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A hyper-relational knowledge graph has been recently studied where a tripletis associated with a set of qualifiers; a qualifier is composed of a relationand an entity, providing auxiliary information for a triplet. While existinghyper-relational knowledge graph embedding methods assume that the entities arediscrete 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, OxfordUniv.) can be associated with a qualifier such as (start time, 1911). In thispaper, we propose a unified framework named HyNT that learns representations ofa hyper-relational knowledge graph containing numeric literals in eithertriplets or qualifiers. We define a context transformer and a predictiontransformer to learn the representations based not only on the correlationsbetween a triplet and its qualifiers but also on the numeric information. Bylearning compact representations of triplets and qualifiers and feeding theminto the transformers, we reduce the computation cost of using transformers.Using HyNT, we can predict missing numeric values in addition to missingentities or relations in a hyper-relational knowledge graph. Experimentalresults show that HyNT significantly outperforms state-of-the-art methods onreal-world datasets.

Representation Learning on Hyper-Relational and Numeric Knowledge Graphs with Transformers