WSDL Information selection for improving web service classification

Abstract

Currently, the increasing number of available Web Services (WS) over the Internet has induced the urgency for proposing new ways for searching and categorizing such software pieces. Normally, WS functionality is detailed through the WSDL description language, resulting in a structured document that includes a great variety of features definition. One of the WSDL inner features ”documentation” is designed to describe the Web Service functionality, in natural language, which could help to classify and find WS. Nevertheless, the majority of WS lack of that description. To tackle this problem, this paper presents an analysis of the WSDL inner feature information that can assist to classify WS, without any extra data. The experiments carried out on three different WSDL collections showed that only with minimal information is possible to increase the performance of automatic WS classification.