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

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

Mingyang Chen, Wen Zhang, Yuxia Geng, Zezhong Xu, Jeff Z. Pan, Huajun Chen

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence
Survey Track. Pages 6574-6582. https://doi.org/10.24963/ijcai.2023/737

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.
Keywords:
Survey: Knowledge Representation and Reasoning
Survey: Natural Language Processing
Survey: Machine Learning