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Clustering Web services to facilitate service discovery

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Abstract

Clustering Web services would greatly boost the ability of Web service search engine to retrieve relevant services. The performance of traditional Web service description language (WSDL)-based Web service clustering is not satisfied, due to the singleness of data source. Recently, Web service search engines such as Seekda! allow users to manually annotate Web services using tags, which describe functions of Web services or provide additional contextual and semantical information. In this paper, we cluster Web services by utilizing both WSDL documents and tags. To handle the clustering performance limitation caused by uneven tag distribution and noisy tags, we propose a hybrid Web service tag recommendation strategy, named WSTRec, which employs tag co-occurrence, tag mining, and semantic relevance measurement for tag recommendation. Extensive experiments are conducted based on our real-world dataset, which consists of 15,968 Web services. The experimental results demonstrate the effectiveness of our proposed service clustering and tag recommendation strategies. Specifically, compared with traditional WSDL-based Web service clustering approaches, the proposed approach produces gains in both precision and recall for up to 14 % in most cases.

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Notes

  1. http://webservices.seekda.com.

  2. http://www.premis.cz/PremisWS/MeteorologyWS.asmx?WSDL.

  3. http://rpc.test.sunnycars.com/CarRentalAgentService121/CarRentalAgentService.asmx?wsdl.

  4. http://www.w3.org/Submission/OWL-S/.

  5. http://www.w3.org/Submission/WSMO/.

  6. http://db.cs.washington.edu/woogle.html.

  7. http://ws.cdyne.com/WeatherWS/Weather.asmx?wsdl.

  8. http://gordan.gorodok.net/sharpcast.wsdl.

  9. STag Dataset 1.0, collected by Liang Chen (cliang@zju.edu.cn) and Johnny Jian (johnnyjian@gmail.com).

  10. http://ccnt.zju.edu.cn:8080.

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Acknowledgments

This research was partially supported by the National Technology Support Program under the grant of 2011BAH16B04, the National Natural Science Foundation of China under the grant of No. 61173176, Science and Technology Program of Zhejiang Province under the grant of 2008C03007, National High-Tech Research and Development Plan of China under the Grant No. 2011AA010501, and a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. CUHK 415409).

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Authors and Affiliations

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

    Jian Wu, Liang Chen & Zhaohui Wu

  2. Department of Computer Science and Engineering, Shenzhen Research Institute, The Chinese University of Hong Kong, New Territories, Hong Kong

    Zibin Zheng & Michael R. Lyu

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  1. Jian Wu
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  2. Liang Chen
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  3. Zibin Zheng
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  4. Michael R. Lyu
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  5. Zhaohui Wu
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Correspondence to Jian Wu.

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Wu, J., Chen, L., Zheng, Z. et al. Clustering Web services to facilitate service discovery. Knowl Inf Syst 38, 207–229 (2014). https://doi.org/10.1007/s10115-013-0623-0

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