基于结构和上下文感知推的异质数据图的半自动构建.
Nan Yin1, Junheng Liang1, Xi Guo2,3
1School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China.
Scientific data
|February 1, 2025
概括
这项研究引入了一种半自动化方法来创建科学数据图表,提高了50.5%的效率,并减少了16.5%的图表扩散. 该方法使用对异质材料数据的上下文感知建议.
科学领域:
- 数据科学数据科学数据科学
- 材料 信息学 信息学
- 科学数据管理科学数据管理
背景情况:
- 科学数据的发布需要灵活地为各种数据类型定制方案.
- 当前的平台导致了模式的扩散和复杂的创建过程.
- 不同质的材料数据带来了独特的方案构建挑战.
研究的目的:
- 开发一种半自动方法,用于构建异质材料数据方案.
- 为了提高效率并减少数据图表创建的复杂性.
- 解决数据发布平台中的图表扩散问题.
主要方法:
- 提出了用于层次数据表示的模式碎片树结构.
- 通过子树匹配实现了上下文意识的推.
- 使用碎片索引,语义搜索和树编辑距离来计算相似性.
主要成果:
- 拟议的算法在模式匹配 (精度,回忆,F1分数) 中超过了TF-IDF和BM25基线.
- 在图表创建效率方面实现了50.5%的改进.
- 与手动方法相比,减少了16.5%的模式扩散.
结论:
- 半自动方法有效地构建异质材料数据方案.
- 基于背景的建议显著提高了图表创建效率,并减少了扩散.
- 该方法为管理各种科学数据结构提供了一个可扩展的解决方案.
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