Coronavirus Knowledge Graph: A Case Study

• URL: http://arxiv.org/abs/2007.10287v1
• Date: Sat, 4 Jul 2020 03:55:31 GMT
• Title: Coronavirus Knowledge Graph: A Case Study
• Authors: Chongyan Chen, Islam Akef Ebeid, Yi Bu and Ying Ding
• Abstract summary: We use several Machine Learning, Deep Learning, and Knowledge Graph construction and mining techniques to identify COVID-19 related experts and bio-entities. We suggest possible techniques to predict related diseases, drug candidates, gene, gene mutations, and related compounds.
• Score: 4.646516629534201
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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