Three Approaches for Mining Definitions from Relational Data in the Web of Data

International audienceIn 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 extensional 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 comple-mentarity 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 redescription mining and MDL

Using Redescriptions and Formal Concept Analysis for Mining Definitions Linked Data

International audienceIn 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

Workshop Notes of the Sixth International Workshop "What can FCA do for Artificial Intelligence?"

International audienc