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Data-Driven Methodology for Knowledge Graph Generation Within the Tourism Domain

Authors :
Alessandro Chessa
Gianni Fenu
Enrico Motta
Francesco Osborne
Diego Reforgiato Recupero
Angelo Salatino
Luca Secchi
Source :
IEEE Access, Vol 11, Pp 67567-67599 (2023)
Publication Year :
2023
Publisher :
IEEE, 2023.

Abstract

The tourism and hospitality sectors have become increasingly important in the last few years and the companies operating in this field are constantly challenged with providing new innovative services. At the same time, (big-) data has become the “new oil” of this century and Knowledge Graphs are emerging as the most natural way to collect, refine, and structure this heterogeneous information. In this paper, we present a methodology for semi-automatic generating a Tourism Knowledge Graph (TKG), which can be used for supporting a variety of intelligent services in this space, and a new ontology for modelling this domain, the Tourism Analytics Ontology (TAO). Our approach processes and integrates data from Booking.com, Airbnb, DBpedia, and GeoNames. Due to its modular structure, it can be easily extended to include new data sources or to apply new enrichment and refinement functions. We report a comprehensive evaluation of the functional, logical, and structural dimensions of TKG and TAO.

Details

Language :
English
ISSN :
21693536
Volume :
11
Database :
Directory of Open Access Journals
Journal :
IEEE Access
Publication Type :
Academic Journal
Accession number :
edsdoj.5a2436ad48d24dfd95865b3cd16c33c5
Document Type :
article
Full Text :
https://doi.org/10.1109/ACCESS.2023.3292153