NOVÁČEK, Vít and Pavel SMRŽ. Empirical Merging of Ontologies A Proposal of Universal Uncertainty Representation Framework. In The Semantic Web: Research and Applications (Lecture notes in Computer Science 4011 / 2006 - Proceedings of ESWC'06 - 3rd European Semantic Web Conference). Berlin: Springer Verlag, 2006, p. 65-79, 14 pp. ISBN 3-540-34544-2.
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Basic information
Original name Empirical Merging of Ontologies A Proposal of Universal Uncertainty Representation Framework
Name in Czech Empiricke spojovani ontologii - navrh ramce pro universalni reprezentaci neurcitosti
Name (in English) Empirical Merging of Ontologies A Proposal of Universal Uncertainty Representation Framework
Authors NOVÁČEK, Vít (203 Czech Republic, guarantor) and Pavel SMRŽ (203 Czech Republic).
Edition Berlin, The Semantic Web: Research and Applications (Lecture notes in Computer Science 4011 / 2006 - Proceedings of ESWC'06 - 3rd European Semantic Web Conference), p. 65-79, 14 pp. 2006.
Publisher Springer Verlag
Other information
Original language Czech
Type of outcome Proceedings paper
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher Czech Republic
Confidentiality degree is not subject to a state or trade secret
WWW URL
RIV identification code RIV/00216224:14330/06:00015341
Organization unit Faculty of Informatics
ISBN 3-540-34544-2
UT WoS 000238574900005
Keywords in English knowledge acquisition; ontology; uncertainty representation
Tags knowledge acquisition, ontology, uncertainty representation
Tags International impact, Reviewed
Changed by Changed by: doc. Mgr. Bc. Vít Nováček, PhD, učo 4049. Changed: 21/11/2006 12:22.
Abstract
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.
Abstract (in English)
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.
Links
1ET100300419, research and development projectName: Inteligentní modely, algoritmy, metody a nástroje pro vytváření sémantického webu
Investor: Academy of Sciences of the Czech Republic, Intelligent Models, Algorithms, Methods and Tools for the Semantic Web (realization)
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