Hidden Bibliographic Details
| ISBN: | 9783631753842
3631753845
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| Notes: | Thesis (Doctoral).
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.
Online resource; title from title screen (viewed December 28, 2018).
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| Summary: | 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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| Other form: | Print version: 9783631606513
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| Standard no.: | 9783631753842
10.3726/b13903
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