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Vision, Deduction and Alignment: An Empirical Study on Multi-Modal Knowledge Graph Alignment by Yangning Li; Jiaoyan Chen; Yinghui Li; Yuejia Xiang; Xi Chen; Hai-Tao Zheng is a scholarly article available to read on EtoBox.

What is Vision, Deduction and Alignment: An Empirical Study on Multi-Modal Knowledge Graph Alignment about?

Entity alignment (EA) for knowledge graphs (KGs) plays a critical role in knowledge engineering. Existing EA methods mostly focus on utilizing the graph structures and entity attributes (including literals), but ignore images that are common in modern multi-modal KGs. In this study we first constructed Multi-OpenEA -eight large-scale, image-equipped EA benchmarks, and then evaluated some existing embedding-based methods for utilizing images. In view of the complementary nature of visual modal information and logical deduction, we further developed a new multi-modal EA method named LODEME using logical deduction and multimodal KG embedding, with state-of-the-art performance achieved on Multi-OpenEA and other existing multi-modal EA benchmarks.

Author

Yangning Li; Jiaoyan Chen; Yinghui Li; Yuejia Xiang; Xi Chen; Hai-Tao Zheng

Publisher

IEEE

Published

2023

Language

EN