Using propositional graphs for soft information fusion
Michael Prentice, Stuart C. Shapiro · 2011
Abstract—Soft information is information contained in natural language messages written by human informants or human intel-ligence gatherers. Tractor is a system that automatically processes natural language messages and represents the information ex-tracted from them as propositional graphs. Propositional graphs have several benefits as a knowledge representation formalism for information fusion: n-ary relations may be represented as simply as binary relations; meta-information and pedigree may be represented in the same format as object-level information; they are amenable to graph matching techniques and fusion with information from other sources; they may be used by reasoning systems to draw inferences from explicitly conveyed information and relevant background information. The propositional graphs produced by Tractor are based on the FrameNet system of deep lexical semantics. A method of producing propositional graphs is proposed using dependency parse information and rules written in the SNePS knowledge representation and reasoning system.