基于地理空间知识图谱的网络弹性测度--以US多商品流网络为例
Measuring Network Resilience via Geospatial Knowledge Graph: a Case Study of the US Multi-Commodity Flow Network
摘要：
量化粮食系统的复原力对粮食安全问题很重要。在这项工作中，我们提出了一个地理空间知识图谱（GeoKG）为基础的方法来衡量多商品流网络的弹性。具体而言，我们开发了一个CFS-GeoKG本体来全面描述多商品流网络的地理空间语义，并设计了弹性度量，用于测量食品供应链中单源、远距离或非相邻供应商/客户的节点级和网络级依赖性。我们进行了一个案例研究，美国国家一级的农业多商品流网络与层次商品类型。结果表明，通过利用GeoKG，我们的方法支持跨空间和时间测量节点级和网络级的弹性，也有助于发现农业资源在不同地理尺度的空间网络中的集中模式。
英文摘要：
Quantifying the resilience in the food system is important for food security issues. In this work, we present a geospatial knowledge graph (GeoKG)-based method for measuring the resilience of a multi-commodity flow network. Specifically, we develop a CFS-GeoKG ontology to describe geospatial semantics of a multi-commodity flow network comprehensively, and design resilience metrics that measure the node-level and network-level dependence of single-sourcing, distant, or non-adjacent suppliers/customers in food supply chains. We conduct a case study of the US state-level agricultural multi-commodity flow network with hierarchical commodity types. The results indicate that, by leveraging GeoKG, our method supports measuring both node-level and network-level resilience across space and over time and also helps discover concentration patterns of agricultural resources in the spatial network at different geographic scales.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
