Advertisement

Interactive Ontology-Based User Knowledge Acquisition: A Case Study

  • Lora Aroyo
  • Ronald Denaux
  • Vania Dimitrova
  • Michael Pye
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4011)

Abstract

On the Semantic Web personalization technologies are needed to deal with user diversity. Our research aims at maximising the automation of acquisition of user knowledge, thus providing an effective solution for multi-faceted user modeling. This paper presents an approach to eliciting a user’s conceptualization by engaging in an ontology-driven dialog. This is implemented as an OWL-based domain-independent diagnostic agent. We show the deployment of the agent in a use case for personalized management of learning content, which has been evaluated in three studies with users. Currently, the system is being deployed in a cultural heritage domain for personalized recommendation of museum resources.

Keywords

User Model Domain Ontology File Operation Cultural Heritage Domain Unix User 
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.

References

  1. 1.
    Henze, N.: Personalization functionality for the semantic web: Identification and description of techniques. Technical report, REWERSE EU NoE (2004)Google Scholar
  2. 2.
    Kobsa, A.: User modeling in dialog systems: Potentials and hazards. Artificial Intelligence and Society 1, 214–240 (1990)Google Scholar
  3. 3.
    Jameson, A.: User-adaptive systems. Technical report, UM03 Tutorial (2003)Google Scholar
  4. 4.
    Wolpers, M., Nejdl, W.: European e-learning: Important research issues and application scenarios. In: ED-Media 2004 Conference, Lugano, Switzerland, pp. 21–25 (2004)Google Scholar
  5. 5.
    Anderson, T., Whitelock, D.: The Educational Semantic Web: Visioning and Practicing the Future of Education, vol. 1 (2004)Google Scholar
  6. 6.
    Stojanovic, L., Staab, S., Studer, R.: elearning based on the semantic web. In: World Conference on the WWW and Internet (WebNet 2001), Florida, USA, pp. 23–27 (2001)Google Scholar
  7. 7.
    Aroyo, L., Dicheva, D.: The new challenges for e-learning: The educational semantic web. Journal of Educational Technology and Society 7, 59–69 (2004)Google Scholar
  8. 8.
    Duval, E.: Learning technology standardization: making sense of it all. International Journal on Computer Science and Information Systems 1, 33–43 (2004)CrossRefGoogle Scholar
  9. 9.
    Simon, B., Dolog, P., Miklós, Z., Olmedilla, D., Sintek, M.: Conceptualising smart spaces for learning. Journal of Interactive Media in Education (September 2004)Google Scholar
  10. 10.
    Stutt, A., Motta, E.: Semantic learning webs. Journal of Interactive Media in Education: Special Issue on the Educational Semantic Web 10 (2004)Google Scholar
  11. 11.
    Dolog, P.: Identifying relevant fragments of learner profile on the semantic web. In: SW-EL 2004 at International Semantic Web Conference, Hiroshima, Japan (2004)Google Scholar
  12. 12.
    Henze, N., Dolog, P., Nejdl, W.: Reasoning and ontologies for personalized e-learning. Journal of Educational Technology & Society 7 (2004)Google Scholar
  13. 13.
    Berners-Lee, T., Hendler, J., Lassila, O.: The defining characteristics of intelligent tutoring systems research: Itss care, precisely. International Journal of Artificial Intelligence in Education 10 (1999)Google Scholar
  14. 14.
    Denaux, R., Aroyo, L., Dimitrova, V.: An approach for ontology-based elicitation of user models for the semantic web. In: WWW 2005 (poster) (2004)Google Scholar
  15. 15.
    Dimitrova, V.: Style-olm: Interactive open learner modelling. Int. Journal of Artificial Intelligence in Education 13, 35–78 (2003)Google Scholar
