On Learning Associative Relationship Memory among Knowledge Concepts

• Published in: The International journal of networked and distributed computing (Online) Vol. 8; no. 3; pp. 124 - 130
• Main Authors: Xie, Zhenping, Wang, Kun, Liu, Yuan
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.06.2020; Springer
• Subjects: Associative memory; dynamic reduction; knowledge concept relationship; knowledge network; memory optimization; Research Article; Associative memory; dynamic reduction; memory optimization; knowledge network; knowledge concept relationship
• ISSN: 2211-7938; 2211-7946; 2211-7946
• DOI: 10.2991/ijndc.k.200515.005

A high-level associative memory modelling method was developed to explore the realization of associative memory. In the proposed method, two stage procedures are progressively performed to construct a unified associative knowledge network. In the first stage, some direct weighted associative links are created according to original context relations, and in the second stage, dynamic link reduction operations are executed to optimize associative access efficiency. Moreover, two kinds of link reduction strategies are designed including a global link reduction strategy and a dynamic link reduction strategy based on Hebb learning rule. Two independent datasets are considered to examine the performance of proposed modelling method. By means of reasonable performance indices, the experimental results displayed that, about 70% original links can be reduced almost without associative access failure but better total associative access efficiency. Particularly, the dynamic reduction strategy based on Hebb learning rule may achieve better associative access performance.