构建气候知识图谱，让科研人员一键查清模型与区域的验证关系。
Querying Climate Knowledge: Semantic Retrieval for Scientific Discovery
- 基于气候文献构建领域专用知识图谱，支持语义查询。
- 可精准回答模型在特定区域是否被验证等结构化问题。
- 适合气候研究者、模型开发者快速获取上下文准确信息。
气候科学文献的复杂性和体量不断增长，使研究人员难以在不同模型、数据集、区域和变量间高效查找相关信息。本文提出一个从气候出版物及更广泛科学文本中构建的领域专用知识图谱（KG），旨在提升气候知识的可访问性与可用性。与传统关键词搜索不同，该KG支持结构化语义查询，帮助研究者发现如‘哪些模型已在特定区域得到验证’或‘哪些数据集常与特定遥相关模式一起使用’等精确关联。我们通过Cypher查询展示了该图谱如何回答此类问题，并阐述其与大语言模型在RAG系统中的集成，以提升气候相关问答的透明度与可靠性。本工作不仅关注知识图谱构建，更强调其在真实世界中对气候研究者、模型开发者等群体的价值。
原文摘要 · Abstract (English)
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.
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