Detailed Information on Publication Record
2006
Empirical Merging of Ontologies A Proposal of Universal Uncertainty Representation Framework
NOVÁČEK, Vít and Pavel SMRŽBasic 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
Language
Czech
Type of outcome
Stať ve sborníku
Field of Study
10201 Computer sciences, information science, bioinformatics
Country of publisher
Czech Republic
Confidentiality degree
není předmětem státního či obchodního tajemství
References:
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
International impact, Reviewed
Změněno: 21/11/2006 12:22, doc. Mgr. Bc. Vít Nováček, PhD
V originále
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.
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 project |
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