A Hybrid-Sorting Semantic Matching Method

Semantic Web Service enables automated service discovery, and even execution. Discovering proper services which match the given request has become an important issue as Internet technology develops and service demands increase. Existing service matching methods in service discovering always cost too much time for obtaining precision and recall ratio. This paper proposes a semantic similarity computing algorithm which considers both information capacity and ontology concept distance. Based on this similarity computing algorithm, we present a hybrid-sorting web service matching method. Compare with other methods like OWLS-MX by experiments, our method reduce average query time and we even improve recall ratio and precision ratio. In addition, this method supports service composition, another important issue in service discovery.