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Matchmaking OWL-S processes: an approach based on path signatures

Published:21 November 2011Publication History

ABSTRACT

With the development of e-commerce over Internet, web service discovery received much interest. A critical aspect of web service discovery is web service similarity search or matchmaking. To enhance the similarity precision, several solutions that do not limit to a syntactic comparison of inputs and outputs of the compared services have been proposed. Most of them introduce the structure of web service operations in the similarity measure. In this paper, we analyze these approaches and point out their time complexity drawback. Then, we propose a more efficient matching algorithm based on the concept of decomposition kernels of graphs. We study the complexity of our approach and present performance analysis.

References

  1. Universal description discovery and integration, http://uddi.xml.org/.Google ScholarGoogle Scholar
  2. Web ontology language for web services, http://www.w3.org/submission/owl-s/.Google ScholarGoogle Scholar
  3. Web services description language, http://www.w3.org/tr/wsdl.Google ScholarGoogle Scholar
  4. M. Beck and B. Freitag. Semantic matchmaking using ranked instance retrieval. In proceedings of SMR'06: 1st International Workshop on Semantic Matchmaking and Resource Retrieval, Co-located with VLDB, 2006.Google ScholarGoogle Scholar
  5. U. Bellur, H. Vadodaria, and A. Gupta. Semantic Matchmaking Algorithms, chapter Greedy Algorithms. Witold Bednorz, InTech, Croatia, 2008.Google ScholarGoogle Scholar
  6. K. Borgwardt and H.-P. Kriegel. Shortest-path kernels on graphs. In 5th Int. Conference on Data Mining, pages 74--81, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. H. Bunke. Recent developments in graph matching. In International Conference on Pattern Recognition, pages 2117--2124, 2000.Google ScholarGoogle ScholarCross RefCross Ref
  8. H. Bunke and G. Allermann. Inexact graph matching for structural pattern recognition. Pattern Recognition Letters, 1: 245--253, 1983.Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. J. C. Corrales, D. Grigori, and M. Bouzeghoub. Behavioral matchmaking for service retrieval: Application to conversation protocols. Information Systems, 33(7--8): 681--698, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. R. Dijkman, M. Dumas, and L. Garcia-Banuelos. Business Process Management, LNCS 570, pages 48--63. 2009.Google ScholarGoogle Scholar
  11. X. Dong, A. Halevy, J. Madhavan, E. Nemes, and J. Zhang. Simlarity search for web services. In VLDB2004, pages 372--383, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. M. Garofalakis and A. Kumar. Correlating xml data streams using tree-edit distance embeddings. In ACM PODS'2003. San Diego, California, June 2003, pages 143--154. ACM Press, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. T. Gartner, P. Flach, and S. Wrobel. On graph kernels: Hardness results and efficient alternatives. In Springer, editor, Annual Conf. Computational Learning Theory, pages 129--143, 2003.Google ScholarGoogle Scholar
  14. Y. Hao and Y. Zhang. Web services discovery based on schema matching. In the thirtieth Australasian conference on Computer science - Volume 62, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. D. Haussler. Convolution kernels on discrete structures. Technical Report UCSC-CRL-99-10, University of California, Santa Cruz, 1999.Google ScholarGoogle Scholar
  16. T. Horvath, T. Gartner, and S. Wrobel. Cyclic pattern kernels for predictive graph mining. KDD 2004, pages 158--167, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. S. Lagraa, H. Seba, R. Khennoufa, and H. Kheddouci. A graph decomposition approach to web service matchmaking. In 7th International Conference on Web Information Systems and Technologies (WEBIST), 2011.Google ScholarGoogle Scholar
  18. C. L. Lu, Z.-Y. Su, and G. Y. Tang. A new measure of edit distance between labeled trees. LNCS, Springer-Verlag Heidelberg, pages 338--348, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. D. Mandell and S. McIlraith. A bottom-up approach to automating web service discovery, customization, and semantic translation. In Proceedings of the Twelfth International World Wide Web Conference Workshop on E-Services and the Semantic Web (ESSW), Budapest, 2003.Google ScholarGoogle Scholar
  20. B. Messmer. Efficient Graph Matching Algorithms for Preprocessed Model Graphs. PhD thesis, University of Bern, Switzerland, 1995.Google ScholarGoogle Scholar
  21. B. T. Messmer and H. Bunke. A decision tree approach to graph and subgraph isomorphism detection. Pattern Recognition, 32: 1979--1998, 1999.Google ScholarGoogle ScholarCross RefCross Ref
  22. M. Neuhaus and H. Bunke. A convolution edit kernel for errortolerant graph matching. In IEEE international conference on pattern recognition, Hong Kong, pages 220--223, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. T. Paolucci, T. Kawmura, and K. Sycara. Semantic matching of web service capabilities. In Springer Verlag, LNCS, Proceedings of the International Semantic Web Conference, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. F. Porto, L.-H. Vu, M. Hauswirth, and K. Aberer. A search engine for qos enabled discovery of semantic web services. International Journal of Business Process Integration and Management, 1(4): 244--255, 2006.Google ScholarGoogle ScholarCross RefCross Ref
  25. J. Ramon and T. Gartner. Expressivity versus efficiency of graph kernels. In First International Workshop on Mining Graphs, Trees and Sequences, 2003.Google ScholarGoogle Scholar
  26. A. Sanfeliu and K. Fu. A distance measure between attributed relational graphs for pattern recognition. IEEE Transactions on Systems, Man, and Cybernetics (Part B), 13(3): 353--363, 1983.Google ScholarGoogle ScholarCross RefCross Ref
  27. K. Shin and T. Kuboyama. A generalization of haussler's convolution kernel-mapping kernel. In the 25th International Conference on Machine Learning, Helsinki, Finland, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Y. Wang and E. Stroulia. Flexible interface matching for web-service discovery. In WISE'2003, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library

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          cover image ACM Other conferences
          MEDES '11: Proceedings of the International Conference on Management of Emergent Digital EcoSystems
          November 2011
          316 pages
          ISBN:9781450310475
          DOI:10.1145/2077489

          Copyright © 2011 ACM

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          Publication History

          • Published: 21 November 2011

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          MEDES '11 Paper Acceptance Rate26of82submissions,32%Overall Acceptance Rate267of682submissions,39%

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