Advertisement

An Approach for Identification of User’s Intentions During the Navigation in Semantic Websites

  • Rafael Liberato Roberto
  • Sérgio Roberto P. da Silva
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4519)

Abstract

The growing need for content customization in websites has fostered the development of systems which try to identify the user’s navigation patterns. These may be, normally, identified by means of log file analysis. However, this solution does not identify the semantic intention behind user’s navigation. This paper provides an approach to incorporating semantic knowledge to the process of identifying the user’s intentions in the navigation of a website with semantic support. The capture of the user’s intentions is achieved by the semantic enrichment of the log files and the use of and approach that takes into account the linguistic and cognitive aspects in the development of the user model.

Keywords

User Model Semantic Web Web Personalization 

References

  1. 1.
    Anderson, J.R.: A spreading activation theory of memory. Journal of Verbal Learning and Verbal Behavior 22 (1983a)Google Scholar
  2. 2.
    Anderson, J.R.: The architecture of cognition. Harvard University Press, Cambridge (1983b)Google Scholar
  3. 3.
    Berners-Lee, T., Hendler, J., Lassila, O. (2001). The Semantic Web. Scientifc American (May 2001)Google Scholar
  4. 4.
    Brusilovsky, P.: Adaptive Hypermedia, User Modeling and User-Adapted Interaction, pp. 87–110. Kluwer Academic Publishers, Dordrecht (2001)Google Scholar
  5. 5.
    Chen, L., Sycara, K.: Web Mate: A Personal Agent for Browsing and Searching. In: Proceedings of the 2nd International Conference on Autonomous Agents and Multi Agent Systems, AGENTS ’98, pp. 132–139. ACM Press, New York (1998)Google Scholar
  6. 6.
    Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D., Sartin, M.: Combining Content-based and Collaborative Filters in an Online Newspaper. In: Proceedings of the ACM SIGIR ’99 Workshop on Recommender Systems: Algorithms and Evaluation, University of California, Berkeley (Aug. 1999)Google Scholar
  7. 7.
    Collins, A.M., Loftus, E.F.: A spreading activation theory of semantic priming. Psychological Review 82, 407–428 (1975)CrossRefGoogle Scholar
  8. 8.
    Dai, H., Mobasher, B.: A Road map to More Effective Web Personalization: Integrating Domain Knowledge with Web Usage Mining. In: Proc. of the International Conference on Internet Computing 2003 (IC’03), Las Vegas, Nevada (June 2003)Google Scholar
  9. 9.
    Eirinaki, M., Vazirgiannis, M., Varlamis, I.: SEWeP: using site semantics and a taxonomy to enhance the Web personalization process. In: KDD 2003, pp. 99–108 (2003)Google Scholar
  10. 10.
    Eirinaki, M., Vazirgiannis, M.: Web Mining for Web Personalization. ACM Transactions on Internet Technology (TOIT) 3(1), 1–27 (2003)CrossRefGoogle Scholar
  11. 11.
    Gauch, S., Chaffee, J., Pretschner, A.: Ontology Based Personalized Search. Web Intelligence and Agent Systems (in press)Google Scholar
  12. 12.
    Hendler, J., Berners-Lee, T., Miller, E.: Integrating Applications on the Semantic Web. Journal of the Institute of Electrical Engineers of Japan 122(10), 676–680 (2002), http://www.w3.org/2002/07/swint Google Scholar
  13. 13.
    Lei, Y., Motta, E., Domingue, J.: Modelling Data- Intensive Web Sites with OntoWeaver. In: International Workshop on Web Information Systems Modelling (WISM 2004), Riga, Latvia (2004)Google Scholar
  14. 14.
    Lieberman, H.: Letizia: An Agent That Assists Web Browsing. In: Proceedings of the International Joint Conference on Artificial Intelligence, Montreal, CA (1995)Google Scholar
  15. 15.
    Mcguinness, D.L., van Harmelen, F.: OWL Web Ontology Language Overview. W3C Recommendation 10 February 2004 (2006). Available in: http://www.w3.org/TR/2004/REC-owl-features-20040210/ (Access in: Jan. 2006)
  16. 16.
    Middleton, S., De Roure, D., Shadbolt, N.: Capturing knowledge of user preferences: ontologies in recommender systems. In: Proceedings of the 1st International Conference on Knowledge Capture (K-Cap2001), Victoria, BC, Canada (2001)Google Scholar
  17. 17.
    Mladenic, D.: Text-learning and related intelligent agents. Revised version in IEEE Expert, special issue on Applications of Intelligent Information Retrieval (1999)Google Scholar
  18. 18.
    Mobasher, B., Daí, H., Luo, T., Sung, Y., Zhu, J.: Integrating Web Usage and Content Mining for More Effective Personalization. In: Proc. of the International Conference on E-Commerce and Web Technologies (ECWeb2000), Greenwich, UK (September 2000)Google Scholar
  19. 19.
    Pazzani, M.A.: Framework for Collaborative, Content-Based and Demographic Filtering. Artificial Intelligence Review, 393–408 (Dec. 1999)Google Scholar
  20. 20.
    Quillian, M.R.: Semantic memory. In: Minsky, M.L. (ed.) Semantic Information Processing, MIT Press, Cambridge (1968)Google Scholar
  21. 21.
    Tanasa, D., Trousse, B.: Advanced data preprocessing for intersites web usage mining. IEEE Intelligent Systems 19(2), 59–65 (2004)CrossRefGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Rafael Liberato Roberto
    • 1
  • Sérgio Roberto P. da Silva
    • 1
  1. 1.Universidade Estadual de Maringá, Av. Colombo 5790, zona 07, Maringá – PRBrasil

Personalised recommendations