Knowledge Graph Question Answering (KGQA) has shown promise for grounded and interpretable reasoning, yet existing approaches 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 methods suffer from critical limitations in both calibration validity and score discriminability, resulting in violated coverage guarantees and excessively large prediction sets. To address these pitfalls, we propose Conformal Path Reasoning (CPR), a trustworthy KGQA framework with two key innovations. First, we perform query-level conformal calibration over path-level scores, preserving the exchangeability while generating path prediction sets. Second, we introduce the Residual Conformal Value Network (RCVNet), a lightweight module trained via PUCT-guided exploration to learn discriminative path-level nonconformity scores. Experiments on benchmarks show that CPR significantly improves the Empirical Coverage Rate by 34% while reducing average prediction set size by 40% compared to conformal baselines. These results validate the efficacy of CPR in satisfying coverage guarantees with substantially more compact answer sets.

翻译：知识图谱问答方法在支持可解释推理方面展现出潜力，但现有方法往往无法对检索结果提供可靠的覆盖保证。尽管保形预测为生成具有统计保证的预测集提供了理论框架，但先前方法在校准有效性与评分可区分性方面存在关键缺陷，导致覆盖保证被违反且预测集规模过大。针对这些不足，我们提出了一种可信的知识图谱问答框架——保形路径推理，其包含两项核心创新：首先，在路径层级评分上执行查询级保形校准，在保证可交换性的同时生成路径预测集；其次，引入残差保形价值网络，该轻量级模块通过PUCT引导式探索训练，学习具有高区分度的路径级非保形评分。基准实验表明，相比保形基线方法，保形路径推理在保持覆盖保证的前提下，将经验覆盖率提升34%，同时将平均预测集规模缩减40%。实验结果验证了保形路径推理在满足覆盖保证的同时生成更紧凑答案集方面的有效性。