A logistic regression model for Semantic Web service matchmaking

Semantic Web service matchmaking, as one of the most challenging problems in Semantic Web services (SWS), aims to filter and rank a set of services with respect to a service query by using a certain matching strategy. In this paper, we propose a logistic regression based method to aggregate several matching strategies instead of a fixed integration (e.g., the weighted sum) for SWS matchmaking. The logistic regression model is trained on training data derived from binary relevance assessments of existing test collections, and then used to predict the probability of relevance between a new pair of query and service according to their matching values obtained from various matching strategies. Services are then ranked according to the probabilities of relevance with respect to each query. Our method is evaluated on two main test collections, SAWSDL-TC2 and Jena Geography Dataset(JGD). Experimental results show that the logistic regression model can effectively predict the relevance between a query and a service, and hence can improve the effectiveness of service matchmaking.