  Entity alignment, aiming to identify equivalent entities across different
knowledge graphs (KGs), is a fundamental problem for constructing Web-scale
KGs. Over the course of its development, the label supervision has been
considered necessary for accurate alignments. Inspired by the recent progress
of self-supervised learning, we explore the extent to which we can get rid of
supervision for entity alignment. Commonly, the label information (positive
entity pairs) is used to supervise the process of pulling the aligned entities
in each positive pair closer. However, our theoretical analysis suggests that
the learning of entity alignment can actually benefit more from pushing
unlabeled negative pairs far away from each other than pulling labeled positive
pairs close. By leveraging this discovery, we develop the self-supervised
learning objective for entity alignment. We present SelfKG with efficient
strategies to optimize this objective for aligning entities without label
supervision. Extensive experiments on benchmark datasets demonstrate that
SelfKG without supervision can match or achieve comparable results with
state-of-the-art supervised baselines. The performance of SelfKG suggests that
self-supervised learning offers great potential for entity alignment in KGs.
The code and data are available at https://github.com/THUDM/SelfKG.
