Knowledge Graph (KG) alignment is to discover the mappings (i.e., equivalent entities, relations, and others) between two KGs. The existing methods can be divided into the embedding-based models, and the conventional reasoning and lexical matching based systems. The former compute the similarity of entities via their cross-KG embeddings, but they usually rely on an ideal supervised learning setting for good performance and lack appropriate reasoning to avoid logically wrong mappings; while the latter address the reasoning issue but are poor at utilizing the KG graph structures and the entity contexts. In this study, we aim at combining the above two solutions and thus propose an iterative framework named PRASE which is based on probabilistic reasoning and semantic embedding. It learns the KG embeddings via entity mappings from a probabilistic reasoning system named PARIS, and feeds the resultant entity mappings and embeddings back into PARIS for augmentation. The PRASE framework is compatible with different embedding-based models, and our experiments on multiple datasets have demonstrated its state-of-the-art performance.

翻译：知识图( KG) 校正是指发现两个 KG 之间的映射( 等效实体、 关系等) 。 现有的方法可以分为嵌入模型, 以及常规推理和词汇匹配系统。 前者通过跨KG 嵌入系统计算实体的相似性, 但是它们通常依靠一个理想的、 受监督的学习环境来取得良好的业绩, 缺乏适当的推理来避免逻辑错误的映射; 而后者处理推理问题, 但是在使用 KG 图形结构和实体背景方面却差强人意。 在这项研究中, 我们的目标是将以上两种解决方案合并, 从而提出一个名为 PRASE 的迭代框架, 以概率推理和语义嵌入为基础。 它从名为PARIS 的实体绘图系统中学习 KG 嵌入, 并将结果实体的映射和嵌入嵌入 回到 PARE 增强功能。 PRAASE 框架与不同的嵌入模型是兼容的, 我们关于多个数据集的实验显示了它的状态性。