With good explanatory power and controllability, rule-based methods play an important role in many tasks such as knowledge reasoning and decision support. However, existing studies primarily focused on learning chain-like rules, which limit their semantic expressions and accurate prediction abilities. As a result, chain-like rules usually fire on the incorrect grounding values, producing inaccurate or even erroneous reasoning results. In this paper, we propose the concept of tree-like rules on knowledge graphs to expand the application scope and improve the reasoning ability of rule-based methods. Meanwhile, we propose an effective framework for refining chain-like rules into tree-like rules. Experimental comparisons on four public datasets show that the proposed framework can easily adapt to other chain-like rule induction methods and the refined tree-like rules consistently achieve better performances than chain-like rules on link prediction. The data and code of this paper can be available at https://anonymous.4open.science/r/tree-rule-E3CD/.

翻译：基于规则的方法因其良好的可解释性和可控性，在知识推理和决策支持等众多任务中发挥着重要作用。然而，现有研究主要集中于学习链式规则，这限制了其语义表达能力和准确预测能力。因此，链式规则通常会在错误的基值上触发，产生不准确甚至错误的推理结果。本文提出知识图谱上树式规则的概念，以扩展规则方法的应用范围并提升其推理能力。同时，我们提出一个有效的框架，用于将链式规则精炼为树式规则。在四个公开数据集上的实验比较表明，所提框架能够轻松适应其他链式规则归纳方法，且精炼后的树式规则在链接预测任务中的表现始终优于链式规则。本文的数据与代码可通过 https://anonymous.4open.science/r/tree-rule-E3CD/ 获取。