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

TL;DR

This paper presents a fast, context-aware machine learning approach for classifying web service descriptions (WSDL, REST, WADL) into categories, enhancing accuracy and efficiency for service discovery.

Contribution

It introduces a novel classification method that incorporates contextual information, significantly improving accuracy across five service categories compared to previous approaches.

Findings

Contextual information improves classification accuracy by 5 categories.

High precision achieved in classifying WSDL, REST, and WADL descriptions.

Machine learning techniques effectively categorize web services for better discovery.

Abstract

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.