Smart cities utilize Big Data and IoT to provide a better life for citizens. Since they are the most complicated human artefact, the adoption of such technologies become a complex task, requiring continuous data collection, aggregation and analysis. In order to transform city problems into concrete actions, a systematic approach aimed at digital transition needs to be followed. There are huge efforts to build city information models for encoding city objects, their relations and supporting the decision-making. This requires a common knowledge base, supported by rich vocabularies and ontologies that are capable to handle information diversity and overload.

In this paper, a methodological framework and an upper-level ontology for building digital city models are presented. The process of digital city modelling follows the concept of digital twin by providing data-driven decision making. The proposed upper-level ontology aims to overcome city modelling problems due to data silos and lack of semantic interoperability.