An associative information retrieval algorithm for a Kanerva-like memory model

This paper presents an associative information retrieval algorithm for a Kanerva-like sparse distributed memory (SDM) model. This memory model is used to implement the associative level of a hierarchical heterogeneous knowledge-base model consisting of multi-levels, starting from an associative level, through to the semantic, rule-based and description-generator level as the top level in the hierarchy. The architecture of knowledge-base was inspired by biological and psychological models. The proposed algorithm retrieves concepts from the associative level based on the similarity between a concept of interest and already stored concepts. The similarity is expressed by a value of the linguistic variable. With this approach it is possible to solve a problem when the inference processes at the semantic level encounter an unknown concept of interest. The algorithm is demonstrated by retrieving concepts that were stored based on the results of psychological experiment.