COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation
Abstract
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.
Keywords
Cite
@article{arxiv.2007.00576,
title = {COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation},
author = {Qingyun Wang and Manling Li and Xuan Wang and Nikolaus Parulian and Guangxing Han and Jiawei Ma and Jingxuan Tu and Ying Lin and Haoran Zhang and Weili Liu and Aabhas Chauhan and Yingjun Guan and Bangzheng Li and Ruisong Li and Xiangchen Song and Yi R. Fung and Heng Ji and Jiawei Han and Shih-Fu Chang and James Pustejovsky and Jasmine Rah and David Liem and Ahmed Elsayed and Martha Palmer and Clare Voss and Cynthia Schneider and Boyan Onyshkevych},
journal= {arXiv preprint arXiv:2007.00576},
year = {2025}
}
Comments
12 pages, Accepted by Proceedings of 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics System Demonstrations, for resources see http://blender.cs.illinois.edu/covid19/, for video see http://159.89.180.81/demo/covid/Covid-KG_DemoVideo.mp4, for slides see https://eaglew.github.io/files/Covid-KG_DemoVideo_with_ethics.pdf