Learning Ontology Relations by Combining Corpus-Based Techniques and Reasoning on Data from Semantic Web Sources
Regular price
€52.99
14 days return policy
Shipping & Delivery
A01=Gerhard Wohlgenannt
Author_Gerhard Wohlgenannt
Based
Category=JNF
Category=JPP
Category=UBJ
Category=UFL
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_society-politics
Product details
- ISBN 9783631606513
- Weight: 390g
- Dimensions: 148 x 210mm
- Publication Date: 13 May 2011
- Publisher: Peter Lang AG
- Publication City/Country: CH
- Product Form: Hardback
Secure
checkout
Fast Shipping
Easy returns
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.
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.
