Knowledge Engineering for Large Belief Networks
This work addresses the challenge of managing and editing large belief networks, particularly in medical domains, but it appears incremental as it builds on existing concepts like noisy-OR and leak probabilities.
The paper tackles the problem of knowledge engineering for large belief networks by introducing techniques like noisyMAX for modeling causal independence and Netview for visualization and version control, resulting in a tool that dynamically updates leak probabilities to reflect missing network portions.
We present several techniques for knowledge engineering of large belief networks (BNs) based on the our experiences with a network derived from a large medical knowledge base. The noisyMAX, a generalization of the noisy-OR gate, is used to model causal in dependence in a BN with multi-valued variables. We describe the use of leak probabilities to enforce the closed-world assumption in our model. We present Netview, a visualization tool based on causal independence and the use of leak probabilities. The Netview software allows knowledge engineers to dynamically view sub-networks for knowledge engineering, and it provides version control for editing a BN. Netview generates sub-networks in which leak probabilities are dynamically updated to reflect the missing portions of the network.