Advertisement

Framework for Collaborative Knowledge Sharing and Recommendation Based on Taxonomic Partial Reputations

  • Dong-Hwee Kim
  • Soon-Ja Kim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4092)

Abstract

We propose a novel system for collaborative knowledge sharing and recommendation based on taxonomic partial reputations on web-based personal knowledge directories. And we developed a prototype of the proposed system as a web-based user interface for personal knowledge management. This system presents a personal knowledge directory to a registered user. Such a directory has a personal ontology to integrate and classify the knowledge collected by a user from the Web. And the knowledge sharing activities among registered users generate partial reputation factors of knowledge items, their domain nodes, users and groups. Then new users can obtain the knowledge items proper to their needs, by referring such reputation values of those elements. In addition, users can also take the stem that is a set of common knowledge items over the domains designated by them. Thus proposed system can prevent cold-start problem because our knowledge recommendation mechanisms depend on the results of the collaborative knowledge sharing activities among users.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Rensick, P., Varian, H.R.: Recommender Systems. Communications of the ACM 40(3), 56–58 (1997)CrossRefGoogle Scholar
  2. 2.
    Goldberg, D., Nicholas, D., Oki, B.M., Terry, D.: Using Collaborative Filtering to Weave an Information Tapestry. Communications of the ACM 35(12), 61–70 (1992)CrossRefGoogle Scholar
  3. 3.
    Maltz, D., Rhrlich, E.: Pointing the way: Active Collaborative Filtering. In: Proc. of CHI 1995, pp. 202–209 (1995)Google Scholar
  4. 4.
    Avery, C., Zeckhauser, R.: Recommender Systems for Evaluating Computer Messages. Communications of the ACM 40(3), 88–89 (1997)CrossRefGoogle Scholar
  5. 5.
    Kidd, A.: Knowledge Acquisition: An Introductory Framework. Knowledge Acquisition for Expert Systems: A Practical Handbook, 1–15 (1987)Google Scholar
  6. 6.
    Massa, P., Avesani, P.: Trust-aware collaborative filtering for recommender systems. In: Meersman, R., Tari, Z. (eds.) OTM 2004. LNCS, vol. 3290, pp. 492–508. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  7. 7.
    Breese, J.S., Heckerman, D., Kadie, C.: Empirical Analysis of Predictive Algorisms for Collaborative Filtering. In: Proc. of the 14th Conference on Uncertainty in Artificial Intelligence, pp. 43–52 (1998)Google Scholar
  8. 8.
    Konstan, J.A., Miller, B.N., Maltz, D., Herlocker, J.L., Gordon, L.R., Riedl, J.: GroupLens: Applying Collaborative Filtering to Usenet News. Communications of the ACM 40(3), 77–87 (1997)CrossRefGoogle Scholar
  9. 9.
    Rensnick, P., Iacovou, N., Suchak, M., Bergstorm, P., Riedl, J.: GroupLens: An Open Architecture for Collaborative Filtering of Netnwes. In: Proc. of CSCW 1994 (1994)Google Scholar
  10. 10.
    Gruber, T.R.: Toward Principles for the Design of Ontologies used for Knowledge Sharing. Technical report, Stanford University (1993)Google Scholar
  11. 11.
    Pretschner, A., Gauch, S.: Ontology based Personalized Search. In: Proc. 11th IEEE Intl. Conference on Tools on Artificial Intelligence, pp. 391–398 (1999)Google Scholar
  12. 12.
    Welty, C.: Toward Semantics for the Web. In: Proc. Dagstuhl-Seminar: Semantics for the Web (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Dong-Hwee Kim
    • 1
  • Soon-Ja Kim
    • 1
  1. 1.School of Electrical Engineering and Computer ScienceKyungpook National UniversityKorea

Personalised recommendations