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A Proposal for Classifying the Content of the Web of Data Based on FCA and Pattern Structures

Justine Reynaud 1 Mehwish Alam 2 Yannick Toussaint 1 Amedeo Napoli 1
1 ORPAILLEUR - Knowledge representation, reasonning
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : This paper focuses on a framework based on Formal Concept Analysis and the Pattern Structures for classifying sets of RDF triples. The first step proposes how the pattern structures allowing the classification of RDF triples w.r.t. domain knowledge can be constructed. More precisely, the poset of classes representing subjects and objects and the poset of predicates in RDF triples are taken into account. A similarity measure is also proposed based on these posets. Then, the paper discusses experimental details using a subset of DBpedia. It shows how the resulting pattern concept lattice is built and how it can be interpreted for discovering significant knowledge units from the obtained classes of RDF triples.
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Justine Reynaud, Mehwish Alam, Yannick Toussaint, Amedeo Napoli. A Proposal for Classifying the Content of the Web of Data Based on FCA and Pattern Structures. ISMIS 2017 - 23rd International Symposium on Methodologies for Intelligent Systems, Jun 2017, Warsaw, Poland. ⟨hal-01667437⟩

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