Ontology Bulding vs Data Harvesting and Cleaning for Smart-city Services
This addresses the challenge of data interoperability and integration for local governments and smart city applications, though it is incremental as it builds on existing ontology and data management approaches.
The paper tackles the problem of integrating diverse and non-interoperable data sets for smart city services by proposing a system that maps static and dynamic data to a smart-city ontology and stores it in an RDF-Store, enabling new services via SPARQL queries.
Presently, a very large number of public and private data sets are available around the local governments. In most cases, they are not semantically interoperable and a huge human effort is needed to create integrated ontologies and knowledge base for smart city. Smart City ontology is not yet standardized, and a lot of research work is needed to identify models that can easily support the data reconciliation, the management of the complexity and reasoning. In this paper, a system for data ingestion and reconciliation smart cities related aspects as road graph, services available on the roads, traffic sensors etc., is proposed. The system allows managing a big volume of data coming from a variety of sources considering both static and dynamic data. These data are mapped to smart-city ontology and stored into an RDF-Store where they are available for applications via SPARQL queries to provide new services to the users. The paper presents the process adopted to produce the ontology and the knowledge base and the mechanisms adopted for the verification, reconciliation and validation. Some examples about the possible usage of the coherent knowledge base produced are also offered and are accessible from the RDF-Store.