9783838355108 - Extracting Systems of Concepts from Text: Automatically Learning Ontologies de Gillam, Lee (6 resultados)

Taschenbuch. Condición: Neu. Extracting Systems of Concepts from Text | Automatically Learning Ontologies | Lee Gillam | Taschenbuch | 196 S. | Englisch | 2010 | LAP LAMBERT Academic Publishing | EAN 9783838355108 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

Taschenbuch. Condición: Neu. Neuware -This work investigates automating the population of knowledge bases with systems of concepts extracted from texts in arbitrary domains, normally undertaken manually by domain experts. It explores issues of terminology extraction from domain texts, the need for and use of knowledge representation, and the means by which terminology extraction and knowledge representation can be combined with international standards for terminology to produce such an initial model of an arbitrary specialist domain. A method is elaborated for identifying evidence of key domain concepts, expressed through terms used in place of and in relation to these concepts. The work presented may contribute to the Semantic Web and related initiatives by helping to overcome the well-documented and unsolved AI problem of producing an initial model of an arbitrary specialist domain from background resources without significant hand-crafting effort and involvement of a domain expert: the so-called 'Knowledge Acquisition Bottleneck'. This bottleneck is usually only overcome through extensive and expensive interactions with domain experts, involving a number of expert interviews.Books on Demand GmbH, Überseering 33, 22297 Hamburg 196 pp. Englisch.

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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This work investigates automating the population of knowledge bases with systems of concepts extracted from texts in arbitrary domains, normally undertaken manually by domain experts. It explores issues of terminology extraction from domain texts, the need for and use of knowledge representation, and the means by which terminology extraction and knowledge representation can be combined with international standards for terminology to produce such an initial model of an arbitrary specialist domain. A method is elaborated for identifying evidence of key domain concepts, expressed through terms used in place of and in relation to these concepts. The work presented may contribute to the Semantic Web and related initiatives by helping to overcome the well-documented and unsolved AI problem of producing an initial model of an arbitrary specialist domain from background resources without significant hand-crafting effort and involvement of a domain expert: the so-called 'Knowledge Acquisition Bottleneck'. This bottleneck is usually only overcome through extensive and expensive interactions with domain experts, involving a number of expert interviews. 196 pp. Englisch.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Gillam LeeDr Lee Gillam MBCS CITP FHEA: Lecturer at the University of Surrey, and a Director of GeoLang Ltd.This work investigates automating the population of knowledge bases with systems of concepts extracted from texts in arbi.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This work investigates automating the population of knowledge bases with systems of concepts extracted from texts in arbitrary domains, normally undertaken manually by domain experts. It explores issues of terminology extraction from domain texts, the need for and use of knowledge representation, and the means by which terminology extraction and knowledge representation can be combined with international standards for terminology to produce such an initial model of an arbitrary specialist domain. A method is elaborated for identifying evidence of key domain concepts, expressed through terms used in place of and in relation to these concepts. The work presented may contribute to the Semantic Web and related initiatives by helping to overcome the well-documented and unsolved AI problem of producing an initial model of an arbitrary specialist domain from background resources without significant hand-crafting effort and involvement of a domain expert: the so-called 'Knowledge Acquisition Bottleneck'. This bottleneck is usually only overcome through extensive and expensive interactions with domain experts, involving a number of expert interviews.