C04-1150 quantitative evaluation of the OntoLearn ontology learning system , under different learning
C04-1150 an evaluation of the OntoLearn ontology learning system . The proposed evaluation
E06-2016 describing our general framework for ontology learning , postponing the solution of
J06-4009 of how great the challenge of ontology learning still remains . The evaluation
D11-1060 in information extraction and ontology learning . For example , Hearst ( 1992
C04-1150 detailed quantitative analysis of the ontology learning algorithms , in order to compute
E09-3011 question-answering and recently also within ontology learning . In the area of automatic glossary
J06-4009 contributions to the field of ontology learning and have organized some of the
J06-4009 ontological relations . In many ways , ontology learning is a specialization of core computational
C04-1150 in-depth evaluation of the Ontolearn ontology learning system . The three basic algorithms
C04-1150 On the computational side , an ontology learning tool is based on a battery of
C04-1150 applications . 3.2 Evaluating the ontology learning algorithms The distinctive task
J06-4009 important workshops in the area . Ontology learning has become a major area of research
D09-1025 for various NLP tasks such as ontology learning ( Cimiano and Staab , 2004 )
D09-1139 analysis ( Chesley et al. , 2006 ) or ontology learning ( Weber and Buitelaar , 2006
E06-2016 Performing a rigorous evaluation of an ontology learning process is not an easy task (
J06-4009 distinguishes ontology enrichment from ontology learning . Faatz and Steinmetz identify
J04-2002 workshops23 have been dedicated to ontology learning and related issues . The majority
D15-1206 informatics ( Wang and Fan , 2014 ) , ontology learning ( Xu et al. , 2014 ) , etc. .
J06-2005 Mihalcea and Moldovan 2001 ) , and ontology learning ( Navigli and Velardi 2004 )
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