Abstract

The popularization and quick growth of Linked Open Data (LOD) has led to challenging aspects re-garding quality assessment and data exploration of the RDF triples that shape the LOD cloud. Particu-larly, we are interested in the completeness of data and its potential to provide concept definitions in terms of necessary and sufficient conditions. In this work we propose a novel technique based on For-mal Concept Analysis which organizes RDF data into a concept lattice. This allows data exploration as well as the discovery of implications, 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. 1