Ontology learning method for Web services clustering

Web service discovery is becoming a challenging and time consuming task due to large number of Web services available on the Internet. Organizing the Web services into functionally similar clusters is one of a very efficient approach for reducing the search space. To cluster Web services, take out the Web services description languages documents and extract the features (e.g., service name) to measure the similarities. Complex terms are used as Web service features in some contexts. Current approaches do not consider about the hidden semantic pattern exists within the complex terms. We present an approach to cluster the Web services into functionally similar Web service clusters that mine Web Service Description Language (WSDL) documents and generate ontologies by using complex terms for the measuring purpose of similarity. We use both logic based reasoning and edge base similarity measuring techniques for calculating the similarity using generated ontology. Experimental results show our clustering approach with ontology learning, has better performance comparing with approaches which are not considering about the latent pattern exists within the complex terms.