Facultad de Matematicas, Universidad Autonoma de Yucatan, Merida, Mexico

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Abstract—Web services (WS) are used in different environments like enterprises, government and industry, providing tools for implementing complex distributed systems. Web service discovery allows a system to find services that meet the requirements of the users. One way to improve this type of discovery would consider not only the functional aspects of the required service, but also relevant aspects such as performance or availability to perform its functions. Moreover, this approach would allow a more efficient discovery process to obtain results closer to the user needs. In this paper we present an approach for Web service discovery through the use of machine learning algorithms for classification of Web services. For Web services matching our proposal takes into account quality of service (QoS) parameters, which include semantic information about each Web service. For semantic Web service specification we use the SAWSDL standard. Whereas the proposed UDDI standard was discontinued, among other reasons, due to limited syntactic Web service discovery, our approach brings important elements to the consolidation of semantic Web service discovery.

Index Terms—Web services discovery, semantic Web services, non-functional properties, naïve bayes, support vector machines, adaboost

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Cite: Martha Varguez-Moo, Francisco Moo-Mena, and Victor Uc-Cetina, " Use of Classification Algorithms for Semantic Web Services Discovery," Journal of Computers vol. 8, no. 7, pp. 1810-1814, 2013.