  Entity alignment (EA) aims to find equivalent entities in different knowledge
graphs (KGs). Current EA approaches suffer from scalability issues, limiting
their usage in real-world EA scenarios. To tackle this challenge, we propose
LargeEA to align entities between large-scale KGs. LargeEA consists of two
channels, i.e., structure channel and name channel. For the structure channel,
we present METIS-CPS, a memory-saving mini-batch generation strategy, to
partition large KGs into smaller mini-batches. LargeEA, designed as a general
tool, can adopt any existing EA approach to learn entities' structural features
within each mini-batch independently. For the name channel, we first introduce
NFF, a name feature fusion method, to capture rich name features of entities
without involving any complex training process. Then, we exploit a name-based
data augmentation to generate seed alignment without any human intervention.
Such design fits common real-world scenarios much better, as seed alignment is
not always available. Finally, LargeEA derives the EA results by fusing the
structural features and name features of entities. Since no widely-acknowledged
benchmark is available for large-scale EA evaluation, we also develop a
large-scale EA benchmark called DBP1M extracted from real-world KGs. Extensive
experiments confirm the superiority of LargeEA against state-of-the-art
competitors.
