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Cited by 3Biomedical Text Mining and OntologiesSemantic Web and OntologiesAdvanced Graph Neural Networks

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

COVID19-OBKG: An Ontology-Based Knowledge Graph and Web Service for COVID-19

Zheng, Xiangwen, Xiao, Yu, Song, Wei, Tong, Fan, Liu, Sheng, Zhao, Dongsheng·2021·2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
DOI 10.1109/bibm52615.2021.9669535
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Abstract

Given the huge amount of data from diverse sources and involving various conceptual fields in heterogeneous formats, researchers have encountered challenges in their effort to process, search for, and access knowledge about coronavirus disease 2019 (COVID-19). In this paper, we built COVID19-OBKG, an ontology-based knowledge graph and web service for COVID-19, to enable the access and retrieval of knowledge. First, we built the schema of COVID19-OBKG based on biomedical ontologies to guide the construction of the instance layer of COVID19-OBKG from top to bottom. Secondly, we collected data sources related to COVID-19, including structured databases and web pages. We acquired entities and relationships from data sources through named entity recognition and relation extraction algorithms and merged them with knowledge in biomedical ontologies. Thirdly, we modeled our data in the form of an attribute graph and stored it in Dgraph. Finally, we built a web service to support the retrieval and visualization of COVID19-OBKG, which verified the effectiveness of our approach to constructing a knowledge graph, and the usability of COVID19-OBKG.

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Research works printed but not linked: 31

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Metadata

Authors
Zheng, X., Xiao, Y., Song, W., Tong, F., Liu, S., & Zhao, D.
Affiliations
Academy of Military Medical Sciences (CN)
ORCID
Venue
2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Publisher
IEEE
Published
2021-12-09
Pages
2456-2462
DOI
10.1109/bibm52615.2021.9669535
OpenAlex
W4206171978
Cited by
3
References
1
Citations by year
2026: 1 · 2023: 1 · 2022: 1
FWCI
1.81
Type
conference paper
Language
en
Open access
closed
Licence
Locations
IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (publishedVersion)
Topics
  • Health Sciences › Medicine › Health Informatics › Biomedical Text Mining and Ontologies
  • Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
  • Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
Keywords
COVID-19 pandemic, knowledge graphs, biomedical ontologies, named entity recognition, relation extraction, entity relation extraction, knowledge graph construction, knowledge retrieval, knowledge visualization, web services, heterogeneous data integration

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CoChat. (2026). Citation check: COVID19-OBKG: An Ontology-Based Knowledge Graph and Web Service for COVID-19. https://app.cochat.ai/papers/10.1109/bibm52615.2021.9669535
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Zheng, X., Xiao, Y., Song, W., Tong, F., Liu, S., & Zhao, D. (2021). COVID19-OBKG: An Ontology-Based Knowledge Graph and Web Service for COVID-19. 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). https://doi.org/10.1109/bibm52615.2021.9669535