Effective Mashup Service Clustering Method by Exploiting LDA Topic Model from Multiple Data Sources
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.
KeywordsMashup 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.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)
- 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
- 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
- 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
- 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.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
- 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.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