GeoRAG: A Question-Answering Approach from a Geographical Perspective

TL;DR

GeoRAG introduces a knowledge-enhanced question-answering framework tailored for geographic domains, combining domain-specific knowledge bases, classification, retrieval, and prompt engineering to improve accuracy and interactivity.

Contribution

It presents a novel GeoRAG framework integrating geographic knowledge, multi-label classification, and dynamic prompting, advancing domain-specific QA capabilities.

Findings

GeoRAG outperforms traditional RAG models in geographic QA tasks.

The knowledge base contains over 145,000 entries and 875,000 QA pairs.

Experiments validate GeoRAG's superior retrieval accuracy and response quality.

Abstract

Geographic Question Answering (GeoQA) addresses natural language queries in geographic domains to fulfill complex user demands and improve information retrieval efficiency. Traditional QA systems, however, suffer from limited comprehension, low retrieval accuracy, weak interactivity, and inadequate handling of complex tasks, hindering precise information acquisition. This study presents GeoRAG, a knowledge-enhanced QA framework integrating domain-specific fine-tuning and prompt engineering with Retrieval-Augmented Generation (RAG) technology to enhance geographic knowledge retrieval accuracy and user interaction. The methodology involves four components: (1) A structured geographic knowledge base constructed from 3267 corpora (research papers, monographs, and technical reports), categorized via a multi-agent approach into seven dimensions: semantic understanding, spatial location, geometric morphology, attribute characteristics, feature relationships, evolutionary processes, and operational mechanisms. This yielded 145234 classified entries and 875432 multi-dimensional QA pairs. (2) A multi-label text classifier based on BERT-Base-Chinese, trained to analyze query types through geographic dimension classification. (3) A retrieval evaluator leveraging QA pair data to assess query-document relevance, optimizing retrieval precision. (4) GeoPrompt templates engineered to dynamically integrate user queries with retrieved information, enhancing response quality through dimension-specific prompting. Comparative experiments demonstrate GeoRAG's superior performance over conventional RAG across multiple base models, validating its generalizability. This work advances geographic AI by proposing a novel paradigm for deploying large language models in domain-specific contexts, with implications for improving GeoQA systems scalability and accuracy in real-world applications.