ICML 2025

Structure Is All You Need: Structural Representation Learning on
Hyper-Relational Knowledge Graphs
Jaejun Lee 1 Joyce Jiyoung Whang 1
Abstract
Hyper-relational knowledge graphs (HKGs) en-
rich knowledge graphs by extending a triplet to
a hyper-relational fact, where a set of qualifiers
adds auxiliary information to a triplet. While
many HKG representation learning methods have
been proposed, they often fail to effectively utilize
the HKG’s structure. This paper demonstrates that
thoroughly leveraging the structure of an HKG
is crucial for reasoning on HKGs, and a purely
structure-based representation learning method
can achieve state-of-the-art performance on var-
ious link prediction tasks. We propose MAYPL,
which learns to initialize representation vectors
based on the structure of an HKG and employs
an attentive neural message passing consisting of
fact-level message computation and entity-centric
and relation-centric aggregations, thereby comput-
ing the representations based solely on the struc-
ture. Due to its structure-driven learning, MAYPL
can conduct inductive inferences on new entities
and relations. MAYPL outperforms 40 knowledge
graph completion methods in 10 datasets, com-
pared with different baseline methods on different
datasets to be tested from diverse perspectives.
1. Introduction
Knowledge graphs (KGs) provide a structured representa-
tion of human knowledge, which eases organizing the re-
lationships between concepts or objects (Ji et al., 2022).
In KGs, information is represented in the form of a
triplet (Toutanova & Chen, 2015; Bordes et al., 2013), e.g.,
(Finding Nemo, set in, Sydney). Since the sim-
ple triplet form oversimplifies information, hyper-relational
knowledge graphs (HKGs) have been recently proposed to
add auxiliary details by adding qualifiers to a triplet (Rosso
1School of Computing, KAIST, Daejeon, South Korea. Corre-
spondence to: Joyce Jiyoung Whang <jjwhang@kaist.ac.kr>.
Proceedings of the 42 nd International Conference o...