{"type":"ScholarlyArticle","title":"Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs","authors":[{"name":"Yuting Wu","slug":"yuting-wu","given":"Yuting","family":"Wu","affiliation":"Institute of Computer Science and Technology , Peking University , China"},{"name":"Xiao Liu","slug":"xiao-liu","given":"Xiao","family":"Liu","affiliation":"Institute of Computer Science and Technology , Peking University , China"},{"name":"Yansong Feng","slug":"yansong-feng","given":"Yansong","family":"Feng","affiliation":"Institute of Computer Science and Technology , Peking University , China"},{"name":"Zheng Wang","slug":"zheng-wang","given":"Zheng","family":"Wang","affiliation":"School of Computing and Communications , Lancaster University , U. K."},{"name":"Rui Yan","slug":"rui-yan","given":"Rui","family":"Yan","affiliation":"Institute of Computer Science and Technology , Peking University , China"},{"name":"Dongyan Zhao","slug":"dongyan-zhao","given":"Dongyan","family":"Zhao","affiliation":"Institute of Computer Science and Technology , Peking University , China"}],"abstract":"Entity alignment is the task of linking entities with the same real-world identity from different knowledge graphs (KGs), which has been recently dominated by embedding-based methods. Such approaches work by learning KG representations so that entity alignment can be performed by measuring the similarities between entity embeddings. While promising, prior works in the field often fail to properly capture complex relation information that commonly exists in multi-relational KGs, leaving much room for improvement. In this paper, we propose a novel Relation-aware Dual-Graph Convolutional Network (RDGCN) to incorporate relation information via attentive interactions between the knowledge graph and its dual relation counterpart, and further capture neighboring structures to learn better entity representations. Experiments on three real-world cross-lingual datasets show that our approach delivers better and more robust results over the state-of-the-art alignment methods by learning better KG representations.","doi":"10.24963/ijcai.2019/733","url":"https://ijcai.org/proceedings/2019/733","pdf":"https://ijcai.org/proceedings/2019/0733.pdf","markdown":"https://ijcai.org/proceedings/2019/733.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/733","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Natural Language Processing","track":"Main track","number":733,"pages":{"first":5278,"last":5284},"keywords":["Natural Language Processing: NLP Applications and Tools","Natural Language Processing: Natural Language Processing","Natural Language Processing: Knowledge Extraction"],"videos":[],"references":[{"position":1,"raw":"Datasets Entities Relations Rel. triples DBP 15K ZH-EN Chinese 66,469 2,830 153,929 English 98,125 2,317 237,674 DBP 15K JA-EN Japanese 65,744 2,043 164,373 English 95,680 2,096 233,319","title":"Datasets Entities Relations Rel. triples DBP 15K ZH-EN Chinese","authors":null,"year":"0929","venue":"Datasets Entities Relations Rel. triples DBP 15K ZH-EN Chinese","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"DBP 15K F R-EN French 66,858 1,379 192,191 English 105,889 2,209 278,590 References","title":"K F R-EN French","authors":null,"year":null,"venue":"K F R-EN French","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Atwood and Towsley, 2016] James Atwood and Don Towsley. 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