Gacitua, R. and Sawyer, Peter and Rayson, P. (2008) A flexible framework to experiment with ontology learning techniques. In: Proceedings of AI-2007 Twenty-seventh SGAI International Conference on Artificial Intelligence. Springer, pp. 153-166. ISBN 978-1-84800-093-3Full text not available from this repository.
Ontology learning refers to extracting conceptual knowledge from several sources and building an ontology from scratch, enriching, or adapting an existing ontology. It uses methods from a diverse spectrum of fields such as Natural Language Processing, Artificial Intelligence and Machine learning. However, a crucial challenging issue is to quantitatively evaluate the usefulness and accuracy of both techniques and combinations of techniques, when applied to ontology learning. It is an interesting problem because there are no published comparative studies. We are developing a flexible framework for ontology learning from text which provides a cyclical process that involves the successive application of various NLP techniques and learning algorithms for concept extraction and ontology modelling. The framework provides support to evaluate the usefulness and accuracy of different techniques and possible combinations of techniques into specific processes, to deal with the above challenge. We show our framework's eficacy as a workbench for testing and evaluating concept identification. Our initial experiment supports our assumption about the usefulness of our approach.
|Item Type:||Contribution in Book/Report/Proceedings|
|Additional Information:||[Also selected for publication in Knowledge-Based Systems journal (to appear)]|
|Subjects:||Q Science > QA Mathematics > QA75 Electronic computers. Computer science|
|Departments:||Faculty of Science and Technology > School of Computing & Communications|
|Deposited On:||30 Mar 2012 17:06|
|Last Modified:||18 Jun 2015 07:05|
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