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In the recent rapid growth of web services, IoT, and cloud computing, manyweb services and APIs appeared on the web. With the failure of global UDDIregistries, different service repositories started to appear, trying to listand categorize various types of web services for client applications' discoverand use. In order to increase the effectiveness and speed up the task offinding compatible Web Services in the brokerage when performing servicecomposition or suggesting Web Services to the requests, high-levelfunctionality of the service needs to be determined. Due to the lack ofstructured support for specifying such functionality, classification ofservices into a set of abstract categories is necessary. We employ a wide rangeof Machine Learning and Signal Processing algorithms and techniques in order tofind the highest precision achievable in the scope of this article for the fastclassification of three type of service descriptions: WSDL, REST, and WADL. Inaddition, we complement our approach by showing the importance and effect ofcontextual information on the classification of the service descriptions andshow that it improves the accuracy in 5 different categories of services.

Fast Context-Annotated Classification of Different Types of Web Service Descriptions