Building a highly consumable semantic model for smarter cities

Abstract-Several challenges are faced by citizens of urban centers while dealing with day-to-day events, and the absence of a centralized reporting mechanism makes event-reporting and redressal a daunting task. With the push on information technology to adapt to the needs of smart-cities and integrate urban civic services, the use of Open311 architecture presents an interesting solution. In this paper, we present a novel approach that uses an existing Open311 ontology to classify and report non-emergency city-events, as well as to guide the citizen to the points of redressal. The use of linked open data and the semantic model serves to provide contextual meaning and make vast amounts of content hyper-connected and easily-searchable. Such a one-size-fits-all model also ensures reusability and effective visualization and analysis of data across several cities. By integrating urban services across various civic bodies, the proposed approach provides a single endpoint to the citizen, which is imperative for smooth functioning of smart cities.

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“…our university-Indian Institute of Information Technology, Allahabad (IIIT-Allahabad). 4 Universal Server. It is a multi-model data server.…”

Section: Methodsmentioning

confidence: 99%

Ontology-based classification and analysis of non-emergency smart-city events

Rani

Alekh

Bhardwaj

et al. 2016

2016 International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT)

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“…Organization ontology, defined by Fox et al [11] , focuses on organization structure, roles, authority and empowerment. It was developed as part of the Toronto Virtual Enterprise Project [12] . It is available at: http://ontology.eil.utoronto.ca/organization.owl.…”

Section: Reuse Of Ontologiesmentioning

confidence: 99%

“…IBM's SCRIBE is a semantic model for cities [7] . It provides a taxonomy of city services including Education, Justice and Correction, Public Safety.…”

Section: Introductionmentioning

confidence: 99%

Achieving interoperability of smart city data: An analysis of 311 data

Nalchigar

Fox

2017

J Smart Cities

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A major challenge in making cities smarter is performing comparative analyses across two or more cities, or within a city across two or more departments. The problem is that data models and the underlying semantics of their content differ, making analysis difficult at best and erroneous at worst. This paper explores the hypothesis that a single, interoperable (i.e., shareable) data model/ontology can be designed for one category of city data: openly published 311 call centre data. 311 is a service provided by many North American cities that responds to non-emergency questions and reports made by the public. It has rapidly become the single point of contact for city services, inquiries, etc. We perform a semantic analysis of the content of 311 open datasets from four cities. The result of the analysis is that existing 311 datasets combine multiple semantic dimensions in their data making it impossible to perform comparative analysis. We then construct a 311 Reference Ontology that separates the semantic dimensions, and show how 311 data from multiple cities can be mapped onto the 311 Reference Ontology. We also demonstrate how the ontology can be used to support analysis.

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“…The first version of SCRIBE includes the ontology and the main sub-taxonomies: the CityPhysicalBase, which contains physical objects in the city, such as landmarks, roads, networks, etc. ; the EntityRoleBase, describing organizations, people, items, and their roles; the EventAndMessageBase, including external events (like storms, road work), and messages; the KPIBase, including indicators related to cost, quality of service or response time; the MeasurementBase, for measurements (height, length) and measurement units; the OrganisationBase, which captures the abstract organization of a city plus the set of service areas; the Pro-tocolBase, describing the city protocols as a set of protocol steps, and the SCGeo, the geospatial core sub-ontology [20] . The main object properties that create horizontal relationships are "hasAttribute" for properties and attributes (name, identifier, etc.…”

Section: Design Of the Smart City Ontologymentioning

confidence: 99%

Smart City Ontologies: Improving the effectiveness of smart city applications

et al. 2016

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This paper addresses the problem of low impact of smart city applications observed in the fields of energy and transport, which constitute high-priority domains for the development of smart cities. However, these are not the only fields where the impact of smart cities has been limited. The paper provides an explanation for the low impact of various individual applications of smart cities and discusses ways of improving their effectiveness. We argue that the impact of applications depends primarily on their ontology, and secondarily on smart technology and programming features. Consequently, we start by creating an overall ontology for the smart city, defining the building blocks of this ontology with respect to the most cited definitions of smart cities, and structuring this ontology with the Protégé 5.0 editor, defining entities, class hierarchy, object properties, and data type properties. We then analyze how the ontologies of a sample of smart city applications fit into the overall Smart City Ontology, the consistency between digital spaces, knowledge processes, city domains targeted by the applications, and the types of innovation that determine their impact. In conclusion, we underline the relationships between innovation and ontology, and discuss how we can improve the effectiveness of smart city applications, combining expert and user-driven ontology design with the integration and or-chestration of applications over platforms and larger city entities such as neighborhoods, districts, clusters, and sectors of city activities.

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Building a highly consumable semantic model for smarter cities

Cited by 22 publications

References 4 publications

Ontology-based classification and analysis of non-emergency smart-city events

Ontology-based classification and analysis of non-emergency smart-city events

Achieving interoperability of smart city data: An analysis of 311 data

Smart City Ontologies: Improving the effectiveness of smart city applications

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