Benefits of Semantics on Web Service Composition from a Complex Network Perspective

  • Chantal Cherifi
  • Vincent Labatut
  • Jean-François Santucci
Part of the Communications in Computer and Information Science book series (CCIS, volume 88)


The number of publicly available Web services (WS) is continuously growing, and in parallel, we are witnessing a rapid development in semantic-related web technologies. The intersection of the semantic web and WS allows the development of semantic WS. In this work, we adopt a complex network perspective to perform a comparative analysis of the syntactic and semantic approaches used to describe WS. From a collection of publicly available WS descriptions, we extract syntactic and semantic WS interaction networks. We take advantage of tools from the complex network field to analyze them and determine their properties. We show that WS interaction networks exhibit some of the typical characteristics observed in real-world networks, such as short average distance between nodes and community structure. By comparing syntactic and semantic networks through their properties, we show the introduction of semantics in WS descriptions should improve the composition process.


Web Services Service Composition Complex Networks Interaction Networks Semantic Web 


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  1. 1.
    Benatallah, B., Dumas, M., Fauvet, M.-C., Rabhi, F.A.: Towards patterns of web services composition. In: Patterns and Skeletons for Parallel and Distributed Computing, pp. 265–296. Springer, Heidelberg (2003)Google Scholar
  2. 2.
    Sycara, K., Paolucci, M., Ankolelar, A., Srinivasan, N.: Automated Discovery, Interaction and Composition of Semantic Web Services. J. Web Semantics 1, 27–46 (2003)Google Scholar
  3. 3.
    Ma, J., Zhang, Y., He, J.: Web Services Discovery Based on Latent Semantic Approach. In: International Conference on Web Services, Beijing, CN, pp. 740–747 (2008)Google Scholar
  4. 4.
    Stroulia, E., Wang, Y.: Semantic structure matching for assessing Web-service similarity. In: International Conference on Service Oriented Computing, Berlin, DE (2003)Google Scholar
  5. 5.
    Wu, J., Wu, Z.: Similarity-based Web service matchmaking. In: Conference on Services Computing, Orlando, FL, vol. 1, pp. 287–294 (2005)Google Scholar
  6. 6.
    Hess, A., Johnston, E., Kushmerick, N.: ASSAM: A Tool for Semi-Automatically Annotating Semantic Web Services. In: McIlraith, S.A., Plexousakis, D., van Harmelen, F. (eds.) ISWC 2004. LNCS, vol. 3298, pp. 320–334. Springer, Heidelberg (2004)Google Scholar
  7. 7.
    Gomadam, K., Verma, K., Brewer, D., Sheth, A.P., Miller, J.A.: Radiant: A tool for semantic annotation of Web Services. In: International Semantic Web Conference, Galway, IE (2005)Google Scholar
  8. 8.
    da Fontura Costa, L., Oliveira Jr., O.N., Travieso, G., Rodrigues, R.A., Villas Boas, P.R., Antiqueira, L., Viana, M.P., da Rocha, L.E.C.: Analyzing and Modeling Real-World Phenomena with Complex Networks: A Survey of Applications (2008), arXiv 0711.3199Google Scholar
  9. 9.
    Newman, M.E.J.: The structure and function of complex networks. SIAM Review 45, 167–256 (2003)zbMATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    Erdos, P., Renyi, A.: On random graphs. Publicationes Mathematicae 6, 290–297 (1959)MathSciNetGoogle Scholar
  11. 11.
    Watts, D.J., Strogatz, S.H.: Collective dynamics of small-world networks. Nature 393, 440–442 (1998)CrossRefGoogle Scholar
  12. 12.
    Albert, R., Jeong, H., Barabasi, A.-L.: The diameter of the world wide web. Nature 401, 130 (1999)CrossRefGoogle Scholar
  13. 13.
    Boccaletti, S., Latora, V., Moreno, Y., Chavez, Y., Hwang, D.: Complex networks: Structure and dynamics. Physics Reports 424, 175–308 (2006)CrossRefMathSciNetGoogle Scholar
  14. 14.
    Orman, G.K., Labatut, V.: A Comparison of Community Detection Algorithms on Artificial Networks. In: Gama, J., Costa, V.S., Jorge, A.M., Brazdil, P.B. (eds.) DS 2009. LNCS (LNAI), vol. 5808, pp. 242–256. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  15. 15.
    Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69, 026113 (2004)CrossRefGoogle Scholar
  16. 16.
    Newman, M.E.J.: Modularity and community structure in networks. PNAS USA 103, 8577–8582 (2006)CrossRefGoogle Scholar
