Parallel architectures for AI semantic network processing

Artificial intelligence (AI) applications are growing in several fields, and in many such applications knowledge bases must be manipulated. This activity is usually performed by an external agent such as a central processor, but often this cannot supply the speed required. Knowledge-oriented architectures provide an efficient execution of knowledge manipulations. This paper provides an introduction to a particular subset of knowledge-oriented architectures, the semantic network approach, which is one of the most commonly used methods of representing and manipulating knowledge in the AI field. A brief overview of the semantic network components is presented in order to provide a background to the topic. The purpose of this paper is to review the proposed, implemented and/or simulated architectures for semantic network processing, and to discuss the capabilities and limitations of such architectures.

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