Providing a context-aware location based web service through semantics and user-defined rules

Viktoratos, Iosif; Tsadiras, Athanasios; Bassiliades, Nick

doi:10.1145/2611040.2611083

Cited by 7 publications

(10 citation statements)

References 19 publications

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“…The service stores and retrieves Resource Description Framework (RDF) triples using the Sesame 3 framework for knowledge sharing and re-usability. The personalised information is provided by SPLIS using Goggle maps, highlighting the recommended POI and the user current location, and enables user rating (Viktoratos et al ., 2014, 2015).…”

Section: Cata-related Workmentioning

confidence: 99%

“…Moreover, they allow the identification of people with similar tastes or behaviours and play an important role both in individual and in group recommendation processes. Regarding the surveyed CATA, the social networks are incorporated in GeOasis (Santiago et al ., 2012), GuideMe (Umanets et al ., 2014), iTravel (Yang & Hwang, 2013), liveCities (Martin et al ., 2011), SPLIS (Viktoratos et al ., 2015, 2014), Mobile TRS (Garcia et al ., 2013), PaTac (Ceccaroni et al ., 2009), Smart-Travel (Hung et al ., 2011), SPETA (García-Crespo et al ., 2009), Turist@ (Batet et al ., 2012) and VISIT (Meehan et al ., 2013). Crowdsourcing is the process of getting work done by a crowd of individuals, that is, corresponds to any collective and collaborative activity performed by a large number of volunteers with the support of information and communication technologies (Howe, 2006).…”

Section: Cata: Knowledge Acquisition Modulementioning

confidence: 99%

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Context-aware tourism technologies

Leal¹,

Malheiro

Burguillo³

2018

The Knowledge Engineering Review

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Nowadays travellers can benefit from the computing capabilities, collection of on board sensors and ubiquitous Internet access provided by mobile devices. These are the three pillars of any tourist support system since they provide the power, means and data to establish the local user context, to access remote services and to provide value-added user-centred context-aware applications. However, making sense of the user context data is not straightforward, as it requires dedicated knowledge acquisition and knowledge representation solutions. Besides, the range and diversity of available data sources is huge, requiring appropriate knowledge processing techniques to provide addequated tourism services. This article presents an updated review, and a comparison of recent context-aware tourism applications (CATA), including supporting technologies; and considering four possible dimensions: knowledge acquisition, knowledge representation, knowledge processing and knowledge-based services. We propose and apply a CATA analysis framework, contemplating these four dimensions to the applications found in the literature. This survey constitutes, not only, a state of the art review on tourism mobile applications, but, also, anticipates the latest development trends in tourism-related applications.

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Section: Cata-related Workmentioning

confidence: 99%

Section: Cata: Knowledge Acquisition Modulementioning

confidence: 99%

Context-aware tourism technologies

Leal¹,

Malheiro

Burguillo³

2018

The Knowledge Engineering Review

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“…Semantic Web technologies were applied to context-aware systems in many application domains: road traffic management (Baumgartner et al, 2010), business meeting assistance (Her et al, 2010), tourist services (Moreno et al, 2013), home health monitoring (Esposito et al, 2008), smart homes (Nguyen et al, 2007;Santofimia et al, 2011), manufacturing (Khilwani et al, 2009;Puttonen et al, 2010;Uddin et al, 2012), and personalized services (Barros et al, 2011;Chernbumroong et al, 2013;Furno and Zimeo, 2014;Hong et al, 2009;Rodriguez et al, 2012;Viktoratos et al, 2014) to name a few. In general, all the mentioned applications use ontologies to capture the relevant context in a model.…”

Section: Semantic Web Technologiesmentioning

confidence: 99%

Context-aware knowledge-based middleware for selective information delivery in data-intensive monitoring systems

Evchina

Puttonen

Dvoryanchikova

et al. 2015

Engineering Applications of Artificial Intelligence

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“…In this section, consider the following scenario explained to present our contextual pre-filtering paradigm used in our recommendation process, followed by ranking of nearby places on the basis of our ranking function in equation (1).…”

Section: Case Studymentioning

confidence: 99%

“…In [1] Geographical Semantic Personalized Location Information/ Geo SPLIS provide user the capability to add their own rules. Rules contain the contextual information and rules are defined using RuleML.…”

Section: Introductionmentioning

confidence: 99%

Location based context aware recommender system through user defined rules

Sharma

Kaur

2015

International Conference on Computing, Communication &Amp; Automation

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Recommender systems are a subclass of information filtering system and are widely used in the ecommerce domain [13]. They filter huge amount of data to provide personalized recommendations on services or products to users. Most of the existing approaches to develop a recommender system do not take into account contextual information such as weather, day, time, distance and location to provide recommendations. This paper proposes a location based context aware recommender system [9] that uses a ranking function to provide top-k recommendations to the user. The contextual data is defined by the users in the form of rules and RuleML [1] is chosen as a rule based language. When an active user needs recommendations of nearby places then contextual data in the user-defined RuleML rules is extracted, evaluated and top-k recommendations of nearby places based on the ranking function are presented to the user.

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Providing a context-aware location based web service through semantics and user-defined rules

Cited by 7 publications

References 19 publications

Context-aware tourism technologies

Context-aware tourism technologies

Context-aware knowledge-based middleware for selective information delivery in data-intensive monitoring systems

Location based context aware recommender system through user defined rules

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