2006
Empirical Merging of Ontologies A Proposal of Universal Uncertainty Representation Framework
NOVÁČEK, Vít a Pavel SMRŽZákladní údaje
Originální název
Empirical Merging of Ontologies A Proposal of Universal Uncertainty Representation Framework
Název česky
Empiricke spojovani ontologii - navrh ramce pro universalni reprezentaci neurcitosti
Název anglicky
Empirical Merging of Ontologies A Proposal of Universal Uncertainty Representation Framework
Autoři
NOVÁČEK, Vít (203 Česká republika, garant) a Pavel SMRŽ (203 Česká republika)
Vydání
Berlin, The Semantic Web: Research and Applications (Lecture notes in Computer Science 4011 / 2006 - Proceedings of ESWC'06 - 3rd European Semantic Web Conference), od s. 65-79, 14 s. 2006
Nakladatel
Springer Verlag
Další údaje
Jazyk
čeština
Typ výsledku
Stať ve sborníku
Obor
10201 Computer sciences, information science, bioinformatics
Stát vydavatele
Česká republika
Utajení
není předmětem státního či obchodního tajemství
Odkazy
Kód RIV
RIV/00216224:14330/06:00015341
Organizační jednotka
Fakulta informatiky
ISBN
3-540-34544-2
UT WoS
000238574900005
Klíčová slova anglicky
knowledge acquisition; ontology; uncertainty representation
Příznaky
Mezinárodní význam, Recenzováno
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.
Anglicky
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.
Návaznosti
| 1ET100300419, projekt VaV |
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