  16. 16.
    Noy, N., Sintek, M., Crubezy, M., Fergerson, R., Musen, M.: Creating semantic web contents with protege-2000. IEEE Intelligent Systems 16(2), 60–71 (2001)CrossRefGoogle Scholar
  17. 17.
    Carroll, J., et al.: Jena: Implementing the semantic web recommendations. In: WWW 2004, pp. 74–83 (2004)Google Scholar
  18. 18.
    Miller, E., Manola, F.: Rdf primer (2004), http://www.w3c.org/TR/
  19. 19.
    Lecoeuche, R., Mellish, C., Barry, C., Robertson, D.: User-system dialogues and the notion of focus. The Knowledge Engineering Review 13 (1998)Google Scholar
  20. 20.
    Levin, J., Moore, J.: Dialogue games: Meta-communication structures for natural language interaction. Cognitive Science (1978)Google Scholar
  21. 21.
    Denaux, R., Dimitrova, V., Aroyo, L.: Interactive ontology-based user modeling for personalized learning content management. In: AH 2004: Workshop Proceedings Part II, pp. 338–347 (2004)Google Scholar
  22. 22.
    Aroyo, L., Dicheva, D.: Aims: Learning and teaching support for www-based education. Int. Journal for Continuing Engineering Education and Life-long Learning (IJCEELL) 11, 152–164 (2001)CrossRefGoogle Scholar
  23. 23.
    Denaux, R., Dimitrova, V., Aroyo, L.: Integrating open user modeling and learning content management for the semantic web. In: Ardissono, L., Brna, P., Mitrović, A. (eds.) UM 2005. LNCS, vol. 3538, pp. 9–18. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  24. 24.
    Brusilovsky, P., Tasso, C.: Special issue on user modelling for web information retrieval. User Modeling and User Adapted Interaction 14 (2004)Google Scholar
  25. 25.
    Micarelli, A., Sciarrone, F.: Anatomy and empirical evaluation of an adaptive web-based information filtering system. User Modeling and User-Adapted Interaction 14, 159–200 (2004)CrossRefGoogle Scholar
  26. 26.
    Stojanovic, N.: On the role of a user’s knowledge gap in an information retrieval process. In: Proceedings of K-CAP 2005, pp. 83–90. ACM Press, New York (2005)Google Scholar
  27. 27.
    Chklovski, T., Gil, Y.: Improving the design of intelligent acquisition interfaces for collecting world knowledge from web contributors. In: Proceedings of K-CAP 2005, pp. 35–42. ACM Press, New York (2005)Google Scholar
  28. 28.
    Bra, P.D., Aroyo, L., Chepegin, V.: The next big thing: Adaptive web-based systems. Journal of Digital Information 5(1) (2004)Google Scholar
  29. 29.
    Klein, M.: Combining and relating ontologies: an analysis of problems and solutions. In: Gomez-Perez, A., Gruninger, M., Stuckenschmidt, H., Uschold, M. (eds.) Workshop on Ontologies and Information Sharing, IJCAI 2001, Seattle, USA (2001)Google Scholar
  30. 30.
    Ehrig, M., Sure, Y.: Ontology mapping - an integrated approach. In: Bussler, C.J., Davies, J., Fensel, D., Studer, R. (eds.) ESWS 2004. LNCS, vol. 3053, pp. 76–91. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  31. 31.
    Noy, N.F., Musen, M.A.: PROMPT: Algorithm and tool for automated ontology merging and alignment. In: IJCAI 2001 Workshop on Ontologies and Information Sharing, pp. 63–70 (2000)Google Scholar
  32. 32.
    Bailin, S.C., Truszkowski, W.: Ontology negotiation between intelligent information agents. The Knowledge Engineering Review 17, 7–19 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Lora Aroyo
    • 1
  • Ronald Denaux
    • 1
  • Vania Dimitrova
    • 2
  • Michael Pye
    • 2
  1. 1.Faculty of Mathematics and Computer ScienceEindhoven University of TechnologyEindhovenThe Netherlands
  2. 2.School of ComputingUniversity of LeedsLeedsUK

Personalised recommendations