TourMISLOD: A tourism linked data set

Sabou, Marta; Arsal, Irem; Brașoveanu, Adrian

doi:10.3233/sw-2012-0087

Cited by 17 publications

(6 citation statements)

References 6 publications

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“…Other important datasets in the LODcloud are GeoNames (Maltese and Farazi 2013), a LOD representation of a database containing more than 25 million geographical names; MusicBrainz (Swartz 2002), a comprehensive LOD representation of knowledge about music-related topics; and SNOMED clinical terms (Stearns et al 2001), a large database of health-care terminologies. From the tourism prospective, the TourMISLOD dataset contains the linked data encoding of European tourism statistics (Sabou et al 2013). The…”

Section: Semantic Web Of Data: Linked Open Datamentioning

confidence: 99%

Semantic Web Empowered E-Tourism

Angele¹,

Fensel²,

Huaman³

et al. 2020

Handbook of E-Tourism

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Smart speakers such as Alexa and later Google Home have introduced Artificial Intelligence (AI) into millions, soon to be billions of households, making AI an everyday experience. These new communication channels present a new challenge for successful e-Marketing and e-Commerce providers. Data, content, and services are becoming semantically annotated, allowing software agents, socalled bots, to search through the web and understand its content. Nowadays, users typically consult their bot to find, aggregate, and personalize information and to reserve, book, or buy products and services. As a consequence, it is becoming increasingly important for touristic providers of information, products, and services to be prominently visible in these new online channels to ensure their future economic maturity. In our chapter, we survey the methods and tools helping to achieve these goals. The core aim is the development and application of machine-processable (semantic) annotations of content, data, and services, as well as their aggregation in large Knowledge Graphs. It is only through these methods bots are able to answer a question in a knowledgeable way and organize a useful dialogue (Knowledge Graphs in Use A significantly extended and generalized version of this article will appear as D.

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Section: Semantic Web Of Data: Linked Open Datamentioning

confidence: 99%

Semantic Web Empowered E-Tourism

Angele¹,

Fensel²,

Huaman³

et al. 2020

Handbook of E-Tourism

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“…To verify its effectiveness, the improved VBPR model was evaluated on the Wisdom Tourism dataset [17] by metrics like root mean square error (RMSE) [18], mean absolute error (MAE) [19], precision, and recall. Different visual features were selected as the bases of recommendation, including scale-invariant feature transform (SIFT), GIST descriptor, hue saturation value (HSV), red green blue (RGB), local binary pattern (LBP), and VGG.…”

Section: Experimental Verificationmentioning

confidence: 99%

A Novel Tourist Attraction Recommendation System Based on Improved Visual Bayesian Personalized Ranking

Liang¹,

Chen²

2020

ISI

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Statistics show that most tourists log into the main tourism websites to view user reviews or scores before selecting their destinations. However, the existing tourist destination recommendation models neither consider the implicit user preferences nor mine the potential semantics of tourist attractions. To solve the problems, this paper predicts user scores of tourist attractions through stratified sampling, and optimizes the predicted scores with Bayesian personalized ranking (BPR) and improved visual BPR (VBPR). Then, the recommendation system was optimized by the improved VBPR, which decomposes the prediction score matrix and considers visual features. Experimental results fully demonstrate the excellence of the proposed tourist attraction recommendation system. The research findings provide a good reference for online travel agencies to recommend tourist attractions.

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“…Linked data [18,19] is defined as a vast, distributed data space that use many different vocabularies in different data formats 4 build on a simple set of standards 5 where the entities are interlinked for creating a vast collection of data graph 6 that spans data sources and enables the discovery of new data sources. However, some approaches deals with linked data cloud in etourism domain, there may be mentioned: TourMISLOD [5] and OpeNER [6,7] (Open Polarity Enhanced Name Entity Recognition). Our framework uses an existing Linked e-tourism Data [7] allowing us to find the binary semantic relationships between two concepts (a computed pair-wise similarity): (1) equivalent concept degree (Exact Match (≡), a symmetric predicate) or (2) subconcept degree (Plugin Match( ô )) defined as below:…”

Section: Interlinked E-tourism Data Layer (L1)mentioning

confidence: 99%

“…Unfortunately combining and booking disparate components is still a time consuming and a challenging task, due to the continuous overloaded travel's information 1 and booking platforms 2 . In order to provide an intelligent and proactive access to relevant high quality online travel information and services, the DP can particularly benefit from: (1) Linked e-Tourism Data 3 [5,6,7] that integrates business offers across different data sources and (2) Semantic Web services (SWSs) [8] technologies to book multiple travel components as a complete travel package. Therefore, this paper describes a tailored framework for consumers that group the appropriate travel atomic processes (indivisible operation defined in OWL-S process model) they are interested in, Called SQUIREL.…”

Section: Introductionmentioning

confidence: 99%

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SQUIREL: Semantic Querying Interlinked OWL-S traveling Process Models

Merazi¹,

Malki²

2015

IJITCS

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With the advent of new forms of information and communication technologies, the consumer needs to combine and customize different travel components as a complete travel package, namely: Dynamic Packaging Technology. Nevertheless, disparate tourist offers and services make it difficult for consumer to use them effectively. Therefore, our paper presents an intelligent querying framework of OWLS travel services, called SQUIREL composition engine. It uses Semantic Web Services (SWSs) technologies combined with the useful of Linked e-tourism Data concept to fulfill the preferences and constraints of the e-tourist any time. This purpose supports SWSs pre-selection through the valuation of the rewritten SPARQL consumer query at runtime that manages dynamic service dependencies extracted from Linked e-tourism Data and returns the SWSs endpoint. Then, SQUIREL catches this endpoint and makes the necessary optimizations to refine it to its relevant atomic processes needed to be composed using matrix computation. However, the experimental results indicate that this method owns both lower computation cost and higher success ratio of fine-grained discoverybased atomic processes composition.

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TourMISLOD: A tourism linked data set

Cited by 17 publications

References 6 publications

Semantic Web Empowered E-Tourism

Semantic Web Empowered E-Tourism

A Novel Tourist Attraction Recommendation System Based on Improved Visual Bayesian Personalized Ranking

SQUIREL: Semantic Querying Interlinked OWL-S traveling Process Models

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