research-article

Multiple-source Data Collection and Processing into a Graph Database Supporting Cultural Heritage Applications

Authors:

Antonio Origlia,

Sergio Di Martino,

Francesco Cutugno,

Maria Laura ChiacchioAuthors Info & Claims

Journal on Computing and Cultural Heritage (JOCCH), Volume 14, Issue 4

Article No.: 55, Pages 1 - 27

https://doi.org/10.1145/3465741

Published: 16 July 2021 Publication History

Abstract

The continuous growth of available resources on the web, both in the form of Linked Open Data and on Social Networks, provides an important opportunity to gather information concerning specific kinds of touristic activities like, for example, cultural tourism, eco-tourism, bike-tourism, and so on. Both decision makers and tourists can take advantage from these data, as demonstrated by previous works, with institutional actors foreseeing an increase in the use of this data to substitute other time-consuming and expensive approaches. However, managing multiple sources built with different goals and structures is not straightforward, so specific design choices must be made when assembling this kind of information. Graph databases represent an ideal way to combine multiple-source data but, to be successful, strategies accounting for inconsistencies and format differences have to be defined to support coherent analysis. Also, the continuously changing nature of crowd-sourced data makes it difficult, for the research community, to compare technological approaches to the different tasks that are linked to cultural heritage, from recommendation to management. To support the research effort in this direction, we describe the data ingestion and enrichment procedure we followed to organise knowledge coming from three different sources, namely Wikidata, Wikipedia, and Flickr, into a single, application-oriented, resource organised as a graph database. We present the potential use of this resource to perform multiple source analyses targeting the specific case of cultural tourism on a nationwide scale, and we propose its use as a shared benchmark for technological applications designed to support optimal management of cultural resources.

References

[1]

Merve Alanyali, Tobias Preis, and Helen Susannah Moat. 2016. Tracking protests using geotagged Flickr photographs. PLoS ONE 11, 3 (2016), e0150466.

[2]

John Aliprantis, Eirini Kalatha, Markos Konstantakis, Kostas Michalakis, and George Caridakis. 2018. Linked open data as universal markers for mobile augmented reality applications in cultural heritage. In Digital Cultural Heritage. Springer, 79–90.

[3]

Nika Balomenou and Brian Garrod. 2019. Photographs in tourism research: Prejudice, power, performance and participant-generated images. Tour. Manage. 70 (2019), 201–217.

[4]

Daniele Barchiesi, Helen Susannah Moat, Christian Alis, Steven Bishop, and Tobias Preis. 2015. Quantifying international travel flows using Flickr. PLoS ONE 10, 7 (2015), e0128470.

[5]

Emma Bell and Jane Davison. 2013. Visual management studies: Empirical and theoretical approaches. Int. J. Manage. Rev. 15, 2 (2013), 167–184.

[6]

Christian Bizer, Tom Heath, and Tim Berners-Lee. 2011. Linked data: The story so far. In Semantic Services, Interoperability and Web Applications: Emerging Concepts. IGI Global, 205–227.

[7]

Andrea Cacho, Mickael Figueredo, Arthur Cassio, Maria Valeria Araujo, Luiz Mendes, José Lucas, Hiarley Farias, Jazon Coelho, Nélio Cacho, and Carlos Prolo. 2016. Social smart destination: A platform to analyze user generated content in smart tourism destinations. In New Advances in Information Systems and Technologies. Springer, 817–826.

[8]

Gustavo Candela, Pilar Escobar, Rafael C. Carrasco, and Manuel Marco-Such. 2019. A linked open data framework to enhance the discoverability and impact of culture heritage. J. Inf. Sci. 45, 6 (2019), 756–766.

Digital Library

[9]

Weeraphan Chanhom and Chutiporn Anutariya. 2019. TOMS: A linked open data system for collaboration and distribution of cultural heritage artifact collections of national museums in thailand. New Gener. Comput. 37, 4 (2019), 479–498.

[10]

Gabriel J. Cherem and B. L. Driver. 1983. Visitor employed photography: A technique to measure common perceptions of natural environments. J. Leis. Res. 15, 1 (1983), 65–83.

[11]

Crm Cidoc. 2003. The CIDOC Conceptual Reference Model.

[12]

Andrea Fronzetti Colladon, Francesca Grippa, and Rosy Innarella. 2020. Studying the association of online brand importance with museum visitors: An application of the semantic brand score. Tour. Manage. Perspect. 33 (2020), 100588.

