1. Carlos Bobed, Laura Douze, Sébastien Ferré, and Romaric Marcilly. PEGASE: A Knowledge Graph for Search and Exploration in Pharmacovigilance Data. In P. Cimiano and O. Corby, editors, EKAW Posters and Demonstrations, volume 2262 of CEUR Workshop Proceedings, pages 33-36, 2018. [WWW] [PDF] Keyword(s): PEGASE, Sparklis, knowledge graph. Abstract:

   Pharmacovigilance is in charge of studying the adverse effects of pharmaceutical products. In this field, pharmacovigilance specialists experience several difficulties when searching and exploring their patient data despite the existence of standardized terminologies (MedDRA). In this paper, we present our approach to enhance the way pharmacovigilance specialists perform search and exploration on their data. First, we have developed a knowledge graph that relies on the OntoADR ontology to semantically enrich the MedDRA terminology with SNOMED CT concepts, and that includes anonymized patient data from FAERS. Second, we have chosen and applied a semantic search tool, Sparklis, according to the user requirements that we have identified in pharmacovigilance.

   
   

   @inproceedings{Pegase2018ekaw,
   author = {Carlos Bobed and Laura Douze and Sébastien Ferré and Romaric Marcilly},
   title = {{PEGASE:} {A} Knowledge Graph for Search and Exploration in Pharmacovigilance Data},
   booktitle = {{EKAW} Posters and Demonstrations},
   pages = {33--36},
   year = {2018},
   editor = {P. Cimiano and O. Corby},
   series = {{CEUR} Workshop Proceedings},
   volume = {2262},
   pdf = {http://ceur-ws.org/Vol-2262/ekaw-poster-23.pdf},
   url = {https://hal.inria.fr/hal-01976818},
   keywords = {PEGASE, Sparklis, knowledge graph},
   abstract = {Pharmacovigilance is in charge of studying the adverse effects of pharmaceutical products. In this field, pharmacovigilance specialists experience several difficulties when searching and exploring their patient data despite the existence of standardized terminologies (MedDRA). In this paper, we present our approach to enhance the way pharmacovigilance specialists perform search and exploration on their data. First, we have developed a knowledge graph that relies on the OntoADR ontology to semantically enrich the MedDRA terminology with SNOMED CT concepts, and that includes anonymized patient data from FAERS. Second, we have chosen and applied a semantic search tool, Sparklis, according to the user requirements that we have identified in pharmacovigilance.},
   
   }
   




2. Carlos Bobed, Laura Douze, Sébastien Ferré, and Romaric Marcilly. Sparklis over PEGASE knowledge graph: a new tool for pharmacovigilance. In A. Waagmeester, C. J. O. Baker, A. Splendiani, O. Deniz Beyan, and M. Scott Marshall, editors, Int. Conf. Semantic Web Applications and Tools for Life Sciences (SWAT4LS), volume 2275 of CEUR Workshop Proceedings, 2018. [WWW] [PDF] Keyword(s): PEGASE, Sparklis, knowledge graph. Abstract:

   Pharmacovigilance is in charge of studying the adverse effects of pharmaceutical products. In this field, pharmacovigilance specialists experience several difficulties when searching and exploring their patient data despite the existence of standardized terminologies (MedDRA). In this paper, we present our approach to enhance the way pharmacovigilance specialists perform search and exploration on their data. First, we have developed a knowledge graph that relies on the OntoADR ontology to semantically enrich the MedDRA terminology with SNOMED CT concepts, and that includes anonymized patient data from FAERS. Second, we have chosen and extended a semantic search tool, Sparklis, according to the user requirements that we have identified in pharmacovigilance. We report the results of a usability evaluation that has been performed by human factors specialists to check the benefits of our proposal.

   
   

   @inproceedings{Pegase2018swat,
   author = {Carlos Bobed and Laura Douze and Sébastien Ferré and Romaric Marcilly},
   title = {Sparklis over {PEGASE} knowledge graph: a new tool for pharmacovigilance},
   booktitle = {Int. Conf. Semantic Web Applications and Tools for Life Sciences ({SWAT4LS})},
   year = {2018},
   editor = {A. Waagmeester and C. J. O. Baker and A. Splendiani and O. Deniz Beyan and M. Scott Marshall},
   series = {{CEUR} Workshop Proceedings},
   volume = {2275},
   pdf = {http://ceur-ws.org/Vol-2275/paper7.pdf},
   url = {https://hal.inria.fr/hal-01976825},
   keywords = {PEGASE, Sparklis, knowledge graph},
   abstract = {Pharmacovigilance is in charge of studying the adverse effects of pharmaceutical products. In this field, pharmacovigilance specialists experience several difficulties when searching and exploring their patient data despite the existence of standardized terminologies (MedDRA). In this paper, we present our approach to enhance the way pharmacovigilance specialists perform search and exploration on their data. First, we have developed a knowledge graph that relies on the OntoADR ontology to semantically enrich the MedDRA terminology with SNOMED CT concepts, and that includes anonymized patient data from FAERS. Second, we have chosen and extended a semantic search tool, Sparklis, according to the user requirements that we have identified in pharmacovigilance. We report the results of a usability evaluation that has been performed by human factors specialists to check the benefits of our proposal.},
   
