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Visual Understanding of COVID-19 Knowledge Graph for Predictive Analysis...

Publication Type
Conference Paper
Book Title
2021 IEEE International Conference on Big Data (Big Data).
Publication Date
Page Numbers
4381 to 4386
Publisher Location
New Jersey, United States of America
Conference Name
IEEE Big Data 2021 Workshop on Big Data Analytics for COVID-19
Conference Location
Orlanda, Florida, United States of America
Conference Sponsor
Conference Date

This study aims to effectively analyze and visualize the concept to concept network derived from the COVID-19 Open Research Dataset (CORD-19) dataset, where we have more than 48,000 concepts with more than 300,000 relationships between concepts. In analyzing networks, we focus on finding relationship patterns between the coronavirus disease 2019 (COVID-19) concepts and other concepts. Given the node and edge datasets, we construct directional graphs and calculate all pair shortest paths based on multiple edge weight schemes. However, statistical metrics are not sufficient to identify specific relationships represented in the network. Therefore, we also propose a visual analytics approach to effectively understand the knowledge graph. Our highly interactive visual analytics allows users to effectively analyze the evolving graphs and (COVID-19) concept nodes and other nodes related to the COVID-19 nodes. We envision that this study will pave the path to develop strategies to provide more accurate and scalable predictive analysis on knowledge graphs related to CORD19 and other biomedical knowledge graphs.