[13]

Christophe Demunter. 2017. Tourism statistics: Early adopters of big data. Technical Report. Publications Office of the European Union: Luxemburg (2017).

[14]

Sara Di Giorgio, Achille Felicetti, Patrizia Martini, and Emilia Masci. 2015. Dati. CulturaItalia: A Use Case of Publishing Linked Open Data Based on CIDOC-CRM. In EMF-CRM@ TPDL. 44–54.

[15]

Felix Dietze, Johannes Karoff, André Calero Valdez, Martina Ziefle, Christoph Greven, and Ulrik Schroeder. 2016. An open-source object-graph-mapping framework for neo4j and scala: Renesca. In Proceedings of the International Conference on Availability, Reliability, and Security. Springer, 204–218.

[16]

Georgios Drakopoulos, Andreas Kanavos, Christos Makris, and Vasileios Megalooikonomou. 2015. On converting community detection algorithms for fuzzy graphs in Neo4j. In Proceedings of the 5th International Workshop on Combinations of Intelligent Methods and Applications (CIMA’15).

[17]

Alain Dupeyras and Neil MacCallum. 2013. Indicators for measuring competitiveness in tourism: A guidance document. (2013).

[18]

Mickael Figueredo, Jose Ribeiro, Nelio Cacho, Antonio Thome, Andrea Cacho, Frederico Lopes, and Valeria Araujo. 2018. From Photos to Travel Itinerary: A tourism recommender system for smart tourism destination. In Proceedings of the 2018 IEEE 4th International Conference on Big Data Computing Service and Applications (BigDataService’18). IEEE, 85–92.

[19]

Simona Giglio, Francesca Bertacchini, Eleonora Bilotta, and Pietro Pantano. 2019. Using social media to identify tourism attractiveness in six Italian cities. Tour. Manage. 72 (2019), 306–312.

[20]

Vuokko Heikinheimo, Enrico Di Minin, Henrikki Tenkanen, Anna Hausmann, Joel Erkkonen, and Tuuli Toivonen. 2017. User-generated geographic information for visitor monitoring in a national park: A comparison of social media data and visitor survey. ISPRS Int. J. Geo-Inf. 6, 3 (2017), 85.

[21]

Johann Höchtl, Peter Parycek, and Ralph Schöllhammer. 2016. Big data in the policy cycle: Policy decision making in the digital era. J. Org. Comput. Electr. Commerce 26, 1-2 (2016), 147–169.

[22]

Antoine Isaac et al. 2013. Europeana data model primer. (2013).

[23]

Pablo Jiménez, Javier Villalba Diez, and Joaquin Ordieres-Mere. 2016. HOSHIN KANRI visualization with neo4j. empowering leaders to operationalize lean structural networks. Proc. CIRP 55 (2016), 284–289.

[24]

Tarmo Kalvet, Maarja Olesk, and Marek Tiits. 2020. Report on cultural tourism leading to sustainable economic and social development. IMPACTOUR Deliverable 1.1. European Commission, Brussels, Belgium.

[25]

Tarmo Kalvet, Maarja Olesk, Marek Tiits, and Janika Raun. 2020. Innovative tools for tourism and cultural tourism impact assessment. Sustainability 12, 18 (2020), 7470.

[26]

Yoonjung Kim, Choong-ki Kim, Dong Kun Lee, Hyun-woo Lee, and Rogelio II T Andrada. 2019. Quantifying nature-based tourism in protected areas in developing countries by using social big data. Tour. Manage. 72 (2019), 249–256.

[27]

Slava Kisilevich, Daniel Keim, Natalia Andrienko, and Gennady Andrienko. 2013. Towards acquisition of semantics of places and events by multi-perspective analysis of geotagged photo collections. In Geospatial Visualisation. Springer, 211–233.

[28]

Kwan Hui Lim. 2015. Recommending tours and places-of-interest based on user interests from geo-tagged photos. In Proceedings of the 2015 ACM SIGMOD on PhD Symposium. 33–38.

Digital Library

[29]

Heinz-Dieter Maas. 1972. Über den zusammenhang zwischen wortschatzumfang und länge eines textes. Zeitschr. Literaturwissensch. Ling. 2, 8 (1972), 73.

[30]

David Malvern, Brian Richards, Ngoni Chipere, and Pilar Durán. 2004. Lexical Diversity and Language Development. Springer.

