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Lifelong knowledge graph embedding via diffusion model
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DOI：10.1016/j.neunet.2026.108630.png)
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Abstract
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• We proposed a unified perspective of embedding space drift for lifelong knowledge graph embedding. • We designed a diffusion-based knowledge graph embedding framework for the lifelong setting. • Our framework avoids catastrophic forgetting and enables efficient learning of new entities. • Our framework achieves improvements in different incremental scenarios.
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LollipopE: Bi-centered lollipop embedding for complex logic query on knowledge graph.png)
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