ArticlePeerJ. Computer science2022
Using logical constraints to validate statistical information about disease outbreaks in collaborative knowledge graphs: the case of COVID-19 epidemiology in Wikidata.
Article in PeerJ. Computer science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
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Who cites it
6 citing papers in PubMed.
- A review on knowledge graphs for healthcare: Resources, applications, and promises.Journal of biomedical informatics · 2025Review
- A framework for integrating biomedical knowledge in Wikidata with open biological and biomedical ontologies and MeSH keywords.Heliyon · 2024Article
- MeSH2Matrix: combining MeSH keywords and machine learning for biomedical relation classification based on PubMed.Journal of biomedical semantics · 2024Article
- Ten quick tips for editing Wikidata.PLoS computational biology · 2023Article
- Editorial: Linked open bibliographic data for real-time research assessment.Frontiers in research metrics and analytics · 2023Article
- Enhancing Knowledge Graph Extraction and Validation From Scholarly Publications Using Bibliographic Metadata.Frontiers in research metrics and analytics · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Urgent global research demands real-time dissemination of precise data. Wikidata, a collaborative and openly licensed knowledge graph available in RDF format, provides an ideal forum for exchanging structured data that can be verified and consolidated using validation schemas and bot edits. In this research article, we catalog an automatable task set necessary to assess and validate the portion of Wikidata relating to the COVID-19 epidemiology. These tasks assess statistical data and are implemented in SPARQL, a query language for semantic databases. We demonstrate the efficiency of our methods for evaluating structured non-relational information on COVID-19 in Wikidata, and its applicability in collaborative ontologies and knowledge graphs more broadly. We show the advantages and limitations of our proposed approach by comparing it to the features of other methods for the validation of linked web data as revealed by previous research.
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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.