J Algorithm for Scientific Knowledge Discovery: Taking Economic Growth Theory as an Example

DOI

10.2991/mmsta-19.2019.21How to use a DOI?

Keywords

scientific knowledge discovery; J algorithm; ABC model

Abstract

Text mining, intelligent algorithms and knowledge maps are the research frontiers and hotspots of current scientific knowledge discovery. However, there is currently no theoretical basis for such research—for example, the theoretical framework for the structure of scientific knowledge systems and the lack of case studies for scientific knowledge discovery. The paper proposes that most scientific knowledge systems are composed of concept set [A], concept set [B], and concept set [C]. Different scientific knowledge systems have different cognitive patterns [P], so the scientific knowledge system consists essentially of four concept sets. And the four concept sets are represented in the four quadrants of the "concept coordinate system", and then study the evolution process from concept to model block to model to model system, revealing new concepts and new structures in the process of scientific knowledge discovery. And through the construction case of the economic growth model of The Synergy Theory, it is shown that the knowledge map provides a powerful analysis tool for the "four-set analysis method" proposed by the J system methodology, thus greatly expanding, deepening and innovating the Swanson’s knowledge discovery method which is based on non-relevant literature—ABC model.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

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

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TY  - CONF
AU  - Qianhui Zhao
AU  - Qi Qian
AU  - Zhaohua Jiang
PY  - 2019/12
DA  - 2019/12
TI  - J Algorithm for Scientific Knowledge Discovery: Taking Economic Growth Theory as an Example
BT  - Proceedings of the 2019 2nd International Conference on Mathematics, Modeling and Simulation Technologies and Applications (MMSTA 2019)
PB  - Atlantis Press
SP  - 96
EP  - 102
SN  - 2352-538X
UR  - https://doi.org/10.2991/mmsta-19.2019.21
DO  - 10.2991/mmsta-19.2019.21
ID  - Zhao2019/12
ER  -