Coronavirus Knowledge Graph: A Case Study
This work aims to help biomedical researchers understand and find cures for COVID-19 by applying knowledge discovery methods to pandemic-related data, but it is incremental as it builds on existing techniques for a new domain.
The authors tackled the problem of organizing and extracting insights from COVID-19 research data by constructing a knowledge graph from the CORD-19 dataset, using machine learning and deep learning techniques to identify experts, bio-entities, and predict related diseases, drugs, genes, mutations, and compounds.
The emergence of the novel COVID-19 pandemic has had a significant impact on global healthcare and the economy over the past few months. The virus's rapid widespread has led to a proliferation in biomedical research addressing the pandemic and its related topics. One of the essential Knowledge Discovery tools that could help the biomedical research community understand and eventually find a cure for COVID-19 are Knowledge Graphs. The CORD-19 dataset is a collection of publicly available full-text research articles that have been recently published on COVID-19 and coronavirus topics. Here, we use several Machine Learning, Deep Learning, and Knowledge Graph construction and mining techniques to formalize and extract insights from the PubMed dataset and the CORD-19 dataset to identify COVID-19 related experts and bio-entities. Besides, we suggest possible techniques to predict related diseases, drug candidates, gene, gene mutations, and related compounds as part of a systematic effort to apply Knowledge Discovery methods to help biomedical researchers tackle the pandemic.