Concept hierarchy memory model: A neural architecture for conceptual knowledge representation, learning, and commonsense reasoning

• Main Authors: TAN, Ah-hwee, SOON, Hui-Shin Vivien
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 1996
• Subjects: Computer Engineering; Databases and Information Systems; OS and Networks
• Online Access: https://ink.library.smu.edu.sg/sis_research/5225; https://ink.library.smu.edu.sg/context/sis_research/article/6228/viewcontent/Concept20Hierarchy20Memory20Model_IJNS96.PDF.pdf
• Institution: Singapore Management University

This article introduces a neural network based cognitive architecture termed Concept Hierarchy Memory Model (CHMM) for conceptual knowledge representation and commonsense reasoning. CHMM is composed of two subnetworks: a Concept Formation Network (CFN), that acquires concepts based on their sensory representations; and a Concept Hierarchy Network (CHN), that encodes hierarchical relationships between concepts. Based on Adaptive Resonance Associative Map (ARAM), a supervised Adaptive Resonance Theory (ART) model, CHMM provides a systematic treatment for concept formation and organization of a concept hierarchy. Specifically, a concept can be learned by sampling activities across multiple sensory fields. By chunking relations between concepts as cognitive codes, a concept hierarchy can be learned/modified through experience. Also, fuzzy relations between concepts can now be represented in terms of the weights on the links connecting them. Using a unified inferencing mechanism based on code firing, CHMM performs an important class of commonsense reasoning, including concept recognition and property inheritance.