On the definition of generic multi-layered ontologies for urban applications

Benslimane, Djamal; Leclercq, Éric; Savonnet, Marinette; Terrasse, Marie-Noölle; Yétongnon, Kokou

doi:10.1016/s0198-9715(99)00059-9

Cited by 26 publications

(11 citation statements)

References 22 publications

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“…However, although ontology-based approaches have reached a good level of maturity in other fields, their applications in the domain of urban planning and modeling is limited (Benslimane et al 2000). Most existing efforts focus on the construction of ontology as a shared vocabulary to improve communication or to facilitate knowledge management (Teller et al 2007).…”

Section: Ontology-based Data Integrationmentioning

confidence: 99%

Data Integration to Create Large-Scale Spatially Detailed Synthetic Populations

Zhu

Ferreira

2015

Lecture Notes in Geoinformation and Cartography

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Many planning support systems and, indeed, some 'smart city' initiatives begin with time consuming efforts to integrate cross-agency data describing current conditions in sufficient detail to support 'what-if' exploration of urban development options. Integrating data from different sources has become increasingly challenged as available datasets, and the relevant urban modeling efforts, become more disaggregated and spatial-temporally detailed. Open data initiatives, with unprecedented amounts of embedded georeferenced information, have made web services and crowdsourcing attractive. However, data from such sources are typically imperfect and their integration is complicated by syntactic and semantic differences. We develop an ontology-based data integration mechanism to fuse data from different sources in generic ways that can utilize semantic information to minimize the labor involved and facilitate updating as new data are acquired. As a test application, we evaluate, filter, adjust, and integrate building information from heterogeneous data sources for use in an agent-based microsimulation model of transportation and land-use dynamics in Singapore. Third-party data about building size, age and use added substantial value to the official datasets generally available from government agencies.

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Section: Ontology-based Data Integrationmentioning

confidence: 99%

Data Integration to Create Large-Scale Spatially Detailed Synthetic Populations

Zhu

Ferreira

2015

Lecture Notes in Geoinformation and Cartography

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“…A few transportation specific ontologies exist, but most of them focus too heavily on the mechanics on the transportation network (Fonseca et al, 2000;Lorenz et al, 2005;Benslimane et al, 2000;Teller et al, 2007). Our research is not focused on how the transportation system is built or organized, beyond the minimum necessary to guide users through it.…”

Section: The Tranquyl Ontologymentioning

confidence: 99%

Robust Natural Language Processing for Urban Trip Planning

Booth¹,

Eugenio

Cruz

et al. 2015

Applied Artificial Intelligence

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Navigating large, urban transportation networks is a complicated task. A user needs to negotiate the available modes of transportation, their schedules, and how they are interconnected. In this paper we present a Natural Language interface for trip planning in complex multimodal urban transportation networks. Our objective is to provide robust understanding of complex requests while giving the user flexibility in their language.We designed TRANQUYL, a Transportation Query Language for trip planning; we developed a usercentric ontology, that defines the concepts covered by the interface and allows for a broad vocabulary. NL2TRANQUYL, the software system built on these foundations, translates English requests into formal TRANQUYL queries. Through a detailed intrinsic evaluation, we show that NL2TRANQUYL is highly accurate and robust with respect to paraphrasing of requests as well as handling fragmented or telegraphic requests.

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“…Urban planning has evolved from a rationalistic model to a transactional model in which public participation, multi-stakeholder partnerships and strategic planning have become a byword. Ontologies can thus be seen as a way of responding to the difficulties that arise out of these new models, enabling them to become inter-operative across systems and people [10][11][12][13][14]. At present, several research groups are developing work in the field of urban ontologies, some of the most relevant of which are listed below.…”

Section: Related Workmentioning

confidence: 99%

A Land Use Planning Ontology: LBCS

et al. 2012

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Urban planning has a considerable impact on the economic performance of cities and on the quality of life of their populations. Efficiency at this level has been hampered by the lack of integrated tools to adequately describe urban space in order to formulate appropriate design solutions. This paper describes an ontology called LBCS-OWL2 specifically developed to overcome this flaw, based on the Land Based Classification Standards (LBCS), a comprehensive and detailed land use standard to describe the different dimensions of urban space. The goal is to provide semantic and computer-readable land use descriptions of geo-referenced spatial data. This will help to make programming strategies available to those involved in the urban development process. There are several advantages to transferring a land use standard to an OWL2 land use ontology: it is modular, it can be shared and reused, it can be extended and data consistency maintained, and it is ready for integration, thereby supporting the interoperability of different urban planning applications. This standard is used as a basic structure for the "City Information Modelling" (CIM) model developed within a larger research project called City Induction, which aims to develop a tool for urban planning and design.

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On the definition of generic multi-layered ontologies for urban applications

Cited by 26 publications

References 22 publications

Data Integration to Create Large-Scale Spatially Detailed Synthetic Populations

Data Integration to Create Large-Scale Spatially Detailed Synthetic Populations

Robust Natural Language Processing for Urban Trip Planning

A Land Use Planning Ontology: LBCS

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