I-Corps: A Trustworthy, Interactive, Up to Date COVID-19 Knowledge Graph

这个i-Corps项目的更广泛的影响/商业潜力是开发一个互动的、易于使用的知识图谱，其中包含来自新冠肺炎上最新发布的医学发现的可靠信息。容易地获得经过审查的医学发现可能会激励人们做出明智的决定，这有望带来更好的健康实践。这个解决方案可能会拯救美国和世界各地的许多人的生命。此外，这个知识图谱可以扩展到其他疾病，并为衰老、癌症、心血管疾病、糖尿病等创建可访问、易于使用、值得信赖的医疗实践。此i-Corps项目基于交互式知识图谱(KG)的开发，其中填充了来自新冠肺炎上最新发布的医学发现的可信信息。目前，现有的社交维护的KG缺乏新冠肺炎的医学发现和可扩展的机制来保持图表的最新。提出的解决方案包括设计和评估新的可伸缩算法和抽象。该技术将COVID症状和可能的疫苗副作用合并到具有不同结构和元数据的非关系表中。该小组已经构建了图表的初步“骨架”，并提议自动处理最近出版物中的表格，以丰富KG。虽然大多数医疗表是复杂的，表现出分层的垂直/水平元数据，但这项技术通过提供新的、可伸缩的混合图、结合新的抽象来处理复杂的表格数据、开发具有新的2D表格嵌入层的多层深度/机器学习网络以及设计新的图形和医疗表搜索引擎来解决基本挑战。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this I-Corps project is the development of an interactive, easy to use knowledge graph populated with trustworthy information from the latest published medical findings on COVID-19. Easy access to the vetted medical findings may motivate people to make informed decisions, which is expected to lead to better health practices. The solution may save many lives in the US and worldwide. Additionally, this knowledge graph can extend to other diseases and create accessible, easy to use, trustworthy medical practices for aging, cancer, cardiovascular diseases, diabetes, etc.This I-Corps project is based on the development of an interactive Knowledge Graph (KG) populated with trustworthy information from the latest published medical findings on COVID-19. Currently existing, socially maintained KGs lack COVID-19 medical findings and scalable mechanisms to keep the graphs up to date. The proposed solution includes the design and evaluation of new scalable algorithms and abstractions. The technology incorporates COVID symptoms and possible vaccine side-effects in non-relational tables having different structures and metadata. The team has constructed the initial “skeleton” of the graph and proposes to automatically process tables from recent publications in order to enrich the KG. While most medical tables are complex, exhibiting hierarchical vertical/horizontal metadata, this technology addresses the fundamental challenges by providing a novel, scalable hybrid graph, incorporating new abstractions to handle complex tabular data, developing a multi-layer deep-/machine-Learning network with new 2D tabular embedding layer, and designing a new search engine for graph and medical tables.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.