Mining Definitions from RDF Annotations Using Formal Concept Analysis

Mehwish Alam (1) , Aleksey Buzmakov (2, 1) , Victor Codocedo (3, 1) , Amedeo Napoli (1)

1 ORPAILLEUR - Knowledge representation, reasonning
2 Department of Applied Mathematics - State University Higher School of Economics
3 DM2L - Data Mining and Machine Learning

The popularization and quick growth of Linked Open Data (LOD) has led to challenging aspects regarding quality assessment and data exploration of the RDF triples that shape the LOD cloud. Particularly, we are interested in the completeness of the data and the their potential to provide concept definitions in terms of necessary and sufficient conditions. In this work we propose a novel technique based on Formal Concept Analysis which organizes RDF data into a concept lattice. This allows data exploration as well as the discovery of implication rules which are used to automatically detect missing information and then to complete RDF data. Moreover, this is a way of reconciling syntax and semantics in the LOD cloud. Finally experiments on the DBpedia knowledge base show that the approach is well-founded and effective.

Mots clés: RDF quality; dbpedia; semantic web; formal concept analysis; association rules; pattern structures; heterogeneous data

Domaines: Intelligence artificielle [cs.AI]; Web

Mehwish Alam, Aleksey Buzmakov, Victor Codocedo, Amedeo Napoli. Mining Definitions from RDF Annotations Using Formal Concept Analysis. International Joint Conference in Artificial Intelligence, Jul 2015, Buenos Aires, Argentina. ⟨hal-01186204⟩