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

LiMin Wang

Mathematical Problems in Engineering, 2013, vol. 2013, 1-10

Abstract:

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.

Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:297121

DOI: 10.1155/2013/297121

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