论文标题
识别科学文献中新兴主题的文献计量范围扫描方法
A Bibliometric Horizon Scanning Methodology for Identifying Emerging Topics in the Scientific Literature
论文作者
论文摘要
描述了针对新兴科学和技术领域进行扫描的文献计量学方法，其中科学，技术和创新企业的主题是使用潜在的Dirichlet分配发现的，它们的增长率是使用一阶速率动力学建模的，并且使用位置点的定位点来测量这些主题中各个实体的研究专业。开发了将这些结果整合在一起以帮助人类分析师的多个交互式可视化界面。通过分析最近五年的出版物，专利和赠款（约1400万个文件）来证明这种方法，例如，对机器视觉的深度学习是增长最快的领域，并且中国在该领域的焦点比美国更加重点。
A bibliometric methodology for scanning for emerging science and technology areas is described, where topics in the science, technology and innovation enterprise are discovered using Latent Dirichlet Allocation, their growth rates are modeled using first-order rate kinetics, and research specialization of various entities in these topics is measured using the location quotient. Multiple interactive visualization interfaces that integrate these results together to assist human analysts are developed. This methodology is demonstrated by analyzing the last five years of publications, patents and grants (~ 14 million documents) showing, for example, that deep learning for machine vision is the fastest growing area, and that China has a stronger focus than the U.S. in this area.
