The identification and labelling of non-hierarchical relations are among the most challenging tasks in ontology learning. This paper describes a bottom-up approach for automatically suggesting ontology link types. The presented method extracts verb-vectors from semantic relations identified in the domain corpus, aggregates them by computing centroids for known relation types, and stores the centroids in a central knowledge base. Comparing verb-vectors extracted from unknown relations with the stored centroids yields link type suggestions. Domain experts evaluate these suggestions, refining the knowledge base and constantly improving the component's accuracy. A final evaluation provides a detailed statistical analysis of the introduced approach.
%0 Journal Article
%1 weichselbraun2009
%A Weichselbraun, Albert
%A Wohlgenannt, Gerhard
%A Scharl, Arno
%A Granitzer, Michael
%A Neidhart, Thomas
%A Juffinger, Andreas
%D 2009
%J International Journal of Metadata, Semantics and Ontologies
%K _self detection, learning, ontology relation
%N 3
%P 212--222
%T Discovery and Evaluation of Non-Taxonomic Relations in Domain Ontologies
%U http://www.inderscience.com/search/index.php?action=record&rec_id=27755
%V 4
%X The identification and labelling of non-hierarchical relations are among the most challenging tasks in ontology learning. This paper describes a bottom-up approach for automatically suggesting ontology link types. The presented method extracts verb-vectors from semantic relations identified in the domain corpus, aggregates them by computing centroids for known relation types, and stores the centroids in a central knowledge base. Comparing verb-vectors extracted from unknown relations with the stored centroids yields link type suggestions. Domain experts evaluate these suggestions, refining the knowledge base and constantly improving the component's accuracy. A final evaluation provides a detailed statistical analysis of the introduced approach.
@article{weichselbraun2009,
abstract = {
The identification and labelling of non-hierarchical relations are among the most challenging tasks in ontology learning. This paper describes a bottom-up approach for automatically suggesting ontology link types. The presented method extracts verb-vectors from semantic relations identified in the domain corpus, aggregates them by computing centroids for known relation types, and stores the centroids in a central knowledge base. Comparing verb-vectors extracted from unknown relations with the stored centroids yields link type suggestions. Domain experts evaluate these suggestions, refining the knowledge base and constantly improving the component's accuracy. A final evaluation provides a detailed statistical analysis of the introduced approach.},
added-at = {2009-10-21T14:25:27.000+0200},
author = {Weichselbraun, Albert and Wohlgenannt, Gerhard and Scharl, Arno and Granitzer, Michael and Neidhart, Thomas and Juffinger, Andreas},
biburl = {https://www.bibsonomy.org/bibtex/2da897e28402667517a617a4a0d7536ee/albert.weichselbraun},
eprint = {http://eprints.weblyzard.com/11/1/ontology_linktype.pdf},
interhash = {4d73376e94606af66f745634055974b5},
intrahash = {da897e28402667517a617a4a0d7536ee},
journal = {International Journal of Metadata, Semantics and Ontologies},
keywords = {_self detection, learning, ontology relation},
number = 3,
owner = {albert},
pages = {212--222},
timestamp = {2009-10-21T22:37:20.000+0200},
title = {Discovery and Evaluation of Non-Taxonomic Relations in Domain Ontologies},
url = {http://www.inderscience.com/search/index.php?action=record\&rec_id=27755},
volume = 4,
year = 2009
}