COVID-19

Since December 2019, a new strain of coronaviruses has appeared in Wuhan, Mainland China, causing a local respiratory disease outbreak characterized by acute respiratory distress syndrome. Due to the contagious power of the disease (so-called COVID-19), the virus has spread all over the world by March 2020 causing a large infectious pandemic. Such a pandemic requires multidisciplinary care rather than simple clinical support. That is why I decided to contribute to a computer science project about the issue as a part of the Data Engineering and Semantics Research Unit of the University of Sfax.

Computer science as a scholarly discipline can contribute a lot to solving this matter through the development of computer systems for COVID-19 tracking and case prediction and for COVID-19 data analysis and management using machine learning, social network analysis, and knowledge graphs like Wikidata.

We thank the Tunisian Ministry of Higher Education and Scientific Research (MoHESR) for funding this research initiative in the framework of Federated Research Project PRFCOV19-D1-P1. We also thank the WikiCred Grants Initiative of Craig Newmark Philanthropies, Facebook, and Microsoft to fund the part of our project about adding reference support to Wikidata statements related to the COVID-19 pandemic.

Useful links:

Wikidata: A large-scale collaborative ontological medical database: https://www.sciencedirect.com/science/article/pii/S1532046419302114

Representing COVID-19 information in collaborative knowledge graphs: the case of Wikidata: https://content.iospress.com/articles/semantic-web/sw210444

Using logical constraints to validate statistical information about disease outbreaks in collaborative knowledge graphs: the case of COVID-19 epidemiology in Wikidata: https://peerj.com/articles/cs-1085/

Enhancing Knowledge Graph Extraction and Validation From Scholarly Publications Using Bibliographic Metadata: https://www.frontiersin.org/articles/10.3389/frma.2021.694307/full

Infectious epidemics and the research output of nations: A data-driven analysis: https://journals.sagepub.com/doi/abs/10.1177/01655515211006605

Enhancing multilingual and biomedical named entity recognition using Wikidata semantic relations: https://wikiworkshop.org/2022/papers/WikiWorkshop2022_paper_12.pdf

How knowledge-driven class generalization affects classical machine learning algorithms for mono-label supervised classification: https://link.springer.com/chapter/10.1007/978-3-030-96308-8_59

MeSH2Matrix: Machine learning-driven biomedical relation classification based on the MeSH keywords of PubMed scholarly publications: https://ceur-ws.org/Vol-3230/paper-07.pdf

Data Models for Annotating Biomedical Scholarly Publications: the Case of CORD-19: https://dl.acm.org/doi/abs/10.1145/3487553.3524675

Letter to the Editor: FHIR RDF - Why the world needs structured electronic health records: https://www.sciencedirect.com/science/article/pii/S1532046422002581

WikiProject COVID-19 in Wikidata: https://www.wikidata.org/wiki/Wikidata:WikiProject_COVID-19 

Adding reference support to Wikidata statements: https://misinfocon.com/refdata-adding-trustworthiness-to-wikidata-d3cc68c21a6f