Document Type

Article

Publication Date

2026

Abstract

Knowledge graphs (KGs) are an established paradigm for integrating heterogeneous data and representing knowledge. As such, there are many different methodologies for producing KGs, which span notions of expressivity, and are tailored for different use-cases and domains. Now, as neurosymbolic methods rise in prominence, it is important to understand how the development of KGs according to these methodologies impact downstream tasks, such as link prediction using KG embeddings (KGEs). In this article, we examine how various perturbations of graph structures impact downstream tasks. These perturbations are sourced from how various methodologies (or design practices) would impact the model, starting with simple inclusions of schema and basic reification constructions. We assess these changes across synthetic graphs and FB15k-237, a common benchmark. We provide visualizations, graph metrics, and performance on the link prediction task as exploration results using various KGE models.

Comments

This work is licensed under CC BY-NC 4.0 Creative Commons Attribution-NonCommercial 4.0 International License

DOI

10.1177/29498732261420038

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