Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1462
Title: An ontology-based similarity measurement for problem-based case reasoning
Authors: Lau, SMA
Tsui, E 
Lee, WB 
Keywords: Knowledge retrieval
Ontology-based similarity measurement
Problem-driven case
Issue Date: Apr-2009
Publisher: Elsevier
Source: Expert systems with applications, Apr. 2009, v. 36, no. 3, pt. 2, p. 6574-6579 How to cite?
Journal: Expert Systems with Applications 
Abstract: Traditional case-based reasoning uses a table/frame or scenario to represent a case. It assumed that similar input/event results in similar output/event state. However, similar cases may not have similar output/event states since problem solver may have different way to break down the problem. Thus, authors previously proposed problem-based case reasoning to overcome the limitation of the traditional approaches and used clustered ontology to represent the problem spaces of a case. However, synonym problem causes the mismatch of similar sub-problems of historical cases for new case. Thus, this paper proposed ontology-based similarity measurement to retrieve the similar sub-problems that overcomes the synonym problems on case retrieval. The recall and precise of ontology-based similarity measurement were higher than that of the traditional similarity measurement.
URI: http://hdl.handle.net/10397/1462
ISSN: 0957-4174
DOI: 10.1016/j.eswa.2008.07.033
Rights: Expert Systems with Applications © 2008 Elsevier Ltd. The journal web site is located at http://www.sciencedirect.com.
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