{"type":"ScholarlyArticle","title":"An Effective and Efficient Time-aware Entity Alignment Framework via Two-aspect Three-view Label Propagation","authors":[{"name":"Li Cai","slug":"li-cai","given":"Li","family":"Cai","affiliation":"School of Computer Science and Technology , East China Normal University , Shanghai 200062 , China; College of Computer Science and Technology , Guizhou University , Guiyang 5220025 , China"},{"name":"Xin Mao","slug":"xin-mao","given":"Xin","family":"Mao","affiliation":"School of Computer Science and Technology , East China Normal University , Shanghai 200062 , China"},{"name":"Youshao Xiao","slug":"youshao-xiao","given":"Youshao","family":"Xiao","affiliation":"Ant Group , China"},{"name":"Changxu Wu","slug":"changxu-wu","given":"Changxu","family":"Wu","affiliation":"Department of Industrial Engineering , Tsinghua University"},{"name":"Man Lan","slug":"man-lan","given":"Man","family":"Lan","affiliation":"School of Computer Science and Technology , East China Normal University , Shanghai 200062 , China; Shanghai Institute of AI for Education , East China; Lingang Laboratory , Shanghai 200031 , China; Normal University"}],"abstract":"Entity alignment (EA) aims to find the equivalent entity pairs between different knowledge graphs (KGs), which is crucial to promote knowledge fusion. With the wide use of temporal knowledge graphs (TKGs), time-aware EA (TEA) methods appear to enhance EA. Existing TEA models are based on Graph Neural Networks (GNN) and achieve state-of-the-art (SOTA) performance, but it is difficult to transfer them to large-scale TKGs due to the scalability issue of GNN. In this paper, we propose an effective and efficient non-neural EA framework between TKGs, namely LightTEA, which consists of four essential components: (1) Two-aspect Three-view Label Propagation, (2) Sparse Similarity with Temporal Constraints, (3) Sinkhorn Operator, and (4) Temporal Iterative Learning. All of these modules work together to improve the performance of EA while reducing the time consumption of the model. Extensive experiments on public datasets indicate that our proposed model significantly outperforms the SOTA methods for EA between TKGs, and the time consumed by LightTEA is only dozens of seconds at most, no more than 10% of the most efficient TEA method.","doi":"10.24963/ijcai.2023/558","url":"https://ijcai.org/proceedings/2023/558","pdf":"https://ijcai.org/proceedings/2023/0558.pdf","markdown":"https://ijcai.org/proceedings/2023/558.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/558","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":"Natural Language Processing","track":"Main Track","number":558,"pages":{"first":5021,"last":5029},"keywords":["Natural Language Processing: NLP: Information retrieval and text mining"],"videos":[],"references":[{"position":1,"raw":"[Bordes et al., 2013] Antoine Bordes, Nicolas Usunier, Alberto García-Durán, Jason Weston, and Oksana Yakhnenko. 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