Entity Alignment for Knowledge Graphs with Multi-order Convolutional Networks (Extended Abstract)
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Thanh Trung, Huynh
yin, hongzhi
TONG, Van Vinh
Sakong, Darnbi
Zheng, Bolong
Nguyen, Quoc Viet Hung
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Abstract
Knowledge graph (KG) entity alignment is the task of identifying corresponding entities across different KGs. Existing alignment techniques often require large amounts of labelled data, are unable to encode multi-modal data simultaneously, and enforce only a few consistency constraints. In this paper, we propose an end-to-end, unsupervised entity alignment framework for cross-lingual KGs using multi-order graph convolutional networks. An evaluation of our method using real-world datasets reveals that it consistently outperforms the state-of-the-art in terms of accuracy, efficiency, and label saving.
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37th IEEE International Conference on Data Engineering
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© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Information systems
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Nguyen, TT; Thanh Trung, H; yin, H; TONG, VV; Sakong, D; Zheng, B; Nguyen, QVH, Entity Alignment for Knowledge Graphs with Multi-order Convolutional Networks (Extended Abstract), 2021