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The task of entity alignment between knowledge graphs (KGs) aims to identifyevery pair of entities from two different KGs that represent the same entity.Many machine learning-based methods have been proposed for this task. However,to our best knowledge, existing methods all require manually crafted seedalignments, which are expensive to obtain. In this paper, we propose the firstfully automatic alignment method named TransAlign, which does not require anymanually crafted seed alignments. Specifically, for predicate embeddings,TransAlign constructs a predicate-proximity-graph to automatically capture thesimilarity between predicates across two KGs by learning the attention ofentity types. For entity embeddings, TransAlign first computes the entityembeddings of each KG independently using TransE, and then shifts the two KGs'entity embeddings into the same vector space by computing the similaritybetween entities based on their attributes. Thus, both predicate alignment andentity alignment can be done without manually crafted seed alignments.TransAlign is not only fully automatic, but also highly effective. Experimentsusing real-world KGs show that TransAlign improves the accuracy of entityalignment significantly compared to state-of-the-art methods.

TransAlign: Fully Automatic and Effective Entity Alignment for Knowledge Graphs