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Verlag: Peter Lang GmbH, Internationaler, 2011
ISBN 10: 3631606516ISBN 13: 9783631606513
Anbieter: Books From California, Simi Valley, CA, USA
Buch
Hardcover. Zustand: Very Good. Cover has some light shelfwear. Pages are clean and intact. There is some slight dirtiness on the textblock/fore edge from handling. Has some minor dirtiness on the outside from handling.
Verlag: Peter Lang Ltd. International Academic Publishers Mai 2011, 2011
ISBN 10: 3631606516ISBN 13: 9783631606513
Anbieter: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Deutschland
Buch Print-on-Demand
Buch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The manual construction of formal domain conceptualizations (ontologies) is labor-intensive. Ontology learning, by contrast, provides (semi-)automatic ontology generation from input data such as domain text. This thesis proposes a novel approach for learning labels of non-taxonomic ontology relations. It combines corpus-based techniques with reasoning on Semantic Web data. Corpus-based methods apply vector space similarity of verbs co-occurring with labeled and unlabeled relations to calculate relation label suggestions from a set of candidates. A meta ontology in combination with Semantic Web sources such as DBpedia and OpenCyc allows reasoning to improve the suggested labels. An extensive formal evaluation demonstrates the superior accuracy of the presented hybrid approach. 222 pp. Englisch.
Verlag: Peter Lang, 2011
ISBN 10: 3631606516ISBN 13: 9783631606513
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Buch
Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - The manual construction of formal domain conceptualizations (ontologies) is labor-intensive. Ontology learning, by contrast, provides (semi-)automatic ontology generation from input data such as domain text. This thesis proposes a novel approach for learning labels of non-taxonomic ontology relations. It combines corpus-based techniques with reasoning on Semantic Web data. Corpus-based methods apply vector space similarity of verbs co-occurring with labeled and unlabeled relations to calculate relation label suggestions from a set of candidates. A meta ontology in combination with Semantic Web sources such as DBpedia and OpenCyc allows reasoning to improve the suggested labels. An extensive formal evaluation demonstrates the superior accuracy of the presented hybrid approach.
Verlag: Peter Lang Ltd. International Academic Publishers, 2011
ISBN 10: 3631606516ISBN 13: 9783631606513
Anbieter: moluna, Greven, Deutschland
Buch Print-on-Demand
Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. The manual construction of formal domain conceptualizations (ontologies) is labor-intensive. Ontology learning, by contrast, provides (semi-)automatic ontology generation from input data such as domain text. This title combines corpus-based techniques with .