Modeling semantics of inconsistent qualitative knowledge for quantitative Bayesian network inference

Rui Chang, Wilfried Brauer, Martin Stetter

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

We propose a novel framework for performing quantitative Bayesian inference based on qualitative knowledge. Here, we focus on the treatment in the case of inconsistent qualitative knowledge. A hierarchical Bayesian model is proposed for integrating inconsistent qualitative knowledge by calculating a prior belief distribution based on a vector of knowledge features. Each inconsistent knowledge component uniquely defines a model class in the hyperspace. A set of constraints within each class is generated to describe the uncertainty in ground Bayesian model space. Quantitative Bayesian inference is approximated by model averaging with Monte Carlo methods. Our method is firstly benchmarked on ASIA network and is applied to a realistic biomolecular interaction modeling problem for breast cancer bone metastasis. Results suggest that our method enables consistently modeling and quantitative Bayesian inference by reconciling a set of inconsistent qualitative knowledge.

Original languageEnglish (US)
Pages (from-to)182-192
Number of pages11
JournalNeural Networks
Volume21
Issue number2-3
DOIs
StatePublished - Mar 2008
Externally publishedYes

Keywords

  • Bayesian inference
  • Bayesian networks
  • Inconsistent knowledge integration
  • Monte Carlo simulation
  • Qualitative knowledge modeling

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Artificial Intelligence

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