HTEA: Heterogeneity-aware Embedding Learning for Temporal Entity Alignment

Temporal entity alignment (TEA), which identifies equivalent entities across temporal knowledge graphs (TKGs), plays a vital role in integrating multiple TKGs. Simply adapting traditional EA models to TKGs cannot achieve satisfactory results, driving the need for dedicated studies in TEA. However, existing TEA models often fail to effectively capture the importance of temporal features and the richness of temporal context during embedding learning. Moreover, the challenge of temporal heterogeneity, which is prevalent in real-world TKGs, has not been adequately studied. In this work, we propose a HTEA framework to address these limitations. Specifically, we introduce a frequency-based temporal embedding module that incorporates the importance of temporal features for each entity, along with a temporal attention mechanism that prioritizes more informative context based on temporal richness. We further design an iterative module to detect temporal heterogeneity and refine the related facts accordingly. In this way, entity embeddings can be improved progressively, yielding more accurate and consistent alignment outcomes. Extensive experiments showcase the efficacy of our HTEA model, especially under the existence of temporal heterogeneity in real-world TKGs.