Uplifting Air Quality Data Using Knowledge Graph

Air quality is one of the most important factors concerning the natural environment. Nowadays, advanced ICT technologies, e.g., sensors, allow to efficiently monitor air quality globally. Often sensor data is available on the Internet as Open Data, facilitating important research on how air quality affects human health. However, these online datasets usually have heterogeneous schemas, traditional tabular formats and are hard to interconnect with data from different domains. In this paper, we present how to transform sensor data from traditional tabular data to knowledge graphs, following FAIR Data Principles (Findable, Accessible, Interoperable, and Reusable). This allows data to become interoperable and semantically interlinked with other data sources. As a result, we show how air quality sensor data can be enriched and become machine-readable, so to positively impact research not only in air quality but also in other domains.