COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation
This addresses the need for clinicians and scientists to efficiently access and utilize vast COVID-19 literature, though it appears incremental as it builds on existing knowledge graph methods applied to a new domain.
The authors tackled the problem of extracting and organizing biomedical knowledge from COVID-19 literature by developing COVID-KG, a framework that constructs multimedia knowledge graphs and uses them for tasks like drug repurposing, with results including detailed evidence such as contextual sentences and subfigures.
To combat COVID-19, both clinicians and scientists need to digest vast amounts of relevant biomedical knowledge in scientific literature to understand the disease mechanism and related biological functions. We have developed a novel and comprehensive knowledge discovery framework, COVID-KG to extract fine-grained multimedia knowledge elements (entities and their visual chemical structures, relations, and events) from scientific literature. We then exploit the constructed multimedia knowledge graphs (KGs) for question answering and report generation, using drug repurposing as a case study. Our framework also provides detailed contextual sentences, subfigures, and knowledge subgraphs as evidence.