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

Qianhui Zhao, Qi Qian, Zhaohua Jiang
2019 Proceedings of the 2019 2nd International Conference on Mathematics, Modeling and Simulation Technologies and Applications (MMSTA 2019)   unpublished
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
more » ... et [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. Keywords-scientific knowledge discovery; J algorithm; ABC model Swanson first discovered the existence of recessive associations in the medical literature [1] . He inferred the recessive associations between [A] and [C] through the complementarity in content of two non-relevant literatures containing concept set [A] and concept set [C]. (Dong Fenghua, Lan Xiaoyu, 2004). For example, [A] indicates that the intake of a substance may cause a physiological change [B], and the physiological change of [B] triggers a disease of an organ [C], so that useful information [A] can be obtained in [C], and this is not found in a single literature, and through the intermediate link [B], this recessive associations can be extracted. Thus, Swanson refers to the knowledge discovery method based on non-relevant literature as the ABC model. The J system theory studied in this paper is the theoretical basis of the ABC model, and the J system method and J algorithm based on the J system theory are further expansion, deepening and innovation of the ABC model. The following is a discussion of various algorithms and methods based on computer knowledge discovery of non-relevant literature: A. Gordon and Lindsay Based on Word Frequency Statistics This method inherits and extends Swanson's research, and its analysis object becomes the complete field of the Medline record. The steps are as follows (Lindsay RK.Gordon MD,1999) :
doi:10.2991/mmsta-19.2019.21 fatcat:3r6sl5thczcx5gqgkhjun3fvye