Imprecise Empirical Ontology Refinement: Application to Taxonomy Acquisition

NOVÁČEK, Vít. Imprecise Empirical Ontology Refinement: Application to Taxonomy Acquisition. In Proceedings of ICEIS 2007, vol. Artificial Intelligence and Decision Support Systems. Portugal: INSTICC, 2007, p. 31-38. ISBN 978-972-8865-89-4.

Basic information

Original name

Imprecise Empirical Ontology Refinement: Application to Taxonomy Acquisition

Name in Czech

Neurcite Empiricke Tribeni Ontologii

Authors

NOVÁČEK, Vít (203 Czech Republic, guarantor, belonging to the institution)

Edition

Portugal, Proceedings of ICEIS 2007, vol. Artificial Intelligence and Decision Support Systems, p. 31-38, 8 pp. 2007

Publisher

INSTICC

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Other information

Language

English

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

RIV identification code

RIV/00216224:14330/07:00040314

Organization unit

Faculty of Informatics

ISBN

978-972-8865-89-4

Keywords in English

ontology engineering; ontology learning; taxonomy acquisiton; uncertainty

Tags

ontology engineering, ontology learning, taxonomy acquisiton, uncertainty

Tags

International impact, Reviewed

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Abstract

V originále

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)\footnote{The project's web page can be found at URL: \url{http://nlp.fi.muni.cz/projects/ole/}.} -- a project aimed at bottom-up generation and merging of ontologies. It utilises a proposal of expressive fuzzy knowledge representation framework called {\sf 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 {\sf ANUIC} can improve the results of initial knowledge extraction methods.

In Czech

Clanek se zabyva predstaveni modelu pro reprezentaci neurcite znalosti a extrakci taxonomii z volneho textu.

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Links

1ET100300419, research and development project

Name: 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)