README.md

Knowledge Graph-Guided Retrieval Augmented Generation

This is the official code release of the following paper:

Xiangrong Zhu, Yuexiang Xie, Yi Liu, Yaliang Li, Wei Hu. Knowledge Graph-Guided Retrieval Augmented Generation, NAACL 2025.

Retrieval-augmented generation (RAG) has emerged as a promising technology for addressing hallucination issues in the responses generated by large language models (LLMs). Existing studies on RAG primarily focus on applying semantic-based approaches to retrieve isolated relevant chunks, which ignore their intrinsic relationships. In this paper, we propose a novel Knowledge Graph-Guided Retrieval Augmented Generation ($KG^2RAG$) framework that utilizes knowledge graphs (KGs) to provide fact-level relationships between chunks, improving the diversity and coherence of the retrieved results. Specifically, after performing a semantic-based retrieval to provide seed chunks, $KG^2RAG$ employs a KG-guided chunk expansion process and a KG-based chunk organization process to deliver relevant and important knowledge in well-organized paragraphs. Extensive experiments conducted on the HotpotQA dataset and its variants demonstrate the advantages of $KG^2RAG$ compared to existing RAG-based approaches, in terms of both response quality and retrieval quality.

Quick Start

Model Preparation

Please refer to the model directory for instructions on downloading and setting up the required models.

Data Preparation

Please refer to the code/preprocess directory for instructions on preparing the datasets.

Run $KG^2RAG$

• To run in distractor setting:

  python kg_rag_distractor.py --dataset hotpotqa --data_path ../data/hotpotqa/hotpot_dev_distractor_v1.json --kg_dir ../data/hotpotqa/kgs/extract_subkgs --result_path ../output/hotpot/hotpot_dev_distractor_v1_kgrag.json

• To run in fullwiki setting:

  python kg_rag_full.py --dataset hotpotqa --data_path ../data/hotpotqa/hotpot_dev_distractor_v1.json --kg_dir ../data/hotpotqa/kgs/extract_subkgs --result_path ../output/hotpot/hotpot_dev_fullwiki_v1_kgrag.json

If you have any difficulty or question in running code and reproducing experimental results, please email to xrzhu.nju@gmail.com.

Citation

If you find the repository helpful, please cite the following paper.

@inproceedings{KG2RAG,
  title = {Knowledge Graph-Guided Retrieval Augmented Generation},
  author = {Zhu, Xiangrong and 
            Xie, Yuexiang and 
            Liu, Yi and 
            Li, Yaliang and 
            Hu, Wei},
  booktitle = {NAACL},
  year = {2025}
}