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.

翻译：超关系知识图谱近年来受到广泛研究，其中三元组与一组限定符相关联；限定符由关系和实体组成，为三元组提供辅助信息。现有超关系知识图谱嵌入方法假设实体为离散对象，但某些信息需用数值表示，例如（J.R.R., 出生年份, 1892）。同时，三元组（J.R.R., 就读于, 牛津大学）可关联诸如（起始时间, 1911）的限定符。本文提出统一框架HyNT，用于学习包含三元组或限定符中数值字面量的超关系知识图谱表示。我们定义上下文变换器和预测变换器，不仅基于三元组与限定符间的相关性，还结合数值信息学习表示。通过学习三元组和限定符的紧凑表示并将其输入变换器，我们降低了使用变换器的计算成本。利用HyNT，我们可预测超关系知识图谱中缺失的数值、实体或关系。实验结果表明，HyNT在真实数据集上显著优于最先进方法。