Linked data based framework for tourism decision support system : case study of chinese tourists in Switzerland

Liu, Zhan (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Le Calvé, Anne (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Cretton, Fabian (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Glassey Balet, Nicole (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Sokhn, Maria (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Délétroz, Nicolas (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis))

Switzerland is one of the most desirable European destinations for Chinese tourists; therefore, a better understanding of Chinese tourists is essential for successful business practices. In China, the largest and leading social media platform—Sina Weibo, a hybrid of Twitter and Facebook—has more than 600 million users. Weibo’s great market penetration suggests that tourism operators and markets need to understand how to build effective and sustainable communications on Chinese social media platforms. In order to offer a better decision support platform to tourism destination managers as well as Chinese tourists, we proposed a framework using linked data on Sina Weibo. Linked Data is a term referring to using the Internet to connect related data. We will show how it can be used and how ontology can be designed to include the users’ context (e.g., GPS locations). Our framework will provide a good theoretical foundation for further understand Chinese tourists’ expectation, experiences, behaviors and new trends in Switzerland.


Keywords:
Article Type:
scientifique
Faculty:
Economie et Services
School:
HEG-VS
Institute:
Institut Informatique de gestion
Subject(s):
Informatique
Date:
2015
Pagination:
9 p.
Published in:
Journal of computer and communications
Numeration (vol. no.):
vol. 3, no. 5, pp. 118-126
DOI:
ISSN:
2327-5219
Appears in Collection:



 Record created 2015-07-30, last modified 2018-12-20

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