An Ontology-based Information Model for Multi-Domain Semantic Modeling and Analysis of Smart City Data

Abstract

Smart city services are typically defined according to domains (e.g., health, education, safety) and supported by different systems. Consequently, the analysis of smart city data is often domain-specific, thus limiting the capabilities of the offered services and hampering decision-making that relies on isolated domain information. To support a suitable analysis across multiple domains, it is necessary having a unified data model able to handle the inherent heterogeneity of smart city data and take into account both geographic and citizen information. This paper presents an ontology-based information model to support multi-domain analysis in smart cities to foster interoperability and powerful automated reasoning upon unambiguous information. The proposed information model follows Linked Data principles and takes advantage of ontologies to define information semantically. The semantic relationships and properties defined in the model also allow inferring new pieces of information that improve accuracy when analyzing multiple city domains. This paper reports an evaluation of the information model through ontological metrics and competence questions.