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Publications of year 2020
Books and proceedings
  1. Peggy Cellier and Kurt Driessens, editors. Machine Learning and Knowledge Discovery in Databases - International Workshops of ECML PKDD 2019, Würzburg, Germany, September 16-20, 2019, Proceedings, Part I, volume 1167 of Communications in Computer and Information Science, 2020. Springer.
    @proceedings{DBLP:conf/pkdd/2019-w1,
    editor = {Peggy Cellier and Kurt Driessens},
    title = {Machine Learning and Knowledge Discovery in Databases - International Workshops of {ECML} {PKDD} 2019, W{\"{u}}rzburg, Germany, September 16-20, 2019, Proceedings, Part {I}},
    series = {Communications in Computer and Information Science},
    volume = {1167},
    publisher = {Springer},
    year = {2020} 
    }
    


  2. Peggy Cellier and Kurt Driessens, editors. Machine Learning and Knowledge Discovery in Databases - International Workshops of ECML PKDD 2019, Würzburg, Germany, September 16-20, 2019, Proceedings, Part II, volume 1168 of Communications in Computer and Information Science, 2020. Springer.
    @proceedings{DBLP:conf/pkdd/2019-w2,
    editor = {Peggy Cellier and Kurt Driessens},
    title = {Machine Learning and Knowledge Discovery in Databases - International Workshops of {ECML} {PKDD} 2019, W{\"{u}}rzburg, Germany, September 16-20, 2019, Proceedings, Part {II}},
    series = {Communications in Computer and Information Science},
    volume = {1168},
    publisher = {Springer},
    year = {2020} 
    }
    


  3. Sébastien Ferré, editor. IC 2020 : 31es Journées francophones d'Ingénierie des Connaissances, Angers, France, June 29 - July 3, 2020, 2020.
    @proceedings{IC2020,
    editor = {Sébastien Ferré},
    title = {{IC} 2020 : 31es Journ{\'{e}}es francophones d'Ing{\'{e}}nierie des Connaissances, Angers, France, June 29 - July 3, 2020},
    year = {2020},
    
    }
    


Thesis
  1. Peggy Cellier. Towards Usable Pattern Mining. PhD thesis, Habilitation à diriger des recherche de l'université de Rennes 1, 30 octobre 2020.
    @PhdThesis{Cellier2020HDR,
    author = {Peggy Cellier},
    title = {Towards Usable Pattern Mining},
    school = {Habilitation à diriger des recherche de l'université de Rennes 1},
    year = {2020},
    month = {30 octobre} 
    }
    


Articles in journal or book chapters
  1. Sébastien Ferré, Mehdi Kaytoue, Marianne Huchard, Sergei O. Kuznetsov, and Amedeo Napoli. A guided tour of artificial intelligence research, volume II, chapter Formal Concept Analysis: from knowledge discovery to knowledge processing (Chapter 13), pages 411-445. Springer, 2020. [WWW] Keyword(s): formal concept analysis, relational concept analysis, Graph-FCA.
    Abstract:
    In this chapter, we introduce Formal Concept Analysis (FCA) and some of its extensions. FCA is a formalism based on lattice theory aimed at data analysis and knowledge processing. FCA allows the design of so-called concept lattices from binary and complex data. These concept lattices provide a realistic basis for knowledge engineering and the design of knowledge-based systems. Indeed, FCA is closely related to knowledge discovery in databases, knowledge representation and reasoning. Accordingly, FCA supports a wide range of complex and intelligent tasks among which classification, information retrieval, recommendation, network analysis, software engineering and data management. Finally, FCA is used in many applications demonstrating its growing importance in data and knowledge sciences.

    @InBook{BookAI:FCA,
    author = {Sébastien Ferré and Mehdi Kaytoue and Marianne Huchard and Sergei O. Kuznetsov and Amedeo Napoli},
    ALTeditor = {},
    title = {A guided tour of artificial intelligence research},
    chapter = {Formal Concept Analysis: from knowledge discovery to knowledge processing (Chapter 13)},
    publisher = {Springer},
    year = {2020},
    OPTkey = {},
    volume = {II},
    pages = {411--445},
    url = {https://www.springer.com/gp/book/9783030061661},
    keywords = {formal concept analysis, relational concept analysis, Graph-FCA},
    abstract = {In this chapter, we introduce Formal Concept Analysis (FCA) and some of its extensions. FCA is a formalism based on lattice theory aimed at data analysis and knowledge processing. FCA allows the design of so-called concept lattices from binary and complex data. These concept lattices provide a realistic basis for knowledge engineering and the design of knowledge-based systems. Indeed, FCA is closely related to knowledge discovery in databases, knowledge representation and reasoning. Accordingly, FCA supports a wide range of complex and intelligent tasks among which classification, information retrieval, recommendation, network analysis, software engineering and data management. Finally, FCA is used in many applications demonstrating its growing importance in data and knowledge sciences.},
    
