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 this https URL

我们提出了一种基于知识图谱的树状规则方法，可以扩展规则的应用范围和提高基于规则的方法的推理能力，并通过在四个公共数据集上的实验证明，相比链状规则，在链状规则归纳方法的基础上改进的树状规则在关联预测上表现更好。