Empirical Merging of Ontologies  A Proposal of Universal Uncertainty Representation Framework

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Authors	NOVÁČEK Vít SMRŽ Pavel
Year of publication	2006
Type	Article in Proceedings
Conference	The Semantic Web: Research and Applications (Lecture notes in Computer Science 4011 / 2006 - Proceedings of ESWC'06 - 3rd European Semantic Web Conference)
MU Faculty or unit	Faculty of Informatics
Citation
Web	http://nlp.fi.muni.cz/projects/ole/pubs.html
Field	Informatics
Keywords	knowledge acquisition; ontology; uncertainty representation
Description	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.
Related projects:	Intelligent Models, Algorithms, Methods and Tools for the Semantic Web (realization)