TY - GEN
T1 - COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation
AU - Wang, Qingyun
AU - Li, Manling
AU - Wang, Xuan
AU - Parulian, Nikolaus
AU - Han, Guangxing
AU - Ma, Jiawei
AU - Tu, Jingxuan
AU - Lin, Ying
AU - Zhang, Haoran
AU - Liu, Weili
AU - Chauhan, Aabhas
AU - Guan, Yingjun
AU - Li, Bangzheng
AU - Li, Ruisong
AU - Song, Xiangchen
AU - Fung, Yi R.
AU - Ji, Heng
AU - Han, Jiawei
AU - Chang, Shih Fu
AU - Pustejovsky, James
AU - Rah, Jasmine
AU - Liem, David
AU - Elsayed, Ahmed
AU - Palmer, Martha
AU - Voss, Clare
AU - Schneider, Cynthia
AU - Onyshkevych, Boyan
N1 - Funding Information:
This research is based upon work supported in part by U.S. DARPA KAIROS Program No. FA8750-19-2-1004, U.S. DARPA AIDA Program # FA8750-18-2-0014, S. DTRA HDTRA I -16-1-0002/Project #1553695, eTASC - Empirical Evidence for a Theoretical Approach to Semantic Components, U.S. NSF No. 1741634, the Office of the Director of National Intelligence (ODNI), and Intelligence Advanced Research Projects Activity (IARPA) via contract FA8650-17-C-9116. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of DARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein.
Funding Information:
This research is based upon work supported in part by U.S. DARPA KAIROS Program No. FA8750-19-2-1004, U.S. DARPA AIDA Program # FA8750-18-2-0014, .S. DTRA HDTRA I -16-1-0002/Project #1553695, eTASC - Empirical Ev-idence for a Theoretical Approach to Semantic Components, U.S. NSF No. 1741634, the Office of the Director of National Intelligence (ODNI), and Intelligence Advanced Research Projects Ac-tivity (IARPA) via contract FA8650-17-C-9116. The views and conclusions contained herein are 73
Publisher Copyright:
© 2021 Association for Computational Linguistics.
PY - 2021
Y1 - 2021
N2 - To combat COVID-19, both clinicians and scientists need to digest vast amounts of relevant biomedical knowledge in scientific literature to understand the disease mechanism and related biological functions. We have developed a novel and comprehensive knowledge discovery framework, COVID-KG to extract fine-grained multimedia knowledge elements (entities and their visual chemical structures, 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.
AB - To combat COVID-19, both clinicians and scientists need to digest vast amounts of relevant biomedical knowledge in scientific literature to understand the disease mechanism and related biological functions. We have developed a novel and comprehensive knowledge discovery framework, COVID-KG to extract fine-grained multimedia knowledge elements (entities and their visual chemical structures, 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.
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M3 - Conference contribution
AN - SCOPUS:85122303251
T3 - NAACL-HLT 2021 - 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Demonstrations
SP - 66
EP - 77
BT - NAACL-HLT 2021 - 2021 Conference of the North American Chapter of the Association for Computational Linguistics
PB - Association for Computational Linguistics (ACL)
T2 - 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2021
Y2 - 6 June 2021 through 11 June 2021
ER -