Fuzzy Semantic Retrieval for Traffic Information Based on Fuzzy Ontology and RDF on the Semantic Web

Abstract

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 expansions in SeRQL query language are constructed by order relation, equivalence relation, inclusion relation and complement relation between fuzzy concepts defined in linguistic variable ontologies. Examples show that the extended query can return all results which satisfy research requirement at semantic level without upgrading current main search algorithm, and this research facilitates the semantic retrieval of traffic information through fuzzy concepts for ITS on the Semantic Web.