Comparison of Metadata Representation Models for Knowledge Graph Embeddings

• URL: http://arxiv.org/abs/2503.21804v2
• Date: Mon, 31 Mar 2025 04:31:23 GMT
• Title: Comparison of Metadata Representation Models for Knowledge Graph Embeddings
• Authors: Shusaku Egami, Kyoumoto Matsushita, Takanori Ugai, Ken Fukuda,
• Abstract summary: Hyper-relational Knowledge Graphs (HRKGs) extend traditional KGs beyond binary relations.<n>This study evaluates the effects of different Metadata Representation Models (MRMs) on KG Embedding (KGE) and Link Prediction (LP) models.<n>We propose a framework that effectively reflects the knowledge representations of the three MRMs in latent space.
• Score: 1.8749305679160366
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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