0.32 #444), I (0.38 #1815, 0.27 #2413, 0.08 #5561), AZ (0.33 #496, 0.33 #348, 0.32 #444), BY (0.33 #332, 0.32 #444, 0.31 #2719), TM (0.33 #488, 0.32 #444, 0.31 #4479)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #10204 for best value:
>> intensional similarity = 11
>> extensional distance = 104
>> proper extension:  ARM;
>> query: (?x232, ?x111) <- ?x232[ has religion ?x187[ is religion of ?x1206[ has government ?x2531; has wasDependentOf ?x78;];]; is neighbor of ?x111[ has religion ?x410; is locatedIn of ?x110;]; is neighbor of ?x130[ has ethnicGroup ?x58; has language ?x555; is neighbor of ?x277;];]
ranks of expected_values: 1, 17
EVAL	CN	neighbor	MNG	CNN-1.+1._MA	0.000	0.000	0.000	0.062	80.000	80.000	230.000	0.899	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	CN	neighbor	LAO	CNN-1.+1._MA	1.000	1.000	1.000	1.000	80.000	80.000	230.000	0.899	http://www.semwebtech.org/mondial/10/meta#neighbor

#61-Black
PRED entity: Black

PRED relation: ethnicGroup!
PRED expected values: CAYM
=> 23 concepts (11 used for prediction)
PRED predicted values (max 10 best out of 189): CAYM (0.50 #152, 0.17 #343, 0.08 #534), CR (0.25 #250, 0.17 #441, 0.16 #632), HELX (0.25 #39, 0.17 #230, 0.08 #421), NCA (0.21 #545, 0.12 #736, 0.05 #927), CO (0.17 #231, 0.12 #422, 0.12 #613), EC (0.17 #346, 0.12 #537, 0.12 #728), NIC (0.17 #270, 0.12 #461, 0.12 #652), SME (0.17 #220, 0.08 #411, 0.08 #602), GUY (0.17 #256, 0.08 #447, 0.08 #638), MART (0.17 #329, 0.08 #520, 0.04 #711)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #152 for best value:
>> intensional similarity = 8
>> extensional distance = 2
>> proper extension:  White; Mixed;
>> query: (?x1009, CAYM) <- ?x1009[ a EthnicGroup; is ethnicGroup of ?x899[ has dependentOf ?x315; has government ?x2535; has religion ?x95; is locatedIn of ?x182;]; is ethnicGroup of ?x1008<BERM>;]
ranks of expected_values: 1
EVAL	Black	ethnicGroup!	CAYM	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	23.000	11.000	189.000	0.500	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup!
PRED expected values: CAYM
=> 57 concepts (52 used for prediction)
PRED predicted values (max 10 best out of 217): CR (0.57 #1020, 0.56 #1210, 0.40 #637), CO (0.57 #1001, 0.56 #1191, 0.33 #2153), CAYM (0.50 #536, 0.33 #152, 0.29 #921), EC (0.44 #1306, 0.43 #1116, 0.33 #346), C (0.44 #1169, 0.43 #979, 0.27 #2131), NIC (0.43 #1040, 0.33 #1230, 0.33 #270), BR (0.43 #1068, 0.33 #1258, 0.20 #2220), DOM (0.43 #1064, 0.33 #1254, 0.20 #2216), SME (0.40 #607, 0.33 #220, 0.29 #990), MART (0.40 #716, 0.22 #1289, 0.18 #1865)
>> best conf = 0.57
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1020 for best value:
>> intensional similarity = 19
>> extensional distance = 5
>> proper extension:  African; European; Mulatto;
>> query: (?x1009, CR) <- ?x1009[ a EthnicGroup; is ethnicGroup of ?x899[ has government ?x2535; is locatedIn of ?x317<CaribbeanSea>; is locatedIn of ?x665[ a Mountain;]; is locatedIn of ?x1557[ a Island;];]; is ethnicGroup of ?x1008[ a Country; has encompassed ?x521<America>; has religion ?x95<Protestant>; has religion ?x2441[ a Religion;]; is locatedIn of ?x182<AtlanticOcean>;];]

*> Best rule #536 for first EXPECTED value:
*> intensional similarity = 19
*> extensional distance = 2
*> proper extension:  Mixed;
*> query: (?x1009, CAYM) <- ?x1009[ is ethnicGroup of ?x80<BVIR>; is ethnicGroup of ?x407[ a Country; has dependentOf ?x81; has encompassed ?x521; has government ?x562<"British Overseas Territories">; has language ?x247<English>; has religion ?x95<Protestant>; has religion ?x713[ is religion of ?x758<SY>;]; is locatedIn of ?x317<CaribbeanSea>;]; is ethnicGroup of ?x1008<BERM>;]
*> conf = 0.50
ranks of expected_values: 3
EVAL	Black	ethnicGroup!	CAYM	CNN-1.+1._MA	0.000	1.000	1.000	0.333	57.000	52.000	217.000	0.571	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#60-WG
PRED entity: WG

PRED relation: wasDependentOf
PRED expected values: GB
=> 29 concepts (29 used for prediction)
PRED predicted values (max 10 best out of 49): GB (0.60 #4, 0.41 #239, 0.40 #359), E (0.40 #183, 0.33 #95, 0.33 #65), F (0.12 #455, 0.11 #269, 0.11 #32), RH (0.11 #48, 0.08 #107, 0.07 #768), P (0.08 #441, 0.07 #289, 0.07 #258), NL (0.08 #835, 0.07 #768, 0.02 #595), USA (0.08 #835), CO (0.07 #768, 0.07 #185, 0.03 #335), BR (0.07 #768, 0.04 #282, 0.04 #251), SovietUnion (0.07 #565, 0.07 #598, 0.06 #662)
>> best conf = 0.60
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4 for best value:
>> intensional similarity = 7
>> extensional distance = 3
>> proper extension:  WV;
>> query: (?x1073, GB) <- ?x1073[ a Country; has encompassed ?x521<America>; has government ?x1947<"parliamentary democracy and a Commonwealth realm">; is locatedIn of ?x317<CaribbeanSea>;]
ranks of expected_values: 1
EVAL	WG	wasDependentOf	GB	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	29.000	29.000	49.000	0.600	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

PRED relation: wasDependentOf
PRED expected values: GB
=> 81 concepts (81 used for prediction)
PRED predicted values (max 10 best out of 98): GB (0.67 #243, 0.60 #91, 0.50 #62), E (0.50 #750, 0.45 #851, 0.43 #682), F (0.20 #184, 0.16 #2223, 0.16 #1351), RH (0.20 #200, 0.16 #2223, 0.14 #811), CO (0.18 #1830, 0.13 #943, 0.12 #982), BR (0.16 #2223, 0.14 #811, 0.14 #2297), P (0.16 #2223, 0.14 #811, 0.14 #2297), UnitedNations (0.16 #2223, 0.14 #811, 0.14 #1941), NL (0.15 #984, 0.13 #947, 0.12 #982), USA (0.15 #984, 0.13 #947, 0.08 #1734)
>> best conf = 0.67
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #243 for best value:
>> intensional similarity = 17
>> extensional distance = 4
>> proper extension:  BZ;
>> query: (?x1073, GB) <- ?x1073[ a Country; has encompassed ?x521<America>; has ethnicGroup ?x162[ is ethnicGroup of ?x318<CR>; is ethnicGroup of ?x561[ a Country; is locatedIn of ?x182<AtlanticOcean>;]; is ethnicGroup of ?x621[ has language ?x247; has neighbor ?x651;]; is ethnicGroup of ?x1072[ has religion ?x116;];]; has government ?x1947<"parliamentary democracy and a Commonwealth realm">;]
ranks of expected_values: 1
EVAL	WG	wasDependentOf	GB	CNN-1.+1._MA	1.000	1.000	1.000	1.000	81.000	81.000	98.000	0.667	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

#59-Ischim
PRED entity: Ischim

PRED relation: locatedIn
PRED expected values: R
=> 42 concepts (38 used for prediction)
PRED predicted values (max 10 best out of 136): R (0.87 #1184, 0.75 #2364, 0.67 #707), CN (0.60 #236, 0.52 #944, 0.51 #1416), TAD (0.40 #1438, 0.25 #22, 0.17 #493), UZB (0.25 #64, 0.21 #1480, 0.20 #1008), USA (0.25 #6919, 0.18 #7155, 0.18 #3140), UA (0.19 #777, 0.07 #3138, 0.06 #6609), ZRE (0.18 #1966, 0.14 #2910, 0.11 #5506), TR (0.18 #2164, 0.04 #6888, 0.04 #3109), D (0.16 #1671, 0.11 #1907, 0.11 #2851), IR (0.16 #2194, 0.13 #2430, 0.05 #1015)
>> best conf = 0.87
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1184 for best value:
>> intensional similarity = 6
>> extensional distance = 28
>> proper extension:  KuybyshevReservoir; OzeroBaikal; OzeroLadoga; OzeroOnega; OzeroPskovskoje;
>> query: (?x1761, R) <- ?x1761[ has flowsInto ?x1748[ has locatedIn ?x73;]; has locatedIn ?x403[ has neighbor ?x130; is locatedIn of ?x127<Bjelucha>;];]
ranks of expected_values: 1
EVAL	Ischim	locatedIn	R	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	42.000	38.000	136.000	0.867	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: R
=> 130 concepts (128 used for prediction)
PRED predicted values (max 10 best out of 211): R (0.95 #12121, 0.94 #10929, 0.94 #12597), CN (0.77 #8378, 0.65 #4990, 0.63 #2376), USA (0.75 #16956, 0.43 #20757, 0.40 #20994), IR (0.74 #25443, 0.33 #6660, 0.33 #6493), UZB (0.74 #25443, 0.33 #64, 0.26 #25203), TM (0.74 #25443, 0.17 #6659, 0.14 #5466), AZ (0.74 #25443, 0.17 #6659, 0.12 #16404), TAD (0.47 #5013, 0.41 #5252, 0.33 #22), D (0.44 #5731, 0.43 #5967, 0.19 #6680), UA (0.43 #1015, 0.23 #1253, 0.21 #1490)
>> best conf = 0.95
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #12121 for best value:
>> intensional similarity = 10
>> extensional distance = 104
>> proper extension:  Morava; Donau; Mur; Amazonas; WesternBug; Limpopo; Tennessee; Pjandsh; Amudarja; Piva; ...
>> query: (?x1761, ?x73) <- ?x1761[ a River; has flowsInto ?x1748[ has locatedIn ?x232;]; has hasEstuary ?x2264[ a Estuary; has locatedIn ?x73[ has ethnicGroup ?x1550; has language ?x555;];]; has locatedIn ?x403[ has ethnicGroup ?x58;];]
ranks of expected_values: 1
EVAL	Ischim	locatedIn	R	CNN-1.+1._MA	1.000	1.000	1.000	1.000	130.000	128.000	211.000	0.949	http://www.semwebtech.org/mondial/10/meta#locatedIn

#58-Tennessee
PRED entity: Tennessee

PRED relation: inMountains
PRED expected values: AppalachianMountains
=> 34 concepts (34 used for prediction)
PRED predicted values (max 10 best out of 30): Alps (0.19 #178, 0.14 #265, 0.10 #700), Andes (0.15 #620, 0.08 #968, 0.08 #1055), EastAfricanRift (0.12 #463, 0.11 #550, 0.07 #724), SnowyMountains (0.08 #195, 0.07 #282, 0.07 #543), RockyMountains (0.07 #877, 0.07 #1138, 0.05 #2176), AppalachianMountains (0.06 #120, 0.06 #33, 0.05 #2176), Adirondacks (0.06 #105, 0.06 #18, 0.05 #2176), EliasRange (0.05 #2176, 0.02 #885, 0.02 #1146), Hawaii (0.05 #2176, 0.02 #938, 0.01 #1199), CascadeRange (0.05 #2176, 0.02 #911, 0.01 #1172)
>> best conf = 0.19
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #178 for best value:
>> intensional similarity = 5
>> extensional distance = 24
>> proper extension:  Volga; RioSanJuan; ColumbiaRiver; Niger; Ticino; Alz; Colorado; EucumbeneRiver; Angara; Aare; ...
>> query: (?x832, Alps) <- ?x832[ is hasSource of ?x268[ a River; has flowsThrough ?x267; has hasEstuary ?x324; has locatedIn ?x315;];]

*> Best rule #120 for first EXPECTED value:
*> intensional similarity = 3
*> extensional distance = 16
*> proper extension:  NiagaraRiver; Mississippi;
*> query: (?x832, AppalachianMountains) <- ?x832[ a Source; has locatedIn ?x315<USA>;]
*> conf = 0.06
ranks of expected_values: 6
EVAL	Tennessee	inMountains	AppalachianMountains	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.167	34.000	34.000	30.000	0.192	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains
PRED expected values: AppalachianMountains
=> 144 concepts (144 used for prediction)
PRED predicted values (max 10 best out of 44): AppalachianMountains (0.33 #33, 0.20 #381, 0.10 #2089), Alps (0.24 #2441, 0.23 #2093, 0.20 #1918), EastAfricanRift (0.20 #1942, 0.17 #898, 0.10 #3771), SnowyMountains (0.18 #1500, 0.17 #804, 0.17 #717), Vogesen (0.17 #918, 0.07 #3529, 0.06 #2485), RockyMountains (0.10 #2089, 0.07 #4968, 0.07 #5142), Adirondacks (0.10 #2089, 0.07 #1236, 0.06 #10098), EliasRange (0.10 #2089, 0.06 #10098, 0.05 #10010), Hawaii (0.10 #2089, 0.06 #10098, 0.05 #10010), CascadeRange (0.10 #2089, 0.06 #10098, 0.05 #10010)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #33 for best value:
>> intensional similarity = 9
>> extensional distance = 1
>> proper extension:  AlleghenyRiver;
>> query: (?x832, AppalachianMountains) <- ?x832[ a Source; has locatedIn ?x315<USA>; is hasSource of ?x268[ a River; has flowsInto ?x759<OhioRiver>; has hasEstuary ?x324[ a Estuary;];];]
ranks of expected_values: 1
EVAL	Tennessee	inMountains	AppalachianMountains	CNN-1.+1._MA	1.000	1.000	1.000	1.000	144.000	144.000	44.000	0.333	http://www.semwebtech.org/mondial/10/meta#inMountains

#57-Rhone
PRED entity: Rhone

PRED relation: inMountains
PRED expected values: Alps
=> 37 concepts (37 used for prediction)
PRED predicted values (max 10 best out of 31): Alps (0.38 #91, 0.29 #265, 0.29 #4), Vogesen (0.30 #222, 0.18 #309, 0.13 #396), CordilleraIberica (0.13 #403, 0.04 #664, 0.01 #1273), Pyrenees (0.10 #236, 0.04 #410, 0.02 #584), EastAfricanRift (0.07 #811, 0.07 #724, 0.06 #898), Jura (0.06 #293, 0.02 #554), Andes (0.06 #1055, 0.06 #1142, 0.05 #1316), TianShan (0.04 #379), Karakorum (0.04 #356), EliasRange (0.04 #972)
>> best conf = 0.38
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #91 for best value:
>> intensional similarity = 6
>> extensional distance = 6
>> proper extension:  Rhein;
>> query: (?x1707, Alps) <- ?x1707[ a Source; has locatedIn ?x234<CH>; is hasSource of ?x1225[ is flowsInto of ?x812[ has locatedIn ?x78;];];]
ranks of expected_values: 1
EVAL	Rhone	inMountains	Alps	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	37.000	37.000	31.000	0.375	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains
PRED expected values: Alps
=> 110 concepts (110 used for prediction)
PRED predicted values (max 10 best out of 40): Alps (0.60 #614, 0.53 #701, 0.52 #1568), Apennin (0.14 #525, 0.13 #700, 0.08 #1396), Vogesen (0.13 #1006, 0.12 #1441, 0.11 #1528), CordilleraIberica (0.13 #1013, 0.07 #1623, 0.03 #2408), Andes (0.12 #1317, 0.11 #2364, 0.09 #3151), EastAfricanRift (0.12 #1945, 0.07 #2730, 0.06 #2994), SnowyMountains (0.09 #456, 0.08 #1240, 0.08 #1153), Pamir (0.08 #1062, 0.04 #1323, 0.04 #1149), BlackForest (0.07 #1656, 0.04 #1046, 0.04 #1307), SudetyMountains (0.07 #2062, 0.07 #2149, 0.04 #2499)
>> best conf = 0.60
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #614 for best value:
>> intensional similarity = 10
>> extensional distance = 13
>> proper extension:  Raab;
>> query: (?x1707, Alps) <- ?x1707[ a Source; has locatedIn ?x234[ has language ?x51; has religion ?x187[ is religion of ?x476<ETH>;]; is locatedIn of ?x756<Inn>; is locatedIn of ?x847[ a Mountain;];];]
ranks of expected_values: 1
EVAL	Rhone	inMountains	Alps	CNN-1.+1._MA	1.000	1.000	1.000	1.000	110.000	110.000	40.000	0.600	http://www.semwebtech.org/mondial/10/meta#inMountains

#56-CDN
PRED entity: CDN

PRED relation: neighbor!
PRED expected values: USA
=> 30 concepts (30 used for prediction)
PRED predicted values (max 10 best out of 232): MEX (0.33 #86, 0.11 #1295, 0.11 #809), R (0.15 #326, 0.12 #1622, 0.12 #1299), EAT (0.15 #1265, 0.09 #941, 0.08 #779), RG (0.15 #595, 0.11 #1295, 0.11 #809), RMM (0.15 #618, 0.09 #1104, 0.07 #1590), SSD (0.13 #690, 0.11 #852, 0.10 #1339), RA (0.13 #229, 0.11 #1295, 0.11 #809), BOL (0.13 #277, 0.09 #971, 0.08 #762), Z (0.13 #1223, 0.09 #971, 0.08 #413), GB (0.12 #2266, 0.11 #1295, 0.11 #809)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #86 for best value:
>> intensional similarity = 7
>> extensional distance = 1
>> proper extension:  USA;
>> query: (?x272, MEX) <- ?x272[ has wasDependentOf ?x81; is locatedIn of ?x218<LakeHuron>; is locatedIn of ?x658<SaintLawrenceRiver>; is locatedIn of ?x1925<SaintMarysRiver>;]

*> Best rule #1295 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 45
*> proper extension:  NAM; TCH; YV; BI; RCB; MW;
*> query: (?x272, ?x315) <- ?x272[ has religion ?x95; has wasDependentOf ?x81; is locatedIn of ?x717[ a Lake;]; is locatedIn of ?x2159[ has locatedIn ?x315;];]
*> conf = 0.11
ranks of expected_values: 29
EVAL	CDN	neighbor!	USA	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.034	30.000	30.000	232.000	0.333	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: USA
=> 121 concepts (121 used for prediction)
PRED predicted values (max 10 best out of 243): MEX (0.65 #11257, 0.33 #247, 0.25 #409), USA (0.54 #6837, 0.52 #13554, 0.50 #6348), CDN (0.54 #6837, 0.52 #13554, 0.50 #6348), CN (0.40 #691, 0.29 #1668, 0.22 #2156), F (0.33 #976, 0.33 #4, 0.22 #4225), AND (0.33 #125, 0.22 #4225, 0.20 #611), P (0.33 #146, 0.20 #632, 0.17 #955), B (0.29 #1395, 0.22 #2369, 0.22 #4225), PE (0.29 #1513, 0.22 #2000, 0.16 #6674), RA (0.29 #1529, 0.22 #2016, 0.16 #6674)
>> best conf = 0.65
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #11257 for best value:
>> intensional similarity = 9
>> extensional distance = 36
>> proper extension:  RN;
>> query: (?x272, ?x482) <- ?x272[ has ethnicGroup ?x197; is locatedIn of ?x218[ a Lake;]; is locatedIn of ?x2128[ a Mountain; has locatedIn ?x315[ has encompassed ?x521; has ethnicGroup ?x79; has neighbor ?x482;];];]

*> Best rule #6837 for first EXPECTED value:
*> intensional similarity = 10
*> extensional distance = 22
*> proper extension:  TCH;
*> query: (?x272, ?x315) <- ?x272[ has religion ?x95; has wasDependentOf ?x81; is locatedIn of ?x514[ a River; has locatedIn ?x315;]; is locatedIn of ?x606[ a Mountain; has inMountains ?x337;]; is locatedIn of ?x691[ a Estuary;];]
*> conf = 0.54
ranks of expected_values: 2
EVAL	CDN	neighbor!	USA	CNN-1.+1._MA	0.000	1.000	1.000	0.500	121.000	121.000	243.000	0.646	http://www.semwebtech.org/mondial/10/meta#neighbor

#55-Balkan
PRED entity: Balkan

PRED relation: inMountains!
PRED expected values: Korab
=> 23 concepts (23 used for prediction)
PRED predicted values (max 10 best out of 276): Tara (0.16 #751, 0.13 #752, 0.09 #2757), Piva (0.16 #751, 0.13 #752, 0.09 #2757), Drin (0.16 #751, 0.13 #752, 0.09 #2757), SouthernMorava (0.16 #751, 0.13 #752, 0.09 #2757), Drina (0.16 #751, 0.13 #752, 0.09 #2757), Buna (0.16 #751, 0.13 #752, 0.09 #2757), WhiteDrin (0.16 #751, 0.13 #752, 0.09 #2757), Korab (0.16 #751, 0.13 #752, 0.09 #2757), SouthernMorava (0.16 #751, 0.13 #752, 0.09 #2757), LakeOhrid (0.16 #751, 0.13 #752, 0.09 #2757)
>> best conf = 0.16
=> the first rule below is the first best rule for 24 predicted values
>> Best rule #751 for best value:
>> intensional similarity = 13
>> extensional distance = 11
>> proper extension:  CordilleraBetica;
>> query: (?x785, ?x656) <- ?x785[ a Mountains; is inMountains of ?x307[ a Source; has locatedIn ?x106[ is neighbor of ?x55;];]; is inMountains of ?x1763[ a Source; has locatedIn ?x701[ has government ?x254; has language ?x511; is locatedIn of ?x656;];]; is inMountains of ?x1941[ a Mountain;];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 8
EVAL	Balkan	inMountains!	Korab	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.125	23.000	23.000	276.000	0.155	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains!
PRED expected values: Korab
=> 59 concepts (59 used for prediction)
PRED predicted values (max 10 best out of 276): MediterraneanSea (0.35 #2273, 0.25 #9850, 0.25 #2274), Piva (0.35 #2273, 0.25 #9850, 0.25 #2274), Piva (0.35 #2273, 0.25 #9850, 0.25 #2274), Buna (0.35 #2273, 0.25 #9850, 0.25 #2274), LakeSkutari (0.35 #2273, 0.25 #9850, 0.25 #2274), WhiteDrin (0.35 #2273, 0.25 #9850, 0.25 #2274), SouthernMorava (0.35 #2273, 0.25 #9850, 0.25 #2274), Tara (0.35 #2273, 0.25 #9850, 0.25 #2274), Moraca (0.35 #2273, 0.25 #9850, 0.25 #2274), Drina (0.35 #2273, 0.25 #9852, 0.23 #252)
>> best conf = 0.35
=> the first rule below is the first best rule for 24 predicted values
>> Best rule #2273 for best value:
>> intensional similarity = 34
>> extensional distance = 2
>> proper extension:  EastAfricanRift; Drakensberge;
>> query: (?x785, ?x183) <- ?x785[ a Mountains; is inMountains of ?x307[ a Source; is hasSource of ?x306[ a River; has flowsInto ?x813;];]; is inMountains of ?x814[ a Source; has locatedIn ?x106[ a Country; has encompassed ?x195; has ethnicGroup ?x775; has government ?x435<"republic">; has religion ?x56; has wasDependentOf ?x904; is neighbor of ?x55;]; is hasSource of ?x473;]; is inMountains of ?x1763[ a Source; is hasSource of ?x656[ a River; has hasEstuary ?x936;];]; is inMountains of ?x1941[ a Mountain; has locatedIn ?x177[ has ethnicGroup ?x164;];]; is inMountains of ?x2445[ a Source; has locatedIn ?x204[ a Country; has ethnicGroup ?x595; has government ?x254; is locatedIn of ?x183;];];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 14
EVAL	Balkan	inMountains!	Korab	CNN-1.+1._MA	0.000	0.000	0.000	0.071	59.000	59.000	276.000	0.355	http://www.semwebtech.org/mondial/10/meta#inMountains

#54-UZB
PRED entity: UZB

PRED relation: ethnicGroup
PRED expected values: Kazak
=> 42 concepts (42 used for prediction)
PRED predicted values (max 10 best out of 231): Ukrainian (0.43 #2279, 0.33 #1013, 0.33 #1), Turkmen (0.40 #1717, 0.33 #958, 0.29 #1970), European (0.35 #3804, 0.30 #4057, 0.28 #4817), Kyrgyz (0.33 #653, 0.33 #147, 0.16 #8859), Uighur (0.33 #161, 0.25 #1426, 0.16 #8859), Pashtun (0.33 #430, 0.16 #8859, 0.16 #8858), Dungan (0.33 #140, 0.16 #8859, 0.16 #8858), Chuvash (0.33 #1188, 0.03 #3719, 0.01 #5238), Bashkir (0.33 #1112, 0.03 #3643, 0.01 #5162), Polish (0.29 #2479, 0.11 #4503, 0.10 #4250)
>> best conf = 0.43
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2279 for best value:
>> intensional similarity = 8
>> extensional distance = 12
>> proper extension:  ARM;
>> query: (?x277, Ukrainian) <- ?x277[ has ethnicGroup ?x1193<Russian>; has ethnicGroup ?x1326[ a EthnicGroup;]; has government ?x1815; has language ?x278; has wasDependentOf ?x903; is neighbor of ?x129;]

*> Best rule #4191 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 61
*> proper extension:  C; IRL; KN; CY; CDN; ROU; SLB; IS; NZ; CL; ...
*> query: (?x277, Kazak) <- ?x277[ a Country; has ethnicGroup ?x1193; has government ?x1815; has language ?x278; has wasDependentOf ?x903; is locatedIn of ?x289;]
*> conf = 0.02
ranks of expected_values: 145
EVAL	UZB	ethnicGroup	Kazak	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.007	42.000	42.000	231.000	0.429	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: Kazak
=> 75 concepts (75 used for prediction)
PRED predicted values (max 10 best out of 247): Ukrainian (0.61 #3810, 0.58 #2284, 0.58 #4319), Belorussian (0.44 #1607, 0.42 #2368, 0.33 #2876), Polish (0.44 #1723, 0.33 #4010, 0.33 #2992), Uighur (0.41 #2537, 0.35 #1268, 0.33 #3045), Kyrgyz (0.41 #2537, 0.35 #1268, 0.33 #3045), German (0.41 #2537, 0.35 #1268, 0.33 #3045), Dungan (0.41 #2537, 0.35 #1268, 0.33 #3045), Pashtun (0.41 #2537, 0.35 #1268, 0.27 #3809), European (0.38 #10182, 0.29 #9165, 0.29 #13999), Turkmen (0.35 #1268, 0.33 #3045, 0.27 #3809)
>> best conf = 0.61
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3810 for best value:
>> intensional similarity = 15
>> extensional distance = 16
>> proper extension:  SK; PL;
>> query: (?x277, Ukrainian) <- ?x277[ has ethnicGroup ?x1193[ is ethnicGroup of ?x130<KGZ>; is ethnicGroup of ?x403<KAZ>; is ethnicGroup of ?x448<LV>; is ethnicGroup of ?x591<EW>; is ethnicGroup of ?x886<MD>;]; has language ?x278; has religion ?x56; is locatedIn of ?x289; is neighbor of ?x381;]

*> Best rule #5226 for first EXPECTED value:
*> intensional similarity = 16
*> extensional distance = 23
*> proper extension:  ROK;
*> query: (?x277, Kazak) <- ?x277[ has government ?x1815; has neighbor ?x129[ is locatedIn of ?x1601[ a Mountain;];]; has neighbor ?x381[ a Country; has language ?x1033; has wasDependentOf ?x81; is locatedIn of ?x82; is neighbor of ?x232<CN>;]; has neighbor ?x403[ a Country; has encompassed ?x175; is locatedIn of ?x127;]; is locatedIn of ?x289;]
*> conf = 0.04
ranks of expected_values: 112
EVAL	UZB	ethnicGroup	Kazak	CNN-1.+1._MA	0.000	0.000	0.000	0.009	75.000	75.000	247.000	0.611	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#53-Persian
PRED entity: Persian

PRED relation: ethnicGroup!
PRED expected values: IR
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 96): EAU (0.03 #132), ETH (0.03 #100), UA (0.03 #57), EAK (0.03 #99), CZ (0.02 #98), SN (0.02 #83), MYA (0.02 #70), IR (0.02 #58), SK (0.02 #25), RCA (0.02 #139)
>> best conf = 0.03
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #132 for best value:
>> intensional similarity = 1
>> extensional distance = 279
>> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
>> query: (?x2510, EAU) <- ?x2510[ a EthnicGroup;]

*> Best rule #58 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 279
*> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
*> query: (?x2510, IR) <- ?x2510[ a EthnicGroup;]
*> conf = 0.02
ranks of expected_values: 8
EVAL	Persian	ethnicGroup!	IR	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.125	2.000	2.000	96.000	0.032	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup!
PRED expected values: IR
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 96): EAU (0.03 #132), ETH (0.03 #100), UA (0.03 #57), EAK (0.03 #99), CZ (0.02 #98), SN (0.02 #83), MYA (0.02 #70), IR (0.02 #58), SK (0.02 #25), RCA (0.02 #139)
>> best conf = 0.03
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #132 for best value:
>> intensional similarity = 1
>> extensional distance = 279
>> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
>> query: (?x2510, EAU) <- ?x2510[ a EthnicGroup;]

*> Best rule #58 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 279
*> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
*> query: (?x2510, IR) <- ?x2510[ a EthnicGroup;]
*> conf = 0.02
ranks of expected_values: 8
EVAL	Persian	ethnicGroup!	IR	CNN-1.+1._MA	0.000	0.000	1.000	0.125	2.000	2.000	96.000	0.032	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#52-AtlanticOcean
PRED entity: AtlanticOcean

PRED relation: locatedInWater!
PRED expected values: Bioko Iceland Principe SaoMiguel PuertoRico Tiree SaoTome Basse-Terre NewProvidence NorthUist WestFalkland
=> 29 concepts (29 used for prediction)
PRED predicted values (max 10 best out of 594): Svalbard (0.33 #1500, 0.33 #279, 0.24 #814), Iceland (0.33 #1473, 0.33 #252, 0.24 #814), PuertoRico (0.33 #129, 0.25 #739, 0.24 #814), Basse-Terre (0.33 #183, 0.25 #793, 0.20 #1201), SanAndres (0.33 #110, 0.25 #720, 0.20 #1128), GrandCayman (0.33 #78, 0.25 #688, 0.20 #1096), Jamaica (0.33 #71, 0.25 #681, 0.20 #1089), LittleCayman (0.33 #39, 0.25 #649, 0.20 #1057), CaymanBrac (0.33 #34, 0.25 #644, 0.20 #1052), Curacao (0.33 #26, 0.25 #636, 0.20 #1044)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1500 for best value:
>> intensional similarity = 4
>> extensional distance = 4
>> proper extension:  Svalbard;
>> query: (?x182, Svalbard) <- ?x182[ has locatedIn ?x179[ a Country;]; has locatedIn ?x973<SVAX>;]
>> Best rule #279 for best value:
>> intensional similarity = 7
>> extensional distance = 1
>> proper extension:  NorwegianSea;
>> query: (?x182, Svalbard) <- ?x182[ has locatedIn ?x179[ has ethnicGroup ?x79;]; has locatedIn ?x357<FARX>; has locatedIn ?x379[ has neighbor ?x404;]; is locatedInWater of ?x112;]

