We present MMKG, a collection of three knowledge graphs that contain both numerical features and (links to) images for all entities as well as entity alignments between pairs of KGs. Therefore, multi-relational link prediction and entity matching communities can benefit from this resource. We believe this data set has the potential to facilitate the development of novel multi-modal learning approaches for knowledge graphs.We validate the utility ofMMKG in the sameAs link prediction task with an extensive set of experiments. These experiments show that the task at hand benefits from learning of multiple feature types.

翻译：我们提出MMKG,这是三张包含所有实体数字特征和(链接)图像的知识图集,以及两组KG之间的实体对齐。因此,多关系链接预测和实体匹配社区可以受益于这一资源。我们认为,这一数据集有潜力促进开发新的知识图多模式学习方法。我们验证MMKG在同一As中的效用,将预测任务与一系列广泛的实验联系起来。这些实验表明,手头的任务从学习多种特征类型中受益。