Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - A flexible framework to experiment with ontology learning techniques
AU - Gacitua, R.
AU - Sawyer, Peter
AU - Rayson, P.
N1 - [Also selected for publication in Knowledge-Based Systems journal (to appear)]
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=40449124704&partnerID=8YFLogxK
U2 - 10.1007/978-1-84800-094-0_12
DO - 10.1007/978-1-84800-094-0_12
M3 - Conference contribution/Paper
SN - 978-1-84800-093-3
SP - 153
EP - 166
BT - Proceedings of AI-2007 Twenty-seventh SGAI International Conference on Artificial Intelligence
A2 - Bramer, M.
A2 - Coenen, F.
A2 - Petridis, M.
PB - Springer
T2 - Proceedings of AI-2007 Twenty-seventh SGAI International Conference on Artificial Intelligence.(Cambridge, UK, December)
Y2 - 1 January 1900
ER -