    }
    


  2. Carlos Bobed, Pierre Maillot, Peggy Cellier, and Sébastien Ferré. Data-driven Assessment of Structural Evolution of RDF Graphs. Semantic Web: Interoperability, Usability, Applicability, 11:831-853, 2020. [WWW] Keyword(s): semantic web, data mining, data quality, data evolution, data-driven approach, MDL principle.
    Abstract:
    Since the birth of the Semantic Web, numerous knowledge bases have appeared. The applications that exploit them rely on the quality of their data through time. In this regard, one of the main dimensions of data quality is conformance to the expected usage of the vocabulary. However, the vocabulary usage (i.e., how classes and properties are actually populated) can vary from one base to another. Moreover, through time, such usage can evolve within a base and diverges from the previous practices. Methods have been proposed to follow the evolution of a knowledge base by the observation of the changes of their intentional schema (or ontology); however, they do not capture the evolution of their actual data, which can vary greatly in practice. In this paper, we propose a data-driven approach to assess the global evolution of vocabulary usage in large RDF graphs. Our proposal relies on two structural measures defined at different granularities (dataset vs update), which are based on pattern mining techniques. We have performed a thorough experimentation which shows that our approach is scalable, and can capture structural evolution through time of both synthetic (LUBM) and real knowledge bases (different snapshots and updates of DBpedia).

    @article{BobMaiCelFer2019swj,
    author = {Carlos Bobed and Pierre Maillot and Peggy Cellier and Sébastien Ferré},
    title = {Data-driven Assessment of Structural Evolution of {RDF} Graphs},
    journal = {Semantic Web: Interoperability, Usability, Applicability},
    publisher = {IOS Press},
    year = {2020},
    volume = {11},
    pages = {831--853},
    url = {http://www.semantic-web-journal.net/content/data-driven-assessment-structural-evolution-rdf-graphs-0},
    keywords = {semantic web, data mining, data quality, data evolution, data-driven approach, MDL principle},
    abstract = { Since the birth of the Semantic Web, numerous knowledge bases have appeared. The applications that exploit them rely on the quality of their data through time. In this regard, one of the main dimensions of data quality is conformance to the expected usage of the vocabulary. However, the vocabulary usage (i.e., how classes and properties are actually populated) can vary from one base to another. Moreover, through time, such usage can evolve within a base and diverges from the previous practices. Methods have been proposed to follow the evolution of a knowledge base by the observation of the changes of their intentional schema (or ontology); however, they do not capture the evolution of their actual data, which can vary greatly in practice. In this paper, we propose a data-driven approach to assess the global evolution of vocabulary usage in large RDF graphs. Our proposal relies on two structural measures defined at different granularities (dataset vs update), which are based on pattern mining techniques. We have performed a thorough experimentation which shows that our approach is scalable, and can capture structural evolution through time of both synthetic (LUBM) and real knowledge bases (different snapshots and updates of DBpedia).},
    
    }
    


  3. Sébastien Ferré. Application of Concepts of Neighbours to Knowledge Graph Completion. Data Science: Methods, Infrastructure, and Applications, 2020. Note: To appear. [WWW] Keyword(s): knowledge graph, link prediction, concepts of enighbours.
    Abstract:
    The open nature of Knowledge Graphs (KG) often implies that they are incomplete. Knowledge graph completion (aka. link prediction) consists in inferring new relationships between the entities of a KG based on existing relationships. Most existing approaches rely on the learning of latent feature vectors for the encoding of entities and relations. In general however, latent features cannot be easily interpreted. Rule-based approaches offer interpretability but a distinct ruleset must be learned for each relation. In both latent- and rule-based approaches, the training phase has to be run again when the KG is updated. We propose a new approach that does not need a training phase, and that can provide interpretable explanations for each inference. It relies on the computation of Concepts of Nearest Neighbours (C-NN) to identify clusters of similar entities based on common graph patterns. Different rules are then derived from those graph patterns, and combined to predict new relationships. We evaluate our approach on standard benchmarks for link prediction, where it gets competitive performance compared to existing approaches.

    @Article{Fer2020ds,
    author = {Sébastien Ferré},
    title = {Application of Concepts of Neighbours to Knowledge Graph Completion},
    journal = {Data Science: Methods, Infrastructure, and Applications},
    year = {2020},
    OPTkey = {},
    OPTvolume = {},
    OPTnumber = {},
    OPTpages = {},
    OPTmonth = {},
    note = {To appear},
    OPTannote = {},
    url = {https://datasciencehub.net/paper/application-concepts-neighbours-knowledge-graph-completion-0},
    keyword = {knowledge graph, link prediction, concepts of enighbours},
    abstract = {The open nature of Knowledge Graphs (KG) often implies that they are incomplete. Knowledge graph completion (aka. link prediction) consists in inferring new relationships between the entities of a KG based on existing relationships. Most existing approaches rely on the learning of latent feature vectors for the encoding of entities and relations. In general however, latent features cannot be easily interpreted. Rule-based approaches offer interpretability but a distinct ruleset must be learned for each relation. In both latent- and rule-based approaches, the training phase has to be run again when the KG is updated. We propose a new approach that does not need a training phase, and that can provide interpretable explanations for each inference. It relies on the computation of Concepts of Nearest Neighbours (C-NN) to identify clusters of similar entities based on common graph patterns. Different rules are then derived from those graph patterns, and combined to predict new relationships. We evaluate our approach on standard benchmarks for link prediction, where it gets competitive performance compared to existing approaches.} 
    }
    


