  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.
