语境意识学习和模式分解对于时间知识图的推理
IEEE transactions on neural networks and learning systems
|November 25, 2025
概括
本研究介绍了TCDR-PD,这是一个用于时间知识图 (TKG) 推理的新型网络. 它通过捕捉局部动态并区分反复和新出现的模式来增强实体和关系表示,以便在不断变化的TKG中进行更好的预测.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 图形神经网络 (GNN) 在时间知识图 (TKG) 推理方面表现出色.
- 现有的方法与当地的上下文动态和在不断发展的技术技能群中出现的模式作斗争.
研究的目的:
- 解决模拟本地动态和处理TKG中新型相互作用的局限性.
- 提出TCDR-PD,这是一个用于增强时间和上下文动态表示的网络,具有模式分解.
主要方法:
- 为全球趋势和查询特定动态引入了一个时间和上下文动态表示学习 (TCDR) 模块.
- 开发了一种模式分解 (PD) 预测模块,以分离反复和新出现的模式.
- 在四个基准数据集上对TKG推理进行了TCDR-PD评估.
主要成果:
- 与最先进的方法相比,TCDR-PD显示出更高的性能.
- TCDR模块有效地捕捉了时间趋势和上下文动态.
- 该PD模块成功处理了反复和新出现的模式,提高了预测准确度.
结论:
- 在不断变化的时间知识图表上,TCDR-PD为稳定的推理提供了强大的解决方案.
- 拟议的方法提高了适应动态环境和新型相互作用的能力.
- 这项工作通过解决代表性学习和预测的关键挑战,推进了TKG推理领域.
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