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Existing knowledge graph (KG) embedding models have primarily focused onstatic KGs. However, real-world KGs do not remain static, but rather evolve andgrow in tandem with the development of KG applications. Consequently, new factsand previously unseen entities and relations continually emerge, necessitatingan embedding model that can quickly learn and transfer new knowledge throughgrowth. Motivated by this, we delve into an expanding field of KG embedding inthis paper, i.e., lifelong KG embedding. We consider knowledge transfer andretention of the learning on growing snapshots of a KG without having to learnembeddings from scratch. The proposed model includes a masked KG autoencoderfor embedding learning and update, with an embedding transfer strategy toinject the learned knowledge into the new entity and relation embeddings, andan embedding regularization method to avoid catastrophic forgetting. Toinvestigate the impacts of different aspects of KG growth, we construct fourdatasets to evaluate the performance of lifelong KG embedding. Experimentalresults show that the proposed model outperforms the state-of-the-art inductiveand lifelong embedding baselines.

Lifelong Embedding Learning and Transfer for Growing Knowledge Graphs