用外部知识图谱提升RAG的准确性和可解释性
KG-Infused RAG: Augmenting Corpus-Based RAG with External Knowledge Graphs
- 将预存知识图谱与文本库结合，通过传播激活检索结构化知识
- 在5个问答数据集上比基础RAG提升3.9%至17.8%
- 低成本获取高质量结构化知识，适合作为RAG增强模块
检索增强生成（RAG）通过引入外部知识提升回答准确性。现有方法或仅依赖文本语料而忽略结构化知识，或构建临时知识图谱成本高且不可靠。为此，我们提出KG-Infused RAG框架，将大规模预存知识图谱融入RAG，并应用传播激活机制增强检索与生成。该方法直接在外部知识图谱上执行传播激活，获取相关结构化知识，用于扩展查询并融合文本段落，实现可解释、语义一致的多源检索。进一步通过采样关键阶段的偏好学习优化模型。在五个问答基准测试中，KG-Infused RAG持续优于基础RAG（提升3.9%至17.8%）。相比GraphRAG、LightRAG等基于知识图谱的方法，本方法以更低成本获取结构化知识且表现更优。此外，与Self-RAG和DeepNote集成后仍获进一步提升，验证其作为即插即用增强模块的有效性与通用性。
原文摘要 · Abstract (English)
Retrieval-Augmented Generation (RAG) improves factual accuracy by grounding responses in external knowledge. However, existing RAG methods either rely solely on text corpora and neglect structural knowledge, or build ad-hoc knowledge graphs (KGs) at high cost and low reliability. To address these issues, we propose KG-Infused RAG, a framework that incorporates pre-existing large-scale KGs into RAG and applies spreading activation to enhance both retrieval and generation. KG-Infused RAG directly performs spreading activation over external KGs to retrieve relevant structured knowledge, which is then used to expand queries and integrated with corpus passages, enabling interpretable and semantically grounded multi-source retrieval. We further improve KG-Infused RAG through preference learning on sampled key stages of the pipeline. Experiments on five QA benchmarks show that KG-Infused RAG consistently outperforms vanilla RAG (by 3.9% to 17.8%). Compared with KG-based approaches such as GraphRAG and LightRAG, our method obtains structured knowledge at lower cost while achieving superior performance. Additionally, integrating KG-Infused RAG with Self-RAG and DeepNote yields further gains, demonstrating its effectiveness and versatility as a plug-and-play enhancement module for corpus-based RAG methods.
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