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A flexible framework to experiment with ontology learning techniques

Research output: Contribution in Book/Report/ProceedingsPaper

Published

Publication date2008
Host publicationProceedings of AI-2007 Twenty-seventh SGAI International Conference on Artificial Intelligence
EditorsM. Bramer, F. Coenen, M. Petridis
PublisherSpringer
Pages153-166
Number of pages14
ISBN (Print)978-1-84800-093-3
Original languageEnglish

Conference

ConferenceProceedings of AI-2007 Twenty-seventh SGAI International Conference on Artificial Intelligence.(Cambridge, UK, December)
Period1/01/00 → …

Conference

ConferenceProceedings of AI-2007 Twenty-seventh SGAI International Conference on Artificial Intelligence.(Cambridge, UK, December)
Period1/01/00 → …

Abstract

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.

Bibliographic note

[Also selected for publication in Knowledge-Based Systems journal (to appear)]