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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.

References

  1. George Z, Athman B (2010) Web service mining. Springer, Berlin

    MATH  Google Scholar 

  2. Ames M, Naaman M (2007) Why we tag: motivations for annotation in mobile and online media. In: Proceedings of the SIGCHI conference on human factors in computing systems (CHI), pp 71–980

  3. Sigurbjrnsson B, van Zwol R (2008) Flickr tag recommendation based on collective knowledge. In: Proceedings of the 17th international conference on World Wide Web (WWW), pp 327–336

  4. Chen L, Hu L, Zheng Z, Wu J (2011) Wtcluster: utilizing tags for web services clustering. In: Proceedings of the 9th international conference on service oriented computing (ICSOC), pp 204–218

  5. Wu J, Chen L, Zheng Z, Lyu MR, Wu Z (2013) Clustering web services to facilitate service discovery. Int J Knowl Inf Syst (KAIS), p. to appear

  6. Averbakh A, Krause D, Skoutas D (2009) Exploiting user feedback to improve semantic web service discovery. In: International semantic web conference (ISWC), pp 33–48

  7. Hou J, Zhang J, Nayak R, Bose A (2012) Semantics-based web service discovery using information retrieval techniques. In: Comparative evaluation of focused retrieval, pp 336–346

  8. Bouillet E, Feblowitz M, Feng H, Liu Z, Ranganathan A, Riabov A (2008) A folksonomy-based model of web services for discovery and automatic composition. In: IEEE international conference on services computing, pp 389–396

  9. Kennedy LS, Chang S-F, Kozintsev IV (2006), To search or to label?: predicting the performance of search-based automatic image classifiers. In: Proceedings of the 8th ACM international workshop on multimedia information retrieval, pp 249–258

  10. Chen L, Zheng Z, Feng Y, Wu J, Lyu MR (2012), Wstrank: ranking tags to facilitate web service mining. In: Proceedings of the 10th international conference on service oriented computing, pp 574–581

  11. Li X, Snoek CG, Worring M (2008), Learning tag relevance by neighbor voting for social image retrieval. In: Proceedings of the 1st ACM international conference on multimedia information retrieval, pp 180–187

  12. Wu L, Yang L, Yu N (2009) Learning to tag. In: Proceedings of the 18st international conference companion on World Wide Web (WWW), pp 361–370

  13. Li L, Shang Y, Zhang W (2002) Improvement of hits-based algorithms on web documents. In: Proceedings of the 11th international World Wide Web conference, pp 527–535

  14. Dong X, Halevy A, Madhavan J, Nemes E, Zhang J (2004) Similarity search for web services. In: International conference on very large databases, pp 372–383

  15. Liu W, Wong W (2009) Web service clustering using text mining techniques. Int J Agent-Oriented Softw Eng 3(1):6–26

    Article  Google Scholar 

  16. Elgazzar K, Hassan AE, Martin P (2009) Clustering wsdl documents to bootstrap the discovery of web services. In: International conference on web services, pp 147–154

  17. Chukmol U, Benharkat A-N, Amghar Y (2011) Bringing socialized semantics into web services based on user-centric collaborative tagging and usage experience. In: IEEE Asia-Pacific services computing conference, pp 450–455

  18. Azmeh Z, My Falleri J-R, Huchard M, Tibermacine C (2011) Automatic web service tagging using machine learning and wordnet synsets. Lecture notes in business information processing 75(1):46–59

  19. Fang L, Wang L, Li M, Zhao J, Zou Y, Shao L (2012) Towards automatic tagging for web services. In: 19th IEEE international conference on web services, pp 528–535

  20. Katakis I, Pallis G, Dikaiakos MD, Onoufriou O (2012), Automated tagging for the retrieval of software resources in grid and cloud infrastructures In: 12th IEEE/ACM international symposium on cluster, cloud and grid computing, pp 628–635

  21. Ding Z, Lei D, Yan J, Bin Z, Lun A (2010) A web service discovery method based on tag. In: International conference on complex, intelligent and software intensive systems, pp 404–408

  22. Fernandez A, Hayes C, Loutas N, Peristeras V (2008) Closing the service discovery gap by collaborative tagging and clustering techniques. In: International workshop on service matchmaking and resource retrieval in the Semantic Web, pp 115–128

  23. Nayak R (2008) Data mining in web service discovery and monitoring. Int J Web Serv Res 5(1):62–80

    Article  Google Scholar 

  24. Porter MF (1980) An algorithm for suffix stripping. Program 14(3):130–137

    Article  Google Scholar 

  25. Church K, Gale W (1995) Inverse document frequency (idf): a measure of deviations from poisson. In: Proceedings of the ACL 3rd workshop on very large corpora, pp 121–130

  26. Cilibrasi RL, Vitnyi PMB (2007) The google similarity distance. IEEE Trans Knowl Data Eng 19(3):370–383

    Article  Google Scholar 

  27. Kleinberg JM (1999) Hubs, authorities, and communities. ACM Comput Surv 31(4):1–3

    MathSciNet  Google Scholar 

  28. Arvelin KJ, Kekalainen J (2002) Cumulated gain-based evaluation of IR techniques. ACM Trans Inf Syst 20(4):422–446

    Article  Google Scholar 

  29. Wu J, Chen L, Xie Y, Zheng Z (2012) Titan: a system for effective web service discovery. In: Proceedings of the 21st international conference companion on World Wide Web (WWW), pp 441–444

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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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  3. Zibin Zheng
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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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