Harnessing Structural Context for Entity Alignment Foundation Models

Entity alignment (EA) across unseen knowledge graph (KG) pairs requires a model to reuse alignment knowledge without learning pair-specific entity embeddings. In a structure-only setting, this raises two connected challenges: evidence from the two KGs must interact through limited seed anchors, and structurally similar candidates must be distinguished beyond coarse similarity. We propose ContextEA, a transferable EA framework that learns cross-KG context construction and candidate discrimination jointly. For each query, its encoder propagates query-relative, relation-conditioned structural signals over an anchor-bridged unified graph. Its decoder then uses entity, neighborhood, relation, and anchor evidence to calibrate ambiguous candidates; the calibration loss also updates the encoder during training. This design enables a pretrained model to apply learned structural alignment patterns to new KG pairs. Across 29 benchmark settings from OpenEA, SRPRS, and DBP, ContextEAimproves over structure-only transferable EA baselines. Without target-specific finetuning, it also exceeds the finetuned EAFM baseline across all three benchmark groups.