让大模型自主构建可追溯的知识图谱，提升问答准确率。
RAGA: Reading-And-Graph-building-Agent for Autonomous Knowledge Graph Construction and Retrieval-Augmented Generation
- 用阅读-搜索-验证-构建的循环机制，让模型边读边建图。
- 在科学问答数据集上，融合知识图谱后答案和证据质量均提升。
- 每条知识都有来源凭证，适合高可信场景使用。
现有基于大模型的知识图谱构建方法多采用无状态批量处理流程，在跨段落语义关系捕捉、实体消歧和构建过程可解释性方面存在结构性缺陷，影响图谱质量、检索精度及高风险领域部署的信任度。本文提出RAGA（Reading And Graph-building Agent），一个基于大模型的自主知识图谱构建与检索增强生成融合框架。RAGA提供支持知识图谱全生命周期增删改查操作的原子工具集，并将‘阅读-搜索-验证-构建’认知约束嵌入到ReAct工具循环中。通过知识图谱-向量同步机制实现符号与向量混合检索，证据锚定验证确保每个知识条目均可追溯至原始文本，实现可审计的来源溯源。在QASPER科学问答数据集子集上的初步实验表明，RAGA的融合检索性能优于零样本基线，知识图谱集成显著提升了答案与证据质量。该框架设计与实验基线为代理驱动的自主知识图谱构建提供了参考。
原文摘要 · Abstract (English)
Existing LLM-driven knowledge graph (KG) construction methods predominantly employ stateless batch processing pipelines, exhibiting structural deficiencies in cross-chunk semantic relation capture, entity disambiguation, and construction process interpretability. These limitations undermine KG quality, retrieval precision, and deployment trust in high-stakes domains. We propose RAGA (Reading And Graph-building Agent), an LLM-based autonomous KG construction and retrieval fusion framework. RAGA provides an atomic toolset supporting full KG lifecycle CRUD operations and embeds a Read-Search-Verify-Construct cognitive constraint into a ReAct tool loop. A KG-vector synchronization mechanism enables hybrid symbolic-vector retrieval, while evidence-anchored verification links every knowledge entry to its source text for auditable provenance. Preliminary experiments on a subset of the QASPER scientific QA dataset indicate that RAGA's fusion retrieval outperforms zero-shot baselines, with KG integration providing measurable gains in both answer and evidence quality. The framework design and experimental baseline serve as a reference for agent-driven autonomous KG construction.
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