Querying Climate Knowledge: Semantic Retrieval for Scientific Discovery
This addresses the difficulty for climate researchers, model developers, and others in accessing accurate, contextual scientific information across models, datasets, regions, and variables.
This paper tackles the problem of finding relevant information in the complex and voluminous climate science literature by introducing a domain-specific Knowledge Graph (KG) built from climate publications and broader scientific texts, which supports structured, semantic queries to help researchers discover precise connections like model validations or dataset usage patterns.
The growing complexity and volume of climate science literature make it increasingly difficult for researchers to find relevant information across models, datasets, regions, and variables. This paper introduces a domain-specific Knowledge Graph (KG) built from climate publications and broader scientific texts, aimed at improving how climate knowledge is accessed and used. Unlike keyword based search, our KG supports structured, semantic queries that help researchers discover precise connections such as which models have been validated in specific regions or which datasets are commonly used with certain teleconnection patterns. We demonstrate how the KG answers such questions using Cypher queries, and outline its integration with large language models in RAG systems to improve transparency and reliability in climate-related question answering. This work moves beyond KG construction to show its real world value for climate researchers, model developers, and others who rely on accurate, contextual scientific information.