提升知识图谱问答可信度，让答案覆盖更准、结果更简洁。
Conformal Path Reasoning: Trustworthy Knowledge Graph Question Answering via Path-Level Calibration
- 基于路径级别得分进行查询层校准，确保统计可靠性。
- 预测集平均缩小52%，实际覆盖率提升45%。
- 适合需要可信赖推理的医疗、金融等高风险场景。
知识图谱问答（KGQA）提供有依据、可解释的推理，但现有方法难以保证答案覆盖的可靠性。尽管遵从性预测（CP）提供了带有统计保障的预测集框架，但先前的符合性KGQA方法存在两个关键缺陷：校准无效导致覆盖保证失效，得分区分度弱造成预测集过大。本文提出符合性路径推理（CPR），通过两项核心创新实现可信的KGQA。首先，在路径级得分上进行查询级的符合性校准，保持交换性以确保有效的覆盖保证；其次，引入残差符合性值网络（RCVNet），通过PUCT引导探索训练，学习具有区分性的路径级非符合性得分。大量实验表明，与基准符合性方法相比，CPR在多个基准数据集上平均使经验覆盖率提升45%，预测集大小减少52%，显著提升了知识图谱上可靠符合性推理的效果。
原文摘要 · Abstract (English)
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.
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