{"type":"ScholarlyArticle","title":"Generalizing to Unseen Elements: A Survey on Knowledge Extrapolation for Knowledge Graphs","authors":[{"name":"Mingyang Chen","slug":"mingyang-chen","given":"Mingyang","family":"Chen","affiliation":"College of Computer Science and Technology , Zhejiang University"},{"name":"Wen Zhang","slug":"wen-zhang","given":"Wen","family":"Zhang","affiliation":"School of Software Technology , Zhejiang University"},{"name":"Yuxia Geng","slug":"yuxia-geng","given":"Yuxia","family":"Geng","affiliation":"College of Computer Science and Technology , Zhejiang University"},{"name":"Zezhong Xu","slug":"zezhong-xu","given":"Zezhong","family":"Xu","affiliation":"College of Computer Science and Technology , Zhejiang University"},{"name":"Jeff Z. Pan","slug":"jeff-z-pan","given":"Jeff Z.","family":"Pan","affiliation":"School of Informatics , The University of Edinburgh"},{"name":"Huajun Chen","slug":"huajun-chen","given":"Huajun","family":"Chen","affiliation":"College of Computer Science and Technology , Zhejiang University; Donghai Laboratory; Alibaba-Zhejiang University Joint Institute of Frontier Technologies {mingyangchen ,"}],"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.","doi":"10.24963/ijcai.2023/737","url":"https://ijcai.org/proceedings/2023/737","pdf":"https://ijcai.org/proceedings/2023/0737.pdf","markdown":"https://ijcai.org/proceedings/2023/737.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/737","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Survey Track","track":"Survey Track","number":737,"pages":{"first":6574,"last":6582},"keywords":["Survey: Natural Language Processing","Survey: Knowledge Representation and Reasoning","Survey: Machine Learning"],"videos":[],"references":[{"position":1,"raw":"[Albooyeh et al., 2020] M. 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