*> Best rule #1473 for first EXPECTED value:
*> intensional similarity = 4
*> extensional distance = 4
*> proper extension:  Svalbard;
*> query: (?x182, Iceland) <- ?x182[ has locatedIn ?x179[ a Country;]; has locatedIn ?x973<SVAX>;]
*> conf = 0.33
ranks of expected_values: 2, 3, 4, 175, 213, 390, 394, 406, 426
EVAL	AtlanticOcean	locatedInWater!	WestFalkland	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.003	29.000	29.000	594.000	0.333	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	AtlanticOcean	locatedInWater!	NorthUist	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	29.000	29.000	594.000	0.333	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	AtlanticOcean	locatedInWater!	NewProvidence	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.003	29.000	29.000	594.000	0.333	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	AtlanticOcean	locatedInWater!	Basse-Terre	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	29.000	29.000	594.000	0.333	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	AtlanticOcean	locatedInWater!	SaoTome	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.003	29.000	29.000	594.000	0.333	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	AtlanticOcean	locatedInWater!	Tiree	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.006	29.000	29.000	594.000	0.333	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	AtlanticOcean	locatedInWater!	PuertoRico	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	29.000	29.000	594.000	0.333	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	AtlanticOcean	locatedInWater!	SaoMiguel	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.005	29.000	29.000	594.000	0.333	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	AtlanticOcean	locatedInWater!	Principe	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.002	29.000	29.000	594.000	0.333	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	AtlanticOcean	locatedInWater!	Iceland	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.500	29.000	29.000	594.000	0.333	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	AtlanticOcean	locatedInWater!	Bioko	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	29.000	29.000	594.000	0.333	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater!
PRED expected values: Bioko Iceland Principe SaoMiguel PuertoRico Tiree SaoTome Basse-Terre NewProvidence NorthUist WestFalkland
=> 88 concepts (87 used for prediction)
PRED predicted values (max 10 best out of 913): DevonIsland (0.71 #2878, 0.70 #2877, 0.43 #2879), EllesmereIsland (0.71 #2878, 0.70 #2877, 0.43 #2879), ShetlandMainland (0.71 #2878, 0.70 #2877, 0.43 #2879), Unalaska (0.71 #2878, 0.70 #2877, 0.43 #2879), Molokai (0.71 #2878, 0.70 #2877, 0.43 #2879), Lanai (0.71 #2878, 0.70 #2877, 0.43 #2879), Kauai (0.71 #2878, 0.70 #2877, 0.43 #2879), Maui (0.71 #2878, 0.70 #2877, 0.43 #2879), Hawaii (0.71 #2878, 0.70 #2877, 0.43 #2879), Niihau (0.71 #2878, 0.70 #2877, 0.43 #2879)
>> best conf = 0.71
=> the first rule below is the first best rule for 36 predicted values
>> Best rule #2878 for best value:
>> intensional similarity = 10
>> extensional distance = 3
>> proper extension:  SaintVincent; Soufriere;
>> query: (?x182, ?x68) <- ?x182[ has locatedIn ?x77[ a Country;]; has locatedIn ?x124<WV>; has locatedIn ?x149[ is locatedIn of ?x68[ a Island;]; is locatedIn of ?x1020[ a Island; has belongsToIslands ?x1068;];];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 11, 22, 29, 30, 31, 219, 224, 287, 288
EVAL	AtlanticOcean	locatedInWater!	WestFalkland	CNN-1.+1._MA	0.000	0.000	0.000	0.037	88.000	87.000	913.000	0.714	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	AtlanticOcean	locatedInWater!	NorthUist	CNN-1.+1._MA	0.000	0.000	0.000	0.000	88.000	87.000	913.000	0.714	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	AtlanticOcean	locatedInWater!	NewProvidence	CNN-1.+1._MA	0.000	0.000	0.000	0.037	88.000	87.000	913.000	0.714	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	AtlanticOcean	locatedInWater!	Basse-Terre	CNN-1.+1._MA	0.000	0.000	0.000	0.005	88.000	87.000	913.000	0.714	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	AtlanticOcean	locatedInWater!	SaoTome	CNN-1.+1._MA	0.000	0.000	0.000	0.004	88.000	87.000	913.000	0.714	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	AtlanticOcean	locatedInWater!	Tiree	CNN-1.+1._MA	0.000	0.000	0.000	0.037	88.000	87.000	913.000	0.714	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	AtlanticOcean	locatedInWater!	PuertoRico	CNN-1.+1._MA	0.000	0.000	0.000	0.091	88.000	87.000	913.000	0.714	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	AtlanticOcean	locatedInWater!	SaoMiguel	CNN-1.+1._MA	0.000	0.000	0.000	0.048	88.000	87.000	913.000	0.714	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	AtlanticOcean	locatedInWater!	Principe	CNN-1.+1._MA	0.000	0.000	0.000	0.004	88.000	87.000	913.000	0.714	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	AtlanticOcean	locatedInWater!	Iceland	CNN-1.+1._MA	0.000	0.000	0.000	0.005	88.000	87.000	913.000	0.714	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	AtlanticOcean	locatedInWater!	Bioko	CNN-1.+1._MA	0.000	0.000	0.000	0.000	88.000	87.000	913.000	0.714	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#51-A
PRED entity: A

PRED relation: neighbor!
PRED expected values: I H
=> 27 concepts (27 used for prediction)
PRED predicted values (max 10 best out of 165): H (0.90 #2504, 0.90 #2818, 0.89 #2975), UA (0.29 #50, 0.26 #2347, 0.26 #2191), A (0.29 #73, 0.26 #2347, 0.26 #2191), PL (0.29 #33, 0.26 #2347, 0.26 #2191), HR (0.26 #2347, 0.26 #2191, 0.26 #2977), NL (0.26 #2347, 0.26 #2191, 0.26 #2977), L (0.26 #2347, 0.26 #2191, 0.26 #2977), B (0.26 #2347, 0.26 #2191, 0.26 #2977), F (0.26 #2347, 0.26 #2191, 0.26 #2661), I (0.26 #2347, 0.26 #2191, 0.26 #2661)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2504 for best value:
>> intensional similarity = 5
>> extensional distance = 134
>> proper extension:  BIH; ET; R; F; GB; PK; LS; THA; DJI; MNE; ...
>> query: (?x424, ?x236) <- ?x424[ has neighbor ?x236[ has ethnicGroup ?x164; has wasDependentOf ?x2352; is locatedIn of ?x523;]; is locatedIn of ?x133;]
ranks of expected_values: 1, 10
EVAL	A	neighbor!	H	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	27.000	27.000	165.000	0.897	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	A	neighbor!	I	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.111	27.000	27.000	165.000	0.897	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: I H
=> 88 concepts (88 used for prediction)
PRED predicted values (max 10 best out of 212): H (0.91 #8841, 0.91 #10744, 0.91 #10268), PL (0.60 #1880, 0.60 #1757, 0.40 #4248), UA (0.57 #2404, 0.52 #4407, 0.51 #8210), RO (0.52 #4407, 0.51 #8210, 0.51 #5197), I (0.52 #4407, 0.51 #8210, 0.51 #5197), A (0.52 #4407, 0.51 #8210, 0.51 #5197), BG (0.52 #4407, 0.51 #8210, 0.51 #5197), HR (0.52 #4407, 0.51 #8210, 0.51 #5197), SRB (0.52 #4407, 0.51 #8210, 0.51 #5197), MD (0.52 #4407, 0.51 #8210, 0.51 #5197)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #8841 for best value:
>> intensional similarity = 12
>> extensional distance = 72
>> proper extension:  FGU;
>> query: (?x424, ?x236) <- ?x424[ a Country; has language ?x511; has neighbor ?x236; has religion ?x95; is locatedIn of ?x133; is neighbor of ?x234[ has government ?x2472; is locatedIn of ?x233;]; is neighbor of ?x446[ a Country; has religion ?x187; has wasDependentOf ?x1197;];]
ranks of expected_values: 1, 5
EVAL	A	neighbor!	H	CNN-1.+1._MA	1.000	1.000	1.000	1.000	88.000	88.000	212.000	0.915	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	A	neighbor!	I	CNN-1.+1._MA	0.000	0.000	1.000	0.250	88.000	88.000	212.000	0.915	http://www.semwebtech.org/mondial/10/meta#neighbor

#50-Guadalquivir
PRED entity: Guadalquivir

PRED relation: hasSource
PRED expected values: Guadalquivir
=> 41 concepts (33 used for prediction)
PRED predicted values (max 10 best out of 188): Douro (0.20 #416, 0.20 #188, 0.05 #873), Guadiana (0.20 #361, 0.20 #133, 0.05 #818), Tajo (0.20 #357, 0.05 #814, 0.05 #585), Ebro (0.20 #135, 0.05 #591, 0.03 #1732), Loire (0.05 #903, 0.04 #1359, 0.04 #1131), Amazonas (0.05 #883, 0.04 #1339, 0.04 #1111), Garonne (0.05 #834, 0.04 #1290, 0.04 #1062), RioSaoFrancisco (0.05 #815, 0.04 #1271, 0.04 #1043), Tocantins (0.05 #755, 0.04 #1211, 0.04 #983), Orinoco (0.05 #748, 0.04 #1204, 0.04 #976)
>> best conf = 0.20
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #416 for best value:
>> intensional similarity = 5
>> extensional distance = 3
>> proper extension:  Tajo;
>> query: (?x1145, Douro) <- ?x1145[ a River; has flowsInto ?x182<AtlanticOcean>; has locatedIn ?x149<E>;]
>> Best rule #188 for best value:
>> intensional similarity = 5
>> extensional distance = 3
>> proper extension:  Guadiana; Douro; Ebro;
>> query: (?x1145, Douro) <- ?x1145[ a River; has flowsInto ?x182; has hasEstuary ?x1249; has locatedIn ?x149<E>;]

*> Best rule #685 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 20
*> proper extension:  Inn; Limmat; Aare; Doubs; Reuss;
*> query: (?x1145, ?x68) <- ?x1145[ a River; has locatedIn ?x149[ is locatedIn of ?x68; is neighbor of ?x78<F>;];]
*> conf = 0.02
ranks of expected_values: 48
EVAL	Guadalquivir	hasSource	Guadalquivir	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.021	41.000	33.000	188.000	0.200	http://www.semwebtech.org/mondial/10/meta#hasSource

PRED relation: hasSource
PRED expected values: Guadalquivir
=> 135 concepts (135 used for prediction)
PRED predicted values (max 10 best out of 224): Guadiana (0.33 #133, 0.25 #361, 0.20 #817), Douro (0.25 #416, 0.20 #872, 0.20 #644), Tajo (0.20 #813, 0.17 #14917, 0.11 #13310), Ebro (0.20 #591, 0.17 #1047, 0.11 #13310), Loire (0.17 #14917, 0.07 #2048, 0.06 #2508), Garonne (0.17 #14917, 0.07 #1979, 0.06 #2439), ConnecticutRiver (0.17 #14917, 0.05 #2640, 0.03 #4244), MerrimackRiver (0.17 #14917, 0.05 #2575, 0.03 #4179), HudsonRiver (0.17 #14917, 0.05 #2557, 0.03 #4161), Parana (0.17 #14917, 0.05 #2638, 0.03 #4242)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #133 for best value:
>> intensional similarity = 8
>> extensional distance = 1
>> proper extension:  Guadiana;
>> query: (?x1145, Guadiana) <- ?x1145[ a River; has flowsInto ?x182<AtlanticOcean>; has hasEstuary ?x1249[ a Estuary; has locatedIn ?x149<E>;]; has locatedIn ?x149<E>;]

*> Best rule #13310 for first EXPECTED value:
*> intensional similarity = 10
*> extensional distance = 164
*> proper extension:  Bahrel-Ghasal; Bahrel-Djebel-Albert-Nil;
*> query: (?x1145, ?x1700) <- ?x1145[ has hasEstuary ?x1249; has locatedIn ?x149[ a Country; has government ?x1657; is locatedIn of ?x275[ is flowsInto of ?x698;]; is locatedIn of ?x1198[ a River;]; is locatedIn of ?x1700[ a Source;];];]
*> conf = 0.11
ranks of expected_values: 22
EVAL	Guadalquivir	hasSource	Guadalquivir	CNN-1.+1._MA	0.000	0.000	0.000	0.045	135.000	135.000	224.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasSource

#49-Australia-Oceania
PRED entity: Australia-Oceania

PRED relation: encompassed!
PRED expected values: VU GUAM
=> 5 concepts (5 used for prediction)
PRED predicted values (max 10 best out of 208): MAL (0.83 #807, 0.81 #202, 0.67 #201), TL (0.83 #807, 0.81 #202, 0.67 #201), BRU (0.81 #202, 0.67 #605, 0.57 #403), THA (0.81 #202, 0.67 #605, 0.57 #403), USA (0.81 #202, 0.60 #407, 0.44 #813), SGP (0.81 #202, 0.50 #404, 0.40 #204), CDN (0.81 #202, 0.40 #204, 0.36 #808), PA (0.81 #202, 0.40 #204, 0.36 #808), HCA (0.81 #202, 0.40 #204, 0.36 #808), EC (0.81 #202, 0.40 #204, 0.36 #808)
>> best conf = 0.83
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #807 for best value:
>> intensional similarity = 41
>> extensional distance = 3
>> proper extension:  Africa;
>> query: (?x211, ?x376) <- ?x211[ is encompassed of ?x217[ a Country; has ethnicGroup ?x425[ a EthnicGroup;]; has government ?x435; has religion ?x187; is locatedIn of ?x333[ has type ?x150;]; is locatedIn of ?x384[ is locatedInWater of ?x518;]; is locatedIn of ?x385[ a Sea;]; is locatedIn of ?x1807[ a Mountain;]; is neighbor of ?x376;]; is encompassed of ?x322[ a Country; has dependentOf ?x315; has ethnicGroup ?x380; has language ?x247<English>;]; is encompassed of ?x564[ has ethnicGroup ?x1335; has government ?x2145; has language ?x51<French>; has religion ?x352[ is religion of ?x272<CDN>; is religion of ?x446<SLO>; is religion of ?x667<TT>;]; is locatedIn of ?x282;]; is encompassed of ?x1002[ a Country; has dependentOf ?x78; has ethnicGroup ?x197; has government ?x916; is locatedIn of ?x1616;];]

*> Best rule #202 for first EXPECTED value:
*> intensional similarity = 52
*> extensional distance = 1
*> proper extension:  Asia;
*> query: (?x211, ?x460) <- ?x211[ is encompassed of ?x217[ a Country; has ethnicGroup ?x425; has ethnicGroup ?x778[ a EthnicGroup;]; has religion ?x95; is locatedIn of ?x333[ a Island;]; is locatedIn of ?x339<AndamanSea>; is locatedIn of ?x385<MalakkaStrait>; is locatedIn of ?x625[ has locatedIn ?x460; has mergesWith ?x677; is locatedInWater of ?x369;]; is neighbor of ?x376;]; is encompassed of ?x322[ a Country; has dependentOf ?x315; has ethnicGroup ?x380[ a EthnicGroup;]; has language ?x247<English>;]; is encompassed of ?x453[ a Country; has ethnicGroup ?x454; has religion ?x116<Christian>;]; is encompassed of ?x564[ has ethnicGroup ?x1335; has government ?x2145; has language ?x51; has religion ?x352; is locatedIn of ?x282<PacificOcean>;]; is encompassed of ?x853[ has wasDependentOf ?x485;]; is encompassed of ?x1002[ has dependentOf ?x78; has ethnicGroup ?x774<Indonesian>; has government ?x916;]; is encompassed of ?x1731[ has ethnicGroup ?x298<Chinese>; has religion ?x187;]; is encompassed of ?x1944[ a Country; has government ?x92<"constitutional monarchy">; is locatedIn of ?x205;];]
*> conf = 0.81
ranks of expected_values: 25, 42
EVAL	Australia-Oceania	encompassed!	GUAM	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.024	5.000	5.000	208.000	0.833	http://www.semwebtech.org/mondial/10/meta#encompassed
EVAL	Australia-Oceania	encompassed!	VU	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.040	5.000	5.000	208.000	0.833	http://www.semwebtech.org/mondial/10/meta#encompassed

PRED relation: encompassed!
PRED expected values: VU GUAM
=> 5 concepts (5 used for prediction)
PRED predicted values (max 10 best out of 216): CDN (0.91 #231, 0.78 #206, 0.57 #443), PA (0.91 #231, 0.78 #206, 0.52 #212), VU (0.91 #231, 0.57 #443, 0.47 #207), USA (0.91 #231, 0.52 #212, 0.52 #210), CR (0.91 #231, 0.52 #212, 0.47 #207), NIC (0.91 #231, 0.52 #212, 0.47 #207), MEX (0.91 #231, 0.52 #212, 0.47 #207), HCA (0.91 #231, 0.52 #212, 0.47 #207), GCA (0.91 #231, 0.52 #212, 0.47 #207), ES (0.91 #231, 0.52 #212, 0.47 #207)
>> best conf = 0.91
=> the first rule below is the first best rule for 18 predicted values
>> Best rule #231 for best value:
>> intensional similarity = 173
>> extensional distance = 1
>> proper extension:  America;
>> query: (?x211, ?x73) <- ?x211[ a Continent; is encompassed of ?x196[ has language ?x1458; is locatedIn of ?x1465[ has type ?x136;];]; is encompassed of ?x217[ a Country; has ethnicGroup ?x425[ a EthnicGroup;]; has religion ?x187; is locatedIn of ?x770[ a Sea; is locatedInWater of ?x937;]; is locatedIn of ?x840[ a Island;]; is locatedIn of ?x1074[ is locatedOnIsland of ?x1697;]; is locatedIn of ?x1101[ has type ?x150<"volcanic">;]; is locatedIn of ?x1102[ a Lake; has type ?x287;]; is locatedIn of ?x1330[ a Source;]; is locatedIn of ?x1510[ a Mountain; a Volcano; has type ?x706<"volcano">;]; is locatedIn of ?x1965[ a Island; has belongsToIslands ?x1099;]; is locatedIn of ?x2024[ a Estuary;]; is neighbor of ?x376[ a Country; has encompassed ?x175; has ethnicGroup ?x959; has government ?x92; is locatedIn of ?x178; is neighbor of ?x91;];]; is encompassed of ?x297[ has ethnicGroup ?x298<Chinese>; has ethnicGroup ?x1335[ a EthnicGroup;]; has government ?x2145; has language ?x51[ a Language; is language of ?x50<NLSM>;]; is locatedIn of ?x2009;]; is encompassed of ?x322[ a Country; has government ?x2110; has language ?x247; has language ?x1155[ a Language;]; is locatedIn of ?x65;]; is encompassed of ?x390[ a Country; has government ?x1947; has religion ?x1082<Baptist>; has religion ?x2460[ a Religion;]; is locatedIn of ?x1083;]; is encompassed of ?x400[ a Country; has government ?x2126; has wasDependentOf ?x485;]; is encompassed of ?x461[ a Country; has ethnicGroup ?x197[ is ethnicGroup of ?x193<SD>; is ethnicGroup of ?x363<ROU>; is ethnicGroup of ?x450<CV>; is ethnicGroup of ?x483<GH>; is ethnicGroup of ?x520<DOM>; is ethnicGroup of ?x525<Z>; is ethnicGroup of ?x542<BR>; is ethnicGroup of ?x1130<BDS>; is ethnicGroup of ?x1755<GNB>;]; has ethnicGroup ?x380[ a EthnicGroup; is ethnicGroup of ?x154<IRL>; is ethnicGroup of ?x1072<WAL>;]; has religion ?x95<Protestant>; has religion ?x410[ a Religion; is religion of ?x179<SME>; is religion of ?x207<I>; is religion of ?x351<GUY>; is religion of ?x633<GUAD>; is religion of ?x667<TT>;]; has religion ?x462[ is religion of ?x460<RP>; is religion of ?x617<VN>;]; has religion ?x713<Anglican>; has wasDependentOf ?x81<GB>; is locatedIn of ?x282[ a Sea; has locatedIn ?x73; has locatedIn ?x181<GCA>; has locatedIn ?x215<CO>; has locatedIn ?x408<NIC>; has locatedIn ?x482<MEX>; has locatedIn ?x783<PA>; has locatedIn ?x1364<HCA>; has mergesWith ?x271; is flowsInto of ?x602; is locatedInWater of ?x205;];]; is encompassed of ?x550[ a Country; has religion ?x1547<Mormon>;]; is encompassed of ?x564[ a Country; has dependentOf ?x78<F>; has language ?x2321;]; is encompassed of ?x728[ a Country; has ethnicGroup ?x1129; has government ?x435<"republic">; is locatedIn of ?x896[ a Island; has belongsToIslands ?x2209;];]; is encompassed of ?x1276[ has ethnicGroup ?x2423[ a EthnicGroup;]; has government ?x2533; has language ?x189[ a Language;]; has religion ?x1277;]; is encompassed of ?x1731[ a Country; has government ?x907; is locatedIn of ?x60[ a Sea; has mergesWith ?x1333; is flowsInto of ?x242; is locatedInWater of ?x226; is mergesWith of ?x182;];];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 3, 18
EVAL	Australia-Oceania	encompassed!	GUAM	CNN-1.+1._MA	0.000	0.000	0.000	0.059	5.000	5.000	216.000	0.906	http://www.semwebtech.org/mondial/10/meta#encompassed
EVAL	Australia-Oceania	encompassed!	VU	CNN-1.+1._MA	0.000	1.000	1.000	0.333	5.000	5.000	216.000	0.906	http://www.semwebtech.org/mondial/10/meta#encompassed

#48-TAD
PRED entity: TAD

PRED relation: neighbor!
PRED expected values: AFG
=> 35 concepts (35 used for prediction)
PRED predicted values (max 10 best out of 172): AFG (0.91 #4475, 0.90 #3351, 0.89 #4476), R (0.53 #1277, 0.50 #800, 0.44 #1437), TM (0.50 #365, 0.33 #47, 0.30 #3353), IND (0.40 #614, 0.30 #3353, 0.29 #2554), TAD (0.37 #636, 0.33 #16, 0.30 #3353), KAZ (0.37 #636, 0.30 #3353, 0.29 #2554), IR (0.33 #51, 0.30 #3353, 0.29 #2554), PK (0.33 #6, 0.30 #3353, 0.29 #2554), VN (0.30 #3353, 0.29 #2554, 0.28 #4153), NEP (0.30 #3353, 0.29 #2554, 0.28 #4153)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4475 for best value:
>> intensional similarity = 8
>> extensional distance = 129
>> proper extension:  SSD;
>> query: (?x129, ?x130) <- ?x129[ has government ?x435; has neighbor ?x130[ has ethnicGroup ?x58;]; has neighbor ?x381[ is neighbor of ?x304[ has encompassed ?x175; is locatedIn of ?x573;];]; is locatedIn of ?x276;]
ranks of expected_values: 1
EVAL	TAD	neighbor!	AFG	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	35.000	35.000	172.000	0.908	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: AFG
=> 84 concepts (84 used for prediction)
PRED predicted values (max 10 best out of 191): AFG (0.92 #6174, 0.92 #6994, 0.92 #5195), TAD (0.49 #2432, 0.46 #1127, 0.44 #1306), TM (0.49 #2432, 0.46 #1127, 0.44 #1337), KAZ (0.49 #2432, 0.46 #1127, 0.40 #711), R (0.45 #2274, 0.38 #2597, 0.36 #161), PK (0.36 #161, 0.35 #1289, 0.33 #329), IND (0.36 #161, 0.33 #460, 0.32 #1451), VN (0.36 #161, 0.33 #422, 0.32 #1451), LAO (0.36 #161, 0.33 #401, 0.32 #1451), NEP (0.36 #161, 0.33 #336, 0.32 #1451)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #6174 for best value:
>> intensional similarity = 15
>> extensional distance = 72
>> proper extension:  IL;
>> query: (?x129, ?x232) <- ?x129[ has ethnicGroup ?x1193; has neighbor ?x130[ a Country; has ethnicGroup ?x58; has language ?x555;]; has neighbor ?x232[ is locatedIn of ?x319[ has flowsInto ?x320;]; is locatedIn of ?x497[ a River;]; is neighbor of ?x111[ has religion ?x187;];]; is locatedIn of ?x276; is locatedIn of ?x300[ a River;];]
ranks of expected_values: 1
EVAL	TAD	neighbor!	AFG	CNN-1.+1._MA	1.000	1.000	1.000	1.000	84.000	84.000	191.000	0.918	http://www.semwebtech.org/mondial/10/meta#neighbor

#47-IR
PRED entity: IR

PRED relation: ethnicGroup
PRED expected values: Persian
=> 43 concepts (43 used for prediction)
PRED predicted values (max 10 best out of 237): Jewish (0.43 #1298, 0.38 #1550, 0.12 #1801), Russian (0.33 #572, 0.26 #4018, 0.26 #4270), Azeri (0.33 #549, 0.26 #4018, 0.26 #4270), Polynesian (0.33 #84, 0.12 #2846, 0.08 #4857), Georgian (0.33 #565, 0.02 #2574, 0.02 #2825), European (0.32 #2519, 0.29 #2770, 0.29 #5284), Armenian (0.26 #4018, 0.26 #4270, 0.25 #1847), Kurdish (0.26 #4018, 0.26 #4270, 0.25 #831), Turkish (0.26 #4018, 0.26 #4270, 0.25 #934), Arabic (0.26 #4018, 0.26 #4270, 0.25 #783)
>> best conf = 0.43
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1298 for best value:
>> intensional similarity = 7
>> extensional distance = 5
>> proper extension:  SA;
>> query: (?x304, Jewish) <- ?x304[ has ethnicGroup ?x244; has language ?x1848<Arabic>; has religion ?x187; is locatedIn of ?x573; is neighbor of ?x332[ has ethnicGroup ?x908;];]

No rule for expected values
ranks of expected_values: 
EVAL	IR	ethnicGroup	Persian	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	43.000	43.000	237.000	0.429	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup
PRED expected values: Persian
=> 111 concepts (111 used for prediction)
PRED predicted values (max 10 best out of 249): Russian (0.71 #6107, 0.67 #5349, 0.60 #4341), Turkish (0.60 #4704, 0.33 #754, 0.33 #432), Uzbek (0.57 #6188, 0.50 #5430, 0.40 #4926), European (0.56 #7304, 0.45 #11074, 0.44 #7052), Tatar (0.50 #5362, 0.43 #6120, 0.40 #4354), Ukrainian (0.43 #6038, 0.40 #4272, 0.33 #5280), Chinese (0.43 #6302, 0.33 #1773, 0.25 #3280), Azeri (0.36 #16854, 0.33 #18616, 0.33 #5577), Armenian (0.36 #16854, 0.33 #18616, 0.33 #5619), Pashtun (0.36 #16854, 0.33 #18616, 0.33 #754)
>> best conf = 0.71
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #6107 for best value:
>> intensional similarity = 10
>> extensional distance = 5
>> proper extension:  KGZ;
>> query: (?x304, Russian) <- ?x304[ a Country; has encompassed ?x175; has ethnicGroup ?x244; has language ?x511; is locatedIn of ?x1337[ has locatedIn ?x403<KAZ>;]; is neighbor of ?x302[ has wasDependentOf ?x485; is locatedIn of ?x255;];]

No rule for expected values
ranks of expected_values: 
EVAL	IR	ethnicGroup	Persian	CNN-1.+1._MA	0.000	0.000	0.000	0.000	111.000	111.000	249.000	0.714	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#46-Tajo
PRED entity: Tajo

PRED relation: hasEstuary!
PRED expected values: Tajo
=> 38 concepts (34 used for prediction)
PRED predicted values (max 10 best out of 94): Guadiana (0.25 #134, 0.09 #2041, 0.05 #360), Douro (0.25 #175, 0.09 #2041, 0.05 #401), Ebro (0.03 #643, 0.02 #870, 0.01 #1097), Ticino (0.03 #587, 0.02 #814, 0.01 #1041), Guadalquivir (0.03 #580, 0.02 #807, 0.01 #1034), Adda (0.03 #563, 0.02 #790, 0.01 #1017), Mincio (0.03 #560, 0.02 #787, 0.01 #1014), Tiber (0.03 #557, 0.02 #784, 0.01 #1011), Etsch (0.03 #538, 0.02 #765, 0.01 #992), Po (0.03 #524, 0.02 #751, 0.01 #978)
>> best conf = 0.25
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #134 for best value:
>> intensional similarity = 3
>> extensional distance = 2
>> proper extension:  Douro; Guadiana;
>> query: (?x2455, Guadiana) <- ?x2455[ a Estuary; has locatedIn ?x1027<P>;]

*> Best rule #906 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 40
*> proper extension:  Leine; Main; Saar; Kemijoki; Oder; Lech; Neckar; Ganges; Breg; Mosel; ...
*> query: (?x2455, ?x182) <- ?x2455[ a Estuary; has locatedIn ?x1027[ has government ?x2551; is locatedIn of ?x182; is locatedIn of ?x827[ has belongsToIslands ?x200;]; is neighbor of ?x149;];]
*> conf = 0.02
ranks of expected_values: 58
EVAL	Tajo	hasEstuary!	Tajo	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.017	38.000	34.000	94.000	0.250	http://www.semwebtech.org/mondial/10/meta#hasEstuary

PRED relation: hasEstuary!
PRED expected values: Tajo
=> 127 concepts (127 used for prediction)
PRED predicted values (max 10 best out of 227): Guadiana (0.38 #909, 0.31 #908, 0.27 #680), Douro (0.38 #909, 0.25 #175, 0.14 #628), Tajo (0.31 #908, 0.27 #680, 0.17 #8441), Parana (0.14 #501, 0.12 #729, 0.09 #958), Uruguay (0.14 #571, 0.12 #799, 0.09 #1028), Paraguay (0.14 #500, 0.12 #728, 0.09 #957), Guadalquivir (0.14 #354, 0.08 #1266, 0.06 #5242), Ebro (0.14 #417, 0.08 #1329, 0.05 #2693), Thames (0.14 #385, 0.01 #9741), Orinoco (0.12 #707, 0.09 #936, 0.06 #5242)
>> best conf = 0.38
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #909 for best value:
>> intensional similarity = 13
>> extensional distance = 6
>> proper extension:  Orinoco;
>> query: (?x2455, ?x1519) <- ?x2455[ a Estuary; has locatedIn ?x1027[ a Country; has government ?x2551; has wasDependentOf ?x149<E>; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x1198[ has hasSource ?x1726;]; is locatedIn of ?x1352[ a Estuary; is hasEstuary of ?x1519;];];]

*> Best rule #908 for first EXPECTED value:
*> intensional similarity = 13
*> extensional distance = 6
*> proper extension:  Orinoco;
*> query: (?x2455, ?x1198) <- ?x2455[ a Estuary; has locatedIn ?x1027[ a Country; has government ?x2551; has wasDependentOf ?x149<E>; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x1198[ has hasSource ?x1726;]; is locatedIn of ?x1352[ a Estuary; is hasEstuary of ?x1519;];];]
*> conf = 0.31
ranks of expected_values: 3
EVAL	Tajo	hasEstuary!	Tajo	CNN-1.+1._MA	0.000	1.000	1.000	0.333	127.000	127.000	227.000	0.375	http://www.semwebtech.org/mondial/10/meta#hasEstuary

#45-IrishSea
PRED entity: IrishSea

PRED relation: mergesWith
PRED expected values: AtlanticOcean
=> 34 concepts (32 used for prediction)
PRED predicted values (max 10 best out of 64): AtlanticOcean (0.83 #578, 0.82 #329, 0.82 #411), TheChannel (0.48 #494, 0.47 #248, 0.47 #663), NorthSea (0.48 #494, 0.47 #248, 0.47 #663), IrishSea (0.47 #248, 0.47 #663, 0.47 #662), PacificOcean (0.27 #222, 0.27 #181, 0.26 #344), GreenlandSea (0.25 #117, 0.25 #76, 0.17 #413), IndianOcean (0.25 #84, 0.19 #330, 0.19 #208), GulfofMexico (0.25 #116, 0.17 #413, 0.16 #412), CaribbeanSea (0.25 #99, 0.17 #413, 0.16 #412), LabradorSea (0.25 #91, 0.17 #413, 0.16 #412)
>> best conf = 0.83
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #578 for best value:
>> intensional similarity = 7
>> extensional distance = 36
>> proper extension:  YellowSea; SuluSea; GulfofAden;
>> query: (?x1833, ?x182) <- ?x1833[ has locatedIn ?x154[ has religion ?x352[ is religion of ?x353<GE>; is religion of ?x446<SLO>;];]; is mergesWith of ?x182;]
ranks of expected_values: 1
EVAL	IrishSea	mergesWith	AtlanticOcean	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	34.000	32.000	64.000	0.829	http://www.semwebtech.org/mondial/10/meta#mergesWith

