This work studies the use of visual semantic representations to align entities in heterogeneous knowledge graphs (KGs). Images are natural components of many existing KGs. By combining visual knowledge with other auxiliary information, we show that the proposed new approach, EVA, creates a holistic entity representation that provides strong signals for cross-graph entity alignment. Besides, previous entity alignment methods require human labelled seed alignment, restricting availability. EVA provides a completely unsupervised solution by leveraging the visual similarity of entities to create an initial seed dictionary (visual pivots). Experiments on benchmark data sets DBP15k and DWY15k show that EVA offers state-of-the-art performance on both monolingual and cross-lingual entity alignment tasks. Furthermore, we discover that images are particularly useful to align long-tail KG entities, which inherently lack the structural contexts necessary for capturing the correspondences.

本文介绍了一种新的方法EVA，通过图像与其他辅助信息相结合，利用视觉相似度来创建一个初始种子词典，从而提供了一个完全无监督的实体对齐的解决方案。实验结果表明，该方法在单语和跨语言实体对齐任务上均能提供最先进的性能。此外，我们发现图像对于对齐长尾KG实体尤其有用。

用于(无监督)实体对齐的视觉枢轴