[31]

Philip M. McCarthy. 2005. An Assessment of the Range and Usefulness of Lexical Diversity Measures and the Potential of the Measure of Textual, Lexical Diversity (MTLD). Ph.D. Dissertation. The University of Memphis.

[32]

Philip M. McCarthy and Scott Jarvis. 2007. vocd: A theoretical and empirical evaluation. Lang. Test. 24, 4 (2007), 459–488.

[33]

Philip M. McCarthy and Scott Jarvis. 2010. MTLD, vocd-D, and HD-D: A validation study of sophisticated approaches to lexical diversity assessment. Behav. Res. Methods 42, 2 (2010), 381–392.

[34]

Daniele Metilli, Valentina Bartalesi, and Carlo Meghini. 2019. A Wikidata-based tool for building and visualising narratives. Int. J. Dig. Libr. 20, 4 (2019), 417–432.

[35]

Shah Jahan Miah, Huy Quan Vu, John Gammack, and Michael McGrath. 2017. A big data analytics method for tourist behaviour analysis. Inf. Manage. 54, 6 (2017), 771–785.

[36]

Sue Newell and Marco Marabelli. 2015. Strategic opportunities (and challenges) of algorithmic decision-making: A call for action on the long-term societal effects of “datification”. J. Strat. Inf. Syst. 24, 1 (2015), 3–14.

Digital Library

[37]

Tuong Tri Nguyen, David Camacho, and Jai E. Jung. 2017. Identifying and ranking cultural heritage resources on geotagged social media for smart cultural tourism services. Pers. Ubiq. Comput. 21, 2 (2017), 267–279.

Digital Library

[38]

Naoko Nitta, Yusuke Kumihashi, Tomochika Kato, and Noboru Babaguchi. 2014. Real-world event detection using flickr images. In Proceedings of the International Conference on Multimedia Modeling. Springer, 307–314.

Digital Library

[39]

World Tourism Organization. 2018. Tourism and Culture Synergies.

[40]

Antonio Origlia, Antonio Rossi, Maria Laura Chiacchio, and Francesco Cutugno. 2016. Cultural heritage presentations with a humanoid robot using implicit feedback. In Proceedings of the Annual Conference on Advanced Visual Interfaces for Cultural Heritage (AVI* CH’16). 19–22.

[41]

Elisa Oteros-Rozas, Berta Martín-López, Nora Fagerholm, Claudia Bieling, and Tobias Plieninger. 2018. Using social media photos to explore the relation between cultural ecosystem services and landscape features across five European sites. Ecol. Indicat. 94 (2018), 74–86.

[42]

Ilhan Ozturk. 2016. The relationships among tourism development, energy demand, and growth factors in developed and developing countries. Int. J. Sust. Dev. World Ecol. 23, 2 (2016), 122–131.

[43]

Juho Pesonen and Mikko Lampi. 2016. Utilizing open data in tourism. In Proceedings of the ENTER 2016 Conference on Information and Communication Technologies in Tourism.

[44]

Ian Robinson, Jim Webber, and Emil Eifrem. 2013. Graph Databases. O’Reilly Media, Inc.

[45]

Silvia Rossi, Francesco Barile, Antonio Caso, and Alessandra Rossi. 2016. Pre-trip ratings and social networks user behaviors for recommendations in touristic web portals. In Web Information Systems and Technologies. Springer International Publishing, Cham, 297–317.

[46]

Silvia Rossi, Antonio Caso, and Francesco Barile. 2015. Combining users and items rankings for group decision support. In Trends in Practical Applications of Agents, Multi-Agent Systems and Sustainability. Springer International Publishing, Cham, 151–158.

[47]

Giuseppe Sansonetti, Fabio Gasparetti, and Alessandro Micarelli. 2019. Cross-Domain recommendation for enhancing cultural heritage experience. In Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization. 413–415.

Digital Library

[48]

Giuseppe Sansonetti, Fabio Gasparetti, Alessandro Micarelli, Federica Cena, and Cristina Gena. 2019. Enhancing cultural recommendations through social and linked open data. User Model. User-Adapt. Interact. 29, 1 (2019), 121–159.

Digital Library

[49]

Carrie Sessions, Spencer A. Wood, Sergey Rabotyagov, and David M. Fisher. 2016. Measuring recreational visitation at US national parks with crowd-sourced photographs. J. Environ. Manage. 183 (2016), 703–711.