PRED relation: mergesWith
PRED expected values: AtlanticOcean
=> 111 concepts (111 used for prediction)
PRED predicted values (max 10 best out of 64): AtlanticOcean (0.82 #1249, 0.80 #705, 0.79 #955), TheChannel (0.55 #378, 0.55 #293, 0.55 #292), NorthSea (0.55 #378, 0.55 #293, 0.55 #292), IrishSea (0.55 #378, 0.55 #293, 0.55 #292), NorwegianSea (0.40 #334, 0.40 #99, 0.34 #1038), Skagerrak (0.40 #334, 0.34 #1038, 0.20 #119), GreenlandSea (0.34 #1038, 0.33 #327, 0.33 #34), IndianOcean (0.34 #1038, 0.33 #1, 0.27 #624), GulfofMexico (0.34 #1038, 0.33 #33, 0.25 #74), CaribbeanSea (0.34 #1038, 0.33 #16, 0.25 #57)
>> best conf = 0.82
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1249 for best value:
>> intensional similarity = 9
>> extensional distance = 28
>> proper extension:  RedSea;
>> query: (?x1833, ?x182) <- ?x1833[ a Sea; has locatedIn ?x154[ has encompassed ?x195; has ethnicGroup ?x162; has language ?x247;]; is mergesWith of ?x182[ has locatedIn ?x138[ has wasDependentOf ?x485;]; is mergesWith of ?x60;];]
ranks of expected_values: 1
EVAL	IrishSea	mergesWith	AtlanticOcean	CNN-1.+1._MA	1.000	1.000	1.000	1.000	111.000	111.000	64.000	0.818	http://www.semwebtech.org/mondial/10/meta#mergesWith

#44-GR
PRED entity: GR

PRED relation: locatedIn!
PRED expected values: Kefallinia
=> 43 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 1401): WhiteDrin (0.50 #434, 0.09 #16890, 0.09 #18298), AtlanticOcean (0.45 #26784, 0.40 #14117, 0.36 #38045), PacificOcean (0.41 #11345, 0.35 #18383, 0.33 #21198), Euphrat (0.40 #2326, 0.29 #3734, 0.10 #9364), SyrianDesert (0.40 #1884, 0.29 #3292, 0.05 #8922), BlackSea (0.29 #2828, 0.20 #1420, 0.14 #16903), Kura (0.29 #3001, 0.09 #16890, 0.09 #18298), NorthSea (0.27 #4245, 0.27 #5652, 0.16 #7059), Drin (0.25 #319, 0.19 #49268, 0.12 #4223), Buna (0.25 #49, 0.10 #8494, 0.09 #16890)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #434 for best value:
>> intensional similarity = 6
>> extensional distance = 2
>> proper extension:  AL; KOS;
>> query: (?x399, WhiteDrin) <- ?x399[ a Country; has encompassed ?x195; has government ?x1174; is locatedIn of ?x275; is neighbor of ?x701<MK>;]

*> Best rule #21113 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 52
*> proper extension:  MACX; ES;
*> query: (?x399, ?x68) <- ?x399[ has encompassed ?x195; has language ?x1567; has religion ?x187; is locatedIn of ?x275[ is locatedInWater of ?x68;]; is neighbor of ?x177;]
*> conf = 0.03
ranks of expected_values: 1021
EVAL	GR	locatedIn!	Kefallinia	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.001	43.000	36.000	1401.000	0.500	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Kefallinia
=> 111 concepts (110 used for prediction)
PRED predicted values (max 10 best out of 1402): AtlanticOcean (0.89 #50767, 0.76 #87404, 0.76 #129696), Donau (0.50 #15519, 0.47 #21130, 0.43 #67633), MontBlanc (0.50 #11377, 0.40 #14194, 0.25 #12785), WhiteDrin (0.50 #10294, 0.33 #3252, 0.25 #8883), Drin (0.47 #21130, 0.34 #43670, 0.33 #3137), BlackDrin (0.47 #21130, 0.34 #43670, 0.33 #3111), BlackSea (0.47 #21130, 0.33 #16916, 0.33 #15506), LakeSkutari (0.47 #21130, 0.33 #4226, 0.33 #2834), PacificOcean (0.47 #45169, 0.43 #21216, 0.40 #56448), Vignemale (0.40 #15459, 0.25 #12642, 0.12 #40815)
>> best conf = 0.89
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #50767 for best value:
>> intensional similarity = 16
>> extensional distance = 17
>> proper extension:  TT; MART; PR; SVAX; BERM; WL;
>> query: (?x399, AtlanticOcean) <- ?x399[ has ethnicGroup ?x595; has government ?x1174; is locatedIn of ?x275[ has locatedIn ?x149<E>; has locatedIn ?x185[ a Country;]; has locatedIn ?x466[ has neighbor ?x302;]; is flowsInto of ?x698; is locatedInWater of ?x68;]; is locatedIn of ?x505[ a Island;]; is locatedIn of ?x1379[ a Island; has belongsToIslands ?x978;];]

*> Best rule #19720 for first EXPECTED value:
*> intensional similarity = 16
*> extensional distance = 4
*> proper extension:  D; N; RI;
*> query: (?x399, ?x68) <- ?x399[ has government ?x1174; is locatedIn of ?x275[ is flowsInto of ?x698; is locatedInWater of ?x68;]; is locatedIn of ?x739[ a Lake;]; is locatedIn of ?x2006[ a Island; has belongsToIslands ?x1053;]; is locatedIn of ?x2467[ a Mountain;]; is neighbor of ?x701[ a Country; has ethnicGroup ?x354; has religion ?x56; is locatedIn of ?x656;];]
*> conf = 0.19
ranks of expected_values: 198
EVAL	GR	locatedIn!	Kefallinia	CNN-1.+1._MA	0.000	0.000	0.000	0.005	111.000	110.000	1402.000	0.895	http://www.semwebtech.org/mondial/10/meta#locatedIn

#43-ClingmansDome
PRED entity: ClingmansDome

PRED relation: inMountains
PRED expected values: AppalachianMountains
=> 28 concepts (28 used for prediction)
PRED predicted values (max 10 best out of 48): RockyMountains (0.35 #7, 0.34 #94, 0.20 #697), EliasRange (0.20 #697, 0.12 #102, 0.12 #15), AppalachianMountains (0.20 #697, 0.08 #33, 0.06 #120), Hawaii (0.20 #697, 0.08 #68, 0.06 #155), CascadeRange (0.20 #697, 0.08 #41, 0.06 #128), SierraNevadaCalifornia (0.20 #697, 0.08 #38, 0.06 #125), Adirondacks (0.20 #697, 0.06 #1830, 0.06 #2092), AlaskaRange (0.20 #697, 0.06 #1830, 0.06 #2092), WrangellMountains (0.20 #697, 0.06 #1830, 0.06 #2092), Andes (0.09 #272, 0.07 #533, 0.06 #795)
>> best conf = 0.35
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #7 for best value:
>> intensional similarity = 3
>> extensional distance = 24
>> proper extension:  MtAdams; MtElbert; KingsPeak; MtSt.Elias; MtBona; MtMitchell; GannettPeak; MaunaKea; HumphreysPeak; MtHood; ...
>> query: (?x2012, RockyMountains) <- ?x2012[ a Mountain; has locatedIn ?x315<USA>;]

*> Best rule #697 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 147
*> proper extension:  PicoBolivar;
*> query: (?x2012, ?x337) <- ?x2012[ a Mountain; has locatedIn ?x315[ is locatedIn of ?x782[ has inMountains ?x337;]; is locatedIn of ?x1084[ a River;];];]
*> conf = 0.20
ranks of expected_values: 3
EVAL	ClingmansDome	inMountains	AppalachianMountains	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.333	28.000	28.000	48.000	0.346	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains
PRED expected values: AppalachianMountains
=> 117 concepts (117 used for prediction)
PRED predicted values (max 10 best out of 71): RockyMountains (0.35 #7, 0.34 #94, 0.26 #269), EliasRange (0.23 #2184, 0.22 #3404, 0.22 #2008), AppalachianMountains (0.23 #2184, 0.22 #3404, 0.22 #2008), Hawaii (0.23 #2184, 0.22 #3404, 0.22 #2008), CascadeRange (0.23 #2184, 0.22 #3404, 0.22 #2008), SierraNevadaCalifornia (0.23 #2184, 0.22 #3404, 0.22 #2008), Adirondacks (0.23 #2184, 0.22 #3404, 0.22 #2008), AlaskaRange (0.23 #2184, 0.22 #3404, 0.22 #2008), WrangellMountains (0.23 #2184, 0.22 #3404, 0.22 #2008), Andes (0.13 #1058, 0.12 #1408, 0.12 #273)
>> best conf = 0.35
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #7 for best value:
>> intensional similarity = 3
>> extensional distance = 24
>> proper extension:  MtAdams; MtElbert; KingsPeak; MtSt.Elias; MtBona; MtMitchell; GannettPeak; MaunaKea; HumphreysPeak; MtHood; ...
>> query: (?x2012, RockyMountains) <- ?x2012[ a Mountain; has locatedIn ?x315<USA>;]

*> Best rule #2184 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 91
*> proper extension:  ChangbaiShan;
*> query: (?x2012, ?x1405) <- ?x2012[ a Mountain; has locatedIn ?x315[ a Country; has neighbor ?x482; has wasDependentOf ?x81; is locatedIn of ?x823[ a Mountain; has inMountains ?x1405;];];]
*> conf = 0.23
ranks of expected_values: 3
EVAL	ClingmansDome	inMountains	AppalachianMountains	CNN-1.+1._MA	0.000	1.000	1.000	0.333	117.000	117.000	71.000	0.346	http://www.semwebtech.org/mondial/10/meta#inMountains

#42-ArcticOcean
PRED entity: ArcticOcean

PRED relation: mergesWith
PRED expected values: LabradorSea BeringSea
=> 31 concepts (27 used for prediction)
PRED predicted values (max 10 best out of 286): LabradorSea (0.83 #213, 0.81 #322, 0.80 #142), BeringSea (0.83 #213, 0.81 #322, 0.80 #142), ArcticOcean (0.51 #358, 0.50 #249, 0.50 #80), AtlanticOcean (0.51 #358, 0.50 #249, 0.50 #321), NorwegianSea (0.51 #358, 0.50 #249, 0.50 #321), IndianOcean (0.33 #143, 0.20 #107, 0.19 #323), PacificOcean (0.30 #190, 0.27 #226, 0.26 #335), EastChinaSea (0.20 #197, 0.17 #162, 0.08 #378), SeaofOkhotsk (0.20 #196, 0.17 #161, 0.08 #232), GulfofMexico (0.20 #135, 0.17 #171, 0.08 #242)
>> best conf = 0.83
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #213 for best value:
>> intensional similarity = 5
>> extensional distance = 8
>> proper extension:  SeaofAzov; BlackSea;
>> query: (?x263, ?x249) <- ?x263[ a Sea; has locatedIn ?x73<R>; has mergesWith ?x248; is mergesWith of ?x249;]
ranks of expected_values: 1, 2
EVAL	ArcticOcean	mergesWith	BeringSea	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	31.000	27.000	286.000	0.833	http://www.semwebtech.org/mondial/10/meta#mergesWith
EVAL	ArcticOcean	mergesWith	LabradorSea	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	31.000	27.000	286.000	0.833	http://www.semwebtech.org/mondial/10/meta#mergesWith

PRED relation: mergesWith
PRED expected values: LabradorSea BeringSea
=> 96 concepts (96 used for prediction)
PRED predicted values (max 10 best out of 287): BeringSea (0.84 #633, 0.83 #631, 0.83 #592), LabradorSea (0.84 #633, 0.83 #631, 0.83 #592), AtlanticOcean (0.78 #226, 0.64 #181, 0.61 #218), ArcticOcean (0.64 #181, 0.61 #218, 0.50 #329), NorwegianSea (0.64 #181, 0.61 #218, 0.50 #329), IndianOcean (0.40 #146, 0.33 #183, 0.25 #110), PacificOcean (0.33 #49, 0.31 #306, 0.30 #344), GulfofMexico (0.25 #138, 0.20 #174, 0.17 #211), CaribbeanSea (0.25 #123, 0.20 #159, 0.17 #196), TheChannel (0.25 #136, 0.20 #172, 0.17 #209)
>> best conf = 0.84
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #633 for best value:
>> intensional similarity = 6
>> extensional distance = 37
>> proper extension:  SeaofAzov;
>> query: (?x263, ?x1419) <- ?x263[ a Sea; has locatedIn ?x272; is mergesWith of ?x1419[ has locatedIn ?x455; is mergesWith of ?x182[ has locatedIn ?x50;];];]
ranks of expected_values: 1, 2
EVAL	ArcticOcean	mergesWith	BeringSea	CNN-1.+1._MA	1.000	1.000	1.000	1.000	96.000	96.000	287.000	0.838	http://www.semwebtech.org/mondial/10/meta#mergesWith
EVAL	ArcticOcean	mergesWith	LabradorSea	CNN-1.+1._MA	1.000	1.000	1.000	1.000	96.000	96.000	287.000	0.838	http://www.semwebtech.org/mondial/10/meta#mergesWith

#41-PNG
PRED entity: PNG

PRED relation: locatedIn!
PRED expected values: PacificOcean Bougainville
=> 40 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 1333): PacificOcean (0.93 #22727, 0.91 #35511, 0.90 #21306), BandaSea (0.93 #22727, 0.91 #35511, 0.90 #21306), SouthChinaSea (0.93 #22727, 0.33 #1561, 0.31 #4401), AndamanSea (0.93 #22727, 0.33 #1539, 0.23 #5681), IndianOcean (0.93 #22727, 0.33 #1423, 0.18 #19888), MalakkaStrait (0.93 #22727, 0.33 #1562, 0.15 #4402), SulawesiSea (0.93 #22727, 0.33 #1702, 0.13 #5682), Timor (0.93 #22727, 0.33 #2069, 0.13 #5682), JavaSea (0.93 #22727, 0.33 #1484, 0.13 #5682), Sulawesi (0.93 #22727, 0.33 #1940, 0.13 #5682)
>> best conf = 0.93
=> the first rule below is the first best rule for 38 predicted values
>> Best rule #22727 for best value:
>> intensional similarity = 7
>> extensional distance = 54
>> proper extension:  GBM; FALK;
>> query: (?x853, ?x60) <- ?x853[ a Country; has government ?x854; has language ?x247; is locatedIn of ?x1074[ a Island; has locatedIn ?x217[ is locatedIn of ?x60;];];]
ranks of expected_values: 1, 451
EVAL	PNG	locatedIn!	Bougainville	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.002	40.000	31.000	1333.000	0.932	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	PNG	locatedIn!	PacificOcean	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	40.000	31.000	1333.000	0.932	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: PacificOcean Bougainville
=> 94 concepts (94 used for prediction)
PRED predicted values (max 10 best out of 1431): PacificOcean (0.98 #31296, 0.98 #65470, 0.98 #22759), BandaSea (0.98 #31296, 0.98 #65470, 0.98 #22759), IndianOcean (0.98 #31296, 0.98 #65470, 0.98 #22759), SouthChinaSea (0.98 #31296, 0.98 #65470, 0.98 #22759), MalakkaStrait (0.98 #31296, 0.98 #65470, 0.98 #22759), AndamanSea (0.98 #31296, 0.98 #65470, 0.98 #22759), JavaSea (0.98 #31296, 0.98 #65470, 0.98 #22759), SulawesiSea (0.98 #31296, 0.98 #65470, 0.98 #22759), LakeToba (0.98 #31296, 0.98 #65470, 0.98 #22759), Timor (0.98 #31296, 0.98 #65470, 0.98 #22759)
>> best conf = 0.98
=> the first rule below is the first best rule for 38 predicted values
>> Best rule #31296 for best value:
>> intensional similarity = 19
>> extensional distance = 8
>> proper extension:  AG;
>> query: (?x853, ?x60) <- ?x853[ a Country; has encompassed ?x211; has government ?x854; has language ?x247<English>; has wasDependentOf ?x485[ is wasDependentOf of ?x138[ has language ?x635; has religion ?x95; is locatedIn of ?x137;]; is wasDependentOf of ?x803[ has ethnicGroup ?x244;];]; is locatedIn of ?x1074[ a Island; has locatedIn ?x217[ has government ?x435; is locatedIn of ?x60;];]; is locatedIn of ?x1964[ has type ?x150;];]
ranks of expected_values: 1, 39
EVAL	PNG	locatedIn!	Bougainville	CNN-1.+1._MA	0.000	0.000	0.000	0.026	94.000	94.000	1431.000	0.984	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	PNG	locatedIn!	PacificOcean	CNN-1.+1._MA	1.000	1.000	1.000	1.000	94.000	94.000	1431.000	0.984	http://www.semwebtech.org/mondial/10/meta#locatedIn

#40-ARM
PRED entity: ARM

PRED relation: neighbor!
PRED expected values: IR GE
=> 42 concepts (42 used for prediction)
PRED predicted values (max 10 best out of 160): GE (0.91 #2410, 0.91 #1444, 0.91 #3865), IR (0.91 #2410, 0.91 #1444, 0.91 #3865), R (0.50 #325, 0.47 #644, 0.47 #485), SYR (0.33 #80, 0.27 #1283, 0.27 #3381), IL (0.33 #46, 0.12 #207, 0.11 #5802), ARM (0.27 #1283, 0.27 #3381, 0.26 #3217), IRQ (0.27 #1283, 0.27 #3381, 0.26 #3217), BG (0.27 #1283, 0.27 #3381, 0.26 #3217), GR (0.27 #1283, 0.27 #3381, 0.26 #3217), TM (0.27 #1283, 0.27 #3381, 0.26 #3217)
>> best conf = 0.91
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #2410 for best value:
>> intensional similarity = 6
>> extensional distance = 83
>> proper extension:  PK; NOK;
>> query: (?x331, ?x304) <- ?x331[ has language ?x555; has neighbor ?x304; is neighbor of ?x185[ has ethnicGroup ?x638; is locatedIn of ?x98; is neighbor of ?x177;];]
ranks of expected_values: 1, 2
EVAL	ARM	neighbor!	GE	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	42.000	42.000	160.000	0.914	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	ARM	neighbor!	IR	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	42.000	42.000	160.000	0.914	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: IR GE
=> 101 concepts (101 used for prediction)
PRED predicted values (max 10 best out of 218): GE (0.96 #5899, 0.94 #7548, 0.94 #7052), IR (0.95 #5238, 0.94 #7548, 0.94 #7052), ARM (0.60 #1195, 0.36 #2170, 0.33 #219), CN (0.42 #2323, 0.39 #3635, 0.35 #3140), D (0.41 #2949, 0.39 #3442, 0.31 #2786), R (0.40 #2111, 0.40 #1953, 0.40 #1142), MK (0.40 #1093, 0.33 #2563, 0.30 #1903), SRB (0.40 #1924, 0.33 #2584, 0.29 #3589), AL (0.40 #1823, 0.33 #2483, 0.29 #3589), UZB (0.40 #861, 0.30 #1622, 0.30 #647)
>> best conf = 0.96
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5899 for best value:
>> intensional similarity = 14
>> extensional distance = 46
>> proper extension:  USA; PY; ES; GAZA;
>> query: (?x331, ?x185) <- ?x331[ a Country; has ethnicGroup ?x1193; has language ?x555[ a Language;]; has neighbor ?x185[ has ethnicGroup ?x638; has neighbor ?x302[ a Country; has religion ?x116;]; has wasDependentOf ?x1656; is locatedIn of ?x1126[ has type ?x150;];]; has religion ?x670[ a Religion;];]
ranks of expected_values: 1, 2
EVAL	ARM	neighbor!	GE	CNN-1.+1._MA	1.000	1.000	1.000	1.000	101.000	101.000	218.000	0.963	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	ARM	neighbor!	IR	CNN-1.+1._MA	1.000	1.000	1.000	1.000	101.000	101.000	218.000	0.963	http://www.semwebtech.org/mondial/10/meta#neighbor

#39-Mantaro
PRED entity: Mantaro

PRED relation: hasSource
PRED expected values: Mantaro
=> 51 concepts (41 used for prediction)
PRED predicted values (max 10 best out of 144): Tambo (0.20 #19, 0.17 #247, 0.14 #475), Maranon (0.20 #196, 0.17 #424, 0.12 #880), Ucayali (0.20 #127, 0.17 #355, 0.12 #811), Huallaga (0.14 #614, 0.12 #842, 0.11 #1070), Ene (0.14 #464, 0.12 #692, 0.11 #920), Urubamba (0.14 #576, 0.12 #804, 0.11 #1032), Perene (0.10 #1235, 0.08 #6166, 0.03 #1692), Apurimac (0.10 #1149, 0.08 #6166, 0.03 #1606), Amazonas (0.08 #6166, 0.04 #1566, 0.02 #1597), Mantaro (0.08 #6166, 0.02 #1597, 0.02 #5252)
>> best conf = 0.20
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #19 for best value:
>> intensional similarity = 5
>> extensional distance = 3
>> proper extension:  Maranon; Ucayali; Tambo;
>> query: (?x1332, Tambo) <- ?x1332[ a River; has flowsInto ?x1207; has locatedIn ?x296<PE>; is flowsInto of ?x1331;]

*> Best rule #6166 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 207
*> proper extension:  Enns; Raab;
*> query: (?x1332, ?x1691) <- ?x1332[ a River; has hasEstuary ?x694; has locatedIn ?x296[ is locatedIn of ?x1691[ a Source;];];]
*> conf = 0.08
ranks of expected_values: 10
EVAL	Mantaro	hasSource	Mantaro	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.100	51.000	41.000	144.000	0.200	http://www.semwebtech.org/mondial/10/meta#hasSource

PRED relation: hasSource
PRED expected values: Mantaro
=> 171 concepts (171 used for prediction)
PRED predicted values (max 10 best out of 237): Ene (0.54 #5724, 0.33 #237, 0.21 #14449), RioSaoFrancisco (0.33 #130, 0.08 #1731, 0.03 #4021), Tambo (0.17 #476, 0.12 #704, 0.11 #8022), Maranon (0.17 #653, 0.12 #881, 0.11 #8022), Ucayali (0.17 #584, 0.12 #812, 0.11 #8022), Huallaga (0.12 #843, 0.11 #8022, 0.11 #1072), Urubamba (0.12 #805, 0.11 #1034, 0.11 #8021), Perene (0.11 #8022, 0.11 #8021, 0.10 #1238), Apurimac (0.11 #8022, 0.11 #8021, 0.10 #1152), Amazonas (0.11 #8022, 0.10 #10089, 0.09 #15137)
>> best conf = 0.54
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5724 for best value:
>> intensional similarity = 9
>> extensional distance = 39
>> proper extension:  LakeMaiNdombe;
>> query: (?x1332, ?x264) <- ?x1332[ has flowsInto ?x1207[ a River; has flowsInto ?x987[ a River; has hasSource ?x1691; has locatedIn ?x296;]; is flowsInto of ?x1049[ has hasEstuary ?x1518; has hasSource ?x264;];];]

*> Best rule #8022 for first EXPECTED value:
*> intensional similarity = 11
*> extensional distance = 57
*> proper extension:  Jubba;
*> query: (?x1332, ?x430) <- ?x1332[ a River; has flowsInto ?x1207; has hasEstuary ?x694; has locatedIn ?x296[ has government ?x700; is locatedIn of ?x430[ a Source;]; is locatedIn of ?x1781[ has hasSource ?x1627;]; is neighbor of ?x202;]; is flowsInto of ?x1331;]
*> conf = 0.11
ranks of expected_values: 11
EVAL	Mantaro	hasSource	Mantaro	CNN-1.+1._MA	0.000	0.000	0.000	0.091	171.000	171.000	237.000	0.542	http://www.semwebtech.org/mondial/10/meta#hasSource

#38-ES
PRED entity: ES

PRED relation: neighbor!
PRED expected values: HCA
=> 47 concepts (46 used for prediction)
PRED predicted values (max 10 best out of 222): HCA (0.90 #5998, 0.90 #3886, 0.89 #4210), CR (0.33 #55, 0.17 #380, 0.12 #543), BZ (0.26 #4372, 0.25 #2267, 0.25 #275), MEX (0.26 #4372, 0.25 #2267, 0.25 #248), ES (0.26 #4372, 0.25 #2267, 0.12 #5668), USA (0.25 #217, 0.17 #379, 0.12 #542), BR (0.23 #1227, 0.15 #741, 0.12 #1711), BOL (0.19 #762, 0.17 #439, 0.13 #1248), PE (0.19 #1022, 0.17 #375, 0.13 #1184), RA (0.17 #391, 0.15 #714, 0.15 #1038)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #5998 for best value:
>> intensional similarity = 7
>> extensional distance = 134
>> proper extension:  UAE; TAD; SP; CN; IR; WEST; LB; WSA; SA; GAZA;
>> query: (?x654, ?x1364) <- ?x654[ has neighbor ?x1364; has religion ?x352[ is religion of ?x156<HR>; is religion of ?x163[ has ethnicGroup ?x58;];]; is neighbor of ?x181;]
ranks of expected_values: 1
EVAL	ES	neighbor!	HCA	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	47.000	46.000	222.000	0.899	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: HCA
=> 96 concepts (96 used for prediction)
PRED predicted values (max 10 best out of 229): HCA (0.94 #4468, 0.93 #4467, 0.91 #12145), BZ (0.36 #3465, 0.33 #1100, 0.33 #443), MEX (0.36 #3465, 0.33 #416, 0.33 #85), USA (0.36 #3465, 0.33 #327, 0.33 #220), ES (0.36 #3465, 0.33 #441, 0.26 #3466), NIC (0.36 #3465, 0.26 #3466, 0.21 #2637), CR (0.36 #3465, 0.25 #658, 0.25 #550), PE (0.33 #1038, 0.25 #1531, 0.22 #1858), BR (0.33 #1901, 0.21 #4226, 0.20 #2565), CO (0.29 #3341, 0.25 #1519, 0.22 #1846)
>> best conf = 0.94
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4468 for best value:
>> intensional similarity = 13
>> extensional distance = 24
>> proper extension:  NAM;
>> query: (?x654, ?x1364) <- ?x654[ has encompassed ?x521; has language ?x796; has neighbor ?x1364[ has ethnicGroup ?x162;]; has religion ?x1547[ a Religion; is religion of ?x550[ has government ?x2004;];]; has wasDependentOf ?x149[ is wasDependentOf of ?x202[ is locatedIn of ?x182<AtlanticOcean>;];]; is locatedIn of ?x282;]
ranks of expected_values: 1
EVAL	ES	neighbor!	HCA	CNN-1.+1._MA	1.000	1.000	1.000	1.000	96.000	96.000	229.000	0.936	http://www.semwebtech.org/mondial/10/meta#neighbor

#37-Dnjestr
PRED entity: Dnjestr

PRED relation: locatedIn
PRED expected values: UA
=> 35 concepts (35 used for prediction)
PRED predicted values (max 10 best out of 76): UA (0.75 #6922, 0.70 #4535, 0.69 #4297), MD (0.69 #4297, 0.60 #7640, 0.59 #5487), R (0.52 #3824, 0.35 #2864, 0.35 #2631), USA (0.47 #4131, 0.20 #5082, 0.18 #5321), PL (0.31 #1952, 0.19 #2191, 0.18 #1474), RO (0.30 #1229, 0.25 #275, 0.25 #37), SF (0.24 #2518, 0.17 #3235, 0.17 #2996), D (0.22 #4318, 0.19 #5746, 0.16 #6225), AUS (0.14 #521, 0.09 #3388, 0.06 #2671), SK (0.14 #4296, 0.14 #3340, 0.13 #1906)
>> best conf = 0.75
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #6922 for best value:
>> intensional similarity = 8
>> extensional distance = 117
>> proper extension:  Vaesterdalaelv; Araguaia; Würm; Lulua;
>> query: (?x1199, ?x303) <- ?x1199[ a Estuary; is hasEstuary of ?x1393[ a River; has flowsInto ?x98[ has locatedIn ?x303[ is locatedIn of ?x1394[ a Source;];];]; has hasSource ?x1394;];]
ranks of expected_values: 1
EVAL	Dnjestr	locatedIn	UA	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	35.000	35.000	76.000	0.746	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: UA
=> 112 concepts (112 used for prediction)
PRED predicted values (max 10 best out of 83): UA (0.80 #24988, 0.75 #26195, 0.73 #6481), MD (0.68 #17782, 0.68 #19943, 0.67 #18743), R (0.54 #16100, 0.53 #9850, 0.46 #15377), RO (0.50 #993, 0.32 #15609, 0.29 #1676), USA (0.48 #18576, 0.48 #17131, 0.27 #4633), D (0.46 #19725, 0.42 #11788, 0.30 #14432), F (0.36 #6249, 0.19 #18753, 0.12 #2406), SRB (0.36 #9073, 0.33 #9550, 0.25 #12435), I (0.33 #3168, 0.27 #13739, 0.21 #9175), SF (0.33 #6614, 0.27 #9737, 0.23 #7338)
>> best conf = 0.80
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #24988 for best value:
>> intensional similarity = 16
>> extensional distance = 68
>> proper extension:  Busira; Lomami; Lualaba; Lukenie; Tshuapa; Lukuga; Fimi; Uelle; Aruwimi; Ruki; ...
>> query: (?x1199, ?x303) <- ?x1199[ a Estuary; is hasEstuary of ?x1393[ a River; has flowsInto ?x98[ has locatedIn ?x303[ a Country; has ethnicGroup ?x58; has neighbor ?x73; has religion ?x95<Protestant>; has wasDependentOf ?x903;];]; has locatedIn ?x303; has locatedIn ?x886[ a Country; has encompassed ?x195; has ethnicGroup ?x1438;];];]
ranks of expected_values: 1
EVAL	Dnjestr	locatedIn	UA	CNN-1.+1._MA	1.000	1.000	1.000	1.000	112.000	112.000	83.000	0.805	http://www.semwebtech.org/mondial/10/meta#locatedIn

#36-SouthChinaSea
PRED entity: SouthChinaSea

PRED relation: mergesWith
PRED expected values: EastChinaSea
=> 35 concepts (27 used for prediction)
PRED predicted values (max 10 best out of 146): EastChinaSea (0.85 #223, 0.85 #222, 0.83 #147), IndianOcean (0.53 #335, 0.48 #260, 0.46 #598), SouthChinaSea (0.53 #335, 0.48 #260, 0.46 #598), SulawesiSea (0.53 #335, 0.48 #260, 0.46 #598), AndamanSea (0.53 #335, 0.48 #260, 0.46 #598), SeaofJapan (0.33 #12, 0.17 #449, 0.10 #385), GulfofBengal (0.33 #46, 0.10 #119, 0.08 #194), YellowSea (0.33 #11, 0.08 #159, 0.07 #309), AtlanticOcean (0.29 #190, 0.29 #115, 0.27 #303), ArcticOcean (0.22 #421, 0.17 #308, 0.17 #270)
>> best conf = 0.85
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #223 for best value:
>> intensional similarity = 5
>> extensional distance = 22
>> proper extension:  SeaofAzov; BlackSea;
>> query: (?x384, ?x620) <- ?x384[ has locatedIn ?x91[ has religion ?x116;]; is flowsInto of ?x1152; is mergesWith of ?x620[ has locatedIn ?x117;];]
>> Best rule #222 for best value:
>> intensional similarity = 6
>> extensional distance = 22
>> proper extension:  SeaofAzov; BlackSea;
>> query: (?x384, ?x241) <- ?x384[ has locatedIn ?x91[ has religion ?x116;]; is flowsInto of ?x1152; is mergesWith of ?x241; is mergesWith of ?x620[ has locatedIn ?x117;];]
ranks of expected_values: 1
EVAL	SouthChinaSea	mergesWith	EastChinaSea	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	35.000	27.000	146.000	0.846	http://www.semwebtech.org/mondial/10/meta#mergesWith

