Learning Ontology Relations by Combining Corpus-Based Techniques and Reasoning on Data from Semantic Web Sources
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Book Details
Format
Hardback or Cased Book
ISBN-10
3631606516
ISBN-13
9783631606513
Edition
New
Publisher
Peter Lang AG
Imprint
Peter Lang AG
Country of Manufacture
DE
Country of Publication
GB
Publication Date
May 13th, 2011
Print length
222 Pages
Weight
396 grams
Dimensions
15.50 x 21.70 x 1.80 cms
Product Classification:
Ethical & social aspects of ITEnterprise software
Ksh 9,700.00
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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 reasoning on Semantic Web data.
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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