  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.
