{"type":"ScholarlyArticle","title":"Global Structure and Local Semantics-Preserved Embeddings for Entity Alignment","authors":[{"name":"Hao Nie","slug":"hao-nie","given":"Hao","family":"Nie","affiliation":"Chinese Information Processing Laboratory , Institute of Software , Chinese Academy of Sciences; University of Chinese Academy of Sciences"},{"name":"Xianpei Han","slug":"xianpei-han","given":"Xianpei","family":"Han","affiliation":"Chinese Information Processing Laboratory , Institute of Software , Chinese Academy of Sciences; State Key Laboratory of Computer Science , Institute of Software , Chinese Academy of Sciences"},{"name":"Le Sun","slug":"le-sun","given":"Le","family":"Sun","affiliation":"Chinese Information Processing Laboratory , Institute of Software , Chinese Academy of Sciences; State Key Laboratory of Computer Science , Institute of Software , Chinese Academy of Sciences"},{"name":"Chi Man Wong","slug":"chi-man-wong","given":"Chi Man","family":"Wong","affiliation":"Alibaba Group"},{"name":"Qiang Chen","slug":"qiang-chen","given":"Qiang","family":"Chen","affiliation":"Alibaba Group"},{"name":"Suhui Wu","slug":"suhui-wu","given":"Suhui","family":"Wu","affiliation":"Alibaba Group"},{"name":"Wei Zhang","slug":"wei-zhang","given":"Wei","family":"Zhang","affiliation":"Alibaba Group"}],"abstract":"Entity alignment (EA) aims to identify entities located in different knowledge graphs (KGs) that refer to the same real-world object. To learn the entity representations, most EA approaches rely on either translation-based methods which capture the local relation semantics of entities or graph convolutional networks (GCNs), which exploit the global KG structure. Afterward, the aligned entities are identified based on their distances. In this paper, we propose to jointly leverage the global KG structure and entity-specific relational triples for better entity alignment. Specifically, a global structure and local semantics preserving network is proposed to learn entity representations in a coarse-to-fine manner. Experiments on several real-world datasets show that our method significantly outperforms other entity alignment approaches and achieves the new state-of-the-art performance.","doi":"10.24963/ijcai.2020/506","url":"https://ijcai.org/proceedings/2020/506","pdf":"https://ijcai.org/proceedings/2020/0506.pdf","markdown":"https://ijcai.org/proceedings/2020/506.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/506","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Natural Language Processing","track":"Main track","number":506,"pages":{"first":3658,"last":3664},"keywords":["Natural Language Processing: Natural Language Processing","Natural Language Processing: Named Entities","Natural Language Processing: Embeddings"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24752/file_24752.m3u8","length_sec":295,"provider":"hls"}],"references":[{"position":1,"raw":"[Bordes et al., 2013] Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 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