  4. Sébastien Ferré and Peggy Cellier. Graph-FCA: An extension of formal concept analysis to knowledge graphs. Discrete Applied Mathematics, 273(5):81-102, 2020. [WWW] [doi:https://doi.org/10.1016/j.dam.2019.03.003] Keyword(s): Formal concept analysis, Knowledge graph, Semantic web, Graph homomorphism.
    Abstract:
    Knowledge graphs offer a versatile knowledge representation, and have been studied under different forms, such as conceptual graphs or RDF graphs in the Semantic Web. A challenge is to discover conceptual structures in those graphs, in the same way as Formal Concept Analysis (FCA) discovers conceptual structures in tables. FCA has been successful for analysing, mining, learning, and exploring tabular data, and our aim is to help transpose those results to graph-based data. Previous several FCA approaches have already addressed relational data, hence graphs, but with various limits. We propose Graph-FCA as an extension of FCA where a dataset is a hypergraph instead of a binary table. We show that it can be formalized simply by replacing objects by tuples of objects. This leads to the notion of "n-ary concept", whose extent is an n-ary relation of objects, and whose intent is a "projected graph pattern". In this paper, we formally reconstruct the fundamental results of FCA for knowledge graphs. We describe in detail the representation of hypergraphs, and the operations on them, as they are much more complex than the sets of attributes that they extend. We also propose an algorithm based on a notion of "pattern basis" to generate and display n-ary concepts in a more efficient and more compact way. We explore a few use cases, in order to study the feasibility and usefulness of Graph-FCA. We consider two use cases: workflow patterns in cooking recipes and linguistic structures from parse trees. In addition, we report on experiments about quantitative aspects of the approach.

    @article{FerCel2019dam,
    title = "Graph-FCA: An extension of formal concept analysis to knowledge graphs",
    journal = "Discrete Applied Mathematics",
    volume = "273",
    number = "5",
    pages = "81--102",
    year = "2020",
    issn = "0166-218X",
    doi = "https://doi.org/10.1016/j.dam.2019.03.003",
    url = "http://www.sciencedirect.com/science/article/pii/S0166218X19301532",
    author = "Sébastien Ferré and Peggy Cellier",
    keywords = "Formal concept analysis, Knowledge graph, Semantic web, Graph homomorphism",
    abstract = {Knowledge graphs offer a versatile knowledge representation, and have been studied under different forms, such as conceptual graphs or RDF graphs in the Semantic Web. A challenge is to discover conceptual structures in those graphs, in the same way as Formal Concept Analysis (FCA) discovers conceptual structures in tables. FCA has been successful for analysing, mining, learning, and exploring tabular data, and our aim is to help transpose those results to graph-based data. Previous several FCA approaches have already addressed relational data, hence graphs, but with various limits. We propose Graph-FCA as an extension of FCA where a dataset is a hypergraph instead of a binary table. We show that it can be formalized simply by replacing objects by tuples of objects. This leads to the notion of "n-ary concept", whose extent is an n-ary relation of objects, and whose intent is a "projected graph pattern". In this paper, we formally reconstruct the fundamental results of FCA for knowledge graphs. We describe in detail the representation of hypergraphs, and the operations on them, as they are much more complex than the sets of attributes that they extend. We also propose an algorithm based on a notion of "pattern basis" to generate and display n-ary concepts in a more efficient and more compact way. We explore a few use cases, in order to study the feasibility and usefulness of Graph-FCA. We consider two use cases: workflow patterns in cooking recipes and linguistic structures from parse trees. In addition, we report on experiments about quantitative aspects of the approach.} 
    }
    


  5. Flavien Lécuyer, Valérie Gouranton, Aurélien Lamercerie, Adrien Reuzeau, Bruno Arnaldi, and Benoît Caillaud. Unveiling the implicit knowledge, one scenario at a time. Visual Computer, pp 1-12, 2020. [WWW] [PDF] [doi:10.1007/s00371-020-01904-7] Keyword(s): Process Mining, demodocos, S3PMSunset, ProcMining.
    @article{lecuyer:hal-02879083,
    TITLE = {{Unveiling the implicit knowledge, one scenario at a time}},
    AUTHOR = {L{\'e}cuyer, Flavien and Gouranton, Val{\'e}rie and Lamercerie, Aur{\'e}lien and Reuzeau, Adrien and Arnaldi, Bruno and Caillaud, Beno{\^i}t},
    URL = {https://hal.inria.fr/hal-02879083},
    JOURNAL = {{Visual Computer}},
    PUBLISHER = {{Springer Verlag}},
    PAGES = {1-12},
    YEAR = {2020},
    DOI = {10.1007/s00371-020-01904-7},
    KEYWORDS = {Process Mining ; demodocos ; S3PMSunset ; ProcMining},
    PDF = {https://hal.inria.fr/hal-02879083/file/Demodocos_x_XRaken__Visual_Computer_-4.pdf},
    HAL_ID = {hal-02879083},
    HAL_VERSION = {v1},
    
