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Learning Ontology Relations by Combining Corpus-Based Techniques and Reasoning on Data from Semantic Web Sources: 44 - Rilegato
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.
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L'autore:
Gerhard Wohlgenannt is a senior researcher at the New Media Technology Department, MODUL University Vienna. He received his PhD from the Institute for Information Business at Vienna University of Economics and Business (WU). His research interests include ontology learning, text mining and the Semantic Web.
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- EditorePeter Lang Pub Inc
- Data di pubblicazione2011
- ISBN 10 3631606516
- ISBN 13 9783631606513
- RilegaturaCopertina rigida
- Numero di pagine221
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Da: Germania a: U.S.A.
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Learning Ontology Relations by Combining Corpus-Based Techniques and Reasoning on Data from Semantic Web Sources
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Peter Lang Ltd. International Academic Publishers Mai 2011
(2011)
ISBN 10: 3631606516
ISBN 13: 9783631606513
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Descrizione libro Buch. Condizione: 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. Codice articolo 9783631606513
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Learning Ontology Relations by Combining Corpus-Based Techniques and Reasoning on Data from Semantic Web Sources
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Descrizione libro Buch. Condizione: 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. Codice articolo 9783631606513
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Learning Ontology Relations by Combining Corpus-Based Techniques and Reasoning on Data from Semantic Web Sources
Editore:
Peter Lang Ltd. International Academic Publishers
(2011)
ISBN 10: 3631606516
ISBN 13: 9783631606513
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Descrizione libro Condizione: 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 . Codice articolo 122925021
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