COVID-Forecast-Graph: An Open Knowledge Graph for Consolidating COVID-19 Forecasts and Economic Indicators via Place and Time

Abstract

Abstract. The longer the COVID-19 pandemic lasts, the more apparent it becomes that understanding its social drivers may be as important as understanding the virus itself. One such social driver is misinformation and distrust in institutions. This is particularly interesting as the scientific process is more transparent than ever before. Numerous scientific teams have published datasets that cover almost any imaginable aspects of COVID-19 during the last two years. However, consistently and efficiently integrating and making sense of these separate data “silos” to scientists, decision makers, journalists, and more importantly the general public remain a key challenge with important implications for transparency. Several types of knowledge graphs have been published to tackle this issue and to enable data crosswalks by providing rich contextual information. Interestingly, none of these graphs has focused on COVID-19 forecasts despite them acting as the underpinning for decision making. In this work we motivate the need for exposing forecasts as a knowledge graph, showcase queries that run against the graph, and geographically interlink forecasts with indicators of economic impacts.

Symbolic and subsymbolicGeoAI: Geospatial knowledge graphs and spatially explicit machine learning

“…It posits the use of declarative knowledge in reasoning and learning as critical to producing intelligent behavior (Goel, 2022). Examples are logical inference, symbolic reasoning, ontology engineering (Allemang & Hendler, 2011; Battle & Kolas, 2011; Hobbs & Pan, 2006; Hu et al, 2013; Janowicz et al, 2019), and in part knowledge graphs (KGs) (Hoffart et al, 2013; Janowicz et al, 2022; Noy et al, 2019; Singhal, 2012; Zhu, Janowicz, Mai, et al, 2022). In contrast, Connectionist AI advocates the idea of explaining intelligence using artificial neural networks (van Eijck & Visser, 2012).…”

EVKG: An interlinked and interoperable electric vehicle knowledge graph for smart transportation system

“…As a novel data paradigm, KGs are a combination of technologies, terminologies, and data cultures for densely interconnecting (Web‐scale) data across domains in a human and machine‐readable format (Bizer et al, 2009; Janowicz et al, 2022). With an ontology, or so‐called KG schema, to encode the terminology semantically, KGs also foster interoperability across different domains (Hitzler, 2021; Zhu, Janowicz, Mai, et al, 2022). Today, open KGs such as DBpedia (Auer et al, 2007), and Wikidata (Vrandečić, 2012; Vrandečić & Krötzsch, 2014) are considered valuable assets for exploiting a broad scope of cross‐domain linked data.…”