{"type":"ScholarlyArticle","title":"Entity Alignment for Cross-lingual Knowledge Graph with Graph Convolutional Networks","authors":[{"name":"Fan Xiong","slug":"fan-xiong","given":"Fan","family":"Xiong","affiliation":"School of Computer Science and Engineering , Central South University , China"},{"name":"Jianliang Gao","slug":"jianliang-gao","given":"Jianliang","family":"Gao","affiliation":"School of Computer Science and Engineering , Central South University , China"}],"abstract":"Graph convolutional network (GCN) is a promising approach that has recently been used to resolve knowledge graph alignment. In this paper, we propose a new method to entity alignment for cross-lingual knowledge graph. In the method, we design a scheme of attribute embedding for GCN training. Furthermore, GCN model utilizes the attribute embedding and structure embedding to abstract graph features simultaneously. Our preliminary experiments show that the proposed method outperforms the state-of-the-art GCN-based method.","doi":"10.24963/ijcai.2019/929","url":"https://ijcai.org/proceedings/2019/929","pdf":"https://ijcai.org/proceedings/2019/0929.pdf","markdown":"https://ijcai.org/proceedings/2019/929.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/929","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":"Doctoral Consortium","track":"Doctoral Consortium","number":929,"pages":{"first":6480,"last":6481},"keywords":["Natural Language Processing: Embeddings","Natural Language Processing: Named Entities"],"videos":[],"references":[{"position":1,"raw":"[Aditya et al., 2018] Somak Aditya, Yezhou Yang, and Chit- ta Baral. Explicit reasoning over end-to-end neural archi- tectures for visual question answering. In AAAI, pages 629-637, 2018.","title":"Explicit reasoning over end-to-end neural architectures for visual question answering","authors":"Somak Aditya, Yezhou Yang, Chitta Baral","year":"2018","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Fang et al., 2017] Yuan Fang, Kingsley Kuan, Jie Lin, Che- ston Tan, and Vijay Chandrasekhar. Object detection meet- s knowledge graphs. In IJCAI, pages 1661-1667, 2017.","title":"Object detection meets knowledge graphs","authors":"Yuan Fang, Kingsley Kuan, Jie Lin, Cheston Tan, Vijay Chandrasekhar","year":"2017","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2017/230"},{"position":3,"raw":"[Sun et al., 2017] Zequn Sun, Wei Hu, and Chengkai Li. Cross-lingual entity alignment via joint attribute- preserving embedding. In International Semantic Web Conference, pages 628-644. Springer, 2017.","title":"Cross-lingual entity alignment via joint attributepreserving embedding","authors":"Sun","year":"2017","venue":"International Semantic Web Conference","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Wang et al., 2018] Zhichun Wang, Qingsong Lv, Xiaohan Lan, and Yu Zhang. Cross-lingual knowledge graph align- ment via graph convolutional networks. In the Conference on Empirical Methods in Natural Language Processing, pages 349-357, 2018.","title":"Cross-lingual knowledge graph alignment via graph convolutional networks","authors":"Zhichun Wang, Qingsong Lv, Xiaohan Lan, Yu Zhang","year":"2018","venue":"the Conference on Empirical Methods in Natural Language Processing","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Wang et al., 2019] Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Peng Cui, P. Yu, and Yanfang Ye. Hetero- geneous graph attention network. arXiv preprint arX- iv:1903.07293, 2019.","title":"Heterogeneous graph attention network","authors":"Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Peng Cui, P Yu, Yanfang Ye","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}