Title | ||
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Systematic causal knowledge acquisition using FCM Constructor for product design decision support |
Abstract | ||
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Despite its usefulness, design knowledge is not often captured or documented, and is therefore lost or damaged after a product design is completed. As a way to address this issue, two major formalisms can be used for modeling, representing, and reasoning about causal design knowledge: fuzzy cognitive map (FCM) and Bayesian belief network (BBN). Although FCM has been used extensively in knowledge engineering, few methodologies exist for systematically constructing it. In this paper, we present a methodology and application-FCM Constructor-to systematically acquire design knowledge from domain experts, and to construct a corresponding BBN. To show the system's usability, we use three realistic product design cases to compare BBNs that are directly generated by domain experts, with BBNs that are generated using the FCM Constructor. We find that the BBN constructed through the FCM Constructor is similar, based on reasoning results, to the BBN constructed directly by specifying conditional probability tables of BBNs. |
Year | DOI | Venue |
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2011 | 10.1016/j.eswa.2011.06.032 | Expert Syst. Appl. |
Keywords | Field | DocType |
fuzzy cognitive map,systematic causal knowledge acquisition,causal reasoning,reasoning result,product design,design knowledge,domain expert,bayesian belief network,product design knowledge,causal design knowledge,product design decision support,knowledge acquisition,fcm constructor,corresponding bbn,realistic product design case,knowledge engineering,conditional probability table,decision support | Data mining,Causal reasoning,Design knowledge,Computer science,Fuzzy cognitive map,Usability,Bayesian network,Knowledge engineering,Artificial intelligence,Product design,Knowledge acquisition,Machine learning | Journal |
Volume | Issue | ISSN |
38 | 12 | Expert Systems With Applications |
Citations | PageRank | References |
14 | 0.75 | 15 |
Authors | ||
4 |
Name | Order | Citations | PageRank |
---|---|---|---|
Wooi Ping Cheah | 1 | 36 | 8.03 |
Yun Seon Kim | 2 | 25 | 2.61 |
Kyoung-Yun Kim | 3 | 210 | 23.90 |
Hyungjeong Yang | 4 | 455 | 47.05 |