Knowledge4COVID-19

A semantic-based approach for constructing a COVID-19 related knowledge graph from various sources and analyzing treatments’ toxicities

verfasst von: Ahmad Sakor, Samaneh Jozashoori, Emetis Niazmand, Ariam Rivas, Konstantinos Bougiatiotis, Fotis Aisopos, Enrique Iglesias, Philipp D. Rohde, Trupti Padiya, Anastasia Krithara, Georgios Paliouras, Maria Esther Vidal
Abstract: In this paper, we present Knowledge4COVID-19, a framework that aims to showcase the power of integrating disparate sources of knowledge to discover adverse drug effects caused by drug–drug interactions among COVID-19 treatments and pre-existing condition drugs. Initially, we focus on constructing the Knowledge4COVID-19 knowledge graph (KG) from the declarative definition of mapping rules using the RDF Mapping Language. Since valuable information about drug treatments, drug–drug interactions, and side effects is present in textual descriptions in scientific databases (e.g., DrugBank) or in scientific literature (e.g., the CORD-19, the Covid-19 Open Research Dataset), the Knowledge4COVID-19 framework implements Natural Language Processing. The Knowledge4COVID-19 framework extracts relevant entities and predicates that enable the fine-grained description of COVID-19 treatments and the potential adverse events that may occur when these treatments are combined with treatments of common comorbidities, e.g., hypertension, diabetes, or asthma. Moreover, on top of the KG, several techniques for the discovery and prediction of interactions and potential adverse effects of drugs have been developed with the aim of suggesting more accurate treatments for treating the virus. We provide services to traverse the KG and visualize the effects that a group of drugs may have on a treatment outcome. Knowledge4COVID-19 was part of the Pan-European hackathon#EUvsVirus in April 2020 and is publicly available as a resource through a GitHub repository and a DOI.
Organisationseinheit(en): Forschungszentrum L3S
Externe Organisation(en): Technische Informationsbibliothek (TIB) Leibniz-Informationszentrum Technik und Naturwissenschaften und Universitätsbibliothek
National Centre For Scientific Research Demokritos (NCSR Demokritos)
University of Athens
Typ: Artikel
Journal: Journal of Web Semantics
Band: 75
Anzahl der Seiten: 22
ISSN: 1570-8268
Publikationsdatum: 01.2023
Publikationsstatus: Veröffentlicht
Peer-reviewed: Ja
ASJC Scopus Sachgebiete: Software, Mensch-Maschine-Interaktion, Computernetzwerke und -kommunikation
Ziele für nachhaltige Entwicklung: SDG 3 – Gute Gesundheit und Wohlergehen
Elektronische Version(en): https://doi.org/10.1016/j.websem.2022.100760 (Zugang: Geschlossen)
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9558693/ (Zugang: Offen)

BibTeX

@article{bc2a2fa5ef444efc925da792427709d8,
title = "Knowledge4COVID-19: A semantic-based approach for constructing a COVID-19 related knowledge graph from various sources and analyzing treatments{\textquoteright} toxicities",
abstract = "In this paper, we present Knowledge4COVID-19, a framework that aims to showcase the power of integrating disparate sources of knowledge to discover adverse drug effects caused by drug–drug interactions among COVID-19 treatments and pre-existing condition drugs. Initially, we focus on constructing the Knowledge4COVID-19 knowledge graph (KG) from the declarative definition of mapping rules using the RDF Mapping Language. Since valuable information about drug treatments, drug–drug interactions, and side effects is present in textual descriptions in scientific databases (e.g., DrugBank) or in scientific literature (e.g., the CORD-19, the Covid-19 Open Research Dataset), the Knowledge4COVID-19 framework implements Natural Language Processing. The Knowledge4COVID-19 framework extracts relevant entities and predicates that enable the fine-grained description of COVID-19 treatments and the potential adverse events that may occur when these treatments are combined with treatments of common comorbidities, e.g., hypertension, diabetes, or asthma. Moreover, on top of the KG, several techniques for the discovery and prediction of interactions and potential adverse effects of drugs have been developed with the aim of suggesting more accurate treatments for treating the virus. We provide services to traverse the KG and visualize the effects that a group of drugs may have on a treatment outcome. Knowledge4COVID-19 was part of the Pan-European hackathon#EUvsVirus in April 2020 and is publicly available as a resource through a GitHub repository and a DOI.",
keywords = "COVID-19, Drug–drug interactions, Knowledge graphs",
author = "Ahmad Sakor and Samaneh Jozashoori and Emetis Niazmand and Ariam Rivas and Konstantinos Bougiatiotis and Fotis Aisopos and Enrique Iglesias and Rohde, {Philipp D.} and Trupti Padiya and Anastasia Krithara and Georgios Paliouras and Vidal, {Maria Esther}",
note = "Funding Information: This work has been supported by the EU H2020 RIA funded projects iASiS with grant agreement No. 727658 , P4-LUCAT with GA No. 53000015 , BigMedilytics with GA No. 780495 , CLARIFY with GA No. 875160 , and Federal Ministry for Economic Affairs and Energy of Germany in the project CoyPu ( No 01MK21007[A-L] ). Maria-Esther Vidal is also partially supported by Leibniz Association in the program “Leibniz Best Minds: Programme for Women Professors”, project TrustKG-Transforming Data in Trustable Insights with grant P99/2020. Knowledge4COVID-19 was originally a project of the Pan-European hackathon #EUvsVirus in April 2020. ",
year = "2023",
month = jan,
doi = "10.1016/j.websem.2022.100760",
language = "English",
volume = "75",
journal = "Journal of Web Semantics",
issn = "1570-8268",
publisher = "Elsevier",
}