COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation

Wang, Qingyun, Li, Manling, Wang, Xuan, Parulian, Nikolaus, Han, Guangxing, Ma, Jiawei, Tu, Jingxuan, Lin, Ying, Zhang, Haoran, Liu, Weili, Chauhan, Aabhas, Guan, Yingjun, Li, Bangzheng, Li, Ruisong, Song, Xiangchen, Ji, Heng, Han, Jiawei, Chang, Shih-Fu, Pustejovsky, James, Rah, Jasmine, Liem, David, Elsayed, Ahmed, Palmer, Martha, Voss, Clare, Schneider, Cynthia, Onyshkevych, Boyan

arXiv.org Artificial Intelligence 

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, \textbf{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, resources and shared services are publicly available.

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