Reviewing and Predicting Human-Machine Cooperation Based on Knowledge Graph Analysis

Abstract

Human-Machine Cooperation involves multiple fields and disciplines such as automated vehicles, robots, machine manufacturing, psychological cognition, and artificial intelligence. As research in these areas progresses rapidly, it is important to keep up with new trends and key turning points in the development of collective knowledge. Based on the visualization method, we can quickly sort out the ins and outs of this field in the vast literature and search for valuable and potential literature. First, we introduced the principles of scientific visualization of knowledge graphs. Then we extracted 3257 articles from Web of Science Core Collection and established a research field of bibliographic records of representative data sets. Next, we carried out a visual analysis of the discipline Dual-Map overlay, Co-words, Co-citation, and reviewed some key Highly-Cited documents. Finally, we summarize and analyze the literature with high betweenness centrality, citation bursts, and Sigma value. This review will help professionals to have a more systematic understanding of the entire field and find opportunities for future Human-Machine Cooperation development.