论文标题
规则：嵌入规则的知识图推理
RulE: Knowledge Graph Reasoning with Rule Embedding
论文作者
论文摘要
知识图（kg）推理是知识图的重要问题。在本文中，我们提出了一个名为\ textbf {rule}（代表{rul} e {e} mbedding）的小说和原则性框架，以有效利用逻辑规则来增强kg的推理。与知识图嵌入（KGE）方法不同，规则通过共同表示\ textbf {entities}，\ textbf {ressection}和\ textbf {logical {logical规则}在统一的嵌入空间中，从现有三重态和一阶{规则}中学习规则嵌入。基于学习的规则嵌入，可以为每个规则计算一个置信度评分，以反映其与观察到的三胞胎的一致性。这使我们能够以柔和的方式执行逻辑规则推论，从而减轻逻辑的脆弱性。另一方面，规则将先前的逻辑规则信息注入嵌入空间，丰富和正规化实体/关系嵌入。这也使KGE独自表现更好。规则在概念上是简单的，并且在经验上有效。我们进行大量实验以验证规则的每个组成部分。多个基准的结果表明，我们的模型的表现优于大多数基于嵌入的基于嵌入的方法和基于规则的方法。
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.
