Integrated semantic similarity model based on ontology

To solve the problem of the inadequacy of semantic processing in the intelligent question answering system, an integrated semantic similarity model which calculates the semantic similarity using the geometric distance and information content is presented in this paper. With the help of interrelationship between concepts, the information content of concepts and the strength of the edges in the ontology network, we can calculate the semantic similarity between two concepts and provide information for the further calculation of the semantic similarity between user’s question and answers in knowledge base. The results of the experiments on the prototype have shown that the semantic problem in natural language processing can also be solved with the help of the knowledge and the abundant semantic information in ontology. More than 90% accuracy with less than 50 ms average searching time in the intelligent question answering prototype system based on ontology has been reached. The result is very satisfied. Foundation item: Supported by the important science and technology item of China of “The 10^th Five-year Plan” (2001BA101A05-04) Biography: LIU Ya-jun (1953-), female, Associate professor, research direction: software engineering, information processing, data-base application.