Incorporating Heterogeneous Information for Mashup Discovery with Consistent Regularization.

With the development of service oriented computing, web mashups which provide composite services are increasing rapidly in recent years, posing a challenge for the searching of appropriate mashups for a given query. To the best of our knowledge, most approaches on service discovery are mainly based on the semantic information of services, and the services are ranked by their QoS values. However, these methods can't be applied to mashup discovery seamlessly, since they merely rely on the description of mashups, but neglecting the information of service components. Besides, those semantic based techniques do not consider the compositive structure of mashups and their components. In this paper, we propose an efficient consistent regularization framework to enhance mashup discovery by leveraging heterogeneous information network between mashups and their components. Our model also integrates mashup discovery and ranking properly. Comprehensive experiments have been conducted on a real-world ProgrammableWeb.com (http://www.programmableweb.com) dataset with mashups and APIs (In ProgrammableWeb.com, APIs are the service components of mashups. Our model verified on the ProgrammableWeb.com dataset could also be applied to other compositive service discovery scenarios.). Experimental results show that our model achieves a better performance compared with ProgrammableWeb.com search engine and a state-of-the-art semantic based model.