  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.
