Join the ERC-funded GeoTrAnsQData project and explore hybrid AI approaches to better understand, structure and formalise geo-analytical questions. This helps shape the future of geospatial reasoning and map-based knowledge discovery.
Your jobGeographic questions like 'What is the potential for reducing urban heat in Amsterdam by installing green roofs on existing buildings?' are important in fields such as urban planning, sustainability, and public health. Answering such a question requires the transformation of maps combining different suitable geodata sources, including heat sources and building layouts to generate an answer map. This problem is called indirect question answering, and it is not straightforward with current GeoQA tools. In such scenarios, maps must be created or transformed from other maps.
The ERC funded project GeoTrAnsQData project addresses this by developing a GeoQA method that converts questions into executable geo-analytical workflows, turning geodata into new answer maps accordingly. We use knowledge graphs to model these transformations and apply AI methods to scale them up across large map repositories, enabling users to explore many ways maps can be reused to answer different kinds of questions. This PhD position focuses on understanding, interpreting, parsing and formalising geo-analytical questions. You will explore hybrid (symbolic and sub-symbolic) AI approaches to help users formulate and translate natural language questions into structured representations that can be linked to geospatial data sources and workflows.
You will contribute to the design of the linguistic and conceptual interface between natural language questions and formal workflow models over a geodata repository. In this project, you will:
- build and annotate a corpus of geo-analytical questions and their associated purposes, data needs, and analytical steps in geo-analytical standard scenarios;
- develop a model of geo-analytical purposes (transformation requests) and a corresponding question grammar;
- perform a user study on geo-analytical question formulation to express such purposes;
- contribute to the formalisation of spatial question types using a purpose-driven taxonomy;
- develop a hybrid question parsing pipeline using NLP and formal semantic representations; investigate Large Language Models (LLMs) as well as symbolic AI for question parsing;
- evaluate models based on a gold standard of geo-analytic purposes and questions;
- collaborate with a technical assistant, another PhD candidate (on geodata source modeling), and a postdoc (on the GeoQA reasoning engine).
This position is ideal for someone interested in natural language processing, geographic information and knowledge representation. It is part of the ERC-funded project GeoTrAnsQData, which develops the foundations of a transformative GeoQA methodology through an integrated research program across geoinformatics, AI, and geography. The project is based at the Department of Human Geography and Spatial Planning, Utrecht University, and contributes to cutting-edge research on spatial reasoning, semantic technologies, and interdisciplinary AI for geosciences and geography.