ArticleBioinformatics (Oxford, England)2021
COVID-19 Knowledge Graph from semantic integration of biomedical literature and databases.
Article in Bioinformatics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Graph databases in systems biology: a systematic review.Briefings in bioinformatics · 2024Pooled it
- Knowledge Graph-Driven AI in Biohealth: From Biomedical Discovery to Health Risk Prediction.Delaware journal of public health · 2026Article
- Overview and Prospects of DNA Sequence Visualization.International journal of molecular sciences · 2025Review
- Petagraph: A large-scale unifying knowledge graph framework for integrating biomolecular and biomedical data.Scientific data · 2024Article
- PubTator 3.0: an AI-powered literature resource for unlocking biomedical knowledge.Nucleic acids research · 2024Article
- Searching COVID-19 Clinical Research Using Graph Queries: Algorithm Development and Validation.Journal of medical Internet research · 2024Article
- A scalable tool for analyzing genomic variants of humans using knowledge graphs and graph machine learning.Frontiers in big data · 2024Article
- Lessons learned to enable question answering on knowledge graphs extracted from scientific publications: A case study on the coronavirus literature.Journal of biomedical informatics · 2023Article
- Linked Argumentation Graphs for Multidisciplinary Decision Support.Healthcare (Basel, Switzerland) · 2023Article
- Knowledge Graph: Applications in Tracing the Source of Large-Scale Outbreak - Beijing Municipality, China, 2020-2021.China CDC weekly · 2023Article
- Graph data science and machine learning for the detection of COVID-19 infection from symptoms.PeerJ. Computer science · 2023Article
- Analysis of the evolution of COVID-19 disease understanding through temporal knowledge graphs.Frontiers in research metrics and analytics · 2023Article
- Novel graph-based machine-learning technique for viral infectious diseases: application to influenza and hepatitis diseases.Annals of medicine · 2023Article
- Semantic interoperability: ontological unpacking of a viral conceptual model.BMC bioinformatics · 2022Article
- AnthraxKP: a knowledge graph-based, Anthrax Knowledge Portal mined from biomedical literature.Database : the journal of biological databases and curation · 2022Article
- CoV2K model, a comprehensive representation of SARS-CoV-2 knowledge and data interplay.Scientific data · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
summaryThe global response to the COVID-19 pandemic has led to a rapid increase of scientific literature on this deadly disease. Extracting knowledge from biomedical literature and integrating it with relevant information from curated biological databases is essential to gain insight into COVID-19 etiology, diagnosis and treatment. We used Semantic Web technology RDF to integrate COVID-19 knowledge mined from literature by iTextMine, PubTator and SemRep with relevant biological databases and formalized the knowledge in a standardized and computable COVID-19 Knowledge Graph (KG). We published the COVID-19 KG via a SPARQL endpoint to support federated queries on the Semantic Web and developed a knowledge portal with browsing and searching interfaces. We also developed a RESTful API to support programmatic access and provided RDF dumps for download. AVAILABILITY AND IMPLEMENTATION: The COVID-19 Knowledge Graph is publicly available under CC-BY 4.0 license at https://research.bioinformatics.udel.edu/covid19kg/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.