E06-1003 main paradigms distinguishing Ontology Population approaches . In the first one
E06-1003 the state-of-the-art methods in Ontology Population . Section 3 presents the three
E06-1003 this Section we present three Ontology Population approaches . Two of them are
E06-1003 approaches . In the first one Ontology Population is performed using patterns (
C02-1083 which includes term recognition , ontology population , and ontology-based inference
E06-1003 weakly supervised approach for ontology population which accepts as a training data
E06-1003 Weakly supervised approaches for Ontology Population In this Section we present three
E06-1003 supervised approach to automatic Ontology Population from text and compare it with
P10-1031 constructing an ontology ) and ontology population ( mapping textual expressions
E06-1003 Weakly Supervised Approaches for Ontology Population </title> Hristo Tanev Tanev Bernardo
C04-1150 OntoLearn system OntoLearn is an ontology population method based on text mining and
E06-1003 weakly supervised approach for Ontology Population , called Class-Example , and
S07-1029 such as taxonomy induction and ontology population . Acknowledgments Claudio Giuliano
J12-3005 instance , Information Extraction and ontology population , although to our knowledge this
W06-0504 introduces challenging extensions to Ontology Population restricted to named entities
W06-0504 , OPTM simplifies the general Ontology Population task , limiting the input textual
C02-1083 used for automatic and systematic ontology population . The paper is organised as follows
E06-1003 approaches . 1 Introduction Automatic Ontology Population ( OP ) from texts has recently
E06-1003 conclusion that the performance of an Ontology Population system improves with the increase
E06-1003 t , ... In our experiments for ontology population we used the patterns described
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