    }
    


  6. Romaric Marcilly, Laura Douze, Sébastien Ferré, Bissan Audeh, Carlos Bobed, Agnès Lillo-Le-Louët, Jean-Baptiste Lamy, and Cédric Bousquet. How to interact with medical terminologies? Formative usability evaluations comparing three approaches for supporting the use of MedDRA by pharmacovigilance specialists. BMC Medical Informatics and Decision Making, 20(261), 2020. [WWW] [doi:https://doi.org/10.1186/s12911-020-01280-1]
    @Article{marcilly2020midm,
    author = {Romaric Marcilly and Laura Douze and Sébastien Ferré and Bissan Audeh and Carlos Bobed and Agnès Lillo-Le-Louët and Jean-Baptiste Lamy and Cédric Bousquet},
    title = {How to interact with medical terminologies? Formative usability evaluations comparing three approaches for supporting the use of {MedDRA} by pharmacovigilance specialists},
    journal = {BMC Medical Informatics and Decision Making},
    year = {2020},
    volume = {20},
    number = {261},
    doi = {https://doi.org/10.1186/s12911-020-01280-1},
    url = {https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-020-01280-1},
    
    }
    


Conference articles
  1. Francesco Bariatti, Peggy Cellier, and Sébastien Ferré. GraphMDL : sélection de motifs de graphes avec le principe MDL. In Extraction et Gestion des Connaissances (EGC), Bruxelles, Belgium, 2020. [WWW] [PDF]
    Abstract:
    Plusieurs algorithmes de fouille de motifs ont été proposés pour identifier des structures récurrentes dans les graphes. Le principal défaut de ces approches est qu'elles produisent généralement trop de motifs pour qu'une analyse humaine soit possible. Récemment, des méthodes de fouille de motifs ont traité ce problème sur des données transactionnelles, séquentielles et relationnelles en utilisant le principe MDL (Minimum Description Length). Dans ce papier, nous proposons une approche MDL pour sélectionner un sous-ensemble représentatif de motifs sur des graphes non-orientés étiquetés. Une notion clé de notre approche est l'introduction de ports pour encoder les connections entre occurrences de motifs, sans perte d'information. Nos expériences montrent que le nombre de motifs est drastiquement réduit et que les motifs sélectionnés peuvent avoir des formes complexes.

    @inproceedings{Bariatti2020egc,
    TITLE = {{GraphMDL : sélection de motifs de graphes avec le principe MDL}},
    AUTHOR = {Bariatti, Francesco and Cellier, Peggy and Ferré, Sébastien},
    URL = {https://hal.inria.fr/hal-02511412},
    BOOKTITLE = {{Extraction et Gestion des Connaissances (EGC)}},
    ADDRESS = {Bruxelles, Belgium},
    YEAR = {2020},
    PDF = {https://hal.inria.fr/hal-02511412/file/egc_graphMDL.pdf},
    ABSTRACT = {Plusieurs algorithmes de fouille de motifs ont été proposés pour identifier des structures récurrentes dans les graphes. Le principal défaut de ces approches est qu'elles produisent généralement trop de motifs pour qu'une analyse humaine soit possible. Récemment, des méthodes de fouille de motifs ont traité ce problème sur des données transactionnelles, séquentielles et relationnelles en utilisant le principe MDL (Minimum Description Length). Dans ce papier, nous proposons une approche MDL pour sélectionner un sous-ensemble représentatif de motifs sur des graphes non-orientés étiquetés. Une notion clé de notre approche est l'introduction de ports pour encoder les connections entre occurrences de motifs, sans perte d'information. Nos expériences montrent que le nombre de motifs est drastiquement réduit et que les motifs sélectionnés peuvent avoir des formes complexes.} 
    }
    


  2. Francesco Bariatti, Peggy Cellier, and Sébastien Ferré. GraphMDL Visualizer: Interactive Visualization of Graph Patterns. In Graph Embedding and Mining (GEM), an ECML-PKDD workshop, 2020. [WWW] Keyword(s): graph mining, graph pattern, MDL principle, visualization.
    Abstract:
    Pattern mining algorithms allow to extract structures from data to highlight interesting and useful knowledge. However, those approaches can only be truly helpful if the users can actually understand their outputs. Thus, visualization techniques play a great role in pattern mining, bridging the gap between the algorithms and the users. In this demo paper we propose GraphMDL Visualizer, a tool for the interactive visualization of the graph patterns extracted with GraphMDL, a graph mining approach based on the MDL principle. GraphMDL Visualizer is structured according to the behavior and needs of users when they analyze GraphMDL results. The tool has different views, ranging from more general (distribution of pattern characteristics), to more specific (visualization of specific patterns). It is also highly interactive, allowing the users to customize the different views, and navigate between them, through simple mouse clicks. GraphMDL Visualizer is freely available online.

