Web Service Categorization Using Normalized Similarity Score

Abstract

 Abstract—Service discovery is one of challenging issues in Service-Oriented computing. Currently, most of the existing service discovering and matching approaches are based on keywords-based strategy. However, this method is inefficient and time-consuming. Based on the current dominating mechanisms of discovering and describing Web services with UDDI and WSDL, a novel approach for Web service categorization is proposed, where WSDL's documentation tag is used as only means to describe information pertaining to the entire Web service's functionality which is used in conjunction with the current Web service standards, to automatically categorize a Web service into a one of the pre-defined categories. The words are extracted from WSDL of a Web service and Nearest Similarity Score (NSS), a Measure of Semantic Relatedness (MSR) of each word is calculated with every pre-defined category. Total value of all the words is calculated through the NSS and then Web service is assigned a category based on the sum of MSR of all the words provided in the Web service description tag. This work enables automatic semantic categorization of Web services.