Knowledge Graph Question Answering (KGQA) offers grounded, interpretable reasoning, but existing methods often fail to provide reliable coverage guarantees over retrieved answers. While Conformal Prediction (CP) offers a principled framework for producing prediction sets with statistical guarantees, prior conformal KGQA methods suffer from two critical pitfalls: violated coverage guarantees due to invalid calibration, and weak score discriminability that yields excessively large prediction sets. We propose Conformal Path Reasoning (CPR), a novel trustworthy KGQA framework built on two key innovations. First, query-level conformal calibration over path-level scores preserves exchangeability to ensure valid coverage guarantees. Second, we introduce the Residual Conformal Value Network (RCVNet), a lightweight module trained via PUCT-guided exploration to learn discriminative path-level nonconformity scores. Extensive experiments show that CPR significantly improves the Empirical Coverage Rate by 45% while reducing prediction set size by 52% on average over conformal baselines across benchmark datasets, highlighting its effectiveness for reliable conformal reasoning over knowledge graphs.

翻译：知识图谱问答（KGQA）提供基于证据的可解释推理，但现有方法通常无法保证检索答案的可靠覆盖。尽管共形预测（CP）提供了生成具有统计保证的预测集的原理框架，但先前的共形KGQA方法存在两个关键缺陷：因无效校准导致的覆盖保证失效，以及因分数判别能力薄弱而生成的预测集过大。本文提出同调路径推理（CPR），这是一个基于两项核心创新的新型可信KGQA框架。首先，针对路径级分数进行查询级共形校准，通过保持可交换性确保有效的覆盖保证。其次，我们引入残差共形价值网络（RCVNet），该轻量模块通过PUCT引导的探索进行训练，学习具有判别能力的路径级非共形分数。大量实验表明，与基准数据集上的共形基线相比，CPR将经验覆盖率提升45%，同时将预测集大小平均减小52%，充分展现了其在知识图谱上进行可靠共形推理的有效性。