Existing knowledge graph (KG) embedding models have primarily focused on static KGs. However, real-world KGs do not remain static, but rather evolve and grow in tandem with the development of KG applications. Consequently, new facts and previously unseen entities and relations continually emerge, necessitating an embedding model that can quickly learn and transfer new knowledge through growth. Motivated by this, we delve into an expanding field of KG embedding in this paper, i.e., lifelong KG embedding. We consider knowledge transfer and retention of the learning on growing snapshots of a KG without having to learn embeddings from scratch. The proposed model includes a masked KG autoencoder for embedding learning and update, with an embedding transfer strategy to inject the learned knowledge into the new entity and relation embeddings, and an embedding regularization method to avoid catastrophic forgetting. To investigate the impacts of different aspects of KG growth, we construct four datasets to evaluate the performance of lifelong KG embedding. Experimental results show that the proposed model outperforms the state-of-the-art inductive and lifelong embedding baselines.

翻译：现有知识图谱（KG）嵌入模型主要关注静态知识图谱。然而，现实中的知识图谱并非静态不变，而是随着知识图谱应用的发展而不断演化和增长。因此，新的事实以及先前未见过的实体和关系不断涌现，这要求嵌入模型能够通过增长过程快速学习并迁移新知识。受此启发，本文深入探讨知识图谱嵌入的一个新兴领域，即终身知识图谱嵌入。我们研究了在知识图谱增长快照上的知识迁移与保留问题，而无需从头学习嵌入。所提模型包括一个用于嵌入学习与更新的掩码知识图谱自编码器，结合一种嵌入迁移策略将已学知识注入新实体和关系的嵌入中，并采用一种嵌入正则化方法以避免灾难性遗忘。为探究知识图谱增长不同方面的影响，我们构建了四个数据集来评估终身知识图谱嵌入的性能。实验结果表明，所提模型优于最先进的归纳式与终身嵌入基线方法。