Learning SKOS relations for terminological ontologies from text
Wei Wang, , Payam Barnaghi, and Andrzej Bargiela, (2011) Learning SKOS relations for terminological ontologies from text In: Ontology Learning and Knowledge Discovery Using the Web: Challenges and Recent Advances. IGI Global, 129 - 152. ISBN 1609606256
barnaghi chap_wong book.pdf
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The problem of learning concept hierarchies and terminological ontologies can be divided into two subtasks: concept extraction and relation learning. The authors of this chapter describe a novel approach to learn relations automatically from unstructured text corpus based on probabilistic topic models. The authors provide definition (Information Theory Principle for Concept Relationship) and quantitative measure for establishing “broader” (or “narrower”) and “related” relations between concepts. They present a relation learning algorithm to automatically interconnect concepts into concept hierarchies and terminological ontologies with the probabilistic topic models learned. In this experiment, around 7,000 ontology statements expressed in terms of “broader” and “related” relations are generated using different combination of model parameters. The ontology statements are evaluated by domain experts and the results show that the highest precision of the learned ontologies is around 86.6% and structures of learned ontologies remain stable when values of the parameters are changed in the ontology learning algorithm.
|Item Type:||Book Section|
|Additional Information:||Copyright © 2011 by IGI Global. All rights reserved. No part of this publication may be reproduced, stored or distributed in any form or by any means, electronic or mechanical, including photocopying, without written permission from the publisher.|
|Divisions:||Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Communication Systems Research|
|Depositing User:||Symplectic Elements|
|Date Deposited:||11 May 2012 12:42|
|Last Modified:||23 Sep 2013 19:28|
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