Knowledge graph (KG) reasoning is an important problem for knowledge graphs. In this paper, we propose a novel and principled framework called \textbf{RulE} (stands for {Rul}e {E}mbedding) to effectively leverage logical rules to enhance KG reasoning. Unlike knowledge graph embedding (KGE) methods, RulE learns rule embeddings from existing triplets and first-order {rules} by jointly representing \textbf{entities}, \textbf{relations} and \textbf{logical rules} in a unified embedding space. Based on the learned rule embeddings, a confidence score can be calculated for each rule, reflecting its consistency with the observed triplets. This allows us to perform logical rule inference in a soft way, thus alleviating the brittleness of logic. On the other hand, RulE injects prior logical rule information into the embedding space, enriching and regularizing the entity/relation embeddings. This makes KGE alone perform better too. RulE is conceptually simple and empirically effective. We conduct extensive experiments to verify each component of RulE. Results on multiple benchmarks reveal that our model outperforms the majority of existing embedding-based and rule-based approaches.

翻译：知识图谱（KG）推理是知识图谱领域的重要问题。本文提出一种新颖且具有原则性的框架——\textbf{RulE}（即{Rul}e {E}mbedding的缩写），旨在有效利用逻辑规则增强知识图谱推理。与知识图谱嵌入（KGE）方法不同，RulE通过将\textbf{实体}、\textbf{关系}和\textbf{逻辑规则}共同表示在统一的嵌入空间中，从现有三元组和一阶{规则}中学习规则嵌入。基于学习到的规则嵌入，可计算每条规则的置信度分数，反映其与观测三元组的一致性。这使我们能够以软性方式执行逻辑规则推理，从而缓解逻辑的脆弱性。另一方面，RulE将先验逻辑规则信息注入嵌入空间，丰富并正则化实体/关系嵌入，使单独的KGE性能也得到提升。RulE概念简洁且经验有效。我们开展大量实验验证RulE的每个组件。多个基准测试的结果表明，该模型优于大多数现有基于嵌入和基于规则的方法。