  Entity alignment (EA) aims to discover the equivalent entities in different
knowledge graphs (KGs). It is a pivotal step for integrating KGs to increase
knowledge coverage and quality. Recent years have witnessed a rapid increase of
EA frameworks. However, state-of-the-art solutions tend to rely on labeled data
for model training. Additionally, they work under the closed-domain setting and
cannot deal with entities that are unmatchable. To address these deficiencies,
we offer an unsupervised framework that performs entity alignment in the open
world. Specifically, we first mine useful features from the side information of
KGs. Then, we devise an unmatchable entity prediction module to filter out
unmatchable entities and produce preliminary alignment results. These
preliminary results are regarded as the pseudo-labeled data and forwarded to
the progressive learning framework to generate structural representations,
which are integrated with the side information to provide a more comprehensive
view for alignment. Finally, the progressive learning framework gradually
improves the quality of structural embeddings and enhances the alignment
performance by enriching the pseudo-labeled data with alignment results from
the previous round. Our solution does not require labeled data and can
effectively filter out unmatchable entities. Comprehensive experimental
evaluations validate its superiority.
