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Mathematical Problems in Engineering
Volume 2013, Article ID 297121, 10 pages
Research Article

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

Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, ChangChun City, JiLin Province 130012, China

Received 22 May 2013; Revised 5 August 2013; Accepted 19 August 2013

Academic Editor: Praveen Agarwal

Copyright © 2013 LiMin Wang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.