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Modeling and exploiting tag relevance for Web service mining

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Abstract

Web service tags, i.e., terms annotated by users to describe the functionality or other aspects of Web services, are being treated as collective user knowledge for Web service mining. Since user tagging is inherently uncontrolled, ambiguous, and overly personalized, a critical and fundamental problem is how to measure the relevance of a user-contributed tag with respect to the functionality of the annotated Web service. In this paper, we propose a hybrid mechanism by using Web Service Description Language documents and service-tag network information to compute the relevance scores of tags by employing semantic computation and Hyperlink-Induced Topic Search model, respectively. Further, we introduce tag relevance measurement mechanism into three applications of Web service mining: (1) Web service clustering; (2) Web service tag recommendation; and (3) tag-based Web service retrieval. To evaluate the accuracy of tag relevance measurement and its impact to Web service mining, experiments are implemented based on Titan which is a Web service search engine constructed based on 15,968 real Web services. Comprehensive experiments demonstrate the effectiveness of the proposed tag relevance measurement mechanism and its active promotion to the usage of tagging data in Web service mining.

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Notes

  1. In this paper, we focus on non-semantic Web services. Non-semantic Web services are described by WSDL documents while semantic Web services use Web ontology languages (OWL-S) or Web Service Modeling Ontology (WSMO) as a description language. Non-semantic Web services are widely supported by both the industry and development tools.

  2. Statistics obtained from SeekDa! (a Web service search engine), http://webservices.seekda.com.

  3. USWeather’s WSDL Address: http://webservices.seekda.com/providers/webservicex.net/USWeather XigniteQuotes’s WSDL Address: http://webservices.seekda.com/providers/xignite.com/XigniteQuotes.

  4. Dataset can be downloaded from http://www.zjujason.com.

  5. Titan is constructed based on 15,968 real Web services, and it has been accepted by WWW 2012 Demo Track. Link to Titan: http://ccnt.zju.edu.cn:8080.

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Acknowledgments

This research was partially supported by the National Technology Support Program under Grant No. 2011BAH16B04, the National Natural Science Foundation of China under Grant No. 61173176, National High-Tech Research and Development Plan of China under Grant No. 2013AA01A604, the Shenzhen Basic Research Program (Project No. JCYJ20120619153834216, JC201104220300A), National Key Science and Technology Research Program of China (2009ZX01043-003- 003), and the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. CUHK 415311 of General Research Fund).

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Authors and Affiliations

  1. College of Computer Science, Zhejiang University, Hangzhou, China

    Liang Chen, Jian Wu & Zhaohui Wu

  2. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China

    Zibin Zheng & Michael R. Lyu

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  1. Liang Chen
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  2. Jian Wu
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  5. Zhaohui Wu
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Correspondence to Jian Wu.

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Chen, L., Wu, J., Zheng, Z. et al. Modeling and exploiting tag relevance for Web service mining. Knowl Inf Syst 39, 153–173 (2014). https://doi.org/10.1007/s10115-013-0703-1

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