Kamath S?, S.S.Ananthanarayana, V.S.2026-02-052016International Journal of Data Science and Analytics, 2016, 2, 46054, pp. 61-742364415Xhttps://doi.org/10.1007/s41060-016-0026-xhttps://idr.nitk.ac.in/handle/123456789/25844With the emergence of the Programmable Web paradigm, the World Wide Web is evolving into a Web of Services, where data and services can be effectively reused across applications. Given the wide diversity and scale of published Web services, the problem of service discovery is a big challenge for service-based application development. This is further compounded by the limited availability of intelligent categorization and service management frameworks. In this paper, an approach that extends service similarity analysis by using morphological analysis and machine learning techniques for capturing the functional semantics of real-world Web services for facilitating effective categorization is presented. To capture the functional diversity of the services, different feature vector selection techniques are used to represent a service in vector space, with the aim of finding the optimal set of features. Using these feature vector models, services are classified as per their domain, using ensemble machine learning methods. Experiments were performed to validate the classification accuracy with respect to the various service feature vector models designed, and the results emphasize the effectiveness of the proposed approach. © 2016, Springer International Publishing Switzerland.Machine learningSemantic WebSemanticsVector spacesVectorsWebsitesClassification accuracyFeature vector selectionFunctional diversityMachine learning methodsMachine learning techniquesMorphological analysisService based applicationsWeb service classificationsWeb services