Representing Context-Sensitive Knowledge in a Network Formalism: A Preliminary Report

TL;DR

This paper introduces a framework for representing complex, context-sensitive knowledge that combines categorical and uncertain information within a network formalism, aiming to improve automated decision-making processes.

Contribution

It presents a novel network-based formalism for integrating categorical and uncertain, context-sensitive knowledge, with analysis of its expressiveness and efficiency.

Findings

Framework effectively models context-sensitive knowledge

Demonstrates potential for improved decision-making

Analyzes expressiveness and computational efficiency

Abstract

Automated decision making is often complicated by the complexity of the knowledge involved. Much of this complexity arises from the context sensitive variations of the underlying phenomena. We propose a framework for representing descriptive, context-sensitive knowledge. Our approach attempts to integrate categorical and uncertain knowledge in a network formalism. This paper outlines the basic representation constructs, examines their expressiveness and efficiency, and discusses the potential applications of the framework.