多跳式可解释的超级学习为短暂的时间知识图完成
概括
这项研究介绍了一种基于多跳式可解释元学习 (Multi-hop Interpretable meta-learning,FTMI) 的新的短拍时间知识图完成模型,以应对短拍关系的挑战. FTMI有效地处理时间信息,并使用元学习和强化学习优化路径完成.
科学领域:
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 传统的多跳跃路径完成模型与时间知识图表作斗争,特别是在少数射击关系中.
- 现有的模型无法考虑时间信息对路径完成的影响.
研究的目的:
- 提出一个基于多跳式可解释元学习 (Multi-hop Interpretable meta-learning,FTMI) 的短拍时间知识图完成模型.
- 增强具有有限关系实例的时间知识图的多跳路径完成.
主要方法:
- FTMI聚合了多跳邻近信息,用于时间意识的实体表示.
- 变压器编码和平均值聚合用于实体对和关系表示.
- 使用新的奖励函数进行强化学习,优化了完成路径选择.
主要成果:
- FTMI模型有效地结合了时间信息,以了解时间动态.
- 超级学习可以在很少的样本中快速适应新任务.
- 在两个数据集上的实验验证证明了该模型的有效性.
结论:
- FTMI显著改善了短暂的时间知识图完成.
- 该模型为处理稀疏的时间关系数据提供了一个强大的解决方案.
- 通过考虑时间上下文,FTMI提供了可解释的完成结果.
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