Automatic tagging web services using machine learning techniques

Web services have become popular and increasingly important in e-business and e-commerce applications especially in large scale distributed systems. As a result, increasing number of web services has been developed. However, this abundance creates a vast collection of web services which makes the task of locating a suitable one more challenging and more difficult. Automatic clustering of web services groups together web services with similar functions. Clustering could greatly boost the power of web service search engines and generate tags to improve the search accuracy of tag-based service recommendation. In this paper, we propose a web service clustering technique based on Carrot search clustering and K-means to group similar services together to generate tags and we use naive bayes algorithm to classify web services. We also develop a tag-based service recommendation for WSDL documents. We demonstrate that the proposed clustering approach is effective for web service discovery.