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In this paper, we present Knowledge4COVID-19, a framework that aims toshowcase the power of integrating disparate sources of knowledge to discoveradverse drug effects caused by drug-drug interactions among COVID-19 treatmentsand pre-existing condition drugs. Initially, we focus on constructing theKnowledge4COVID-19 knowledge graph (KG) from the declarative definition ofmapping rules using the RDF Mapping Language. Since valuable information aboutdrug treatments, drug-drug interactions, and side effects is present in textualdescriptions in scientific databases (e.g., DrugBank) or in scientificliterature (e.g., the CORD-19, the Covid-19 Open Research Dataset), theKnowledge4COVID-19 framework implements Natural Language Processing. TheKnowledge4COVID-19 framework extracts relevant entities and predicates thatenable the fine-grained description of COVID-19 treatments and the potentialadverse events that may occur when these treatments are combined withtreatments of common comorbidities, e.g., hypertension, diabetes, or asthma.Moreover, on top of the KG, several techniques for the discovery and predictionof interactions and potential adverse effects of drugs have been developed withthe aim of suggesting more accurate treatments for treating the virus. Weprovide services to traverse the KG and visualize the effects that a group ofdrugs may have on a treatment outcome. Knowledge4COVID-19 was part of thePan-European hackathon#EUvsVirus in April 2020 and is publicly available as aresource through a GitHub repository(https://github.com/SDM-TIB/Knowledge4COVID-19) and a DOI(https://zenodo.org/record/4701817#.YH336-8zbol).

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