An unsupervised multi-view contrastive learning framework with attention-based reranking strategy for entity alignment.

Entity alignment is a crucial task in knowledge graphs, aiming to match corresponding entities from different knowledge graphs. Due to the scarcity of pre-aligned entities in real-world scenarios, research focused on unsupervised entity alignment has become more popular. However, current unsupervised entity alignment methods suffer from a lack of informative entity guidance, hindering their ability to accurately predict challenging entities with similar names and structures. To solve these problems, we present an unsupervised multi-view contrastive learning framework with an attention-based reranking strategy for entity alignment, named AR-Align. In AR-Align, two kinds of data augmentation methods are employed to provide a complementary view for neighborhood and attribute, respectively. Next, a multi-view contrastive learning method is introduced to reduce the semantic gap between different views of the augmented entities. Moreover, an attention-based reranking strategy is proposed to rerank the hard entities through calculating their weighted sum of embedding similarities on different structures. Experimental results indicate that AR-Align outperforms most both supervised and unsupervised state-of-the-art methods on three benchmark datasets.