  Entity alignment is a crucial step in integrating knowledge graphs (KGs) from
multiple sources. Previous attempts at entity alignment have explored different
KG structures, such as neighborhood-based and path-based contexts, to learn
entity embeddings, but they are limited in capturing the multi-context
features. Moreover, most approaches directly utilize the embedding similarity
to determine entity alignment without considering the global interaction among
entities and relations. In this work, we propose an Informed Multi-context
Entity Alignment (IMEA) model to address these issues. In particular, we
introduce Transformer to flexibly capture the relation, path, and neighborhood
contexts, and design holistic reasoning to estimate alignment probabilities
based on both embedding similarity and the relation/entity functionality. The
alignment evidence obtained from holistic reasoning is further injected back
into the Transformer via the proposed soft label editing to inform embedding
learning. Experimental results on several benchmark datasets demonstrate the
superiority of our IMEA model compared with existing state-of-the-art entity
alignment methods.
