  Hyper-relational knowledge graphs (KGs) (e.g., Wikidata) enable associating
additional key-value pairs along with the main triple to disambiguate, or
restrict the validity of a fact. In this work, we propose a message passing
based graph encoder - StarE capable of modeling such hyper-relational KGs.
Unlike existing approaches, StarE can encode an arbitrary number of additional
information (qualifiers) along with the main triple while keeping the semantic
roles of qualifiers and triples intact. We also demonstrate that existing
benchmarks for evaluating link prediction (LP) performance on hyper-relational
KGs suffer from fundamental flaws and thus develop a new Wikidata-based dataset
- WD50K. Our experiments demonstrate that StarE based LP model outperforms
existing approaches across multiple benchmarks. We also confirm that leveraging
qualifiers is vital for link prediction with gains up to 25 MRR points compared
to triple-based representations.
