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Towards structural Web Services matching based on Kernel methods

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Abstract

This paper describes a kernel methods based Web Services matching mechanism for Web Services discovery and integration. The matching mechanism tries to exploit the latent semantics by the structure of Web Services. In this paper, Web Services are schemed by WSDL (Web Services Description Language) as tree-structured XML documents, and their matching degree is calculated by our novel algorithm designed for loosely tree matching against the traditional methods. In order to achieve the task, we bring forward the concept of path subsequence to model WSDL documents in the vector space. Then, an advanced n-spectrum kernel function is defined, so that the similarity of two WSDL documents can be drawn by implementing the kernel function in the space. Using textual similarity and n-spectrum kernel values as features of low-level and mid-level, we build up a model to estimate the functional similarity between Web Services, whose parameters are learned by a ranking-SVM. Finally, a set of experiments were designed to verify the model, and the results showed that several metrics for the retrieval of Web Services have been improved by our approach.

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References

  1. Paolucci M, Kawmura T, Payne T, et al. Semantic matching of web services capabilities. In: Proceedings of ISWC. Berlin: Springer, 2002, 333–347

    Google Scholar 

  2. Carmel D, Maarek Y S, Mandelbrod M, et al. Searching XML documents via XML fragments. In: Proceedings of ACM SIGIR. New York: ACM Press, 2003, 151–158

    Google Scholar 

  3. Kokash N. A comparison of web service interface similarity measures. Technical Report. University of Trento, DIT-06-025, 2006

  4. Wang Y, Stroulia E. Semantic structure matching for assessing web service similarity. In: Proceedings of ICSOC. Berlin: Springer, 2003, 194–207

    Google Scholar 

  5. Yu J, Guo S, Su H, et al. A Kernel based structure matching for web services search. In: Proceedings of WWW. New York: ACM Press, 2007, 1249–1250

    Google Scholar 

  6. Cost S, Salzberg S. A weighted nearest neighbor algorithm for learning with symbolic features. Machine Learning, 1993, 57(10): 57–78

    Google Scholar 

  7. Larkey L S, Croft W. Combining classifiers in text categorization. In: Proceedings of SIGIR. New York: ACM Press, 1996, 289–297

    Google Scholar 

  8. Nigam K, Mccallum A K, Thrun S, et al. Text classification from labeled and unlabeled documents using EM. Machine Learning, 2000, 39(2–3): 103–134

    Article  MATH  Google Scholar 

  9. Dong X, Halevy A, Madhavan J, et al. Similarity search for web services. In: Proceedings of VLDB. San Fransisco: Morgan Kaufmann, 2004, 372–383

    Google Scholar 

  10. Aumuller D, Do H H, Massmann S, et al. Schema and ontology matching with COMA++. In: Proceedings of ACM SIGMOD. New York: ACM Press, 2005, 906–908

    Chapter  Google Scholar 

  11. Madhavan J, Bernstein P, Rahm E. Generic schema matching with cupid. In: Proceedings of VLDB. San Francisco: Morgan Kaufmann, 2001, 49–58

    Google Scholar 

  12. Melnik S, Garcia-Molina H, Rahm E. Similarity flooding: a versatile graph matching algorithm. In: Proceedings of ICDE. Washington DC: IEEE Computer Society, 2002, 117–128

    Google Scholar 

  13. Flesca S, Manco G, Masciari E, et al. Detecting structural similarities between XML documents. In: Proceedings of WebDB. Wisconsin, 2002, 55–60

  14. Lian W, Cheung D W I, Mamoulis N, et al. An efficient and scalable algorithm for clustering XML documents by structure. IEEE Transaction on Knowledge and Data Engineering, 2004, 16(1): 82–96

    Article  Google Scholar 

  15. Zhang K, Shasha D. Simple fast algorithms for the editing distance between trees and related problems. SIAM Journal on Computing, 1989, 18(16): 1245–1262

    Article  MATH  MathSciNet  Google Scholar 

  16. Gartner T. A survey of kernels for structured data. ACM SIGKDD Explorations Newsletter, 2003, 5(1): 49–58

    Article  MathSciNet  Google Scholar 

  17. Shawe-Taylor J, Cristianini N. Kernel Methods for Pattern Analysis. United Kingdom: Cambridge University Press, 2004, 344–372

    Google Scholar 

  18. Vishwanathan S, Smola A. Fast kernels for string and tree matching. Advances in Neural Information Processing Systems, 2002, 569–576

  19. Charfi A, Mezini M. Using aspects for security engineering of web service compositions. In: Proceedings of ICWS. Washington DC: IEEE Computer Society, 2005, 59–66

    Google Scholar 

  20. Milanovic N, Malek M. Current solutions for web service composition. Internet Computing, 2004, 8(6): 51–59

    Article  Google Scholar 

  21. Hopcroft J E, Karp R M. A n2/5 algorithm for maximum matchings in bipartite graphs. SIAM Journal on Computing, 1973, 2(4): 225–231

    Article  MATH  MathSciNet  Google Scholar 

  22. Xu J, Cao Y, Li H, et al. Ranking definitions with supervised learning methods. In: Proceedings of WWW. New York: ACM Press, 2005, 811–819

    Chapter  Google Scholar 

  23. Valiente G. Algorithms on Trees and Graphs. New York: Springer-Verlag, 2002

    MATH  Google Scholar 

Download references

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Correspondence to Yu Jianjun.

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Nan, K., Yu, J., Su, H. et al. Towards structural Web Services matching based on Kernel methods. Front. Comput. Sc. China 1, 450–458 (2007). https://doi.org/10.1007/s11704-007-0043-y

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  • DOI: https://doi.org/10.1007/s11704-007-0043-y

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