Evolving Semantics for Agent-Based Collaborative Search

  • Murat Şensoy
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7068)

Abstract

Millions of users search the Web every day to locate Web resources relevant to their interests. Unfortunately, the Web resources found by a user with a specific interest are usually not shared with others having the same or similar interests. In this paper, we propose an agent-based approach for collaborative distributed semantic search of the Web. Our approach enables a human user to semantically describe his search interest to an agent. Depending on the interests of their users, the agents evolve their vocabularies and create search concepts. Based on these search concepts, the agents discover other agents having similar search interests and collaborate with them to locate Web resources relevant to their search interests. Our empirical evaluations and the analysis of the proposed approach show that our approach enables agents with similar interests to coordinate and compose virtual communities. Within these communities, the agents interact to locate and share pointers to the Web resources relevant to the search interests of their users.

Keywords

Multiagent System Description Logic Virtual Community Search Interest Semantic Search 
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.

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References

  1. 1.
    Aberer, K., Cudre-Mauroux, P., Hauswirth, M.: Start making sense: The chatty web approach for global semantic agreements. Journal of Web Semantics 1(1), 89–114 (2003)CrossRefGoogle Scholar
  2. 2.
    Blanchard, E., Harzallah, M., Kuntz, P.: A generic framework for comparing semantic similarities on a subsumption hierarchy. In: Proceeding of the ECAI 2008, pp. 20–24 (2008)Google Scholar
  3. 3.
    Şensoy, M.: Distributed semantic search for the web: A multiagent approach. In: Proceedings of the AAMAS 2010, pp. 1561–1562 (2010)Google Scholar
  4. 4.
    Şensoy, M., Zhang, J., Yolum, P., Cohen, R.: Poyraz: Context-aware service selection under deception. Computational Intelligence 25(4), 335–366 (2009)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Dellarocas, C.: Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior. In: Proceedings of the Second ACM Conference on Electronic Commerce, pp. 150–157 (2000)Google Scholar
  6. 6.
    Massa, P., Avesani, P.: Trust metrics in recommender systems. In: Golbeck, J. (ed.) Computing with Social Trust, pp. 259–285. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  7. 7.
    Ramchurn, S., Huynh, D., Jennings, N.: Trust in multiagent systems. Knowledge Engineering Review 19(1), 1–25 (2004)CrossRefGoogle Scholar
  8. 8.
    Resnik, P.: Semantic similarity in a taxonomy: An information-based measure and its application to problems of ambiguity in natural language. Journal of Artificial Intelligence Research 11, 95–130 (1999)MATHGoogle Scholar
  9. 9.
    Sirin, E., Parsia, B., Grau, B.C., Kalyanpur, A., Katz, Y.: Pellet: A practical OWL-DL reasoner. Web Semant. 5(2), 51–53 (2007)CrossRefGoogle Scholar
  10. 10.
    Wu, Z., Palmer, M.: Verbs semantics and lexical selection. In: Proceedings of the 32nd Annual Meeting on Association for Computational Linguistics, pp. 133–138 (1994)Google Scholar
  11. 11.
    Yolum, P., Singh, M.P.: Engineering self-organizing referral networks for trustworthy service selection. IEEE Transactions on Systems, Man, and Cybernetics A35(3), 396–407 (2005)CrossRefGoogle Scholar
  12. 12.
    Yu, B., Singh, M.P.: Searching social networks. In: Proceedings of International Conference on Autonomous Agents and Multiagent Systems, pp. 65–72 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Murat Şensoy
    • 1
  1. 1.Department of Computing ScienceUniversity of AberdeenAberdeenUK

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