PRED relation: mergesWith
PRED expected values: EastChinaSea
=> 80 concepts (80 used for prediction)
PRED predicted values (max 10 best out of 574): EastChinaSea (0.85 #756, 0.85 #1256, 0.84 #569), IndianOcean (0.49 #1217, 0.48 #1141, 0.46 #1373), SouthChinaSea (0.49 #1217, 0.48 #1141, 0.46 #1373), SulawesiSea (0.49 #1217, 0.48 #1141, 0.46 #1373), AndamanSea (0.49 #1217, 0.48 #1141, 0.46 #1373), BandaSea (0.44 #439, 0.40 #327, 0.33 #154), AtlanticOcean (0.36 #685, 0.35 #537, 0.32 #724), SeaofJapan (0.33 #356, 0.33 #129, 0.33 #12), YellowSea (0.33 #128, 0.25 #79, 0.23 #457), GulfofBengal (0.33 #164, 0.22 #426, 0.20 #314)
>> best conf = 0.85
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #756 for best value:
>> intensional similarity = 12
>> extensional distance = 20
>> proper extension:  SeaofAzov; BlackSea;
>> query: (?x384, ?x241) <- ?x384[ has locatedIn ?x773[ has ethnicGroup ?x298[ a EthnicGroup;]; has language ?x247;]; has mergesWith ?x282[ has locatedIn ?x272[ has encompassed ?x521;]; has locatedIn ?x1944[ has government ?x92;];]; is flowsInto of ?x1152[ has locatedIn ?x463;]; is mergesWith of ?x241;]
ranks of expected_values: 1
EVAL	SouthChinaSea	mergesWith	EastChinaSea	CNN-1.+1._MA	1.000	1.000	1.000	1.000	80.000	80.000	574.000	0.852	http://www.semwebtech.org/mondial/10/meta#mergesWith

#35-Gheschm
PRED entity: Gheschm

PRED relation: locatedInWater
PRED expected values: GulfofOman
=> 16 concepts (13 used for prediction)
PRED predicted values (max 10 best out of 50): MediterraneanSea (0.76 #58, 0.12 #188, 0.12 #231), AtlanticOcean (0.45 #92, 0.30 #135, 0.29 #179), PacificOcean (0.35 #102, 0.24 #145, 0.23 #189), GulfofOman (0.20 #34, 0.09 #171, 0.05 #85), IndianOcean (0.15 #87, 0.10 #130, 0.09 #174), ArabianSea (0.11 #172), CaribbeanSea (0.11 #104, 0.11 #147, 0.10 #191), Donau (0.11 #46, 0.02 #176, 0.02 #394), NorthSea (0.09 #131, 0.08 #175, 0.08 #218), Dascht-e-Lut (0.05 #85, 0.02 #215, 0.01 #128)
>> best conf = 0.76
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #58 for best value:
>> intensional similarity = 13
>> extensional distance = 36
>> proper extension:  VelikiRatnoOstrvo; ZitnyOstrov; MargitSziget; MalyZitnyOstrov;
>> query: (?x1736, MediterraneanSea) <- ?x1736[ a Island; has locatedInWater ?x918[ has locatedIn ?x304[ has ethnicGroup ?x305; has government ?x2318; has neighbor ?x185<TR>;]; has locatedIn ?x639[ a Country; has government ?x640;]; has locatedIn ?x1963[ has ethnicGroup ?x2169; has wasDependentOf ?x81;];];]

*> Best rule #34 for first EXPECTED value:
*> intensional similarity = 3
*> extensional distance = 3
*> proper extension:  Khark; Bahrain; Lavan;
*> query: (?x1736, GulfofOman) <- ?x1736[ a Island; has locatedInWater ?x918<PersianGulf>;]
*> conf = 0.20
ranks of expected_values: 4
EVAL	Gheschm	locatedInWater	GulfofOman	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.250	16.000	13.000	50.000	0.763	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater
PRED expected values: GulfofOman
=> 32 concepts (32 used for prediction)
PRED predicted values (max 10 best out of 61): PacificOcean (0.92 #479, 0.89 #526, 0.77 #573), MediterraneanSea (0.85 #244, 0.81 #290, 0.76 #337), IndianOcean (0.82 #137, 0.74 #183, 0.40 #417), SouthChinaSea (0.71 #111, 0.21 #390, 0.16 #670), AtlanticOcean (0.69 #748, 0.54 #841, 0.45 #888), Bahrain (0.36 #86, 0.15 #412, 0.05 #925), JavaSea (0.21 #144, 0.19 #190, 0.12 #98), GulfofOman (0.20 #34, 0.19 #1389, 0.17 #85), NorthSea (0.20 #744, 0.09 #1117, 0.09 #1164), SyrianDesert (0.18 #273, 0.12 #87, 0.11 #462)
>> best conf = 0.92
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #479 for best value:
>> intensional similarity = 34
>> extensional distance = 58
>> proper extension:  Tongatapu; Fakaofo; Tasmania; Hokkaido; Futuna; Tinian; Niihau; Guadalcanal; Niue; Tutuila; ...
>> query: (?x1736, PacificOcean) <- ?x1736[ a Island; has locatedInWater ?x918[ has locatedIn ?x174[ a Country; has ethnicGroup ?x826; has wasDependentOf ?x81<GB>;]; has locatedIn ?x302[ a Country; has ethnicGroup ?x557; has government ?x254; has neighbor ?x185; has religion ?x116; has wasDependentOf ?x485;]; has locatedIn ?x304[ has ethnicGroup ?x244; has language ?x511; has neighbor ?x83; has religion ?x187<Muslim>; is locatedIn of ?x1337<CaspianSea>; is neighbor of ?x332<AZ>;]; has locatedIn ?x639[ a Country; has encompassed ?x175; has government ?x640; has wasDependentOf ?x1027; is neighbor of ?x668;]; has locatedIn ?x1705[ a Country; has ethnicGroup ?x380<Asian>;];];]

*> Best rule #34 for first EXPECTED value:
*> intensional similarity = 3
*> extensional distance = 3
*> proper extension:  Khark; Bahrain; Lavan;
*> query: (?x1736, GulfofOman) <- ?x1736[ a Island; has locatedInWater ?x918<PersianGulf>;]
*> conf = 0.20
ranks of expected_values: 8
EVAL	Gheschm	locatedInWater	GulfofOman	CNN-1.+1._MA	0.000	0.000	1.000	0.125	32.000	32.000	61.000	0.917	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#34-Uluru
PRED entity: Uluru

PRED relation: type
PRED expected values: "monolith"
=> 51 concepts (51 used for prediction)
PRED predicted values (max 10 best out of 10): "volcano" (0.36 #183, 0.32 #102, 0.27 #297), "volcanic" (0.21 #98, 0.18 #66, 0.17 #374), "sand" (0.20 #389, 0.15 #226, 0.14 #340), "dam" (0.20 #389, 0.15 #226, 0.14 #340), "salt" (0.20 #389, 0.15 #226, 0.14 #340), "lime" (0.20 #389, 0.15 #226, 0.14 #340), "atoll" (0.02 #217), "caldera" (0.02 #212), "granite" (0.01 #386, 0.01 #207), "monolith" (0.01 #204)
>> best conf = 0.36
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #183 for best value:
>> intensional similarity = 6
>> extensional distance = 74
>> proper extension:  Mt.Victoria;
>> query: (?x2394, "volcano") <- ?x2394[ a Mountain; has locatedIn ?x196[ has religion ?x56; has wasDependentOf ?x81; is locatedIn of ?x282<PacificOcean>;];]

*> Best rule #204 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 98
*> proper extension:  Karisimbi; AsoRock; Ruwenzori; Fako; PicoBolivar;
*> query: (?x2394, "monolith") <- ?x2394[ a Mountain; has locatedIn ?x196[ has religion ?x56; has wasDependentOf ?x81; is locatedIn of ?x1021[ a Lake;];];]
*> conf = 0.01
ranks of expected_values: 10
EVAL	Uluru	type	"monolith"	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.100	51.000	51.000	10.000	0.355	http://www.semwebtech.org/mondial/10/meta#type

PRED relation: type
PRED expected values: "monolith"
=> 134 concepts (134 used for prediction)
PRED predicted values (max 10 best out of 12): "volcano" (0.40 #122, 0.40 #106, 0.32 #186), "sand" (0.21 #215, 0.21 #214, 0.21 #213), "dam" (0.21 #215, 0.21 #214, 0.21 #213), "salt" (0.21 #215, 0.21 #214, 0.21 #213), "lime" (0.21 #215, 0.21 #214, 0.21 #213), "volcanic" (0.20 #118, 0.16 #182, 0.15 #510), "granite" (0.04 #194, 0.04 #210, 0.02 #474), "monolith" (0.04 #207, 0.01 #471, 0.01 #487), "caldera" (0.03 #414, 0.02 #447, 0.02 #576), "atoll" (0.02 #1167, 0.01 #1296)
>> best conf = 0.40
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #122 for best value:
>> intensional similarity = 14
>> extensional distance = 18
>> proper extension:  Pulog; Leuser; MountApo; Tambora; Mantalingajan; Kerinci; Kanlaon; Rantekombola; Semeru; Rinjani; ...
>> query: (?x2394, "volcano") <- ?x2394[ a Mountain; has locatedIn ?x196[ has ethnicGroup ?x197; has religion ?x56[ is religion of ?x73<R>;]; has religion ?x95<Protestant>; has religion ?x187<Muslim>; has religion ?x462<Buddhist>; is locatedIn of ?x433[ a Island;];];]
>> Best rule #106 for best value:
>> intensional similarity = 9
>> extensional distance = 18
>> proper extension:  Meru; Kilimanjaro; Mawenzi;
>> query: (?x2394, "volcano") <- ?x2394[ a Mountain; has locatedIn ?x196[ has ethnicGroup ?x197; has religion ?x56[ a Religion; is religion of ?x130<KGZ>;]; is locatedIn of ?x60<IndianOcean>;];]

*> Best rule #207 for first EXPECTED value:
*> intensional similarity = 8
*> extensional distance = 26
*> proper extension:  Soufriere;
*> query: (?x2394, "monolith") <- ?x2394[ a Mountain; has locatedIn ?x196[ a Country; has government ?x1903; has wasDependentOf ?x81<GB>; is locatedIn of ?x1465[ has type ?x136;];];]
*> conf = 0.04
ranks of expected_values: 8
EVAL	Uluru	type	"monolith"	CNN-1.+1._MA	0.000	0.000	1.000	0.125	134.000	134.000	12.000	0.400	http://www.semwebtech.org/mondial/10/meta#type

#33-GUY
PRED entity: GUY

PRED relation: neighbor!
PRED expected values: YV BR
=> 48 concepts (44 used for prediction)
PRED predicted values (max 10 best out of 230): BR (0.91 #3075, 0.90 #1131, 0.90 #1943), YV (0.91 #3075, 0.90 #1131, 0.90 #1943), MYA (0.38 #64, 0.27 #225, 0.25 #548), PE (0.27 #5201, 0.26 #5529, 0.26 #5530), GUY (0.27 #5201, 0.26 #5529, 0.26 #5530), FGU (0.27 #5201, 0.26 #5529, 0.26 #5530), BOL (0.27 #5201, 0.26 #5529, 0.26 #5530), CO (0.27 #5201, 0.26 #5529, 0.26 #5530), PY (0.27 #5201, 0.26 #5529, 0.26 #5530), RA (0.27 #5201, 0.26 #5529, 0.12 #710)
>> best conf = 0.91
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #3075 for best value:
>> intensional similarity = 7
>> extensional distance = 54
>> proper extension:  UAE; D; TAD; SP; CN; CH; IR; KAZ; ETH; SA;
>> query: (?x351, ?x179) <- ?x351[ a Country; has encompassed ?x521; has ethnicGroup ?x79; has government ?x435; has neighbor ?x179; has religion ?x187<Muslim>;]
ranks of expected_values: 1, 2
EVAL	GUY	neighbor!	BR	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	48.000	44.000	230.000	0.911	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	GUY	neighbor!	YV	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	48.000	44.000	230.000	0.911	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: YV BR
=> 107 concepts (107 used for prediction)
PRED predicted values (max 10 best out of 239): YV (0.93 #9580, 0.92 #9578, 0.92 #9577), BR (0.92 #5764, 0.92 #9578, 0.92 #9577), BOL (0.50 #2412, 0.47 #2297, 0.40 #1263), CO (0.47 #2297, 0.40 #695, 0.36 #2298), PE (0.47 #2297, 0.33 #2348, 0.32 #10410), PY (0.47 #2297, 0.33 #2369, 0.32 #10410), ROU (0.47 #2297, 0.28 #11409, 0.27 #13407), EC (0.47 #2297, 0.20 #794, 0.19 #9582), PA (0.47 #2297, 0.19 #9582, 0.17 #1437), RCH (0.40 #692, 0.20 #1184, 0.17 #2333)
>> best conf = 0.93
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #9580 for best value:
>> intensional similarity = 8
>> extensional distance = 50
>> proper extension:  BG; IRQ; AZ; CAM; EAT; MA; MNG; WAL;
>> query: (?x351, ?x345) <- ?x351[ has ethnicGroup ?x79; has government ?x435; has neighbor ?x345[ has wasDependentOf ?x149;]; has religion ?x187<Muslim>; has wasDependentOf ?x81; is locatedIn of ?x182;]
ranks of expected_values: 1, 2
EVAL	GUY	neighbor!	BR	CNN-1.+1._MA	1.000	1.000	1.000	1.000	107.000	107.000	239.000	0.931	http://www.semwebtech.org/mondial/10/meta#neighbor
EVAL	GUY	neighbor!	YV	CNN-1.+1._MA	1.000	1.000	1.000	1.000	107.000	107.000	239.000	0.931	http://www.semwebtech.org/mondial/10/meta#neighbor

#32-Loire
PRED entity: Loire

PRED relation: locatedIn
PRED expected values: F
=> 28 concepts (28 used for prediction)
PRED predicted values (max 10 best out of 120): F (0.82 #2854, 0.81 #2616, 0.68 #3566), ZRE (0.40 #2377, 0.40 #2218, 0.08 #4598), USA (0.36 #2449, 0.33 #2688, 0.22 #1022), D (0.29 #1920, 0.06 #1425, 0.05 #6202), R (0.27 #3332, 0.25 #3571, 0.25 #4285), CH (0.20 #770, 0.11 #294, 0.10 #532), A (0.17 #1999, 0.04 #4141, 0.03 #5569), E (0.15 #1215, 0.14 #1453, 0.11 #977), CN (0.14 #2433, 0.13 #2672, 0.08 #3860), PE (0.13 #1729, 0.06 #1017, 0.05 #1255)
>> best conf = 0.82
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2854 for best value:
>> intensional similarity = 8
>> extensional distance = 37
>> proper extension:  DarlingRiver; EucumbeneRiver; MurrumbidgeeRiver;
>> query: (?x2420, ?x78) <- ?x2420[ a Source; is hasSource of ?x1257[ a River; has flowsInto ?x182; has hasEstuary ?x2424; has locatedIn ?x78[ has encompassed ?x195; is dependentOf of ?x61;];];]
ranks of expected_values: 1
EVAL	Loire	locatedIn	F	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	28.000	28.000	120.000	0.821	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: F
=> 80 concepts (80 used for prediction)
PRED predicted values (max 10 best out of 126): F (0.81 #10504, 0.76 #10029, 0.74 #12892), E (0.50 #742, 0.23 #2649, 0.23 #2409), USA (0.48 #9862, 0.24 #12964, 0.23 #7398), CDN (0.47 #5076, 0.31 #10092, 0.27 #10329), CH (0.40 #3396, 0.38 #3636, 0.27 #7457), R (0.39 #12658, 0.38 #14085, 0.27 #14801), I (0.35 #4104, 0.33 #4584, 0.27 #7687), UA (0.33 #4845, 0.29 #6756, 0.18 #11291), D (0.33 #9331, 0.11 #16725, 0.10 #15772), A (0.30 #5349, 0.15 #9410, 0.07 #15851)
>> best conf = 0.81
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #10504 for best value:
>> intensional similarity = 11
>> extensional distance = 28
>> proper extension:  DetroitRiver;
>> query: (?x2420, ?x78) <- ?x2420[ is hasSource of ?x1257[ a River; has flowsInto ?x182[ has locatedIn ?x50;]; has locatedIn ?x78[ has encompassed ?x195; has government ?x435; has language ?x51<French>; has religion ?x352<RomanCatholic>;];];]
ranks of expected_values: 1
EVAL	Loire	locatedIn	F	CNN-1.+1._MA	1.000	1.000	1.000	1.000	80.000	80.000	126.000	0.812	http://www.semwebtech.org/mondial/10/meta#locatedIn

#31-ZRE
PRED entity: ZRE

PRED relation: religion
PRED expected values: RomanCatholic
=> 22 concepts (22 used for prediction)
PRED predicted values (max 10 best out of 23): RomanCatholic (0.60 #240, 0.59 #279, 0.56 #318), Christian (0.48 #586, 0.48 #626, 0.47 #627), ChristianOrthodox (0.40 #235, 0.29 #274, 0.28 #313), Jewish (0.16 #823, 0.12 #392, 0.10 #236), Buddhist (0.16 #823, 0.10 #400, 0.10 #283), Hindu (0.16 #823, 0.10 #635, 0.08 #674), Mormon (0.16 #823, 0.09 #219, 0.05 #258), Anglican (0.16 #823, 0.08 #643, 0.08 #721), JehovasWitnesses (0.16 #823, 0.05 #565, 0.04 #605), Seventh-DayAdventist (0.16 #823, 0.03 #714, 0.03 #636)
>> best conf = 0.60
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #240 for best value:
>> intensional similarity = 6
>> extensional distance = 18
>> proper extension:  AUS;
>> query: (?x348, RomanCatholic) <- ?x348[ has wasDependentOf ?x543; is locatedIn of ?x389[ a Estuary;]; is locatedIn of ?x1434[ a Source; has inMountains ?x1066;];]
ranks of expected_values: 1
EVAL	ZRE	religion	RomanCatholic	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	22.000	22.000	23.000	0.600	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion
PRED expected values: RomanCatholic
=> 82 concepts (82 used for prediction)
PRED predicted values (max 10 best out of 33): RomanCatholic (0.83 #753, 0.79 #1777, 0.79 #1738), Christian (0.67 #357, 0.65 #826, 0.64 #1141), ChristianOrthodox (0.40 #2563, 0.37 #2999, 0.30 #1337), Catholic (0.40 #2563, 0.37 #2999, 0.29 #120), Jewish (0.31 #1652, 0.30 #513, 0.29 #120), Anglican (0.31 #1652, 0.29 #120, 0.23 #2087), Hindu (0.31 #1652, 0.29 #120, 0.23 #2087), Buddhist (0.31 #1652, 0.29 #120, 0.23 #2087), JehovasWitnesses (0.31 #1652, 0.29 #120, 0.23 #2087), Mormon (0.31 #1652, 0.29 #120, 0.22 #1928)
>> best conf = 0.83
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #753 for best value:
>> intensional similarity = 9
>> extensional distance = 21
>> proper extension:  BVIR; FARX; AXA; GUAD; TT; PR; BERM; BDS; WL;
>> query: (?x348, RomanCatholic) <- ?x348[ a Country; has encompassed ?x213; has ethnicGroup ?x2121; has religion ?x95<Protestant>; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x1770[ is flowsInto of ?x601;];]
ranks of expected_values: 1
EVAL	ZRE	religion	RomanCatholic	CNN-1.+1._MA	1.000	1.000	1.000	1.000	82.000	82.000	33.000	0.826	http://www.semwebtech.org/mondial/10/meta#religion

#30-Europe
PRED entity: Europe

PRED relation: encompassed!
PRED expected values: BG S B GBM
=> 5 concepts (5 used for prediction)
PRED predicted values (max 10 best out of 193): B (0.81 #703, 0.81 #702, 0.81 #175), S (0.81 #703, 0.81 #702, 0.81 #175), BG (0.81 #703, 0.81 #702, 0.73 #527), SYR (0.81 #702, 0.81 #175, 0.73 #527), UZB (0.81 #702, 0.73 #527, 0.46 #349), IR (0.81 #702, 0.73 #527, 0.46 #349), CN (0.81 #702, 0.73 #527, 0.46 #349), IRQ (0.81 #702, 0.73 #527, 0.46 #349), GE (0.81 #702, 0.73 #527, 0.33 #53), KGZ (0.81 #702, 0.73 #527, 0.33 #14)
>> best conf = 0.81
=> the first rule below is the first best rule for 3 predicted values
>> Best rule #703 for best value:
>> intensional similarity = 32
>> extensional distance = 4
>> proper extension:  Africa;
>> query: (?x195, ?x543) <- ?x195[ a Continent; is encompassed of ?x78[ a Country; has government ?x435; has language ?x51; has neighbor ?x543; is locatedIn of ?x1967[ a Island;];]; is encompassed of ?x154[ has ethnicGroup ?x162; has government ?x2243; is locatedIn of ?x153;]; is encompassed of ?x156[ a Country; has ethnicGroup ?x160;]; is encompassed of ?x176[ a Country; is locatedIn of ?x133[ is flowsInto of ?x132;]; is neighbor of ?x177;]; is encompassed of ?x575[ a Country; is locatedIn of ?x1866[ has belongsToIslands ?x795;];]; is encompassed of ?x1027[ is locatedIn of ?x1162[ has type ?x150;]; is locatedIn of ?x1352[ is hasEstuary of ?x1519;]; is wasDependentOf of ?x192;]; is encompassed of ?x1826[ has religion ?x95;];]
ranks of expected_values: 1, 2, 3, 23
EVAL	Europe	encompassed!	GBM	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.050	5.000	5.000	193.000	0.815	http://www.semwebtech.org/mondial/10/meta#encompassed
EVAL	Europe	encompassed!	B	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	5.000	5.000	193.000	0.815	http://www.semwebtech.org/mondial/10/meta#encompassed
EVAL	Europe	encompassed!	S	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	5.000	5.000	193.000	0.815	http://www.semwebtech.org/mondial/10/meta#encompassed
EVAL	Europe	encompassed!	BG	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	5.000	5.000	193.000	0.815	http://www.semwebtech.org/mondial/10/meta#encompassed

PRED relation: encompassed!
PRED expected values: BG S B GBM
=> 5 concepts (5 used for prediction)
PRED predicted values (max 10 best out of 200): BG (0.93 #541, 0.91 #542, 0.90 #544), S (0.93 #541, 0.91 #542, 0.90 #544), CN (0.93 #541, 0.91 #542, 0.90 #544), UZB (0.93 #541, 0.91 #542, 0.90 #544), KGZ (0.93 #541, 0.91 #542, 0.90 #544), B (0.93 #541, 0.91 #542, 0.90 #544), IR (0.93 #541, 0.91 #542, 0.90 #544), GE (0.93 #541, 0.91 #542, 0.90 #544), MNG (0.93 #541, 0.91 #542, 0.90 #544), AZ (0.93 #541, 0.91 #542, 0.90 #544)
>> best conf = 0.93
=> the first rule below is the first best rule for 13 predicted values
>> Best rule #541 for best value:
>> intensional similarity = 94
>> extensional distance = 1
>> proper extension:  America;
>> query: (?x195, ?x302) <- ?x195[ is encompassed of ?x73[ has ethnicGroup ?x1326; has neighbor ?x232[ a Country; is locatedIn of ?x231;]; is locatedIn of ?x72; is locatedIn of ?x465[ a River;];]; is encompassed of ?x106[ a Country; has ethnicGroup ?x775; has language ?x2123; is locatedIn of ?x105[ a Estuary;]; is locatedIn of ?x203[ has hasSource ?x183;]; is locatedIn of ?x307[ a Source;];]; is encompassed of ?x154[ has government ?x2243; is locatedIn of ?x153[ a Island;]; is locatedIn of ?x1833[ is locatedInWater of ?x495;];]; is encompassed of ?x185[ has neighbor ?x302; is locatedIn of ?x184;]; is encompassed of ?x207[ a Country; is locatedIn of ?x86; is locatedIn of ?x1156[ a Estuary;]; is wasDependentOf of ?x1184;]; is encompassed of ?x222[ has language ?x555; has religion ?x95<Protestant>; is locatedIn of ?x221;]; is encompassed of ?x399[ a Country; has ethnicGroup ?x595; has wasDependentOf ?x1656; is locatedIn of ?x2057[ has inMountains ?x1323;];]; is encompassed of ?x424[ has ethnicGroup ?x160; has language ?x684; is locatedIn of ?x1455[ a Source;]; is locatedIn of ?x1838[ a River;];]; is encompassed of ?x455[ has language ?x1850; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x806[ has type ?x150;]; is locatedIn of ?x1874[ a Estuary;]; is locatedIn of ?x2272[ a Source;];]; is encompassed of ?x701[ a Country; has ethnicGroup ?x2224[ a EthnicGroup;]; has government ?x254; has language ?x511; has religion ?x56[ is religion of ?x476<ETH>;]; has religion ?x187[ a Religion; is religion of ?x351<GUY>;]; has religion ?x352<RomanCatholic>; is locatedIn of ?x656[ a River; has flowsInto ?x698;]; is locatedIn of ?x1374[ a Lake;]; is neighbor of ?x177;]; is encompassed of ?x793[ a Country; has government ?x92; is locatedIn of ?x146[ is flowsInto of ?x660; is locatedInWater of ?x145;];]; is encompassed of ?x850[ has government ?x435<"republic">; has language ?x247; is locatedIn of ?x777;];]
ranks of expected_values: 1, 2, 6, 39
EVAL	Europe	encompassed!	GBM	CNN-1.+1._MA	0.000	0.000	0.000	0.028	5.000	5.000	200.000	0.926	http://www.semwebtech.org/mondial/10/meta#encompassed
EVAL	Europe	encompassed!	B	CNN-1.+1._MA	0.000	0.000	1.000	0.250	5.000	5.000	200.000	0.926	http://www.semwebtech.org/mondial/10/meta#encompassed
EVAL	Europe	encompassed!	S	CNN-1.+1._MA	1.000	1.000	1.000	1.000	5.000	5.000	200.000	0.926	http://www.semwebtech.org/mondial/10/meta#encompassed
EVAL	Europe	encompassed!	BG	CNN-1.+1._MA	1.000	1.000	1.000	1.000	5.000	5.000	200.000	0.926	http://www.semwebtech.org/mondial/10/meta#encompassed

#29-Murgab
PRED entity: Murgab

PRED relation: hasEstuary
PRED expected values: Murgab
=> 46 concepts (40 used for prediction)
PRED predicted values (max 10 best out of 184): Pjandsh (0.17 #239, 0.17 #13, 0.06 #465), Bartang (0.17 #36, 0.06 #488, 0.03 #715), Syrdarja (0.17 #198, 0.06 #650, 0.03 #877), Irtysch (0.03 #764, 0.02 #1444, 0.02 #1671), Ili (0.03 #780, 0.02 #1460, 0.01 #2365), Ischim (0.03 #868, 0.02 #1548, 0.01 #3132), Ural (0.03 #732, 0.02 #1412), Ammer (0.03 #1101, 0.03 #1327, 0.02 #1781), Bahrel-Djebel-Albert-Nil (0.03 #1084, 0.03 #1310, 0.02 #1764), Luvua (0.03 #1083, 0.03 #1309, 0.02 #1763)
>> best conf = 0.17
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #239 for best value:
>> intensional similarity = 4
>> extensional distance = 4
>> proper extension:  Pjandsh; Pjandsh; Amudarja;
>> query: (?x682, Pjandsh) <- ?x682[ has locatedIn ?x129<TAD>; has locatedIn ?x381<AFG>;]
>> Best rule #13 for best value:
>> intensional similarity = 7
>> extensional distance = 4
>> proper extension:  KoliSarez;
>> query: (?x682, Pjandsh) <- ?x682[ has flowsInto ?x592; has locatedIn ?x129<TAD>; has locatedIn ?x381[ is neighbor of ?x83; is neighbor of ?x290[ a Country;];];]

No rule for expected values
ranks of expected_values: 
EVAL	Murgab	hasEstuary	Murgab	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	46.000	40.000	184.000	0.167	http://www.semwebtech.org/mondial/10/meta#hasEstuary

PRED relation: hasEstuary
PRED expected values: Murgab
=> 184 concepts (184 used for prediction)
PRED predicted values (max 10 best out of 223): Pjandsh (0.33 #240, 0.25 #693, 0.25 #466), Bartang (0.25 #716, 0.20 #1170, 0.17 #1624), Amudarja (0.25 #516, 0.17 #1651, 0.12 #2333), Dnepr (0.20 #968, 0.17 #1422, 0.12 #2559), Irtysch (0.20 #1219, 0.14 #2127, 0.12 #2811), EucumbeneRiver (0.20 #941, 0.06 #8447, 0.03 #15953), SnowyRiver (0.20 #1115, 0.04 #10668, 0.03 #16127), Syrdarja (0.17 #1786, 0.11 #3379, 0.09 #4741), Angara (0.17 #1543, 0.06 #7913, 0.05 #9733), Volga (0.17 #1548, 0.06 #7918, 0.04 #10647)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #240 for best value:
>> intensional similarity = 12
>> extensional distance = 1
>> proper extension:  Pjandsh;
>> query: (?x682, Pjandsh) <- ?x682[ has flowsInto ?x592[ has flowsInto ?x300; has hasSource ?x652;]; has hasSource ?x1106[ a Source; has inMountains ?x749<Pamir>;]; has locatedIn ?x129<TAD>; has locatedIn ?x381<AFG>; is flowsInto of ?x683;]

No rule for expected values
ranks of expected_values: 
EVAL	Murgab	hasEstuary	Murgab	CNN-1.+1._MA	0.000	0.000	0.000	0.000	184.000	184.000	223.000	0.333	http://www.semwebtech.org/mondial/10/meta#hasEstuary

#28-Tiree
PRED entity: Tiree

PRED relation: locatedInWater
PRED expected values: AtlanticOcean
=> 54 concepts (48 used for prediction)
PRED predicted values (max 10 best out of 63): AtlanticOcean (0.71 #50, 0.71 #611, 0.67 #7), NorthSea (0.38 #745, 0.36 #790, 0.29 #89), NorwegianSea (0.38 #745, 0.36 #790, 0.14 #216), TheChannel (0.38 #745, 0.36 #790, 0.09 #1454), IrishSea (0.36 #790, 0.18 #128, 0.16 #171), PacificOcean (0.27 #628, 0.27 #584, 0.26 #363), MediterraneanSea (0.17 #716, 0.17 #762, 0.16 #807), CaribbeanSea (0.15 #586, 0.15 #630, 0.10 #1208), JavaSea (0.12 #398, 0.12 #442, 0.09 #531), IndianOcean (0.12 #569, 0.12 #613, 0.09 #391)
>> best conf = 0.71
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #50 for best value:
>> intensional similarity = 3
>> extensional distance = 5
>> proper extension:  Mull;
>> query: (?x1599, AtlanticOcean) <- ?x1599[ a Island; has belongsToIslands ?x2364<InnerHebrides>;]
ranks of expected_values: 1
EVAL	Tiree	locatedInWater	AtlanticOcean	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	54.000	48.000	63.000	0.714	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater
PRED expected values: AtlanticOcean
=> 138 concepts (138 used for prediction)
PRED predicted values (max 10 best out of 79): AtlanticOcean (0.90 #1215, 0.90 #1178, 0.89 #443), NorthSea (0.49 #1170, 0.49 #1127, 0.45 #4015), TheChannel (0.45 #4015, 0.42 #3600, 0.41 #3602), NorwegianSea (0.45 #4015, 0.42 #3600, 0.41 #3461), IrishSea (0.45 #4015, 0.41 #3461, 0.41 #4016), PacificOcean (0.40 #1233, 0.33 #1737, 0.30 #2610), CaribbeanSea (0.34 #1650, 0.31 #1190, 0.27 #2109), MediterraneanSea (0.20 #1600, 0.19 #3018, 0.19 #2194), JavaSea (0.16 #1821, 0.16 #1913, 0.12 #2233), BalticSea (0.16 #945, 0.12 #1268, 0.07 #2460)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1215 for best value:
>> intensional similarity = 8
>> extensional distance = 37
>> proper extension:  SaintVincent; Grenada;
>> query: (?x1599, ?x182) <- ?x1599[ a Island; has belongsToIslands ?x2364[ a Islands; is belongsToIslands of ?x674[ has locatedInWater ?x182<AtlanticOcean>; has type ?x150;];]; has locatedIn ?x81;]
>> Best rule #1178 for best value:
>> intensional similarity = 8
>> extensional distance = 37
>> proper extension:  SaintVincent; Grenada;
>> query: (?x1599, AtlanticOcean) <- ?x1599[ a Island; has belongsToIslands ?x2364[ a Islands; is belongsToIslands of ?x674[ has locatedInWater ?x182<AtlanticOcean>; has type ?x150;];]; has locatedIn ?x81;]
ranks of expected_values: 1
EVAL	Tiree	locatedInWater	AtlanticOcean	CNN-1.+1._MA	1.000	1.000	1.000	1.000	138.000	138.000	79.000	0.897	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#27-NorthwesternBantu
PRED entity: NorthwesternBantu

