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Author Wohlgenannt, Gerhard. Author.

Title Learning Ontology Relations by Combining Corpus-Based Techniques and Reasoning on Data from Semantic Web Sources Gerhard Wohlgenannt.

Imprint Frankfurt a.M. Peter Lang GmbH, Internationaler Verlag der Wissenschaften [2018], ©2011.

Copies

Location Call No. OPAC Message Status
 Axe JSTOR Open Ebooks  Electronic Book    ---  Available
Edition 1st, New ed.
Description 1 online resource.
text txt rdacontent
computer c rdamedia
online resource cr rdacarrier
Series Forschungsergebnisse der Wirtschaftsuniversität Wien 44.
Free online access: JSTOR.
Thesis Thesis (Doctoral).
Contents Ontology learning fundamentals and techniques -- Overview of ontology relation detection and labeling methods -- A novel hybrid approach for labeling non-taxonomic relations which combines corpus-based methods with ontology reasoning based on Semantic Web sources -- Improved accuracy demonstrated with an extensive formal evaluation.
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.
Biography 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.
Note Online resource; title from title screen (viewed December 28, 2018).
Subject Conceptual structures (Information theory)
Ontologies (Information retrieval)
Expert systems (Computer science)
Semantic Web.
Conceptual structures (Information theory) (OCoLC)fst00872985
Expert systems (Computer science) (OCoLC)fst00918516
Ontologies (Information retrieval) (OCoLC)fst01739863
Semantic Web. (OCoLC)fst01112076
Genre/Form Academic theses. (OCoLC)fst01726453
Electronic books.
Academic theses.
Other Form: Print version: 9783631606513
ISBN 9783631753842 (electronic bk.)
3631753845 (electronic bk.)
Standard No. 9783631753842
10.3726/b13903 doi

 
    
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