Mining and clustering service goals for RESTful service discovery

摘要

In recent years, RESTful services that are mainly described using short texts are becoming increasingly popular. The keyword-based discovery technology adopted by existing service registries usually suffers from low recall and is insufficient to retrieve accurate RESTful services according to users’ functional goals. Moreover, it is often difficult for users to specify queries that can precisely represent their requirements due to the lack of knowledge on their desired service functionalities. Toward these issues, we propose a RESTful service discovery approach by leveraging service goal (i.e., service functionality) knowledge mined from services’ textual descriptions. The approach first groups the available services into clusters using probabilistic topic models. Then, service goals are extracted from the textual descriptions of services and also clustered based on the topic modeling results of services. Based on service goal clusters, we design a mechanism to recommend semantically relevant service goals to help users refine their initial queries. Relevant services are retrieved by matching user selected service goals with those of candidate services. To improve the recall of the goal-based service discovery approach, we further propose a hybrid approach by integrating it with two existing service discovery approaches. A series of experiments conducted on real-world services crawled from a publicly accessible registry, ProgrammableWeb, demonstrate the effectiveness of the proposed approaches.