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Cited by 40

The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.

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

Chessa, Alessandro, Fenu, Gianni, Motta, Enrico, Osborne, Francesco, Reforgiato Recupero, Diego, Salatino, Angelo, Secchi, Luca·2023·IEEE Access
DOI 10.1109/access.2023.3292153
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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.

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Cites

Show all 53 references

Research works printed but not linked: 11

  1. Chaves, Towards a multilingual ontology for ontology-driven content mining in social web sites, 687, 1
  2. Consoli, Towards emergency vehicle routing using geolinked open data: The case study of the municipality of Catania, 1329, 31
  3. Ou, Development and alignment of a domain-specific ontology for question answering, 2221
  4. Delpeuch, 2019, OpenTapioca: Lightweight entity linking for Wikidata, arXiv:1904.09131 · arXiv
  5. Dimou, RML: A generic language for integrated RDF mappings of heterogeneous data, 1184, 1
  6. Dimou, Automated metadata generation for linked data generation and publishing workflows, 1593, 1
  7. Verborgh, Web-scale querying through linked data fragments, 1184
  8. Gangemi, Modelling ontology evaluation and valilidation, 15
  9. Bordes, Translating embeddings for modeling multi-relational data, 2, 2787
  10. Shuyo, 2010, Language Detection Library for Java
  11. 10.1145/2063518.2063519 · DOI · Paper page

Metadata

Authors
Chessa, A., Fenu, G., Motta, E., Osborne, F., Reforgiato Recupero, D., Salatino, A., & Secchi, L.
Venue
IEEE Access
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2023
Volume
11
Pages
67567-67599
ISSN
2169-3536
DOI
10.1109/access.2023.3292153
Cited by
40
References
53
Licence
creativecommons.org/licenses/by-nc-nd/4.0/ (vor)

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CoChat. (2026). Citation check: Data-Driven Methodology for Knowledge Graph Generation Within the Tourism Domain. https://app.cochat.ai/papers/10.1109/access.2023.3292153
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Chessa, A., Fenu, G., Motta, E., Osborne, F., Reforgiato Recupero, D., Salatino, A., & Secchi, L. (2023). Data-Driven Methodology for Knowledge Graph Generation Within the Tourism Domain. IEEE Access. https://doi.org/10.1109/access.2023.3292153

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.