Knowledge4COVID-19: A Semantic-based Approach for Constructing a COVID-19 related Knowledge Graph from Various Sources and Analysing Treatments' Toxicities

TL;DR

Knowledge4COVID-19 is a framework that constructs a comprehensive COVID-19 knowledge graph from diverse sources, integrating NLP and semantic techniques to analyze drug interactions and predict adverse effects for improved treatment strategies.

Contribution

It introduces a novel semantic-based framework that combines knowledge graph construction, NLP extraction, and interaction prediction specifically for COVID-19 treatments.

Findings

Successfully integrated multiple data sources into a COVID-19 knowledge graph.

Developed techniques for predicting drug-drug interactions and adverse effects.

Provided visualization tools for exploring treatment impacts.

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 (https://github.com/SDM-TIB/Knowledge4COVID-19) and a DOI (https://zenodo.org/record/4701817#.YH336-8zbol).