Uncertain Knowledge Gathering: An Evolutionary Approach

  • Dennis Hooijmaijers
  • Damien Bright
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3488)


Information and the knowledge pertaining to it are important to making informed decisions. In domains where knowledge changes quickly, such as business and open source intelligence gathering there is a need to represent and manage the collection of dynamic information from multiple sources. Some of this information may be transitory due to its situated context and time-limited value (eg driven by a changing business market). Ontologies offer a way to model concepts and relationships from information sources and for dynamic domains it is important to look at how to support evolution of knowledge represented in an ontology, the change management of an ontology and how to maintain consistency when mapping and merging knowledge from multiple sources to an ontology base. One of the difficulties is that often contradictions can occur and sources may be unreliable. In this paper we introduce a technique for merging knowledge while ensuring that the reliability of that knowledge is captured and present a model which supports ontology evolution through the inclusion of trust and belief measures.


Knowledge Expert Domain Expert Belief Revision Information Fusion Belief Function 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Van Harmelen, F., et al.: Ontology-based information visualisation. In: Proc. of Fifth International Conference on Information Visualisation, London, England, July 25 - 27 (2001)Google Scholar
  2. 2.
    Noy, N.F., Klein, M.: Ontology evolution: Not the same as schema evolution. Knowledge and Information Systems 6(4), 428–440 (2004)CrossRefGoogle Scholar
  3. 3.
    Stojanovic, L., et al.: User-driven Ontology Evolution Management. In: Proc. of 13th Intl’ Conf. on Knowledge Engineering and Knowledge Management (2002)Google Scholar
  4. 4.
    Sindt, T.: Formal Operations for Ontology Evolution. In: Proc. of ICET 2003, Minneapolis, MN, USA (2003)Google Scholar
  5. 5.
    McGuinness, D.L., Van Harmelen, F.: OWL Web Ontology Language Overview (2003), (accessed November 2004)
  6. 6.
    Klein, M., Noy, N.F.: A component-based framework for ontology evolution. In: Proc. of the Workshop on Ontologies and Distributed Systemsm, Acpulco, Mexico (2003)Google Scholar
  7. 7.
    Noy, N.F., Musen, M.A.: PROMPT: Algorithm and Tool for Automated Ontology Merging and Alignment. In: Proc. of the 17th National Conference on Artificial Intelligence, Austin, Texas (2000)Google Scholar
  8. 8.
    Dou, D., McDermott, D., Qi, P.: Ontology Translation on the Semantic Web. In: Proc. of Int’l Conf. on Ontologies, Databases and Applications of Semantics (2003)Google Scholar
  9. 9.
    McGuinness, D.L., et al.: The Chimaera Ontology Environment. In: Proc. of The 17th National Conference on Artificial Intelligence, Austin, Texas (2000)Google Scholar
  10. 10.
    McGuinness, D.L.: Ontologies for Information Fusion. In: Proc. of Fusion 2003 The 6th International Conference on Information Fusion, Cairns, Australia (2003)Google Scholar
  11. 11.
    Noy, N.F., Musen, M.A.: The PROMPT Suite: Interactive Tools For Ontology Merging And Mapping. International Journal of Human-Computer Studies 59, 983–1024 (2003)CrossRefGoogle Scholar
  12. 12.
    Dahl, F.A.: Representing Human Uncertainty by Subjective Likelihood Estimates. Preprint 2004 series, ISSN 0806-3842, Department of Mathematics, University of Oslo (2004)Google Scholar
  13. 13.
    Gennari, J.H., et al.: The Evolution of Protege: An Environment for Knowledge-Based Systems Development. Tech. Report SMI-2002-0943, Stanford Medical Informatics (2002)Google Scholar
  14. 14.
    Resnick, P., Zeckhauser, R., Friedman, E., Kuwabara, K.: Reputation Systems. Communications of the ACM 43(12), 45–48 (2000)CrossRefGoogle Scholar
  15. 15.
    Noy, N.F., Kunnatur, S., Klein, M., Musen, M.A.: Tracking changes during ontology evolution. In: 3rd Int. Semantic Web Conference (ISWC 2004), Hiroshima, Japan (2004)Google Scholar
  16. 16.
    Parsons, S., Hunter, A.: A Review of Uncertainty Handling Formalisms. In: Hunter, A., Parsons, S. (eds.) Applications of Uncertainty Formalisms. LNCS (LNAI), vol. 1455, pp. 8–37. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  17. 17.
    Ding, Z., Peng, Y., Pan, R.: A Bayesian Approach to Uncertainty Modeling in OWL Ontology. In: Proc. of the Int. Conf. on Advances in Intelligent Systems (November 2004)Google Scholar
  18. 18.
    Noble, D.F.: Assessing the Reliability of open Source Information. In: Proc. of 7th International Conference on Information Fusion, Stockholm, Sweden (2004)Google Scholar
  19. 19.
    Buchanan, B.G., Shortliffe, E.H.: Rule-Based Expert Systems. AddisonWesley, Reading (1984)Google Scholar
  20. 20.
    Johnson, C.H. (eds): Competitive Intelligence: Where are the ethical limits. Special issue, Ethics in Economics, (3&4) (1998)Google Scholar
  21. 21.
    Pattison, T.R., Phillips, M.P.: View Coordination Architecture for Information Visualization. In: Australian Symposium on Information Visualisation, Sydney, Australia (2001)Google Scholar
  22. 22.
    Gauvin, M., Boury-Brisset, A.C., Auger, A.: Context, Ontology and Portfolio: Key Concepts for a Situational Awareness Knowledge Portal. In: Proc. of the 37th Hawaii International Conference on System Sciences (2004)Google Scholar
  23. 23.
    Noh, N., Rector, A.: Defining N-ary Relations on the Semantic Web: Use with Individuals W3C Working Draft (accessed December 2004),

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Dennis Hooijmaijers
    • 1
  • Damien Bright
    • 1
  1. 1.School of Computer and Information ScienceUniversity of South AustraliaMawson LakesAustralia

Personalised recommendations