An efficient and large-scale reasoning method for the semantic Web

摘要

We present an extended version of the \(\mathcal {C}\mathcal {E}\mathcal {D}\mathcal {A}\mathcal {R}\) taxonomic reasoner for large ontologies. This new version provides fuller support for TBox reasoning, checking consistency, and retrieving instances. The \(\mathcal {C}\mathcal {E}\mathcal {D}\mathcal {A}\mathcal {R}\) system is based upon the \(\mathcal {O}\mathcal {S}\mathcal {F}\) formalism. It is implemented on an entirely new architecture which includes several optimization techniques. We define a bidirectional mapping between \(\mathcal {O}\mathcal {S}\mathcal {F}\) graph structures and the Resource Description Framework (RDF) allowing a translation from \(\mathcal {O}\mathcal {S}\mathcal {F}\) queries into SPARQL for retrieving instances from an RDF triplestore. We carried out comparative performance evaluation experiments using \(\mathcal {C}\mathcal {E}\mathcal {D}\mathcal {A}\mathcal {R}\) as well as well-known Semantic Web reasoners (such as FaCT++, Pellet, HermiT, TrOWL, and RacerPro) on very large public ontologies. For the same queries on the same ontologies, the results achieved by \(\mathcal {C}\mathcal {E}\mathcal {D}\mathcal {A}\mathcal {R}\) were compared to those obtained by all the other reasoners. The results of experiments show that \(\mathcal {C}\mathcal {E}\mathcal {D}\mathcal {A}\mathcal {R}\) consistently performs on a par with the fastest systems for concept classification, and several orders of magnitude more efficiently in terms of response time for Boolean query-answering over attributed concepts, as well as for ABox triplestore querying. The latter result is irrespective of the triplestore management used because the \(\mathcal {C}\mathcal {E}\mathcal {D}\mathcal {A}\mathcal {R}\) reasoner uses its knowledge to optimize SPARQL queries before submitting them to the triplestore.