COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation
Qingyun Wang, Manling Li, Xuan Wang, Nikolaus Parulian, Guangxing Han, Jiawei Ma, Jingxuan Tu, Ying Lin
Abstract
To combat COVID-19, both clinicians and scientists need to digest the vast amount of relevant biomedical knowledge in literature to understand the disease mechanism and the related biological functions. We have developed a novel and comprehensive knowledge discovery framework, COVID-KG to extract fine-grained multimedia knowledge elements (entities, relations and events) from scientific literature. We then exploit the constructed multimedia knowledge graphs (KGs) for question answering and report generation, using drug repurposing as a case study. Our framework also provides detailed contextual sentences, subfigures, and knowledge subgraphs as evidence. All of the data, KGs, reports.
BibTeX
@inproceedings{wang-etal-2021-covid,
title = "{COVID}-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation",
author = "Wang, Qingyun and
Li, Manling and
Wang, Xuan and
Parulian, Nikolaus and
Han, Guangxing and
Ma, Jiawei and
Tu, Jingxuan and
Lin, Ying and
Zhang, Ranran Haoran and
Liu, Weili and
Chauhan, Aabhas and
Guan, Yingjun and
Li, Bangzheng and
Li, Ruisong and
Song, Xiangchen and
Fung, Yi and
Ji, Heng and
Han, Jiawei and
Chang, Shih-Fu and
Pustejovsky, James and
Rah, Jasmine and
Liem, David and
ELsayed, Ahmed and
Palmer, Martha and
Voss, Clare and
Schneider, Cynthia and
Onyshkevych, Boyan",
editor = "Sil, Avi and
Lin, Xi Victoria",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-demos.8/",
doi = "10.18653/v1/2021.naacl-demos.8",
pages = "66--77"
}