Propositional deductive inference by semantic vectors

Abstract

Representing symbols by high-dimensional vectors makes it easier to perform analogical and associational reasoning, but performing multi-step deductive reasoning typically requires a discrete knowledge base. In this paper, we show a method by which deductive inference can be performed directly on high-dimensional semantic vectors, and characterize some limitations and advantages of this approach. We provide a method for taking a set of semantic vectors representing propositions and encoding a knowledge base telling how those propositions are logically related.