@Article{info:doi/10.2196/37215,
author="Wang, Haofen
and Du, Huifang
and Qi, Guilin
and Chen, Huajun
and Hu, Wei
and Chen, Zhuo",
title="Construction of a Linked Data Set of COVID-19 Knowledge Graphs: Development and Applications",
journal="JMIR Med Inform",
year="2022",
month="May",
day="13",
volume="10",
number="5",
pages="e37215",
keywords="knowledge graph; linked data; COVID-19; knowledge extraction; knowledge fusion; natural language processing; artificial intelligence; data set; schema modeling; semantic search",
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. ",
issn="2291-9694",
doi="10.2196/37215",
url="https://medinform.jmir.org/2022/5/e37215",
url="https://doi.org/10.2196/37215",
url="http://www.ncbi.nlm.nih.gov/pubmed/35476822"
}