Recurrent Multi-task Graph Convolutional Networks for COVID-19 Knowledge Graph Link Prediction
21st Smoky Mountains Computational Sciences and Engineering Conference, SMC 2021
; 1512 CCIS:411-419, 2022.
Article
in English
| Scopus | ID: covidwho-1777654
ABSTRACT
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. © 2022, Springer Nature Switzerland AG.
COVID-19 knowledge graph; Link prediction; Multi-task learning; Recurrent graph convolutional networks; Convolution; Convolutional neural networks; Domain Knowledge; Forecasting; Learning systems; Natural language processing systems; Convolutional networks; Domain-specific knowledge; Knowledge graphs; Modeling data; Multi tasks; Oak ridge National Laboratory; Recurrent graph convolutional network; Tasks graph; Knowledge graph
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
21st Smoky Mountains Computational Sciences and Engineering Conference, SMC 2021
Year:
2022
Document Type:
Article
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