2021
NAACL
NAACL 2021
COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation
Abstract
AbstractTo 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.
👥
Mega-Team
— 27 authors
🌉
Interdisciplinary Bridge
— Knowledge & Reasoning and Natural Language Processing
🐝
Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Qingyun Wang
,
Manling Li
,
Xuan Wang
,
Nikolaus Parulian
,
Guangxing Han
,
Jiawei Ma
,
Jingxuan Tu
,
Ying Lin
,
Ranran Haoran Zhang
,
Weili Liu
,
Aabhas Chauhan
,
Yingjun Guan
,
Bangzheng Li
,
Ruisong Li
,
Xiangchen Song
,
Yi Fung
,
Heng Ji
,
Jiawei Han
,
Shih-fu Chang
,
James Pustejovsky
,
Jasmine Rah
,
David Liem
,
Ahmed Elsayed
,
Martha Palmer
,
Clare Voss
,
Cynthia Schneider
,
Boyan Onyshkevych