Presently, a very large number of public and private data sets are availablefrom local governments. In most cases, they are not semantically interoperableand a huge human effort would be needed to create integrated ontologies andknowledge base for smart city. Smart City ontology is not yet standardized, anda lot of research work is needed to identify models that can easily support thedata reconciliation, the management of the complexity, to allow the datareasoning. In this paper, a system for data ingestion and reconciliation ofsmart cities related aspects as road graph, services available on the roads,traffic sensors etc., is proposed. The system allows managing a big data volumeof data coming from a variety of sources considering both static and dynamicdata. These data are mapped to a smart-city ontology, called KM4City (KnowledgeModel for City), and stored into an RDF-Store where they are available forapplications via SPARQL queries to provide new services to the users viaspecific applications of public administration and enterprises. The paperpresents the process adopted to produce the ontology and the big dataarchitecture for the knowledge base feeding on the basis of open and privatedata, and the mechanisms adopted for the data verification, reconciliation andvalidation. Some examples about the possible usage of the coherent big dataknowledge base produced are also offered and are accessible from the RDF-Storeand related services. The article also presented the work performed aboutreconciliation algorithms and their comparative assessment and selection.
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