COVID-19 preVIEW: Semantic Search to Explore COVID-19 Research Preprints

被引：11

作者：

Langnickel, Lisa ^{[1
,2
]}

Baum, Roman ^{[1
]}

Darms, Johannes ^{[1
]}

Madan, Sumit ^{[3
,4
]}

Fluck, Juliane ^{[1
,3
,4
]}

机构：

[1] ZB MED Informat Ctr Life Sci, Cologne, Germany

[2] Bielefeld Univ, Fac Technol, Bielefeld Inst Bioinformat Infrastruct BIBI, Grad Sch DILS, Bielefeld, Germany

[3] Univ Bonn, Bonn, Germany

[4] Fraunhofer Inst Algorithms & Sci Comp, St Augustin, Germany

来源：

PUBLIC HEALTH AND INFORMATICS, PROCEEDINGS OF MIE 2021 | 2021年 / 281卷

关键词：

COVID-19; Information Retrieval; Biomedical Text Mining;

D O I：

10.3233/SHTI210124

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

During the current COVID-19 pandemic, the rapid availability of profound information is crucial in order to derive information about diagnosis, disease trajectory, treatment or to adapt the rules of conduct in public. The increased importance of preprints for COVID-19 research initiated the design of the preprint search engine preVIEW. Conceptually, it is a lightweight semantic search engine focusing on easy inclusion of specialized COVID-19 textual collections and provides a user friendly web interface for semantic information retrieval. In order to support semantic search functionality, we integrated a text mining workflow for indexing with relevant terminologies. Currently, diseases, human genes and SARS-CoV-2 proteins are annotated, and more will be added in future. The system integrates collections from several different preprint servers that are used in the biomedical domain to publish non-peer-reviewed work, thereby enabling one central access point for the users. In addition, our service offers facet searching, export functionality and an API access. COVID-19 preVIEW is publicly available at https://preview.zbmed.de.

引用

页码：78 / 82

页数：5

共 12 条

[1] Large-scale biomedical concept recognition: an evaluation of current automatic annotators and their parameters [J].