Knowledge Graphs (KGs) and their machine learning counterpart, Knowledge Graph Embedding Models (KGEMs), have seen ever-increasing use in a wide variety of academic and applied settings. In particular, KGEMs are typically applied to KGs to solve the link prediction task; i.e. to predict new facts in the domain of a KG based on existing, observed facts. While this approach has been shown substantial power in many end-use cases, it remains incompletely characterised in terms of how KGEMs react differently to KG structure. This is of particular concern in light of recent studies showing that KG structure can be a significant source of bias as well as partially determinant of overall KGEM performance. This paper seeks to address this gap in the state-of-the-art. This paper provides, to the authors' knowledge, the first comprehensive survey exploring established relationships of Knowledge Graph Embedding Models and Graph structure in the literature. It is the hope of the authors that this work will inspire further studies in this area, and contribute to a more holistic understanding of KGs, KGEMs, and the link prediction task.

翻译：知识图谱及其机器学习对应物——知识图谱嵌入模型——在各类学术与应用场景中的使用日益广泛。知识图谱嵌入模型通常应用于知识图谱以解决链接预测任务，即基于已有观测事实预测知识图谱领域中的新事实。尽管该方法在许多终端应用场景中展现出显著效能，但关于知识图谱嵌入模型如何对知识图谱结构产生差异化反应，其特性尚未得到完整刻画。鉴于近期研究表明知识图谱结构可能成为显著偏差来源，并在一定程度上决定知识图谱嵌入模型的整体性能，这一问题尤为值得关注。本文致力于填补当前研究领域的这一空白。据作者所知，本文首次在文献中对知识图谱嵌入模型与图结构之间已确立的关系进行了系统性综述。作者期望本工作能够推动该领域的进一步研究，并促进对知识图谱、知识图谱嵌入模型及链接预测任务更全面的理解。