Modeling semantics of inconsistent qualitative knowledge for quantitative Bayesian network inference

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.