RAPID: COVIDGeoGraph – A Geographically Integrated Cross-Domain Knowledge Graph for Studying Regional Disruptions

这一新冠肺炎快速研究项目将开发一个地理整合的知识图谱，以支持数据科学家、行业和政府决策者采取特定地区的步骤，以重新开放国家。为了实现这一目标，该项目将结合各种主题的数据，包括交通、社会距离衡量、人口和环境因素以及经济影响。知识图谱是一种语境化技术。它们通过提供来自邻近学科的可操作的见解，使用户能够对复杂的社会和科学问题有更全面的了解。例如，关于当地经济和食品系统的决策需要了解经常变化的社会距离措施、交通管制和限制，包括邻近地区、人口因素和康复公民的百分比。该项目将与行业合作伙伴合作，了解如何在他们的预测模型中利用图表知识。最后，该项目将延伸到其他知识图谱，共同形成一个开放的新冠肺炎相关知识网络。在技术上，我们将利用一系列开放源码技术和国际标准，开发一个高度集成的基于数据的链接知识图谱，将不同地理尺度和类型的地方的跨域数据结合在一起。我们将利用机器学习技术来学习图表嵌入，以预测我们图表中的链接，以及通过使用城市或县等地作为集成点来对齐到其他图表。我们还将提供与更广泛的schema.org工作协作的功能。我们尤其感兴趣的是研究如何表示和调整目前在不同地理比例和聚合下报告的新冠肺炎数据，从而促进互操作性和打破数据孤岛。我们将直接与我们的行业合作伙伴合作，从我们的和外部图表中开发工程(空间上明确的)功能。这些功能将被整合到行业下游预测模型中，特别关注食品系统。最后，我们将利用我们合作伙伴的专业知识创建可视化数据仪表板，以更好地传达我们的发现。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。

This COVID-19 RAPID research program will develop a geographically integrated knowledge graph to support data scientists and decision-makers in industry and the government in taking region-specific steps towards reopening the country. Towards this goal, the project will combine data from themes as diverse as transportation, social distancing measures, demographic and environmental factors, as well as economic impacts. Knowledge graphs are contextualization technologies. They enable their users to gain a more holistic understanding of complex social and scientific questions by providing actionable insights from neighboring disciplines. For instance, making decisions about local economies and food systems require an understanding of the frequently changing social distancing measures, traffic control and restrictions including neighboring regions, demographic factors, and the percentage of recovered citizens. The project will work together with industry partners to understand how graphed knowledge can be utilized in their forecasting models. Finally, the project will reach out to other knowledge graphs to jointly form an open knowledge network related to COVID-19. More technically, we will utilize a stack of open source technologies and international standards to develop a highly integrated Linked Data-based knowledge graph that combines cross-domain data across different geographic scales and types of places. We will utilize machine learning technologies to learn graph embeddings for predicting links within our graph as well as alignments to other graphs by using places such as cities or counties as points of integration. We will also provide the functionality to collaborate with the broader schema.org effort. We are particularly interested in studying how to represent and align COVID-19 data that is currently being reported at different geographic scales and aggregates, thereby fostering interoperability and breaking up data silos. We will work directly with our industry partners on engineering (spatially-explicit) features from our and external graphs. These features will be integrated into industry downstream forecasting models with a particular focus on food systems. Finally, We will utilize the expertise of our partners in creating visual data dashboards to better communicate our findings.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.