An ontology-based method for similarity calculation of concepts in the semantic web

In the semantic web, evaluating the semantic similarity between concepts in a same ontology is a central component of techniques such as clustering, data-mining, semantic sense disambiguation, ontology translations, automatic database schema matching, and simple object comparison. Traditionally, the distance based approach and the information content based approach are the two major methods. In this paper, on the basis of analyzing these previous approaches, a new method based on hierarchy information content and attribute information content is provided and a similarity calculating algorithm, HIC-AIC, based on this theory is presented. In terms of theoretical analysis and experiments, the new approach obtains higher accuracy in calculating the semantic similarity between concepts.