On Learning Associative Relationship Memory among Knowledge Concepts
- https://doi.org/10.2991/ijndc.k.200515.005How to use a DOI?
- Associative memory, knowledge concept relationship, memory optimization, knowledge network, dynamic reduction
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.
- © 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/).
Cite this article
TY - JOUR AU - Zhenping Xie AU - Kun Wang AU - Yuan Liu PY - 2020 DA - 2020/05 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 - https://doi.org/10.2991/ijndc.k.200515.005 ID - Xie2020 ER -