Construction of a Linked Data Set of COVID-19 Knowledge Graphs: Development and Applications

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Abstract

Background

With the continuous spread of COVID-19, information about the worldwide pandemic is exploding. Therefore, it is necessary and significant to organize such a large amount of information. As the key branch of artificial intelligence, a knowledge graph (KG) is helpful to structure, reason, and understand data.

Objective

To improve the utilization value of the information and effectively aid researchers to combat COVID-19, we have constructed and successively released a unified linked data set named OpenKG-COVID19, which is one of the largest existing KGs related to COVID-19. OpenKG-COVID19 includes 10 interlinked COVID-19 subgraphs covering the topics of encyclopedia, concept, medical, research, event, health, epidemiology, goods, prevention, and character.

Methods

In this paper, we introduce the key techniques exploited in building COVID-19 KGs in a top-down manner. First, the schema of the modeling process for each KG in OpenKG-COVID19 is described. Second, we propose different methods for extracting knowledge from open government sites, professional texts, public domain–specific sources, and public encyclopedia sites. The curated 10 COVID-19 KGs are further linked together at both the schema and data levels. In addition, we present the naming convention for OpenKG-COVID19.

Results

OpenKG-COVID19 has more than 2572 concepts, 329,600 entities, 513 properties, and 2,687,329 facts, and the data set will be updated continuously. Each COVID-19 KG was evaluated, and the average precision was found to be above 93%. We have developed search and browse interfaces and a SPARQL endpoint to improve user access. Possible intelligent applications based on OpenKG-COVID19 for further development are also described.

Conclusions

A KG is useful for intelligent question-answering, semantic searches, recommendation systems, visualization analysis, and decision-making support. Research related to COVID-19, biomedicine, and many other communities can benefit from OpenKG-COVID19. Furthermore, the 10 KGs will be continuously updated to ensure that the public will have access to sufficient and up-to-date knowledge.

Related collections

Most cited references 39

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Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions

Zifeng Yang, Zhiqi Zeng, Ke Wang … (2020)

Background The coronavirus disease 2019 (COVID-19) outbreak originating in Wuhan, Hubei province, China, coincided with chunyun, the period of mass migration for the annual Spring Festival. To contain its spread, China adopted unprecedented nationwide interventions on January 23 2020. These policies included large-scale quarantine, strict controls on travel and extensive monitoring of suspected cases. However, it is unknown whether these policies have had an impact on the epidemic. We sought to show how these control measures impacted the containment of the epidemic. Methods We integrated population migration data before and after January 23 and most updated COVID-19 epidemiological data into the Susceptible-Exposed-Infectious-Removed (SEIR) model to derive the epidemic curve. We also used an artificial intelligence (AI) approach, trained on the 2003 SARS data, to predict the epidemic. Results We found that the epidemic of China should peak by late February, showing gradual decline by end of April. A five-day delay in implementation would have increased epidemic size in mainland China three-fold. Lifting the Hubei quarantine would lead to a second epidemic peak in Hubei province in mid-March and extend the epidemic to late April, a result corroborated by the machine learning prediction. Conclusions Our dynamic SEIR model was effective in predicting the COVID-19 epidemic peaks and sizes. The implementation of control measures on January 23 2020 was indispensable in reducing the eventual COVID-19 epidemic size.

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COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images

Linda. Wang, Zhong Qiu Lin, Alexander Wong (2020)

The Coronavirus Disease 2019 (COVID-19) pandemic continues to have a devastating effect on the health and well-being of the global population. A critical step in the fight against COVID-19 is effective screening of infected patients, with one of the key screening approaches being radiology examination using chest radiography. It was found in early studies that patients present abnormalities in chest radiography images that are characteristic of those infected with COVID-19. Motivated by this and inspired by the open source efforts of the research community, in this study we introduce COVID-Net, a deep convolutional neural network design tailored for the detection of COVID-19 cases from chest X-ray (CXR) images that is open source and available to the general public. To the best of the authors’ knowledge, COVID-Net is one of the first open source network designs for COVID-19 detection from CXR images at the time of initial release. We also introduce COVIDx, an open access benchmark dataset that we generated comprising of 13,975 CXR images across 13,870 patient patient cases, with the largest number of publicly available COVID-19 positive cases to the best of the authors’ knowledge. Furthermore, we investigate how COVID-Net makes predictions using an explainability method in an attempt to not only gain deeper insights into critical factors associated with COVID cases, which can aid clinicians in improved screening, but also audit COVID-Net in a responsible and transparent manner to validate that it is making decisions based on relevant information from the CXR images. By no means a production-ready solution, the hope is that the open access COVID-Net, along with the description on constructing the open source COVIDx dataset, will be leveraged and build upon by both researchers and citizen data scientists alike to accelerate the development of highly accurate yet practical deep learning solutions for detecting COVID-19 cases and accelerate treatment of those who need it the most.

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Kurt Bollacker, Colin Evans, Praveen Paritosh … (2008)

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Author and article information

Contributors

Haofen Wang:

ORCID: https://orcid.org/0000-0003-3018-3824

College of Design and InnovationTongji UniversityNo. 281 Fuxin Road, Yangpu DistrictShanghai, 200092China86 13918586855carter.whfcarter@gmail.com

Journal

Journal ID (nlm-ta): JMIR Med Inform

Journal ID (iso-abbrev): JMIR Med Inform

Journal ID (publisher-id): JMI

Title: JMIR Medical Informatics

Publisher: JMIR Publications (Toronto, Canada )

ISSN (Electronic): 2291-9694

Publication date Collection: May 2022

Publication date (Electronic): 13 May 2022

Publication date PMC-release: 13 May 2022

Volume: 10

Issue: 5

Electronic Location Identifier: e37215

Affiliations

[1 ] College of Design and Innovation Tongji University Shanghai China

[2 ] School of Computer Science and Engineering Southeast University Nanjing China

[3 ] College of Computer Science and Technology Zhejiang University Hangzhou China

[4 ] National Institute of Healthcare Data Science Nanjing University Nanjing China

Author notes

Corresponding Author: Haofen Wang carter.whfcarter@ 123456gmail.com

Author information

Haofen Wang https://orcid.org/0000-0003-3018-3824

Huifang Du https://orcid.org/0000-0001-9152-8027

Guilin Qi https://orcid.org/0000-0002-1957-6961

Huajun Chen https://orcid.org/0000-0001-5496-7442

Wei Hu https://orcid.org/0000-0003-3635-6335

Zhuo Chen https://orcid.org/0000-0001-9991-6892

Article

Publisher ID: v10i5e37215

DOI: 10.2196/37215

PMC ID: 9109781

PubMed ID: 35476822

SO-VID: bde41a5e-05c6-47e5-930b-610998778350

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on https://medinform.jmir.org/, as well as this copyright and license information must be included.

History

Date received : 7 March 2022

Date revision requested : 20 April 2022

Date revision received : 23 April 2022

Date accepted : 26 April 2022

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