OWSDR: An Ontology-based Web Service Discovery and Selection System

With the rapid growth of Web of Things (WoT) and corresponding Web services, there is a pressing need to develop an effective computational method for services discovery and recommendation. Despite personalized services discovery methods have been studied before, few attempts have been made to explore ontological user profiling and probabilistic language modeling approach for Web service contextualization and ranking. This paper makes a novel contribution in terms of developing an ontology-based user profiling method to improve the Web service discovery and recommendations. In particular, a novel probabilistic language model is developed to conduct Web service contextualization and ranking. Our preliminary experimental results reveal that the proposed service personalization approach outperforms a classical baseline method.