  17. 17.
    Oh, S.-C.: Effective Web Services Composition in diverse and large-scale services networks. Thesis, Pennsylvania State University (2006)Google Scholar
  18. 18.
    Liu, J., Liu, J., Chao, L.: Design and Implementation of an Extended UDDI Registration Center for Web Service Graph. In: International Conference on Web Services, Salt Lake City, UT, pp. 1174–1175 (2007)Google Scholar
  19. 19.
    Talantikite, H.N., Aissani, D., Boudjlida, N.: Semantic annotations for web services discovery and composition. Comput. Stand. Interfaces 31, 1108–1117 (2009)CrossRefGoogle Scholar
  20. 20.
    Gekas, J., Fasli, M.: Employing Graph Network Analysis for Web Service Composition. In: Alkhatib, G.I., Rine, D.C. (eds.) Agent Technologies and Web Engineering (2008)Google Scholar
  21. 21.
    Shiaa, M.M., Fladmark, J.O., Thiell, B.: An Incremental Graph-based Approach to Automatic Service Composition. In: International Conference on Services Computing, Hawaii, HI (2008)Google Scholar
  22. 22.
    Kwon, J., Park, K., Lee, D., Lee, S.: PSR: Pre-computing Solutions in RDBMS for FastWeb Services Composition Search. In: International Conference on Web Services, Salt Lake City, UT (2007)Google Scholar
  23. 23.
    Hashemian, S.V., Mavaddat, F.: A Graph-Based Approach to Web Services Composition Symposium on Applications and the Internet, Trento, IT (2005)Google Scholar
  24. 24.
    Shvaiko, P., Euzenat, J.: A Survey of Schema-Based Matching Approaches. J. Data Semantics IV, 146–171 (2005)Google Scholar
  25. 25.
    Paolucci, M., Kawamura, T., Payne, T.R., Sycara, K.P.: Semantic Matching of Web Services Capabilities. In: International Conference on Web Services, Sardinia, IT, pp. 333–347 (2002)Google Scholar
  26. 26.
    Kil, H., Oh, S., Lee, D.: On the Topological Landscape of Semantic Web services Matchmaking. In: Proc. of 1st International Workshop on Semantic Matchmaking and Resource Retreival (SMR 2006), Seoul, Korea, pp. 19–34 (2006)Google Scholar
  27. 27.
    Keller, U., Lara, R., Lausen, H., Polleres, A., Fensel, D.: Automatic Location of Web Services. In: Gómez-Pérez, A., Euzenat, J. (eds.) ESWC 2005. LNCS, vol. 3532, pp. 1–16. Springer, Heidelberg (2005)Google Scholar
  28. 28.
    Küster, U., König-Ries, B.: Evaluating Semantic Web Service Matchmaking Effectiveness Based on Graded Relevance. In: International Semantic Web Conference/Workshop on Service Matchmaking and Resource Retrieval in the Semantic Web, Karlsruhe, DE (2008)Google Scholar
  29. 29.
    Li, L., Horrocks, I.: A Software Framework for Matchmaking Based on Semantic Web Technology. In: International Conference on World Wide Web, Budapest, HU (2003)Google Scholar
  30. 30.
    Lecue, F., Delteil, A., Leger, A.: Applying Abduction in Semantic Web Service Composition. In: International Conference on Web Services, pp. 94–101 (2007)Google Scholar
  31. 31.
    Klusch, M., Kapahnke, P.: Semantic web service selection with SAWSDL-MX. In: International Semantic Web Conference, pp. 3–16 (2008)Google Scholar
  32. 32.
  33. 33.
  34. 34.
    SWS-TC OPOSSum Web Services Collection,
  35. 35.
    Jena Geography Dataset OPOSSum,
  36. 36.
    Clauset, A., Shalizi, C.R., Newman, M.E.J.: Power-Law Distributions in Empirical Data. SIAM Review 51, 661–703 (2009)zbMATHCrossRefMathSciNetGoogle Scholar
  37. 37.
    Cohen, W.W., Ravikumar, P.D., Fienberg, S.E.: A Comparison of String Distance Metrics for Name-Matching Tasks. In: Proceedings of IJCAI 2003 / IIWeb 2003, Acapulco, MX (2003)Google Scholar
  38. 38.
    Oh, S.-C., Lee, D., Kumara, S.R.T.: Effective Web Services Composition in Diverse and Large-Scale Services Networks. IEEE Transactions on Services Computing 1 (2008)Google Scholar
  39. 39.
    Cherifi, C., Labatut, V., Santucci, J.-F.: Web Services Dependency Networks Analysis. In: International Conference on New Media and Interactivity, Istanbul, TR (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Chantal Cherifi
    • 1
    • 2
  • Vincent Labatut
    • 1
  • Jean-François Santucci
    • 2
  1. 1.Computer Science DepartmentGalatasaray UniversityOrtaköyTurkey
  2. 2.SPE LaboratoryUniversity of Corsica, UMR CNRSFrance

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