Efficient clustering index for semantic Web service based on user preference

Abstract

A great deal of Web services exist in Web environment. Web services matchmaking based on semantic can improve accuracy of service discovery, and discover similar web services allowing users to have more choices. Because of complicated semantic calculation, the reaction rate of Web service matchmaking was slow. In the process of semantic Web service matchmaking, a large amount of semantic calculation exited in function matching phase. With features of the ontology and user preferences, this paper propose an optimized method of semantic Web services matching with efficient index, which includes the creation of efficient index based on entity clustering index and an efficient algorithm for discovering in each cluster. Finally, the proposed method was proved to be feasible and rational via an instance experiment. It can reduce semantic calculation and promote reaction rate by filtering some irrelevant Web services. Furthermore, the experiences of users can be improved.