   }
   




3. Sébastien Ferré. Answers Partitioning and Lazy Joins for Efficient Query Relaxation and Application to Similarity Search. In A. Gangemi and others, editors, Int. Conf. The Semantic Web (ESWC), LNCS 10843, pages 209-224, 2018. Springer. [WWW] [doi:10.1007/978-3-319-93417-4_14] Keyword(s): query relaxation, approximate answer, similarity search, semantic similarity, TDF, graph pattern, partition, join. Abstract:

   Query relaxation has been studied as a way to find approximate answers when user queries are too specific or do not align well with the data schema. We are here interested in the application of query relaxation to similarity search of RDF nodes based on their description. However, this is challenging because existing approaches have a complexity that grows in a combinatorial way with the size of the query and the number of relaxation steps. We introduce two algorithms, answers partitioning and lazy join, that together significantly improve the efficiency of query relaxation. Our experiments show that our approach scales much better with the size of queries and the number of relaxation steps, to the point where it becomes possible to relax large node descriptions in order to find similar nodes. Moreover, the relaxed descriptions provide explanations for their semantic similarity.

   
   

   @inproceedings{Fer2018eswc,
   author = {Sébastien Ferré},
   title = {Answers Partitioning and Lazy Joins for Efficient Query Relaxation and Application to Similarity Search},
   booktitle = {Int. Conf. The Semantic Web ({ESWC})},
   pages = {209--224},
   year = {2018},
   url = {https://hal.archives-ouvertes.fr/hal-01945454},
   doi = {10.1007/978-3-319-93417-4\_14},
   editor = {A. Gangemi and others},
   series = {LNCS 10843},
   publisher = {Springer},
   keywords = {query relaxation, approximate answer, similarity search, semantic similarity, TDF, graph pattern, partition, join},
   abstract = {Query relaxation has been studied as a way to find approximate answers when user queries are too specific or do not align well with the data schema. We are here interested in the application of query relaxation to similarity search of RDF nodes based on their description. However, this is challenging because existing approaches have a complexity that grows in a combinatorial way with the size of the query and the number of relaxation steps. We introduce two algorithms, answers partitioning and lazy join, that together significantly improve the efficiency of query relaxation. Our experiments show that our approach scales much better with the size of queries and the number of relaxation steps, to the point where it becomes possible to relax large node descriptions in order to find similar nodes. Moreover, the relaxed descriptions provide explanations for their semantic similarity.},
   
   }
   




4. Sébastien Ferré. Responsive and Flexible Controlled Natural Language Authoring with Zipper-Based Transformations. In B. Davis, C. M. Keet, and A. Wyner, editors, Int. Work. Controlled Natural Language (CNL), volume 304 of Frontiers in Artificial Intelligence and Applications, pages 21-30, 2018. IOS Press. [WWW] [doi:10.3233/978-1-61499-904-1-21] Keyword(s): controlled natural language, authoring, user interaction, abstract syntax, zipper, focus. Abstract:

   Controlled natural languages (CNL) have the benefits to combine the readability of natural languages, and the accuracy of formal languages. They have been used to help users express facts, rules or queries. While generally easy to read, CNLs remain difficult to write because of the constrained syntax. A common solution is a grammar-based auto-completion mechanism to suggest the next possible words in a sentence. However, this solution has two limitations: (a) partial sentences may have no semantics, which prevents giving intermediate results or feedback, and (b) the suggestion is often limited to adding words at the end of the sentence. % We propose a more responsive and flexible CNL authoring by designing it as a sequence of sentence transformations. Responsiveness is obtained by having a complete, and hence interpretable, sentence at each time. Flexibility is obtained by allowing insertion and deletion on any part of the sentence. Technically, this is realized by working directly on the abstract syntax, rather than on the concrete syntax, and by using Huet's zippers to manage the focus on a query part, the equivalent of the text cursor of a word processor.

