Applied Intelligence

, Volume 25, Issue 2, pp 223–237 | Cite as

Service matching in agent systems

  • Anton Naumenko
  • Sergiy NikitinEmail author
  • Vagan Terziyan


The problem of service and resource matching is being actively discussed currently as a new challenging task for the next generation of semantic discovery approaches for Web services and Web agents. A significant advantage is expected when using an ontological approach to semantically describe and query services. A matchmaking problem arises when a service is being queried and it includes the distance measure between the required service description and the one from the service registry. We realized the need to analyze the applicability of different matchmaking methods to agent development tools when implemented according to agent technology specifications such as FIPA. We consider three main groups of cases: matchmaking between classes of service profiles in pure taxonomies, matchmaking between classes in faceted taxonomies, and matchmaking between instances of faceted taxonomies.


Agent technology Ontology Service matching Similarity 


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  1. 1.
    Kaikova H, Khriyenko O, Kononenko O, Terziyan V, Zharko A (2004) Proactive Self-Maintained Resources in Semantic Web. Eastern-European J Enter Technol, 2(1):4–16, ISSN: 1729-3774Google Scholar
  2. 2.
  3. 3.
    Adaptive Services Grid, Integrated project supported by the European Commission,
  4. 4.
    FIPA, Foundation for Intellegent Physical Agents,
  5. 5.
    Tailor C, Tudhope D (1996) Semantic Closeness and Classification Schema Based Hypermedia Access. In: Proceedings of the 3-rd International Conference on Electronic Library and Visual Information Research (ELVIRA′96), Milton, KeynesGoogle Scholar
  6. 6.
    Brooks T (1995) Topical Subject Expertise and the Semantic Distance Model of Relevance Assessment. J Doc, 51(4):370–387Google Scholar
  7. 7.
    Foo N, Garner B, Rao A, Tsui E (1992) Semantic Distance in Conceptual Graphs. In: Gerhotz L (ed) Current Directions in Conceptual Structure Research. Ellis Horwood, pp 149–154Google Scholar
  8. 8.
    Rada R, Mili H, Bicknell E, Blettner M (1989) Development and application of a metric on semantic nets. IEEE Tran Sys, Man, and Cybernetics 19(1):17–30CrossRefGoogle Scholar
  9. 9.
    Wilson D, Martinez T (1997) Improved Heterogeneous Distance Functions. J Art Intell Rese 6:1–34zbMATHMathSciNetGoogle Scholar
  10. 10.
    FIPA Abstract Architecture Specification,
  11. 11.
    FIPA Agent Management Specification,
  12. 12.
    Stojanovic N, Meadche A, Staab S, Studer R, Sure Y (2001) SEAL: A Framework for Developing Semantic PortALs. In: Proceedings of the International Conference on Knowledge Capture, Victoria, British Columbia, Canada, ACM Press. pp 155–162Google Scholar
  13. 13.
    Glossary of Content Management Professionals,
  14. 14.
    Cost S, Salzberg S (1993) A weighted nearest neighbor algorithm for learning with symbolic features. Mach Learn 10(1):57–78Google Scholar
  15. 15.
    Puuronen S, Tsymbal A, Terziyan V (2000) Distance Functions in Dynamic Integration of Data Mining Techniques. In: Dasarathy BV (ed) Data Mining and Knowledge Discovery: Theory, Tools and Technology II, Proceedings of SPIE, vol. 4057, The Society of Photo-Optical Instrumentation Engineers, USA, pp 22–32Google Scholar
  16. 16.
    Haase P, Agarwal S, Sure Y (2004) Service-Oriented Semantic Peer-to-Peer Systems, Lecture Notes in Computer Science, vol. 3307, pp 46–57Google Scholar
  17. 17.
    Ludwig SA, Reyhani SMS (2005) Semantic Approach to Service Discovery in a Grid Environment. J Web Semant, 3(4) ElsevierGoogle Scholar
  18. 18.
    Hau J, Lee W, Darlington J (2005) A Semantic Similarity Measure for Semantic Web Services, Web Service Semantics WorkshopGoogle Scholar

Copyright information

© Springer Science + Business Media, LLC 2006

Authors and Affiliations

  • Anton Naumenko
    • 1
  • Sergiy Nikitin
    • 1
    Email author
  • Vagan Terziyan
    • 1
  1. 1.Industrial Ontologies Group, MIT DepartmentUniversity of JyvaskylaJyvaskylaFinland

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