Two Negatives Make a Positive: Dual Negative Learning for Continual Knowledge Graph Embedding

Continual Knowledge Graph Embedding (CKGE) aims to efficiently update Knowledge Graph (KG) representations to accommodate emerging facts while preserving previously learned knowledge. As new facts arrive, evolving graph structures and interactions between new and existing knowledge complicate continual adaptation. Existing CKGE methods primarily address these challenges by adapting representations, while the learning signals conveyed by negative samples remain insufficiently explored. Negative supervision in CKGE changes in two respects: accumulated facts alter the validity of negative samples, while model evolution affects their informativeness for training. To address these challenges, we propose EvoNS (Evolution-Aware Negative Supervision), a framework that jointly adapts negative construction and utilization through dual negative learning. Specifically, EvoNS exploits accumulated observations to construct history-consistent negatives. It further reweights individual negatives according to their model-perceived difficulty, enabling more informative negative signals to guide continual optimization. Extensive experiments on seven CKGE benchmarks demonstrate that EvoNS consistently outperforms existing baselines, achieving up to 12.40% relative improvement in MRR and reducing training time by up to 18.92% in several settings. The results demonstrate the effectiveness and efficiency of EvoNS for CKGE.