{"type":"ScholarlyArticle","title":"Temporal Knowledge Graph Extrapolation via Causal Subhistory Identification","authors":[{"name":"Kai Chen","slug":"kai-chen","given":"Kai","family":"Chen","affiliation":"National University of Defense Technology , Changsha , China"},{"name":"Ye Wang","slug":"ye-wang","given":"Ye","family":"Wang","affiliation":"National University of Defense Technology , Changsha , China"},{"name":"Xin Song","slug":"xin-song","given":"Xin","family":"Song","affiliation":"National University of Defense Technology , Changsha , China"},{"name":"Siwei Chen","slug":"siwei-chen","given":"Siwei","family":"Chen","affiliation":"Defense Innovation Institute , Beijing , China"},{"name":"Han Yu","slug":"han-yu","given":"Han","family":"Yu","affiliation":"National University of Defense Technology , Changsha , China"},{"name":"Aiping Li","slug":"aiping-li","given":"Aiping","family":"Li","affiliation":"National University of Defense Technology , Changsha , China"}],"abstract":"Temporal knowledge graph extrapolation has become a prominent area of study interest in recent years. Numerous methods for extrapolation have been put forth, mining query-relevant information from history to generate forecasts. However, existing approaches normally do not discriminate between causal and non-causal effects in reasoning; instead, they focus on analyzing the statistical correlation between the future events to be predicted and the historical data given, which may be deceptive and hinder the model's capacity to learn real causal information that actually affects the reasoning conclusions. To tackle it, we propose a novel approach called Causal Subhistory Identification (CSI), which focuses on extracting the causal subhistory for reasoning purposes from a large amount of historical data. CSI can improve the clarity and transparency of the reasoning process and more effectively convey the logic behind conclusions by giving priority to the causal subhistory and eliminating non-causal correlations. Extensive experiments demonstrate the remarkable potential of our CSI in the following aspects: superiority, improvement, explainability, and robustness.","doi":"10.24963/ijcai.2024/365","url":"https://ijcai.org/proceedings/2024/365","pdf":"https://ijcai.org/proceedings/2024/0365.pdf","markdown":"https://ijcai.org/proceedings/2024/365.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/365","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Knowledge Representation and Reasoning","track":"Main Track","number":365,"pages":{"first":3298,"last":3306},"keywords":["Data Mining: DM: Knowledge graphs and knowledge base completion","Natural Language Processing: NLP: Applications","Knowledge Representation and Reasoning: KRR: Learning and reasoning"],"videos":[],"references":[{"position":1,"raw":"[Bai et al., 2023] Luyi Bai, Wenting Yu, Die Chai, Wen- jun Zhao, and Mingzhuo Chen. 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