在维基数据中整合生物医学知识与开放的生物和生物医学本体学以及MeSH关键词的框架
Houcemeddine Turki1,2,3, Khalil Chebil4, Bonaventure F P Dossou2,5,6,7
1Data Engineering and Semantics Research Unit, Faculty of Sciences of Sfax, University of Sfax, Sfax, Tunisia.
Heliyon
|October 15, 2024
概括
这项研究通过整合生物医学本体学和PubMed关键词来增强维基数据,提高知识图的准确性. 它验证语义对齐并完善关系提取,以提高生物医学数据的互操作性.
科学领域:
- 生物信息学是一种生物信息学.
- 知识表示 知识表示
- 生物医学信息学 生物医学信息学
背景情况:
- 维基数据是一个开放的知识图,需要用结构化生物医学数据来增强.
- 整合开放的生物和生物医学本体 (OBO) 和医学主题标题 (MeSH) 可以改善维基数据中的生物医学信息.
- 现有的OBO和Wikidata之间的语义对齐显示了差距和不准确性.
研究的目的:
- 开发一个框架,以 OBO 和 MeSH 数据丰富维基数据.
- 评估和改进OBO本体和维基数据之间的语义对齐.
- 从PubMed.使用MeSH关键字提取和分类生物医学关系.
主要方法:
- 使用SPARQL查询来收集和分类OBO本体和MeSH关键字.
- 从MeSH关键词中提取生物医学关系的应用点向点的相互信息.
- 采用集成梯度来完善分类并删除无关的MeSH合格符.
- 使用MeSH关键字来识别支持维基数据关系的PubMed评论.
主要成果:
- 在基于超级类的分类中达到89.40%的准确性,在基于类型的关系分类中达到75.32%的准确性.
- 确定了OBO-Wikidata语义对齐中的重大差距和扭曲表示.
- 发现45.8%的未支持的Wikidata关系在PubMed中不存在,这表明潜在的不一致性.
- 展示了改进的方法,以生物医学信息丰富维基数据.
结论:
- 拟议的框架有效地通过生物医学数据增强了维基数据.
- 经过验证的语义对齐和高效的分类流程可以提高知识图的准确性.
- MeSH关键词对于验证语义关系,提高生物医学知识图的稳定性和多语言能力至关重要.
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