一个中国的知识图数据集在科学适应性领域
Shutong Du1,2, Zhitong Liu1,2, Bingyu Pan3,4
1School of Sports Engineering, Beijing Sport University, Beijing, China.
Scientific data
|February 5, 2025
概括
我们开发了FitKG-CN (Chinese Knowledge Graph Dataset in Scientific Fitness) 中的中国知识图数据集,这是一个包含超过10,000个术语的全面资源. 这个数据集通过先进的数据处理和训练有素的SpERT模型来增强健身研究和实践.
科学领域:
- 运动科学 运动科学 运动科学
- 生物医学信息学 生物医学信息学
- 数据科学数据科学数据科学
背景情况:
- 科学健身研究需要有结构的,可访问的数据来推进.
- 现有的资源缺乏针对中国健身术语的全面的,特定领域的知识图.
- 专门数据集的开发对于改善健身研究和实践至关重要.
研究的目的:
- 在科学适应性领域引入中国知识图数据集 (FitKG-CN).
- 建立一个强大的知识图,涵盖超过10,000个与健身相关的术语.
- 开发和验证用于科学健康知识的自动化数据处理模型.
主要方法:
- 使用权威来源与严格的预处理 (消除噪音,标准化,规范化) 构建FitKG-CN.
- 在专业平台上手动注释健身实体和关系.
- 在实体和关系提取的注释数据集上开发和培训中国SpERT模型.
主要成果:
- FitKG-CN数据集包括八个类别 (例如,身体部位,运动类型,设备) 的超过10,000个术语.
- 经过训练的中国SpERT模型实现了高性能:94.05%的F1分数用于实体识别和82.00%用于关系提取.
- 开发的模型在自动化数据处理方面表现出有效性,并提高了数据集的可扩展性.
结论:
- FitKG-CN为中国人的科学健身研究提供了有价值的,结构化的知识基础.
- 中国SpERT模型的高性能验证了其用于自动化知识图构建的实用性.
- 这项工作显著提升了开发和扩展科学健身信息学的潜力.
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