On the Combination of Textual and Semantic Descriptions for Automated Semantic Web Service Classification

Semantic Web services have emerged as the solution to the need for automating, several aspects related to service-oriented architectures, such as service discovery and composition, and they are realized by combining Semantic Web technologies and Web service standards. In the present paper, we tackle the problem of automated classification of Web services according to their application domain taking into account both the textual description and the semantic annotations of OWL-S advertisements. We present results that we obtained by applying machine learning algorithms on textual and semantic descriptions separately and we propose methods for increasing the overall classification accuracy through an extended feature vector and an ensemble of classifiers.