    @InProceedings{BarCelFer2020gem,
    author = {Francesco Bariatti and Peggy Cellier and Sébastien Ferré},
    title = {{GraphMDL} Visualizer: Interactive Visualization of Graph Patterns},
    booktitle = {Graph Embedding and Mining ({GEM}), an {ECML-PKDD} workshop},
    year = {2020},
    url = {https://gem-ecmlpkdd.github.io/papers/GEM2020_paper_7.pdf},
    keywords = {graph mining, graph pattern, MDL principle, visualization},
    abstract = {Pattern mining algorithms allow to extract structures from data to highlight interesting and useful knowledge. However, those approaches can only be truly helpful if the users can actually understand their outputs. Thus, visualization techniques play a great role in pattern mining, bridging the gap between the algorithms and the users. In this demo paper we propose GraphMDL Visualizer, a tool for the interactive visualization of the graph patterns extracted with GraphMDL, a graph mining approach based on the MDL principle. GraphMDL Visualizer is structured according to the behavior and needs of users when they analyze GraphMDL results. The tool has different views, ranging from more general (distribution of pattern characteristics), to more specific (visualization of specific patterns). It is also highly interactive, allowing the users to customize the different views, and navigate between them, through simple mouse clicks. GraphMDL Visualizer is freely available online.} 
    }
    


  3. Francesco Bariatti, Peggy Cellier, and Sébastien Ferré. GraphMDL: Graph Pattern Selection based on Minimum Description Length. In Symposium on Intelligent Data Analysis (IDA), 2020. [WWW] [PDF] Keyword(s): Minimum Description Length, Graph Mining, Pattern Mining.
    Abstract:
    Many graph pattern mining algorithms have been designed to identify recurring structures in graphs. The main drawback of these approaches is that they often extract too many patterns for human analysis. Recently, pattern mining methods using the Minimum Description Length (MDL) principle have been proposed to select a characteristic subset of patterns from transactional, sequential and relational data. In this paper, we propose an MDL-based approach for selecting a characteristic subset of patterns on labeled graphs. A key notion in this paper is the introduction of ports to encode connections between pattern occurrences without any loss of information. Experiments show that the number of patterns is drastically reduced. The selected patterns have complex shapes and are representative of the data.

    @inproceedings{Bariatti2020ida,
    TITLE = {{GraphMDL: Graph Pattern Selection based on Minimum Description Length}},
    AUTHOR = {Bariatti, Francesco and Cellier, Peggy and Ferré, Sébastien},
    URL = {https://hal.inria.fr/hal-02510517},
    BOOKTITLE = {{Symposium on Intelligent Data Analysis (IDA)}},
    YEAR = {2020},
    KEYWORDS = {Minimum Description Length ; Graph Mining ; Pattern Mining},
    PDF = {https://hal.inria.fr/hal-02510517/file/ida_graphmdl.pdf},
    OPTHAL_ID = {hal-02510517},
    OPTHAL_VERSION = {v1},
    ABSTRACT = {Many graph pattern mining algorithms have been designed to identify recurring structures in graphs. The main drawback of these approaches is that they often extract too many patterns for human analysis. Recently, pattern mining methods using the Minimum Description Length (MDL) principle have been proposed to select a characteristic subset of patterns from transactional, sequential and relational data. In this paper, we propose an MDL-based approach for selecting a characteristic subset of patterns on labeled graphs. A key notion in this paper is the introduction of ports to encode connections between pattern occurrences without any loss of information. Experiments show that the number of patterns is drastically reduced. The selected patterns have complex shapes and are representative of the data.} 
    }
    


  4. Mireille Ducassé. Kartu-Verbs: A Semantic Web Base of Inflected Georgian Verb Forms to Bypass Georgian Verb Lemmatization Issues. In Zoe Gavriilidou, editor, First Proceedings of XIX EURALEX Conference, November 2020. Euralex association. [WWW] Keyword(s): Georgian verbs, Inflected forms, Dictionary front-end, Semantic web tool, Prolog.
    Abstract:
    The Georgian language has a complex verbal system, both agglutinative and inflectional, with many irregularities. Inflected forms of a given verb can differ greatly from one another and it is still a controversial issue to determine which lemmas should represent a verb in dictionaries. Verb tables help people to track lemmas starting from inflected forms but these tables are tedious and error-prone to browse. We propose Kartu-Verbs, a Semantic Web base of inflected Georgian verb forms. For a given verb, all its inflected forms are present. Knowledge can easily be traversed in all directions: from Georgian to French and English; from an inflected form to a masdar (a verbal noun, the form that comes closest to an infinitive), and conversely from a masdar to any inflected form; from component(s) to forms and from a form to its components. Users can easily retrieve the lemmas that are relevant to access their preferred dictionaries. Kartu-Verbs can be seen as a front-end to any Georgian dictionary, thus bypassing the lemmatization issues.

