  Entity Alignment (EA), which aims to detect entity mappings (i.e. equivalent
entity pairs) in different Knowledge Graphs (KGs), is critical for KG fusion.
Neural EA methods dominate current EA research but still suffer from their
reliance on labelled mappings. To solve this problem, a few works have explored
boosting the training of EA models with self-training, which adds confidently
predicted mappings into the training data iteratively. Though the effectiveness
of self-training can be glimpsed in some specific settings, we still have very
limited knowledge about it. One reason is the existing works concentrate on
devising EA models and only treat self-training as an auxiliary tool. To fill
this knowledge gap, we change the perspective to self-training to shed light on
it. In addition, the existing self-training strategies have limited impact
because they introduce either much False Positive noise or a low quantity of
True Positive pseudo mappings. To improve self-training for EA, we propose
exploiting the dependencies between entities, a particularity of EA, to
suppress the noise without hurting the recall of True Positive mappings.
Through extensive experiments, we show that the introduction of dependency
makes the self-training strategy for EA reach a new level. The value of
self-training in alleviating the reliance on annotation is actually much higher
than what has been realised. Furthermore, we suggest future study on smart data
annotation to break the ceiling of EA performance.
