Multi-channel graph neural network for entity alignment

• Main Authors: CAO, Yixin, LIU, Zhiyuan, LI, Chengjiang, LI, Juanzi, CHUA, Tat-Seng
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: Databases and Information Systems; Graphics and Human Computer Interfaces; OS and Networks
• Online Access: https://ink.library.smu.edu.sg/sis_research/7461; https://ink.library.smu.edu.sg/context/sis_research/article/8464/viewcontent/P19_1140.pdf
• Institution: Singapore Management University

Entity alignment typically suffers from the issues of structural heterogeneity and limited seed alignments. In this paper, we propose a novel Multi-channel Graph Neural Network model (MuGNN) to learn alignment-oriented knowledge graph (KG) embeddings by robustly encoding two KGs via multiple channel...