COVID-19 Ontologies and their Application in Medical Sciences: Reviewing BioPortal

Yousefianzadeh, Omid; Taheri, Abolfazl

doi:10.18502/ahit.v1i1.5259

Cited by 4 publications

(3 citation statements)

References 8 publications

(8 reference statements)

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“…From a biomedical perspective, Yousefianzadeh et al [ 15 ] explored ontologies in the field of COVID-19 published in a medical ontology database called BioPortal [ 16 ]. Coronavirus Infectious Disease Ontology (CIDO) has been developed as a community-based ontology that supports coronavirus disease knowledge and data standardization, integration, sharing, and analysis [ 17 ].…”

Section: Related Workmentioning

confidence: 99%

CIRO: COVID-19 infection risk ontology

Egami

Yamamoto²,

Ohmukai³

et al. 2023

PLoS ONE

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Public health authorities perform contact tracing for highly contagious agents to identify close contacts with the infected cases. However, during the pandemic caused by coronavirus disease 2019 (COVID-19), this operation was not employed in countries with high patient volumes. Meanwhile, the Japanese government conducted this operation, thereby contributing to the control of infections, at the cost of arduous manual labor by public health officials. To ease the burden of the officials, this study attempted to automate the assessment of each person’s infection risk through an ontology, called COVID-19 Infection Risk Ontology (CIRO). This ontology expresses infection risks of COVID-19 formulated by the Japanese government, toward automated assessment of infection risks of individuals, using Resource Description Framework (RDF) and SPARQL (SPARQL Protocol and RDF Query Language) queries. For evaluation, we demonstrated that the knowledge graph built could infer the risks, formulated by the government. Moreover, we conducted reasoning experiments to analyze the computational efficiency. The experiments demonstrated usefulness of the knowledge processing, and identified issues left for deployment.

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Section: Related Workmentioning

confidence: 99%

CIRO: COVID-19 infection risk ontology

Egami

Yamamoto²,

Ohmukai³

et al. 2023

PLoS ONE

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“…According to FAIR principles, several COVID-19 ontologies offer robustly supported data integration, sharing, reproducibility, and computer-assisted data analysis [ 22 ], indicating that all research data should be findable, accessible, interoperability and reusable [ 24 ]. These include CIDO ontology that brings together various models to represent aspects such as similarity to other viruses, common symptoms and drugs that have been attempted to treat the virus, etc.…”

Section: Related Workmentioning

confidence: 99%

Ontological engineering for the definition of a COVID-19 pandemic ontology

González-Eras

Santos

Aguilar

et al. 2022

Informatics in Medicine Unlocked

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COVID-19 has generated a lot of information in different formats, and one of them is in the ontology format. Also, there are previous ontologies from other disciplines that can help to analyze the COVID-19 pandemic. Thus, due to the large quantity of COVID-19 information in the form of ontologies, approaches to ontology integration and interoperability could be beneficial. In this context, this research proposes a new ontology, called COVID-19 Pandemic ontology, which is the product of an ontological engineering process proposed in this research that allows the integration of several ontologies to cover all the aspects of this infectious disease. The ontological engineering process defines tasks of fusion, alignment, and linking for integrating the ontologies. The resulting pandemic ontology provides a simple repository for storing information about the COVID-19, reusing existing ontologies, to offer multiple views about the disease, including the social context. This ontology has been tested in different case studies to prove its capabilities to infer useful information about the COVID-19 pandemic.

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“…Sinha and Dutta (2020) proposed a set of general parameters for reviewing ontologies and reviewed flood ontologies based on those parameters. Recently, Yousefianzadeh and Taheri (2020) compared the available existing COVID-19 ontologies, which were available in BioPortal (https://bioportal.bioontology.org/), to explore their specific application in medical sciences.…”

Section: State-of-the-art Workmentioning

confidence: 99%

A review of data mining ontologies

Sinha

Gajbe

Debnath

et al. 2021

DTA

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PurposeThis work provides a generic review of the existing data mining ontologies (DMOs) and also provides a base platform for ontology developers and researchers for gauging the ontologies for satisfactory coverage and usage.Design/methodology/approachThe study uses a systematic literature review approach to identify 35 DMOs in the domain between the years 2003 and 2021. Various parameters, like purpose, design methodology, operations used, language representation, etc. are available in the literature to review ontologies. Accompanying the existing parameters, a few parameters, like semantic reasoner used, knowledge representation formalism was added and a list of 20 parameters was prepared. It was then segregated into two groups as generic parameters and core parameters to review DMOs.FindingsIt was observed that among the 35 papers under the study, 26 papers were published between the years 2006 and 2016. Larisa Soldatova, Saso Dzeroski and Pance Panov were the most productive authors of these DMO-related publications. The ontological review indicated that most of the DMOs were domain and task ontologies. Majority of ontologies were formal, modular and represented using web ontology language (OWL). The data revealed that Ontology development 101, METHONTOLOGY was the preferred design methodology, and application-based approaches were preferred for evaluation. It was also observed that around eight ontologies were accessible, and among them, three were available in ontology libraries as well. The most reused ontologies were OntoDM, BFO, OBO-RO, OBI, IAO, OntoDT, SWO and DMOP. The most preferred ontology editor was Protégé, whereas the most used semantic reasoner was Pellet. Even ontology metrics for 16 DMOs were also available.Originality/valueThis paper carries out a basic level review of DMOs employing a parametric approach, which makes this study the first of a kind for the review of DMOs.

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COVID-19 Ontologies and their Application in Medical Sciences: Reviewing BioPortal

Cited by 4 publications

References 8 publications

CIRO: COVID-19 infection risk ontology

CIRO: COVID-19 infection risk ontology

Ontological engineering for the definition of a COVID-19 pandemic ontology

A review of data mining ontologies

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