Research on Graph-Retrieval Augmented Generation Based on Historical Text Knowledge Graphs
On the map: Retrieval-augmented generation → Knowledge-graph and structured-knowledge RAG
Abstract (arXiv)
This article addresses domain knowledge gaps in general large language models for historical text analysis in the context of computational humanities and AIGC technology. We propose the Graph RAG framework, combining chain-of-thought prompting, self-instruction generation, and process supervision to create a The First Four Histories character relationship dataset with minimal manual annotation. This dataset supports automated historical knowledge extraction, reducing labor costs. In the graph-augmented generation phase, we introduce a collaborative mechanism between knowledge graphs and retrieval-augmented generation, improving the alignment of general models with historical knowledge. Experiments show that the domain-specific model Xunzi-Qwen1.5-14B, with Simplified Chinese input and chain-of-thought prompting, achieves optimal performance in relation extraction (F1 = 0.68). The DeepSeek model integrated with GraphRAG improves F1 by 11% (0.08-0.19) on the open-domain C-CLUE relation extraction dataset, surpassing the F1 value of Xunzi-Qwen1.5-14B (0.12), effectively alleviating hallucinations phenomenon, and improving interpretability. This framework offers a low-resource solution for classical text knowledge extraction, advancing historical knowledge services and humanities research.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Knowledge-graph and structured-knowledge RAG, one of 15 directions in Retrieval-augmented generation. Builds on 14 of its 29 references that are in the map. First posted on arXiv 18 Jun 2025.
Builds on
14 of 29 references.
Closest ancestors · 2
Same direction, newest first
- 2024Graph Retrieval-Augmented Generation: A Survey15 Aug
- 2024G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering12 Feb
Other references · 12
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
- 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,754
- 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,724
- 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
- 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,601
- 2024Improve Mathematical Reasoning in Language Models by Automated Process Supervisioncited by 441
- 2024A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Modelscited by 269
- 2022Promptagator: Few-shot Dense Retrieval From 8 Examplescited by 235
- 2023Knowledge Graph Prompting for Multi-Document Question Answeringcited by 152
- 2023Knowledge Graph-Augmented Language Models for Knowledge-Grounded Dialogue Generationcited by 54
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Citing papers per year: 2026: 2.
2 papers.
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Close in wording, no citation either way
- 2025A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models
- 2025Pseudo-Knowledge Graph: Meta-Path Guided Retrieval and In-Graph Text for RAG-Equipped LLM
- 2025Knowledge Graph-Guided Retrieval Augmented Generation
- 2025RAKG:Document-level Retrieval Augmented Knowledge Graph Construction
- 2026Automated Tree Knowledge Graph Construction using Ontology Expansion and Retrieval from Vietnamese History Textbooks
- 2025A Query-Aware Multi-Path Knowledge Graph Fusion Approach for Enhancing Retrieval-Augmented Generation in Large Language Models
- 2026Efficient Retrieval-Augmented Generation via Token Co-occurrence Graphs
- 2025LinearRAG: Linear Graph Retrieval Augmented Generation on Large-scale Corpora