Abstract
We propose a novel framework to automatically discover service communities that group together related services in a diverse and large scale service space. Community discovery is a key enabler to address a set of fundamental issues in service computing, which include service discovery, service composition, and quality-based service selection. The standard Web service description language, WSDL, primarily describes a service from the syntactic perspective and rarely provides rich service descriptions. This hinders the direct application of traditional document clustering approaches. In order to attack this central challenge, the proposed framework applies Non-negative Matrix Factorization (NMF) to the WSDL corpus for service community discovery. NMF has demonstrated its effectiveness in clustering high-dimensional sparse data while offering intuitive interpretability of the clustering result. NMF-based community discovery is further augmented via semantic extensions of the WSDL descriptions. The extended semantics are first computed based on the information sources outside the WSDL corpus. They are then seamlessly integrated with NMF, which makes the semantic extensions fit in the context of the original services. The experiments on real world Web services are presented to show the effectiveness of the proposed framework.
Chapter PDF
Similar content being viewed by others
References
Baeza-Yates, R.A., Ribeiro-Neto, B.: Modern Information Retrieval. Addison-Wesley Longman Publishing Co., Inc, Boston (1999)
Bose, A., Nayak, R., Bruza, P.: Improving web service discovery by using semantic models. In: Bailey, J., Maier, D., Schewe, K.-D., Thalheim, B., Wang, X.S. (eds.) WISE 2008. LNCS, vol. 5175, pp. 366–380. Springer, Heidelberg (2008)
Bouguettaya, A., Yu, Q., Liu, X., Malik, Z.: Service-centric framework for a digital government application. In: IEEE Transactions on Services Computing, vol. 99(PrePrints) (2010)
Cai, D., He, X., Han, J.: Document clustering using locality preserving indexing. IEEE Trans. Knowl. Data Eng. 17(12), 1624–1637 (2005)
Dhillon, I.S.: Co-clustering documents and words using bipartite spectral graph partitioning. In: KDD ’01: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 269–274. ACM, New York (2001)
Ding, C.H.Q., Li, T., Peng, W., Park, H.: Orthogonal nonnegative matrix t-factorizations for clustering. In: KDD, pp. 126–135 (2006)
Dong, X., Halevy, A., Madhavan, J., Nemes, E., Zhang, J.: Similarity search for web services. In: VLDB 2004: Proceedings of the Thirtieth International Conference on Very Large Data Bases, pp. 372–383, VLDB Endowment (2004)
Elgazzar, K., Hassan, A.E., Martin, P.: Clustering wsdl documents to bootstrap the discovery of web services. In: ICWS, pp. 147–154 (2010)
Klusch, M., Fries, B., Sycara, K.: Automated semantic web service discovery with owls-mx. In: Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multiagent Systems AAMAS 2006, pp. 915–922. ACM Press, New York (2006)
Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401, 788–791 (1999)
Liu, F., Shi, Y., Yu, J., Wang, T., Wu, J.: Measuring similarity of web services based on wsdl. In: ICWS, pp. 155–162 (2010)
Liu, W., Wong, W.: Discovering homogenous service communities through web service clustering. In: Kowalczyk, R., Huhns, M.N., Klusch, M., Maamar, Z., Vo, Q.B. (eds.) SOCASE 2008. LNCS, vol. 5006, pp. 69–82. Springer, Heidelberg (2008)
Liu, X., Huang, G., Mei, H.: Discovering homogeneous web service community in the user-centric web environment. IEEE T. Services Computing 2(2), 167–181 (2009)
Lovasz, L.: Matching Theory (North-Holland Mathematics Studies). Elsevier Science Ltd. (1986)
Ma, J., Zhang, Y., He, J.: Efficiently finding web services using a clustering semantic approach. In: CSSSIA 2008: Proceedings of the 2008 International Workshop on Context Enabled Source and Service Selection, Integration and Adaptation, pp. 1–8. ACM, New York (2008)
Sahami, M., Heilman, T.D.: A web-based kernel function for measuring the similarity of short text snippets. In: Proceedings of the 15th International Conference on World Wide Web, WWW 2006, pp. 377–386. ACM, New York (2006)
Xu, W., Liu, X., Gong, Y.: Document clustering based on non-negative matrix factorization. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Informaion Retrieval, SIGIR 2003, pp. 267–273. ACM, New York (2003)
Yu, Q., Bouguettaya, A.: Framework for web service query algebra and optimization. TWEBÂ 2(1) (2008)
Yu, Q., Liu, X., Bouguettaya, A., Medjahed, B.: Deploying and managing web services: issues, solutions, and directions. VLDB Journal 17(3), 537–572 (2008)
Yu, Q., Rege, M.: On service community learning: A co-clustering approach. In: ICWS, pp. 283–290 (2010)
Yu, T., Zhang, Y., Lin, K.-J.: Efficient algorithms for web services selection with end-to-end qos constraints. ACM Trans. Web 1(1), 6 (2007)
Zeng, L., Benatallah, B., Ngu, A., Dumas, M., Kalagnanam, J., Chang, H.: Qos-aware middleware for web services composition. IEEE Trans. Softw. Eng. 30(5), 311–327 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yu, Q. (2011). Place Semantics into Context: Service Community Discovery from the WSDL Corpus. In: Kappel, G., Maamar, Z., Motahari-Nezhad, H.R. (eds) Service-Oriented Computing. ICSOC 2011. Lecture Notes in Computer Science, vol 7084. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25535-9_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-25535-9_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25534-2
Online ISBN: 978-3-642-25535-9
eBook Packages: Computer ScienceComputer Science (R0)