Continual Knowledge Graph Embedding (CKGE) aims to continually learn embeddings for new knowledge, i.e., entities and relations, while retaining previously acquired knowledge. Most existing CKGE methods mitigate catastrophic forgetting via regularization or replaying old knowledge. They conflate new and old knowledge of an entity within the same embedding space to seek a balance between them. However, entities inherently exhibit multi-faceted semantics that evolve dynamically as their relational contexts change over time. A shared embedding fails to capture and distinguish these temporal semantic variations, degrading lifelong link prediction accuracy across snapshots. To address this, we propose a Multi-Faceted CKGE framework (MF-CKGE) for semantic-aware link prediction. During offline learning, MF-CKGE separates temporal old and new knowledge into distinct embedding spaces to prevent knowledge entanglement and employs semantic decoupling to reduce semantic redundancy, thereby improving space efficiency. During online inference, MF-CKGE adaptively identifies semantically query-relevant entity embeddings by quantifying their semantic importance, reducing interference from query-irrelevant noise. Experiments on eight datasets show that MF-CKGE achieves an average (maximum) improvement of 1.7% (2.7%) and 1.4% (3.8%) in MRR and Hits@10, respectively, over the best baseline. Our source code and datasets are available at: https://anonymous.4open.science/r/MF-CKGE-04E5.

翻译：持续知识图谱嵌入（CKGE）旨在持续学习新知识（即实体和关系）的嵌入表示，同时保留先前习得的知识。现有CKGE方法大多通过正则化或重放旧知识来缓解灾难性遗忘，将实体的新旧知识混淆在同一嵌入空间中以寻求平衡。然而，实体本质上具有多面语义，且随着其关系上下文随时间动态演变。共享嵌入无法捕获和区分这些时间语义变化，导致跨快照的生命周期链接预测精度下降。为此，我们提出面向语义感知链接预测的多面持续知识图谱嵌入框架（MF-CKGE）。在离线学习阶段，MF-CKGE将时间维度的新旧知识分离到不同的嵌入空间以防止知识纠缠，并采用语义解耦技术降低语义冗余，从而提升空间效率。在在线推理阶段，MF-CKGE通过量化语义重要性自适应识别与查询语义相关的实体嵌入，减少查询无关噪声的干扰。在八个数据集上的实验表明，与最优基线相比，MF-CKGE在MRR和Hits@10指标上分别平均提升1.7%（最高2.7%）和1.4%（最高3.8%）。我们的源代码和数据集可访问：https://anonymous.4open.science/r/MF-CKGE-04E5。