    @InProceedings{ducasse2020,
    Author={Mireille Ducassé},
    Title={Kartu-Verbs: A Semantic Web Base of Inflected Georgian Verb Forms to Bypass Georgian Verb Lemmatization Issues},
    Pages={ },
    BookTitle={First Proceedings of XIX EURALEX Conference},
    Year={2020},
    Editor={Zoe Gavriilidou},
    Publisher={Euralex association},
    Month={November},
    Keywords={Georgian verbs, Inflected forms, Dictionary front-end, Semantic web tool, Prolog},
    OPTUrl={https://euralex.org/publications/},
    URL = {https://hal.archives-ouvertes.fr/hal-02924019},
    Abstract={ The Georgian language has a complex verbal system, both agglutinative and inflectional, with many irregularities. Inflected forms of a given verb can differ greatly from one another and it is still a controversial issue to determine which lemmas should represent a verb in dictionaries. Verb tables help people to track lemmas starting from inflected forms but these tables are tedious and error-prone to browse. We propose Kartu-Verbs, a Semantic Web base of inflected Georgian verb forms. For a given verb, all its inflected forms are present. Knowledge can easily be traversed in all directions: from Georgian to French and English; from an inflected form to a masdar (a verbal noun, the form that comes closest to an infinitive), and conversely from a masdar to any inflected form; from component(s) to forms and from a form to its components. Users can easily retrieve the lemmas that are relevant to access their preferred dictionaries. Kartu-Verbs can be seen as a front-end to any Georgian dictionary, thus bypassing the lemmatization issues. }
    }
    


  5. Sébastien Ferré. A Proposal for Nested Results in SPARQL. In K. Taylor, R. Gonçalves, F. Lecue, and J. Yan, editors, ISWC 2020 Posters, Demos, and Industry Tracks, volume 2721 of CEUR Workshop Proceedings, pages 114-119, 2020. [WWW] Keyword(s): SPARQL, nested tables.
    Abstract:
    Tables are a common form of query results, notably in SPARQL. However, due to the flat structure of tables, all structure from the RDF graph is lost, and this can lead to duplications in the table contents, and difficulties to interpret the results. We propose an extension of SPARQL 1.1 aggregations to get nested results, i.e. tables where cells may contain embedded tables instead of RDF terms, and so recursively

    @InProceedings{Fer2020iswc,
    author = {Sébastien Ferré},
    title = {A Proposal for Nested Results in {SPARQL}},
    booktitle = {{ISWC} 2020 Posters, Demos, and Industry Tracks},
    year = {2020},
    editor = {K. Taylor and R. Gonçalves and F. Lecue and J. Yan},
    series = {{CEUR} Workshop Proceedings},
    volume = {2721},
    pages = {114-119},
    url = {http://ceur-ws.org/Vol-2721/paper527.pdf},
    keywords = {SPARQL, nested tables},
    abstract = {Tables are a common form of query results, notably in SPARQL. However, due to the flat structure of tables, all structure from the RDF graph is lost, and this can lead to duplications in the table contents, and difficulties to interpret the results. We propose an extension of SPARQL 1.1 aggregations to get nested results, i.e. tables where cells may contain embedded tables instead of RDF terms, and so recursively},
    
    }
    


  6. Sébastien Ferré. Construction guidée de requêtes analytiques sur des graphes RDF. In Atelier Web des Données, Bruxelles, Belgium, 2020. [WWW] [PDF] Keyword(s): RDF, SPARQL, analytical query, statistical query, Sparklis, query builder.
    Abstract:
    As more and more data are available as RDF graphs, the availability of tools for analytical queries beyond semantic search becomes a key issue of the Semantic Web. Previous work require the modelling of data cubes on top of RDF graphs. We propose an approach that directly answers analytical queries on unmodified RDF graphs by exploiting the computation features of SPARQL 1.1 (aggregations, expressions). We rely on the NAF design pattern to design a query builder user interface that is user-friendly by completely hiding SPARQL behind a verbalization in natural language; and responsive by giving intermediate results and suggestions at each step. Our evaluations show that our approach covers a large range of use cases, and scales well on large datasets.

    @inproceedings{Fer2020awd,
    TITLE = {{Construction guid{\'e}e de requ{\^e}tes analytiques sur des graphes RDF}},
    AUTHOR = {Ferr{\'e}, S{\'e}bastien},
    URL = {https://hal.inria.fr/hal-02452395},
    BOOKTITLE = {{Atelier Web des Donn{\'e}es}},
    ADDRESS = {Bruxelles, Belgium},
    YEAR = {2020},
    PDF = {https://hal.inria.fr/hal-02452395/file/paper.pdf},
    keywords = {RDF, SPARQL, analytical query, statistical query, Sparklis, query builder},
    abstract = {As more and more data are available as RDF graphs, the availability of tools for analytical queries beyond semantic search becomes a key issue of the Semantic Web. Previous work require the modelling of data cubes on top of RDF graphs. We propose an approach that directly answers analytical queries on unmodified RDF graphs by exploiting the computation features of SPARQL 1.1 (aggregations, expressions). We rely on the NAF design pattern to design a query builder user interface that is user-friendly by completely hiding SPARQL behind a verbalization in natural language; and responsive by giving intermediate results and suggestions at each step. Our evaluations show that our approach covers a large range of use cases, and scales well on large datasets.},
    
