Adaptive Hybrid Selection of Semantic Services: The iSeM Matchmaker

  • Patrick Kapahnke
  • Matthias Klusch


We present the intelligent service matchmaker iSeM, which exhaustively exploits functional service descriptions in terms of logical signature annotations in OWL and specifications of preconditions and effects in SWRL. In particular, besides strict logical matching filters, text and structural similarity, it adopts approximated reasoning based on logical concept abduction and contraction for the description logic subset SH with information-theoretic valuation for matching inputs and outputs. In addition, it uses stateless logical specification matching in terms of the incomplete but decidable \(\theta \)-subsumption algorithm for preconditions and effects. The optimal aggregation strategy of the above mentioned matching aspects is adapted off-line by means of a binary SVM-based service relevance classifier in combination with evidential coherence-based pruning to improve ranking precision with respect to false classification of any such variant on its own. We demonstrate the additional benefit of the presented approximation and the adaptive hybrid combination by example and by presenting an experimental performance analysis.


Average Precision Service Selection Signature Match Query Response Time Signature Concept 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    A.L. Blum, P. Langley, Selection of relevant features and examples in machine learning. Artif. Intell. 97, 245–271 (1997)Google Scholar
  2. 2.
    S. Colucci et al., Concept abduction and contraction for semantic-based discovery of matches and negotiation spaces in an e-marketplace. Electron. Commerce Res. Appl. 4(4), 345–361 (2005)Google Scholar
  3. 3.
    T. Di Noia, E. Di Sciascio, F.M. Donini, A Tableaux-based calculus for abduction in expressive description logics: preliminary results, in Proceedings of 22nd International Workshop on Description Logics (DL), Oxford, 2009Google Scholar
  4. 4.
    D. H. Glass, Inference to the best explanation: a comparison of approaches, in Proceedings of the AISB 2009 Convention, Edinburgh, 2009,
  5. 5.
    P. Idestam-Almquist, Generalization of clauses under implication. Artif. Intell. Res. 3, 467–489 (1995)Google Scholar
  6. 6.
    C. Kiefer, A. Bernstein, The creation and evaluation of iSPARQL strategies for matchmaking, in Proceedings of the 5th European Semantic Web Conference (ESWC). (Springer, Berlin/ New York, 2008)Google Scholar
  7. 7.
    M. Klusch, Semantic web service coordination, in CASCOM – Intelligent Service Coordination in the Semantic Web, Chapter 4, ed. by M. Schumacher, H. Helin, H. Schuldt (Birkhäuser Verlag/Springer, Basel, 2008)Google Scholar
  8. 8.
    M. Klusch, P. Kapahnke, OWLS-MX3: an adaptive hybrid semantic service atchmaker for OWL-S, in Proceedings of 3rd International Workshop on Semantic Matchmaking and Resource Retrieval (SMR2), USA; CEUR, vol. 525, Washington, 2009Google Scholar
  9. 9.
    M. Klusch, P. Kapahnke, iSeM: approximated reasoning for adaptive hybrid selection of semantic services, in Proceedings of 4th IEEE International Conference on Semantic Computing (ICSC), Pittsburgh, 2010Google Scholar
  10. 10.
    M. Klusch, Z. Xing, Deployed semantic services for the common user of the web: a reality check, in Proceedings of the 2nd IEEE International Conference on Semantic Computing (ICSC), (IEEE Press, Santa Clara, 2008)Google Scholar
  11. 11.
    M. Klusch, B. Fries, K. Sycara, OWLS-MX: A hybrid semantic web service matchmaker for OWL-S services. Web Semant. 7(2),121–133 (2009), ElsevierGoogle Scholar
  12. 12.
    M. Klusch, P. Kapahnke, I. Zinnikus, Hybrid adaptive web service selection with SAWSDL-MX and WSDL analyzer, in Proceedings of 6th European Semantic Web Conference (ESWC) (IOS Press, Heraklion, 2009)Google Scholar
  13. 13.
    Y. Li, A. Bandar, D. McLean, An approach for measuring semantic similarity between words using multiple information sources. Trans. Knowl. Data Eng. 15, 871–882 (2003)Google Scholar
  14. 14.
    D. Lin, An information-theoretic definition of similarity, in Proceedings of the 15th International Conference on Machine Learning, Madison, 1998Google Scholar
  15. 15.
    P. Resnik, Semantic similarity in a taxonomy: an information-based measure and its application to problems of ambiguity in natural language. J. Artif. Intell. Res. 11, 95–130 (1999)Google Scholar
  16. 16.
    K. Samuel et al., Translating OWL and semantic web rules into prolog: Moving toward description logic programs. Theory Pract. Logic Program. 8(3), 301–322 (2008)Google Scholar
  17. 17.
    T. Scheffer, R. Herbrich, F. Wysotzki, Efficient theta-Subsumption based on Graph Algorithms. Lecture Notes In Computer Science, vol. 1314 (Springer, Berlin/New York, 1996)Google Scholar
  18. 18.
    K. Sycara, S. Widoff, M. Klusch, J. Lu, LARKS: dynamic matchmaking among heterogeneous software agents in cyberspace. Auton. Agent Multi-Agent Syst. 5, 173–203 (2002), KluwerGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.German Research Center for Artificial Intelligence (DFKI)SaarbrueckenGermany

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