Ontonym

Stevenson, Graeme; Knox, Stephen; Dobson, Simon; Nixon, Paddy

doi:10.1145/1552262.1552271

Cited by 36 publications

(2 citation statements)

References 24 publications

(27 reference statements)

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“…The Ontonym-Sensor [35] ontology provides a high-level description of sensors and capabilities such as their frequency, coverage, accuracy, and precision pairs. In addition, sensor observations (observation-specific information, metadata, sensor, timestamp, and the time period over which the value is valid, the rate of change) are modelled as well [35]. Due to its generic nature, Ontonym-Sensor can be reused in different scenarios that involve both sensors and sensor data.…”

Section: Semantic Models For Sensor Datamentioning

confidence: 99%

The smashHitCore Ontology for GDPR-Compliant Sensor Data Sharing in Smart Cities

Kurteva

Chhetri

Tauqeer

et al. 2023

Sensors

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The adoption of the General Data Protection Regulation (GDPR) has resulted in a significant shift in how the data of European Union citizens is handled. A variety of data sharing challenges in scenarios such as smart cities have arisen, especially when attempting to semantically represent GDPR legal bases, such as consent, contracts and the data types and specific sources related to them. Most of the existing ontologies that model GDPR focus mainly on consent. In order to represent other GDPR bases, such as contracts, multiple ontologies need to be simultaneously reused and combined, which can result in inconsistent and conflicting knowledge representation. To address this challenge, we present the smashHitCore ontology. smashHitCore provides a unified and coherent model for both consent and contracts, as well as the sensor data and data processing associated with them. The ontology was developed in response to real-world sensor data sharing use cases in the insurance and smart city domains. The ontology has been successfully utilised to enable GDPR-complaint data sharing in a connected car for insurance use cases and in a city feedback system as part of a smart city use case.

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Section: Semantic Models For Sensor Datamentioning

confidence: 99%

The smashHitCore Ontology for GDPR-Compliant Sensor Data Sharing in Smart Cities

Kurteva

Chhetri

Tauqeer

et al. 2023

Sensors

View full text Add to dashboard Cite

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“…The modelling capabilities have been designed with minimum semantic commitment to guarantee maximal interoperability. As such, the ontologies can be aligned with relevant foundational ontologies, such as SEM (Van Hage, Malaisé, Segers, Hollink, & Schreiber, 2011) and Ontonym (Stevenson, Knox, Dobson, & Nixon, 2009), reusing existing vocabularies for modelling different aspects of activities, e.g. entities, places and so forth.…”

Section: Semantic Interpretation Layermentioning

confidence: 99%

Multi-sensing Monitoring and Knowledge-driven Analysis for Dementia Assessment

Stavropoulos

Meditskos

Kontopoulos

et al. 2015

International Journal of E-Health and Medical Communications

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DemaWare is a Service-Oriented platform that aids in the timely assessment and monitoring of people with dementia in an Ambient Assisted Living context. This work presents in detail the underlying modules integrated in DemaWare, providing both software and hardware services. The system coordinates the retrieval of raw sensor data from a variety of sources, such as ambient and wearable sensors, and their processing into a common knowledge base. The semantic interpretation performed afterwards reasons upon collected knowledge and infers higher level observations. Finally, all knowledge is presented in suitable end-user applications that support various scenarios, e.g. lab assessment trials and monitoring in nursing home environments.

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A multidimensional resource model for dynamic resource matching in internet of things

Zhao

Zhang

et al. 2013

Concurrency and Computation

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SummaryWith the development of Internet of Things (IoT), many middleware solutions have been proposed for the integration of physical world with the Web. However, most leading middleware solutions provide only limited resource matching and selection functionality. When significant amounts of resources are available, selecting the appropriate resources becomes challenging and time‐consuming. This paper addresses the growing issues of resource matching and selection in IoT solutions. In this paper, we propose a novel resource model to describe the IoT resources in a multidimensional manner. Based on the resource model, a resource matching algorithm that selects the well‐matched resources by matching the similarity between resources is proposed. Moreover, we constructed a combined resource set and used it and OWLS‐TC to evaluate our work comprehensively. Experimental results show that the proposed resource matching approach is more effective and efficient than existing approaches. Copyright © 2013 John Wiley & Sons, Ltd.

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Ontonym

Cited by 36 publications

References 24 publications

The smashHitCore Ontology for GDPR-Compliant Sensor Data Sharing in Smart Cities

The smashHitCore Ontology for GDPR-Compliant Sensor Data Sharing in Smart Cities

Multi-sensing Monitoring and Knowledge-driven Analysis for Dementia Assessment

A multidimensional resource model for dynamic resource matching in internet of things

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