Kadaster and the UT have joined forces to operate at the forefront of knowledge about federated data; the goal is to advance the research field and develop methods and techniques to extract, combine, and analyse information from distributed data sources while accounting for the principles of ethical conduct, scientific integrity, and open science, to benefit the society.
To realise that goal, Kadaster and UT work together on a collaborative project under the umbrella of the Centre for Security and Digitalisation (CVD), a collaborative knowledge centre based in Apeldoorn, uniting educational institutions, businesses, and public organisations to address challenges in security and digital transformation. The project aims to work on trusted federative data infrastructures based on KG technology and simultaneously explore the potential of mutual augmentation of AI (particularly LLMs) and KG for land administration applications. The project will be conducted by a scientific team from Kadaster and UT, where five young researchers will join the team as EngD and/or PhD candidates, including the PhD Candidate employed through this call.
From the UT side, the
Geo-Information Processing (GIP) department from the Faculty ITC and the
Semantics, Cybersecurity & Services (SCS) department from the Faculty EEMCS will supervise the PhD and EngD candidates. The supervisory team will also include colleagues from the
Kadaster Data Science. Artificial intelligence (AI) and knowledge graphs (KGs) have become ground-breaking technologies across numerous domains, enabling advanced decision-making, data integration, and knowledge extraction. AI, particularly large language models (LLMs), excels in natural language understanding and reasoning, while KGs provide structured, interpretable representations of data that enhance the contextual relevance and reliability of AI systems. Their combination paves the way for impactful innovations in various fields, including healthcare, urban planning, environmental monitoring, and public governance.
Research at the intersection of AI/LLMs and KGs is critical for addressing complex, data-driven challenges, especially in land administration, where reliable and interpretable data is paramount. Organisations like the Netherlands' Cadastre, Land Registry, and Mapping Agency (Kadaster) rely on structured data to maintain legal certainty and provide insights for spatial planning and property rights. Integrating KGs and LLMs can enhance the utility of Kadaster's datasets, enabling more advanced applications such as multi-modal data analysis (geospatial) reasoning and developing trustworthy AI tools for land administration.
You will have to conduct research at the intersection of LLMs and KG. The research will focus on enhancing LLMs with the Kadaster knowledge graph (KKG) for Multi-modal applications. The project focuses on improving LLMs' interpretability, applicability, and security in tackling land administration challenges based on KKG. The research contributes to a collaborative project between Kadaster and the University of Twente (UT) under the Centre for Safety and Digitalization (CVD) objectives (see Organization and Project section for more information).
You would have to develop techniques for LLM pre-training/fine-tuning/aligning using KKG, explore multi-modal data integration with KKG-enhanced LLMs, and address challenges such as hallucination mitigation and interpretability in land administration applications.