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The Climate Change Knowledge Graph: Supporting Climate Services

Miguel Ceriani, Fiorela Ciroku, Alessandro Russo, Massimiliano Schembri, Fai Fung, Neha Mittal, Vito Trianni, Andrea Giovanni Nuzzolese

TL;DR

The Climate Change Knowledge Graph (KG) addresses fragmentation in climate data by integrating diverse data sources, metadata, and vocabularies into a coherent, interoperable graph to enable complex queries across climate models, simulations, and outputs. The authors adopt Extreme Design (XD), a three-step data handling workflow ( cleansing, RDF mapping with RDF Mapping Language, consolidation via SPARQL updates ), and a three-module ontology network (top-level Common Terms, Core Climate Services Ontology, and data) to represent climate models, simulations, variables, and datasets, including CF/CMIP/CORDEX content. Implementation is OWL-based with RML mappings, tested via Protégé, deployed in a Dockerized stack (GraphDB, LodView, Nginx), and evaluated through competency-question verification demonstrating ontology coverage and queryability. The KG, open under CC BY 4.0 and containing roughly 14 million triples across 13 named graphs, supports climate services by providing a shared vocabulary and interoperable access to climate projections, with future work on formalizing climate-service provisioning processes. This work advances practical data exploration and decision support for climate adaptation and mitigation by enabling precise, scalable querying across multiple data sources and spatio-temporal granularities.

Abstract

Climate change impacts a broad spectrum of human resources and activities, necessitating the use of climate models to project long-term effects and inform mitigation and adaptation strategies. These models generate multiple datasets by running simulations across various scenarios and configurations, thereby covering a range of potential future outcomes. Currently, researchers rely on traditional search interfaces and APIs to retrieve such datasets, often piecing together information from metadata and community vocabularies. The Climate Change Knowledge Graph is designed to address these challenges by integrating diverse data sources related to climate simulations into a coherent and interoperable knowledge graph. This innovative resource allows for executing complex queries involving climate models, simulations, variables, spatio-temporal domains, and granularities. Developed with input from domain experts, the knowledge graph and its underlying ontology are published with open access license and provide a comprehensive framework that enhances the exploration of climate data, facilitating more informed decision-making in addressing climate change issues.

The Climate Change Knowledge Graph: Supporting Climate Services

TL;DR

The Climate Change Knowledge Graph (KG) addresses fragmentation in climate data by integrating diverse data sources, metadata, and vocabularies into a coherent, interoperable graph to enable complex queries across climate models, simulations, and outputs. The authors adopt Extreme Design (XD), a three-step data handling workflow ( cleansing, RDF mapping with RDF Mapping Language, consolidation via SPARQL updates ), and a three-module ontology network (top-level Common Terms, Core Climate Services Ontology, and data) to represent climate models, simulations, variables, and datasets, including CF/CMIP/CORDEX content. Implementation is OWL-based with RML mappings, tested via Protégé, deployed in a Dockerized stack (GraphDB, LodView, Nginx), and evaluated through competency-question verification demonstrating ontology coverage and queryability. The KG, open under CC BY 4.0 and containing roughly 14 million triples across 13 named graphs, supports climate services by providing a shared vocabulary and interoperable access to climate projections, with future work on formalizing climate-service provisioning processes. This work advances practical data exploration and decision support for climate adaptation and mitigation by enabling precise, scalable querying across multiple data sources and spatio-temporal granularities.

Abstract

Climate change impacts a broad spectrum of human resources and activities, necessitating the use of climate models to project long-term effects and inform mitigation and adaptation strategies. These models generate multiple datasets by running simulations across various scenarios and configurations, thereby covering a range of potential future outcomes. Currently, researchers rely on traditional search interfaces and APIs to retrieve such datasets, often piecing together information from metadata and community vocabularies. The Climate Change Knowledge Graph is designed to address these challenges by integrating diverse data sources related to climate simulations into a coherent and interoperable knowledge graph. This innovative resource allows for executing complex queries involving climate models, simulations, variables, spatio-temporal domains, and granularities. Developed with input from domain experts, the knowledge graph and its underlying ontology are published with open access license and provide a comprehensive framework that enhances the exploration of climate data, facilitating more informed decision-making in addressing climate change issues.
Paper Structure (32 sections, 5 figures, 1 table)

This paper contains 32 sections, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. Ontology Design
  5. Incorporating Existing Knowledge
  6. Scope and Requirements
  7. Climate projections
  8. Climate service
  9. The HACID project
  10. Competency Questions
  11. Data Sources
  12. Resource: the Knowledge Graph
  13. Ontology Network
  14. Common Terms (top-level).
  15. Climate Models.
  16. ...and 17 more sections

Figures (5)

  • Figure 1: Process adopted to integrate existing data-sources into the KG
  • Figure 2: Graffoo diagram depicting the part of ontology related to climate simulations.
  • Figure 3: Graffoo diagram depicting the part of ontology related to variables.
  • Figure 4: Example SPARQL query to filter simulations (prefix declarations omitted for brevity)
  • Figure 5: Example SPARQL query to filter output datasets (prefix declarations omitted for brevity)