   
   

   @inproceedings{Fer2018cnl,
   author = {Sébastien Ferré},
   title = {Responsive and Flexible Controlled Natural Language Authoring with Zipper-Based Transformations},
   booktitle = {Int. Work. Controlled Natural Language ({CNL})},
   pages = {21--30},
   year = {2018},
   crossref = {DBLP:conf/cnl/2018},
   url = {https://hal.inria.fr/hal-01976766},
   doi = {10.3233/978-1-61499-904-1-21},
   editor = {B. Davis and C. M. Keet and A. Wyner},
   series = {Frontiers in Artificial Intelligence and Applications},
   volume = {304},
   publisher = {{IOS} Press},
   keywords = {controlled natural language, authoring, user interaction, abstract syntax, zipper, focus},
   abstract = {Controlled natural languages (CNL) have the benefits to combine the readability of natural languages, and the accuracy of formal languages. They have been used to help users express facts, rules or queries. While generally easy to read, CNLs remain difficult to write because of the constrained syntax. A common solution is a grammar-based auto-completion mechanism to suggest the next possible words in a sentence. However, this solution has two limitations: (a) partial sentences may have no semantics, which prevents giving intermediate results or feedback, and (b) the suggestion is often limited to adding words at the end of the sentence. % We propose a more responsive and flexible CNL authoring by designing it as a sequence of sentence transformations. Responsiveness is obtained by having a complete, and hence interpretable, sentence at each time. Flexibility is obtained by allowing insertion and deletion on any part of the sentence. Technically, this is realized by working directly on the abstract syntax, rather than on the concrete syntax, and by using Huet's zippers to manage the focus on a query part, the equivalent of the text cursor of a word processor.} 
   }
   




5. Sébastien Ferré. What's New in SPARKLIS. In P. Cimiano and O. Corby, editors, EKAW Posters and Demonstrations, volume 2262 of CEUR Workshop Proceedings, pages 41-44, 2018. [WWW] [PDF] Keyword(s): Sparklis, demo. Abstract:

   Sparklis is a SPARQL query builder that can connect to any endpoint, and that interacts with users in natural language only. Users are guided in the building of their queries so that they do not have to know the schema, and so that empty results are almost completely avoided. This demo paper presents a number of recent extensions to Sparklis. Most notably, it now supports analytical queries, Wikidata statement qualifiers, and the display of results on a map or as a slideshow.

   
   

   @inproceedings{Fer2018ekaw,
   author = {Sébastien Ferré},
   title = {What's New in {SPARKLIS}},
   booktitle = {{EKAW} Posters and Demonstrations},
   pages = {41--44},
   year = {2018},
   editor = {P. Cimiano and O. Corby},
   series = {{CEUR} Workshop Proceedings},
   volume = {2262},
   pdf = {http://ceur-ws.org/Vol-2262/ekaw-demo-10.pdf},
   url = {https://hal.inria.fr/hal-01976799},
   keywords = {Sparklis, demo},
   abstract = {Sparklis is a SPARQL query builder that can connect to any endpoint, and that interacts with users in natural language only. Users are guided in the building of their queries so that they do not have to know the schema, and so that empty results are almost completely avoided. This demo paper presents a number of recent extensions to Sparklis. Most notably, it now supports analytical queries, Wikidata statement qualifiers, and the display of results on a map or as a slideshow.},
   
   }
   




6. Sébastien Ferré and Peggy Cellier. How Hierarchies of Concept Graphs Can Facilitate the Interpretation of RCA Lattices?. In D. I. Ignatov and L. Nourine, editors, Int. Conf. Concept Lattices and Their Applications (CLA), CEUR 2123, pages 69-80, 2018. CEUR-WS.org. [WWW] [PDF] Keyword(s): formal concept analysis, relational concept analysis, data mining, concept graph. Abstract:

   Relational Concept Analysis (RCA) has been introduced in order to allow concept analysis on multi-relational data. It significantly widens the field of application of Formal Concept Analysis (FCA), and it produces richer concept intents that are similar to concept definitions in Description Logics (DL). However, reading and interpreting RCA concept lattices is notoriously difficult. Nica {\em et al} have proposed to represent RCA intents by cpo-patterns in the special case of sequence structures. We propose an equivalent representation of a family of RCA concept lattices in the form of a hierarchy of concept graphs. Each concept belongs to one concept graph, and each concept graph exhibits the relationships between several concepts. A concept graph is generally transversal to several lattices, and therefore highlights the relationships between different types of objects. We show the benefits of our approach on several use cases from the RCA litterature.

