Calculating web service similarity using ontology learning with machine learning

The Web is a popular, easy and common way to propagate information today and according to the growth of the Web, Web service discovery has become a challenging task. Clustering Web services into similar clusters through calculating the semantic similarity of Web services is one way for overcome this issue. Several methods are used for current similarity calculation process such as knowledge based, information-retrieval based, text mining, ontology based and context-aware based methods. Through this paper, present a method for calculating Web service similarity using both ontology learning and machine learning that uses a support vector machine for similarity calculation in generated ontology instead of edge count base method. Experimental results show that our hybrid approach of combining ontology learning and machine learning works efficiently and give accurate results than previous two approaches.