By Topic

Improving Ontologies through Ontology Learning: a University Case

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
$33 $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

4 Author(s)
Richard Gil ; Dept. of Processes & Syst., Simon Bolivar Univ., Caracas, Venezuela ; Ana María Borges ; Leonardo Contreras ; Maria J. Martin-Bautista

Ontology learning (OL) arises as an area to support semantic engineering because it enables to recover and to extract knowledge from the Web documents to improve the development of domain ontologies. One of the most fruitful fields of OL is artificial intelligence (AI), since it sustains new methods, techniques and tools, particularly related with Web- and text-mining. In this work, we are dealing with a meta-model incrementally developed, proposing an OL experiment with open source tools. To reach that, firstly, ontology about a university institution previously developed is increasable synthesized. Secondly, a particular methodology and strategy for OL is used to illustrate how could be included these text-mining and OL tools into a kind of (semi-) intelligent-agent, and how the overall ontological development process could be improved. Particularly, the OL "agent" test tool is applied to update/extend the university ontology by a semi-supervised machine learning approach.

Published in:

Computer Science and Information Engineering, 2009 WRI World Congress on  (Volume:4 )

Date of Conference:

March 31 2009-April 2 2009