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Knowledge Graph (KG) alignment is to discover the mappings (i.e., equivalententities, relations, and others) between two KGs. The existing methods can bedivided into the embedding-based models, and the conventional reasoning andlexical matching based systems. The former compute the similarity of entitiesvia their cross-KG embeddings, but they usually rely on an ideal supervisedlearning setting for good performance and lack appropriate reasoning to avoidlogically wrong mappings; while the latter address the reasoning issue but arepoor at utilizing the KG graph structures and the entity contexts. In thisstudy, we aim at combining the above two solutions and thus propose aniterative framework named PRASE which is based on probabilistic reasoning andsemantic embedding. It learns the KG embeddings via entity mappings from aprobabilistic reasoning system named PARIS, and feeds the resultant entitymappings and embeddings back into PARIS for augmentation. The PRASE frameworkis compatible with different embedding-based models, and our experiments onmultiple datasets have demonstrated its state-of-the-art performance.

Unsupervised Knowledge Graph Alignment by Probabilistic Reasoning and Semantic Embedding