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...

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Bibliographic Details
Published in2021 Photonics & Electromagnetics Research Symposium (PIERS) pp. 2347 - 2350
Main Authors Wu, Jiantao, Orlandi, Fabrizio, Gollini, Isabella, Pisoni, Enrico, Dev, Soumyabrata
Format Conference Proceeding
LanguageEnglish
Published IEEE 21.11.2021
Subjects
Air quality
Electromagnetics
Internet
Monitoring
Open data
Soft sensors
Transforms
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Summary: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.
ISSN:2694-5053
DOI:10.1109/PIERS53385.2021.9695102