# Template-Based Question Answering over Linked Geospatial Data

arXiv: https://arxiv.org/abs/2007.07060 · HTML: https://mlsanity.com/paper/2007.07060/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2020.

## Built on by (most cited first, 12 of 12)

- [RS5M and GeoRSCLIP: A Large Scale Vision-Language Dataset and A Large Vision-Language Model for Remote Sensing](https://mlsanity.com/paper/2306.11300/) (2023)
- [CityGPT: Empowering Urban Spatial Cognition of Large Language Models](https://mlsanity.com/paper/2406.13948/) (2024)
- [Geographic Question Answering: Challenges, Uniqueness, Classification, and Future Directions](https://mlsanity.com/paper/2105.09392/) (2021)
- [Towards General-Purpose Representation Learning of Polygonal Geometries](https://mlsanity.com/paper/2209.15458/) (2022)
- [MapQA: Open-domain Geospatial Question Answering on Map Data](https://mlsanity.com/paper/2503.07871/) (2025)
- [Poly2Vec: Polymorphic Fourier-Based Encoding of Geospatial Objects for GeoAI Applications](https://mlsanity.com/paper/2408.14806/) (2024)
- [Location Aware Modular Biencoder for Tourism Question Answering](https://mlsanity.com/paper/2401.02187/) (2024)
- [Semantic Parsing for Question Answering over Knowledge Graphs](https://mlsanity.com/paper/2401.06772/) (2023)
- [Measuring Linguistic Diversity During COVID-19](https://mlsanity.com/paper/2104.01290/) (2021)
- [Question Answering Over Spatio-Temporal Knowledge Graph](https://mlsanity.com/paper/2402.11542/) (2024)
- [EVGeoQA: Benchmarking LLMs on Dynamic, Multi-Objective Geo-Spatial Exploration](https://mlsanity.com/paper/2604.07070/) (2026)
- [Much of Geospatial Web Search Is Beyond Traditional GIS](https://mlsanity.com/paper/2605.11336/) (2026)

## Abstract (arXiv)

Large amounts of geospatial data have been made available recently on the linked open data cloud and the portals of many national cartographic agencies (e.g., OpenStreetMap data, administrative geographies of various countries, or land cover/land use data sets). These datasets use various geospatial vocabularies and can be queried using SPARQL or its OGC-standardized extension GeoSPARQL. In this paper, we go beyond these approaches to offer a question-answering engine for natural language questions on top of linked geospatial data sources. Our system has been implemented as re-usable components of the Frankenstein question answering architecture. We give a detailed description of the system's architecture, its underlying algorithms, and its evaluation using a set of 201 natural language questions. The set of questions is offered to the research community as a gold standard dataset for the comparative evaluation of future geospatial question answering engines.