Research on the construction of COVID-19 knowledge graph for literature organization

Corresponding author: Li Junlian, li.junlian@imicams.ac.cn

DOI: 10.12201/bmr.202010.00840

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  Abstract: This paper presents a COVID-19 knowledge model covering the modern medicine and traditional Chinese medicine, as well as a Natural Language Processing based automatic knowledge discovery, followed by knowledge organization and semantic association from hierarchical structure, subject classification and relation types. Additionally, the COVID-19 literature, diagnosis and treatment regimens, also the new media data were collected for knowledge discovery and manual annotation, resulting a COVID-19 knowledge graph and its visualization system to support the well organization and effective utilization of biomedical literature.

  Key words: COVID-19; Knowledge Graph; Knowledge Discovery; Knowledge Organization; Natural Language Processing

  Submit time: 8 December 2020

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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  1	2020-10-21	bmr.202010.00840V1	Download

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