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 in-company 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.

Open Governmental Data publishing has had mixed success. While many governmental bodies are publishing an increasing number of datasets online, the potential usefulness is rather low. This paper describes action research conducted within the context of the Dutch Cadastre's open data platform. We start by observing contemporary (Dutch) Open Data platforms and observe that dataset reuse is not always realized. We introduce Linked Open Data, which promises to deliver solutions to the lack of Open Data reuse. In the process of implementing Linked Data in practice, we observe that users face a knowledge and skill and that contemporary Linked Open Data tooling is often unable to properly advertise the usefulness of datasets to potential users, thereby hampering reuse. We therefore develop four components for Linked Data viewing to enhance the current situation, making it easier to observe what a dataset is about and which potential use cases it could serve.

Linked Data is an innovative approach for publishing heterogeneous data sources on the web. As such, it can transcend the traditional confines of separate databases, as well as the confines of separate institutions. At the same time, businesses and governmental organizations alike are trying to cope with ever-increasing quantities of heterogeneous data that must be used across multiple departments, manufacturing locations, and governmental bodies. Linked Data would, therefore, be a great technological solution for today’s organizational problems. However, we observe that a serious gap exists between Linked Data research and business research. While Linked Data research is almost exclusively technologically oriented, the business research literature has not devoted much attention to the use of Linked Data solutions yet. In this paper, we seek to bridge this gap, by introducing a real-world use case where Linked Data technologies are applied in large-scale government settings. We argue in detail that Linked Data provides a major contribution to the business vision of a modern governmental institution based on the experience of the Netherlands’ Cadastre Land Registry and Mapping Agency (Kadaster), so far, the largest implementation of Linked Data in the governments of the Netherlands.

In the second half of the twentieth century, the Soviet school of cartographic thought offered an alternative theoretical framework for cartography. Konstantin Salishchev, the school’s founder, was a prominent leader of the International Cartographic Association who debated against the communication paradigm widely accepted by Western scholars. The reason was the widespread adoption of the Marxism-Leninism ideology by Soviet geographers and cartographers. The concept of “map as a model of reality” was a key idea of the Soviet school. It was aligned with Lenin’s reflection theory and the practical demand for high-quantity map production. Having dialectic materialism at its core, Marxism-Leninism imposed the principle of non-reductionism on Soviet science, which led to the rejection of the cartographic communication paradigm. An analysis of the proceedings of the International Cartographic Conference of 1976 and 1980 brought evidence of the underrepresentation of user study research within the Soviet school. Although several other theoreticians of cartography opposed Salishchev (cartographic dissidents), they did not change the school of thought. The successors of Salishchev “inherited” the school, also applying a cognitive paradigm to geoinformation science and remote sensing. Due to the lack of cartographic communication research, adopting the geovisualization paradigm seems challenging in contemporary Russia.