Distance based knowledge graph embedding methods show promising results on link prediction task, on which two topics have been widely studied: one is the ability to handle complex relations, such as N-to-1, 1-to-N and N-to-N, the other is to encode various relation patterns, such as symmetry/antisymmetry. However, the existing methods fail to solve these two problems at the same time, which leads to unsatisfactory results. To mitigate this problem, we propose PairRE, a model with paired vectors for each relation representation. The paired vectors enable an adaptive adjustment of the margin in loss function to fit for complex relations. Besides, PairRE is capable of encoding three important relation patterns, symmetry/antisymmetry, inverse and composition. Given simple constraints on relation representations, PairRE can encode subrelation further. Experiments on link prediction benchmarks demonstrate the proposed key capabilities of PairRE. Moreover, We set a new state-of-the-art on two knowledge graph datasets of the challenging Open Graph Benchmark.

翻译：远程知识图嵌入方法显示了连接预测任务方面有希望的结果,对此已进行了广泛研究的两个专题:一是处理复杂关系的能力,如N-1、1-N和N-N-N,另一个是编码各种关系模式,如对称/对称。然而,现有方法未能同时解决这两个问题,从而导致不令人满意的结果。为缓解这一问题,我们提议PairRE,一个为每个关系代表制配对矢量的模型。配对矢量使损失函数的差值能够适应适应复杂关系。此外,PairRE能够对三种重要关系模式进行编码,即对称/对称、反义和构成。鉴于关系表达的简单限制,PairRE可以对子关系进行进一步编码。对连接预测基准的实验显示了PairRE的拟议关键能力。此外,我们为挑战性的公开图表基准设置了两个知识图表数据集。