Enhanced Semantic Web Service Discovery Using Machine Learning on Mapped WSMO Services

摘要

Web services were traditionally defined using the Web Service Description Language (WSDL) as it was the World Wide Web Consortium?s standard recommendation for web service description. Later on, with the advent of knowledge representation using ontologies, more and more services were designed using ontological models, more specifically the Sematic Mark-up for Web Ontology Language (OWL-S). And to make use of the huge number of existing web service repositories, a mapping from WSDL to OWL-S was created. But, OWL-S was just an extension of OWL and required additional support such as external Rule Languages in order to bring out the best of its semantic capabilities. Another better ontological knowledge representation model, the Web Service Modeling Ontology (WSMO), is an entirely conceptual model having its own semantic framework for handling web services, but lacks the extensibility of OWL-S. Therefore, a model for mapping WSDL to WSMO is proposed. And thereafter the automated semantic web service discovery mechanism, for WSMO based web services, is to be enhanced using machine learning algorithm.