2008
Automatic Knowledge Acquisition and Integration Technique: Application to Large Scale Taxonomy Extraction and Document Annotation
NOVÁČEK, VítZákladní údaje
Originální název
Automatic Knowledge Acquisition and Integration Technique: Application to Large Scale Taxonomy Extraction and Document Annotation
Název česky
Automatic Knowledge Acquisition and Integration Technique: Application to Large Scale Taxonomy Extraction and Document Annotation
Autoři
Vydání
Berlin/Heidelberg, Enterprise Information Systems (ICEIS 2007, revised selected papers), od s. 160-172, 13 s. IX, 2008
Nakladatel
Springer-Verlag
Další údaje
Jazyk
angličtina
Typ výsledku
Kapitola resp. kapitoly v odborné knize
Obor
10201 Computer sciences, information science, bioinformatics
Stát vydavatele
Irsko
Utajení
není předmětem státního či obchodního tajemství
Odkazy
Označené pro přenos do RIV
Ano
Kód RIV
RIV/00216224:14330/08:00040380
Organizační jednotka
Fakulta informatiky
ISSN
UT WoS
Klíčová slova anglicky
ontology engineering; ontology learning; ontology integration; taxonomy acquisiton; uncertainty; knowledge representation
Štítky
Příznaky
Mezinárodní význam, Recenzováno
Změněno: 26. 4. 2011 20:52, doc. Mgr. Bc. Vít Nováček, PhD
V originále
We present new results of our research on integration of on- tologies created automatically by means of Human Language Technolo- gies. The research is related to OLE (Ontology LEarning)1 - a project aimed at bottom-up generation and merging of ontologies. It utilises a proposal of expressive uncertain 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. We also suggest an algorithm for large-scale automatic annotation of natural language documents, applying uncertain knowledge bases created using our approach.
Česky
We present new results of our research on integration of on- tologies created automatically by means of Human Language Technolo- gies. The research is related to OLE (Ontology LEarning)1 - a project aimed at bottom-up generation and merging of ontologies. It utilises a proposal of expressive uncertain 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. We also suggest an algorithm for large-scale automatic annotation of natural language documents, applying uncertain knowledge bases created using our approach.
Návaznosti
| 1ET100300419, projekt VaV |
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