Smart cities generate vast, heterogeneous data streams from transportation networks, energy grids, environmental sensors, and public services, yet the semantic fragmentation of these data silos prevents urban operators from deriving actionable, cross-domain intelligence. Knowledge graphs (KGs) have emerged as a powerful paradigm for integrating diverse, large-scale data collections through graph-based representations of entities and their relationships. This paper applies the Design Science Research Methodology (DSRM) to design, develop, and evaluate UrbanKG, a layered artifact that deploys knowledge graphs as the semantic backbone of smart city data infrastructure. We demonstrate the framework through a proof-of-concept implementation using publicly available urban datasets across five domains, yielding a 287,000-triple knowledge graph validated through cross-domain SPARQL queries and accessibility analysis. Following the six DSRM process steps—problem identification, objective definition, design and development, demonstration, evaluation, and communication—the framework addresses ontology design, multi-source data fusion, federated governance, temporal reasoning, and hybrid deductive–inductive inference. The artifact satisfies all five design objectives and contributes four transferable design principles. Six open research challenges are identified as the forward research agenda.
Khantong et al. (Mon,) studied this question.