Semantic approach for Web service classification using machine learning and measures of semantic relatedness

Abstract

Web service classification, a task of assigning a category to the service from a predefined set, is a challenging task nowadays as manually organizing and searching services are simply not feasible, given the time constraints or the exponentially growing number of services. In this paper, a hybrid approach independent of service description models is suggested for automatic classification of Web services to improve classification accuracy. The proposed classification approach assists the repository administrator and the users during registration and service retrieval, respectively. It utilizes the semantic as well as syntactic information present within the service description by combining the techniques from machine learning, data mining, logical reasoning, statistical methods and measures of semantic relatedness. The proposed approach applies Omiotis measure of semantic relatedness to transform the service vectors into semantically enriched service vectors which are used by the classification algorithms. Supervised machine learning-based support vector machines and k-Nearest Neighbor classifiers are used to categorize service profiles under different categories. Empirical evaluation and comparison of the proposed approach implemented on OWL-X dataset is presented for enabling the discovery and reusability of the existing services.