Predicting Similarity of Web Services Using WordNet

Abstract

As vast numbers of web services have been developed over a broad range of functionalities, it becomes a challenging task to find relevant or similar web services using web services registry such as UDDI. Current UDDI search uses keywords from web service and company information in its registry to retrieve web services. This method cannot fully capture user’s needs and may miss out on potential matches. Underlying functionality and semantics of web services need to be considered. This chapter introduces a methodology for predicting similarity of web services by integrating hierarchical clustering, nearest neighbor classification, and algorithms for natural language processing using WordNet. It can be used to facilitate the development of intelligent applications for retrieving web services with imprecise or vague requests. The authors explore semantics of web services using WSDL operation names and parameter names along with WordNet. They compute semantic interface similarity of web services and use this data to generate clusters. Then, they represent each cluster by a set of characteristic operations to predict similarity of new web services using nearest neighbor approach. The empirical result is promising.