Entity alignment aims to discover unique equivalent entity pairs with the same meaning across different knowledge graphs (KG). It has been a compelling but challenging task for knowledge integration or fusion. Existing models have primarily focused on projecting KGs into a latent embedding space to capture inherent semantics between entities for entity alignment. However, the adverse impacts of alignment conflicts have been largely overlooked during training, thus limiting the entity alignment performance. To address this issue, we propose a novel Conflict-aware Pseudo Labeling via Optimal Transport model (CPL-OT) for entity alignment. The key idea of CPL-OT is to iteratively pseudo-label alignment pairs empowered with conflict-aware Optimal Transport modeling to boost the precision of entity alignment. CPL-OT is composed of two key components-entity embedding learning with global-local aggregation and iterative conflict-aware pseudo labeling-that mutually reinforce each other. To mitigate alignment conflicts during pseudo labeling, we propose to use optimal transport (OT) as an effective means to warrant one-to-one entity alignment between two KGs with the minimal overall transport cost. The transport cost is calculated as the rectified distance between entity embeddings obtained via graph convolution augmented with global-level semantics. Extensive experiments on benchmark datasets show that CPL-OT can markedly outperform state-of-the-art baselines under both settings with and without prior alignment seeds.

翻译：实体对齐的目的是通过不同的知识图表(KG)发现具有相同含义的独特对应实体对等实体。这是知识整合或融合方面一项令人信服但具有挑战性的任务。现有的模型主要侧重于将KGs投射到一个潜在的嵌入空间,以捕捉各实体之间固有的语义调整。然而,在培训期间,调整冲突的不利影响在很大程度上被忽视,从而限制了实体对齐性业绩。为了解决这一问题,我们提议通过优化运输模型(CPL-OT),为实体对齐而采用新的冲突意识匹配。CPL-OT的关键想法是让具有冲突意识的最佳运输模型的对齐化假假标签对齐来增强实体的精确性。CPL-OT由两个关键组成部分组成,将学习与全球本地组合和反复冲突认知的假标签相结合,从而限制了实体的对齐性。为了减轻对齐性标签使用假标签模式(CPL-OT),我们提议使用最佳运输工具(OT)作为有效手段,以证明两个KGs和两个具备冲突意识优化优化的假标签对齐配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配配