Hierarchical Tree-structured Knowledge Graph For Academic Insight Survey

Research surveys have always posed a challenge for novice researchers who lack research training. These researchers struggle to understand the directions within their research topic and the discovery of new research findings within a short time. One way to provide intuitive assistance to novice researchers is by offering relevant knowledge graphs $(KG)$ and recommending related academic papers. However, existing navigation knowledge graphs mainly rely on keywords or meta information in the research field to guide researchers, which makes it difficult to clearly present the hierarchical relationships, such as inheritance and relevance between multiple related papers. Moreover, most recommendation systems for academic papers simply rely on high text similarity, confusing researchers as to why a particular article is recommended. They may lack the grasp of important information about the insight connection between ‘Issue resolved’ and ‘Issue finding’ that they hope to obtain. This study aims to support research insight surveys for novice researchers by establishing a hierarchical tree-structured knowledge graph that reflects the inheritance insight and the relevance insight among multiple academic papers on specific research topics to address these issues.