By Topic

A statistical approach for semantic relation extraction

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Imsombut, A. ; Fac. of Inf. Technol., Dhurakij Pundit Univ., Bangkok, Thailand

Semantic relations are an important component of ontologies that can support many applications e.g. text mining, question answering, and information extraction. Automatic semantic relation extraction system is a crucial tool that can reduce the bottleneck of knowledge acquisition in the ontologies construction. In this paper, we present a statistical approach for learning the semantic relations between concepts of an ontology in the agricultural domain. The semantic relations are acquired by using verbs to indicate the relations between ontology concepts. The co-occurrences of domain-verbs with their components, which are annotated the concepts, are analyzed by using several statistical methodologies. Moreover, we expand the sets of verb expressing the same semantic relation by using the extracted patterns of concept pairs of the seed verb's component. Our experiment has been done on a collection of Thai shallow parsed texts in the domain of agriculture. The precision and recall of the presented system is 65% and 82%, respectively.

Published in:

Natural Language Processing, 2009. SNLP '09. Eighth International Symposium on

Date of Conference:

20-22 Oct. 2009