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，同时保持先前习得的知识。现有大多数CKGE方法通过正则化或重放旧知识来缓解灾难性遗忘。它们将实体的新旧知识混淆在同一嵌入空间中，以寻求两者之间的平衡。然而，实体本质上具有多方面的语义，这些语义会随着其关系上下文随时间动态演变。共享嵌入无法捕捉和区分这些时态语义变化，从而降低了跨快照的全周期链接预测准确性。为解决这一问题，我们提出了一个面向语义感知链接预测的多方面CKGE框架（MF-CKGE）。在离线学习阶段，MF-CKGE将时态旧知识与新知识分离到不同的嵌入空间中以防止知识纠缠，并采用语义解耦减少语义冗余，从而提高空间效率。在线推理阶段，MF-CKGE通过量化查询相关实体的语义重要性，自适应地识别与查询语义相关的实体嵌入，减少了与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上分别实现了平均（最大）1.7%（2.7%）和1.4%（3.8%）的提升。我们的源代码和数据集可在 https://anonymous.4open.science/r/MF-CKGE-04E5 获取。