    }
    


  7. Nicolas Fouqué, Sébastien Ferré, and Peggy Cellier. Concepts de voisins dans les graphes RDF : Une extension Jena et une interface graphique. In Antoine Cornuéjols and Etienne Cuvelier, editors, Extraction et Gestion des Connaissances (EGC), volume E-36 of RNTI, pages 483-490, 2020. Éditions RNTI. [WWW] Keyword(s): RDF, concepts de voisins.
    Abstract:
    Les concepts de voisins définissent une forme symbolique de similarité entre les entités d'un graphe de connaissances. Partant d'une entité, chaque concept de voisins est un cluster d'entités voisines partageant un même motif de graphe centré sur l'entité. Dans ce papier démo, nous rappelons les définitions des concepts de voisins et nous présentons une extension de la librairie Jena dont l'API permet de calculer les concepts de voisins pour un modèle RDF(S) Jena. Nous présentons également une interface graphique permettant à un utilisateur d'effectuer ces calculs de façon simple et interactive.

    @inproceedings{Fouque2020egc,
    author = {Nicolas Fouqué and Sébastien Ferré and Peggy Cellier},
    editor = {Antoine Cornu{\'{e}}jols and Etienne Cuvelier},
    title = {Concepts de voisins dans les graphes {RDF} : Une extension Jena et une interface graphique},
    booktitle = {Extraction et Gestion des Connaissances ({EGC})},
    series = {{RNTI}},
    volume = {{E-36}},
    pages = {483--490},
    publisher = {{\'{E}}ditions {RNTI}},
    year = {2020},
    url = {http://editions-rnti.fr/?inprocid=1002617},
    keywords = {RDF, concepts de voisins},
    abstract = {Les concepts de voisins définissent une forme symbolique de similarité entre les entités d'un graphe de connaissances. Partant d'une entité, chaque concept de voisins est un cluster d'entités voisines partageant un même motif de graphe centré sur l'entité. Dans ce papier démo, nous rappelons les définitions des concepts de voisins et nous présentons une extension de la librairie Jena dont l'API permet de calculer les concepts de voisins pour un modèle RDF(S) Jena. Nous présentons également une interface graphique permettant à un utilisateur d'effectuer ces calculs de façon simple et interactive.} 
    }
    


  8. Clément Gautrais, Peggy Cellier, Matthijs van Leeuwen, and Alexandre Termier. Widening for MDL-Based Retail Signature Discovery. In Michael R. Berthold, Ad Feelders, and Georg Krempl, editors, Advances in Intelligent Data Analysis XVIII - 18th International Symposium on Intelligent Data Analysis, IDA 2020, Konstanz, Germany, April 27-29, 2020, Proceedings, volume 12080 of Lecture Notes in Computer Science, pages 197-209, 2020. Springer.
    @inproceedings{IDA20Gautrais,
    author = {Cl{\'{e}}ment Gautrais and Peggy Cellier and Matthijs van Leeuwen and Alexandre Termier},
    editor = {Michael R. Berthold and Ad Feelders and Georg Krempl},
    title = {Widening for MDL-Based Retail Signature Discovery},
    booktitle = {Advances in Intelligent Data Analysis {XVIII} - 18th International Symposium on Intelligent Data Analysis, {IDA} 2020, Konstanz, Germany, April 27-29, 2020, Proceedings},
    series = {Lecture Notes in Computer Science},
    volume = {12080},
    pages = {197--209},
    publisher = {Springer},
    year = {2020} 
    }
    


  9. Priscilla Keip, Sébastien Ferré, Alain Gutierrez, Marianne Huchard, Pierre Silvie, and Pierre Martin. Practical Comparison of FCA Extensions to Model Indeterminate Value of Ternary Data. In F. J. Valverde-Albacete and M. Trnecka, editors, Int. Conf. Concept Lattices and Their Applications, volume 2668 of CEUR Workshop Proceedings, pages 197-208, 2020. CEUR-WS.org. [WWW] Keyword(s): FCA, Graph-FCA, RCA, triadic FCA, relational data.
    Abstract:
    The Knomana knowledge base brings together knowledge from the scientific literature on the use of plants with pesticidal or antibiotic effects on animals, plants, and human beings to propose protection solutions using local plants. In this literature, the elements of the 3-tuple (protected organism, protecting plant, pest) are named using the binomial nomenclature consisting of the genus name followed by the species name. In some instances, authors use the abbreviation "sp." in the singular or "spp." in the plural, as species name, to indicate the indeterminate status of the species for a guaranteed genus. To suggest protection solutions, the indeterminacy of the species has to be hypothesized based on assigning the sp./spp. to the other species in the same genus and conversely. This paper discusses the classification of ternary data containing some indeterminate values generated by three extensions of Formal Concept Analysis.

