Semantic Web service discovery using natural language processing techniques

This paper proposes a semantic Web service discovery framework for finding semantic Web services by making use of natural language processing techniques. The framework allows searching through a set of semantic Web services in order to find a match with a user query consisting of keywords. By specifying the search goal using keywords, end-users do not need to have knowledge about semantic languages, which makes it easy to express the desired semantic Web services. For matching keywords with semantic Web service descriptions given in WSMO, techniques like part-of-speech tagging, lemmatization, and word sense disambiguation are used. After determining the senses of relevant words gathered from Web service descriptions and the user query, a matching process takes place. The performance evaluation shows that the three proposed matching algorithms are able to effectively perform matching and approximate matching.

► We propose the semantic Web service discovery framework for WSMO services. ► It finds Web services by matching query keywords and service descriptions. ► Natural language processing techniques are employed for parsing descriptions. ► A genetic algorithm is used to learn the importance of description features. ► We evaluate three matching algorithms for exact and approximate matching.