论文标题
视觉旋转（无监督）实体对齐
Visual Pivoting for (Unsupervised) Entity Alignment
论文作者
论文摘要
这项工作研究了视觉语义表示在异质知识图（KGS）中对齐实体的使用。图像是许多现有公斤的天然组成部分。通过将视觉知识与其他辅助信息相结合，我们表明拟议的新方法EVA创建了一个整体实体表示，该表示为跨编码实体对齐提供了强大的信号。此外，以前的实体比对方法需要人类标记的种子对准，从而限制了可用性。 EVA通过利用实体的视觉相似性来创建初始种子词典（视觉枢轴），从而提供了完全无监督的解决方案。基准数据集DBP15K和DWY15K上的实验表明，EVA在单语言和跨语性实体对准任务上都提供最先进的性能。此外，我们发现图像对于对齐长尾kg实体特别有用，而长尾kg实体缺乏捕获对应关系所需的结构上下文。
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.
