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Discovering associations in COVID-19 related research papers
arXiv - CS - Artificial Intelligence Pub Date : 2020-04-06 , DOI: arxiv-2004.03397
Iztok Fister Jr., Karin Fister, Iztok Fister

A COVID-19 pandemic has already proven itself to be a global challenge. It proves how vulnerable humanity can be. It has also mobilized researchers from different sciences and different countries in the search for a way to fight this potentially fatal disease. In line with this, our study analyses the abstracts of papers related to COVID-19 and coronavirus-related-research using association rule text mining in order to find the most interestingness words, on the one hand, and relationships between them on the other. Then, a method, called information cartography, was applied for extracting structured knowledge from a huge amount of association rules. On the basis of these methods, the purpose of our study was to show how researchers have responded in similar epidemic/pandemic situations throughout history.

中文翻译:

在 COVID-19 相关研究论文中发现关联

COVID-19 大流行已被证明是一项全球性挑战。它证明了人类是多么脆弱。它还动员了来自不同学科和不同国家的研究人员,寻找对抗这种潜在致命疾病的方法。与此相一致,我们的研究使用关联规则文本挖掘分析了与 COVID-19 和冠状病毒相关研究相关的论文摘要,以便一方面找到最有趣的词,另一方面找到它们之间的关系。然后,应用一种称为信息制图的方法从大量关联规则中提取结构化知识。在这些方法的基础上,我们研究的目的是展示研究人员如何应对历史上类似的流行病/大流行情况。
更新日期:2020-04-08
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