TY - GEN
T1 - Recurrent Multi-task Graph Convolutional Networks for COVID-19 Knowledge Graph Link Prediction
AU - Kim, Remington
AU - Ning, Yue
N1 - Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Knowledge graphs (KGs) are a way to model data involving intricate relations between a number of entities. Understanding the information contained in KGs and predicting what hidden relations may be present can provide valuable domain-specific knowledge. Thus, we use data provided by the 5th Annual Oak Ridge National Laboratory Smoky Mountains Computational Sciences Data Challenge 2 as well as auxiliary textual data processed with natural language processing techniques to form and analyze a COVID-19 KG of biomedical concepts and research papers. Moreover, we propose a recurrent graph convolutional network model that predicts both the existence of novel links between concepts in this COVID-19 KG and the time at which the link will form. We demonstrate our model’s promising performance against several baseline models. The utilization of our work can give insights that are useful in COVID-19-related fields such as drug development and public health. All code for our paper is publicly available at https://github.com/RemingtonKim/SMCDC2021.
AB - Knowledge graphs (KGs) are a way to model data involving intricate relations between a number of entities. Understanding the information contained in KGs and predicting what hidden relations may be present can provide valuable domain-specific knowledge. Thus, we use data provided by the 5th Annual Oak Ridge National Laboratory Smoky Mountains Computational Sciences Data Challenge 2 as well as auxiliary textual data processed with natural language processing techniques to form and analyze a COVID-19 KG of biomedical concepts and research papers. Moreover, we propose a recurrent graph convolutional network model that predicts both the existence of novel links between concepts in this COVID-19 KG and the time at which the link will form. We demonstrate our model’s promising performance against several baseline models. The utilization of our work can give insights that are useful in COVID-19-related fields such as drug development and public health. All code for our paper is publicly available at https://github.com/RemingtonKim/SMCDC2021.
KW - COVID-19 knowledge graph
KW - Link prediction
KW - Multi-task learning
KW - Recurrent graph convolutional networks
UR - http://www.scopus.com/inward/record.url?scp=85127065654&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85127065654&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-96498-6_24
DO - 10.1007/978-3-030-96498-6_24
M3 - Conference contribution
AN - SCOPUS:85127065654
SN - 9783030964979
T3 - Communications in Computer and Information Science
SP - 411
EP - 419
BT - Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation - 21st Smoky Mountains Computational Sciences and Engineering, SMC 2021, Revised Selected Papers
A2 - Nichols, [given-name]Jeffrey
A2 - Maccabe, [given-name]Arthur ‘Barney’
A2 - Nutaro, James
A2 - Pophale, Swaroop
A2 - Devineni, Pravallika
A2 - Ahearn, Theresa
A2 - Verastegui, Becky
T2 - 21st Smoky Mountains Computational Sciences and Engineering Conference, SMC 2021
Y2 - 18 October 2021 through 20 October 2021
ER -