The Kadaster Knowledge Graph (KKG) is an integrated publication of multiple large-scale spatial datasets based on the IMX-Geo model. The Kadaster Knowledge Graph allows researchers to explore and analyze cadastral data through a structured, semantically rich model. Among others, the KKG contains data from the Key Register of Addresses and Buildings (BAG), the Key Register of Large-scale Topography (BGT), the Top10NL from the Key Register of Topography (BRT), the Administrative Areas from the Key Register of Cadastres (BRK) and data from the Public Law Restrictions (PB). There is currently no Service Level Agreement offered on the Kadaster Knowledge Graph. You can read more about the KKG at this page (in Dutch). The data can be queried via https://data.kkg.kadaster.nl/sparql/.
The Kadaster Knowledge Graph allows researchers to explore and analyze cadastral data through a structured, semantically rich model. Concrete use cases include:
Combination of address and statistical information: The knowledge graph is a rich source of building and address information; combined with CBS statistics it is possible to explore neighbourhood and district level statistics based on a given address.
Environmental and spatial planning research: In addition to the building and statistical information, it is also possible to retrieve public law restrictions associated with a building or address. Researchers are able to use this when combining datasets for the purpose of environmental or spatial planning a given area.
Thematic Integration: Using federated SPARQL queries, researchers can enrich Kadaster data with external sources such as the Dutch Basic Registration of Addresses and Buildings (BAG) and the Rijksdienst voor het Cultureel Erfgoed (RCE). For example, one could study correlations between building age, ownership structure, and energy label performance and monument status.
These use cases can be implemented through SPARQL queries directly on the endpoint, or embedded in research workflows using tools like Python (e.g., RDFLib, SPARQLWrapper), R, or Jupyter Notebooks.
