Title of article :
Constructing probabilistic graphical model from predicate formulas for fusing logical and probabilistic knowledge
Author/Authors :
Wei-Yi Liu، نويسنده , , Kun Yue، نويسنده , , Ming-Hai Gao، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2011
Pages :
18
From page :
3828
To page :
3845
Abstract :
Expressing knowledge as expert experience and discovering knowledge implied in data are two important ways for knowledge acquisition. Consistent combination of these two kinds of knowledge has attracted much attention due to the potential applications to knowledge fusion and wide requirements of decision support. In this paper, we focus on the probabilistic modeling of expert experience represented as logical predicate formulas, aiming at the effective fusion of logical and probabilistic knowledge. Taking qualitative probabilistic network (QPN) as the underlying framework of probabilistic knowledge implied in data as well as the abstraction of general Bayesian networks (BNs), we are to construct the probabilistic graphical model for both the given predicate formulas and the ultimate result of knowledge fusion. We first propose the concept and the construction algorithm of predicate graph (PG) to describe the dependence relations among predicate formulas, and discuss PG’s probabilistic semantics correspondingly. We then prove that PG is a probability dependency model and has the same semantics with a general probabilistic graphical model. Consequently, we give the method for fusing PG and QPN. Experimental results show the effectiveness of our methods.
Keywords :
Qualitative probabilistic network (QPN) , Fusion , Predicate graph (PG) , knowledge acquisition , Bayesian network (BN) , Predicate formula
Journal title :
Information Sciences
Serial Year :
2011
Journal title :
Information Sciences
Record number :
1214594
Link To Document :
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