Abstract
The resources in the Semantic Web are described using particular metadata called “Semantic annotations”. A semantic annotation is a particular case of annotation which refers to ontology. The Web content is, for the most part, subject to uncertainty or imperfection. FSAQL is a fuzzy query language proposed to query semantic annotations defined with fuzzy RDFS. The efficiency of Datalog systems to query large amount of data has been proven in the literature. We propose in this paper an efficient Datalog based approach for the evaluation of the FSAQL query language. The particularity of our approach consists on the fact that we use crisp Datalog programs instead of fuzzy ones. In fact, there is no known implementation of fuzzy Datalog systems and the use of crisp Datalog allows the interporability of our query language. Two approaches have been proposed for a correct mapping of fuzzy RDFS to crisp Datalog programs. The defuzzification approach defines crisp “α −cut” classes and properties and maps them to crisp Datalog predicates. The skolemisation approach represents fuzzy classes and properties with crisp Datalog predicates having the same names. The membership degrees are then defined as terms of theses predicates. The two approaches are implemented and evaluated using the \(\mathcal{F}\)lora-2 Datalog system.
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Bahri, A., Bouaziz, R., Gargouri, F. (2013). Towards an Efficient Datalog Based Evaluation of the FSAQL Query Language. In: Lacroix, Z., Ruckhaus, E., Vidal, ME. (eds) Resource Discovery. RED 2012. Lecture Notes in Computer Science, vol 8194. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45263-5_7
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DOI: https://doi.org/10.1007/978-3-642-45263-5_7
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