   
   

   @inproceedings{FerCel2018cla,
   author = {Sébastien Ferré and Peggy Cellier},
   title = {How Hierarchies of Concept Graphs Can Facilitate the Interpretation of {RCA} Lattices?},
   booktitle = {Int. Conf. Concept Lattices and Their Applications ({CLA})},
   pages = {69--80},
   year = {2018},
   crossref = {DBLP:conf/cla/2018},
   pdf = {http://ceur-ws.org/Vol-2123/paper6.pdf},
   url = {https://hal.inria.fr/hal-01976754},
   editor = {D. I. Ignatov and L. Nourine},
   series = {{CEUR} 2123},
   publisher = {CEUR-WS.org},
   keywords = {formal concept analysis, relational concept analysis, data mining, concept graph},
   abstract = {Relational Concept Analysis (RCA) has been introduced in order to allow concept analysis on multi-relational data. It significantly widens the field of application of Formal Concept Analysis (FCA), and it produces richer concept intents that are similar to concept definitions in Description Logics (DL). However, reading and interpreting RCA concept lattices is notoriously difficult. Nica {\em et al} have proposed to represent RCA intents by cpo-patterns in the special case of sequence structures. We propose an equivalent representation of a family of RCA concept lattices in the form of a hierarchy of concept graphs. Each concept belongs to one concept graph, and each concept graph exhibits the relationships between several concepts. A concept graph is generally transversal to several lattices, and therefore highlights the relationships between different types of objects. We show the benefits of our approach on several use cases from the RCA litterature.} 
   }
   

1. Peggy Cellier, Mireille Ducassé, Sébastien Ferré, and Olivier Ridoux. Data Mining for Fault Localization: towards a Global Debugging Process. Research Report, INSA RENNES ; Univ Rennes, CNRS, IRISA, France, 2018. [WWW] [PDF]
   

   @techreport{cellier:hal-02003069,
   TITLE = {{Data Mining for Fault Localization: towards a Global Debugging Process}},
   AUTHOR = {Cellier, Peggy and Ducass{\'e}, Mireille and Ferr{\'e}, S{\'e}bastien and Ridoux, Olivier},
   URL = {https://hal.archives-ouvertes.fr/hal-02003069},
   TYPE = {Research Report},
   INSTITUTION = {{INSA RENNES ; Univ Rennes, CNRS, IRISA, France}},
   YEAR = {2018},
   PDF = {https://hal.archives-ouvertes.fr/hal-02003069/file/DebuggingAsDataMining.pdf},
   HAL_ID = {hal-02003069},
   HAL_VERSION = {v1},
   
   }
   




2. Sébastien Ferré. A SPARQL 1.1 Query Builder for the Data Analytics of Vanilla RDF Graphs. Research Report, IRISA, team SemLIS, 2018. [WWW] [PDF] Keyword(s): RDF, SPARQL, OLAP, natural language interface, data analytics, query builder, expressivity. Abstract:

   As more and more data are available as RDF graphs, the availability of tools for data analytics beyond semantic search becomes a key issue of the Semantic Web. Previous work has focused on adapting OLAP-like approaches and question answering by modelling RDF data cubes on top of RDF graphs. We propose a more direct -- and more expressive -- approach by guiding users in the incremental building of SPARQL~1.1 queries that combine several computation features (aggregations, expressions, bindings and filters), and by evaluating those queries on unmodified (vanilla) RDF graphs. We rely on the NF design pattern to hide SPARQL behind a natural language interface, and to provide results and suggestions at every step. We have implemented our approach on top of {\sc Sparklis}, and we report on three experiments to assess its expressivity, usability, and scalability.

   
   

   @techreport{Fer2018analytics,
   TITLE = {A {SPARQL 1.1} Query Builder for the Data Analytics of Vanilla {RDF} Graphs},
   AUTHOR = {Ferré, Sébastien},
   URL = {https://hal.inria.fr/hal-01820469},
   TYPE = {Research Report},
   INSTITUTION = {{IRISA, team SemLIS}},
   YEAR = {2018},
   KEYWORDS = {RDF, SPARQL, OLAP, natural language interface, data analytics, query builder, expressivity},
   PDF = {https://hal.inria.fr/hal-01820469/file/paper_as_report.pdf},
   HAL_ID = {hal-01820469},
   HAL_VERSION = {v1},
   ABSTRACT = {As more and more data are available as RDF graphs, the availability of tools for data analytics beyond semantic search becomes a key issue of the Semantic Web. Previous work has focused on adapting OLAP-like approaches and question answering by modelling RDF data cubes on top of RDF graphs. We propose a more direct -- and more expressive -- approach by guiding users in the incremental building of SPARQL~1.1 queries that combine several computation features (aggregations, expressions, bindings and filters), and by evaluating those queries on unmodified (vanilla) RDF graphs. We rely on the NF design pattern to hide SPARQL behind a natural language interface, and to provide results and suggestions at every step. We have implemented our approach on top of {\sc Sparklis}, and we report on three experiments to assess its expressivity, usability, and scalability.},
   
   }
   

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