# Hierarchical Tree-structured Knowledge Graph For Academic Insight Survey

arXiv: https://arxiv.org/abs/2402.04854 · HTML: https://mlsanity.com/paper/2402.04854/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 23 references that are in the map.
- First posted on arXiv 7 Feb 2024.

## Builds on (most cited first, 12 of 12)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing](https://mlsanity.com/paper/1902.07669/) (2019)
- [Construction of the Literature Graph in Semantic Scholar](https://mlsanity.com/paper/1805.02262/) (2018)
- [The Semantic Scholar Open Data Platform](https://mlsanity.com/paper/2301.10140/) (2023)
- [Building a PubMed knowledge graph](https://mlsanity.com/paper/2005.04308/) (2020)
- [unarXive 2022: All arXiv Publications Pre-Processed for NLP, Including Structured Full-Text and Citation Network](https://mlsanity.com/paper/2303.14957/) (2023)
- [What's New? Summarizing Contributions in Scientific Literature](https://mlsanity.com/paper/2011.03161/) (2020)
- [Network visualizations with Pyvis and VisJS](https://mlsanity.com/paper/2006.04951/) (2020)
- [SKG: A Versatile Information Retrieval and Analysis Framework for Academic Papers with Semantic Knowledge Graphs](https://mlsanity.com/paper/2306.04758/) (2023)
- [Object Recognition from Scientific Document based on Compartment Refinement Framework](https://mlsanity.com/paper/2312.09038/) (2023)

## Built on by (most cited first, 3 of 3)

- [AgenticSCR: An Autonomous Agentic Secure Code Review for Immature Vulnerabilities Detection](https://mlsanity.com/paper/2601.19138/) (2026)
- [Fish-bone diagram of research issue: Gain a bird's-eye view on a specific research topic](https://mlsanity.com/paper/2407.01553/) (2024)
- [A Survey Forest Diagram : Gain a Divergent Insight View on a Specific Research Topic](https://mlsanity.com/paper/2407.17081/) (2024)

## Abstract (arXiv)

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.