  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.
