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

被引：12

作者：

Chessa, Alessandro ^{[1
]}

Fenu, Gianni ^{[2
]}

Motta, Enrico ^{[3
]}

Osborne, Francesco ^{[3
,4
]}

Recupero, Diego Reforgiato ^{[2
]}

Salatino, Angelo ^{[3
]}

Secchi, Luca ^{[1
,2
]}

机构：

[1] Linkalab, I-09122 Cagliari, Italy

[2] Univ Cagliari, Dept Math & Comp Sci, I-09124 Cagliari, Italy

[3] Open Univ, Knowledge Media Inst, Milton Keynes MK7 6AA, England

[4] Univ Milano Bicocca, Dept Business & Law, I-20126 Milan, Italy

来源：

IEEE ACCESS | 2023年 / 11卷

关键词：

Knowledge graphs; ontology design; tourism ontology; web science; web mining; tourism; hospitality; ONTOLOGY;

D O I：

10.1109/ACCESS.2023.3292153

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：67567 / 67599

页数：33

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