On the Combination of Textual and Semantic Descriptions for Automated Semantic Web Service Classification

  • Ioannis Katakis
  • Georgios Meditskos
  • Grigorios Tsoumakas
  • Nick Bassiliades
  • Vlahavas
Part of the IFIP International Federation for Information Processing book series (IFIPAICT, volume 296)


Semantic Web services have emerged as the solution to the need for automating several aspects related to service-oriented architectures, such as service discovery and composition, and they are realized by combining Semantic Web technologies and Web service standards. In the present paper, we tackle the problem of automated classification of Web services according to their application domain taking into account both the textual description and the semantic annotations of OWL-S advertisements. We present results that we obtained by applying machine learning algorithms on textual and semantic descriptions separately and we propose methods for increasing the overall classification accuracy through an extended feature vector and an ensemble of classifiers.


Description Logic Textual Description Service Discovery Semantic Annotation Semantic Description 


  1. 1.
    Aha, D., Kibler, D.: Instance-based learning algorithms. Machine Learning 6, 37–66 (1991)MATHGoogle Scholar
  2. 2.
    Baader, F., Calvanese, D., McGuinness, D.L., Nardi, D., Patel-Schneider, P.F.: The Description Logic Handbook: Theory, Implementation, and Applications. Cambridge University Press (2003)Google Scholar
  3. 3.
    Bruno, M., Canfora, G., Penta, M.D., Scognamiglio, R.: An approach to support web service classification and annotation. In: Proceedings IEEE International Conference on e-Technology, e-Commerce and e-Service, pp. 138–143. Washington, DC (2005). DOI http://dx.doi.org/10.1109/EEE.2005.31
  4. 4.
    Cohen, W.W.: Fast effective rule induction. In: Proceedings 12th International Conference on Machine Learning, pp. 115–123 (1995)Google Scholar
  5. 5.
    Corella, M., Castells, P.: Semi-automatic semantic-based web service classification. In: J. Eder, S. Dustdar (eds.) Business Process Mangement Workshops, Springer Verlag Lecture Notes in Computer Science, vol. 4103, pp. 459–470. Vienna, Austria (2006)Google Scholar
  6. 6.
    Heβ, A., Johnston, E., Kushmerick, N.: ASSAM: A tool for semi-automatically annotating semantic web services. In: Proceedings 3rd International Semantic Web Conference (2004)Google Scholar
  7. 7.
    Hess, A., Kushmerick, N.: Learning to attach semantic metadata to web services. In: Proceedings International Semantic Web Conference (ISWC'03), pp. 258–273 (2003)Google Scholar
  8. 8.
    John, G.H., Langley, P.: Estimating continuous distributions in bayesian classifiers. In: Proceedings 11th Conference on Uncertainty in Artificial Intelligence, pp. 338–345. Morgan Kaufmann, San Mateo (1995)Google Scholar
  9. 9.
    Kiefer, C., Bernstein, A.: The creation and evaluation of isparql strategies for matchmaking. In: M. Hauswirth, M. Koubarakis, S. Bechhofer (eds.) Proceedings 5th European Semantic Web Conference, LNCS. Springer Verlag, Berlin, Heidelberg (2008). URL http://data.semanticweb.org/conference/eswc/2008/papers/133Google Scholar
  10. 10.
    Klusch, M., Kapahnke, P., Fries, B.: Hybrid semantic web service retrieval: A case study with OWLS-MX. In: International Conference on Semantic Computing, pp. 323–330. IEEE Computer Society, Los Alamitos, CA (2008). DOI http://doi.ieeecomputersociety.org/10.1109/ICSC.2008.20Google Scholar
  11. 11.
    Kopecký, J., Vitvar, T., Bournez, C., Farrell, J.: Sawsdl: Semantic annotations for wsdl and xml schema. IEEE Internet Computing 11(6), 60–67 (2007). DOI http://dx.doi.org/10.1109/MIC.2007.134 CrossRefGoogle Scholar
  12. 12.
    Meditskos, G., Bassiliades, N.: Object-oriented similarity measures for semantic web service matchmaking. In: Proceedings 5th IEEE European Conference on Web Services (ECOWS'07), pp. 57–66. Halle (Saale), Germany (2007)Google Scholar
  13. 13.
    Oldham, N., Thomas, C., Sheth, A., Verma, K.: Meteor-s web service annotation framework with machine learning classification. In: Proceedings 1st International Workshop on Semantic Web Services and Web Process Composition (SWSWPC'04), pp. 137–146 (2005)Google Scholar
  14. 14.
    Paolucci, M., Kawamura, T., Payne, T.R., Sycara, K.P.: Importing the semantic web in uddi. In: Revised Papers from the International Workshop on Web Services, E-Business, and the Semantic Web (CAiSE'02/WES'02), pp. 225–236. Springer-Verlag, London, UK (2002)CrossRefGoogle Scholar
  15. 15.
    Paolucci, M., Kawamura, T., Payne, T.R., Sycara, K.P.: Semantic matching of web services capabilities. In: Proceedings 1st International Semantic Web Conference on The Semantic Web (ISWC'02), pp. 333–347. Springer-Verlag, London, UK (2002)Google Scholar
  16. 16.
    Platt, J.: Machines using sequential minimal optimization. In: B. Schoelkopf, C. Burges, A. Smola (eds.) Advances in Kernel Methods - Support Vector Learning. MIT Press (1998). URL http://research.microsoft.com/jplatt/smo.html
  17. 17.
    Quinlan, R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Ma-teo, CA (1993)Google Scholar
  18. 18.
    Saha, S., Murthy, C.A., Pal, S.K.: Classification of web services using tensor space model and rough ensemble classifier. In: Proceedings 17th International Symposiumon Foundations of Intelligent Systems (ISMIS'08), pp. 508–513. Toronto, Canada (2008)Google Scholar
  19. 19.
    Sirin, E., Parsia, B., Grau, B.C., Kalyanpur, A., Katz, Y.: Pellet: A practical owl-dl reasoner. Web Semant. 5(2), 51–53 (2007). DOI http://dx.doi.org/10.1016/j.websem.2007.03.004 CrossRefGoogle Scholar
  20. 20.
    Tsoumakas, G., Katakis, I.: Multi-label classification: An overview. International Journal of Data Warehousing and Mining 3(3), 1–13 (2007)CrossRefGoogle Scholar
  21. 21.
    Vapnik, V.N.: The nature of statistical learning theory. Springer-Verlag, NY, USA (1995)CrossRefMATHGoogle Scholar
  22. 22.
    Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd Edition. Morgan Kaufmann Publishers Inc., San Francisco, CA (2005)MATHGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2009

Authors and Affiliations

  • Ioannis Katakis
    • 1
  • Georgios Meditskos
    • 1
  • Grigorios Tsoumakas
    • 1
  • Nick Bassiliades
    • 1
  • Vlahavas
    • 1
  1. 1.Department of InformaticsAristotle University of ThessalonikiThessalonikiGreece

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