用强化学习让知识图谱直接为问答任务服务，效果更优。
AutoGraph-R1: End-to-End Reinforcement Learning for Knowledge Graph Construction
- 将图谱构建转为策略学习，奖励由问答表现决定。
- 在多个基准上，问答准确率显著优于传统图谱。
- 适合想提升知识图谱应用效果的研究者和开发者。
为检索增强生成（RAG）构建高效的知识图谱（KG）对提升问答系统性能至关重要。然而，其效果受限于构建过程与下游应用脱节，导致图谱结构不优。为此，我们提出AutoGraph-R1，首个直接通过强化学习（RL）优化图谱构建以提升任务性能的框架。该框架将图谱生成视为策略学习问题，奖励信号来自图谱在RAG流程中的功能效用。设计了两种任务感知的奖励函数：一种用于图谱作为知识载体，另一种用于图谱作为知识索引。在多个问答基准测试中，AutoGraph-R1使图谱增强型RAG方法相比使用任务无关基线图谱，持续实现显著性能提升。本工作证明可闭合构建与应用之间的闭环，推动范式从构建‘内在优质’图谱转向构建‘实际有用’图谱。
原文摘要 · Abstract (English)
Building effective knowledge graphs (KGs) for Retrieval-Augmented Generation (RAG) is pivotal for advancing question answering (QA) systems. However, its effectiveness is hindered by a fundamental disconnect: the knowledge graph (KG) construction process is decoupled from its downstream application, yielding suboptimal graph structures. To bridge this gap, we introduce AutoGraph-R1, the first framework to directly optimize KG construction for task performance using Reinforcement Learning (RL). AutoGraph-R1 trains an LLM constructor by framing graph generation as a policy learning problem, where the reward is derived from the graph's functional utility in a RAG pipeline. We design two novel, task-aware reward functions, one for graphs as knowledge carriers and another as knowledge indices. Across multiple QA benchmarks, AutoGraph-R1 consistently enables graph RAG methods to achieve significant performance gains over using task-agnostic baseline graphs. Our work shows it is possible to close the loop between construction and application, shifting the paradigm from building intrinsically ``good'' graphs to building demonstrably ``useful'' ones.
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