Semantic data models for hiking trail difficulty assessment

Calbimonte, Jean-Paul (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Martin, Simon (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Calvaresi, Davide (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Zappelaz, Nancy (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Cotting, Alexandre (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis))

Hiking is a popular outdoor activity that if practiced regularly, can bring significant health benefits. Moreover, considering that hikers range from expert mountaineers to older adults with limited physical capabilities, it touches a large target audience, and is strategically included in several tourism packages across the globe. Thus, a precise characterization of the tracks, especially regarding their points of difficulty, is crucial to effectively cope with the challenge of identifying the best-suited hiking trails for heterogeneous users. This paper introduces a semantic model for representing and integrating the main characteristics of a track, including their different types of difficulties, using Semantic Web ontologies. The construction of knowledge graphs that use such a model may constitute a first step towards a system for personalized recommendations of trails based on difficulty-classification criteria.


Keywords:
Conference Type:
full paper
Faculty:
Economie et Services
School:
HEG-VS
Institute:
Institut Informatique de gestion
Subject(s):
Informatique
Publisher:
Surrey, United Kingdom, 8-10 January 2020
Date:
2020-01
Surrey, United Kingdom
8-10 January 2020
Pagination:
Pp. 295-306
Published in:
Proceedings of Information and Communication Technologies in Tourism 2020 : proceedings of the International Conference in Surrey
DOI:
ISBN:
978-3-030-36736-7
Appears in Collection:

Note: The status of this file is: restricted


 Record created 2020-02-17, last modified 2020-03-25

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