    @inproceedings{Keip2020cla,
    author = {Priscilla Keip and Sébastien Ferré and Alain Gutierrez and Marianne Huchard and Pierre Silvie and Pierre Martin},
    editor = {F. J. Valverde{-}Albacete and M. Trnecka},
    title = {Practical Comparison of {FCA} Extensions to Model Indeterminate Value of Ternary Data},
    booktitle = {Int. Conf. Concept Lattices and Their Applications},
    series = {{CEUR} Workshop Proceedings},
    volume = {2668},
    pages = {197--208},
    publisher = {CEUR-WS.org},
    year = {2020},
    url = {http://ceur-ws.org/Vol-2668/paper15.pdf},
    keywords = {FCA, Graph-FCA, RCA, triadic FCA, relational data},
    abstract = {The Knomana knowledge base brings together knowledge from the scientific literature on the use of plants with pesticidal or antibiotic effects on animals, plants, and human beings to propose protection solutions using local plants. In this literature, the elements of the 3-tuple (protected organism, protecting plant, pest) are named using the binomial nomenclature consisting of the genus name followed by the species name. In some instances, authors use the abbreviation "sp." in the singular or "spp." in the plural, as species name, to indicate the indeterminate status of the species for a guaranteed genus. To suggest protection solutions, the indeterminacy of the species has to be hypothesized based on assigning the sp./spp. to the other species in the same genus and conversely. This paper discusses the classification of ternary data containing some indeterminate values generated by three extensions of Formal Concept Analysis.} 
    }
    


  10. Aurélien Lamercerie. ARES : un extracteur d'exigences pour la modélisation de systèmes. In EGC 2020 - Extraction et Gestion des Connaissances (Atelier - Fouille de Textes - Text Mine), Bruxelles, Belgium, pages 1-4, January 2020. [WWW] [PDF]
    @inproceedings{lamercerie:hal-02971727,
    TITLE = {{ARES : un extracteur d'exigences pour la mod{\'e}lisation de syst{\`e}mes}},
    AUTHOR = {Lamercerie, Aur{\'e}lien},
    URL = {https://hal.archives-ouvertes.fr/hal-02971727},
    BOOKTITLE = {{EGC 2020 - Extraction et Gestion des Connaissances (Atelier - Fouille de Textes - Text Mine)}},
    ADDRESS = {Bruxelles, Belgium},
    PAGES = {1-4},
    YEAR = {2020},
    MONTH = Jan,
    PDF = {https://hal.archives-ouvertes.fr/hal-02971727/file/TM%202020%20-%20Ares.pdf},
    HAL_ID = {hal-02971727},
    HAL_VERSION = {v1},
    
    }
    


  11. Aurélien Lamercerie. Transduction sémantique pour la modélisation de système. In PFIA 2020 - Plate-Forme de l'Intelligence Artificielle (PFIA), rencontres RJCIA, Angers, France, pages 1-6, June 2020. [WWW] [PDF] Keyword(s): System Design, semantic parsing, abstract representation, formalization.
    @inproceedings{lamercerie:hal-02971742,
    TITLE = {{Transduction s{\'e}mantique pour la mod{\'e}lisation de syst{\`e}me}},
    AUTHOR = {Lamercerie, Aur{\'e}lien},
    URL = {https://hal.archives-ouvertes.fr/hal-02971742},
    BOOKTITLE = {{PFIA 2020 - Plate-Forme de l'Intelligence Artificielle (PFIA), rencontres RJCIA}},
    ADDRESS = {Angers, France},
    PAGES = {1-6},
    YEAR = {2020},
    MONTH = Jun,
    KEYWORDS = {System Design ; semantic parsing ; abstract representation ; formalization},
    PDF = {https://hal.archives-ouvertes.fr/hal-02971742/file/RJCIA%202020%20-%20Transduction%20s%C3%A9mantique.pdf},
    HAL_ID = {hal-02971742},
    HAL_VERSION = {v1},
    
    }
    


  12. Aurélien Lamercerie and Benoît Caillaud. An Algebra of Deterministic Propositional Acceptance Automata (DPAA). In FDL 2020 - Forum on specification & Design Languages, Kiel, Germany, pages 1-8, September 2020. [WWW] [PDF] Keyword(s): Interface Theory, Automata for System Analysis, Discrete Time Reactive System, Requirements Engineering.
    @inproceedings{lamercerie:hal-02971772,
    TITLE = {{An Algebra of Deterministic Propositional Acceptance Automata (DPAA)}},
    AUTHOR = {Lamercerie, Aur{\'e}lien and Caillaud, Beno{\^i}t},
    URL = {https://hal.archives-ouvertes.fr/hal-02971772},
    BOOKTITLE = {{FDL 2020 - Forum on specification \& Design Languages}},
    ADDRESS = {Kiel, Germany},
    PAGES = {1-8},
    YEAR = {2020},
    MONTH = Sep,
    KEYWORDS = {Interface Theory ; Automata for System Analysis ; Discrete Time Reactive System ; Requirements Engineering},
    PDF = {https://hal.archives-ouvertes.fr/hal-02971772/file/FDL%202020%20-%20DPAA%20Algebra.pdf},
    HAL_ID = {hal-02971772},
    HAL_VERSION = {v1},
    
    }
    



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