Abstract

With the rapid growth of Web services, service discovery becomes an important and difficult issue. Traditional UDDI-based and WSDL-based methods of service discovery have low precision, and semantic-based service discovery methods are usually inefficient and time-consuming. We observe that social annotations can optimize both precision and efficiency of service discovery. In this paper, we propose a social-annotation-based service discovery method by using a learning to rank method, and propose two algorithms, Query Annotation Relevance (QAR) and Service Annotation Ranking (SAR), to calculate the dynamic Query-dependent feature and the static Query-independent feature respectively. Our experiments show that our method is effective for improving service discovery performance.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.