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 multi-modal KG embedding, with state-of-the-art performance achieved on Multi-OpenEA and other existing multi-modal EA benchmarks.
@article{arxiv.2302.08774,
title = {Vision, Deduction and Alignment: An Empirical Study on Multi-modal Knowledge Graph Alignment},
author = {Yangning Li and Jiaoyan Chen and Yinghui Li and Yuejia Xiang and Xi Chen and Hai-Tao Zheng},
journal= {arXiv preprint arXiv:2302.08774},
year = {2023}
}