PRED relation: ethnicGroup!
PRED expected values: CAM
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 96): EAU (0.03 #132), ETH (0.03 #100), UA (0.03 #57), EAK (0.03 #99), CZ (0.02 #98), SN (0.02 #83), MYA (0.02 #70), IR (0.02 #58), SK (0.02 #25), RCA (0.02 #139)
>> best conf = 0.03
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #132 for best value:
>> intensional similarity = 1
>> extensional distance = 279
>> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
>> query: (?x2399, EAU) <- ?x2399[ a EthnicGroup;]

*> Best rule #107 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 279
*> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
*> query: (?x2399, CAM) <- ?x2399[ a EthnicGroup;]
*> conf = 0.02
ranks of expected_values: 12
EVAL	NorthwesternBantu	ethnicGroup!	CAM	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.083	2.000	2.000	96.000	0.032	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup!
PRED expected values: CAM
=> 2 concepts (2 used for prediction)
PRED predicted values (max 10 best out of 96): EAU (0.03 #132), ETH (0.03 #100), UA (0.03 #57), EAK (0.03 #99), CZ (0.02 #98), SN (0.02 #83), MYA (0.02 #70), IR (0.02 #58), SK (0.02 #25), RCA (0.02 #139)
>> best conf = 0.03
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #132 for best value:
>> intensional similarity = 1
>> extensional distance = 279
>> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
>> query: (?x2399, EAU) <- ?x2399[ a EthnicGroup;]

*> Best rule #107 for first EXPECTED value:
*> intensional similarity = 1
*> extensional distance = 279
*> proper extension:  Ukrainian; Amerindian; Fulani; Bounty; Croat; African; Roma; European; Serbian; German; ...
*> query: (?x2399, CAM) <- ?x2399[ a EthnicGroup;]
*> conf = 0.02
ranks of expected_values: 12
EVAL	NorthwesternBantu	ethnicGroup!	CAM	CNN-1.+1._MA	0.000	0.000	0.000	0.083	2.000	2.000	96.000	0.032	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#26-Turkic
PRED entity: Turkic

PRED relation: language!
PRED expected values: AFG
=> 25 concepts (21 used for prediction)
PRED predicted values (max 10 best out of 182): SF (0.47 #561, 0.19 #1826, 0.16 #1170), TR (0.24 #1703, 0.22 #1704, 0.21 #1950), TM (0.24 #1703, 0.22 #1704, 0.21 #1950), AFG (0.24 #1703, 0.22 #1704, 0.21 #1950), AZ (0.24 #1703, 0.22 #1704, 0.21 #1950), IRQ (0.24 #1703, 0.22 #1704, 0.21 #1950), GE (0.24 #776, 0.14 #2073, 0.13 #2198), IL (0.22 #157, 0.19 #1826, 0.15 #1948), GAZA (0.22 #239, 0.19 #1826, 0.15 #1948), WEST (0.22 #199, 0.19 #1826, 0.15 #1948)
>> best conf = 0.47
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #561 for best value:
>> intensional similarity = 9
>> extensional distance = 13
>> proper extension:  Finnish; Estonian; Somali; Swedish;
>> query: (?x2439, SF) <- ?x2439[ is language of ?x304[ a Country; has ethnicGroup ?x244; has government ?x2318; has language ?x1848<Arabic>; has religion ?x187; is locatedIn of ?x573; is neighbor of ?x185;];]

*> Best rule #1703 for first EXPECTED value:
*> intensional similarity = 9
*> extensional distance = 65
*> proper extension:  Slovene; Guarani; Luxembourgish;
*> query: (?x2439, ?x185) <- ?x2439[ is language of ?x304[ a Country; has ethnicGroup ?x244; has neighbor ?x185[ has ethnicGroup ?x638; has wasDependentOf ?x1656; is locatedIn of ?x98;]; has religion ?x187; is locatedIn of ?x573;];]
*> conf = 0.24
ranks of expected_values: 4
EVAL	Turkic	language!	AFG	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.250	25.000	21.000	182.000	0.467	http://www.semwebtech.org/mondial/10/meta#language

PRED relation: language!
PRED expected values: AFG
=> 52 concepts (52 used for prediction)
PRED predicted values (max 10 best out of 191): PK (0.55 #2157, 0.53 #2932, 0.41 #757), SF (0.55 #2157, 0.50 #2876, 0.45 #755), IL (0.55 #2157, 0.45 #755, 0.42 #1141), SA (0.55 #2157, 0.45 #755, 0.42 #1141), AUS (0.55 #2157, 0.45 #755, 0.42 #1141), GAZA (0.55 #2157, 0.45 #755, 0.42 #1141), WEST (0.55 #2157, 0.45 #755, 0.42 #1141), MK (0.55 #2157, 0.41 #757, 0.37 #5475), TR (0.55 #2157, 0.41 #757, 0.37 #5475), BG (0.55 #2157, 0.41 #757, 0.37 #5475)
>> best conf = 0.55
=> the first rule below is the first best rule for 12 predicted values
>> Best rule #2157 for best value:
>> intensional similarity = 20
>> extensional distance = 11
>> proper extension:  Tongan;
>> query: (?x2439, ?x177) <- ?x2439[ a Language; is language of ?x304[ a Country; has ethnicGroup ?x244; has ethnicGroup ?x1329[ a EthnicGroup;]; has government ?x2318; has language ?x511[ a Language; is language of ?x177;]; has religion ?x187[ is religion of ?x207<I>; is religion of ?x403<KAZ>;]; is locatedIn of ?x1693[ has type ?x150<"volcanic">;]; is locatedIn of ?x2355[ a Island;];];]

*> Best rule #1769 for first EXPECTED value:
*> intensional similarity = 23
*> extensional distance = 10
*> proper extension:  Aymara; Quechua; Miskito;
*> query: (?x2439, ?x332) <- ?x2439[ a Language; is language of ?x304[ has ethnicGroup ?x244; has ethnicGroup ?x305[ a EthnicGroup;]; has government ?x2318; has religion ?x187; is locatedIn of ?x573[ a Mountain;]; is locatedIn of ?x1092[ a Lake;]; is locatedIn of ?x1422[ has hasEstuary ?x1756;]; is locatedIn of ?x1620[ a River;]; is locatedIn of ?x1693[ a Volcano; has type ?x150;]; is neighbor of ?x332[ a Country; has ethnicGroup ?x908; has language ?x843; is locatedIn of ?x468;];];]
*> conf = 0.31
ranks of expected_values: 16
EVAL	Turkic	language!	AFG	CNN-1.+1._MA	0.000	0.000	0.000	0.062	52.000	52.000	191.000	0.550	http://www.semwebtech.org/mondial/10/meta#language

#25-Parana
PRED entity: Parana

PRED relation: locatedIn
PRED expected values: BR
=> 31 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 105): PY (0.63 #951, 0.57 #5945, 0.57 #5230), RA (0.57 #5945, 0.57 #5230, 0.57 #6897), BR (0.50 #4751, 0.44 #2375, 0.43 #3088), PE (0.42 #779, 0.17 #67, 0.13 #1019), ZRE (0.40 #1665, 0.40 #1506, 0.17 #79), USA (0.22 #546, 0.17 #1024, 0.17 #72), CDN (0.17 #63, 0.11 #300, 0.10 #1728), LS (0.17 #11, 0.11 #248, 0.06 #485), BOL (0.16 #865, 0.09 #1105, 0.05 #4989), R (0.14 #3093, 0.13 #4757, 0.13 #2619)
>> best conf = 0.63
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #951 for best value:
>> intensional similarity = 6
>> extensional distance = 17
>> proper extension:  Apurimac; RioNegro; RioMagdalena; Perene; Uruguay;
>> query: (?x1625, ?x404) <- ?x1625[ is hasSource of ?x513[ a River; has locatedIn ?x404[ has ethnicGroup ?x676; is neighbor of ?x542<BR>;];];]

*> Best rule #4751 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 90
*> proper extension:  Reuss; RioDesaguadero; Murgab; Vuoksi; DetroitRiver;
*> query: (?x1625, ?x379) <- ?x1625[ is hasSource of ?x513[ a River; has flowsInto ?x182[ has locatedIn ?x50;]; is flowsInto of ?x512[ has locatedIn ?x379;];];]
*> conf = 0.50
ranks of expected_values: 3
EVAL	Parana	locatedIn	BR	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.333	31.000	31.000	105.000	0.632	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: BR
=> 94 concepts (94 used for prediction)
PRED predicted values (max 10 best out of 110): RA (0.71 #5985, 0.71 #4306, 0.66 #10300), PY (0.71 #5985, 0.71 #4306, 0.65 #18678), PE (0.57 #1506, 0.50 #1746, 0.47 #6054), BR (0.56 #16283, 0.56 #16282, 0.50 #16284), USA (0.52 #10135, 0.50 #4621, 0.48 #10543), CDN (0.37 #7189, 0.37 #7012, 0.35 #5987), E (0.33 #1197, 0.33 #987, 0.20 #4335), RG (0.33 #866, 0.15 #3018, 0.13 #4549), BOL (0.29 #1198, 0.14 #4221, 0.14 #1592), RCH (0.29 #1198, 0.12 #958, 0.12 #19877)
>> best conf = 0.71
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #5985 for best value:
>> intensional similarity = 13
>> extensional distance = 15
>> proper extension:  SaskatchewanRiver; Manicouagan;
>> query: (?x1625, ?x404) <- ?x1625[ a Source; is hasSource of ?x513[ a River; has flowsInto ?x182[ has locatedIn ?x272<CDN>;]; has hasEstuary ?x1150; has locatedIn ?x404[ a Country; has encompassed ?x521<America>; has religion ?x95<Protestant>;];];]
>> Best rule #4306 for best value:
>> intensional similarity = 12
>> extensional distance = 12
>> proper extension:  RioMamore;
>> query: (?x1625, ?x404) <- ?x1625[ a Source; is hasSource of ?x513[ a River; has flowsInto ?x182[ has locatedIn ?x542<BR>;]; has hasEstuary ?x1150[ a Estuary;]; has locatedIn ?x404[ has language ?x796<Spanish>; has neighbor ?x690;];];]

*> Best rule #16283 for first EXPECTED value:
*> intensional similarity = 12
*> extensional distance = 59
*> proper extension:  Würm;
*> query: (?x1625, ?x542) <- ?x1625[ a Source; is hasSource of ?x513[ a River; has flowsInto ?x182[ has locatedIn ?x379[ a Country; is locatedIn of ?x512[ has locatedIn ?x542;];]; has locatedIn ?x542;]; has hasEstuary ?x1150[ a Estuary;]; is flowsInto of ?x512;];]
*> conf = 0.56
ranks of expected_values: 4
EVAL	Parana	locatedIn	BR	CNN-1.+1._MA	0.000	0.000	1.000	0.250	94.000	94.000	110.000	0.714	http://www.semwebtech.org/mondial/10/meta#locatedIn

#24-AND
PRED entity: AND

PRED relation: government
PRED expected values: "parliamentary democracy that retains as its chiefs of state a co-principality"
=> 40 concepts (40 used for prediction)
PRED predicted values (max 10 best out of 63): "parliamentary monarchy" (0.33 #28, 0.24 #939, 0.18 #1518), "republic" (0.31 #2318, 0.31 #2100, 0.30 #2245), "republic; parliamentary democracy" (0.25 #142, 0.10 #502, 0.04 #864), "parliamentary democracy" (0.23 #1161, 0.23 #1089, 0.14 #293), "constitutional monarchy" (0.20 #146, 0.14 #218, 0.11 #362), "formally a confederation but similar in structure to a federal republic" (0.20 #202, 0.14 #274, 0.11 #418), "parliamentary representative democratic French overseas collectivity" (0.18 #552, 0.14 #263, 0.05 #1059), "federal republic" (0.14 #291, 0.12 #797, 0.11 #652), "a parliamentary democracy, a federation, and a constitutional monarchy" (0.14 #271, 0.09 #560, 0.03 #1067), "parliamentary republic" (0.14 #235, 0.08 #1103, 0.07 #1175)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #28 for best value:
>> intensional similarity = 7
>> extensional distance = 1
>> proper extension:  E;
>> query: (?x789, "parliamentary monarchy") <- ?x789[ has encompassed ?x195; has ethnicGroup ?x746; has language ?x539[ is language of ?x643[ is locatedIn of ?x642;];]; has language ?x790<Catalan>;]

No rule for expected values
ranks of expected_values: 
EVAL	AND	government	"parliamentary democracy that retains as its chiefs of state a co-principality"	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	40.000	40.000	63.000	0.333	http://www.semwebtech.org/mondial/10/meta#government

PRED relation: government
PRED expected values: "parliamentary democracy that retains as its chiefs of state a co-principality"
=> 102 concepts (102 used for prediction)
PRED predicted values (max 10 best out of 71): "republic" (0.35 #4198, 0.33 #2176, 0.33 #1309), "parliamentary" (0.33 #185, 0.12 #506, 0.08 #723), "parliamentary democracy" (0.25 #294, 0.24 #3186, 0.20 #2680), "constitutional monarchy" (0.20 #435, 0.18 #5424, 0.18 #3255), "formally a confederation but similar in structure to a federal republic" (0.20 #491, 0.18 #5424, 0.18 #3255), "federal republic" (0.20 #509, 0.18 #5424, 0.17 #581), "republic; parliamentary democracy" (0.20 #431, 0.18 #5424, 0.15 #6146), "a parliamentary democracy, a federation, and a constitutional monarchy" (0.20 #488, 0.18 #3255, 0.18 #3254), "dependent territory of the UK" (0.20 #418, 0.04 #2227, 0.03 #5857), "parliamentary monarchy" (0.18 #5063, 0.18 #2964, 0.18 #5424)
>> best conf = 0.35
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #4198 for best value:
>> intensional similarity = 16
>> extensional distance = 58
>> proper extension:  TN; RIM;
>> query: (?x789, "republic") <- ?x789[ a Country; has encompassed ?x195; has ethnicGroup ?x746; is neighbor of ?x78[ a Country; has religion ?x352[ is religion of ?x50<NLSM>; is religion of ?x363<ROU>;]; is locatedIn of ?x121[ is locatedInWater of ?x495;]; is locatedIn of ?x1088[ a Mountain;]; is neighbor of ?x207[ is locatedIn of ?x86;];];]

No rule for expected values
ranks of expected_values: 
EVAL	AND	government	"parliamentary democracy that retains as its chiefs of state a co-principality"	CNN-1.+1._MA	0.000	0.000	0.000	0.000	102.000	102.000	71.000	0.350	http://www.semwebtech.org/mondial/10/meta#government

#23-Azeri
PRED entity: Azeri

PRED relation: language!
PRED expected values: AZ
=> 26 concepts (22 used for prediction)
PRED predicted values (max 10 best out of 200): ARM (0.50 #169, 0.40 #49, 0.31 #979), IR (0.44 #288, 0.17 #1271, 0.14 #1762), SF (0.32 #444, 0.31 #979, 0.30 #566), AZ (0.31 #979, 0.30 #1224, 0.30 #1225), R (0.31 #979, 0.30 #1224, 0.30 #1225), KGZ (0.31 #979, 0.20 #2580, 0.20 #2332), MD (0.31 #979, 0.20 #2580, 0.20 #2332), LT (0.31 #979, 0.20 #2580, 0.20 #2332), TM (0.31 #979, 0.20 #2580, 0.20 #2332), UZB (0.31 #979, 0.20 #2580, 0.20 #2332)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #169 for best value:
>> intensional similarity = 9
>> extensional distance = 4
>> proper extension:  Kurd;
>> query: (?x2073, ARM) <- ?x2073[ a Language; is language of ?x353[ a Country; has government ?x435; has religion ?x56; has wasDependentOf ?x903<SovietUnion>; is neighbor of ?x332<AZ>;];]

*> Best rule #979 for first EXPECTED value:
*> intensional similarity = 11
*> extensional distance = 33
*> proper extension:  French; Turkish; Dutch; German; Hungarian; Aymara; Slovenian; Spanish; Croatian; Czech; ...
*> query: (?x2073, ?x130) <- ?x2073[ a Language; is language of ?x353[ a Country; has government ?x435; has language ?x555[ is language of ?x130;]; has neighbor ?x73; has religion ?x352<RomanCatholic>; has wasDependentOf ?x903; is locatedIn of ?x98;];]
*> conf = 0.31
ranks of expected_values: 4
EVAL	Azeri	language!	AZ	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.250	26.000	22.000	200.000	0.500	http://www.semwebtech.org/mondial/10/meta#language

PRED relation: language!
PRED expected values: AZ
=> 47 concepts (47 used for prediction)
PRED predicted values (max 10 best out of 210): SF (0.51 #1248, 0.50 #2337, 0.44 #1497), KGZ (0.51 #1248, 0.44 #1010, 0.44 #1497), ARM (0.51 #1248, 0.44 #1497, 0.43 #673), R (0.51 #1248, 0.44 #1497, 0.38 #1123), AZ (0.51 #1248, 0.44 #1497, 0.34 #869), LT (0.51 #1248, 0.44 #1497, 0.33 #1357), MD (0.51 #1248, 0.44 #1497, 0.33 #223), BY (0.51 #1248, 0.44 #1497, 0.31 #493), LV (0.51 #1248, 0.44 #1497, 0.31 #493), TM (0.51 #1248, 0.44 #1497, 0.31 #493)
>> best conf = 0.51
=> the first rule below is the first best rule for 12 predicted values
>> Best rule #1248 for best value:
>> intensional similarity = 17
>> extensional distance = 7
>> proper extension:  Maltese;
>> query: (?x2073, ?x331) <- ?x2073[ a Language; is language of ?x353[ a Country; has encompassed ?x175; has language ?x1031[ a Language; is language of ?x331;]; has wasDependentOf ?x903; is locatedIn of ?x98[ a Sea; has locatedIn ?x185<TR>; is flowsInto of ?x133; is mergesWith of ?x1633<MarmaraSea>;]; is locatedIn of ?x876[ has type ?x150;];];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 5
EVAL	Azeri	language!	AZ	CNN-1.+1._MA	0.000	0.000	1.000	0.200	47.000	47.000	210.000	0.509	http://www.semwebtech.org/mondial/10/meta#language

#22-BZ
PRED entity: BZ

PRED relation: neighbor!
PRED expected values: GCA
=> 48 concepts (44 used for prediction)
PRED predicted values (max 10 best out of 231): GCA (0.93 #1933, 0.91 #2908, 0.91 #4040), R (0.38 #1774, 0.16 #3719, 0.14 #3234), D (0.33 #339, 0.11 #501, 0.10 #3894), F (0.33 #328, 0.11 #490, 0.09 #1132), A (0.33 #401, 0.09 #3147, 0.09 #2822), BZ (0.29 #5340, 0.27 #5667, 0.26 #5829), USA (0.29 #5340, 0.27 #5667, 0.26 #5829), HCA (0.29 #5340, 0.27 #5667, 0.26 #6643), ES (0.29 #5340, 0.27 #5667, 0.26 #6643), BR (0.24 #1541, 0.23 #1381, 0.20 #93)
>> best conf = 0.93
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1933 for best value:
>> intensional similarity = 5
>> extensional distance = 27
>> proper extension:  CN; MAL;
>> query: (?x671, ?x482) <- ?x671[ has ethnicGroup ?x676; has neighbor ?x482[ has ethnicGroup ?x79; is locatedIn of ?x282<PacificOcean>;];]
ranks of expected_values: 1
EVAL	BZ	neighbor!	GCA	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	48.000	44.000	231.000	0.932	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: GCA
=> 112 concepts (112 used for prediction)
PRED predicted values (max 10 best out of 245): GCA (0.91 #11781, 0.91 #16966, 0.90 #14474), D (0.50 #681, 0.33 #162, 0.29 #2159), F (0.50 #670, 0.33 #162, 0.29 #2148), EAT (0.40 #1457, 0.20 #828, 0.18 #6087), SSD (0.40 #1369, 0.13 #5167, 0.12 #5334), ES (0.33 #16970, 0.33 #500, 0.33 #446), HCA (0.33 #16970, 0.33 #489, 0.33 #162), BZ (0.33 #16970, 0.33 #448, 0.33 #162), USA (0.33 #16970, 0.33 #162, 0.32 #16971), SME (0.33 #662, 0.33 #530, 0.20 #1355)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #11781 for best value:
>> intensional similarity = 16
>> extensional distance = 48
>> proper extension:  BD;
>> query: (?x671, ?x181) <- ?x671[ a Country; has encompassed ?x521; has ethnicGroup ?x676; has government ?x1947; has neighbor ?x181; has wasDependentOf ?x81[ has government ?x1854; has language ?x247; is locatedIn of ?x121[ is locatedInWater of ?x848;]; is wasDependentOf of ?x63[ is locatedIn of ?x62; is neighbor of ?x186;]; is wasDependentOf of ?x279[ a Country; has religion ?x95;];];]
ranks of expected_values: 1
EVAL	BZ	neighbor!	GCA	CNN-1.+1._MA	1.000	1.000	1.000	1.000	112.000	112.000	245.000	0.907	http://www.semwebtech.org/mondial/10/meta#neighbor

#21-Mormon
PRED entity: Mormon

PRED relation: religion!
PRED expected values: KIR
=> 21 concepts (21 used for prediction)
PRED predicted values (max 10 best out of 226): GB (0.71 #1081, 0.67 #865, 0.54 #1512), MEX (0.61 #1287, 0.60 #752, 0.58 #1719), GCA (0.61 #1287, 0.58 #1719, 0.54 #1071), GUY (0.60 #716, 0.50 #502, 0.43 #1146), NIC (0.60 #732, 0.50 #518, 0.34 #1935), CR (0.60 #710, 0.50 #496, 0.34 #1935), RCH (0.60 #686, 0.50 #472, 0.34 #1935), BZ (0.60 #781, 0.50 #567, 0.33 #995), CUR (0.60 #700, 0.50 #486, 0.33 #914), AUS (0.57 #1115, 0.50 #899, 0.50 #471)
>> best conf = 0.71
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1081 for best value:
>> intensional similarity = 13
>> extensional distance = 5
>> proper extension:  Muslim;
>> query: (?x1547, GB) <- ?x1547[ is religion of ?x315[ is locatedIn of ?x514<SaintMarysRiver>; is locatedIn of ?x615<MtBona>; is locatedIn of ?x733<YukonRiver>; is locatedIn of ?x1273[ is flowsInto of ?x1890;];]; is religion of ?x654[ a Country; has language ?x796; is neighbor of ?x181;];]

*> Best rule #147 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 1
*> proper extension:  RomanCatholic;
*> query: (?x1547, KIR) <- ?x1547[ a Religion; is religion of ?x315<USA>; is religion of ?x550<NIUE>; is religion of ?x654<ES>;]
*> conf = 0.33
ranks of expected_values: 106
EVAL	Mormon	religion!	KIR	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.009	21.000	21.000	226.000	0.714	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion!
PRED expected values: KIR
=> 33 concepts (33 used for prediction)
PRED predicted values (max 10 best out of 226): MEX (0.82 #861, 0.80 #862, 0.78 #1079), GCA (0.82 #861, 0.80 #862, 0.78 #1079), HCA (0.82 #861, 0.55 #429, 0.52 #6512), BZ (0.82 #861, 0.50 #570, 0.40 #784), GB (0.80 #2605, 0.71 #1089, 0.67 #3477), AUS (0.67 #1771, 0.60 #1987, 0.60 #688), UA (0.62 #1578, 0.50 #2226, 0.50 #928), I (0.60 #691, 0.57 #1126, 0.56 #1774), RI (0.60 #694, 0.57 #1129, 0.50 #1344), RP (0.60 #748, 0.57 #1183, 0.50 #1398)
>> best conf = 0.82
=> the first rule below is the first best rule for 4 predicted values
>> Best rule #861 for best value:
>> intensional similarity = 20
>> extensional distance = 3
>> proper extension:  Buddhist;
>> query: (?x1547, ?x482) <- ?x1547[ is religion of ?x315<USA>; is religion of ?x550[ a Country; has encompassed ?x211<Australia-Oceania>; has ethnicGroup ?x1335; has government ?x2004;]; is religion of ?x654[ has encompassed ?x521<America>; has ethnicGroup ?x79; has language ?x796; has religion ?x352<RomanCatholic>; has wasDependentOf ?x149; is locatedIn of ?x282<PacificOcean>; is neighbor of ?x181[ has neighbor ?x482;];];]

*> Best rule #430 for first EXPECTED value:
*> intensional similarity = 17
*> extensional distance = 2
*> proper extension:  EkalesiaNiue;
*> query: (?x1547, ?x482) <- ?x1547[ a Religion; is religion of ?x315[ has ethnicGroup ?x79; is locatedIn of ?x182[ is flowsInto of ?x137; is locatedInWater of ?x112; is mergesWith of ?x60;]; is locatedIn of ?x723[ a Island;]; is locatedIn of ?x809[ has mergesWith ?x452;]; is locatedIn of ?x1023[ has locatedIn ?x482;]; is locatedIn of ?x1221[ has type ?x136;];]; is religion of ?x550<NIUE>;]
*> conf = 0.40
ranks of expected_values: 99
EVAL	Mormon	religion!	KIR	CNN-1.+1._MA	0.000	0.000	0.000	0.010	33.000	33.000	226.000	0.818	http://www.semwebtech.org/mondial/10/meta#religion

#20-SYR
PRED entity: SYR

PRED relation: locatedIn!
PRED expected values: Tigris
=> 35 concepts (26 used for prediction)
PRED predicted values (max 10 best out of 1313): AtlanticOcean (0.74 #9977, 0.32 #8558, 0.29 #29855), DeadSea (0.33 #1662, 0.33 #243, 0.16 #35493), RedSea (0.33 #2296, 0.10 #22712, 0.10 #34073), Negev (0.33 #1481, 0.10 #22712, 0.10 #34073), Jordan (0.33 #160, 0.05 #5677, 0.03 #7257), SouthChinaSea (0.27 #7237, 0.22 #4397, 0.18 #2978), LakeKeban (0.19 #15613, 0.18 #31233, 0.16 #35493), Nile (0.19 #15613, 0.18 #31233, 0.15 #7097), Rhone (0.19 #15613, 0.18 #31233, 0.15 #7097), Drin (0.19 #15613, 0.18 #31233, 0.15 #7097)
>> best conf = 0.74
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #9977 for best value:
>> intensional similarity = 7
>> extensional distance = 76
>> proper extension:  NLSM;
>> query: (?x466, AtlanticOcean) <- ?x466[ has religion ?x187; is locatedIn of ?x275[ has locatedIn ?x156[ has ethnicGroup ?x160;]; has locatedIn ?x851<MA>; is locatedInWater of ?x68;];]

*> Best rule #35494 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 139
*> proper extension:  RSM; V;
*> query: (?x466, ?x468) <- ?x466[ is neighbor of ?x115[ has encompassed ?x175; has government ?x435;]; is neighbor of ?x185[ is locatedIn of ?x468[ has flowsInto ?x1337;];];]
*> conf = 0.12
ranks of expected_values: 26
EVAL	SYR	locatedIn!	Tigris	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.038	35.000	26.000	1313.000	0.744	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Tigris
=> 71 concepts (71 used for prediction)
PRED predicted values (max 10 best out of 1356): DeadSea (0.75 #41208, 0.75 #46904, 0.66 #39788), SchattalArab (0.75 #41208, 0.75 #46904, 0.66 #39788), Jordan (0.67 #46903, 0.63 #63977, 0.60 #68243), Euphrat (0.67 #46903, 0.63 #63977, 0.60 #68243), PersianGulf (0.63 #36943, 0.50 #8977, 0.45 #24150), RedSea (0.63 #36943, 0.40 #12241, 0.40 #11364), AtlanticOcean (0.56 #39830, 0.55 #42677, 0.48 #54061), CaribbeanSea (0.52 #54124, 0.18 #92522, 0.18 #93947), RubAlChali (0.50 #13070, 0.29 #24149, 0.27 #23014), LakeKeban (0.47 #55445, 0.36 #14204, 0.33 #692)
>> best conf = 0.75
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #41208 for best value:
>> intensional similarity = 15
>> extensional distance = 23
>> proper extension:  CAM;
>> query: (?x466, ?x567) <- ?x466[ has ethnicGroup ?x244; is locatedIn of ?x275[ a Sea; has locatedIn ?x149<E>; has locatedIn ?x235[ has language ?x511; has religion ?x56;]; is flowsInto of ?x698; is locatedInWater of ?x68[ a Island;];]; is locatedIn of ?x419[ has flowsInto ?x567;]; is neighbor of ?x239[ has religion ?x109;];]

*> Best rule #14204 for first EXPECTED value:
*> intensional similarity = 14
*> extensional distance = 4
*> proper extension:  YE;
*> query: (?x466, ?x238) <- ?x466[ has encompassed ?x175; has neighbor ?x239[ a Country; has ethnicGroup ?x244; has language ?x1398; is locatedIn of ?x238;]; has neighbor ?x803[ a Country; has government ?x92; has neighbor ?x751<SA>; has religion ?x187<Muslim>;]; is locatedIn of ?x275;]
*> conf = 0.36
ranks of expected_values: 14
EVAL	SYR	locatedIn!	Tigris	CNN-1.+1._MA	0.000	0.000	0.000	0.071	71.000	71.000	1356.000	0.750	http://www.semwebtech.org/mondial/10/meta#locatedIn

#19-Arab
PRED entity: Arab

PRED relation: ethnicGroup!
PRED expected values: IRQ EAU BRN KWT
=> 22 concepts (22 used for prediction)
PRED predicted values (max 10 best out of 167): ET (0.40 #743, 0.25 #1486, 0.24 #1485), WEST (0.33 #294, 0.25 #1486, 0.24 #1485), GAZA (0.33 #360, 0.25 #1486, 0.24 #1485), AZ (0.33 #59, 0.25 #1486, 0.24 #1485), ARM (0.33 #58, 0.25 #1486, 0.23 #1672), TN (0.33 #198, 0.20 #753, 0.08 #556), UA (0.33 #239, 0.09 #1353, 0.09 #1539), MA (0.33 #329, 0.08 #556, 0.06 #3527), BG (0.33 #27, 0.06 #1327, 0.06 #1699), ETH (0.26 #1206, 0.25 #1486, 0.24 #1485)
>> best conf = 0.40
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #743 for best value:
>> intensional similarity = 7
>> extensional distance = 3
>> proper extension:  EasternHamitic;
>> query: (?x244, ET) <- ?x244[ is ethnicGroup of ?x186[ has neighbor ?x63; is locatedIn of ?x2124<Nile>; is neighbor of ?x169;]; is ethnicGroup of ?x508[ has religion ?x116;];]

*> Best rule #1237 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 33
*> proper extension:  Lugbara; Bunyoro; Basogo; Mboum; Langi; Welaita; Tigraway; Acholi; Hadiya; Baya; ...
*> query: (?x244, EAU) <- ?x244[ is ethnicGroup of ?x186[ has neighbor ?x63; is locatedIn of ?x531; is neighbor of ?x229<SSD>;];]
*> conf = 0.26
ranks of expected_values: 11, 13, 23, 78
EVAL	Arab	ethnicGroup!	KWT	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.048	22.000	22.000	167.000	0.400	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	Arab	ethnicGroup!	BRN	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.013	22.000	22.000	167.000	0.400	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	Arab	ethnicGroup!	EAU	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.091	22.000	22.000	167.000	0.400	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	Arab	ethnicGroup!	IRQ	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.083	22.000	22.000	167.000	0.400	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

