ARAN - Access to Research at NUI Galway

Imprecise Empirical Ontology Refinement: Application to Taxonomy Acquisition

ARAN - Access to Research at NUI Galway

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dc.contributor.author Novacek, Vit en
dc.date.accessioned 2009-11-27T12:37:38Z en
dc.date.available 2009-11-27T12:37:38Z en
dc.date.issued 2007 en
dc.identifier.citation Vit Novacek "Imprecise Empirical Ontology Refinement: Application to Taxonomy Acquisition", Proceedings of ICEIS 2007, vol. Artificial Intelligence and Decision Support Systems, INSTICC, 2007. en
dc.identifier.uri http://hdl.handle.net/10379/425 en
dc.description.abstract 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) a ¿ a project aimed at bottom-up generation and merging of ontologies. It utilises a proposal of expressive fuzzy knowledge representation framework called 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 ANUIC can improve the results of initial knowledge extraction methods. en
dc.format application/pdf en
dc.language.iso en en
dc.publisher INSTICC en
dc.subject Ontology engineering en
dc.subject Ontology learning en
dc.subject Taxonomy acquisiton en
dc.subject Uncertainty en
dc.subject.lcsh Knowledge representation (Information theory) en
dc.subject.lcsh Conceptual structure (Information theory) en
dc.subject.lcsh Ontology en
dc.subject.lcsh Expert systems (Computer science) en
dc.subject.lcsh Knowledge acquisition (Expert systems) en
dc.subject.lcsh Knowledge management en
dc.subject.lcsh Uncertainty (Information theory) en
dc.title Imprecise Empirical Ontology Refinement: Application to Taxonomy Acquisition en
dc.type Conference paper en
dc.description.peer-reviewed peer-reviewed en
dc.contributor.funder Knowledge Web en

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