Abstract— 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 of 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 and related services. The article also presented the work performed about reconciliation algorithms and their comparative assessment and selection.

Ontology Bulding vs Data Harvesting and Cleaning for Smart-city Services / Pierfrancesco Bellini; Monica Benigni; Riccardo Billero; Paolo Nesi; Nadia Rauch. - STAMPA. - (2014), pp. 211-220. (Intervento presentato al convegno DMS 2014 tenutosi a Pittsburgh nel 27-29 Agosto 2014).

Ontology Bulding vs Data Harvesting and Cleaning for Smart-city Services

BELLINI, PIERFRANCESCO;BILLERO, RICCARDO;NESI, PAOLO;RAUCH, NADIA
2014

Abstract

Abstract— 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 of 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 and related services. The article also presented the work performed about reconciliation algorithms and their comparative assessment and selection.
2014
DMS 2014
DMS 2014
Pittsburgh
27-29 Agosto 2014
Pierfrancesco Bellini; Monica Benigni; Riccardo Billero; Paolo Nesi; Nadia Rauch
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/922730
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