Abductive reasoning in knowledge graphs aims to generate plausible logical hypotheses from observed entities, with broad applications in areas such as clinical diagnosis and scientific discovery. However, due to a lack of controllability, a single observation may yield numerous plausible but redundant or irrelevant hypotheses on large-scale knowledge graphs. To address this limitation, we introduce the task of controllable hypothesis generation to improve the practical utility of abductive reasoning. This task faces two key challenges when controlling for generating long and complex logical hypotheses: hypothesis space collapse and hypothesis oversensitivity. To address these challenges, we propose CtrlHGen, a Controllable logcial Hypothesis Generation framework for abductive reasoning over knowledge graphs, trained in a two-stage paradigm including supervised learning and subsequent reinforcement learning. To mitigate hypothesis space collapse, we design a dataset augmentation strategy based on sub-logical decomposition, enabling the model to learn complex logical structures by leveraging semantic patterns in simpler components. To address hypothesis oversensitivity, we incorporate smoothed semantic rewards including Dice and Overlap scores, and introduce a condition-adherence reward to guide the generation toward user-specified control constraints. Extensive experiments on three benchmark datasets demonstrate that our model not only better adheres to control conditions but also achieves superior semantic similarity performance compared to baselines. Our code is available at https://github.com/HKUST-KnowComp/CtrlHGen.

翻译：知识图谱中的溯因推理旨在从观测实体生成合理的逻辑假设，在临床诊断和科学发现等领域具有广泛应用。然而，由于缺乏可控性，单一观测在大规模知识图谱上可能产生大量合理但冗余或不相关的假设。为解决这一局限，我们引入了可控假设生成任务以提升溯因推理的实际效用。该任务在控制生成长而复杂的逻辑假设时面临两个关键挑战：假设空间坍缩与假设过度敏感。为应对这些挑战，我们提出CtrlHGen——一种面向知识图谱溯因推理的可控逻辑假设生成框架，采用包含监督学习与后续强化学习的两阶段训练范式。为缓解假设空间坍缩，我们设计了基于子逻辑分解的数据集增强策略，使模型能够通过利用更简单组件的语义模式来学习复杂逻辑结构。针对假设过度敏感问题，我们引入了包含Dice系数与重叠分数的平滑语义奖励，并提出条件遵循奖励以引导生成过程朝向用户指定的控制约束。在三个基准数据集上的大量实验表明，相较于基线模型，我们的方法不仅能更好地遵循控制条件，同时实现了更优的语义相似性性能。代码已发布于https://github.com/HKUST-KnowComp/CtrlHGen。