Skip to main content
Springer Nature Link
Log in

Common Topic Group Mining for Web Service Discovery

  • Conference paper
  • First Online:
Advances in Services Computing (APSCC 2015)

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

Included in the following conference series:

  • 973 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    http://www.programmableweb.com/

  2. 2.

    http://www.nltk.org/

  3. 3.

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

References

  1. Pantazoglou, M., Tsalgatidou, A.: A generic query model for the unified discovery of heterogeneous services. IEEE Trans. Serv. Comput. 6, 201–213 (2013)

    Article  Google Scholar 

  2. Yu, Q., Liu, X., Bouguettaya, A., Medjahed, B.: Deploying and managing web services: issues, solutions, and directions. VLDB J. 17, 537–572 (2008)

    Article  Google Scholar 

  3. Paliwal, A.V., Shafiq, B., Vaidya, J., Xiong, H., Adam, N.: Semantics-based automated service discovery. IEEE Trans. Serv. Comput. 5, 260–275 (2012)

    Article  Google Scholar 

  4. Klusch, M., Kapahnke, P., Zinnikus, I.: Hybrid adaptive web service selection with SAWSDL-MX and WSDL-analyzer. In: Aroyo, L., Traverso, P., Ciravegna, F., Cimiano, P., Heath, T., Hyvönen, E., Mizoguchi, R., Oren, Eyal, Sabou, M., Simperl, E. (eds.) ESWC 2009. LNCS, vol. 5554, pp. 550–564. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  5. Wang, J., Zeng, C., He, C., et al.: Context-aware role mining for mobile service recommendation. In: 27th Annual ACM Symposium on Applied Computing, pp. 173–178. ACM Press, New York (2012)

    Google Scholar 

  6. Yao, L., Mimno, D., McCallum, A.: Efficient methods for topic model inference on streaming document collections. In: 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 937–946. ACM Press, New York (2009)

    Google Scholar 

  7. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

    MATH  Google Scholar 

  8. Wei, X., Croft, W.B.: LDA-based document models for ad-hoc retrieval. In: 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 178–185. ACM Press, New York (2006)

    Google Scholar 

  9. Vaidya, J., Atluri, V., Guo, Q.: The role mining problem: finding a minimal descriptive set of roles. In: 12th ACM Symposium on Access Control Models and Technologies, pp. 175–184. ACM Press, New York (2007)

    Google Scholar 

  10. Klusch, M., Fries, B., Sycara, K.: OWLS-MX: a hybrid semantic web service matchmaker for OWL-S services. J. Web Semant. 7, 121–133 (2009)

    Article  Google Scholar 

  11. Klusch, M., Kaufer, F.: WSMO-MX: a hybrid semantic web service matchmaker. Web Intell. Agent Syst. 7, 23–42 (2009)

    Google Scholar 

  12. Mohebbi, K., Ibrahim, S., Khezrian, M., et al.: A comparative evaluation of semantic web service discovery approaches. In: 12th International Conference on Information Integration and Web-based Applications and Services, pp. 33–39. ACM Press, New York (2010)

    Google Scholar 

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

    Article  Google Scholar 

  14. Chen, L., Wang, Y., Yu, Q., Zheng, Z., Wu, J.: WT-LDA: user tagging augmented LDA for web service clustering. In: Basu, S., Pautasso, C., Zhang, L., Fu, X. (eds.) ICSOC 2013. LNCS, vol. 8274, pp. 162–176. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  15. Aznag, M., Quafafou, M., Jarir, Z.: Leveraging formal concept analysis with topic correlation for service clustering and discovery. In: 2014 IEEE International Conference on Web Services, pp. 153–160. IEEE Press, New York (2014)

    Google Scholar 

  16. Blei, D.M., Lafferty, J.D.: Correlated topic models. Adv. Neural Inf. Process. Syst. 18, 147–154 (2006)

    Google Scholar 

  17. Cheng, X., Yan, X., Lan, Y., Guo, J.: BTM: topic modeling over short texts. IEEE Trans. Knowl. Data Eng. 26, 2928–2941 (2014)

    Article  Google Scholar 

  18. Chu, V.W., Wong, R.K., Chi, C.H.: Online role mining without over-fitting for service recommendation. In: 20th IEEE International Conference on Web Services, pp. 58–65. IEEE Press, New York (2013)

    Google Scholar 

  19. Wong, R.K., Chu, V.W., Hao, T.: Online role mining for context-aware mobile service recommendation. Pers. Ubiquit. Comput. 18, 1029–1046 (2014)

    Article  Google Scholar 

  20. Bellur, U., Kulkarni, R.: Improved matchmaking algorithm for semantic web services based on bipartite graph matching. In: 2007 IEEE International Conference on Web Service, pp. 86–93. IEEE Press, New York (2007)

    Google Scholar 

  21. Wu, J., Chen, L., Zheng, Z., Lyu, M.R., Wu, Z.: Clustering web services to facilitate service discovery. Int. J. Knowl. Inf. Syst. 38, 207–229 (2014)

    Article  Google Scholar 

  22. Elgazzar, K., Hassan, A.E., Martin, P.: Clustering WSDL documents to bootstrap the discovery of web services. In: 2009 IEEE International Conference on Web Services, pp. 147–154. IEEE Press, New York (2009)

    Google Scholar 

  23. Wang, J., Zhang, N., Zeng, C., Li, Z., He, K.Q.: Towards services discovery based on service goal extraction and recommendation. In: 2013 IEEE International Conference on Services Computing, pp. 65–72. IEEE Press, New York (2013)

    Google Scholar 

  24. Chen, Z., Liu, B.: Mining topics in documents: standing on the shoulders of big data. In: 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1116–1125. ACM Press, New York (2014)

    Google Scholar 

  25. Aznag, M., Quafafou, M., Jarir, Z.: Correlated topic model for web services ranking. Int. J. Adv. Comput. Sci. Appl. 4, 283–291 (2013)

    Google Scholar 

  26. Yu, Q.: Place semantics into context: service community discovery from the WSDL corpus. In: Kappel, G., Maamar, Z., Motahari-Nezhad, H.R. (eds.) ICSOC 2011. LNCS, vol. 7084, pp. 188–203. Springer, Heidelberg (2011)

    Google Scholar 

Download references

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
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Panpan Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yutao Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Keqing He
    View author publications

    You can also search for this author in PubMed Google Scholar

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

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

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

Download citation

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

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26978-8

  • Online ISBN: 978-3-319-26979-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics