Effective Mashup Service Clustering Method by Exploiting LDA Topic Model from Multiple Data Sources

  • Buqing CaoEmail author
  • Xiaoqing (Frank) Liu
  • Jianxun Liu
  • Mingdong Tang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9464)


Mashup is emerging as a promising software development method for allowing software developers to compose existing Web APIs to create new or value-added composite Web services. However, the rapid growth in the number of available Mashup services makes it difficult for software developers to select a suitable Mashup service to satisfy their requirements. Even though clustering based Mashup discovery technique shows a promise of improving the quality of Mashup service discovery, Mashup service clustering with high accuracy for discovery of Mashup services is still a challenge problem. In this paper, we propose a novel Mashup service clustering method for Mashup service discovery with high accuracy by exploiting LDA topic model built from multiple data sources. It enables to infer topic probability distribution of Mashup services, which serves as a basis of computation of similarity of Mashup services. K-means and Agnes algorithm are used to perform Mashup service clustering in terms of their similarities. Compared with other service clustering approaches, experimental results show that our approach achieves significant improvement in terms of precision, recall and F-measure rate, which will improve Mashup service discovery.


Mashup service LDA topic model Multiple data source Service clustering 



The work was supported by National Natural Science Foundation of China under grant No. 61402168, 61402167 and 61272063, State Key Laboratory of Software Engineering (SKLSE) of China (Wuhan University) under grant No. SKLSE2014-10-10, and Scientific Research Fund of Hunan Provincial Education Department under grant 11C0689 and 11C0535.


  1. 1.
    Xia, B., Fan, Y., Tan, W., Huang, K., Zhang, J., Wu, C.: Category-aware API clustering and distributed recommendation for automatic Mashup creation. IEEE Trans. Serv. Comput. doi: 10.1109/TSC.2014.2379251 (preprinted)
  2. 2.
    Cao, B., Liu, J., Tang, M., Zheng, Z., Wang, G.: Mashup service recommendation based on usage history and service network. Int. J. Web Serv. Res. 10(4), 82–101 (2013)CrossRefGoogle Scholar
  3. 3.
    Liu, W., Liu, W.: Web service clustering using text mining techniques. Int. J. Agent-Oriented Softw. Eng. 3(1), 6–26 (2009)CrossRefGoogle Scholar
  4. 4.
    Liu, W., Liu, W.: Discovering homogeneous service communities through web service clustering. Serv.-Oriented Comput. Agents Semant. Eng. 5006, 69–82 (2008)CrossRefGoogle Scholar
  5. 5.
    Sun, P., Jiang, C.: Using service clustering to facilitate process-oriented semantic web service discovery. J. Comput. 31(8), 1340–1353 (2008). (In Chinese)Google Scholar
  6. 6.
    Platzer, C., Rosenberg, F., Dustdar, S.: Web service clustering using multidimensional angles as proximity measures. ACM Trans. Internet Technol. 9(3), 1–26 (2009)CrossRefGoogle Scholar
  7. 7.
    Chen, L., Hu, L., Wu, J., Zheng, Z., Ying, J., Li, Y., Deng, S.: Wtcluster: utilizing tags for web service clustering. In: Proceedings of International Conference on Service-oriented Computing, pp. 204–218, Paphos, Cyprus (2011)Google Scholar
  8. 8.
    Wu, J., Chen, L., Zheng, Z., Lyu, R., Wu, Z.: Clustering Web services to facilitate service discovery. Knowl. Inf. Syst. 38(1), 207–229 (2014)CrossRefGoogle Scholar
  9. 9.
    Wu, J., Chen, L., Xie, Y., Zheng, Z.: Titan: a system for effective web service discovery. In: Proceedings of the 21st International Conference on World Wide Web, pp. 441–444. ACM, New York, USA (2012)Google Scholar
  10. 10.
    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)CrossRefGoogle Scholar
  11. 11.
    Li, Z., Wang, J., Zhang, N., Li, Z., He, C., He, K.: A topic-oriented clustering approach for domain services. J. Comput. Res. Dev. 51(2), 408–419 (2014). (In Chinese)Google Scholar
  12. 12.
    Yang, H., Chen, J., Meng, X., Qiu, B.: Dynamically traveling web service clustering based on spatial and temporal aspects. In: Hainaut, J.-L., Rundensteiner, E.A., Kirchberg, M., Bertolotto, M., Brochhausen, M., Chen, Y.-P.P., Cherfi, S.S.-S., Doerr, M., Han, H., Hartmann, S., Parsons, J., Poels, G., Rolland, C., Trujillo, J., Yu, E., Zimányie, E. (eds.) ER Workshops 2007. LNCS, vol. 4802, pp. 348–357. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  13. 13.
    Zhou, Z., Sellami, M., Gaaloul, W., Barhamgi, M., Defude, B.: Data providing services clustering and management for facilitating service discovery and replacement. IEEE Trans. Autom. Sci. Eng. 10(4), 1131–1146 (2013)CrossRefGoogle Scholar
  14. 14.
    Zhang, L., Cheng, S., Chang, C., Zhou, Q.: A pattern-recognition-based algorithm and case study for clustering and selecting business services. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 42(1), 102–114 (2012)CrossRefGoogle Scholar
  15. 15.
    Cassar, G., Barnaghi, P., Moessner, K.: Probabilistic methods for service clustering. In: Proceedings of the 4th International Workshop on SMR2 Conjunction with the International Semantic Web Conference, pp. 4–20, Shanghai, China (2010)Google Scholar
  16. 16.
    Mustapha, A., Mohamed, Q., Zahi, J.: Leveraging formal concept analysis with topic correlation for service clustering and discovery. In: 2014 IEEE International Conference on Web Services, pp. 153–160, Alaska, USA, 27 June–2 July 2014Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Buqing Cao
    • 1
    • 2
    • 3
    Email author
  • Xiaoqing (Frank) Liu
    • 2
  • Jianxun Liu
    • 1
  • Mingdong Tang
    • 1
  1. 1.School of Computer Science and EngineeringHunan University of Science and TechnologyXiangtanChina
  2. 2.Computer Science and Computer Engineering DepartmentUniversity of Arkansas in FayettevilleFayettevilleUSA
  3. 3.State Key Laboratory of Software EngineeringWuhan UniversityWuhanChina

Personalised recommendations