COVIDKG.ORG - a Web-scale COVID-19 Interactive, Trustworthy Knowledge Graph, Constructed and Interrogated for Bias using Deep-Learning
COVIDKG.ORG - a Web-scale COVID-19 Interactive, Trustworthy Knowledge Graph, Constructed and Interrogated for Bias using Deep-Learning
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DOI：
10.48786/edbt.2023.63
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Bhimesh Kandibedala;A. Pyayt;Nick Piraino;Chris Caballero;M. Gubanov
中科院分区：
文献类型：
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作者：
Bhimesh Kandibedala;A. Pyayt;Nick Piraino;Chris Caballero;M. Gubanov
我们描述了一个网络规模的交互式知识图（KG），其中填充了来自最新发布的有关 COVID-19 的医学发现的可信信息。目前现有的、社会维护的知识图谱（例如 YAGO 或 DBPedia）或更专业的医学本体（例如 NCBI、病毒和 COVID-19 相关知识）很快就会过时，缺乏任何最新的 COVID-19 医学发现 - 最重要的是缺乏任何可扩展的机制来保持最新。在这里，我们描述了 COVIDKG.ORG - 一个在线、交互式、值得信赖的 COVID-19 网络规模知识图和几个高级搜索引擎。其内容是从最新的医学研究中提取和更新的。因此，它不会受到任何偏见或错误信息的影响，通常主导公共信息来源。
We describe a Web-scale interactive Knowledge Graph (KG) , populated with trustworthy information from the latest published medical findings on COVID-19. Currently existing, socially maintained KGs, such as YAGO or DBPedia or more specialized medical ontologies, such as NCBI, Virus-, and COVID-19-related are getting stale very quickly, lack any latest COVID-19 medical findings - most importantly lack any scalable mechanism to keep them up to date. Here we describe COVIDKG.ORG - an online, interactive, trust-worthy COVID-19 Web-scale Knowledge Graph and several advanced search-engines. Its content is extracted and updated from the latest medical research. Because of that it does not suffer from any bias or misinformation, often dominating public information sources.