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Expert Systems with Applications
Volume 20, Issue 2, February 2001, Pages 153-162
 
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doi:10.1016/S0957-4174(00)00055-5    How to Cite or Link Using DOI (Opens New Window)
Copyright © 2001 Elsevier Science Ltd. All rights reserved.

An easy-maintenance, reusable approach for building knowledge-based systems: application to landscape assessment

R. Martínez-BéjarCorresponding Author Contact Information, E-mail The Corresponding Author, a, F. Ibañez-Cruza, P. ComptonE-mail The Corresponding Author, b and T. M. Caob

a Departamento de Informatica, Inteligencia Artificial y Electronica, Universidad de Murcia, 30071 Espinardo, Murcia, Spain b Department of Artificial Intelligence, School of Computer Science and Engineering, University of New South Wales, Sydney, Australia

Available online 16 February 2001.

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Abstract

Multiple Classification Ripple-Down Rules (MCRDR) is an extended methodology which allows an expert to build and maintain a knowledge-based system for multiple classification without technical assistance. Current MCRDR knowledge bases do not exhibit an explicit model of the relationships for the domain terms used by the expert. This is a strong impediment for both reusing and sharing of MCRDR knowledge bases, as well as for rapid development and maintenance. In this work, we describe how a domain knowledge ontological framework can be integrated with MCRDR, so providing this with explicit reusable knowledge components.

Author Keywords: Ripple-down rules; Ontology; Landscape assessment

Article Outline

1. Introduction
2. Ripple-down rules
3. The ontological model
4. Example
5. About the tool
6. Conclusion
Acknowledgements
References









 
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