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Redescription mining for learning definitions and disjointness axioms in Linked Open Data

Justine Reynaud 1 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 : In this article, we present an original use of Redescription Mining (RM) for discovering definitions of classes and incompatibility (disjointness) axioms between classes of individuals in the web of data. RM is aimed at mining alternate descriptions from two datasets related to the same set of individuals. We reuse this process for providing definitions in terms of necessary and sufficient conditions to categories in DBpedia. Firstly, we recall the basics of redescription mining and make precise the principles of our definitional process. Then we detail experiments carried out on datasets extracted from DBpedia. Based on the output of the experiments, we discuss the strengths and the possible extensions of our approach.
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Submitted on : Tuesday, July 2, 2019 - 1:25:28 PM
Last modification on : Wednesday, November 3, 2021 - 4:48:39 AM


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  • HAL Id : hal-02170763, version 1


Justine Reynaud, Yannick Toussaint, Amedeo Napoli. Redescription mining for learning definitions and disjointness axioms in Linked Open Data. ICCS 2019 - 24th International Conference on Conceptual Structures, Jul 2019, Marburg, Germany. ⟨hal-02170763⟩



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