Fuzzy Cognitive Map and Bayesian Belief Network for Causal Knowledge Engineering: A Comparative Study


KIPS Transactions on Software and Data Engineering, Vol. 15, No. 2, pp. 147-158, Feb. 2008
10.3745/KIPSTB.2008.15.2.147, Full Text:

Abstract

Fuzzy Cognitive Map (FCM) and Bayesian Belief Network (BBN) are two major frameworks for modeling, representing and reasoning about causal knowledge. Despite their extensive use in causal knowledge engineering, there is no reported work which compares their respective roles. This paper aims to fill the gap by providing a qualitative comparison of the two frameworks through a systematic analysis based on some inherent features of the frameworks. We proposed a set of comparison criteria which covers the entire process of causal knowledge engineering, including modeling, representation, and reasoning. These criteria are usability, expressiveness, reasoning capability, formality, and soundness. The results of comparison have revealed some important facts about the characteristics of FCM and BBN, which will help to determine how FCM and BBN should be used, with respect to each other, in causal knowledge engineering.


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Cite this article
[IEEE Style]
K. Y. Kim, H. J. Yang, S. H. Kim and J. S. Kim, "Fuzzy Cognitive Map and Bayesian Belief Network for Causal Knowledge Engineering: A Comparative Study," KIPS Journal B (2001 ~ 2012) , vol. 15, no. 2, pp. 147-158, 2008. DOI: 10.3745/KIPSTB.2008.15.2.147.

[ACM Style]
Kyoung Yun Kim, Hyung Jeong Yang, Soo Hyung Kim, and Jeong Sik Kim. 2008. Fuzzy Cognitive Map and Bayesian Belief Network for Causal Knowledge Engineering: A Comparative Study. KIPS Journal B (2001 ~ 2012) , 15, 2, (2008), 147-158. DOI: 10.3745/KIPSTB.2008.15.2.147.