English(EN) DyHist: Query-Aware Historical Dynamics Modeling for Temporal Knowledge Graph Extrapolation

DyHist框架增强时间知识图谱外推能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-24 05:51

研究人员开发了DyHist，一个旨在改进时间知识图谱（TKGs）中从历史数据预测未来事实的新框架。DyHist通过显式建模近期和中程历史背景中与查询相关的偏差来解决现有方法的局限性。它还整合了一个面向关系的先验历史信息，以根据时间衰减、近期趋势和关系特定强度动态调整历史复现信号的可靠性。实验表明，DyHist在TKG外推任务中优于当前基线，在四个基准数据集上取得了优异的结果。 AI

影响这项研究可能导致知识图谱中未来事件预测的准确性提高，从而改进依赖于时间数据的应用。

排序理由该条目是一篇学术论文，详细介绍了时间知识图谱外推的新框架。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Qiao Liu · 2026-10-24 05:51

DyHist：面向时序知识图谱外推的查询感知历史动态建模

Temporal Knowledge Graph (TKG) extrapolation aims to predict future facts from observed historical events. Existing methods usually achieve this goal by jointly modeling local historical evolution and long-range historical contexts. First, local evolution modeling often summarize…

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DyHist：面向时序知识图谱外推的查询感知历史动态建模

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