The COVID-19 Ontology

Abstract

Motivation The COVID-19 pandemic has prompted an impressive, worldwide response by the academic community. In order to support text mining approaches as well as data description, linking and harmonization in the context of COVID-19, we have developed an ontology representing major novel coronavirus (SARS-CoV-2) entities. The ontology has a strong scope on chemical entities suited for drug repurposing, as this is a major target of ongoing COVID-19 therapeutic development. Results The ontology comprises 2270 classes of concepts and 38 987 axioms (2622 logical axioms and 2434 declaration axioms). It depicts the roles of molecular and cellular entities in virus-host interactions and in the virus life cycle, as well as a wide spectrum of medical and epidemiological concepts linked to COVID-19. The performance of the ontology has been tested on Medline and the COVID-19 corpus provided by the Allen Institute. Availabilityand implementation COVID-19 Ontology is released under a Creative Commons 4.0 License and shared via https://github.com/covid-19-ontology/covid-19. The ontology is also deposited in BioPortal at https://bioportal.bioontology.org/ontologies/COVID-19. Supplementary information Supplementary data are available at Bioinformatics online.

Curating, collecting, and cataloguing global COVID-19 datasets for the aim of predicting personalized risk

“…However, a global standard data model that comprises all COVID-19 related terms is not available as of now. Therefore, we have used three controlled vocabularies to establish the interoperability and mapping towards various datasets and data points, namely, German Corona Consensus Dataset (GECCO) [18] , the COVID-19 Ontology [19] and the Corona Virus Vocabulary (COVOC) (https://github.com/EBISPOT/covoc/raw/master/covoc.owl).…”

Enhancing Knowledge Graph Extraction and Validation From Scholarly Publications Using Bibliographic Metadata

“…This is particularly true for the domain of STEM (Science, Technology, Engineering, and Mathematics) since it features, compared to arts and humanities as well as social sciences, an agreed-upon vocabulary which mostly addresses directly its subjects. Semantic similarity measures are driven by external knowledge resources like knowledge graphs and ontologies and can consequently compare brief texts with high accuracy and speed (Hadj Taieb et al, 2015) and full transparency (Turki et al, 2021b) by contrast to other advanced techniques applied to full texts, particularly deep learning, semantic embeddings (Sargsyan et al, 2020), TF-IDF 1 (White, 2018), and Latent Dirichlet Allocation (Jeong et al, 2014).…”

“…The status of a research publication can be also important for ensuring the quality of the extraction of scientific knowledge. Although bibliographic databases like PubMed 2 and features like Crossmark 3 state whether a publication is a preprint or a partially or fully retracted paper, most of the projects for the creation and validation of knowledge graphs do not consider this factor when retrieving facts from research papers (Sargsyan et al, 2020). The matter with considering preprints in information retrieval is that these publications have not undergone peer review (Glasziou et al, 2020) and their outputs can be dynamically changed over months (Oikonomidi et al, 2020).…”

“…Fully structured semantic resources representing facts in the form of triples (i.e., knowledge graphs) have a major function in driving computer applications, particularly the ones related to biomedicine, to library and information science and to digital humanities (Haslhofer et al, 2018;Sargsyan et al, 2020). They can be easily processed using Application Programming Interfaces (APIs, like REST APIs) and query languages (mainly SPARQL) to assess the reference semantic information and to generate accurate and precise interpretations and predictions, particularly when the analyzed data is multifactorial and ever-changing such as the COVID-19 knowledge (Turki et al, 2021c), information about the laureates of Nobel Prize in Literature (Lebuda and Karwowski, 2016), and the findings of scholarly publications (Fathalla et al, 2017).…”

CIDO-COVID-19: An Ontology for COVID-19 Based on CIDO

“…CIDO mainly focuses on common terms of the coronavirus category, and it can be applied to discovery of coronavirus pathogenic factors and development of therapeutic drugs. COVID-19 Ontology [6] is a domain ontology for COVID-19, which mainly describes the role of molecules and cells in the virus-host interaction and virus life cycle. It aims to provide support for drug development and repurposing of COVID-19.…”