Incremental Knowledge Management of Web Community Groups on Web Portals

  • Yang Sok Kim
  • Sung Sik Park
  • Byeong Ho Kang
  • Young Ju Choi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3336)

Abstract

The concept of the web portal was introduced in around 1998 when the web became a standard medium for accessing information. While HTML-based static web pages were also popular, people used the search engine websites, or specific web pages, such as the personal web page or the web browser company default page, as their web portals. Since their inception, providing information for users has been the most important function of web portals, and many of them try to provide adapted information to different users. Offering this level of service is difficult because of the quantity of information and the various types of information classification for different user groups involved. In most web portals, the collection and classification of the information is still carried out manually. Automation of this task requires domain-specific classification knowledge, which is not easy to acquire. Automated web information management and publication system has been developed using the Multiple Classification Ripple Down Rules (MCRDR) knowledge acquisition engine. Various prototype web portals are being developed and the evaluation study proves the potential of the out-of-box style web portal generation tool for the adapted service.

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References

  1. 1.
    Wege, C.: Portal Server Technology. IEEE Internet Computing 6(3), 73–77 (2002)CrossRefGoogle Scholar
  2. 2.
    Priebe, T., Pernul, G.: Towards integrative enterprise knowledge portals. In: Twelfth International Conference on Information and Knowledge Management, New Orleans, LA, USA. ACM Press, New York (2003)Google Scholar
  3. 3.
    Park, S.S., Kim, S.K., Kang, B.H.: Web Information Management System: Personalization and Generalization. In: the IADIS International Confernece WWW/Internet 2003 (2003)Google Scholar
  4. 4.
    Alavi, M., Leidner, D.E.: Knowledge management systems: issues, challenges, and benefits. Communications of the AIS 1(2) (1999)Google Scholar
  5. 5.
    Manber, U., Patel, A., Robison, J.: Experience with personalization on Yahoo! Communications of the ACM 43(8), 35–39 (2000)CrossRefGoogle Scholar
  6. 6.
    Bellas, F., Fernandez, D., Muino, A.: A flexible framework for engineering “my” portals. In: 13th conference on World Wide Web. ACM Press, New York (2004)Google Scholar
  7. 7.
    Rossi, G., Schwabe, D., Guimaraes, R.: Designing personalized web applications. In: tenth international conference on World Wide Web, Hong Kong. ACM Press, New York (2001)Google Scholar
  8. 8.
    Brusilovsky, P., Maybury, M.T.: From adaptive hypermedia to the adaptive web. Communications of the ACM 45(5), 30–33 (2002)CrossRefGoogle Scholar
  9. 9.
    Feigenbaum, E.A.: Knowledge engineering: The applied side of artificial intelligence. Annals of the New York Academy Sciences 246, 91–107 (1984)CrossRefGoogle Scholar
  10. 10.
    Kang, B., Compton, P., Preston, P.: Multiple Classification Ripple Down Rules: Evaluation and Possibilities. In: 9th AAAI-Sponsored Banff Knowledge Acquisition for Knowledge-Based Systems Workshop, Banff, Canada, University of Calgary (1995)Google Scholar
  11. 11.
    Kang, B.H., Gambetta, W., Compton, P.: Verification and validation with ripple-down rules. International Journal of Human-Computer Studies 44(2), 257–269 (1996)CrossRefGoogle Scholar
  12. 12.
    Kim, Y.S., et al.: Adaptive Web Document Classification with MCRDR. In: International Conference on Information Technology: Coding and Computing ITCC 2004, Orleans, Las Vegas, Nevada, USA (2004)Google Scholar
  13. 13.
    Lu, B., Hui, S.C., Zhang, Y.: Personalized Information Monitoring Over the Web. In: First International Conference On Information Technology & Applications (ICITA 2002), Bathurst, Australia (2002)Google Scholar
  14. 14.
    Liu, L., Pu, C., Tang, W.: WebCQ: Detecting and Delivering Information Changes on the Web. In: International Conference on Information and Knowledge Management (CIKM). ACM Press, Washington (2000)Google Scholar
  15. 15.
    Pandey, S., Ramamritham, K., Chakrabarti, S.: Monitoring the dynamic web to respond to continuous queries. In: International World Wide Web Conference, Budapest, Hungary (2003)Google Scholar
  16. 16.
    Tang, W., Liu, L., Pu, C.: WebCQ Detecting and Delivering Information Changes on the Web. In: Proc. Int. Conf. on Information and Knowledge Management, CIKM (2000)Google Scholar
  17. 17.
    Tan, B., Foo, S., Hui, S.C.: Web Information Monitoring: an Analysis of Web Page Updates. Online Information Review 25(1), 6–18 (2001)CrossRefGoogle Scholar
  18. 18.
    Brandt, S., Kristensen, A.: Web push as an internet notification service (1997)Google Scholar
  19. 19.
    Hahn, J., Kim, J.: Why are some representations (sometimes) more effective? In: 20th International Conference on Information Systems, Charlotte, North Carolina, United States: Association for Information Systems, Atlanta, GA, USA (1999)Google Scholar
  20. 20.
    Larkin, J., Simon, H.: Why a diagram is (sometimes) worth ten thousand words. Cognitive Science 11, 65–99 (1987)CrossRefGoogle Scholar
  21. 21.
    Ford, K.M., et al.: Knowledge acquisition as a constructive modeling activity. International Journal of Intelligent Systems 8(1), 9–32 (1993)CrossRefGoogle Scholar
  22. 22.
    Kang, B.H., Compton, P., Preston, P.: Validating incremental knowledge acquisition for multiple classifications. In: Critical Technology: Proceedings of the Third World Congress on Expert Systems, pp. 856–868 (1996)Google Scholar
  23. 23.
    Compton, P., Richards, D.: Generalising ripple-down rules. In: Dieng, R., Corby, O. (eds.) EKAW 2000. LNCS (LNAI), vol. 1937, pp. 380–386. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  24. 24.
    Mladenic, D.: Text-learning and Related Intelligent Agents. Applications of Intelligent Information Retrieval (1999)Google Scholar
  25. 25.
    Wada, T., et al.: Integrating Inductive learning and Knowledge Acquisition in the Ripple Down Rules Method. In: 6th Pacific Knowledge Acquisition Workshop, Sydney, Australia (2000)Google Scholar
  26. 26.
    Suryanto, H., Compton, P.: Intermediate Concept Discovery in Ripple Down Rule Knowledge Bases. In: 2002 Pacific Rim Knowledge Acquisition Workshop, Tokyo, Japan (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Yang Sok Kim
    • 1
  • Sung Sik Park
    • 1
  • Byeong Ho Kang
    • 1
  • Young Ju Choi
    • 1
  1. 1.School of ComputingUniversity of TasmaniaHobart, TasmaniaAustralia

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