通过传播模型嵌入终身知识图表
Deyu Chen1, Caicai Guo2, Qiyuan Li2
1School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.
概括
本研究引入了一个新的终身知识图嵌入 (KGE) 框架,以防止灾难性遗忘和提高学习效率. 这种新的方法解决了嵌入空间漂移的问题,在实验中优于现有的KGE方法.
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:
- 终身知识图嵌入 (KGE) 方法对于大型不断变化的知识图中的持续学习至关重要.
- 现有的KGE方法因嵌入空间漂移而扎于灾难性的遗忘和低效的学习.
- 对于在学习新事实时保留旧知识的方法的需求正在增长.
研究的目的:
- 提出一个新的终身KGE框架,解决嵌入空间漂移和灾难性遗忘问题.
- 通过统一新事实的学习和旧事实的保存来实现持续的学习.
- 提高知识的保留和转移,同时降低培训成本.
主要方法:
- 一种基于扩散的嵌入方法,以捕捉上下文变化并产生可转移的嵌入.
- 使用对比学习进行重建和生成策略,以管理嵌入空间漂移和学习效率.
- 分布规范化以防止灾难性遗忘和稳定嵌入分布.
主要成果:
- 与现有的终身KGE方法相比,拟议的框架显示出更高的性能.
- 在七个基准数据集上进行了广泛的实验,证实了该框架在各种增量学习场景中的有效性.
- 这些方法成功地解决了嵌入空间漂移的问题,并提高了学习效率.
结论:
- 新的终身KGE框架有效地缓解了灾难性遗忘,并提高了学习效率.
- 基于扩散的嵌入和对比的学习策略是保持知识和适应新信息的关键.
- 这项工作为终身学习提供了显著的进步.
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