GeoRAG: A Question-Answering Approach from a Geographical Perspective

• URL: http://arxiv.org/abs/2504.01458v2
• Date: Thu, 03 Apr 2025 02:36:26 GMT
• Title: GeoRAG: A Question-Answering Approach from a Geographical Perspective
• Authors: Jian Wang, Zhuo Zhao, Zeng Jie Wang, Bo Da Cheng, Lei Nie, Wen Luo, Zhao Yuan Yu, Ling Wang Yuan,
• Abstract summary: Geographic Question Answering (GeoQA) addresses natural language queries in geographic domains.<n>Traditional QA systems suffer from limited comprehension, low retrieval accuracy, weak interactivity, and inadequate handling of complex tasks.<n>This study presents GeoRAG, a knowledge-enhanced QA framework integrating domain-specific fine-tuning and prompt engineering.
• Score: 3.243241445980849
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• OmniGeo: Towards a Multimodal Large Language Models for Geospatial Artificial Intelligence [51.0456395687016]
  multimodal large language models (LLMs) have opened new frontiers in artificial intelligence. We propose a MLLM (OmniGeo) tailored to geospatial applications. By combining the strengths of natural language understanding and spatial reasoning, our model enhances the ability of instruction following and the accuracy of GeoAI systems.
  arXiv  Detail & Related papers  (2025-03-20T16:45:48Z)
• Towards a Barrier-free GeoQA Portal: Natural Language Interaction with Geospatial Data Using Multi-Agent LLMs and Semantic Search [2.9658923973538034]
  We propose a GeoQA Portal using a multi-agent Large Language Model framework for seamless natural language interaction with geospatial data.<n>Case studies, evaluations, and user tests confirm its effectiveness for non-experts, bridging GIS complexity and public access.
  arXiv  Detail & Related papers  (2025-03-18T13:39:46Z)
• Geo-Semantic-Parsing: AI-powered geoparsing by traversing semantic knowledge graphs [0.7422344184734279]
  We introduce a novel geoparsing and geotagging technique called Geo-Semantic-Parsing (GSP)<n>GSP identifies location references in free text and extracts the corresponding geographic coordinates.<n>We evaluate GSP on a well-known reference dataset including almost 10k event-related tweets.
  arXiv  Detail & Related papers  (2025-03-03T10:30:23Z)
• Geolocation with Real Human Gameplay Data: A Large-Scale Dataset and Human-Like Reasoning Framework [59.42946541163632]
  We introduce a comprehensive geolocation framework with three key components. GeoComp, a large-scale dataset; GeoCoT, a novel reasoning method; and GeoEval, an evaluation metric. We demonstrate that GeoCoT significantly boosts geolocation accuracy by up to 25% while enhancing interpretability.
  arXiv  Detail & Related papers  (2025-02-19T14:21:25Z)
• Geo-FuB: A Method for Constructing an Operator-Function Knowledge Base for Geospatial Code Generation Tasks Using Large Language Models [0.5242869847419834]
  This study introduces a framework to construct such a knowledge base, leveraging geospatial script semantics. An example knowledge base, Geo-FuB, built from 154,075 Google Earth Engine scripts, is available on GitHub.
  arXiv  Detail & Related papers  (2024-10-28T12:50:27Z)
• Swarm Intelligence in Geo-Localization: A Multi-Agent Large Vision-Language Model Collaborative Framework [51.26566634946208]
  We introduce smileGeo, a novel visual geo-localization framework. By inter-agent communication, smileGeo integrates the inherent knowledge of these agents with additional retrieved information. Results show that our approach significantly outperforms current state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-08-21T03:31:30Z)
• Geode: A Zero-shot Geospatial Question-Answering Agent with Explicit Reasoning and Precise Spatio-Temporal Retrieval [0.0]
  We introduce a pioneering system designed to tackle zero-shot geospatial question-answering tasks with high precision. Our approach represents a significant improvement in addressing the limitations of current large language models.
  arXiv  Detail & Related papers  (2024-06-26T21:59:54Z)
• Geo-Encoder: A Chunk-Argument Bi-Encoder Framework for Chinese Geographic Re-Ranking [61.60169764507917]
  Chinese geographic re-ranking task aims to find the most relevant addresses among retrieved candidates. We propose an innovative framework, namely Geo-Encoder, to more effectively integrate Chinese geographical semantics into re-ranking pipelines.
  arXiv  Detail & Related papers  (2023-09-04T13:44:50Z)
• GeoGLUE: A GeoGraphic Language Understanding Evaluation Benchmark [56.08664336835741]
  We propose a GeoGraphic Language Understanding Evaluation benchmark, named GeoGLUE. We collect data from open-released geographic resources and introduce six natural language understanding tasks. We pro vide evaluation experiments and analysis of general baselines, indicating the effectiveness and significance of the GeoGLUE benchmark.
  arXiv  Detail & Related papers  (2023-05-11T03:21:56Z)
• MGeo: Multi-Modal Geographic Pre-Training Method [49.78466122982627]
  We propose a novel query-POI matching method Multi-modal Geographic language model (MGeo) MGeo represents GC as a new modality and is able to fully extract multi-modal correlations for accurate query-POI matching. Our proposed multi-modal pre-training method can significantly improve the query-POI matching capability of generic PTMs.
  arXiv  Detail & Related papers  (2023-01-11T03:05:12Z)
• EQG-RACE: Examination-Type Question Generation [21.17100754955864]
  We propose an innovative Examination-type Question Generation approach (EQG-RACE) to generate exam-like questions based on a dataset extracted from RACE. Two main strategies are employed in EQG-RACE for dealing with discrete answer information and reasoning among long contexts. Experimental results show a state-of-the-art performance of EQG-RACE, which is apparently superior to the baselines.
  arXiv  Detail & Related papers  (2020-12-11T03:52:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.