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Using Redescriptions and Formal Concept Analysis for Mining Definitions in Linked Data

  • Justine ReynaudEmail author
  • Yannick Toussaint
  • Amedeo Napoli
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11511)

Abstract

In this article, we compare the use of Redescription Mining (RM) and Association Rule Mining (ARM) for discovering class definitions in Linked Open Data (LOD). RM is aimed at mining alternate descriptions from two datasets related to the same set of individuals. We reuse RM for providing category definitions in DBpedia in terms of necessary and sufficient conditions (NSC). Implications and AR can be jointly used for mining category definitions still in terms of NSC. In this paper, we firstly, recall the basics of redescription mining and make precise the principles of definition discovery. Then we detail a series of experiments carried out on datasets extracted from DBpedia. We analyze the different outputs related to RM and ARM applications, and we discuss the strengths and limitations of both approaches. Finally, we point out possible improvements of the approaches.

Keywords

Redescription Mining Association Rule Mining Concept Analysis Linked Open Data Definition of categories 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Justine Reynaud
    • 1
    Email author
  • Yannick Toussaint
    • 1
  • Amedeo Napoli
    • 1
  1. 1.Université de Lorraine, CNRS, Inria, LORIANancyFrance

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