COVID-19研究的影响:使用机器学习和独立于领域的知识图来预测有影响力的学术文件的研究
Gollam Rabby1,2, Jennifer D'Souza3, Allard Oelen3
1L3S Research Center, Leibniz University Hannover, Hanover, Germany. gollam.rabby@l3s.de.
Journal of biomedical semantics
|November 29, 2023
概括
这项研究通过使用独立于领域的知识图表来增强学术文档预测,优于在分类内容中对有影响力的文档识别的传统方法.
科学领域:
- 信息科学 信息科学 信息科学
- 计算机科学 计算机科学
- 图书统计学 图书统计学
背景情况:
- 学术文档分析通常依赖于文献计量数据和未分类内容.
- 预测有影响力的学术文件需要探索超越传统元数据的方法.
- 分类学术文件为更细致的分析提供了潜力.
研究的目的:
- 预测有影响力的学术文件,使用超越文献计量元数据的方法.
- 在分类的学术文件中调查有影响力的学术文件预测.
- 引入一种新的方法,通过知识图来增强文档表示.
主要方法:
- 利用世卫组织的COVID-19学术文件.
- 文件表示方法的比较:TF-IDF,词包 (BOW) 和BERT嵌入.
- 采用机器学习算法,包括后勤回归和随机森林.
- 使用域独立知识图表 DBpedia 增强了文档表示.
主要成果:
- TF-IDF的性能优于其他文档表示方法.
- 后勤回归是学术文档类别分类的最佳方法.
- 随机森林在有影响力的学术文档预测方面取得了最好的结果.
- 使用DBpedia增强的文档表示影响了分类影响性文档预测,但没有影响类别分类.
结论:
- 独立于领域的知识图可以改善对分类内容的有影响力的学术文档预测.
- 将机器学习与增强的文档表示相结合,提供了一个有前途的方向.
- 进一步的研究可以探索知识图增强对不同学术数据集的影响.
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