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Automated Information Mediator for HTML and XML Based Web Information Delivery Service

  • Sung Sik Park
  • Yang Sok Kim
  • Gil Cheol Park
  • Byeong Ho Kang
  • Paul Compton
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3809)

Abstract

The World Wide Web (Web) was not designed to ‘push’ information to clients but for clients to ‘pull’ information from servers (providers). This type of technology is not efficient in prompt information delivery from changing sources. Recently, XML-based ‘RSS’, or ‘Weblog’, has become popular, because they simulate real time information delivery using automated client pull technology. However, this is still inefficient because people have to manually manage large quantities of Web information, causing information overflow. Secondly, most current Web information still uses HTML instead of XML. Our automated information mediator (AIMS) collects new information from both traditional HTML sites and XML sites and alleviates the information overload problem by using narrowcasting from the server side, and information filtering from the client side using Multiple Classification Ripple-Down Rules (MCRDR) knowledge acquisition for document classification. The approach overcomes the traditional knowledge acquisition problem with an exception based knowledge representation and case based validation and verification. By employing this approach, the system allows domain experts, or even naive end users to manage their knowledge and personalize their agent system without help from a knowledge engineer.

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References

  1. 1.
    Compton, P., Jansen, R.: A philosophical basis for knowledge acquisition. Knowledge Acquisition 2(3), 241–258 (1990)CrossRefGoogle Scholar
  2. 2.
    Compton, P., et al.: Ripple down rules: possibilities and limitations. In: 6th Bannf AAAI Knowledge Acquisition for Knowledge Based Systems Workshop, Banff, Canada (1991)Google Scholar
  3. 3.
    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
  4. 4.
    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
  5. 5.
    Park, S.S., Kim, Y.S., Kang, B.H.: Web Document Classification: Managing Context Change. In: IADIS International Conference WWW/Internet 2004, Madrid, Spain (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Sung Sik Park
    • 1
  • Yang Sok Kim
    • 1
  • Gil Cheol Park
    • 2
  • Byeong Ho Kang
    • 1
  • Paul Compton
    • 3
  1. 1.School of ComputingUniversity of TasmaniaHobartAustralia
  2. 2.School of Information & MultimediaHannam UniversityDaejeonKorea
  3. 3.School of Computer Science and EngineeringUniversity of New South WalesSydneyAustralia

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