A composite classification model for web services based on semantic & syntactic information integration

Abstract

Automatic and semi-automatic approaches for classification of web services have garnered much interest due to their positive impact on tasks like service discovery, matchmaking and composition. Currently, service registries support only human classification, which results in limited recall and low precision in response to queries, due to keyword based matching. The syntactic features of a service along with certain semantics based measures used during classification can result in accurate and meaningful results. We propose an approach for web service classification based on conversion of services into a class dependent vector by applying the concept of semantic relatedness and to generate classes of services ranked by their semantic relatedness to a given query. We used the OWLS-tc service dataset for evaluating our approach and the experimental results are presented in this work. � 2015 IEEE.