Lifelong knowledge graph embedding via diffusion model

Lifelong knowledge graph embedding (KGE) methods aim to learn new knowledge continuously while retaining old knowledge. This line of work has received much attention for its potential to enable knowledge retention and transfer and to reduce training costs under knowledge graphs' growing scale and flexibility. However, embedding space drift under different contexts is a crucial reason for catastrophic forgetting and inefficient learning of new facts, and existing work ignores this perspective. In order to address the above issues, we proposed a novel lifelong KGE framework that considers learning new facts and preserving old facts in a unified perspective. We propose a diffusion-based embedding method that captures the contextual variation of entity representations and obtains transferable embeddings. In order to handle the drift of the embedding space and balance the learning efficiency, we adopt a reconstruction and generation strategy based on contrastive learning. To avoid catastrophic forgetting and maintain the stability of the embedding distribution, we proposed an effective distribution regularization method. We conduct extensive experiments on seven benchmark datasets with different construction strategies and incremental speed. Experimental results show that our proposed framework outperforms existing lifelong KGE methods.