Automated Climate Analyses Using Knowledge Graph

TL;DR

This paper presents a method to model climate data as knowledge graphs using RDF and Semantic Web technologies, enabling automated analytics and direct querying via SPARQL for improved climate research.

Contribution

It introduces a novel Climate Analysis ontology and demonstrates how heterogeneous climate data can be made FAIR and queried as Linked Data.

Findings

Climate data modeled as knowledge graphs enhances data interoperability.

SPARQL endpoint enables automated climate data analysis.

Use cases show practical advantages of knowledge graph representation.

Abstract

The FAIR (Findable, Accessible, Interoperable, Reusable) data principles are fundamental for climate researchers and all stakeholders in the current digital ecosystem. In this paper, we demonstrate how relational climate data can be "FAIR" and modeled using RDF, in line with Semantic Web technologies and our Climate Analysis ontology. Thus, heterogeneous climate data can be stored in graph databases and offered as Linked Data on the Web. As a result, climate researchers will be able to use the standard SPARQL query language to query these sources directly on the Web. In this paper, we demonstrate the usefulness of our SPARQL endpoint for automated climate analytics. We illustrate two sample use cases that establish the advantage of representing climate data as knowledge graphs.