Extraction of Belief Knowledge from a Relational Database for Quantitative Bayesian Network Inference

Abstract EN

The problem of extracting knowledge from a relational database for probabilistic reasoning is still unsolved.

On the basis of a three-phase learning framework, we propose the integration of a Bayesian network (BN) with the functional dependency (FD) discovery technique.

Association rule analysis is employed to discover FDs and expert knowledge encoded within a BN; that is, key relationships between attributes are emphasized.

Moreover, the BN can be updated by using an expert-driven annotation process wherein redundant nodes and edges are removed.

Experimental results show the effectiveness and efficiency of the proposed approach.