Trois approches pour classifier les données du web des données

Justine Reynaud 1, 2 Yannick Toussaint 1, 2 Amedeo Napoli 1, 2
2 ORPAILLEUR - Knowledge representation, reasonning
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : In this paper we study a classification process on relational data that can be applied to the web of data. We start with a set of objects and relations between objects, and exten-sional classes of objects. We then study how to provide a definition to classes, i.e. to build an intensional description of the class, w.r.t. the relations involving class objects. To this end, we propose three different approaches based on Formal Concept Analysis (FCA), redescription mining and Minimum Description Length (MDL). Relying on some experiments on RDF data from DBpedia, where objects correspond to resources, relations to predicates and classes to categories, we compare the capabilities and the com-plementarity of the three approaches. This research work is a contribution to understanding the connections existing between FCA and other data mining formalisms which are gaining importance in knowledge discovery, namely redes-cription mining and MDL.
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Justine Reynaud, Yannick Toussaint, Amedeo Napoli. Trois approches pour classifier les données du web des données. CNIA/RJCIA 2018 - Conférence Nationale d'Intelligence Artificielle et Rencontres des Jeunes Chercheurs en Intelligence Artificielle, Jul 2018, Nancy, France. ⟨hal-01887820⟩

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