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A flexible framework to experiment with ontology learning techniques

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Published

Standard

A flexible framework to experiment with ontology learning techniques. / Gacitua, R.; Sawyer, Peter; Rayson, P.
Proceedings of AI-2007 Twenty-seventh SGAI International Conference on Artificial Intelligence. ed. / M. Bramer; F. Coenen; M. Petridis. Springer, 2008. p. 153-166.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Gacitua, R, Sawyer, P & Rayson, P 2008, A flexible framework to experiment with ontology learning techniques. in M Bramer, F Coenen & M Petridis (eds), Proceedings of AI-2007 Twenty-seventh SGAI International Conference on Artificial Intelligence. Springer, pp. 153-166, Proceedings of AI-2007 Twenty-seventh SGAI International Conference on Artificial Intelligence.(Cambridge, UK, December), 1/01/00. https://doi.org/10.1007/978-1-84800-094-0_12

APA

Gacitua, R., Sawyer, P., & Rayson, P. (2008). A flexible framework to experiment with ontology learning techniques. In M. Bramer, F. Coenen, & M. Petridis (Eds.), Proceedings of AI-2007 Twenty-seventh SGAI International Conference on Artificial Intelligence (pp. 153-166). Springer. https://doi.org/10.1007/978-1-84800-094-0_12

Vancouver

Gacitua R, Sawyer P, Rayson P. A flexible framework to experiment with ontology learning techniques. In Bramer M, Coenen F, Petridis M, editors, Proceedings of AI-2007 Twenty-seventh SGAI International Conference on Artificial Intelligence. Springer. 2008. p. 153-166 doi: 10.1007/978-1-84800-094-0_12

Author

Gacitua, R. ; Sawyer, Peter ; Rayson, P. / A flexible framework to experiment with ontology learning techniques. Proceedings of AI-2007 Twenty-seventh SGAI International Conference on Artificial Intelligence. editor / M. Bramer ; F. Coenen ; M. Petridis. Springer, 2008. pp. 153-166

Bibtex

@inproceedings{9dffeba910c74716ae0fc2110f81db70,
title = "A flexible framework to experiment with ontology learning techniques",
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.",
author = "R. Gacitua and Peter Sawyer and P. Rayson",
note = "[Also selected for publication in Knowledge-Based Systems journal (to appear)]; Proceedings of AI-2007 Twenty-seventh SGAI International Conference on Artificial Intelligence.(Cambridge, UK, December) ; Conference date: 01-01-1900",
year = "2008",
doi = "10.1007/978-1-84800-094-0_12",
language = "English",
isbn = "978-1-84800-093-3",
pages = "153--166",
editor = "M. Bramer and F. Coenen and M. Petridis",
booktitle = "Proceedings of AI-2007 Twenty-seventh SGAI International Conference on Artificial Intelligence",
publisher = "Springer",

}

RIS

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 -