对于方案复杂异质信息网络的诱导性元路径学习
概括
SchemaWalk有效地学习复杂的异质信息网络 (HIN) 中的元路径. 该框架使用图表层次表示和强化学习代理来改进知识库的元路径发现.
科学领域:
- 计算机科学 计算机科学
- 数据挖掘 数据挖掘
- 人工智能的人工智能
背景情况:
- 异质信息网络 (HIN) 具有多种节点和边缘类型.
- 元路径提供语义解释,但面临的可扩展性问题,在方案复杂的HIN如知识库.
- 对于大规模的HIN来说,详尽的元路径列举是计算密集的.
研究的目的:
- 引入SchemaWalk,这是一个用于模式复杂HINs的诱导性元路径学习框架.
- 在大型HIN中解决元路径计数和评估的计算挑战.
- 开发一种学习元路径得分的方法,而不是列举所有路径实例.
主要方法:
- SchemaWalk使用了对元路径的模式级表示.
- 一个基于强化学习的代理导航网络模式,以发现元路径.
- 该框架学习了跨多个关系的高覆盖率和高信任度元路径的政策.
主要成果:
- SchemaWalk有效地学习了模式复杂的HIN中的元路径.
- 拟议的方法减轻了对详尽的路径实例列举的需求.
- 在真实数据集上的实验验验证了框架的有效性.
结论:
- SchemaWalk 提供了一个高效的解决方案,用于复杂的 HIN 中的元路径学习.
- 该框架提高了知识库中元路径的可解释性和实用性.
- 这种诱导方法为传统的元路径方法提供了一个可扩展的替代方案.
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