Knowledge representations for sensemaking

Abstract

I discuss the lessons learned during the design and implementation of three knowledge representations systems for sensemaking. The focus is on the tension that exists between a knowledge representation's role as a surrogate for the world and its role as a facilitator of computational reasoning. Each system accepts natural language inputs and implements a bidirectional model of sensemaking. One system emphasizes inference while the other two systems emphasize their role as a representation for the world. I discuss the differences between these systems and what gives rise to these differences.