Journal of Intelligent Information Systems

, Volume 33, Issue 2, pp 145-178

First online:

Open Access This content is freely available online to anyone, anywhere at any time.

A join tree probability propagation architecture for semantic modeling

  • C. J. ButzAffiliated withDepartment of Computer Science, University of Regina Email author 
  • , H. YaoAffiliated withDepartment of Computer Science, University of Regina
  • , S. HuaAffiliated withDepartment of Computer Science, University of Regina


We propose the first join tree (JT) propagation architecture that labels the probability information passed between JT nodes in terms of conditional probability tables (CPTs) rather than potentials. By modeling the task of inference involving evidence, we can generate three work schedules that are more time-efficient for LAZY propagation. Our experimental results, involving five real-world or benchmark Bayesian networks (BNs), demonstrate a reasonable improvement over LAZY propagation. Our architecture also models inference not involving evidence. After the CPTs identified by our architecture have been physically constructed, we show that each JT node has a sound, local BN that preserves all conditional independencies of the original BN. Exploiting inference not involving evidence is used to develop an automated procedure for building multiply sectioned BNs. It also allows direct computation techniques to answer localized queries in local BNs, for which the empirical results on a real-world medical BN are promising. Screen shots of our implemented system demonstrate the improvements in semantic knowledge.


Bayesian networks Join trees Probabilistic inference Conditional independence