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Common Topic Group Mining for Web Service Discovery

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Advances in Services Computing (APSCC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9464))

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Abstract

Recent years have witnessed an increasing number of services published on the Internet. How to find suitable services according to user queries remains a challenging issue in the services computing field. Many prior studies have been reported towards this direction. In this paper, we propose a novel service discovery approach by mining and matching common topic groups. In our approach, we mine the common topic groups based on the service-topic distribution matrix generated by topic modeling, and the extracted common topic groups can then be leveraged to match user queries to relevant services, so as to make a better trade-off between the number of available services and the accuracy of service discovery. The results of experiments conducted on a publicly available data set show that compared with other widely used methods, our approach can improve the performance of service discovery by decreasing the number of candidate services.

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Notes

  1. 1.

    http://www.programmableweb.com/

  2. 2.

    http://www.nltk.org/

  3. 3.

    http://www.semwebcentral.org/projects/sawsdl-tc

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Acknowledgements

The work is supported by the National Basic Research Program of China under grant No. 2014CB340404, the National Natural Science Foundation of China under grant Nos. 61202031, 61272111, and 61373037, and the central grant funded Cloud Computing demonstration project of China undertaken by Kingdee Software (China).

Author information

Authors and Affiliations

  1. State Key Laboratory of Software Engineering, Wuhan University, Wuhan, 430072, China

    Jian Wang, Panpan Gao, Yutao Ma & Keqing He

Authors
  1. Jian Wang
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  2. Panpan Gao
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  3. Yutao Ma
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  4. Keqing He
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Corresponding author

Correspondence to Yutao Ma .

Editor information

Editors and Affiliations

  1. School of Computer Sci,Level 4, University of Adelaide, Adelaide, South Australia, Australia

    Lina Yao

  2. School of Computer Cience & Technol, Huazhong University of Science &, Wuhan, China

    Xia Xie

  3. School of Software Technology, Dalian University of Technology, Dalian, China

    Qingchen Zhang

  4. Department of Computer Science, St. Francis Xavier University, Antigonish, Nova Scotia, Canada

    Laurence T. Yang

  5. School of Information Technologies, University of Sydney, Sydney, New South Wales, Australia

    Albert Y. Zomaya

  6. School of Comp. Science and Technology, Huazhong Univ. of Science and Technology, Wuhan, China

    Hai Jin

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Wang, J., Gao, P., Ma, Y., He, K. (2015). Common Topic Group Mining for Web Service Discovery. In: Yao, L., Xie, X., Zhang, Q., Yang, L., Zomaya, A., Jin, H. (eds) Advances in Services Computing. APSCC 2015. Lecture Notes in Computer Science(), vol 9464. Springer, Cham. https://doi.org/10.1007/978-3-319-26979-5_7

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  • DOI: https://doi.org/10.1007/978-3-319-26979-5_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26978-8

  • Online ISBN: 978-3-319-26979-5

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