Leveraging Formal Concept Analysis with Topic Correlation for Service Clustering and Discovery

With a growing number of web services, discovering services that can match with a user's query becomes a challenging task. It's very tedious for a service consumer to select the appropriate one according to her/his needs. In this paper, we propose a non-logic-based matchmaking approach that uses the Correlated Topic Model (CTM) to extract topic from semantic service descriptions and model the correlation between the extracted topics. Based on the topic correlation, service descriptions can be grouped into hierarchical clusters. In our approach, we use the Formal Concept Analysis (FCA) formalism to organize the constructed hierarchical clusters into concept lattices according to their topics. Thus, service discovery may be achieved more easily using the concept lattice. In our approach, topic models are used as efficient dimension reduction techniques, which are able to capture semantic relationships between word-topic and topic-service interpreted in terms of probability distributions. In our experiment, we compared the accuracy of the our hierarchical clustering algorithm with that of a classical hierarchical agglomerative clustering. The comparisons of Precision@n and Normalised Discounted Cumulative Gain (NDCGn) values for our approach, Apache lucene and SAWSDL-MX2 Matchmaker indicate that the method based on CTM presented in this paper outperform all the others matchmakers in terms of ranking of the most relevant services.