Empirical Merging of Ontologies — A Proposal of Universal Uncertainty Representation Framework

  • Vít Nováček
  • Pavel Smrž
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4011)


The significance of uncertainty representation has become obvious in the Semantic Web community recently. This paper presents our research on uncertainty handling in automatically created ontologies. A new framework for uncertain information processing is proposed. The research is related to OLE (Ontology LEarning) — a project aimed at bottom–up generation and merging of domain–specific ontologies. Formal systems that underlie the uncertainty representation are briefly introduced. We discuss the universal internal format of uncertain conceptual structures in OLE then and offer a utilisation example then. The proposed format serves as a basis for empirical improvement of initial knowledge acquisition methods as well as for general explicit inference tasks.


Query Processing Description Logic Uncertainty Representation Graph Concept Knowledge Repository 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Vít Nováček
    • 1
  • Pavel Smrž
    • 2
  1. 1.Faculty of InformaticsMasaryk UniversityBrnoCzech Republic
  2. 2.Faculty of Information TechnologyBrno University of TechnologyBrnoCzech Republic

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