Controllable Logical Hypothesis Generation for Abductive Reasoning in Knowledge Graphs

• URL: http://arxiv.org/abs/2505.20948v1
• Date: Tue, 27 May 2025 09:36:47 GMT
• Title: Controllable Logical Hypothesis Generation for Abductive Reasoning in Knowledge Graphs
• Authors: Yisen Gao, Jiaxin Bai, Tianshi Zheng, Qingyun Sun, Ziwei Zhang, Jianxin Li, Yangqiu Song, Xingcheng Fu,
• Abstract summary: Abductive reasoning in knowledge graphs aims to generate plausible logical hypotheses from observed entities.<n>Due to a lack of controllability, a single observation may yield numerous plausible but redundant or irrelevant hypotheses.<n>We introduce the task of controllable hypothesis generation to improve the practical utility of abductive reasoning.
• Score: 54.596180382762036
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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