Hyper-relational Knowledge Graphs (HRKGs) extend traditional KGs beyond binary relations, enabling the representation of contextual, provenance, and temporal information in domains, such as historical events, sensor data, video content, and narratives. HRKGs can be structured using several Metadata Representation Models (MRMs), including Reification (REF), Singleton Property (SGP), and RDF-star (RDR). However, the effects of different MRMs on KG Embedding (KGE) and Link Prediction (LP) models remain unclear. This study evaluates MRMs in the context of LP tasks, identifies the limitations of existing evaluation frameworks, and introduces a new task that ensures fair comparisons across MRMs. Furthermore, we propose a framework that effectively reflects the knowledge representations of the three MRMs in latent space. Experiments on two types of datasets reveal that REF performs well in simple HRKGs, whereas SGP is less effective. However, in complex HRKGs, the differences among MRMs in the LP tasks are minimal. Our findings contribute to an optimal knowledge representation strategy for HRKGs in LP tasks.

翻译：超关系知识图谱（HRKGs）将传统知识图谱从二元关系扩展至能够表示领域中的上下文、溯源和时序信息，例如历史事件、传感器数据、视频内容和叙事文本。HRKGs可采用多种元数据表示模型（MRMs）进行结构化，包括具体化（REF）、单例属性（SGP）和RDF-star（RDR）。然而，不同MRMs对知识图谱嵌入（KGE）和链接预测（LP）模型的影响尚不明确。本研究在LP任务背景下评估了MRMs，指出了现有评估框架的局限性，并提出了一种确保跨MRM公平比较的新任务。此外，我们设计了一个能有效在潜在空间中反映三种MRM知识表示的框架。在两类数据集上的实验表明：REF在简单HRKGs中表现良好，而SGP效果较差；但在复杂HRKGs中，各MRM在LP任务中的差异微乎其微。我们的研究成果为LP任务中HRKGs的最优知识表示策略提供了理论依据。