The recently developed retrieval-augmented generation (RAG) technology has enabled the efficient construction of domain-specific applications. However, it also has limitations, including the gap between vector similarity and the relevance of knowledge reasoning, as well as insensitivity to knowledge logic, such as numerical values, temporal relations, expert rules, and others, which hinder the effectiveness of professional knowledge services. In this work, we introduce a professional domain knowledge service framework called Knowledge Augmented Generation (KAG). KAG is designed to address the aforementioned challenges with the motivation of making full use of the advantages of knowledge graph(KG) and vector retrieval, and to improve generation and reasoning performance by bidirectionally enhancing large language models (LLMs) and KGs through five key aspects: (1) LLM-friendly knowledge representation, (2) mutual-indexing between knowledge graphs and original chunks, (3) logical-form-guided hybrid reasoning engine, (4) knowledge alignment with semantic reasoning, and (5) model capability enhancement for KAG. We compared KAG with existing RAG methods in multihop question answering and found that it significantly outperforms state-of-theart methods, achieving a relative improvement of 19.6% on 2wiki and 33.5% on hotpotQA in terms of F1 score. We have successfully applied KAG to two professional knowledge Q&A tasks of Ant Group, including E-Government Q&A and E-Health Q&A, achieving significant improvement in professionalism compared to RAG methods.

翻译：近期发展的检索增强生成（RAG）技术已能高效构建领域专用应用。然而，该方法仍存在局限，包括向量相似性与知识推理相关性之间的差距，以及对数值、时序关系、专家规则等知识逻辑的不敏感性，这些因素制约了专业知识服务的效能。本研究提出一种名为知识增强生成（KAG）的专业领域知识服务框架。KAG旨在通过充分发挥知识图谱（KG）与向量检索的优势，应对上述挑战，并通过以下五个关键维度实现大型语言模型（LLMs）与知识图谱的双向增强，以提升生成与推理性能：（1）LLM友好的知识表示，（2）知识图谱与原始文本块的双向索引，（3）逻辑形式引导的混合推理引擎，（4）语义推理驱动的知识对齐，以及（5）面向KAG的模型能力增强。我们将KAG与现有RAG方法在多跳问答任务中进行比较，发现其显著优于当前最优方法，在2wiki和hotpotQA数据集上的F1分数分别实现了19.6%和33.5%的相对提升。我们已成功将KAG应用于蚂蚁集团的两个专业知识问答任务——电子政务问答与电子健康问答，相较于RAG方法在专业度方面取得了显著提升。