_∗
_
.
_∗
_∗
_∗
.
Noting that
PV_D1 HD1 H and P
V
H

D1 L
represents the membership degrees incorporated in
step 3 in the CPT of the virtual node respectively
high
and
low
.
5 CONCLUSIONS
The overall goal of this paper is to develop a
solution to deal with vague and imprecise
knowledge in MEBNs, thus dealing with tow kind of
uncertainty at the same time, for this, we have
introduced a new extension of the MultiEntity
Bayesian Networks based on fuzzy logic in order to
improve the classical MEBN by extending the
classical MFrags to a FuZzy MFrags, our approach
based on a strong probabilistic graphical model
enabling the reasoning with uncertainty under a
complex problems. Moreover, we have proposed a
complete process to do the fuzzy inference in the
extended MEBNs, where the inference task in
FzMEBN is divided in four steps, the first one
consist to generate a minimal fuzzy Bayesian
networks (Fuzzy SSBN) capable to answer the query
as the classical MEBN using Laskey algorithm . The
second one consists to computing the fuzzy
evidence, the third consist to incorporate the fuzzy
evidence in the Fuzzy SSBN and finally, fuzzy
Bayesian inference can be done using classical
Bayesian inference on the generated Fuzzy SSBN.
Currently, we are focusing on evaluating the
ability of the proposed FuZzy MultiEntity Bayesian
Networks by apply it on a complex real world
problems, thus in our next work we are interesting to
evaluate the performance of our solution taking the
diabetes disease as a case of study.
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