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 of 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)