# Querying Climate Knowledge: Semantic Retrieval for Scientific Discovery

arXiv: https://arxiv.org/abs/2509.10087 · HTML: https://mlsanity.com/paper/2509.10087/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 42 references that are in the map.
- First posted on arXiv 12 Sep 2025.

## Builds on (most cited first, 10 of 10)

- [From Local to Global: A Graph RAG Approach to Query-Focused Summarization](https://mlsanity.com/paper/2404.16130/) (2024)
- [Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task](https://mlsanity.com/paper/1809.08887/) (2018)
- [Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answering](https://mlsanity.com/paper/2404.17723/) (2024)
- [Knowledge Graphs: Opportunities and Challenges](https://mlsanity.com/paper/2303.13948/) (2023)
- [Graph-constrained Reasoning: Faithful Reasoning on Knowledge Graphs with Large Language Models](https://mlsanity.com/paper/2410.13080/) (2024)
- [SciER: An Entity and Relation Extraction Dataset for Datasets, Methods, and Tasks in Scientific Documents](https://mlsanity.com/paper/2410.21155/) (2024)
- [Link Climate: An Interoperable Knowledge Graph Platform for Climate Data](https://mlsanity.com/paper/2210.16050/) (2022)
- [A Knowledge Graph-Based Method for Automating Systematic Literature Reviews](https://mlsanity.com/paper/2208.02334/) (2022)
- [DynClean: Training Dynamics-based Label Cleaning for Distantly-Supervised Named Entity Recognition](https://mlsanity.com/paper/2504.04616/) (2025)
- [Comprehensive Review on Semantic Information Retrieval and Ontology Engineering](https://mlsanity.com/paper/2307.13427/) (2023)

## Abstract (arXiv)

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.