Querying Climate Knowledge: Semantic Retrieval for Scientific Discovery
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866918139855372288 |
|---|---|
| author | Adamu, Mustapha Zhang, Qi Pan, Huitong Latecki, Longin Jan Dragut, Eduard C. |
| author_facet | Adamu, Mustapha Zhang, Qi Pan, Huitong Latecki, Longin Jan Dragut, Eduard C. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_10087 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Querying Climate Knowledge: Semantic Retrieval for Scientific Discovery Adamu, Mustapha Zhang, Qi Pan, Huitong Latecki, Longin Jan Dragut, Eduard C. Computation and Language 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. |
| title | Querying Climate Knowledge: Semantic Retrieval for Scientific Discovery |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2509.10087 |