Geospatial Question Answering on Historical Maps Using Spatio-Temporal Knowledge Graphs and Large Language Models
- URL: http://arxiv.org/abs/2508.21491v1
- Date: Fri, 29 Aug 2025 10:16:37 GMT
- Title: Geospatial Question Answering on Historical Maps Using Spatio-Temporal Knowledge Graphs and Large Language Models
- Authors: Ziyi Liu, Sidi Wu, Lorenz Hurni,
- Abstract summary: One approach is question answering (QA), which allows users -- especially those unfamiliar languages -- to retrieve knowledge in a natural and intuitive manner.<n>We developed a GeoQA system by integrating atemporal knowledge graph (KG) constructed from historical map data with large language models.<n>Additional data sources, such as historical map images and internet search results are incorporated into our framework to provide extra context for GeoQA.
- Score: 4.25934967090365
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recent advances have enabled the extraction of vectorized features from digital historical maps. To fully leverage this information, however, the extracted features must be organized in a structured and meaningful way that supports efficient access and use. One promising approach is question answering (QA), which allows users -- especially those unfamiliar with database query languages -- to retrieve knowledge in a natural and intuitive manner. In this project, we developed a GeoQA system by integrating a spatio-temporal knowledge graph (KG) constructed from historical map data with large language models (LLMs). Specifically, we have defined the ontology to guide the construction of the spatio-temporal KG and investigated workflows of two different types of GeoQA: factual and descriptive. Additional data sources, such as historical map images and internet search results, are incorporated into our framework to provide extra context for descriptive GeoQA. Evaluation results demonstrate that the system can generate answers with a high delivery rate and a high semantic accuracy. To make the framework accessible, we further developed a web application that supports interactive querying and visualization.
Related papers
- CartoMapQA: A Fundamental Benchmark Dataset Evaluating Vision-Language Models on Cartographic Map Understanding [5.925837407110905]
We introduce CartoMapQA, a benchmark to evaluate Visual-Language Models' understanding of cartographic maps.<n>The dataset includes over 2000 samples, each composed of a cartographic map, a question (with open-ended or multiple-choice answers), and a ground-truth answer.
arXiv Detail & Related papers (2025-12-03T08:25:22Z) - Plan Then Retrieve: Reinforcement Learning-Guided Complex Reasoning over Knowledge Graphs [52.16166558205338]
Graph-RFT is a novel two-stage reinforcement fine-tuning KGQA framework with a 'plan-KGsearch-and-Websearch-during-think' paradigm.<n>It enables LLMs to perform autonomous planning and adaptive retrieval scheduling across KG and web sources under incomplete knowledge conditions.
arXiv Detail & Related papers (2025-10-23T16:04:13Z) - GeoRAG: A Question-Answering Approach from a Geographical Perspective [3.243241445980849]
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.
arXiv Detail & Related papers (2025-04-02T08:11:05Z) - MapQaTor: An Extensible Framework for Efficient Annotation of Map-Based QA Datasets [3.3856216159724983]
We introduce MapQaTor, an open-source framework that streamlines the creation of traceable map-based QA datasets.<n>MapQaTor enables seamless integration with any maps API, allowing users to gather and visualize data from diverse sources.
arXiv Detail & Related papers (2024-12-30T15:33:19Z) - Ontology-grounded Automatic Knowledge Graph Construction by LLM under Wikidata schema [60.42231674887294]
We propose an ontology-grounded approach to Knowledge Graph (KG) construction using Large Language Models (LLMs) on a knowledge base.<n>We ground generation of KG with the authored ontology based on extracted relations to ensure consistency and interpretability.<n>Our work presents a promising direction for scalable KG construction pipeline with minimal human intervention, that yields high quality and human-interpretable KGs.
arXiv Detail & Related papers (2024-12-30T13:36:05Z) - 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) - 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) - 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) - KILT: a Benchmark for Knowledge Intensive Language Tasks [102.33046195554886]
We present a benchmark for knowledge-intensive language tasks (KILT)
All tasks in KILT are grounded in the same snapshot of Wikipedia.
We find that a shared dense vector index coupled with a seq2seq model is a strong baseline.
arXiv Detail & Related papers (2020-09-04T15:32:19Z) - Semantically-Enriched Search Engine for Geoportals: A Case Study with
ArcGIS Online [7.005838154484841]
We propose a semantically-enriched search engine for geoportals using Lucene-based techniques.
A benchmark dataset is constructed to evaluate the proposed framework.
Our evaluation results show that the proposed semantic query expansion framework is very effective in capturing a user's search intention.
arXiv Detail & Related papers (2020-03-14T06:16:30Z)
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.