The NASA/IPAC Extragalactic Database (NED) is a comprehensive online service that combines fundamental multi-wavelength information for known objects beyond the Milky Way and provides value-added, derived quantities and tools to search and access the data. The contents and relationships between measurements in the database are continuously augmented and revised to stay current with astrophysics literature and new sky surveys. The conventional process of distilling and extracting data from the literature involves human experts to review the journal articles and determine if an article is of extragalactic nature, and if so, what types of data it contains. This is both labor intensive and unsustainable, especially given the ever-increasing number of publications each year. We present here a machine learning (ML) approach developed and integrated into the NED production pipeline to help automate the classification of journal article topics and their data content for inclusion into NED. We show that this ML application can successfully reproduce the classifications of a human expert to an accuracy of over 90% in a fraction of the time it takes a human, allowing us to focus human expertise on tasks that are more difficult to automate.
Evaluation of research publications and publication channels in astronomy and astrophysics
天文学界通常求助于天体物理学数据系统进行文献计量学。当上下文是跨学科的，商业产品，如Web of Science和Scopus与相关的分析工具一起使用。结果往往受到所选分类系统固有问题的影响。最常见的挑战和陷阱的审查。在不久的将来，可将商业替代指标产品添加到评估工具箱中，尽管它们最适合于推广而不是评估。挪威、丹麦和芬兰已经创建了用于国家资助模式的期刊和出版商排名系统。天文学期刊在这些系统中的权重差异可能与国家一级发表的论文数量有关。
