Self-Supervised Entity Alignment Based on Multi-Modal Contrastive Learning  

作　　者：Bo Liu Ruoyi Song Yuejia Xiang Junbo Du Weijian Ruan Jinhui Hu 

机构地区：[1]the School of Software Engineering,Xi’an Jiaotong University,Xi’an 710000 [2]the China Electronics Technology Group Corporation(CETC)Key Laboratory of Smart City Model Simulation and Intelligent Technology,the Smart City Research Institute of CETC and National Center for Applied Mathematics Shenzhen(NCAMS),Shenzhen 518000,China [3]the Chinese University of Hong Kong,Shenzhen 518000,China [4]Tencent,Shenzhen 518000,China [5]the CETC Key Laboratory of Smart City Model Simulation and Intelligent Technology,the Smart City Research Institute of CETC and National Center for Applied Mathematics Shenzhen(NCAMS),Shenzhen 518000,China [6]the Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen 518055

出　　处：《IEEE/CAA Journal of Automatica Sinica》2022年第11期2031-2033,共3页自动化学报（英文版）

基　　金：supported by the National Key Research and Development Project(2019YFB2102500);the National Nature Science Foundations of China(U20B2052)。

摘　　要：Dear Editor,This letter proposes an unsupervised entity alignment method,which realizes integration of multiple multi-modal knowledge graphs adaptively.In recent years,Large-scale multi-modal knowledge graphs(LMKGs),containing text and image,have been widely applied in numerous knowledge-driven topics,such as question answering,entity linking,information extraction,reasoning and recommendation.

关 键 词：MODAL LINKING LETTER 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]