  Entity alignment (EA) plays an important role in automatically integrating
knowledge graphs (KGs) from multiple sources. Recent approaches based on Graph
Neural Network (GNN) obtain entity representation from relation information and
have achieved promising results. Besides, more and more methods introduce
semi-supervision to ask for more labeled training data. However, two challenges
still exist in GNN-based EA methods: (1) Deeper GNN Encoder: The GNN encoder of
current methods has limited depth (usually 2-layers). (2) Low-quality
Bootstrapping: The generated semi-supervised data is of low quality. In this
paper, we propose a novel framework, Echo Entity Alignment (EchoEA), which
leverages 4-levels self-attention mechanism to spread entity information to
relations and echo back to entities. Furthermore, we propose attribute-combined
bi-directional global-filtered strategy (ABGS) to improve bootstrapping, reduce
false samples and generate high-quality training data. The experimental results
on three real-world cross-lingual datasets are stable at around 96\% at hits@1
on average, showing that our approach not only significantly outperforms the
state-of-the-art GNN-based methods, but also is universal and transferable for
existing EA methods.