PRED relation: ethnicGroup!
PRED expected values: IRQ EAU BRN KWT
=> 56 concepts (56 used for prediction)
PRED predicted values (max 10 best out of 229): EAU (0.82 #2419, 0.47 #3564, 0.38 #4141), MOC (0.60 #986, 0.40 #1178, 0.33 #761), WEST (0.52 #1340, 0.49 #1333, 0.42 #1339), GAZA (0.52 #1340, 0.49 #1333, 0.42 #1339), TM (0.52 #1340, 0.42 #1338, 0.42 #1339), AFG (0.52 #1340, 0.42 #1338, 0.42 #1339), AZ (0.52 #1340, 0.42 #1338, 0.42 #1339), TR (0.52 #1340, 0.42 #1339, 0.38 #1140), ETH (0.50 #3152, 0.37 #1334, 0.35 #1147), ET (0.49 #1333, 0.42 #1338, 0.42 #1339)
>> best conf = 0.82
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2419 for best value:
>> intensional similarity = 18
>> extensional distance = 9
>> proper extension:  Lugbara; Bunyoro; Basogo; Langi; Acholi; Iteso; Baganda; Batobo; Bagisu;
>> query: (?x244, EAU) <- ?x244[ is ethnicGroup of ?x466[ has religion ?x187<Muslim>; has wasDependentOf ?x485; is neighbor of ?x185[ has encompassed ?x175; has ethnicGroup ?x638; has wasDependentOf ?x1656; is locatedIn of ?x98;];]; is ethnicGroup of ?x474[ is locatedIn of ?x730<Elgon>; is locatedIn of ?x1195<LakeVictoria>; is neighbor of ?x220;]; is ethnicGroup of ?x751[ has government ?x640; is locatedIn of ?x637;];]
ranks of expected_values: 1, 11, 37, 119
EVAL	Arab	ethnicGroup!	KWT	CNN-1.+1._MA	0.000	0.000	0.000	0.029	56.000	56.000	229.000	0.818	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	Arab	ethnicGroup!	BRN	CNN-1.+1._MA	0.000	0.000	0.000	0.009	56.000	56.000	229.000	0.818	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	Arab	ethnicGroup!	EAU	CNN-1.+1._MA	1.000	1.000	1.000	1.000	56.000	56.000	229.000	0.818	http://www.semwebtech.org/mondial/10/meta#ethnicGroup
EVAL	Arab	ethnicGroup!	IRQ	CNN-1.+1._MA	0.000	0.000	1.000	0.100	56.000	56.000	229.000	0.818	http://www.semwebtech.org/mondial/10/meta#ethnicGroup

#18-FGU
PRED entity: FGU

PRED relation: neighbor
PRED expected values: BR
=> 43 concepts (43 used for prediction)
PRED predicted values (max 10 best out of 222): F (0.40 #483, 0.18 #3868, 0.11 #6293), CN (0.40 #364, 0.11 #1334, 0.10 #3588), SMAR (0.33 #104, 0.25 #265, 0.18 #3868), GUY (0.25 #2904, 0.25 #5485, 0.25 #5646), FGU (0.25 #2904, 0.25 #5485, 0.25 #5646), BR (0.25 #5485, 0.25 #5646, 0.25 #901), BOL (0.25 #921, 0.20 #1245, 0.15 #1731), PE (0.19 #1667, 0.17 #2149, 0.17 #857), YV (0.18 #3868, 0.17 #866, 0.11 #6293), SN (0.18 #3868, 0.12 #2656, 0.11 #6294)
>> best conf = 0.40
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #483 for best value:
>> intensional similarity = 6
>> extensional distance = 3
>> proper extension:  MACX; HONX; GBZ;
>> query: (?x816, ?x78) <- ?x816[ a Country; has dependentOf ?x78[ is wasDependentOf of ?x94;]; has religion ?x352; is locatedIn of ?x182; is neighbor of ?x179;]

*> Best rule #5485 for first EXPECTED value:
*> intensional similarity = 7
*> extensional distance = 132
*> proper extension:  PK; SSD;
*> query: (?x816, ?x351) <- ?x816[ a Country; has government ?x828; is locatedIn of ?x182; is neighbor of ?x179[ has ethnicGroup ?x79; has religion ?x95; is neighbor of ?x351;];]
*> conf = 0.25
ranks of expected_values: 6
EVAL	FGU	neighbor	BR	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.167	43.000	43.000	222.000	0.400	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor
PRED expected values: BR
=> 79 concepts (79 used for prediction)
PRED predicted values (max 10 best out of 233): BR (0.57 #2046, 0.34 #1953, 0.33 #581), F (0.50 #973, 0.20 #9359, 0.16 #5076), CN (0.50 #851, 0.11 #9073, 0.08 #9240), BOL (0.43 #2066, 0.43 #1905, 0.33 #277), PE (0.43 #2002, 0.33 #4305, 0.33 #213), PY (0.43 #2024, 0.33 #235, 0.33 #161), FGU (0.34 #1953, 0.33 #163, 0.33 #162), GUY (0.34 #1953, 0.33 #163, 0.33 #162), YV (0.33 #546, 0.33 #222, 0.33 #161), ROU (0.33 #227, 0.33 #161, 0.29 #2016)
>> best conf = 0.57
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2046 for best value:
>> intensional similarity = 13
>> extensional distance = 5
>> proper extension:  PY;
>> query: (?x816, BR) <- ?x816[ a Country; has language ?x51; has neighbor ?x179[ a Country; has ethnicGroup ?x79; has government ?x180; has religion ?x95<Protestant>; is neighbor of ?x351<GUY>;]; has religion ?x352<RomanCatholic>; is locatedIn of ?x182;]
ranks of expected_values: 1
EVAL	FGU	neighbor	BR	CNN-1.+1._MA	1.000	1.000	1.000	1.000	79.000	79.000	233.000	0.571	http://www.semwebtech.org/mondial/10/meta#neighbor

#17-NevadodelRuiz
PRED entity: NevadodelRuiz

PRED relation: type
PRED expected values: "volcano"
=> 54 concepts (54 used for prediction)
PRED predicted values (max 10 best out of 7): "volcano" (0.64 #54, 0.62 #134, 0.58 #246), "volcanic" (0.49 #273, 0.38 #162, 0.36 #178), "salt" (0.03 #665, 0.02 #713, 0.02 #745), "acid" (0.02 #223, 0.01 #288), "dam" (0.01 #723, 0.01 #563, 0.01 #643), "granite" (0.01 #270, 0.01 #464, 0.01 #512), "lime" (0.01 #278)
>> best conf = 0.64
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #54 for best value:
>> intensional similarity = 4
>> extensional distance = 9
>> proper extension:  Cayambe; Sajama;
>> query: (?x1717, "volcano") <- ?x1717[ a Mountain; a Volcano; has inMountains ?x431<Andes>;]
ranks of expected_values: 1
EVAL	NevadodelRuiz	type	"volcano"	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	54.000	54.000	7.000	0.636	http://www.semwebtech.org/mondial/10/meta#type

PRED relation: type
PRED expected values: "volcano"
=> 141 concepts (141 used for prediction)
PRED predicted values (max 10 best out of 13): "volcano" (0.75 #535, 0.72 #631, 0.72 #551), "volcanic" (0.64 #855, 0.64 #854, 0.64 #837), "granite" (0.08 #415, 0.04 #463, 0.02 #917), "monolith" (0.06 #219, 0.02 #1107, 0.01 #1271), "dam" (0.05 #722, 0.05 #1341, 0.05 #1097), "salt" (0.04 #1844, 0.03 #2020, 0.03 #1299), "acid" (0.04 #448, 0.02 #752, 0.02 #801), "sand" (0.03 #1344, 0.02 #1616, 0.02 #1664), "caldera" (0.03 #1198, 0.02 #1359, 0.02 #1744), "lime" (0.02 #791, 0.02 #924, 0.02 #1004)
>> best conf = 0.75
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #535 for best value:
>> intensional similarity = 11
>> extensional distance = 30
>> proper extension:  Ruapehu;
>> query: (?x1717, "volcano") <- ?x1717[ a Mountain; a Volcano; has locatedIn ?x215[ has ethnicGroup ?x79; has religion ?x352<RomanCatholic>; has wasDependentOf ?x149; is locatedIn of ?x317[ has locatedIn ?x482<MEX>; is locatedInWater of ?x123;];];]
ranks of expected_values: 1
EVAL	NevadodelRuiz	type	"volcano"	CNN-1.+1._MA	1.000	1.000	1.000	1.000	141.000	141.000	13.000	0.750	http://www.semwebtech.org/mondial/10/meta#type

#16-KaraSea
PRED entity: KaraSea

PRED relation: locatedIn
PRED expected values: R
=> 32 concepts (32 used for prediction)
PRED predicted values (max 10 best out of 223): R (0.91 #3083, 0.89 #3795, 0.85 #4032), CN (0.51 #4505, 0.50 #239, 0.50 #237), KAZ (0.51 #4505, 0.50 #239, 0.50 #237), USA (0.45 #3867, 0.20 #2207, 0.20 #1021), CDN (0.30 #3858, 0.15 #1012, 0.12 #2198), RI (0.25 #2660, 0.24 #2898, 0.24 #764), GROX (0.25 #643, 0.18 #879, 0.14 #1589), SVAX (0.25 #668, 0.14 #431, 0.14 #1614), F (0.20 #956, 0.18 #1429, 0.15 #3090), SF (0.20 #2029, 0.16 #6639, 0.05 #6056)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3083 for best value:
>> intensional similarity = 5
>> extensional distance = 31
>> proper extension:  IrishSea;
>> query: (?x801, ?x73) <- ?x801[ a Sea; is locatedInWater of ?x931[ a Island; has locatedIn ?x73;]; is mergesWith of ?x263;]
ranks of expected_values: 1
EVAL	KaraSea	locatedIn	R	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	32.000	32.000	223.000	0.906	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: R
=> 76 concepts (76 used for prediction)
PRED predicted values (max 10 best out of 227): R (0.91 #12636, 0.90 #11679, 0.89 #11918), USA (0.61 #3573, 0.47 #13900, 0.44 #3095), GROX (0.61 #3573, 0.44 #3095, 0.44 #2143), SVAX (0.61 #3573, 0.44 #3095, 0.42 #12637), N (0.61 #3573, 0.44 #3095, 0.42 #12637), CDN (0.61 #3573, 0.44 #2143, 0.42 #12637), KAZ (0.56 #15503, 0.56 #15502, 0.52 #15981), CN (0.56 #15503, 0.56 #15502, 0.52 #15981), IS (0.44 #3095, 0.27 #4047, 0.20 #1776), F (0.36 #3580, 0.33 #1913, 0.24 #5246)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #12636 for best value:
>> intensional similarity = 10
>> extensional distance = 30
>> proper extension:  LakeToba;
>> query: (?x801, ?x73) <- ?x801[ is locatedInWater of ?x931[ a Island; has locatedIn ?x73[ a Country; has encompassed ?x195; has ethnicGroup ?x58; has religion ?x56; is neighbor of ?x194;]; has locatedInWater ?x251[ has locatedIn ?x170;];];]
ranks of expected_values: 1
EVAL	KaraSea	locatedIn	R	CNN-1.+1._MA	1.000	1.000	1.000	1.000	76.000	76.000	227.000	0.907	http://www.semwebtech.org/mondial/10/meta#locatedIn

#15-LS
PRED entity: LS

PRED relation: wasDependentOf
PRED expected values: GB
=> 39 concepts (39 used for prediction)
PRED predicted values (max 10 best out of 27): GB (0.38 #95, 0.35 #185, 0.35 #34), F (0.33 #425, 0.32 #363, 0.30 #242), E (0.23 #460, 0.21 #553, 0.16 #616), UnitedNations (0.21 #75, 0.16 #254, 0.16 #344), ET (0.12 #1, 0.07 #1087, 0.06 #31), P (0.11 #233, 0.09 #507, 0.08 #383), SovietUnion (0.09 #916, 0.09 #757, 0.08 #883), I (0.07 #1087, 0.03 #218, 0.03 #368), ETH (0.07 #1087, 0.03 #224, 0.03 #374), B (0.07 #1087, 0.02 #439, 0.01 #656)
>> best conf = 0.38
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #95 for best value:
>> intensional similarity = 8
>> extensional distance = 24
>> proper extension:  SD;
>> query: (?x89, GB) <- ?x89[ a Country; has encompassed ?x213; has ethnicGroup ?x2491[ a EthnicGroup;]; has government ?x90; has neighbor ?x243; has religion ?x116<Christian>;]
ranks of expected_values: 1
EVAL	LS	wasDependentOf	GB	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	39.000	39.000	27.000	0.385	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

PRED relation: wasDependentOf
PRED expected values: GB
=> 110 concepts (110 used for prediction)
PRED predicted values (max 10 best out of 134): GB (0.50 #133, 0.43 #972, 0.43 #714), UnitedNations (0.48 #2081, 0.42 #835, 0.33 #44), P (0.48 #2081, 0.42 #835, 0.33 #23), F (0.44 #939, 0.44 #807, 0.43 #683), E (0.36 #483, 0.27 #2292, 0.22 #1954), ET (0.20 #253, 0.20 #222, 0.20 #159), SovietUnion (0.17 #305, 0.14 #2036, 0.12 #2772), B (0.17 #335, 0.12 #430, 0.11 #1872), OttomanEmpire (0.11 #893, 0.11 #1362, 0.10 #1025), NAM (0.11 #283, 0.08 #1303, 0.03 #769)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #133 for best value:
>> intensional similarity = 15
>> extensional distance = 2
>> proper extension:  RB; ZW;
>> query: (?x89, GB) <- ?x89[ a Country; has encompassed ?x213; has ethnicGroup ?x2491; has government ?x90; has neighbor ?x243<RSA>; has religion ?x116<Christian>; is locatedIn of ?x137[ a River; has flowsInto ?x182; has locatedIn ?x138<NAM>; is flowsInto of ?x1054[ a River;];];]
ranks of expected_values: 1
EVAL	LS	wasDependentOf	GB	CNN-1.+1._MA	1.000	1.000	1.000	1.000	110.000	110.000	134.000	0.500	http://www.semwebtech.org/mondial/10/meta#wasDependentOf

#14-GCA
PRED entity: GCA

PRED relation: religion
PRED expected values: Mayan
=> 51 concepts (51 used for prediction)
PRED predicted values (max 10 best out of 38): JehovasWitnesses (0.71 #162, 0.60 #81, 0.56 #1005), Mormon (0.71 #162, 0.60 #81, 0.56 #1005), Muslim (0.56 #1772, 0.54 #1491, 0.54 #1571), Christian (0.33 #846, 0.30 #685, 0.30 #1530), ChristianOrthodox (0.33 #483, 0.33 #443, 0.30 #523), Jewish (0.24 #1207, 0.15 #524, 0.14 #404), Anglican (0.24 #1207, 0.14 #418, 0.14 #778), Buddhist (0.24 #1207, 0.14 #212, 0.13 #853), Seventh-DayAdventist (0.24 #1207, 0.12 #331, 0.07 #291), Hindu (0.24 #1207, 0.11 #1255, 0.11 #1816)
>> best conf = 0.71
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #162 for best value:
>> intensional similarity = 6
>> extensional distance = 14
>> proper extension:  RCH; CO; PE; USA; YV; RA; MEX; ES; BZ; BOL; ...
>> query: (?x181, ?x95) <- ?x181[ has wasDependentOf ?x149; is neighbor of ?x654[ has ethnicGroup ?x676<Mestizo>; has language ?x796; has religion ?x95;];]

No rule for expected values
ranks of expected_values: 
EVAL	GCA	religion	Mayan	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	51.000	51.000	38.000	0.706	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion
PRED expected values: Mayan
=> 95 concepts (95 used for prediction)
PRED predicted values (max 10 best out of 39): JehovasWitnesses (0.62 #301, 0.57 #765, 0.57 #524), Muslim (0.60 #2230, 0.58 #3084, 0.58 #2922), Mormon (0.57 #524, 0.57 #523, 0.57 #282), Christian (0.44 #1987, 0.42 #1904, 0.39 #1577), Jewish (0.37 #3285, 0.36 #2796, 0.32 #2552), Hindu (0.37 #3285, 0.36 #2796, 0.32 #2552), CopticChristian (0.37 #3285, 0.36 #2796, 0.32 #2552), Buddhist (0.37 #3285, 0.32 #2552, 0.30 #2593), ChristianOrthodox (0.37 #2187, 0.35 #2267, 0.33 #1859), Anglican (0.30 #2593, 0.29 #2307, 0.28 #3121)
>> best conf = 0.62
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #301 for best value:
>> intensional similarity = 15
>> extensional distance = 6
>> proper extension:  GUY;
>> query: (?x181, JehovasWitnesses) <- ?x181[ has ethnicGroup ?x79<Amerindian>; has ethnicGroup ?x197[ is ethnicGroup of ?x542<BR>;]; has ethnicGroup ?x676[ a EthnicGroup; is ethnicGroup of ?x215<CO>;]; has neighbor ?x482; has religion ?x95<Protestant>; is locatedIn of ?x282; is neighbor of ?x671[ has government ?x1947;];]

No rule for expected values
ranks of expected_values: 
EVAL	GCA	religion	Mayan	CNN-1.+1._MA	0.000	0.000	0.000	0.000	95.000	95.000	39.000	0.625	http://www.semwebtech.org/mondial/10/meta#religion

#13-Fakaofo
PRED entity: Fakaofo

PRED relation: locatedIn
PRED expected values: TOK
=> 28 concepts (28 used for prediction)
PRED predicted values (max 10 best out of 102): KIR (0.33 #158, 0.25 #394, 0.09 #868), USA (0.28 #1257, 0.26 #546, 0.23 #1493), TUV (0.17 #150, 0.12 #386, 0.05 #6179), MH (0.17 #91, 0.12 #327, 0.05 #6179), J (0.15 #493, 0.12 #729, 0.12 #965), COCO (0.12 #422, 0.01 #2081, 0.01 #2318), MV (0.12 #364, 0.01 #2023, 0.01 #2260), P (0.12 #1854, 0.10 #2329, 0.10 #2092), RI (0.10 #2420, 0.08 #4089, 0.07 #4327), GB (0.10 #3329, 0.09 #3568, 0.09 #3807)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #158 for best value:
>> intensional similarity = 7
>> extensional distance = 4
>> proper extension:  Kiritimati; Tarawa; Majuro; Fongafale;
>> query: (?x414, KIR) <- ?x414[ a Island; has belongsToIslands ?x1623[ a Islands;]; has locatedInWater ?x282<PacificOcean>; has type ?x1160<"atoll">;]

*> Best rule #5937 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 268
*> proper extension:  Olkhon;
*> query: (?x414, ?x315) <- ?x414[ a Island; has locatedInWater ?x282[ has locatedIn ?x217[ has wasDependentOf ?x575;]; has locatedIn ?x315[ has religion ?x95;];];]
*> conf = 0.03
ranks of expected_values: 55
EVAL	Fakaofo	locatedIn	TOK	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.018	28.000	28.000	102.000	0.333	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: TOK
=> 36 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 139): KIR (0.33 #158, 0.09 #630, 0.07 #394), USA (0.28 #1028, 0.26 #308, 0.23 #1264), RI (0.17 #3693, 0.12 #949, 0.12 #760), TUV (0.17 #150, 0.05 #8297, 0.05 #8296), MH (0.17 #91, 0.05 #8297, 0.05 #8296), J (0.15 #255, 0.12 #491, 0.10 #727), GR (0.11 #4220, 0.05 #5440, 0.05 #5688), RP (0.11 #2281, 0.10 #3750, 0.08 #3992), P (0.10 #2863, 0.09 #4574, 0.07 #4810), I (0.10 #4178, 0.08 #2714, 0.07 #4425)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #158 for best value:
>> intensional similarity = 7
>> extensional distance = 4
>> proper extension:  Kiritimati; Tarawa; Majuro; Fongafale;
>> query: (?x414, KIR) <- ?x414[ a Island; has belongsToIslands ?x1623[ a Islands;]; has locatedInWater ?x282<PacificOcean>; has type ?x1160<"atoll">;]

*> Best rule #4124 for first EXPECTED value:
*> intensional similarity = 27
*> extensional distance = 85
*> proper extension:  Bangka;
*> query: (?x414, ?x550) <- ?x414[ a Island; has locatedInWater ?x282[ has locatedIn ?x73[ a Country; has ethnicGroup ?x58; has religion ?x56; is neighbor of ?x170;]; has locatedIn ?x158[ a Country; has wasDependentOf ?x81;]; has locatedIn ?x296[ has neighbor ?x542;]; has locatedIn ?x550[ has religion ?x352;]; has locatedIn ?x728[ has encompassed ?x211; has ethnicGroup ?x1129; has government ?x435;]; has mergesWith ?x620[ a Sea; has mergesWith ?x270; is locatedInWater of ?x340;]; has mergesWith ?x770<BandaSea>; is locatedInWater of ?x504[ a Island; has locatedIn ?x322;];];]
*> conf = 0.03
ranks of expected_values: 51
EVAL	Fakaofo	locatedIn	TOK	CNN-1.+1._MA	0.000	0.000	0.000	0.020	36.000	36.000	139.000	0.333	http://www.semwebtech.org/mondial/10/meta#locatedIn

#12-Jewish
PRED entity: Jewish

PRED relation: religion!
PRED expected values: GAZA
=> 17 concepts (14 used for prediction)
PRED predicted values (max 10 best out of 224): I (0.60 #852, 0.58 #810, 0.58 #811), AUS (0.60 #849, 0.56 #645, 0.50 #1258), SRB (0.58 #810, 0.58 #811, 0.56 #1422), RO (0.58 #810, 0.58 #811, 0.56 #1422), SK (0.58 #810, 0.58 #811, 0.56 #1422), RCH (0.58 #810, 0.58 #811, 0.56 #1422), H (0.58 #810, 0.58 #811, 0.56 #1422), BR (0.58 #810, 0.58 #811, 0.56 #1422), PY (0.58 #810, 0.58 #811, 0.56 #1422), B (0.58 #810, 0.58 #811, 0.56 #1422)
>> best conf = 0.60
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #852 for best value:
>> intensional similarity = 14
>> extensional distance = 8
>> proper extension:  ChristianOrthodox;
>> query: (?x109, I) <- ?x109[ is religion of ?x50[ has language ?x51;]; is religion of ?x81[ is locatedIn of ?x121; is wasDependentOf of ?x161[ has language ?x247; is locatedIn of ?x317;]; is wasDependentOf of ?x196[ has encompassed ?x211; is locatedIn of ?x60;]; is wasDependentOf of ?x1209[ has government ?x2377;];]; is religion of ?x108[ has ethnicGroup ?x197;];]

*> Best rule #810 for first EXPECTED value:
*> intensional similarity = 14
*> extensional distance = 7
*> proper extension:  Muslim; Hindu; Buddhist; Anglican; Sikh;
*> query: (?x109, ?x1184) <- ?x109[ a Religion; is religion of ?x78[ is locatedIn of ?x121; is wasDependentOf of ?x94;]; is religion of ?x81<GB>; is religion of ?x108[ is neighbor of ?x1184;]; is religion of ?x363[ has encompassed ?x521; is locatedIn of ?x182;]; is religion of ?x718[ is locatedIn of ?x742; is neighbor of ?x120;];]
*> conf = 0.58
ranks of expected_values: 12
EVAL	Jewish	religion!	GAZA	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.083	17.000	14.000	224.000	0.600	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion!
PRED expected values: GAZA
=> 36 concepts (36 used for prediction)
PRED predicted values (max 10 best out of 233): RO (0.79 #6149, 0.74 #407, 0.73 #406), PY (0.79 #6149, 0.72 #1431, 0.72 #1849), MEX (0.79 #6149, 0.72 #1431, 0.72 #1849), CDN (0.79 #6149, 0.50 #1075, 0.47 #405), SK (0.74 #407, 0.73 #406, 0.72 #1431), H (0.74 #407, 0.73 #406, 0.72 #1431), PL (0.74 #407, 0.73 #406, 0.72 #1431), RCH (0.74 #407, 0.73 #406, 0.72 #1431), BOL (0.74 #407, 0.73 #406, 0.72 #1431), BR (0.74 #407, 0.73 #406, 0.72 #1431)
>> best conf = 0.79
=> the first rule below is the first best rule for 4 predicted values
>> Best rule #6149 for best value:
>> intensional similarity = 15
>> extensional distance = 17
>> proper extension:  CaoDai; HoaHao;
>> query: (?x109, ?x272) <- ?x109[ a Religion; is religion of ?x50[ has government ?x2058;]; is religion of ?x315[ has ethnicGroup ?x79; is locatedIn of ?x219[ a River;]; is locatedIn of ?x809[ has mergesWith ?x452;]; is locatedIn of ?x2458[ a Estuary; has locatedIn ?x272;];]; is religion of ?x718[ is neighbor of ?x543[ a Country;];];]

*> Best rule #1431 for first EXPECTED value:
*> intensional similarity = 18
*> extensional distance = 3
*> proper extension:  Buddhist;
*> query: (?x109, ?x120) <- ?x109[ is religion of ?x239[ has neighbor ?x63[ has encompassed ?x213; has religion ?x187<Muslim>;]; has neighbor ?x803[ has encompassed ?x175; has ethnicGroup ?x244; is locatedIn of ?x953;]; has wasDependentOf ?x485; is locatedIn of ?x1552[ is mergesWith of ?x2407;];]; is religion of ?x315<USA>; is religion of ?x718[ has ethnicGroup ?x237; has language ?x51; is neighbor of ?x120;];]
*> conf = 0.72
ranks of expected_values: 23
EVAL	Jewish	religion!	GAZA	CNN-1.+1._MA	0.000	0.000	0.000	0.043	36.000	36.000	233.000	0.789	http://www.semwebtech.org/mondial/10/meta#religion

#11-P
PRED entity: P

PRED relation: locatedIn!
PRED expected values: Flores Douro TorredeEstrela
=> 44 concepts (41 used for prediction)
PRED predicted values (max 10 best out of 1391): Douro (0.88 #12661, 0.13 #56258, 0.07 #50628), Guadiana (0.51 #42190, 0.07 #50628, 0.06 #6590), Tajo (0.51 #42190, 0.07 #50628, 0.06 #6576), PacificOcean (0.40 #31025, 0.38 #33837, 0.37 #28213), CaribbeanSea (0.39 #22609, 0.31 #21203, 0.30 #12765), MediterraneanSea (0.28 #18368, 0.27 #25398, 0.25 #81), NorthSea (0.25 #22, 0.24 #7053, 0.22 #8460), Maas (0.25 #396, 0.22 #4614, 0.20 #1802), Loire (0.25 #672, 0.13 #56258, 0.06 #6297), Garonne (0.25 #418, 0.13 #56258, 0.06 #6043)
>> best conf = 0.88
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #12661 for best value:
>> intensional similarity = 5
>> extensional distance = 19
>> proper extension:  ROU;
>> query: (?x1027, ?x1519) <- ?x1027[ has encompassed ?x195; is locatedIn of ?x182<AtlanticOcean>; is locatedIn of ?x1352[ is hasEstuary of ?x1519;];]
ranks of expected_values: 1, 828
EVAL	P	locatedIn!	TorredeEstrela	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.000	44.000	41.000	1391.000	0.882	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	P	locatedIn!	Douro	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	44.000	41.000	1391.000	0.882	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	P	locatedIn!	Flores	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.001	44.000	41.000	1391.000	0.882	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn!
PRED expected values: Flores Douro TorredeEstrela
=> 121 concepts (117 used for prediction)
PRED predicted values (max 10 best out of 1424): Douro (0.91 #8453, 0.89 #46464, 0.41 #29569), Flores (0.63 #98558, 0.50 #11271, 0.25 #16896), MediterraneanSea (0.62 #56325, 0.56 #64855, 0.53 #74711), Guadiana (0.61 #85889, 0.61 #29570, 0.59 #59139), Tajo (0.61 #85889, 0.61 #29570, 0.59 #59139), NorthSea (0.57 #25364, 0.42 #74626, 0.40 #92925), CaribbeanSea (0.54 #52205, 0.50 #40939, 0.48 #115551), PacificOcean (0.50 #71896, 0.50 #22613, 0.46 #56410), TheChannel (0.42 #74626, 0.40 #92925, 0.38 #66182), IrishSea (0.42 #74626, 0.40 #92925, 0.38 #66182)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #8453 for best value:
>> intensional similarity = 14
>> extensional distance = 2
>> proper extension:  USA;
>> query: (?x1027, ?x1519) <- ?x1027[ has encompassed ?x195; is locatedIn of ?x827[ a Island; has belongsToIslands ?x200;]; is locatedIn of ?x1162[ a Island; has type ?x150;]; is locatedIn of ?x1352[ is hasEstuary of ?x1519;]; is neighbor of ?x149[ a Country; has ethnicGroup ?x2540; has language ?x790; is locatedIn of ?x68;];]
ranks of expected_values: 1, 2
EVAL	P	locatedIn!	TorredeEstrela	CNN-1.+1._MA	0.000	0.000	0.000	0.000	121.000	117.000	1424.000	0.913	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	P	locatedIn!	Douro	CNN-1.+1._MA	1.000	1.000	1.000	1.000	121.000	117.000	1424.000	0.913	http://www.semwebtech.org/mondial/10/meta#locatedIn
EVAL	P	locatedIn!	Flores	CNN-1.+1._MA	1.000	1.000	1.000	1.000	121.000	117.000	1424.000	0.913	http://www.semwebtech.org/mondial/10/meta#locatedIn

#10-BarentsSea
PRED entity: BarentsSea

PRED relation: mergesWith
PRED expected values: KaraSea
=> 27 concepts (23 used for prediction)
PRED predicted values (max 10 best out of 69): AtlanticOcean (0.55 #154, 0.55 #120, 0.50 #194), BarentsSea (0.45 #393, 0.07 #164, 0.05 #204), KaraSea (0.45 #393, 0.07 #180, 0.05 #220), GreenlandSea (0.40 #70, 0.33 #108, 0.21 #187), PacificOcean (0.21 #250, 0.19 #368, 0.09 #129), IndianOcean (0.20 #39, 0.18 #116, 0.17 #237), TheChannel (0.20 #67, 0.18 #144, 0.17 #105), GulfofMexico (0.20 #69, 0.18 #146, 0.17 #107), CaribbeanSea (0.20 #53, 0.18 #130, 0.17 #91), LabradorSea (0.20 #46, 0.17 #84, 0.09 #123)
>> best conf = 0.55
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #154 for best value:
>> intensional similarity = 5
>> extensional distance = 9
>> proper extension:  BeringSea;
>> query: (?x251, ?x182) <- ?x251[ a Sea; has locatedIn ?x973[ is locatedIn of ?x182<AtlanticOcean>;]; is flowsInto of ?x631;]
>> Best rule #120 for best value:
>> intensional similarity = 5
>> extensional distance = 9
>> proper extension:  BeringSea;
>> query: (?x251, AtlanticOcean) <- ?x251[ a Sea; has locatedIn ?x973[ is locatedIn of ?x182<AtlanticOcean>;]; is flowsInto of ?x631;]

*> Best rule #393 for first EXPECTED value:
*> intensional similarity = 4
*> extensional distance = 41
*> proper extension:  Araguaia;
*> query: (?x251, ?x263) <- ?x251[ is locatedInWater of ?x931[ a Island; has locatedIn ?x73; has locatedInWater ?x263;];]
*> conf = 0.45
ranks of expected_values: 3
EVAL	BarentsSea	mergesWith	KaraSea	CNN-0.1+0.1_MA	0.000	1.000	1.000	0.333	27.000	23.000	69.000	0.545	http://www.semwebtech.org/mondial/10/meta#mergesWith

PRED relation: mergesWith
PRED expected values: KaraSea
=> 93 concepts (93 used for prediction)
PRED predicted values (max 10 best out of 145): BarentsSea (0.50 #155, 0.48 #506, 0.45 #866), KaraSea (0.50 #155, 0.48 #506, 0.45 #866), PacificOcean (0.39 #389, 0.39 #364, 0.38 #170), BeringSea (0.33 #104, 0.32 #626, 0.32 #666), EastSibirianSea (0.33 #19, 0.32 #626, 0.32 #666), GreenlandSea (0.33 #32, 0.18 #625, 0.17 #745), HudsonBay (0.33 #7, 0.18 #625, 0.17 #745), SeaofOkhotsk (0.32 #626, 0.32 #666, 0.29 #545), NorthSea (0.32 #626, 0.32 #666, 0.29 #545), SeaofJapan (0.32 #626, 0.32 #666, 0.29 #545)
>> best conf = 0.50
=> the first rule below is the first best rule for 2 predicted values
>> Best rule #155 for best value:
>> intensional similarity = 7
>> extensional distance = 5
>> proper extension:  OzeroBaikal;
>> query: (?x251, ?x263) <- ?x251[ has locatedIn ?x73<R>; is flowsInto of ?x631[ a River;]; is locatedInWater of ?x931[ a Island; has locatedInWater ?x263;];]

