Ontology-based models in pervasive computing systems

Ye, Juan; Coyle, Lorcan; Dobson, Simon; Nixon, Paddy

doi:10.1017/s0269888907001208

Cited by 159 publications

(94 citation statements)

References 59 publications

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“…There are a number of surveys which review the literature relevant to context modeling in general [4], [5] or focusing on ontology-based models [6], [7]. In addition, Costabello [8] presented and compared a number of ontology-based context models against a set of requirements.…”

Section: A Context Representationmentioning

confidence: 99%

“…Recommender System Context (RSCtx) extends PRISSMA 7 , a vocabulary based on Dey's definition of context [30]. PRISSMA relies on the W3C Model-Based User Interface Incubator Group proposal 8 , which describes mobile context as an encompassing term, defined as the sum of three different dimensions: user model and preferences, device features, and the environment in which the action is performed.…”

Section: The Recommender System Context Ontologymentioning

confidence: 99%

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An Ontology-based Contextual Pre-filtering Technique for Recommender Systems

Karpus

Vagliano

Goczyła

et al. 2016

Annals of Computer Science and Information Systems

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Abstract-Context-aware Recommender Systems aim to provide users with the most adequate recommendations for their current situation. However, an exact context obtained from a user could be too specific and may not have enough data for accurate rating prediction. This is known as the data sparsity problem. Moreover, often user preference representation depends on the domain or the specific recommendation approach used. Therefore, a big effort is required to change the method used. In this paper we present a new approach for contextual pre-filtering (i.e. using the current context to select a relevant subset of data). Our approach can be used with existing recommendation algorithms. It is based on two ontologies: Recommender System Context ontology, which represents the context, and Contextual Ontological User Profile ontology, which represents user preferences. We evaluated our approach through an offline study which showed that when used with well-known recommendation algorithms it can significantly improve the accuracy of prediction.

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Section: A Context Representationmentioning

confidence: 99%

Section: The Recommender System Context Ontologymentioning

confidence: 99%

An Ontology-based Contextual Pre-filtering Technique for Recommender Systems

Karpus

Vagliano

Goczyła

et al. 2016

Annals of Computer Science and Information Systems

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“…Hydrogen [19]; directed state graphs such as the one proposed in [20]; key-value models such as the one used by Dey in the Context Toolkit [13]; graphical models such as ContextUML [21], CML [22] and the one proposed in [23]; etc. However, several studies [14,24,25] state that the use of ontologies to model context is one of the best choices. They state that this model guarantees a high degree of expressiveness, formality, and semantic richness.…”

Section: A Context Ontologymentioning

confidence: 99%

Context-Adaptive Coordination of Pervasive Services by Interpreting Models during Runtime

Serral¹,

Valderas²,

Pelechano³

2012

The Computer Journal

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One of the most important goals of pervasive systems is to help users in their daily life by automating their behaviour patterns. To achieve this, pervasive services must be dynamically coordinated, executed, and adapted to context according to user behaviour patterns. In this work, we propose a model-driven solution to meet this challenge. We propose a task model and a context ontology to design context-adaptive coordination of services at a high level of abstraction. This design facilitates the coordination analysis at design time and is also reused at runtime. We propose a software architecture that interprets the models at runtime in order to coordinate the service execution that is required to support user behaviour patterns. This coordination is done in a contextadaptive way and decoupled from service implementation. This approach makes the models the only representation of service coordination, which facilitates the maintenance and evolution of the executed service coordination after deployment.

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“…Ontology-based modelling [5] combines the advantages of object-based and logic models, i.e., encapsulation, extendibility and reusability, and formalism and inference capabilities, respectively. They facilitate information fusion from heterogeneous data and knowledge sources, also providing support for automated reasoning.…”

Section: State Of the Artmentioning

confidence: 99%

Towards a Lightweight Mobile Semantic-Based Approach for Enhancing Interaction with Smart Objects

Iglesias

Bernardos

Bergesio

et al. 2012

Advances in Intelligent and Soft Computing

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This work describes a semantic extension for a user-smart object interaction model based on the ECA paradigm (Event-Condition-Action). In this approach, smart objects publish their sensing (event) and action capabilities in the cloud and mobile devices are prepared to retrieve them and act as mediators to configure personalized behaviours for the objects. In this paper, the information handled by this interaction system has been shaped according several semantic models that, together with the integration of an embedded ontological and rulebased reasoner, are exploited in order to (i) automatically detect incompatible ECA rules configurations and to (ii) support complex ECA rules definitions and execution. This semantic extension may significantly improve the management of smart spaces populated with numerous smart objects from mobile personal devices, as it facilitates the configuration of coherent ECA rules.

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Ontology-based models in pervasive computing systems

Cited by 159 publications

References 59 publications

An Ontology-based Contextual Pre-filtering Technique for Recommender Systems

An Ontology-based Contextual Pre-filtering Technique for Recommender Systems

Context-Adaptive Coordination of Pervasive Services by Interpreting Models during Runtime

Towards a Lightweight Mobile Semantic-Based Approach for Enhancing Interaction with Smart Objects

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