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arXiv · 2512.01890

Elastic Weight Consolidation for Knowledge Graph Continual Learning: An Empirical Evaluation

Gaganpreet Jhajj·Fuhua Lin
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Abstract

Knowledge graphs (KGs) require continual updates as new information emerges, but neural embedding models suffer from catastrophic forgetting when learning new tasks sequentially. We evaluate Elastic Weight Consolidation (EWC), a regularization-based continual learning method, on KG link prediction using TransE embeddings on FB15k-237. Across multiple experiments with five random seeds, we find that EWC reduces catastrophic forgetting from 12.62% to 6.85%, a 45.7% reduction compared to naive sequential training. We observe that the task partitioning strategy affects the magnitude of forgetting: relation-based partitioning (grouping triples by relation type) exhibits 9.8 percentage points higher forgetting than randomly partitioned tasks (12.62% vs 2.81%), suggesting that task construction influences evaluation outcomes. While focused on a single embedding model and dataset, our results demonstrate that EWC effectively mitigates catastrophic forgetting in KG continual learning and highlight the importance of evaluation protocol design.

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Gaganpreet Jhajj, Fuhua Lin. 2025-12-01. Elastic Weight Consolidation for Knowledge Graph Continual Learning: An Empirical Evaluation. https://arxiv.org/abs/2512.01890

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