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Dual Gated Graph Attention Networks with Dynamic Iterative Training for Cross-Lingual Entity Alignment

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成果类型:
期刊论文
作者:
Xie, Zhiwen;Zhu, Runjie;Zhao, Kunsong;Liu, Jin;Zhou, Guangyou;...
通讯作者:
Liu, Jin(jinliu@whu.edu.cn)
作者机构:
[Liu, Jin; Xie, Zhiwen; Zhao, Kunsong] Wuhan Univ, Sch Comp Sci, Wuhan, Peoples R China.
[Zhu, Runjie] York Univ, Lassonde Sch Engn, Informat Retrieval & Knowledge Management Res Lab, Toronto, ON, Canada.
[Zhu, Runjie] AI Singapore, Singapore, Singapore.
[Zhou, Guangyou] Cent China Normal Univ, Sch Comp Sci, Wuhan, Peoples R China.
[Huang, Jimmy Xiangji] York Univ, Sch Informat Technol, Informat Retrieval & Knowledge Management Res Lab, Toronto, ON, Canada.
通讯机构:
[Liu, J.] S
School of Computer Science, Wuhan University, Wuhan, China
语种:
英文
关键词:
Knowledge graph;cross graph attention;entity alignment;iterative
期刊:
ACM Transactions on Information Systems
ISSN:
1046-8188
年:
2022
卷:
40
期:
3
页码:
1–30
基金类别:
This work was supported by the National Natural Science Foundation of China under grants 61972290 and 61972173, and supported by the National Key R&D Program of China under grant 2018YFC1604000. This research was also supported in part by a research grant from the Natural Sciences and Engineering Research Council (NSERC) of Canada and York Research Chairs (YRC) program. In addition, this research was supported by the National Research Foundation, Singapore under its AI Singapore program. Authors’ addresses: Z. Xie, K. Zhao, and J. Liu (corresponding author), School of Computer Science, Wuhan University, Wuhan, China; emails: {xiezhiwen, kszhao, jinliu}@whu.edu.cn; R. Zhu, Information Retrieval and Knowledge Management Research Lab, Lassonde School of Engineering, York University, Toronto, Canada and AI Singapore, Singapore; email: sherryzh@cse.yorku.ca, sherryzhu@aisingapore.org; G. Zhou (corresponding author), School of Computer Science, Central China Normal University, Wuhan, China; email: gyzhou@mail.ccnu.edu.cn; J. X. Huang, Information Retrieval and Knowledge Management Research Lab, School of Information Technology, York University, Toronto, Canada; email: jhuang@yorku.ca. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. © 2021 Association for Computing Machinery. 1046-8188/2021/11-ART44 $15.00 https://doi.org/10.1145/3471165
机构署名:
本校为其他机构
院系归属:
计算机学院
摘要:
Cross-lingual entity alignment has attracted considerable attention in recent years. Past studies using conventional approaches to match entities share the common problem of missing important structural information beyond entities in the modeling process. This allows graph neural network models to step in. Most existing graph neural network approaches model individual knowledge graphs (KGs) separately with a small amount of pre-Aligned entities served as anchors to connect different KG embedding spaces. However, this characteristic can cause se...

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