A hyper-relational knowledge graph has been recently studied where a triplet
is associated with a set of qualifiers; a qualifier is composed of a relation
and an entity, providing auxiliary information for a triplet. While existing
hyper-relational knowledge graph embedding methods assume that the entities are
discrete objects, some information should be represented using numeric values,
e.g., (J.R.R., was born in, 1892). Also, a triplet (J.R.R., educated at, Oxford
Univ.) can be associated with a qualifier such as (start time, 1911). In this
paper, we propose a unified framework named HyNT that learns representations of
a hyper-relational knowledge graph containing numeric literals in either
triplets or qualifiers. We define a context transformer and a prediction
transformer to learn the representations based not only on the correlations
between a triplet and its qualifiers but also on the numeric information. By
learning compact representations of triplets and qualifiers and feeding them
into the transformers, we reduce the computation cost of using transformers.
Using HyNT, we can predict missing numeric values in addition to missing
entities or relations in a hyper-relational knowledge graph. Experimental
results show that HyNT significantly outperforms state-of-the-art methods on
real-world datasets.

本文提出了一个名为 HyNT 的统一框架，它能够学习含数字量词的超关系知识图的表示，并且可以通过利用三元组与其限定词之间的关联和数字信息，来减少使用变形器的计算成本，同时可用于预测超关系知识图中缺失的数字值、实体或关系，并在真实数据集上展示了显著的性能优势。