清晰原则:将数据和元数据组织成具有语义意义的FAIR数字对象类型,以增加它们的人类可探索性和认知互操作性
1TIB Leibniz Information Centre for Science and Technology, Welfengarten 1B, 30167, Hanover, Germany. lars.m.vogt@googlemail.com.
Journal of biomedical semantics
|October 29, 2025
概括
这项研究强调了知识图中人类可操作性的需要,提出语义单元和CLEAR原则,以增强研究人员的认知互操作性和数据可探索性.
科学领域:
- 数据科学数据科学数据科学
- 信息科学 信息科学 信息科学
- 计算机科学 计算机科学
背景情况:
- 公平的数据原则 (可查找,可访问,可互操作,可重复使用) 对研究和工业至关重要.
- 知识图和本体是实现FAIR数据,特别是互操作性的关键.
- 目前专注于知识图中的机器可操作性,忽视了基本的人类可操作性.
研究的目的:
- 论证人类可操作性在知识图中的重要性.
- 引入认知互操作性作为数据框架中的重要层.
- 为增强人类数据探索提出CLEAR原则和语义单元.
主要方法:
- 这篇论文讨论了人类探索性和认知互操作性之间的关系.
- 它引入了CLEAR原则,以支持认知互操作性和上下文数据探索.
- 语义单位的概念是作为一种方法来构建知识图的概念.
主要成果:
- 语义单元将知识图组织成可识别的有意义的子图.
- 每个语义单元都作为一个FAIR数字对象 (FDO) 运作,实例化一个FDO类.
- 语义单元可以被视觉化为像思维图一样的图表或自然语言文本.
结论:
- 语义单元增强了数据的认知互操作性和人类上下文探索性.
- 该组织支持不同细分度和参考框架的知识图.
- 基于语义单元的创新用户界面将改善数据访问和探索效率.
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