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Improving continual knowledge graph embedding via informed initialization
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AI
DOI：10.1016/j.neucom.2026.134045.png)
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Abstract
En 中文
Knowledge Graphs (KGs) represent knowledge as structured graphs of entities and their relationships. Knowledge Graph Embeddings (KGEs) enable learning and inference over KGs by representing their entities and relations as continuous vectors. Many KGs are frequently updated, requiring their KGEs to adapt to these changes. Continual learning methods for KGEs address this by incorporating new embeddings while updating existing ones to account for the new facts without retraining from scratch. A necessary step in these methods is the initialization of new embeddings, which has an important impact both on the accuracy of the resulting embeddings, and in the time required for their training. Current continual learning methods either use a random initialization as in non-continual scenarios, ignoring relevant information in existing embeddings, or rely on a model-dependent initialization, which is only compatible with specific KGE training models. We propose a novel informed embedding initialization strategy that can be seamlessly integrated into existing continual learning methods for KGEs. Our approach leverages the KG schema along with previously learned embeddings to generate semantically informed initial representations at every continual learning step, enhancing knowledge acquisition while reducing catastrophic forgetting in the final embeddings obtained. Experimental results show that our strategy improves the predictive performance of the resulting KGEs and enhances knowledge retention. Moreover, it accelerates the learning process, reducing the number of epochs, and thus the time required to incrementally learn new embeddings. Finally, our evaluation demonstrates that these benefits are consistent regardless of the KGE learning model used.
Keywords:
Knowledge graph embeddings
Continual learning
Knowledge graphs
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