Storing and Querying Fuzzy Knowledge in the Semantic Web

The great evolution of ontologies during the last decade, bred the need for storage and querying for the Semantic Web. For that purpose, many RDF tools capable of storing a knowledge base, and also performing queries on it, were constructed. Recently, fuzzy extensions to description logics have gained considerable attention especially for the purposes of handling vague information in many applications. In this paper we investigate on the issue of using classical RDF storing systems in order to provide persistent storing and querying over large-scale fuzzy information. To accomplish this we first propose a novel way for serializing fuzzy information into RDF triples thus classical storing systems can be used without any extensions. Additionally, we extend the existing query languages of RDF stores in order to support expressive fuzzy queries proposed in the literature. These extensions are implemented through the FiRE fuzzy reasoning engine, which is a fuzzy DL reasoner for fuzzy-SHIN . Finally, the proposed architecture is evaluated using an industrial application scenario about casting for TV commercials and spots.