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Learning ontology relations by combining corpus-based techniques and reasoning on data from semantic web sources

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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 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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Detail Information

Series Title

Forschungsergebnisse der Wirtschaftsuniversitaet Wien, 44

Call Number

004.678 WOH l

Publisher

Bern, Switzerland : Peter Lang International Academic Publishers., 2018

Collation

221 p.

Language

English

ISBN/ISSN

9783631606513

Classification

004.678

Content Type

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Media Type

-

Carrier Type

-

Edition

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Subject(s)

Semantic Web
Ontologies (Information retrieval)

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