Best rule for first EXPECTED value is the SAME
ranks of expected_values: 2
EVAL	BarentsSea	mergesWith	KaraSea	CNN-1.+1._MA	0.000	1.000	1.000	0.500	93.000	93.000	145.000	0.500	http://www.semwebtech.org/mondial/10/meta#mergesWith

#9-Himalaya
PRED entity: Himalaya

PRED relation: inMountains!
PRED expected values: Lhotse Shishapangma
=> 16 concepts (16 used for prediction)
PRED predicted values (max 10 best out of 307): Tarim-Yarkend (0.14 #194, 0.12 #444, 0.09 #946), BroadPeak (0.14 #124, 0.12 #374, 0.09 #876), PikChan-Tengri (0.14 #104, 0.12 #354, 0.09 #856), Ili (0.14 #71, 0.12 #321, 0.09 #823), LiushiShan (0.14 #67, 0.12 #317, 0.09 #819), Kailash (0.14 #61, 0.12 #311, 0.09 #813), Kongur (0.14 #55, 0.12 #305, 0.09 #807), UlugMuztag (0.14 #26, 0.12 #276, 0.09 #778), PikKarl-Marx (0.14 #224, 0.12 #474, 0.09 #976), Naryn (0.14 #201, 0.12 #451, 0.09 #953)
>> best conf = 0.14
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #194 for best value:
>> intensional similarity = 9
>> extensional distance = 5
>> proper extension:  Karakorum; Kunlun; Pamir; Transhimalaya; TianShan;
>> query: (?x309, Tarim-Yarkend) <- ?x309[ a Mountains; is inMountains of ?x576[ a Mountain; has locatedIn ?x232<CN>;]; is inMountains of ?x1280[ a Mountain; has locatedIn ?x111[ has government ?x1779;];];]

*> Best rule #2509 for first EXPECTED value:
*> intensional similarity = 9
*> extensional distance = 51
*> proper extension:  BlackForest; Ahaggar; Apennin; Alps; Andes; Zagros; BayrischerWald; CordilleraBetica; Balkan; Elburs; ...
*> query: (?x309, ?x231) <- ?x309[ is inMountains of ?x576[ has locatedIn ?x232[ has government ?x831; has neighbor ?x334; is locatedIn of ?x231; is neighbor of ?x73;];]; is inMountains of ?x1280[ a Mountain; has locatedIn ?x111;];]
*> conf = 0.09
ranks of expected_values: 40, 75
EVAL	Himalaya	inMountains!	Shishapangma	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.025	16.000	16.000	307.000	0.143	http://www.semwebtech.org/mondial/10/meta#inMountains
EVAL	Himalaya	inMountains!	Lhotse	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.014	16.000	16.000	307.000	0.143	http://www.semwebtech.org/mondial/10/meta#inMountains

PRED relation: inMountains!
PRED expected values: Lhotse Shishapangma
=> 43 concepts (43 used for prediction)
PRED predicted values (max 10 best out of 307): BroadPeak (0.33 #376, 0.25 #1887, 0.25 #879), Tarim-Yarkend (0.33 #446, 0.25 #1957, 0.25 #949), PikChan-Tengri (0.33 #104, 0.25 #1615, 0.20 #3127), Ili (0.33 #71, 0.25 #1582, 0.20 #3094), Naryn (0.33 #201, 0.25 #1712, 0.20 #3224), BarredesEcrins (0.25 #1004, 0.20 #3020, 0.20 #2516), Grossglockner (0.25 #1003, 0.20 #3019, 0.20 #2515), GranParadiso (0.25 #995, 0.20 #3011, 0.20 #2507), Marmolata (0.25 #992, 0.20 #3008, 0.20 #2504), CrapSognGion (0.25 #991, 0.20 #3007, 0.20 #2503)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #376 for best value:
>> intensional similarity = 26
>> extensional distance = 1
>> proper extension:  Karakorum;
>> query: (?x309, BroadPeak) <- ?x309[ a Mountains; is inMountains of ?x489[ a Mountain; has locatedIn ?x924[ a Country; has language ?x2392; is locatedIn of ?x1877<Thar>; is neighbor of ?x366;];]; is inMountains of ?x576[ a Mountain; has locatedIn ?x111[ a Country; has encompassed ?x175<Asia>; has government ?x1779; has religion ?x187<Muslim>; is locatedIn of ?x328<Lhotse>;]; has locatedIn ?x232<CN>;]; is inMountains of ?x1441[ a Mountain; has locatedIn ?x83<PK>;];]

*> Best rule #3777 for first EXPECTED value:
*> intensional similarity = 24
*> extensional distance = 4
*> proper extension:  SierraParima;
*> query: (?x309, ?x231) <- ?x309[ a Mountains; is inMountains of ?x110[ has locatedIn ?x111[ a Country; has encompassed ?x175; has government ?x1779; has religion ?x187;];]; is inMountains of ?x489[ has locatedIn ?x924[ a Country; has government ?x140<"federal republic">; has religion ?x116[ is religion of ?x232[ a Country; has government ?x831; has neighbor ?x641[ a Country;]; is locatedIn of ?x231; is locatedIn of ?x1771; is neighbor of ?x463;];]; has wasDependentOf ?x81; is neighbor of ?x943;];]; is inMountains of ?x1771;]
*> conf = 0.18
ranks of expected_values: 76, 89
EVAL	Himalaya	inMountains!	Shishapangma	CNN-1.+1._MA	0.000	0.000	0.000	0.011	43.000	43.000	307.000	0.333	http://www.semwebtech.org/mondial/10/meta#inMountains
EVAL	Himalaya	inMountains!	Lhotse	CNN-1.+1._MA	0.000	0.000	0.000	0.013	43.000	43.000	307.000	0.333	http://www.semwebtech.org/mondial/10/meta#inMountains

#8-PortoSanto
PRED entity: PortoSanto

PRED relation: type
PRED expected values: "volcanic"
=> 41 concepts (41 used for prediction)
PRED predicted values (max 10 best out of 8): "volcanic" (0.82 #18, 0.80 #2, 0.57 #50), "volcano" (0.23 #258, 0.16 #145, 0.15 #403), "lime" (0.04 #117, 0.04 #85, 0.03 #101), "salt" (0.04 #474, 0.03 #558, 0.03 #541), "atoll" (0.04 #217, 0.03 #201, 0.03 #249), "coral" (0.02 #202, 0.02 #250, 0.02 #73), "dam" (0.02 #468, 0.01 #387, 0.01 #552), "sand" (0.02 #471, 0.01 #538, 0.01 #488)
>> best conf = 0.82
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #18 for best value:
>> intensional similarity = 3
>> extensional distance = 9
>> proper extension:  SaoMiguel;
>> query: (?x2198, "volcanic") <- ?x2198[ a Island; has locatedIn ?x1027<P>;]
ranks of expected_values: 1
EVAL	PortoSanto	type	"volcanic"	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	41.000	41.000	8.000	0.818	http://www.semwebtech.org/mondial/10/meta#type

PRED relation: type
PRED expected values: "volcanic"
=> 108 concepts (108 used for prediction)
PRED predicted values (max 10 best out of 10): "volcanic" (0.82 #18, 0.80 #2, 0.66 #82), "volcano" (0.28 #377, 0.23 #180, 0.23 #163), "atoll" (0.06 #368, 0.06 #483, 0.06 #613), "lime" (0.05 #102, 0.05 #365, 0.05 #480), "salt" (0.03 #1512, 0.02 #1597, 0.02 #1695), "dam" (0.02 #769, 0.02 #785, 0.02 #818), "coral" (0.02 #681, 0.02 #729, 0.02 #614), "caldera" (0.02 #1048, 0.01 #1132, 0.01 #1183), "sand" (0.01 #1379, 0.01 #1577, 0.01 #1594), "impact" (0.01 #599)
>> best conf = 0.82
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #18 for best value:
>> intensional similarity = 3
>> extensional distance = 9
>> proper extension:  SaoMiguel;
>> query: (?x2198, "volcanic") <- ?x2198[ a Island; has locatedIn ?x1027<P>;]
ranks of expected_values: 1
EVAL	PortoSanto	type	"volcanic"	CNN-1.+1._MA	1.000	1.000	1.000	1.000	108.000	108.000	10.000	0.818	http://www.semwebtech.org/mondial/10/meta#type

#7-Tambo
PRED entity: Tambo

PRED relation: flowsInto!
PRED expected values: Perene Apurimac
=> 54 concepts (52 used for prediction)
PRED predicted values (max 10 best out of 323): Urubamba (0.04 #554, 0.04 #856, 0.03 #3618), Tambo (0.04 #475, 0.04 #777, 0.03 #3618), Murgab (0.04 #393, 0.04 #695, 0.02 #1598), Salzach (0.04 #512, 0.04 #814, 0.02 #1717), Alz (0.04 #467, 0.04 #769, 0.02 #1672), Bartang (0.04 #378, 0.04 #680, 0.02 #1583), Ammer (0.04 #372, 0.04 #674, 0.02 #1577), Ammersee (0.04 #412, 0.04 #714, 0.02 #1617), Würm (0.04 #341, 0.04 #643, 0.02 #1546), Ucayali (0.04 #442, 0.03 #3618, 0.03 #7533)
>> best conf = 0.04
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #554 for best value:
>> intensional similarity = 5
>> extensional distance = 24
>> proper extension:  Mississippi; Drin;
>> query: (?x1207, Urubamba) <- ?x1207[ has flowsInto ?x987; has hasEstuary ?x1208; is flowsInto of ?x1332[ a River; is flowsInto of ?x1331;];]

*> Best rule #3618 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 62
*> proper extension:  DeadSea;
*> query: (?x1207, ?x264) <- ?x1207[ is flowsInto of ?x1049[ has locatedIn ?x296[ has wasDependentOf ?x149; is locatedIn of ?x264;];]; is flowsInto of ?x1332[ is flowsInto of ?x1331;];]
*> conf = 0.03
ranks of expected_values: 93, 100
EVAL	Tambo	flowsInto!	Apurimac	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.011	54.000	52.000	323.000	0.038	http://www.semwebtech.org/mondial/10/meta#flowsInto
EVAL	Tambo	flowsInto!	Perene	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.010	54.000	52.000	323.000	0.038	http://www.semwebtech.org/mondial/10/meta#flowsInto

PRED relation: flowsInto!
PRED expected values: Perene Apurimac
=> 157 concepts (157 used for prediction)
PRED predicted values (max 10 best out of 365): Tambo (0.33 #475, 0.25 #1077, 0.20 #1377), Urubamba (0.33 #554, 0.25 #1156, 0.20 #1456), ColumbiaRiver (0.33 #274, 0.08 #3288, 0.06 #23565), Colorado (0.33 #175, 0.08 #3189, 0.06 #23565), SnowyRiver (0.33 #146, 0.08 #3160, 0.06 #23565), RioLerma (0.33 #80, 0.08 #3094, 0.06 #23565), Huallaga (0.25 #1034, 0.20 #1334, 0.14 #1938), LagoJunin (0.20 #1396, 0.11 #2601, 0.08 #3812), Ucayali (0.12 #2248, 0.12 #3315, 0.11 #12686), Maranon (0.12 #2239, 0.12 #3315, 0.11 #12686)
>> best conf = 0.33
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #475 for best value:
>> intensional similarity = 9
>> extensional distance = 1
>> proper extension:  Ucayali;
>> query: (?x1207, Tambo) <- ?x1207[ a River; has hasEstuary ?x1208; has hasSource ?x430; has locatedIn ?x296<PE>; is flowsInto of ?x1332[ a River; has hasEstuary ?x694; is flowsInto of ?x1331;];]

*> Best rule #3315 for first EXPECTED value:
*> intensional similarity = 12
*> extensional distance = 10
*> proper extension:  AtlanticOcean;
*> query: (?x1207, ?x1350) <- ?x1207[ has locatedIn ?x296[ has ethnicGroup ?x79; has neighbor ?x690<BOL>; is locatedIn of ?x1146[ a Mountain; has inMountains ?x1453;]; is locatedIn of ?x1350[ a River;]; is locatedIn of ?x1759[ a Estuary;];]; is flowsInto of ?x1049;]
*> conf = 0.12
ranks of expected_values: 15, 16
EVAL	Tambo	flowsInto!	Apurimac	CNN-1.+1._MA	0.000	0.000	0.000	0.067	157.000	157.000	365.000	0.333	http://www.semwebtech.org/mondial/10/meta#flowsInto
EVAL	Tambo	flowsInto!	Perene	CNN-1.+1._MA	0.000	0.000	0.000	0.067	157.000	157.000	365.000	0.333	http://www.semwebtech.org/mondial/10/meta#flowsInto

#6-P
PRED entity: P

PRED relation: religion
PRED expected values: Protestant
=> 55 concepts (55 used for prediction)
PRED predicted values (max 10 best out of 36): Protestant (0.72 #740, 0.71 #535, 0.67 #289), Muslim (0.67 #292, 0.64 #415, 0.64 #210), ChristianOrthodox (0.49 #1067, 0.28 #1149, 0.27 #1231), Jewish (0.40 #44, 0.33 #3, 0.25 #331), Buddhist (0.36 #216, 0.36 #421, 0.33 #298), Christian (0.33 #1357, 0.33 #865, 0.32 #906), Hindu (0.27 #214, 0.25 #296, 0.21 #419), JehovasWitnesses (0.24 #594, 0.17 #2092, 0.13 #717), Anglican (0.22 #140, 0.18 #509, 0.17 #2092), Sikh (0.18 #238, 0.17 #2092, 0.17 #320)
>> best conf = 0.72
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #740 for best value:
>> intensional similarity = 6
>> extensional distance = 23
>> proper extension:  SK; PL; H;
>> query: (?x1027, Protestant) <- ?x1027[ has government ?x2551; has religion ?x352; is locatedIn of ?x707[ a Island;]; is locatedIn of ?x2455[ a Estuary;];]
ranks of expected_values: 1
EVAL	P	religion	Protestant	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	55.000	55.000	36.000	0.720	http://www.semwebtech.org/mondial/10/meta#religion

PRED relation: religion
PRED expected values: Protestant
=> 136 concepts (136 used for prediction)
PRED predicted values (max 10 best out of 41): Protestant (0.82 #2921, 0.78 #2346, 0.77 #3209), Muslim (0.68 #5068, 0.65 #4161, 0.62 #4079), ChristianOrthodox (0.63 #2592, 0.53 #2181, 0.41 #4034), Buddhist (0.62 #752, 0.40 #1080, 0.40 #300), Christian (0.49 #1316, 0.47 #1892, 0.47 #1645), Jewish (0.45 #3908, 0.40 #374, 0.40 #250), JehovasWitnesses (0.45 #3908, 0.33 #62, 0.29 #3783), Mormon (0.45 #3908, 0.29 #3783, 0.26 #5022), Anglican (0.41 #2279, 0.40 #2649, 0.39 #2485), Hindu (0.38 #750, 0.26 #4281, 0.25 #1366)
>> best conf = 0.82
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #2921 for best value:
>> intensional similarity = 16
>> extensional distance = 20
>> proper extension:  EW;
>> query: (?x1027, Protestant) <- ?x1027[ has government ?x2551; has religion ?x352[ is religion of ?x77[ has encompassed ?x521;]; is religion of ?x234<CH>; is religion of ?x246<CUR>; is religion of ?x407<AXA>; is religion of ?x1826<GBZ>;]; is locatedIn of ?x199[ a Island;]; is locatedIn of ?x1479[ a River;];]
ranks of expected_values: 1
EVAL	P	religion	Protestant	CNN-1.+1._MA	1.000	1.000	1.000	1.000	136.000	136.000	41.000	0.818	http://www.semwebtech.org/mondial/10/meta#religion

#5-CO
PRED entity: CO

PRED relation: neighbor!
PRED expected values: YV
=> 43 concepts (43 used for prediction)
PRED predicted values (max 10 best out of 221): YV (0.91 #3481, 0.91 #4116, 0.91 #3322), BOL (0.50 #588, 0.33 #272, 0.31 #3483), RCH (0.50 #511, 0.31 #3483, 0.26 #5709), RA (0.33 #541, 0.33 #225, 0.26 #5709), PY (0.33 #546, 0.33 #230, 0.26 #5709), CO (0.33 #198, 0.31 #3483, 0.26 #5709), GUY (0.33 #220, 0.26 #5709, 0.26 #5869), NIC (0.33 #389, 0.13 #2529, 0.11 #6027), SME (0.33 #29, 0.11 #6027, 0.10 #5388), CR (0.31 #3483, 0.26 #5709, 0.26 #5869)
>> best conf = 0.91
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #3481 for best value:
>> intensional similarity = 5
>> extensional distance = 78
>> proper extension:  ARM;
>> query: (?x215, ?x296) <- ?x215[ has language ?x796; has neighbor ?x296[ has neighbor ?x202; has wasDependentOf ?x149; is locatedIn of ?x264;];]
ranks of expected_values: 1
EVAL	CO	neighbor!	YV	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	43.000	43.000	221.000	0.914	http://www.semwebtech.org/mondial/10/meta#neighbor

PRED relation: neighbor!
PRED expected values: YV
=> 116 concepts (116 used for prediction)
PRED predicted values (max 10 best out of 230): YV (0.90 #13105, 0.90 #12945, 0.89 #5732), CO (0.43 #2540, 0.33 #672, 0.33 #515), RCH (0.33 #986, 0.33 #196, 0.29 #2381), BOL (0.33 #273, 0.29 #2381, 0.29 #11665), RA (0.33 #1016, 0.27 #13104, 0.26 #16480), GUY (0.33 #537, 0.27 #13104, 0.26 #16480), PY (0.29 #2381, 0.29 #11665, 0.29 #11664), SME (0.29 #2381, 0.29 #11665, 0.29 #11664), ROU (0.29 #2381, 0.29 #11665, 0.29 #11664), CR (0.29 #2381, 0.28 #11504, 0.28 #13424)
>> best conf = 0.90
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #13105 for best value:
>> intensional similarity = 10
>> extensional distance = 74
>> proper extension:  ARM;
>> query: (?x215, ?x345) <- ?x215[ a Country; has ethnicGroup ?x79; has language ?x796; has neighbor ?x345; is neighbor of ?x783[ a Country; has encompassed ?x521; has religion ?x95; is neighbor of ?x318[ is locatedIn of ?x282;];];]
ranks of expected_values: 1
EVAL	CO	neighbor!	YV	CNN-1.+1._MA	1.000	1.000	1.000	1.000	116.000	116.000	230.000	0.905	http://www.semwebtech.org/mondial/10/meta#neighbor

#4-MediterraneanSea
PRED entity: MediterraneanSea

PRED relation: locatedInWater!
PRED expected values: Samos Kreta Sicilia
=> 36 concepts (32 used for prediction)
PRED predicted values (max 10 best out of 422): Hokkaido (0.21 #1734, 0.10 #1978, 0.09 #3199), Kyushu (0.21 #1828, 0.10 #2072, 0.09 #3293), Sulawesi (0.21 #1795, 0.09 #3260, 0.09 #3505), Sumatra (0.20 #2010, 0.18 #2742, 0.16 #3231), Cuba (0.15 #2150, 0.14 #1906, 0.14 #2882), Taiwan (0.15 #2007, 0.14 #1763, 0.14 #2739), GreatBritain (0.15 #1983, 0.14 #2715, 0.12 #3204), Hispaniola (0.14 #1938, 0.10 #2182, 0.10 #2670), St.Barthelemy (0.14 #1933, 0.10 #2177, 0.10 #2665), Martinique (0.14 #1898, 0.10 #2142, 0.10 #2630)
>> best conf = 0.21
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1734 for best value:
>> intensional similarity = 5
>> extensional distance = 12
>> proper extension:  JavaSea; SeaofJapan; NorwegianSea; SulawesiSea; BandaSea;
>> query: (?x275, Hokkaido) <- ?x275[ a Sea; has locatedIn ?x185[ has neighbor ?x177;]; is locatedInWater of ?x1729[ has type ?x150;];]

*> Best rule #2932 for first EXPECTED value:
*> intensional similarity = 5
*> extensional distance = 20
*> proper extension:  EastSibirianSea; BeringSea;
*> query: (?x275, ?x166) <- ?x275[ a Sea; has locatedIn ?x207[ is locatedIn of ?x166;]; is flowsInto of ?x698; is locatedInWater of ?x68;]
*> conf = 0.08
ranks of expected_values: 90, 125, 126
EVAL	MediterraneanSea	locatedInWater!	Sicilia	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.011	36.000	32.000	422.000	0.214	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	MediterraneanSea	locatedInWater!	Kreta	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.008	36.000	32.000	422.000	0.214	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	MediterraneanSea	locatedInWater!	Samos	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.008	36.000	32.000	422.000	0.214	http://www.semwebtech.org/mondial/10/meta#locatedInWater

PRED relation: locatedInWater!
PRED expected values: Samos Kreta Sicilia
=> 104 concepts (103 used for prediction)
PRED predicted values (max 10 best out of 537): GreatBritain (0.60 #1006, 0.27 #2721, 0.23 #3210), VelikiRatnoOstrvo (0.40 #1227, 0.25 #738, 0.09 #2206), Sumatra (0.27 #3729, 0.24 #5199, 0.19 #8386), Taiwan (0.21 #3479, 0.20 #3726, 0.18 #5685), Cuba (0.20 #1173, 0.18 #5828, 0.18 #5339), Hispaniola (0.20 #1205, 0.17 #1938, 0.15 #3409), St.Barthelemy (0.20 #1200, 0.17 #1933, 0.15 #3404), Martinique (0.20 #1165, 0.17 #1898, 0.15 #3369), Antigua (0.20 #1163, 0.17 #1896, 0.15 #3367), SaintThomas (0.20 #1116, 0.17 #1849, 0.15 #3320)
>> best conf = 0.60
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #1006 for best value:
>> intensional similarity = 11
>> extensional distance = 3
>> proper extension:  NorthSea; AtlanticOcean; TheChannel;
>> query: (?x275, GreatBritain) <- ?x275[ has locatedIn ?x78<F>; has locatedIn ?x156[ has ethnicGroup ?x160;]; has mergesWith ?x182; is flowsInto of ?x966[ a River;]; is locatedInWater of ?x68[ a Island;]; is locatedInWater of ?x327[ has belongsToIslands ?x1715;];]

*> Best rule #1223 for first EXPECTED value:
*> intensional similarity = 12
*> extensional distance = 3
*> proper extension:  NorthSea; AtlanticOcean; TheChannel;
*> query: (?x275, ?x133) <- ?x275[ has locatedIn ?x78<F>; has locatedIn ?x156[ has ethnicGroup ?x160; is locatedIn of ?x133;]; has mergesWith ?x182; is flowsInto of ?x966[ a River;]; is locatedInWater of ?x68[ a Island;]; is locatedInWater of ?x327[ has belongsToIslands ?x1715;];]
*> conf = 0.12
ranks of expected_values: 194, 195, 315
EVAL	MediterraneanSea	locatedInWater!	Sicilia	CNN-1.+1._MA	0.000	0.000	0.000	0.003	104.000	103.000	537.000	0.600	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	MediterraneanSea	locatedInWater!	Kreta	CNN-1.+1._MA	0.000	0.000	0.000	0.005	104.000	103.000	537.000	0.600	http://www.semwebtech.org/mondial/10/meta#locatedInWater
EVAL	MediterraneanSea	locatedInWater!	Samos	CNN-1.+1._MA	0.000	0.000	0.000	0.005	104.000	103.000	537.000	0.600	http://www.semwebtech.org/mondial/10/meta#locatedInWater

#3-D
PRED entity: D

PRED relation: language
PRED expected values: German
=> 31 concepts (31 used for prediction)
PRED predicted values (max 10 best out of 74): Hungarian (0.60 #311, 0.57 #409, 0.45 #605), Roma (0.43 #537, 0.29 #439, 0.27 #635), Serbian (0.33 #137, 0.29 #431, 0.27 #627), Turkish (0.33 #106, 0.29 #498, 0.25 #204), Slovene (0.33 #170, 0.25 #268, 0.21 #687), Polish (0.33 #41, 0.21 #687, 0.02 #1316), Ukrainian (0.29 #520, 0.29 #422, 0.27 #618), French (0.25 #197, 0.21 #687, 0.08 #1080), German (0.25 #211, 0.21 #687, 0.07 #800), Italian (0.25 #203, 0.21 #687, 0.04 #988)
>> best conf = 0.60
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #311 for best value:
>> intensional similarity = 6
>> extensional distance = 3
>> proper extension:  SK; H; SRB;
>> query: (?x120, Hungarian) <- ?x120[ has neighbor ?x78; has religion ?x95; is locatedIn of ?x133<Donau>; is locatedIn of ?x1100[ a Island;];]

*> Best rule #211 for first EXPECTED value:
*> intensional similarity = 6
*> extensional distance = 2
*> proper extension:  CH;
*> query: (?x120, German) <- ?x120[ has ethnicGroup ?x237; has neighbor ?x78; is locatedIn of ?x133[ is flowsInto of ?x132;]; is locatedIn of ?x1602<Bodensee>;]
*> conf = 0.25
ranks of expected_values: 9
EVAL	D	language	German	CNN-0.1+0.1_MA	0.000	0.000	1.000	0.111	31.000	31.000	74.000	0.600	http://www.semwebtech.org/mondial/10/meta#language

PRED relation: language
PRED expected values: German
=> 109 concepts (109 used for prediction)
PRED predicted values (max 10 best out of 98): French (0.50 #690, 0.43 #1376, 0.38 #395), English (0.43 #1379, 0.39 #3934, 0.39 #4526), Spanish (0.38 #5526, 0.35 #5624, 0.34 #4642), German (0.38 #395, 0.33 #508, 0.33 #311), Slovak (0.38 #395, 0.33 #437, 0.33 #42), Hungarian (0.38 #395, 0.33 #412, 0.33 #213), Italian (0.38 #395, 0.33 #500, 0.33 #105), Romansch (0.38 #395, 0.33 #545, 0.25 #643), Dutch (0.38 #395, 0.33 #306, 0.22 #9534), Turkish (0.38 #395, 0.33 #204, 0.22 #9534)
>> best conf = 0.50
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #690 for best value:
>> intensional similarity = 15
>> extensional distance = 2
>> proper extension:  F;
>> query: (?x120, French) <- ?x120[ has encompassed ?x195; has neighbor ?x78[ is locatedIn of ?x323<MontBlanc>;]; has neighbor ?x424[ has ethnicGroup ?x160; has neighbor ?x163; is locatedIn of ?x155;]; is locatedIn of ?x889[ a River; has hasSource ?x1111;]; is locatedIn of ?x1270[ a Island;]; is locatedIn of ?x1482[ has inMountains ?x1483;];]

*> Best rule #395 for first EXPECTED value:
*> intensional similarity = 13
*> extensional distance = 1
*> proper extension:  B;
*> query: (?x120, ?x1035) <- ?x120[ a Country; has ethnicGroup ?x2136; has neighbor ?x78<F>; has neighbor ?x471[ has ethnicGroup ?x164; has language ?x1035; is locatedIn of ?x442;]; has religion ?x95; is locatedIn of ?x121<NorthSea>; is locatedIn of ?x312[ a River;];]
*> conf = 0.38
ranks of expected_values: 4
EVAL	D	language	German	CNN-1.+1._MA	0.000	0.000	1.000	0.250	109.000	109.000	98.000	0.500	http://www.semwebtech.org/mondial/10/meta#language

#2-SanAndres
PRED entity: SanAndres

PRED relation: locatedIn
PRED expected values: CO
=> 14 concepts (14 used for prediction)
PRED predicted values (max 10 best out of 88): USA (0.16 #549, 0.08 #790, 0.07 #1030), GB (0.15 #245, 0.07 #1206, 0.07 #967), P (0.11 #433, 0.04 #915, 0.04 #1155), E (0.09 #263, 0.04 #745, 0.04 #985), CAYM (0.08 #180, 0.05 #2883, 0.05 #2882), TT (0.08 #148, 0.05 #2883, 0.05 #2882), AG (0.08 #208, 0.05 #2883, 0.05 #2882), RI (0.06 #770, 0.06 #1727, 0.06 #2454), D (0.05 #1456, 0.05 #1937, 0.05 #2179), RH (0.05 #2883, 0.05 #2882, 0.05 #2881)
>> best conf = 0.16
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #549 for best value:
>> intensional similarity = 15
>> extensional distance = 79
>> proper extension:  Tongatapu; Fakaofo; Tasmania; Hokkaido; Futuna; Tinian; Niihau; Guadalcanal; Niue; Tutuila; ...
>> query: (?x1373, USA) <- ?x1373[ a Island; has locatedInWater ?x317[ has locatedIn ?x124[ has encompassed ?x521;]; has locatedIn ?x181<GCA>; has locatedIn ?x697[ a Country;]; has locatedIn ?x1209[ has government ?x2377;]; is locatedInWater of ?x1219[ a Island; has belongsToIslands ?x877;]; is locatedInWater of ?x1928[ is locatedOnIsland of ?x1918;];];]

*> Best rule #1434 for first EXPECTED value:
*> intensional similarity = 10
*> extensional distance = 234
*> proper extension:  ReneLevasseurIsland; VictoriaIsland; PrinceofWalesIsland; BaffinIsland; NowajaSemlja; Svalbard; EllesmereIsland; IsleofMan; IsladaOmetepe; Aust-Vagoey; ...
*> query: (?x1373, ?x697) <- ?x1373[ a Island; has locatedInWater ?x317[ has locatedIn ?x80[ has ethnicGroup ?x79;]; has locatedIn ?x697[ a Country; has language ?x2186; has religion ?x95;]; is locatedInWater of ?x1928[ is locatedOnIsland of ?x1918;];];]
*> conf = 0.03
ranks of expected_values: 39
EVAL	SanAndres	locatedIn	CO	CNN-0.1+0.1_MA	0.000	0.000	0.000	0.026	14.000	14.000	88.000	0.160	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: CO
=> 28 concepts (28 used for prediction)
PRED predicted values (max 10 best out of 120): USA (0.16 #555, 0.16 #802, 0.12 #1052), GB (0.15 #245, 0.10 #1239, 0.09 #989), P (0.11 #433, 0.07 #1177, 0.06 #1427), MART (0.11 #730, 0.07 #5208, 0.07 #2717), WD (0.11 #730, 0.07 #5208, 0.07 #2717), WV (0.11 #730, 0.07 #5208, 0.07 #2717), E (0.09 #263, 0.06 #1753, 0.05 #478), CAYM (0.08 #180, 0.05 #6458, 0.05 #6457), TT (0.08 #148, 0.05 #6458, 0.05 #6457), AG (0.08 #208, 0.05 #6458, 0.05 #6457)
>> best conf = 0.16
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #555 for best value:
>> intensional similarity = 37
>> extensional distance = 79
>> proper extension:  Tongatapu; Fakaofo; Tasmania; Hokkaido; Futuna; Tinian; Niihau; Guadalcanal; Niue; Tutuila; ...
>> query: (?x1373, USA) <- ?x1373[ a Island; has locatedInWater ?x317[ has locatedIn ?x215<CO>; has locatedIn ?x321[ has ethnicGroup ?x162; has religion ?x95;]; has locatedIn ?x482<MEX>; has locatedIn ?x745[ a Country; has encompassed ?x521; has government ?x828; has religion ?x352;]; has locatedIn ?x783<PA>; has locatedIn ?x865[ a Country; has ethnicGroup ?x1147; has government ?x254<"parliamentary democracy">;]; has locatedIn ?x1444[ a Country; has government ?x562<"British Overseas Territories">;]; is flowsInto of ?x311; is locatedInWater of ?x123[ has locatedIn ?x124; has type ?x150; is locatedOnIsland of ?x1806;]; is locatedInWater of ?x1397[ has belongsToIslands ?x877;]; is mergesWith of ?x182[ a Sea; has locatedIn ?x77; is flowsInto of ?x137; is locatedInWater of ?x112;];];]

