COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation
COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation
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DOI：
10.18653/v1/2021.naacl-demos.8
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
Qingyun Wang;Manling Li;Xuan Wang;Nikolaus Nova Parulian;G. Han;Jiawei Ma;Jingxuan Tu;Ying Lin;H. Zhang;Weili Liu;Aabhas Chauhan;Yingjun Guan;Bangzheng Li;Ruisong Li;Xiangchen Song;Heng Ji;Jiawei Han;Shih-Fu Chang;J. Pustejovsky;D. Liem;Ahmed Elsayed;Martha Palmer;Jasmine Rah;Cynthia Schneider;Boyan A. Onyshkevych
中科院分区：
文献类型：
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作者：
Qingyun Wang;Manling Li;Xuan Wang;Nikolaus Nova Parulian;G. Han;Jiawei Ma;Jingxuan Tu;Ying Lin;H. Zhang;Weili Liu;Aabhas Chauhan;Yingjun Guan;Bangzheng Li;Ruisong Li;Xiangchen Song;Heng Ji;Jiawei Han;Shih-Fu Chang;J. Pustejovsky;D. Liem;Ahmed Elsayed;Martha Palmer;Jasmine Rah;Cynthia Schneider;Boyan A. Onyshkevych
为了对抗COVID-19，临床医生和科学家都需要消化文献中大量的相关生物医学知识，以了解疾病机制和相关生物功能。我们开发了一个新颖而全面的知识发现框架COVID-KG，用于从科学文献中提取细粒度的多媒体知识元素（实体，关系和事件）。然后，我们利用构建的多媒体知识图（KG）的问题回答和报告生成，使用药物再利用作为案例研究。我们的框架还提供了详细的上下文句子，子图和知识子图作为证据。所有的数据，KG，报告。
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.