Advances in Machine Learning Research Using Knowledge Graphs

TL;DR

This study analyzes China’s machine learning research using CSSCI-indexed CNKI literature (2007–2017) via CiteSpace to generate knowledge graphs that map institutional and author networks and keyword co-occurrence. It identifies leading institutions (Shanghai Jiao Tong University, Jilin University, Zhejiang University) and prolific authors (notably Soochow University), while revealing sparse cross-institution collaboration (network density around 0.0119–0.0372). The keyword analysis shows dispersed hotspots with emphasis on support vector machines, neural networks, and data mining, suggesting the field has yet to converge on a single research focus. The findings offer actionable insights for research planning and policy to foster collaboration and guide future ML development in China.

Abstract

The study uses CSSCI-indexed literature from the China National Knowledge Infrastructure (CNKI) database as the data source. It utilizes the CiteSpace visualization software to draw knowledge graphs on aspects such as institutional collaboration and keyword co-occurrence. This analysis provides insights into the current state of research and emerging trends in the field of machine learning in China. Additionally, it identifies the challenges faced in the field of machine learning research and offers suggestions that could serve as valuable references for future research.

Figures (5)

• Figure 1: Trends in the Volume of Machine Learning Literature
• Figure 2: High-Output Institutions for Machine Learning Research
• Figure 3: Author Collaboration Map
• Figure 4: Prolific Authors and Publication Volume
• Figure 5: Keyword Co-Occurrence Knowledge Map