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

• URL: http://arxiv.org/abs/2302.01859v2
• Date: Sat, 16 Dec 2023 06:06:03 GMT
• Title: Generalizing to Unseen Elements: A Survey on Knowledge Extrapolation for Knowledge Graphs
• Authors: Mingyang Chen, Wen Zhang, Yuxia Geng, Zezhong Xu, Jeff Z. Pan, Huajun Chen
• Abstract summary: Knowledge graphs (KGs) have become valuable knowledge resources in various applications. Conventional knowledge graph embedding (KGE) methods still face challenges when it comes to handling unseen entities or relations during model testing. We use a set of general terminologies to unify these methods and refer to them collectively as Knowledge Extrapolation.
• Score: 46.202752149611776
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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