Definition of an Ontology Matching Algorithm for Context Integration in Smart Cities

Otero-Cerdeira, Lorena; Rodríguez-Martínez, Francisco Javier; Gómez-Rodríguez, Alma

doi:10.3390/s141223581

Cited by 19 publications

(11 citation statements)

References 37 publications

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“…It can also reason about new or hidden knowledge based on known factual knowledge. Ontologies are widely used in many domains, such as information science [30,31], medical science [32,33], and education [8,34].…”

Section: The Ontology In Sarmentioning

confidence: 99%

High-Level Smart Decision Making of a Robot Based on Ontology in a Search and Rescue Scenario

Sun

Chen

2019

Future Internet

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The search and rescue (SAR) scenario is complex and uncertain where a robot needs to understand the scenario to make smart decisions. Aiming at the knowledge representation (KR) in the field of SAR, this paper builds an ontology model that enables a robot to understand how to make smart decisions. The ontology is divided into three parts, namely entity ontology, environment ontology, and task ontology. Web Ontology Language (OWL) is adopted to represent these three types of ontology. Through ontology and Semantic Web Rule Language (SWRL) rules, the robot infers the tasks to be performed according to the environment state and at the same time obtains the semantic information of the victims. Then, the paper proposes an ontology-based algorithm for task planning to get a sequence of atomic actions so as to complete the high-level inferred task. In addition, an indoor experiment was designed and built for the SAR scenario using a real robot platform—TurtleBot3. The correctness and usability of the ontology and the proposed methods are verified by experiments.

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Section: The Ontology In Sarmentioning

confidence: 99%

High-Level Smart Decision Making of a Robot Based on Ontology in a Search and Rescue Scenario

Sun

Chen

2019

Future Internet

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“…OntoPhil is an ontology matching algorithm specifically designed for smart cities [17] and had been evaluated with the Ontology Alignment Evaluation Initiative (OAEI) benchmark. OntoPhil is adapted to those requirements: modular ontology size and lightweight matching process.…”

Section: A Semantics-based Smart City Projectsmentioning

confidence: 99%

“…While the Semantic Sensor Networks (SSN) ontology specification [24] has been released as a W3C Recommendation in October 2017, several surveys related to sensor ontologies 17 Further details regarding the survey of sensor ontologies can be found on the W3C SSN documentation. It is the continuation of the SSN ontology work published in 2012 [9].…”

Section: Existing Surveys For Iot and Smart City Ontologiesmentioning

confidence: 99%

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Building IoT-Based Applications for Smart Cities: How Can Ontology Catalogs Help?

Gyrard

Zimmermann

Sheth

2018

IEEE Internet Things J.

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The Internet of Things (IoT) plays an ever-increasing role in enabling Smart City applications. An ontology-based semantic approach can help improve interoperability between a variety of IoT-generated as well as complementary data needed to drive these applications. While multiple ontology catalogs exist, using them for IoT and smart city applications require significant amount of work. In this paper, we demonstrate how can ontology catalogs be more effectively used to design and develop smart city applications? We consider four ontology catalogs that are relevant for IoT and smart cities: READY4SmartCities, LOV, OpenSensingCity (OSC) and, LOV4IoT. To support semantic interoperability with the reuse of ontology-based smart city applications, we present a methodology to enrich ontology catalogs with those ontologies. Our methodology is generic enough to be applied to any other domains as is demonstrated by its adoption by OSC and LOV4IoT ontology catalogs. Researchers and developers have completed a survey based evaluation of the LOV4IoT catalog. The usefulness of ontology catalogs ascertained through this evaluation has encouraged their ongoing growth and maintenance. The quality of IoT and smart city ontologies have been evaluated to improve the ontology catalog quality. We also share the lessons learned regarding ontology best practices and provide suggestions for ontology improvements with a set of software tools.

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“…It works on the basis of information provided by different sources in the city, such as sensors, administration services, users, and institutions. Interoperability is achieved by limiting the ontologies used by different systems, as each ontology deployed must comply with one of the templates already defined in the platform [16,17] .…”

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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Definition of an Ontology Matching Algorithm for Context Integration in Smart Cities

Cited by 19 publications

References 37 publications

High-Level Smart Decision Making of a Robot Based on Ontology in a Search and Rescue Scenario

High-Level Smart Decision Making of a Robot Based on Ontology in a Search and Rescue Scenario

Building IoT-Based Applications for Smart Cities: How Can Ontology Catalogs Help?

Smart City Ontologies: Improving the effectiveness of smart city applications

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