Generalizing to Unseen Elements: A Survey on Knowledge Extrapolation for Knowledge Graphs

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.

Authors

• Mingyang Chen College of Computer Science and Technology, Zhejiang University
• Wen Zhang
• Yuxia Geng College of Computer Science and Technology, Zhejiang University Alibaba-Zhejiang University Joint Institute of Frontier Technologies
• Zezhong Xu
• Jeff Z. Pan The University of Edinburgh
• Huajun Chen College of Computer Science and Technology, Zhejiang University ZJU-Ant Group Joint Lab of Knowledge Graph Zhejiang Key Laboratory of Big Data Intelligent Computing

Keywords

• Survey: Knowledge Representation and Reasoning
• Survey: Machine Learning
• Survey: Natural Language Processing