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

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.

1. Introduction

Traffic Information Service System is one of the key components in urban Intelligent Transportation Systems (ITS)^[1]. Several extremely important aspects describe the current problems relating to the access and distribution of information^[2]: (1)

A great amount of traffic information is distributed among different web sites.

(2)

It is only possible to obtain as a result information that has been explicitly detailed in advance, since it is no possible to make inferences.