Imprecise Empirical Ontology Refinement: Application to Taxonomy Acquisition

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Authors	NOVÁČEK Vít
Year of publication	2007
Type	Article in Proceedings
Conference	Proceedings of ICEIS 2007, vol. Artificial Intelligence and Decision Support Systems
MU Faculty or unit	Faculty of Informatics
Citation
Field	Informatics
Keywords	ontology engineering; ontology learning; taxonomy acquisiton; uncertainty
Description	The significance of uncertainty representation has become obvious in the Semantic Web community recently. This paper presents new results of our research on uncertainty incorporation into ontologies created automatically by means of Human Language Technologies. The research is related to OLE (Ontology LEarning)\footnote{The project's web page can be found at URL: \url{http://nlp.fi.muni.cz/projects/ole/}.} -- a project aimed at bottom-up generation and merging of ontologies. It utilises a proposal of expressive fuzzy knowledge representation framework called {\sf ANUIC} (Adaptive Net of Universally Interrelated Concepts). We discuss our recent achievements in taxonomy acquisition and show how even simple application of the principles of {\sf ANUIC} can improve the results of initial knowledge extraction methods.
Related projects:	Intelligent Models, Algorithms, Methods and Tools for the Semantic Web (realization)