An intelligent information retrieval approach based on two degrees of uncertainty fuzzy ontology
Advances in Fuzzy Systems - Special issue on Fuzzy Functions, Relations, and Fuzzy Transforms: Theoretical Aspects and Applications to Fuzzy Systems
Semantic infrastructure system with feedback: application on traffic
Proceedings of the 6th Euro American Conference on Telematics and Information Systems
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Information retrieval is the essential task for Traffic Information Service System in Intelligent Transportation Systems (ITS). There a lot of fuzzy traffic information derived from human factor. To achieve fuzzy semantic retrieval, this paper proposes an approach using Resource Description Framework (RDF) and fuzzy ontology. First, we apply RDF data model to represent traffic information on the Semantic Web. Then we present fuzzy linguistic variable ontology models and its formal representation with RDF. Introducing new data type referred as fuzzy linguistic variables to RDF data model, the semantic query expansion in SeRQL query language is constructed by order relation, equivalence relation, inclusion relation, and complement relation between fuzzy concepts defined in linguistic variable ontologies. Examples show that this research facilitates the semantic retrieval of traffic information through fuzzy concepts for ITS on the Semantic Web.