TempReasoner:用于事件时间线构建的神经时间图形网络.
1Department of Computer Engineering and Information,College of Engineering, Prince Sattam Bin Abdulaziz University, Wadi Ad Dwaser, 16273, Al-Kharj, Saudi Arabia. Mohammed.aldawsari@psau.edu.sa.
Scientific reports
|January 10, 2026
概括
TempReasoner是一个新的神经时间图网络,可以自动构建事件时间线. 该系统在排序事件方面达到很高的准确性,为各种应用程序实现实时分析.
科学领域:
- 人工智能的人工智能
- 提取知识 提取知识
- 时间推理 时间推理
背景情况:
- 从非结构化的时间数据构建自动事件时间表是具有挑战性的.
- 现有的方法与细粒度的时间依赖,稀疏的事件相互作用和跨域的因果顺序作斗争.
研究的目的:
- 提出TempReasoner,一种用于构建自动事件时间线的新型神经时间图形网络.
- 针对复杂数据集的现有时间推理方法的局限性.
主要方法:
- 使用神经时间图网络,具有动态的时空注意力和强化的时间推理.
- 结合时间知识图与自适应图神经网络和多尺度时间注意力模型.
- 采用一个层次性的时间编码器,有封闭的反复单位和一个新的时间一致性损失.
主要成果:
- 在五个基准数据集中订购事件时间表时达到94.3%的准确性.
- 实时运行,每个事件序列的平均延迟为127ms.
- 在法律,新闻和生物医学事件分析方面表现出强的性能.
结论:
- TempReasoner通过共同建模本地和全球时间模式,有效地构建自动事件时间线.
- 该系统为各种企业应用中的时间推理提供了可扩展和高效的解决方案.
- 拟议的架构增强了知识提取任务的时间连贯性和准确性.
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