{"type":"ScholarlyArticle","title":"Adaptive Path-Memory Network for Temporal Knowledge Graph Reasoning","authors":[{"name":"Hao Dong","slug":"hao-dong","given":"Hao","family":"Dong","affiliation":"Computer Network Information Center , Chinese Academy of Sciences , Beijing; University of Chinese Academy of Sciences , Beijing"},{"name":"Zhiyuan Ning","slug":"zhiyuan-ning","given":"Zhiyuan","family":"Ning","affiliation":"Computer Network Information Center , Chinese Academy of Sciences , Beijing; University of Chinese Academy of Sciences , Beijing"},{"name":"Pengyang Wang","slug":"pengyang-wang","given":"Pengyang","family":"Wang","affiliation":"State Key Laboratory of Internet of Things for Smart City , University of Macau , Macau"},{"name":"Ziyue Qiao","slug":"ziyue-qiao","given":"Ziyue","family":"Qiao","affiliation":"The Hong Kong University of Science and Technology (Guangzhou) , Guangzhou"},{"name":"Pengfei Wang","slug":"pengfei-wang","given":"Pengfei","family":"Wang","affiliation":"Computer Network Information Center , Chinese Academy of Sciences , Beijing; University of Chinese Academy of Sciences , Beijing"},{"name":"Yuanchun Zhou","slug":"yuanchun-zhou","given":"Yuanchun","family":"Zhou","affiliation":"Computer Network Information Center , Chinese Academy of Sciences , Beijing; University of Chinese Academy of Sciences , Beijing"},{"name":"Yanjie Fu","slug":"yanjie-fu","given":"Yanjie","family":"Fu","affiliation":"Department of Computer Science , University of Central Florida , Orlando"}],"abstract":"Temporal knowledge graph (TKG) reasoning aims to predict the future missing facts based on historical information and has gained increasing research interest recently. Lots of works have been made to model the historical structural and temporal characteristics for the reasoning task. Most existing works model the graph structure mainly depending on entity representation. However, the magnitude of TKG entities in real-world scenarios is considerable, and an increasing number of new entities will arise as time goes on. Therefore, we propose a novel architecture modeling with relation feature of TKG, namely aDAptivE path-MemOry Network (DaeMon), which adaptively models the temporal path information between query subject and each object candidate across history time. It models the historical information without depending on entity representation. Specifically, DaeMon uses path memory to record the temporal path information derived from path aggregation unit across timeline considering the memory passing strategy between adjacent timestamps. Extensive experiments conducted on four real-world TKG datasets demonstrate that our proposed model obtains substantial performance improvement and outperforms the state-of-the-art up to 4.8% absolute in MRR.","doi":"10.24963/ijcai.2023/232","url":"https://ijcai.org/proceedings/2023/232","pdf":"https://ijcai.org/proceedings/2023/0232.pdf","markdown":"https://ijcai.org/proceedings/2023/232.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/232","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":"Data Mining","track":"Main Track","number":232,"pages":{"first":2086,"last":2094},"keywords":["Machine Learning: ML: Sequence and graph learning","Knowledge Representation and Reasoning: KRR: Learning and reasoning","Data Mining: DM: Knowledge graphs and knowledge base completion"],"videos":[],"references":[{"position":1,"raw":"[Bordes et al., 2013] Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 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