An interactive knowledge graph based platform for covid-19 clinical research
WSDM 2022 - Proceedings of the 15th ACM International Conference on Web Search and Data Mining
Since the first identified case of COVID-19 in December 2019, a plethora of pharmaceuticals and therapeutics have been tested for COVID-19 treatment. While medical advancements and breakthroughs are well underway, the sheer number of studies, treatments, and associated reports makes it extremely challenging to keep track of the rapidly growing COVID-19 research landscape. While existing scientific literature search systems provide basic document retrieval, they fundamentally lack the ability to explore data, and in addition, do not help develop a deeper understanding of COVID-19 related clinical experiments and findings. As research expands, results do so as well, resulting in a position that is complicated and overwhelming. To address this issue, we present a named entity recognition based framework that accurately extracts COVID-19 related information from clinical test results articles, and generates an efficient and interactive visual knowledge graph. This knowledge graph platform is user friendly, and provides intuitive and convenient tools to explore and analyze COVID-19 research data and results including medicinal performances, side effects and target populations.
Su, J., Dougherty, E., Jiang, S., & Jin, F. (2022). An interactive knowledge graph based platform for covid-19 clinical research. WSDM 2022 - Proceedings of the 15th ACM International Conference on Web Search and Data Mining, 1609-1612. https://doi.org/10.1145/3488560.3502193