论文标题
冠状病毒知识图：案例研究
Coronavirus Knowledge Graph: A Case Study
论文作者
论文摘要
在过去的几个月中，新颖的Covid-19- 19日大流行对全球医疗保健和经济产生了重大影响。该病毒的快速广泛广泛导致生物医学研究的扩散，以解决大流行及其相关主题。可以帮助生物医学研究社区理解并最终找到Covid-19的治疗方法的基本知识发现工具之一是知识图。 CORD-19数据集是最近在Covid-19和Coronavirus主题上发表的公开全文研究文章的集合。在这里，我们使用几种机器学习，深度学习和知识图构造和采矿技术来形式化和从PubMed数据集和Cord-19数据集中提取见解，以识别COVID-19的相关专家和生物本质。此外，我们建议可能的技术来预测相关疾病，候选药物，基因，基因突变和相关化合物，这是采用知识发现方法的系统努力的一部分，以帮助生物医学研究人员解决大流行。
The emergence of the novel COVID-19 pandemic has had a significant impact on global healthcare and the economy over the past few months. The virus's rapid widespread has led to a proliferation in biomedical research addressing the pandemic and its related topics. One of the essential Knowledge Discovery tools that could help the biomedical research community understand and eventually find a cure for COVID-19 are Knowledge Graphs. The CORD-19 dataset is a collection of publicly available full-text research articles that have been recently published on COVID-19 and coronavirus topics. Here, we use several Machine Learning, Deep Learning, and Knowledge Graph construction and mining techniques to formalize and extract insights from the PubMed dataset and the CORD-19 dataset to identify COVID-19 related experts and bio-entities. Besides, we suggest possible techniques to predict related diseases, drug candidates, gene, gene mutations, and related compounds as part of a systematic effort to apply Knowledge Discovery methods to help biomedical researchers tackle the pandemic.
