TY - GEN
T1 - An interactive knowledge graph based platform for covid-19 clinical research
AU - Su, Juntao
AU - Dougherty, Edward T.
AU - Jiang, Shuang
AU - Jin, Fang
N1 - Publisher Copyright:
© 2022 ACM.
PY - 2022/2/11
Y1 - 2022/2/11
N2 - 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.
AB - 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.
KW - Clinical results
KW - Covid-19
KW - Knowledge graph
KW - Named entity recognition
UR - http://www.scopus.com/inward/record.url?scp=85125794164&partnerID=8YFLogxK
U2 - 10.1145/3488560.3502193
DO - 10.1145/3488560.3502193
M3 - Conference contribution
AN - SCOPUS:85125794164
T3 - WSDM 2022 - Proceedings of the 15th ACM International Conference on Web Search and Data Mining
SP - 1609
EP - 1612
BT - WSDM 2022 - Proceedings of the 15th ACM International Conference on Web Search and Data Mining
PB - Association for Computing Machinery, Inc
T2 - 15th ACM International Conference on Web Search and Data Mining, WSDM 2022
Y2 - 21 February 2022 through 25 February 2022
ER -