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

摘要

We present an extended version of the taxonomic reasoner for large ontologies. This new version provides fuller support for TBox reasoning, checking consistency, and retrieving instances. The system is based upon the formalism. It is implemented on an entirely new architecture which includes several optimization techniques. We define a bidirectional mapping between graph structures and the Resource Description Framework (RDF) allowing a translation from queries into SPARQL for retrieving instances from an RDF triplestore. We carried out comparative performance evaluation experiments using 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 were compared to those obtained by all the other reasoners. The results of experiments show that 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 reasoner uses its knowledge to optimize SPARQL queries before submitting them to the triplestore.