Entity interference from new knowledge graph entities causes up to 25% overestimation of CKGE method performance by disrupting prior predictions, requiring a corrected evaluation protocol.
Ahybridlearningapproachforcontinualknowledgegraphembedding:Contrastivemasking and joint anti-forgetting, in: International Conference on Artificial Neural Networks, Springer
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Revisiting Catastrophic Forgetting in Continual Knowledge Graph Embedding
Entity interference from new knowledge graph entities causes up to 25% overestimation of CKGE method performance by disrupting prior predictions, requiring a corrected evaluation protocol.