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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2007.00576 |
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| _version_ | 1866909644030476288 |
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| author | 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 Fung, Yi R. Ji, Heng Han, Jiawei Chang, Shih-Fu Pustejovsky, James Rah, Jasmine Liem, David Elsayed, Ahmed Palmer, Martha Voss, Clare Schneider, Cynthia Onyshkevych, Boyan |
| author_facet | 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 Fung, Yi R. Ji, Heng Han, Jiawei Chang, Shih-Fu Pustejovsky, James Rah, Jasmine Liem, David Elsayed, Ahmed Palmer, Martha Voss, Clare Schneider, Cynthia Onyshkevych, Boyan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2007_00576 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | 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 Fung, Yi R. Ji, Heng Han, Jiawei Chang, Shih-Fu Pustejovsky, James Rah, Jasmine Liem, David Elsayed, Ahmed Palmer, Martha Voss, Clare Schneider, Cynthia Onyshkevych, Boyan Computation and Language Artificial Intelligence 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. |
| title | COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2007.00576 |