Research surveys have always posed a challenge for beginner researchers who
lack of 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
beginner researchers is by offering relevant knowledge graphs(KG) and
recommending related academic papers. However, existing navigation knowledge
graphs primarily rely on keywords in the research field and often fail to
present the logical hierarchy among multiple related papers clearly. Moreover,
most recommendation systems for academic papers simply rely on high text
similarity, which can leave researchers confused as to why a particular article
is being recommended. They may lack of grasp important information about the
insight connection between "Issue resolved" and "Issue finding" that they hope
to obtain. To address these issues, this study aims to support research insight
surveys for beginner researchers by establishing a hierarchical tree-structured
knowledge graph that reflects the inheritance insight of research topics and
the relevance insight among the academic papers.

为了支持初学者研究人员进行研究调查，本研究旨在建立一种具有层次结构的树状知识图，反映研究主题的继承洞察和学术论文之间的相关洞察。