动态调整医学文本图结构，让模型跟上知识演变速度。
Drift-Aware Temporal Graph Rewiring (DATGR) for Adaptive Semantic Modeling in Biomedical Text
- 根据语义漂移自动重连词语关系边，不重新训练嵌入
- 在生物医学数据集上提升约6.6%的AUROC指标
- 轻量级更新适合实时应用，结果可解释性强
医学语言随新发现快速演进，传统静态嵌入和共现图难以捕捉这种变化，导致检索与知识发现任务性能下降。本文提出一种漂移感知时间图重连（DATGR）框架，通过估计语义漂移动态更新共现边。无需对每个时间片重新训练嵌入，而是采用基于逻辑回归的轻量级反馈式边权重更新。在生物医学多关系语料库（BIOMRC）上，该方法相比静态基线平均AUROC提升约0.066（0.699 vs. 0.633），AUPRC保持稳定（0.738 vs. 0.744），表明漂移感知适应在不损失精度的前提下提升了链接预测召回率。结果证明，边级动态调整能有效捕捉生物医学文本中的时序语义变化，同时具备计算高效与可解释性。
原文摘要 · Abstract (English)
Biomedical language evolves rapidly as new discoveries emerge, causing traditional text models to lose semantic fidelity over time. Static embeddings and co-occurrence graphs cannot capture such evolution, leading to performance degradation in retrieval and knowledge discovery tasks. This paper introduces a Drift-Aware Temporal Graph Rewiring (DATGR) framework that models concept evolution by dynamically updating co-occurrence edges based on estimated semantic drift. Instead of retraining embeddings for each time slice, DATGR performs lightweight, feedback-driven rewiring using a logistic update rule applied to edge weights. Evaluated on the Biomedical Multi-Relation Corpus (BIOMRC), the method achieved a mean Area Under the Receiver Operating Characteristic (AUROC) improvement of approximately 0.066 absolute difference (0.699 vs. 0.633) over a static baseline. Area Under the Precision-Recall Curve (AUPRC) remained comparable (0.738 vs. 0.744), showing that drift-aware adaptation enhances link-prediction recall without a loss in precision. These results demonstrate that edge-level adaptation effectively captures temporal semantic change in evolving biomedical text while remaining computationally efficient and interpretable.
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