A Self-supervised Method for Entity Alignment
This work addresses the fundamental challenge of entity alignment for constructing large-scale knowledge graphs, offering a novel self-supervised approach that could reduce reliance on labeled data.
The paper tackles the problem of entity alignment across knowledge graphs by proposing a self-supervised method that eliminates the need for supervision, achieving results comparable to state-of-the-art supervised baselines on benchmark datasets.
Entity alignment, aiming to identify equivalent entities across different knowledge graphs (KGs), is a fundamental problem for constructing large-scale KGs. Over the course of its development, 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. Existing supervised methods for this task focus on pulling each pair of positive (labeled) entities close to each other. However, our analysis suggests that the learning of entity alignment can actually benefit more from pushing sampled (unlabeled) negatives far away than pulling positive aligned pairs close. We present SelfKG by leveraging this discovery to design a contrastive learning strategy across two KGs. 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 demonstrates self-supervised learning offers great potential for entity alignment in KGs.