<ul><li>Hyper-relational Knowledge Graphs (HRKGs) extend traditional KGs beyond binary relations, enabling the representation of contextual, provenance, and temporal information.</li><li>This study evaluates different Metadata Representation Models (MRMs) and their effects on KG Embedding (KGE) and Link Prediction (LP) models.</li><li>Experimental results show that the Reification (REF) MRM performs well in simple HRKGs, while the Singleton Property (SGP) MRM is less effective.</li><li>Findings contribute to optimal knowledge representation strategies for HRKGs in LP tasks.</li></ul>

Comparison of Metadata Representation Models for Knowledge Graph Embeddings

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