穆兰:多层次的注意力增强匹配网络,用于完成短暂的知识图
Qianyu Li1, Bozheng Feng1, Xiaoli Tang2
1School of Software Engineering, South China University of Technology, Guangzhou, China.
概括
本研究介绍了多层次注意力增强匹配网络 (MuLAN),用于快速完成知识图. 通过捕捉邻居之间的互动和优化特征尺寸,MuLAN显著提高了性能.
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:
- 短暂的知识图完成 (KGC) 对于增强具有有限关系数据的知识图至关重要.
- 当前的方法通常依赖于从一跳邻居的实体嵌入,忽视了关键的邻居之间的动态和特征重要性.
研究的目的:
- 解决现有的少数KGC方法的局限性,特别是关于邻近相互作用和特征维度意义的局限性.
- 提出一个新的网络,MuLAN,用于增强短暂的知识图表完成.
主要方法:
- 开发了一种多层次的注意力增强匹配网络 (MuLAN),采用多头自我注意力邻居编码器.
- 实施实体级,实例级和功能级的注意力机制,以实现全面匹配.
- 引入了一种一致性约束,以提高支持实例嵌入的稳定性.
主要成果:
- 在NELL-One和Wiki-One数据集上,MuLAN在11个最先进的竞争对手上表现出显著的优势.
- 与最佳基线相比,MRR平均改善14.5%,Hits@K平均改善13.3%.
- 有效地处理邻居之间的互动,本地实体匹配,以及不同的特征维度意义.
结论:
- 通过有效地建模复杂的关系和特征的重要性,MuLAN提供了一种优越的方法来完成短暂的知识图.
- 提出的注意力机制和一致性约束导致KGC任务的性能大幅提高.
- 这项工作推进了知识图中的少量学习领域,为更强大,更准确的知识图增强铺平了道路.
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