Publication

Imprecise Empirical Ontology Refinement: Application to Taxonomy Acquisition

Nováček, Vít
Thumbnail Image
Files
Thumbnail Image
iceis07novacek.pdf
Adobe PDF, 236.99 KB
Repository DOI
Publication Date
2007
Keywords
Ontology engineering, Ontology learning, Taxonomy acquisiton, Uncertainty
Type
Conference Paper
Downloads
Show all metadata
Citation
Vit Novacek "Imprecise Empirical Ontology Refinement: Application to Taxonomy Acquisition", Proceedings of ICEIS 2007, vol. Artificial Intelligence and Decision Support Systems, INSTICC, 2007.
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.
Funder
Knowledge Web
Publisher
INSTICC
Publisher DOI
Rights
Attribution-NonCommercial-NoDerivs 3.0 Ireland
cbn
Collections
Data Science Institute (Conference Papers)