A Scalable Architecture for Integrating Associative and Semantic Memory

Abstract

Abstract Traditionally, semantic memory is considered to be composed of a single layer of knowledge. This layer can be thought of as encoding the systematic relations that underlie the regularities in our cognitive world. In this article, this notion is extended to include a second layer, so that semantic memory now consists of two tiers. The second tier asserts that each of our concepts has attached to it an associational cloud of knowledge that encodes the non-systematic knowledge associated with these concepts, or what Fillmore (1982) calls a concept's background frame knowledge. Semantic memory is constructed from co-occurrence statistics gathered from the Wall Street Journal text corpus. The associational knowledge is encoded from a set of semantic features extracted from the categories o/Roget's Thesaurus. This approach of the encoding and use of world knowledge is significant in that it supports an architecture that can scale up.