Accelerated Sparse Learning on Tag Annotation for Web Service Discovery

Learning latent features of Web services will greatly boost the ability of search engine to discover relevant services. Extracted information from Web Service Description Language (WSDL) documents of services is less efficient due to the limited usage of data source. Recently, a number of ongoing works have indicated incorporating service tag, a textual symbol provides additional contextual and semantic information, helps to enhance the process of service discovery. However, a large number of relevant tags for Web services are difficult to obtain in practice. In this paper, we propose a Web service Tag Learning system to address this issue. WT Learning system adopts sparse learning technique to fully understand the structure of high dimensional textual information extracted from WSDL documents and tags. Meanwhile, our proposed system implements Alternative Direction Method of Multiplier (ADMM) strategy, which accelerates solving process in Big Data environment. Extensive experiments are conducted based on real-world dataset, which consists of 24,569 Web services. The results demonstrate the effectiveness of WT Learning system. Specifically, our system outperforms other state-of-the-art frameworks in tag classification and recommendation tasks, with 29.6% and 27.1% performance gaining respectively.