  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.
