{"type":"ScholarlyArticle","title":"Temporal Knowledge Graph Completion: A Survey","authors":[{"name":"Borui Cai","slug":"borui-cai","given":"Borui","family":"Cai","affiliation":"School of Information Technology , Deakin University"},{"name":"Yong Xiang","slug":"yong-xiang","given":"Yong","family":"Xiang","affiliation":"School of Information Technology , Deakin University"},{"name":"Longxiang Gao","slug":"longxiang-gao","given":"Longxiang","family":"Gao","affiliation":"Qilu University of Technology"},{"name":"He Zhang","slug":"he-zhang","given":"He","family":"Zhang","affiliation":"CNPIEC KEXIN , Ltd"},{"name":"Yunfeng Li","slug":"yunfeng-li","given":"Yunfeng","family":"Li","affiliation":"CNPIEC KEXIN , Ltd"},{"name":"Jianxin Li","slug":"jianxin-li","given":"Jianxin","family":"Li","affiliation":"School of Information Technology , Deakin University"}],"abstract":"Knowledge graph completion (KGC) predicts missing links and is crucial for real-life knowledge graphs, which widely suffer from incompleteness. \r\nKGC methods assume a knowledge graph is static, but that may lead to inaccurate prediction results because many facts in the knowledge graphs change over time. \r\nEmerging methods have recently shown improved prediction results by further incorporating the temporal validity of facts; namely, temporal knowledge graph completion (TKGC). \r\nWith this temporal information, TKGC methods explicitly learn the dynamic evolution of the knowledge graph that KGC methods fail to capture.\r\nIn this paper, for the first time, we comprehensively summarize the recent advances in TKGC research. \r\nFirst, we detail the background of TKGC, including the preliminary knowledge, benchmark datasets, and evaluation metrics. \r\nThen, we summarize existing TKGC methods based on how the temporal validity of facts is used to capture the temporal dynamics. \r\nFinally, we conclude the paper and present future research directions of TKGC.","doi":"10.24963/ijcai.2023/734","url":"https://ijcai.org/proceedings/2023/734","pdf":"https://ijcai.org/proceedings/2023/0734.pdf","markdown":"https://ijcai.org/proceedings/2023/734.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/734","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":734,"pages":{"first":6545,"last":6553},"keywords":["Survey: Knowledge Representation and Reasoning"],"videos":[],"references":[{"position":1,"raw":"[Aminikhanghahi and Cook, 2017] Samaneh Aminikhang- hahi and Diane J Cook. 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