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Los Angeles, CA
March 31, 2009 to April 2, 2009
ISBN: 978-0-7695-3507-4
pp: 558-563
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.
Ontology Development, Ontology Learning Tools, Text-mining, Machine Learning, Supervised Learning
Richard Gil, Ana María Borges, Leonardo Contreras, Maria J. Martín-Bautista, "Improving Ontologies through Ontology Learning: a University Case", CSIE, 2009, 2009 WRI World Congress on Computer Science and Information Engineering, CSIE, 2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009, pp. 558-563, doi:10.1109/CSIE.2009.957
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