COVID-19 Knowledge Graph: Accelerating Information Retrieval and Discovery for Scientific Literature

Colby Wise, Miguel Romero Calvo, Pariminder Bhatia, Vassilis Ioannidis, George Karypus, George Price, Xiang Song, Ryan Brand, Ninad Kulkani


Abstract
The coronavirus disease (COVID-19) has claimed the lives of over one million people and infected more than thirty-five million people worldwide. Several search engines have surfaced to provide researchers with additional tools to find and retrieve information from the rapidly growing corpora on COVID19. These engines lack extraction and visualization tools necessary to retrieve and interpret complex relations inherent to scientific literature. Moreover, because these engines mainly rely upon semantic information, their ability to capture complex global relationships across documents is limited, which reduces the quality of similarity-based article recommendations for users. In this work, we present the COVID-19 Knowledge Graph (CKG), a heterogeneous graph for extracting and visualizing complex relationships between COVID-19 scientific articles. The CKG combines semantic information with document topological information for the application of similar document retrieval. The CKG is constructed using the latent schema of the data, and then enriched with biomedical entity information extracted from the unstructured text of articles using scalable AWS technologies to form relations in the graph. Finally, we propose a document similarity engine that leverages low-dimensional graph embeddings from the CKG with semantic embeddings for similar article retrieval. Analysis demonstrates the quality of relationships in the CKG and shows that it can be used to uncover meaningful information in COVID-19 scientific articles. The CKG helps power www.cord19.aws and is publicly available.
Anthology ID:
2020.knlp-1.1
Volume:
Proceedings of Knowledgeable NLP: the First Workshop on Integrating Structured Knowledge and Neural Networks for NLP
Month:
December
Year:
2020
Address:
Suzhou, China
Editors:
Oren Sar Shalom, Alexander Panchenko, Cicero dos Santos, Varvara Logacheva, Alessandro Moschitti, Ido Dagan
Venue:
knlp
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–10
Language:
URL:
https://aclanthology.org/2020.knlp-1.1
DOI:
Bibkey:
Cite (ACL):
Colby Wise, Miguel Romero Calvo, Pariminder Bhatia, Vassilis Ioannidis, George Karypus, George Price, Xiang Song, Ryan Brand, and Ninad Kulkani. 2020. COVID-19 Knowledge Graph: Accelerating Information Retrieval and Discovery for Scientific Literature. In Proceedings of Knowledgeable NLP: the First Workshop on Integrating Structured Knowledge and Neural Networks for NLP, pages 1–10, Suzhou, China. Association for Computational Linguistics.
Cite (Informal):
COVID-19 Knowledge Graph: Accelerating Information Retrieval and Discovery for Scientific Literature (Wise et al., knlp 2020)
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PDF:
https://aclanthology.org/2020.knlp-1.1.pdf
Data
CORD-19