The Representation of Knowledge in Minds and Machines

Abstract

Human knowledge can be represented as a propositional network in which the meaning of a node is defined by its position in the network. That is, the relationship between a node and its neighbours determines how this node is used in language understanding and production, i.e., its meaning. The propositions that make up such a network are predicate‐argument structures with time and location slots. Schemas, frames, and production rules can be expressed in the same formalism. Implications for this contextual view of meaning are discussed. Since the construction of such a propositional network depends on hand coding and is therefore impractical, an alternative automatic statistical procedure is explored that yields a high‐dimensional semantic space. Vectors in this space correspond to nodes in the propositional network, in that the meaning of a vector in the Latent Semantic Analysis space is given by its neighbouring vectors in that space.

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