Title: AutoGraph-R1: End-to-End Reinforcement Learning for Knowledge Graph Construction
Primary area: learning on graphs and other geometries & topologies
Keywords: Knowledge Graph Construction, Reinforcement Learning, Retrieval-Augmented Generation, RAG

Abstract: 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.

TL;DR: We optimize knowledge graph construction directly for downstream task performance using reinforcement learning, improving RAG system effectiveness.