COVID-19知识解构和检索:一个智能图书识别解决方案
Mengjia Wu1, Yi Zhang1, Mark Markley2
1Australian Artificial Intelligence Institute, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia.
Scientometrics
|June 26, 2023
概括
本研究介绍了一个框架和仪表板,以帮助科学家导航COVID-19研究. 它综合新兴信息,确定研究趋势,并将新发现与现有知识联系起来.
科学领域:
- 传染性疾病 传染性疾病
- 信息科学 信息科学 信息科学
- 公共卫生 公共卫生
背景情况:
- 随着COVID-19的流行,大量的科学文献出现了.
- 研究人员在合成这些信息并将其与基础知识联系起来时面临挑战.
- 需要工具来有效管理和理解快速扩展的COVID-19研究环境.
研究的目的:
- 开发一个框架和仪表板,用于识别,检索和理解COVID-19知识.
- 帮助科学家浏览大量关于COVID-19的学术文章.
- 弥合新兴研究与现有知识库之间的差距.
主要方法:
- 使用主要组件分解 (PCD) 进行主题分析.
- 采用基于知识模式的搜索方法.
- 应用层次主题树 (HTT) 分析,以描述研究景观和可视化知识结构.
- 纳入了疫苗研究的知识模型,参考了COVID前的出版物.
主要成果:
- 对127,971篇COVID-19论文的分析通过趋势分析确定了35个研究热点.
- HTT将全球知识细分为临床和公共卫生分支.
- 确定了未来的关键研究领域,包括单克隆抗体治疗,糖尿病疫苗接种,疫苗有效性和过敏敏感化.
结论:
- 拟议的框架和仪表板有效地综合了COVID-19研究.
- 这些工具有助于理解研究趋势和识别基础知识.
- 突出了疫苗接种和治疗的未来研究方向,为科学界提供了宝贵的见解.
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