Advances in Machine Learning Research Using Knowledge Graphs

TL;DR

This paper analyzes Chinese machine learning research using knowledge graphs and visualization tools to reveal current trends, collaborations, challenges, and future directions in the field.

Contribution

It introduces a novel approach of using knowledge graphs and visualization software to analyze research trends and collaborations in Chinese machine learning literature.

Findings

Identifies key research trends and emerging topics.

Maps institutional collaborations and keyword networks.

Highlights challenges and future research directions.

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.