Knowledge base completion (KBC) aims to automatically infer missing facts by exploiting information already present in a knowledge base (KB). A promising approach for KBC is to embed knowledge into latent spaces and make predictions from learned embeddings. However, existing embedding models are subject to at least one of the following limitations: (1) theoretical inexpressivity, (2) lack of support for prominent inference patterns (e.g., hierarchies), (3) lack of support for KBC over higher-arity relations, and (4) lack of support for incorporating logical rules. Here, we propose a spatio-translational embedding model, called BoxE, that simultaneously addresses all these limitations. BoxE embeds entities as points, and relations as a set of hyper-rectangles (or boxes), which spatially characterize basic logical properties. This seemingly simple abstraction yields a fully expressive model offering a natural encoding for many desired logical properties. BoxE can both capture and inject rules from rich classes of rule languages, going well beyond individual inference patterns. By design, BoxE naturally applies to higher-arity KBs. We conduct a detailed experimental analysis, and show that BoxE achieves state-of-the-art performance, both on benchmark knowledge graphs and on more general KBs, and we empirically show the power of integrating logical rules.

中文翻译：

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BoxE：用于知识库完成的框嵌入模型

知识库补全 (KBC) 旨在通过利用知识库 (KB) 中已有的信息来自动推断缺失的事实。KBC 的一种有前途的方法是将知识嵌入到潜在空间中，并从学习到的嵌入中进行预测。然而，现有的嵌入模型至少受到以下限制之一：（1）理论上的不可表达性，（2）缺乏对突出推理模式（例如层次结构）的支持，（3）缺乏对更高数量的 KBC 的支持(4) 缺乏对合并逻辑规则的支持。在这里，我们提出了一个称为 BoxE 的空间平移嵌入模型，它同时解决了所有这些限制。BoxE 将实体嵌入为点，将关系嵌入为一组超矩形（或框），它们在空间上表征基本逻辑属性。这种看似简单的抽象产生了一个完全表达的模型，为许多所需的逻辑属性提供了自然的编码。BoxE 可以从丰富的规则语言类别中捕获和注入规则，远远超出单个推理模式。按照设计，BoxE 自然适用于更高数量的知识库。我们进行了详细的实验分析，并表明 BoxE 在基准知识图和更一般的知识库上都达到了最先进的性能，并且我们凭经验展示了集成逻辑规则的能力。