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

被引:11
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
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
收藏
页码:67567 / 67599
页数:33
相关论文
共 63 条
[1]   La Rioja Turismo: The Construction and Exploitation of a Queryable Tourism Knowledge Graph [J].
Alonso-Maturana, Ricardo ;
Alvarado-Cortes, Elena ;
Lopez-Sola, Susana ;
Ortega Martinez-Losa, Maria ;
Hermoso-Gonzalez, Pablo .
CURRENT TRENDS IN WEB ENGINEERING (ICWE 2018), 2018, 11153 :213-220
[2]   Multi-domain sentiment analysis with mimicked and polarized word embeddings for human-robot interaction [J].
Atzeni, Mattia ;
Recupero, Diego Reforgiato .
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2020, 110 :984-999
[3]  
Barthelmie R., 2009, European Wind Energy Conference, P1
[4]  
Blomqvist E., 2020, LECT NOTES COMPUTER, V7603, P216
[5]  
Bordes A., 2013, P 26 INT C NEUR INF, V2, P2787
[6]   CAFE: Knowledge graph completion using neighborhood-aware features [J].
Borrego, Agustin ;
Ayala, Daniel ;
Hernandez, Inma ;
Rivero, Carlos R. ;
Ruiz, David .
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2021, 103
[7]   DBtravel: A Tourism-Oriented Semantic Graph [J].
Calleja, Pablo ;
Priyatna, Freddy ;
Mihindukulasooriya, Nandana ;
Rico, Mariano .
CURRENT TRENDS IN WEB ENGINEERING (ICWE 2018), 2018, 11153 :206-212
[8]   Pattern-based design applied to cultural heritage knowledge graphs [J].
Carriero, Valentina Anita ;
Gangemi, Aldo ;
Mancinelli, Maria Letizia ;
Nuzzolese, Andrea Giovanni ;
Presutti, Valentina ;
Veninata, Chiara .
SEMANTIC WEB, 2021, 12 (02) :313-357
[9]  
Chaves M. S., 2010, PROC CEUR WORKSHOP, V687, P1
[10]  
Consoli S., SEMANTIC WEB ESWC 20, V8798