  Entity Alignment (EA) aims to match equivalent entities that refer to the
same real-world objects and is a key step for Knowledge Graph (KG) fusion. Most
neural EA models cannot be applied to large-scale real-life KGs due to their
excessive consumption of GPU memory and time. One promising solution is to
divide a large EA task into several subtasks such that each subtask only needs
to match two small subgraphs of the original KGs. However, it is challenging to
divide the EA task without losing effectiveness. Existing methods display low
coverage of potential mappings, insufficient evidence in context graphs, and
largely differing subtask sizes.
  In this work, we design the DivEA framework for large-scale EA with
high-quality task division. To include in the EA subtasks a high proportion of
the potential mappings originally present in the large EA task, we devise a
counterpart discovery method that exploits the locality principle of the EA
task and the power of trained EA models. Unique to our counterpart discovery
method is the explicit modelling of the chance of a potential mapping. We also
introduce an evidence passing mechanism to quantify the informativeness of
context entities and find the most informative context graphs with flexible
control of the subtask size. Extensive experiments show that DivEA achieves
higher EA performance than alternative state-of-the-art solutions.

Artificial Intelligence;Information Retrieval;Machine Learning