  Entity alignment (EA) seeks identical entities in different knowledge graphs,
which is a long-standing task in the database research. Recent work leverages
deep learning to embed entities in vector space and align them via nearest
neighbor search. Although embedding-based EA has gained marked success in
recent years, it lacks explanations for alignment decisions. In this paper, we
present the first framework that can generate explanations for understanding
and repairing embedding-based EA results. Given an EA pair produced by an
embedding model, we first compare its neighbor entities and relations to build
a matching subgraph as a local explanation. We then construct an alignment
dependency graph to understand the pair from an abstract perspective. Finally,
we repair the pair by resolving three types of alignment conflicts based on
dependency graphs. Experiments on a variety of EA datasets demonstrate the
effectiveness, generalization, and robustness of our framework in explaining
and repairing embedding-based EA results.

Computation and Language;Databases