TY  - JOUR
AU  - Wang, Haofen
AU  - Du, Huifang
AU  - Qi, Guilin
AU  - Chen, Huajun
AU  - Hu, Wei
AU  - Chen, Zhuo
PY  - 2022
DA  - 2022/5/13
TI  - Construction of a Linked Data Set of COVID-19 Knowledge Graphs: Development and Applications
JO  - JMIR Med Inform
SP  - e37215
VL  - 10
IS  - 5
KW  - knowledge graph
KW  - linked data
KW  - COVID-19
KW  - knowledge extraction
KW  - knowledge fusion
KW  - natural language processing
KW  - artificial intelligence
KW  - data set
KW  - schema modeling
KW  - semantic search
AB  - 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. 
SN  - 2291-9694
UR  - https://medinform.jmir.org/2022/5/e37215
UR  - https://doi.org/10.2196/37215
UR  - http://www.ncbi.nlm.nih.gov/pubmed/35476822
DO  - 10.2196/37215
ID  - info:doi/10.2196/37215
ER  -