A Semantic Approach of Service Clustering and Web Service Discovery
Yu Yue Du,
Yong Jun Zhang
Xing Lin Zhang
Web service discovery has always been a hot issue in the research field of Web services. In this study, services are grouped into functionally similar service clusters through calculating semantic similarity with WordNet. A Concept Position Vector model of service clusters is proposed, which can sharply cuts in the number of services that do not completely match the service requests, thus can quickly build up the set of candidate services. Therefore, the time efficiency of the service discovery can be improved compared with the general cluster-based method of service discovery.
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