Urban Knowledge Extraction, Representation and Reasoning as a Bridge from Data City towards Smart City

Miguel-Rodríguez, Jaime de; Galán-Páez, Juan; Aranda-Corral, Gonzalo A.; Borrego-Dí­az, Joaquín

doi:10.1109/uic-atc-scalcom-cbdcom-iop-smartworld.2016.0152

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…Automatic knowledge extraction of non-taxonomic relations from smart city documents is demonstrated in [30] by combining semantic graph-based method with context information. Formal concept lattices are constructed for smart city problems [31] to extract knowledge that can be utilized for smart city services.…”

Section: Related Workmentioning

confidence: 99%

Semantic Fusion with Deep Learning and Formal Ontologies for Evaluation of Policies and Initiatives in the Smart City Domain

Kilicay‐Ergin

Barb

2021

Applied Sciences

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Decision makers and policy analysts at different administrative levels often lack a holistic view of the problem as there are semantic variations in policy documents due to domain-specific content. For example, smart city initiatives are derived from national and international initiatives which may influence the incentives for local participants, but local initiatives reflect the local contextual elements of the city. Balanced assessment of smart city initiatives should include a systemic evaluation of the initiatives at multiple levels including the city, the country in which the city resides as well as at international level. In this paper, a knowledge elicitation methodology is presented for multi-granularity evaluation of policies and initiatives. The methodology is demonstrated on the evaluation of smart city initiatives generated at different administrative levels. Semantic networks are constructed using formal ontologies and deep learning methods for automatic semantic evaluation of initiatives to abstract knowledge found in text. Three smart city initiatives published by different administrative levels including international, national, and city level are evaluated in terms of relevance, coherence, and alignment of multi-level smart city initiatives. Experiments and analysis ultimately provide a holistic view of the problem which is necessary for decision makers and policy analysts of smart cities.

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Section: Related Workmentioning

confidence: 99%

Semantic Fusion with Deep Learning and Formal Ontologies for Evaluation of Policies and Initiatives in the Smart City Domain

Kilicay‐Ergin

Barb

2021

Applied Sciences

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Enabling Knowledge Extraction on Bike Sharing Systems Throughout Open Data

Marquez-Saldaña

Aranda-Corral

Borrego-Díaz

2022

HCI in Mobility, Transport, and Automotive Systems

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A Systematic Review for Smart City Data Analytics

2018

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Smart cities (SCs) are becoming highly sophisticated ecosystems at which innovative solutions and smart services are being deployed. These ecosystems consider SCs as data production and sharing engines, setting new challenges for building effective SC architectures and novel services. The aim of this article is to “connect the pieces” among Data Science and SC domains, with a systematic literature review which identifies the core topics, services, and methods applied in SC data monitoring. The survey focuses on data harvesting and data mining processes over repeated SC data cycles. A survey protocol is followed to reach both quantitative and semantically important entities. The review results generate useful taxonomies for data scientists in the SC context, which offers clear guidelines for corresponding future works. In particular, a taxonomy is proposed for each of the main SC data entities, namely, the “D Taxonomy” for the data production, the “M Taxonomy” for data analytics methods, and the “S Taxonomy” for smart services. Each of these taxonomies clearly places entities in a classification which is beneficial for multiple stakeholders and for multiple domains in urban smartness targeting. Such indicative scenarios are outlined and conclusions are quite promising for systemizing.

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Urban Knowledge Extraction, Representation and Reasoning as a Bridge from Data City towards Smart City

Cited by 3 publications

References 11 publications

Semantic Fusion with Deep Learning and Formal Ontologies for Evaluation of Policies and Initiatives in the Smart City Domain

Semantic Fusion with Deep Learning and Formal Ontologies for Evaluation of Policies and Initiatives in the Smart City Domain

Enabling Knowledge Extraction on Bike Sharing Systems Throughout Open Data

A Systematic Review for Smart City Data Analytics

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