A ranking method for social-annotation-based service discovery

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 an...

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Bibliographic Details
Published in2011 IEEE 6th International Symposium on Service Oriented System pp. 114 - 121
Main Authors Duo Qu, Xudong Liu, Hailong Sun, Zicheng Huang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2011
Subjects
Machine learning
Ontologies
Semantic Web
Semantics
service discovery
social annotation
tag
Tagging
Vectors
Web service
Web services
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Summary: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.
ISBN:9781467304115
1467304115
DOI:10.1109/SOSE.2011.6139099