Semantic Approach for Classification of Web Services Using Unsupervised Normalized Similarity Measure

TLDR: An automatic approach for service categorization is proposed that uses a lexical semantic network constructed from the web snippets as a knowledge base for semantic similarity calculation between the service profiles and the categories and aims at alleviating the administrator's job by automatically providing them with a set of categories ranked based on the degree of semantic similarity.

Abstract: Automatic or semiautomatic categorization of web services facilitates the relevant service retrieval as well as it helps the administrators in attaining globally consistent classification decisions that are independent of the administrator’s knowledge of application domain, organization of taxonomies and service characteristics. Lack of automatic mechanisms to help service publishers in the classification task, irrelevant and huge number of services returned by the UDDI, and lack of standard mechanisms that helps in the discovery of desired web services are some of the issues that need focus. In this paper, an automatic approach for service categorization is proposed that uses a lexical semantic network constructed from the web snippets as a knowledge base for semantic similarity calculation between the service profiles and the categories. Our approach involves mapping of service profiles to a category based dimension vector by using the notion of semantic similarity and aims at alleviating the administrator’s job by automatically providing them with a set of categories ranked based on the degree of semantic similarity. Empirical evaluation on a set of OWL-S services shows that the proposed approach helps in better decisions for relevant classification of services by giving an ordered set of categories based on the similarity scores.