Querying incomplete knowledge graphs with neural predictors is powerful but dangerous. Errors compound across multi-hop pipelines with no formal bound on the completeness of results. We introduce ConRAD, the first framework to enforce declarative recall guarantees natively within a neural graph database query engine. Given a user-specified risk budget, ConRAD automatically derives per-operator prediction thresholds that satisfy the recall target with finite-sample, distribution-free statistical validity via Conformal Risk Control, while maximizing end-to-end precision. To scale calibration across multi-operator query topologies, we introduce a quantile-space scalarization that reduces intractable high-dimensional threshold searches to a single parameter. We further design the conformal gate, a novel physical operator that dynamically bypasses neural inference when local graph evidence suffices, eliminating unnecessary model inferences in dense graph regions. Evaluated across three benchmarks and three query topologies, ConRAD strictly satisfies all risk budgets, with empirical recall falling below the target by at most 0.046 across all settings. It reduces neural invocations to zero in near-complete graph regions, and achieves precision that matches or exceeds best-case static baselines that offer no guarantees and require manual threshold search.

翻译：通过神经预测器查询不完整知识图谱强大但危险。在多跳流水线中，错误会累积且结果完整性缺乏形式化界限。我们提出ConRAD，这是首个在神经图数据库查询引擎内原生强制执行声明式召回率保证的框架。给定用户指定的风险预算，ConRAD通过保形风险控制自动推导每个算子的预测阈值，以有限样本、无分布假设的统计有效性满足召回率目标，同时最大化端到端精确率。为在多算子查询拓扑结构上实现可扩展的校准，我们引入分位数空间标量化方法，将难以处理的高维阈值搜索降维至单一参数。我们进一步设计了保形门控——一种新型物理算子，可在局部图证据充分时动态绕过神经推理，消除密集图区域中不必要的模型调用。在三个基准测试和三种查询拓扑结构上的评估表明，ConRAD严格满足所有风险预算，所有设置下经验召回率低于目标值的最大偏差不超过0.046。它在近乎完整的图区域中将神经调用次数降至零，且精确率达到或超过提供无保证且需人工阈值搜索的最佳静态基线。