Knowledge graphs (KGs) have become valuable knowledge resources in various applications, and knowledge graph embedding (KGE) methods have garnered increasing attention in recent years. However, conventional KGE methods still face challenges when it comes to handling unseen entities or relations during model testing. To address this issue, much effort has been devoted to various fields of KGs. In this paper, we use a set of general terminologies to unify these methods and refer to them collectively as Knowledge Extrapolation. We comprehensively summarize these methods, classified by our proposed taxonomy, and describe their interrelationships. Additionally, we introduce benchmarks and provide comparisons of these methods based on aspects that are not captured by the taxonomy. Finally, we suggest potential directions for future research.

翻译：知识图谱（KG）已成为各类应用中宝贵的知识资源，知识图谱嵌入（KGE）方法近年来日益受到关注。然而，传统KGE方法在模型测试阶段处理未见实体或关系时仍面临挑战。为解决这一问题，学界已在KG相关领域投入大量研究工作。本文采用一套通用术语体系对这些方法进行统一，并将其统称为"知识外推"。我们根据提出的分类体系全面梳理了这些方法，并阐述其内在关联。此外，我们引入基准数据集，并从分类体系未涵盖的维度对这些方法进行对比分析。最后，我们展望了未来潜在的研究方向。