Learning logical rules is critical to improving reasoning in KGs. This is due to their ability to provide logical and interpretable explanations when used for predictions, as well as their ability to generalize to other tasks, domains, and data. While recent methods have been proposed to learn logical rules, the majority of these methods are either restricted by their computational complexity and can not handle the large search space of large-scale KGs, or show poor generalization when exposed to data outside the training set. In this paper, we propose an end-to-end neural model for learning compositional logical rules called NCRL. NCRL detects the best compositional structure of a rule body, and breaks it into small compositions in order to infer the rule head. By recurrently merging compositions in the rule body with a recurrent attention unit, NCRL finally predicts a single rule head. Experimental results show that NCRL learns high-quality rules, as well as being generalizable. Specifically, we show that NCRL is scalable, efficient, and yields state-of-the-art results for knowledge graph completion on large-scale KGs. Moreover, we test NCRL for systematic generalization by learning to reason on small-scale observed graphs and evaluating on larger unseen ones.

翻译：学习逻辑规则对于提升知识图谱中的推理能力至关重要，这源于其在进行预测时能提供逻辑清晰且可解释的说明，以及其能泛化到其他任务、领域和数据的特性。尽管近年来已有一些学习逻辑规则的方法被提出，但大多数方法要么受限于计算复杂度，无法处理大规模知识图谱中庞大的搜索空间，要么在面对训练集之外的数据时表现出的泛化能力较差。本文提出了一种端到端的神经模型，用于学习组合逻辑规则，命名为NCRL。NCRL能够检测规则体的最佳组合结构，并将其分解为小型的组合以推断规则头部。通过使用循环注意力单元对规则体中的组合进行循环合并，NCRL最终预测出一个规则头部。实验结果表明，NCRL不仅能学习到高质量的规则，还具有优异的泛化能力。具体而言，我们展示了NCRL的可扩展性、高效性，并在大规模知识图谱补全任务上取得了顶尖成果。此外，我们还通过在小型观测图谱上学习推理并在未见过的大型图谱上评估，测试了NCRL在系统化泛化方面的能力。