让大模型精准回答关系型知识图谱问题，找对连接模式。
UniRel: Relation-Centric Knowledge Graph Question Answering with RL-Tuned LLM Reasoning
- 用强化学习微调大模型，筛选出简洁且信息丰富的子图作为答案
- 在多个数据集上比基线方法连接更紧密、奖励更高，泛化性好
- 既适用于关系型问答，也能提升传统实体查询的性能
知识图谱问答（KGQA）主要针对返回单一实体的答案的实体中心型查询。然而，许多真实问题本质上是关系型的，旨在理解实体间的关联方式，而非找出满足条件的实体。本文提出关系中心型KGQA，其答案为描述实体间语义关系的子图。核心挑战在于候选子图数量庞大，平凡或过于常见的连接会掩盖独特且有意义的答案。为此，我们提出UniRel——一个统一的模块化框架，结合子图检索器与经强化学习微调的大语言模型。该框架采用奖励函数，偏好结构紧凑、关系信息丰富且中间节点度数低的子图。实验表明，UniRel在连接性和奖励值上均优于提示基线方法，并能有效泛化至未见实体与关系。此外，UniRel亦可应用于传统实体中心型KGQA，在多个设置下达到竞争性或更优表现。
原文摘要 · Abstract (English)
Knowledge Graph Question Answering (KGQA) has largely focused on entity-centric queries that return a single answer entity. However, many real-world questions are inherently relational, aiming to understand how entities are associated rather than which entity satisfies a query. In this work, we introduce relation-centric KGQA, a complementary setting in which the answer is a subgraph that represents the semantic relations among entities. The main challenge lies in the abundance of candidate subgraphs, where trivial or overly common connections often obscure the identification of unique and informative answers. To tackle this, we propose UniRel, a unified modular framework that combines a subgraph retriever with an LLM fine-tuned using reinforcement learning. The framework uses a reward function to prefer compact and specific subgraphs with informative relations and low-degree intermediate entities. Experiments show that UniRel improves connectivity and reward over Prompting baselines and generalizes well to unseen entities and relations. Moreover, UniRel can be applied to conventional entity-centric KGQA, achieving competitive or improved performance in several settings.
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