Professor Susan Craw s.craw@rgu.ac.uk
Emeritus Professor
Maintaining retrieval knowledge in a case-based reasoning system.
Craw, Susan; Jarmulak, Jacek; Rowe, Ray
Authors
Jacek Jarmulak
Ray Rowe
Abstract
The knowledge stored in a case base is central to the problem solving of a case-based reasoning (CBR) system. Therefore, case-base maintenance is a key component of maintaining a CBR system. However, other knowledge sources, such as indexing and similarity knowledge for improved case retrieval, also play an important role in CBR problem solving. For many CBR applications, the refinement of this retrieval knowledge is a necessary component of CBR maintenance. This article focuses on optimization of the parameters and feature selections/weights for the indexing and nearest-neighbor algorithms used by CBR retrieval. Optimization is applied after case-base maintenance and refines the CBR retrieval to reflect changes that have occurred to cases in the case base. The optimization process is generic and automatic, using knowledge contained in the cases. In this article we demonstrate its effectiveness on a real tablet formulation application in two maintenance scenarios. One scenario, a growing case base, is provided by two snapshots of a formulation database. A change in the company's formulation policy results in a second, more fundamental requirement for CBR maintenance. We show that after case-base maintenance, the CBR system did indeed benefit from also refining the retrieval knowledge. We believe that existing CBR shells would benefit from including an option to automatically optimize the retrieval process.
Citation
CRAW, S., JARMULAK, J. and ROWE, R. 2001. Maintaining retrieval knowledge in a case-based reasoning system. Computational intelligence [online], 17(2), pages 346-363. Available from: https://doi.org/10.1111/0824-7935.00149
Journal Article Type | Article |
---|---|
Acceptance Date | May 31, 2001 |
Online Publication Date | May 31, 2001 |
Publication Date | May 31, 2001 |
Deposit Date | May 31, 2007 |
Publicly Available Date | May 31, 2007 |
Journal | Computational intelligence |
Print ISSN | 0824-7935 |
Electronic ISSN | 1467-8640 |
Publisher | Wiley |
Peer Reviewed | Peer Reviewed |
Volume | 17 |
Issue | 2 |
Pages | 346-363 |
DOI | https://doi.org/10.1111/0824-7935.00149 |
Keywords | Case based reasoning; Maintenance; Retrieval optimisation; Indexing knowledge; Similarity knowledge |
Public URL | http://hdl.handle.net/10059/66 |
Contract Date | May 31, 2007 |
Files
CRAW 2001 Maintaining retrieval knowledge
(228 Kb)
PDF
Publisher Licence URL
https://creativecommons.org/licenses/by-nc-nd/4.0/
You might also like
Fall prediction using behavioural modelling from sensor data in smart homes.
(2019)
Journal Article
Improving e-learning recommendation by using background knowledge.
(2018)
Journal Article
Case-base maintenance with multi-objective evolutionary algorithms.
(2015)
Journal Article
Learning pseudo-tags to augment sparse tagging in hybrid music recommender systems.
(2014)
Journal Article
Learning adaptation knowledge to improve case-based reasoning.
(2006)
Journal Article
Downloadable Citations
About OpenAIR@RGU
Administrator e-mail: publications@rgu.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search