Fast Context-Annotated Classification of Different Types of Web Service Descriptions
This work addresses the need for efficient web service classification in repositories to enhance discovery for client applications, though it is incremental as it builds on existing methods with contextual enhancements.
The paper tackled the problem of classifying web service descriptions (WSDL, REST, WADL) to improve service discovery and composition, achieving high precision by employing machine learning and signal processing algorithms, with contextual information boosting accuracy across five service categories.
In the recent rapid growth of web services, IoT, and cloud computing, many web services and APIs appeared on the web. With the failure of global UDDI registries, different service repositories started to appear, trying to list and categorize various types of web services for client applications' discover and use. In order to increase the effectiveness and speed up the task of finding compatible Web Services in the brokerage when performing service composition or suggesting Web Services to the requests, high-level functionality of the service needs to be determined. Due to the lack of structured support for specifying such functionality, classification of services into a set of abstract categories is necessary. We employ a wide range of Machine Learning and Signal Processing algorithms and techniques in order to find the highest precision achievable in the scope of this article for the fast classification of three type of service descriptions: WSDL, REST, and WADL. In addition, we complement our approach by showing the importance and effect of contextual information on the classification of the service descriptions and show that it improves the accuracy in 5 different categories of services.