AutoGraph-R1: End-to-End Reinforcement Learning for Knowledge Graph Construction
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866917426905481216 |
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| author | Tsang, Hong Ting Bai, Jiaxin Huang, Haoyu Xiao, Qiao Zheng, Tianshi Xu, Baixuan Liu, Shujie Song, Yangqiu |
| author_facet | Tsang, Hong Ting Bai, Jiaxin Huang, Haoyu Xiao, Qiao Zheng, Tianshi Xu, Baixuan Liu, Shujie Song, Yangqiu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_15339 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AutoGraph-R1: End-to-End Reinforcement Learning for Knowledge Graph Construction Tsang, Hong Ting Bai, Jiaxin Huang, Haoyu Xiao, Qiao Zheng, Tianshi Xu, Baixuan Liu, Shujie Song, Yangqiu Computation and Language Artificial Intelligence 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. |
| title | AutoGraph-R1: End-to-End Reinforcement Learning for Knowledge Graph Construction |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2510.15339 |