# Automated Climate Analyses Using Knowledge Graph

arXiv: https://arxiv.org/abs/2110.11039 · HTML: https://mlsanity.com/paper/2110.11039/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2021.

## Built on by (most cited first, 2 of 2)

- [Knowledge Graphs: The Future of Data Integration and Insightful Discovery](https://mlsanity.com/paper/2502.15689/) (2024)
- [A semantic web approach to uplift decentralized household energy data](https://mlsanity.com/paper/2208.10265/) (2022)

## Abstract (arXiv)

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.