On Learning Associative Relationship Memory among Knowledge Concepts

DOI

10.2991/ijndc.k.200515.005How to use a DOI?

Keywords

Associative memory; knowledge concept relationship; memory optimization; knowledge network; dynamic reduction

Abstract

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.

Copyright

© 2020 The Authors. Published by Atlantis Press SARL.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Zhenping Xie
AU  - Kun Wang
AU  - Yuan Liu
PY  - 2020
DA  - 2020/05/27
TI  - On Learning Associative Relationship Memory among Knowledge Concepts
JO  - International Journal of Networked and Distributed Computing
SP  - 124
EP  - 130
VL  - 8
IS  - 3
SN  - 2211-7946
UR  - https://doi.org/10.2991/ijndc.k.200515.005
DO  - 10.2991/ijndc.k.200515.005
ID  - Xie2020
ER  -