  The task of entity alignment between knowledge graphs (KGs) aims to identify
every 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 seed
alignments, which are expensive to obtain. In this paper, we propose the first
fully automatic alignment method named TransAlign, which does not require any
manually crafted seed alignments. Specifically, for predicate embeddings,
TransAlign constructs a predicate-proximity-graph to automatically capture the
similarity between predicates across two KGs by learning the attention of
entity types. For entity embeddings, TransAlign first computes the entity
embeddings of each KG independently using TransE, and then shifts the two KGs'
entity embeddings into the same vector space by computing the similarity
between entities based on their attributes. Thus, both predicate alignment and
entity alignment can be done without manually crafted seed alignments.
TransAlign is not only fully automatic, but also highly effective. Experiments
using real-world KGs show that TransAlign improves the accuracy of entity
alignment significantly compared to state-of-the-art methods.
