Kadaster knowledge graph: Beyond the fifth star of open data

Stanislav Ronzhin*, Erwin Folmer, Pano Maria, Marco Brattinga, Wouter Beek, Rob Lemmens, Rein van't Veer

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review


After more than a decade, the supply-driven approach to publishing public (open) data has resulted in an ever-growing number of data silos. Hundreds of thousands of datasets have been catalogued and can be accessed at data portals at different administrative levels. However, usually, users do not think in terms of datasets when they search for information. Instead, they are interested in information that is most likely scattered across several datasets. In the world of proprietary incompany data, organizations invest heavily in connecting data in knowledge graphs and/or store data in data lakes with the intention of having an integrated view of the data for analysis. With the rise of machine learning, it is a common belief that governments can improve their services, for example, by allowing citizens to get answers related to government information from virtual assistants like Alexa or Siri. To provide high-quality answers, these systems need to be fed with knowledge graphs. In this paper, we share our experience of constructing and using the first open government knowledge graph in the Netherlands. Based on the developed demonstrators, we elaborate on the value of having such a graph and demonstrate its use in the context of improved data browsing, multicriteria analysis for urban planning, and the development of location-aware chat bots.

Original languageEnglish
Article number310
Pages (from-to)1-19
Number of pages19
JournalInformation (Switzerland)
Issue number10
Publication statusPublished - 9 Oct 2019


  • Governmental open data
  • Knowledge graph
  • Linked data
  • Location-aware chat bots
  • Semantic enrichment


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