*> Best rule #478 for first EXPECTED value:
*> intensional similarity = 40
*> extensional distance = 77
*> proper extension:  SaintPierre; Ireland; Pico; Flores; GrandBermuda; Fogo; Arran; GreatBritain; BishopRock; Benbecula; ...
*> query: (?x1373, ?x149) <- ?x1373[ a Island; has locatedInWater ?x317[ has locatedIn ?x148<C>; has locatedIn ?x407<AXA>; has locatedIn ?x482[ has ethnicGroup ?x79; has government ?x140; has neighbor ?x315; has wasDependentOf ?x149;]; has locatedIn ?x628<SMAR>; has locatedIn ?x633<GUAD>; has locatedIn ?x697<RH>; has locatedIn ?x745<MART>; is flowsInto of ?x311; is locatedInWater of ?x477<Tobago>; is locatedInWater of ?x703<Barbuda>; is locatedInWater of ?x817<Montserrat>; is locatedInWater of ?x1017[ is locatedOnIsland of ?x1410;]; is locatedInWater of ?x1117<Trinidad>; is locatedInWater of ?x1185<Tortola>; is locatedInWater of ?x1397<SaintThomas>; is locatedInWater of ?x1829<Antigua>; is locatedInWater of ?x1928<Cuba>; is mergesWith of ?x1371<GulfofMexico>;];]
*> conf = 0.05
ranks of expected_values: 21
EVAL	SanAndres	locatedIn	CO	CNN-1.+1._MA	0.000	0.000	0.000	0.048	28.000	28.000	120.000	0.160	http://www.semwebtech.org/mondial/10/meta#locatedIn

#1-Flores
PRED entity: Flores

PRED relation: locatedIn
PRED expected values: P
=> 33 concepts (33 used for prediction)
PRED predicted values (max 10 best out of 128): P (0.78 #433, 0.75 #197, 0.35 #5686), E (0.20 #736, 0.17 #499, 0.11 #972), GB (0.17 #2140, 0.15 #3089, 0.15 #2851), USA (0.13 #1017, 0.12 #1255, 0.11 #1492), I (0.10 #1231, 0.10 #1942, 0.10 #2415), CDN (0.10 #3143, 0.07 #3856, 0.05 #7352), RI (0.10 #3845, 0.09 #4081, 0.09 #4318), RP (0.09 #3902, 0.04 #4848, 0.04 #5085), RH (0.08 #3792, 0.05 #7352, 0.05 #7351), BR (0.08 #3792, 0.03 #7113, 0.03 #7112)
>> best conf = 0.78
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #433 for best value:
>> intensional similarity = 5
>> extensional distance = 7
>> proper extension:  SaoMiguel;
>> query: (?x299, P) <- ?x299[ a Island; has belongsToIslands ?x200<Azores>; has type ?x150<"volcanic">;]
ranks of expected_values: 1
EVAL	Flores	locatedIn	P	CNN-0.1+0.1_MA	1.000	1.000	1.000	1.000	33.000	33.000	128.000	0.778	http://www.semwebtech.org/mondial/10/meta#locatedIn

PRED relation: locatedIn
PRED expected values: P
=> 60 concepts (60 used for prediction)
PRED predicted values (max 10 best out of 157): P (0.78 #670, 0.78 #473, 0.78 #433), E (0.21 #736, 0.20 #1928, 0.20 #973), GB (0.19 #5981, 0.19 #4779, 0.19 #5016), I (0.18 #2664, 0.16 #3618, 0.10 #5534), USA (0.15 #4362, 0.14 #4125, 0.13 #4605), RI (0.13 #7214, 0.09 #4105, 0.09 #4585), D (0.12 #9342, 0.06 #10310, 0.06 #12244), J (0.09 #4072, 0.09 #4552, 0.09 #4309), CV (0.08 #815, 0.08 #1052, 0.08 #1289), CDN (0.07 #10287, 0.07 #10110, 0.06 #10353)
>> best conf = 0.78
=> the first rule below is the first best rule for 1 predicted values
>> Best rule #670 for best value:
>> intensional similarity = 5
>> extensional distance = 7
>> proper extension:  SaoMiguel;
>> query: (?x299, P) <- ?x299[ a Island; has belongsToIslands ?x200<Azores>; has type ?x150<"volcanic">;]
>> Best rule #473 for best value:
>> intensional similarity = 14
>> extensional distance = 7
>> proper extension:  Madeira;
>> query: (?x299, ?x1027) <- ?x299[ a Island; has belongsToIslands ?x200[ a Islands; is belongsToIslands of ?x199[ a Island; is locatedOnIsland of ?x1026;]; is belongsToIslands of ?x827[ a Island; has locatedIn ?x1027<P>;];]; has locatedInWater ?x182<AtlanticOcean>; has type ?x150<"volcanic">;]
>> Best rule #433 for best value:
>> intensional similarity = 14
>> extensional distance = 7
>> proper extension:  Madeira;
>> query: (?x299, P) <- ?x299[ a Island; has belongsToIslands ?x200[ a Islands; is belongsToIslands of ?x199[ a Island; is locatedOnIsland of ?x1026;]; is belongsToIslands of ?x827[ a Island; has locatedIn ?x1027<P>;];]; has locatedInWater ?x182<AtlanticOcean>; has type ?x150<"volcanic">;]
ranks of expected_values: 1
EVAL	Flores	locatedIn	P	CNN-1.+1._MA	1.000	1.000	1.000	1.000	60.000	60.000	157.000	0.778	http://www.semwebtech.org/mondial/10/meta#locatedIn
AVERAGE MEASURES:
AVG	ALGO	COUNT	Hits@1	Hits@3	Hits@10	MRR	$nb_concepts	$nb_concepts_used	$nb_predicted_values	$max_measure	CAT
AVG	CNN-0.1+0.1_MA	1354	0.388	0.479	0.596	0.455	33.144	31.027	307.680	0.568	all
AVG	CNN-0.1+0.1_MA	43	0.349	0.442	0.465	0.394	32.047	30.442	579.349	0.620	all-0
AVG	CNN-0.1+0.1_MA	230	0.417	0.491	0.604	0.475	34.717	32.470	295.704	0.566	all-1
AVG	CNN-0.1+0.1_MA	41	0.244	0.341	0.415	0.307	30.366	29.146	505.366	0.641	all-2
AVG	CNN-0.1+0.1_MA	226	0.372	0.504	0.619	0.457	32.469	30.257	280.004	0.543	all-3
AVG	CNN-0.1+0.1_MA	42	0.262	0.333	0.524	0.345	28.190	26.357	519.405	0.618	all-4
AVG	CNN-0.1+0.1_MA	233	0.421	0.502	0.635	0.487	33.382	31.549	245.232	0.558	all-5
AVG	CNN-0.1+0.1_MA	42	0.405	0.524	0.667	0.484	30.095	28.381	538.881	0.665	all-6
AVG	CNN-0.1+0.1_MA	232	0.358	0.453	0.573	0.428	33.664	31.319	260.810	0.546	all-7
AVG	CNN-0.1+0.1_MA	45	0.333	0.378	0.422	0.374	29.467	27.578	432.889	0.637	all-8
AVG	CNN-0.1+0.1_MA	220	0.436	0.518	0.641	0.500	34.405	32.018	264.086	0.567	all-9
AVG	CNN-0.1+0.1_MA	701	0.548	0.668	0.779	0.626	37.046	35.449	111.023	0.609	d-fwd
AVG	CNN-0.1+0.1_MA	653	0.216	0.277	0.400	0.273	28.956	26.280	518.793	0.524	d-gbwd
AVG	CNN-0.1+0.1_MA	531	0.663	0.731	0.806	0.708	33.608	32.105	331.169	0.766	k-ana
AVG	CNN-0.1+0.1_MA	821	0.211	0.318	0.462	0.293	32.920	30.401	293.238	0.442	k-val
AVG	CNN-0.1+0.1_MA	19	0.526	0.895	1.000	0.725	45.579	45.579	44.263	0.516	p-http://www.semwebtech.org/mondial/10/meta#belongsToIslands
AVG	CNN-0.1+0.1_MA	19	0.105	0.158	0.526	0.194	18.158	18.053	225.789	0.317	p-http://www.semwebtech.org/mondial/10/meta#belongsToIslands!
AVG	CNN-0.1+0.1_MA	4	1.000	1.000	1.000	1.000	33.500	33.500	27.750	0.417	p-http://www.semwebtech.org/mondial/10/meta#dependentOf
AVG	CNN-0.1+0.1_MA	4	0.000	0.000	0.000	0.006	35.000	35.000	54.000	0.142	p-http://www.semwebtech.org/mondial/10/meta#dependentOf!
AVG	CNN-0.1+0.1_MA	24	0.833	1.000	1.000	0.917	37.833	37.833	4.917	0.749	p-http://www.semwebtech.org/mondial/10/meta#encompassed
AVG	CNN-0.1+0.1_MA	24	0.417	0.667	0.667	0.512	4.708	4.708	187.250	0.823	p-http://www.semwebtech.org/mondial/10/meta#encompassed!
AVG	CNN-0.1+0.1_MA	66	0.152	0.333	0.485	0.267	38.530	38.530	219.333	0.550	p-http://www.semwebtech.org/mondial/10/meta#ethnicGroup
AVG	CNN-0.1+0.1_MA	66	0.152	0.212	0.394	0.230	18.258	14.227	163.136	0.346	p-http://www.semwebtech.org/mondial/10/meta#ethnicGroup!
AVG	CNN-0.1+0.1_MA	30	0.233	0.433	0.700	0.370	36.200	28.300	113.733	0.306	p-http://www.semwebtech.org/mondial/10/meta#flowsInto
AVG	CNN-0.1+0.1_MA	30	0.033	0.033	0.033	0.043	36.267	31.233	328.500	0.259	p-http://www.semwebtech.org/mondial/10/meta#flowsInto!
AVG	CNN-0.1+0.1_MA	1	1.000	1.000	1.000	1.000	45.000	31.000	71.000	0.600	p-http://www.semwebtech.org/mondial/10/meta#flowsThrough
AVG	CNN-0.1+0.1_MA	1	1.000	1.000	1.000	1.000	56.000	48.000	68.000	0.727	p-http://www.semwebtech.org/mondial/10/meta#flowsThrough!
AVG	CNN-0.1+0.1_MA	19	0.211	0.368	0.526	0.323	36.737	36.737	57.158	0.431	p-http://www.semwebtech.org/mondial/10/meta#government
AVG	CNN-0.1+0.1_MA	22	0.000	0.000	0.136	0.040	42.455	35.682	161.636	0.238	p-http://www.semwebtech.org/mondial/10/meta#hasEstuary
AVG	CNN-0.1+0.1_MA	22	0.000	0.000	0.318	0.061	28.000	22.455	78.636	0.166	p-http://www.semwebtech.org/mondial/10/meta#hasEstuary!
AVG	CNN-0.1+0.1_MA	20	0.000	0.000	0.050	0.038	43.250	35.500	166.950	0.228	p-http://www.semwebtech.org/mondial/10/meta#hasSource
AVG	CNN-0.1+0.1_MA	20	0.050	0.100	0.300	0.132	35.350	29.350	115.850	0.161	p-http://www.semwebtech.org/mondial/10/meta#hasSource!
AVG	CNN-0.1+0.1_MA	34	0.559	0.706	0.794	0.629	37.382	37.382	35.647	0.392	p-http://www.semwebtech.org/mondial/10/meta#inMountains
AVG	CNN-0.1+0.1_MA	34	0.000	0.000	0.029	0.015	18.824	18.676	307.971	0.199	p-http://www.semwebtech.org/mondial/10/meta#inMountains!
AVG	CNN-0.1+0.1_MA	32	0.250	0.375	0.594	0.347	40.500	40.500	88.906	0.501	p-http://www.semwebtech.org/mondial/10/meta#language
AVG	CNN-0.1+0.1_MA	32	0.031	0.219	0.438	0.157	19.875	18.938	170.156	0.457	p-http://www.semwebtech.org/mondial/10/meta#language!
AVG	CNN-0.1+0.1_MA	184	0.609	0.717	0.832	0.682	31.239	29.951	124.837	0.689	p-http://www.semwebtech.org/mondial/10/meta#locatedIn
AVG	CNN-0.1+0.1_MA	184	0.168	0.239	0.299	0.214	35.973	32.022	1299.804	0.674	p-http://www.semwebtech.org/mondial/10/meta#locatedIn!
AVG	CNN-0.1+0.1_MA	43	0.767	0.953	1.000	0.864	46.256	43.140	58.698	0.750	p-http://www.semwebtech.org/mondial/10/meta#locatedInWater
AVG	CNN-0.1+0.1_MA	43	0.000	0.116	0.186	0.074	31.721	28.000	455.349	0.363	p-http://www.semwebtech.org/mondial/10/meta#locatedInWater!
AVG	CNN-0.1+0.1_MA	6	0.000	0.000	0.500	0.104	45.333	38.333	39.833	0.230	p-http://www.semwebtech.org/mondial/10/meta#locatedOnIsland
AVG	CNN-0.1+0.1_MA	6	0.167	0.167	0.333	0.203	46.667	45.333	48.500	0.210	p-http://www.semwebtech.org/mondial/10/meta#locatedOnIsland!
AVG	CNN-0.1+0.1_MA	19	0.947	1.000	1.000	0.965	34.526	29.474	153.263	0.807	p-http://www.semwebtech.org/mondial/10/meta#mergesWith
AVG	CNN-0.1+0.1_MA	19	0.842	0.947	1.000	0.901	34.474	30.842	181.105	0.801	p-http://www.semwebtech.org/mondial/10/meta#mergesWith!
AVG	CNN-0.1+0.1_MA	80	0.825	0.875	0.938	0.858	37.325	36.675	194.100	0.843	p-http://www.semwebtech.org/mondial/10/meta#neighbor
AVG	CNN-0.1+0.1_MA	80	0.825	0.825	0.925	0.845	37.913	37.087	196.775	0.847	p-http://www.semwebtech.org/mondial/10/meta#neighbor!
AVG	CNN-0.1+0.1_MA	50	0.700	0.860	0.960	0.788	36.860	36.860	32.020	0.717	p-http://www.semwebtech.org/mondial/10/meta#religion
AVG	CNN-0.1+0.1_MA	50	0.020	0.060	0.340	0.110	15.600	15.340	153.080	0.548	p-http://www.semwebtech.org/mondial/10/meta#religion!
AVG	CNN-0.1+0.1_MA	29	0.828	0.897	1.000	0.881	41.207	41.207	8.034	0.592	p-http://www.semwebtech.org/mondial/10/meta#type
AVG	CNN-0.1+0.1_MA	19	0.684	0.684	0.789	0.715	33.368	33.211	39.632	0.434	p-http://www.semwebtech.org/mondial/10/meta#wasDependentOf
AVG	CNN-0.1+0.1_MA	19	0.000	0.000	0.211	0.051	33.211	33.211	170.895	0.277	p-http://www.semwebtech.org/mondial/10/meta#wasDependentOf!
AVG	CNN-1.+1._MA	1354	0.409	0.511	0.645	0.485	85.365	84.815	353.572	0.659	all
AVG	CNN-1.+1._MA	227	0.489	0.581	0.683	0.553	86.026	85.410	295.119	0.662	all-0
AVG	CNN-1.+1._MA	41	0.268	0.341	0.488	0.343	79.317	78.707	587.707	0.735	all-1
AVG	CNN-1.+1._MA	230	0.430	0.522	0.639	0.498	88.391	87.548	353.678	0.648	all-2
AVG	CNN-1.+1._MA	45	0.267	0.333	0.444	0.321	83.867	83.622	555.467	0.698	all-3
AVG	CNN-1.+1._MA	229	0.397	0.524	0.677	0.487	86.022	85.454	321.568	0.634	all-4
AVG	CNN-1.+1._MA	38	0.289	0.368	0.526	0.369	78.053	77.263	611.842	0.762	all-5
AVG	CNN-1.+1._MA	229	0.424	0.524	0.686	0.504	84.843	84.485	280.332	0.639	all-6
AVG	CNN-1.+1._MA	39	0.359	0.487	0.641	0.443	78.282	77.974	578.897	0.781	all-7
AVG	CNN-1.+1._MA	226	0.416	0.531	0.673	0.498	87.779	87.301	312.527	0.629	all-8
AVG	CNN-1.+1._MA	50	0.280	0.360	0.440	0.333	74.300	74.040	540.260	0.775	all-9
AVG	CNN-1.+1._MA	701	0.562	0.688	0.796	0.642	95.074	94.555	142.291	0.683	d-fwd
AVG	CNN-1.+1._MA	653	0.245	0.322	0.482	0.316	74.942	74.358	580.383	0.634	d-gbwd
AVG	CNN-1.+1._MA	703	0.600	0.670	0.762	0.653	88.324	87.750	412.488	0.782	k-ana
AVG	CNN-1.+1._MA	649	0.203	0.341	0.519	0.303	82.416	81.891	290.843	0.529	k-val
AVG	CNN-1.+1._MA	19	0.526	0.947	1.000	0.747	111.789	111.789	56.211	0.569	p-http://www.semwebtech.org/mondial/10/meta#belongsToIslands
AVG	CNN-1.+1._MA	19	0.158	0.263	0.474	0.261	37.526	37.368	226.211	0.442	p-http://www.semwebtech.org/mondial/10/meta#belongsToIslands!
AVG	CNN-1.+1._MA	4	0.750	0.750	1.000	0.786	62.750	62.750	38.000	0.449	p-http://www.semwebtech.org/mondial/10/meta#dependentOf
AVG	CNN-1.+1._MA	4	0.000	0.000	0.000	0.010	108.000	108.000	163.750	0.333	p-http://www.semwebtech.org/mondial/10/meta#dependentOf!
AVG	CNN-1.+1._MA	24	0.917	1.000	1.000	0.951	88.792	88.792	4.917	0.824	p-http://www.semwebtech.org/mondial/10/meta#encompassed
AVG	CNN-1.+1._MA	24	0.500	0.667	0.708	0.573	5.000	5.000	196.250	0.883	p-http://www.semwebtech.org/mondial/10/meta#encompassed!
AVG	CNN-1.+1._MA	66	0.121	0.333	0.485	0.245	94.833	94.833	245.212	0.651	p-http://www.semwebtech.org/mondial/10/meta#ethnicGroup
AVG	CNN-1.+1._MA	66	0.091	0.167	0.485	0.187	44.333	43.500	178.000	0.440	p-http://www.semwebtech.org/mondial/10/meta#ethnicGroup!
AVG	CNN-1.+1._MA	30	0.300	0.433	0.700	0.415	103.700	103.333	160.100	0.372	p-http://www.semwebtech.org/mondial/10/meta#flowsInto
AVG	CNN-1.+1._MA	30	0.033	0.033	0.033	0.054	111.000	110.567	395.433	0.321	p-http://www.semwebtech.org/mondial/10/meta#flowsInto!
AVG	CNN-1.+1._MA	1	1.000	1.000	1.000	1.000	153.000	151.000	91.000	0.727	p-http://www.semwebtech.org/mondial/10/meta#flowsThrough
AVG	CNN-1.+1._MA	1	1.000	1.000	1.000	1.000	141.000	141.000	68.000	0.714	p-http://www.semwebtech.org/mondial/10/meta#flowsThrough!
AVG	CNN-1.+1._MA	19	0.211	0.421	0.526	0.310	86.632	86.632	67.789	0.494	p-http://www.semwebtech.org/mondial/10/meta#government
AVG	CNN-1.+1._MA	22	0.000	0.000	0.136	0.053	129.500	129.455	250.909	0.341	p-http://www.semwebtech.org/mondial/10/meta#hasEstuary
AVG	CNN-1.+1._MA	22	0.000	0.091	0.455	0.119	82.773	82.364	147.591	0.179	p-http://www.semwebtech.org/mondial/10/meta#hasEstuary!
AVG	CNN-1.+1._MA	20	0.000	0.000	0.100	0.049	124.800	123.850	244.500	0.342	p-http://www.semwebtech.org/mondial/10/meta#hasSource
AVG	CNN-1.+1._MA	20	0.000	0.000	0.450	0.090	99.050	98.600	181.900	0.190	p-http://www.semwebtech.org/mondial/10/meta#hasSource!
AVG	CNN-1.+1._MA	34	0.588	0.735	0.794	0.655	106.529	106.529	52.735	0.463	p-http://www.semwebtech.org/mondial/10/meta#inMountains
AVG	CNN-1.+1._MA	34	0.000	0.000	0.029	0.021	46.059	46.059	308.118	0.270	p-http://www.semwebtech.org/mondial/10/meta#inMountains!
AVG	CNN-1.+1._MA	32	0.219	0.406	0.656	0.353	93.656	93.656	93.125	0.656	p-http://www.semwebtech.org/mondial/10/meta#language
AVG	CNN-1.+1._MA	32	0.000	0.062	0.375	0.099	40.219	40.219	183.094	0.550	p-http://www.semwebtech.org/mondial/10/meta#language!
AVG	CNN-1.+1._MA	184	0.636	0.750	0.848	0.709	80.750	79.234	146.147	0.738	p-http://www.semwebtech.org/mondial/10/meta#locatedIn
AVG	CNN-1.+1._MA	184	0.272	0.370	0.473	0.341	95.065	93.728	1382.658	0.844	p-http://www.semwebtech.org/mondial/10/meta#locatedIn!
AVG	CNN-1.+1._MA	43	0.767	0.907	1.000	0.853	113.767	113.023	111.442	0.852	p-http://www.semwebtech.org/mondial/10/meta#locatedInWater
AVG	CNN-1.+1._MA	43	0.093	0.163	0.209	0.146	90.419	89.721	734.674	0.544	p-http://www.semwebtech.org/mondial/10/meta#locatedInWater!
AVG	CNN-1.+1._MA	6	0.333	0.333	0.833	0.400	109.167	105.833	53.167	0.393	p-http://www.semwebtech.org/mondial/10/meta#locatedOnIsland
AVG	CNN-1.+1._MA	6	0.000	0.500	0.667	0.253	118.500	118.500	62.833	0.267	p-http://www.semwebtech.org/mondial/10/meta#locatedOnIsland!
AVG	CNN-1.+1._MA	19	0.895	1.000	1.000	0.947	99.526	99.526	386.105	0.815	p-http://www.semwebtech.org/mondial/10/meta#mergesWith
AVG	CNN-1.+1._MA	19	0.895	1.000	1.000	0.930	95.053	94.211	319.842	0.826	p-http://www.semwebtech.org/mondial/10/meta#mergesWith!
AVG	CNN-1.+1._MA	80	0.825	0.887	0.975	0.873	91.963	91.963	220.550	0.878	p-http://www.semwebtech.org/mondial/10/meta#neighbor
AVG	CNN-1.+1._MA	80	0.825	0.887	0.975	0.871	93.838	93.838	222.250	0.877	p-http://www.semwebtech.org/mondial/10/meta#neighbor!
AVG	CNN-1.+1._MA	50	0.800	0.900	0.940	0.855	90.380	90.380	37.560	0.814	p-http://www.semwebtech.org/mondial/10/meta#religion
AVG	CNN-1.+1._MA	50	0.000	0.080	0.400	0.099	29.720	29.720	167.360	0.670	p-http://www.semwebtech.org/mondial/10/meta#religion!
AVG	CNN-1.+1._MA	29	0.828	0.931	1.000	0.879	110.345	110.345	10.241	0.656	p-http://www.semwebtech.org/mondial/10/meta#type
AVG	CNN-1.+1._MA	19	0.579	0.737	0.895	0.671	86.105	86.105	89.579	0.575	p-http://www.semwebtech.org/mondial/10/meta#wasDependentOf
AVG	CNN-1.+1._MA	19	0.000	0.000	0.316	0.069	91.053	91.053	201.368	0.488	p-http://www.semwebtech.org/mondial/10/meta#wasDependentOf!
// Profiling...
#calls	#calls/s	Time (s)	Percentage	Section
//         1	 1000000.0	    0.0	  0.0%	Intrel2.union
//         1	  333333.3	    0.0	  0.0%	Store.transitive_closure
//         1	  333333.3	    0.0	  0.0%	Store.symmetric_closure
//         2	  285714.3	    0.0	  0.0%	Store.expr_property#direct_relation
//        96	 1600000.0	    0.0	  0.0%	Store_match.store#add
//        25	  240384.6	    0.0	  0.0%	Store.expr_property#strict_direct_relation
//         1	    6211.2	    0.0	  0.0%	Turtle.triples_of_turtle
//        86	  413461.5	    0.0	  0.0%	Store.property_data#fold_predecessors
//       384	 1476923.1	    0.0	  0.0%	Store_order.store#add
//        24	   69565.2	    0.0	  0.0%	Store.expr_property#strict_relation
//        24	   52173.9	    0.0	  0.0%	Store.expr_property#relation
//      2088	 2361990.9	    0.0	  0.0%	Cnn.node#fold_concepts
//        25	   15654.4	    0.0	  0.0%	Extension.fold_intset
//        22	   13173.7	    0.0	  0.0%	Extension.t#intset
//      2854	 1628066.2	    0.0	  0.0%	Unicode.codepoint_of_utf8_char
//        22	   11375.4	    0.0	  0.0%	Store.expr_class#relation
//      2635	 1011516.3	    0.0	  0.0%	Store.store#make_name
//        19	    6427.6	    0.0	  0.0%	Store.property_data#types
//      2128	  632580.3	    0.0	  0.0%	Intrel2.keys
//      2088	  402079.7	    0.0	  0.0%	Intreln.remove
//      2808	  453268.8	    0.0	  0.0%	Store.expr_class#add
//      2560	  291074.5	    0.0	  0.0%	Store.store#get_entity/new
//      6584	  633259.6	    0.0	  0.0%	Store.add_link
//         2	      51.5	    0.0	  0.0%	Store.store#pagerank_iterations
//      6584	  133957.3	    0.0	  0.0%	Store.expr_property#add
//      3192	   55707.8	    0.1	  0.0%	Cnn.print_seed_intension
//         2	      31.1	    0.1	  0.0%	Store.store#import_triple_iteration
//         2	      31.1	    0.1	  0.0%	Store.store#import_triples
//      2088	   31276.7	    0.1	  0.0%	Cnn_expe.eval_measures
//         1	      12.1	    0.1	  0.0%	Dcg.once_fold
//         1	      12.1	    0.1	  0.0%	Turtle.parse_file
//    647240	 3947379.7	    0.2	  0.0%	Cnn.node#get_cardinal
//         2	       8.5	    0.2	  0.0%	Dcg.once
//         2	       6.7	    0.3	  0.0%	Store.store#import_ntriples
//         2	       5.9	    0.3	  0.0%	Store.store#import_rdf_file
//   2126940	 3584690.1	    0.6	  0.0%	Intreln.singleton
//   1049403	 1507713.1	    0.7	  0.0%	Cnn_sewelis.adjacent_nodes_edges_raw/classes
//   1615694	 1888914.1	    0.9	  0.0%	Intreln.diff
//   1049403	 1067467.8	    1.0	  0.1%	Cnn_sewelis.adjacent_nodes_edges_raw/structs
//     39489	   31234.9	    1.3	  0.1%	Cnn.Table.join/inter_inv
//      7157	    4501.1	    1.6	  0.1%	Cnn.Table.join/vars2_contains_vars1
//   4859681	 2220592.9	    2.2	  0.1%	Store.property_data#fold_successors
//   3071020	 1188546.5	    2.6	  0.1%	Intreln.fold_assoc
//  10004602	 3493633.2	    2.9	  0.2%	Find_merge.Set.merge_roots
//    543058	  156188.9	    3.5	  0.2%	Cnn.Partition.add_part
//   1762638	  478102.6	    3.7	  0.2%	Intreln.domains
//   1881501	  449810.6	    4.2	  0.2%	Intreln.fold
//    701256	  153786.7	    4.6	  0.3%	Cnn.QuantumExtension.joinable_edge_subset
//   1049403	  209096.7	    5.0	  0.3%	Cnn_sewelis.adjacent_nodes_edges_raw/fwd_properties
//   1049403	  193751.3	    5.4	  0.3%	Cnn_sewelis.adjacent_nodes_edges_raw/bwd_properties
//      8236	    1160.4	    7.1	  0.4%	Kgraph.core_nodes/propagate_edge_constraints
//      8236	    1117.4	    7.4	  0.4%	Patterns.pattern#core_vars/loop
//      3192	     421.4	    7.6	  0.4%	Cnn_expe.core_intension
//    262365	   32460.6	    8.1	  0.4%	Intrel2.inter
//   1771650	  207594.6	    8.5	  0.5%	Cnn.Partition.split_with_subset
//  36451516	 3460040.5	   10.5	  0.6%	Find_merge.Set.find_root
//   9354549	  847132.4	   11.0	  0.6%	Cnn.Table.join/inter
//    124614	   10334.7	   12.1	  0.7%	Intrel2.fold_assoc
//    124614	   10247.3	   12.2	  0.7%	Cnn_expe.cnn_predict_gen/iter_values
//   1762638	  140614.7	   12.5	  0.7%	Cnn.node#next_child/hom_vars
//   1061382	   74772.3	   14.2	  0.8%	Cnn_sewelis.adjacent_nodes_edges_raw
//    524730	   29222.8	   18.0	  1.0%	Intrel2.cardinal
//    347194	   18282.1	   19.0	  1.0%	Cnn.QuantumException.root_plus_one_extensions/group_by
//    127650	    6681.4	   19.1	  1.1%	Cnn.node#has_connected_intension
//  53367134	 2567548.8	   20.8	  1.1%	Intreln.cardinal
//   5921043	  273514.9	   21.6	  1.2%	Cnn_sewelis.relaxed_edge
//  21489810	  783336.1	   27.4	  1.5%	Intreln.inter_assoc
//   5200216	  157540.9	   33.0	  1.8%	Cnn.MetaTable.filter_delta
//   1762638	   50431.0	   35.0	  1.9%	Cnn.node#next_child/edge_weighted_random_choice
//     45686	    1258.5	   36.3	  2.0%	Cnn.Table.join/vars1_contains_vars2
//  21489810	  553942.5	   38.8	  2.1%	Cnn.Table.join/inter_assoc
//  25734739	  628150.4	   41.0	  2.3%	Cnn.MetaTable.cardinals
//   1762638	   42574.2	   41.4	  2.3%	Cnn.QuantumExtension.join/aux_splitting
//    230196	    3751.0	   61.4	  3.4%	Intreln.group_by
//     97842	    1501.0	   65.2	  3.6%	Cnn.Table.join/extension_1_inv
//   5253059	   77860.9	   67.5	  3.7%	Intreln.filter
//    256540	    3155.8	   81.3	  4.5%	Cnn.Table.join/extension_1
// 303313070	 2627460.3	  115.4	  6.4%	Intreln.apply
//     13915	     111.3	  125.1	  6.9%	Cnn.Table.join/general_case
//    368297	    1603.9	  229.6	 12.7%	Intreln.extension
// 598258418	 2000077.8	  299.1	 16.5%	Intreln.inter
//  27497377	   86253.2	  318.8	 17.6%	Cnn.MetaTable.proj
//  58793648	  123010.6	  478.0	 26.4%	Intreln.project
//  18958687	   36940.8	  513.2	 28.3%	Intreln.map_assoc
//  84986263	  163530.0	  519.7	 28.7%	Cnn.Table.proj
//  18958687	   36222.4	  523.4	 28.9%	Cnn.Table.join/map_assoc
//  16136875	   29638.7	  544.5	 30.0%	Cnn.MetaTable.join
//    124614	     194.0	  642.5	 35.4%	Cnn.QuantumExtension.root_plus_one_extensions
//   1615694	    2508.5	  644.1	 35.5%	Cnn.QuantumExtension.join/downward
//    124614	     185.8	  670.9	 37.0%	Cnn_expe.cnn_predict_gen/iter_vars
//      2088	       3.0	  702.9	 38.8%	Cnn_expe.cnn_predict_gen/fold_concepts
//      2088	       2.9	  716.4	 39.5%	Cnn_expe.cnn_predict_gen
//  50263675	   55050.2	  913.1	 50.4%	Cnn.Table.join
//   1762638	    1779.7	  990.4	 54.6%	Cnn.QuantumExtension.join
//   2289205	    2092.8	 1093.8	 60.3%	Cnn.node#next_child
//      2088	       1.9	 1095.6	 60.4%	Cnn.discriminate
//         1	       0.0	 1812.7	100.0%	Cnn_expe.main

real	118m52.401s
user	30m12.666s
sys	17m58.024s