Temporal Residual Memory for Mitigating Catastrophic Forgetting in Continual Knowledge Graph Embedding

Continual Knowledge Graph Embedding (CKGE) aims to sequentially incorporate newly arriving facts into a knowledge graph while preserving the knowledge learned from previous snapshots. A key challenge is catastrophic forgetting, where the knowledge acquired from past snapshots can be rapidly lost as the model adapts to a new snapshot. Existing studies have mainly focused on preserving past knowledge and transferring it stably across snapshots to mitigate such forgetting. This work starts from the observation that relations and entities may exhibit different degrees of semantic stability as a knowledge graph grows. Based on this perspective, we propose Temporal Residual Memory (TRM). TRM introduces snapshot-wise entity-side residual memories through the BoxE bump path, freezes memories from previous snapshots, and aggregates valid memories according to each entity's birth snapshot for current prediction. Experimental results show that the proposed method achieves strong performance on diverse CKGE benchmarks and improves knowledge retention metrics. In addition, experiments extending the core idea of TRM to TransE and DistMult also show a tendency toward mitigating forgetting. These results suggest that leveraging entity-side memory formed from past snapshots for current prediction can serve as an effective strategy for mitigating forgetting in CKGE.