Network Fragments: Representing Knowledge for Constructing Probabilistic Models

TL;DR

This paper introduces a framework for representing domain knowledge as larger, semantically meaningful network fragments, enabling the construction of tailored probabilistic models for complex problem domains.

Contribution

It proposes a novel knowledge representation using network fragments, allowing flexible and modular construction of probabilistic models beyond rule-based systems.

Findings

Framework supports asymmetric independence and intercausal interaction

Enables combining fragments for problem-specific models

Illustrated with military situation awareness examples

Abstract

In most current applications of belief networks, domain knowledge is represented by a single belief network that applies to all problem instances in the domain. In more complex domains, problem-specific models must be constructed from a knowledge base encoding probabilistic relationships in the domain. Most work in knowledge-based model construction takes the rule as the basic unit of knowledge. We present a knowledge representation framework that permits the knowledge base designer to specify knowledge in larger semantically meaningful units which we call network fragments. Our framework provides for representation of asymmetric independence and canonical intercausal interaction. We discuss the combination of network fragments to form problem-specific models to reason about particular problem instances. The framework is illustrated using examples from the domain of military situation awareness.