[50]

Hu Shao, Yi Zhang, and Wenwen Li. 2017. Extraction and analysis of city’s tourism districts based on social media data. Comput. Environ. Urban Syst. 65 (2017), 66–78.

[51]

Evaggelos Spyrou and Phivos Mylonas. 2016. A survey on Flickr multimedia research challenges. Eng. Appl. Artif. Intell. 51 (2016), 71–91.

Digital Library

[52]

Shungo Suzuki and Judit Kormos. 2020. Linguistic dimensions of comprehensibility and perceived fluency: An investigation of complexity, accuracy, and fluency in second language argumentative speech. Stud. Sec. Lang. Acquis. 42, 1 (2020), 143–167.

[53]

M. C. Templin. 1957. Certain language skills in children. The Institute of Child Welfare, University of Minnesota, Minneapolis.

[54]

Steven Van Canneyt, Steven Schockaert, Olivier Van Laere, and Bart Dhoedt. 2011. Time-dependent recommendation of tourist attractions using Flickr. In Proceedings of the 23rd Benelux Conference on Artificial Intelligence (BNAIC’11).

[55]

Chelsey Walden-Schreiner, Sebastian Dario Rossi, Agustina Barros, Catherine Pickering, and Yu-Fai Leung. 2018. Using crowd-sourced photos to assess seasonal patterns of visitor use in mountain-protected areas. Ambio 47, 7 (2018), 781–793.

[56]

Jim Webber. 2012. A programmatic introduction to Neo4j. In Proceedings of the 3rd Annual Conference on Systems, Programming, and Applications: Aoftware for Humanity. ACM, 217–218.

Digital Library

[57]

Spencer A. Wood, Anne D. Guerry, Jessica M. Silver, and Martin Lacayo. 2013. Using social media to quantify nature-based tourism and recreation. Sci. Rep. 3, 1 (2013), 1–7.

[58]

Trong Wu. 1993. An accurate computation of the hypergeometric distribution function. ACM Trans. Math. Softw. 19, 1 (1993), 33–43.

Digital Library

[59]

Wenpu Xing and Ali Ghorbani. 2004. Weighted pagerank algorithm. In Proceedings of the 2nd Annual Conference on Communication Networks and Services Research. IEEE, 305–314.

[60]

Zhenguo Yang, Qing Li, Zheng Lu, Yun Ma, Zhiguo Gong, and Wenyin Liu. 2017. Dual structure constrained multimodal feature coding for social event detection from flickr data. ACM Trans. Internet Technol. 17, 2 (2017), 1–20.

Digital Library

[61]

Yihong Yuan and Monica Medel. 2016. Characterizing international travel behavior from geotagged photos: A case study of flickr. PLoS ONE 11, 5 (2016), e0154885.

[62]

Yu Zheng, Lizhu Zhang, Zhengxin Ma, Xing Xie, and Wei-Ying Ma. 2011. Recommending friends and locations based on individual location history. ACM Trans. Web 5, 1 (2011), 1–44.

Digital Library

[63]

Yu Zheng, Lizhu Zhang, Xing Xie, and Wei-Ying Ma. 2009. Mining interesting locations and travel sequences from GPS trajectories. In Proceedings of the 18th International Conference on World Wide Web. 791–800.

Digital Library

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Index Terms

Multiple-source Data Collection and Processing into a Graph Database Supporting Cultural Heritage Applications
1. Information systems
  1. Data management systems
    1. Database design and models
      1. Graph-based database models
        Hierarchical data models
    2. Information integration
  2. Information retrieval
    1. Document representation
      1. Document collection models
    2. Retrieval tasks and goals
      1. Information extraction

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cover image Journal on Computing and Cultural Heritage

Journal on Computing and Cultural Heritage Volume 14, Issue 4

December 2021

328 pages

ISSN:1556-4673

EISSN:1556-4711

DOI:10.1145/3476246

Editor:
Franco Niccolucci
VAST-LAB at PIN, University of Florence, Italy

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Copyright © 2021 ACM.

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Publication History

Published: 16 July 2021

Accepted: 01 May 2021

Received: 01 October 2020

Published in JOCCH Volume 14, Issue 4

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Italian PRIN project Cultural Heritage Resources Orienting Multimodal Experience (CHROME)

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Ranjgar BSadeghi-Niaraki AShakeri MRahimi FChoi S(2024)Cultural Heritage Information Retrieval: Past, Present, and Future TrendsIEEE Access10.1109/ACCESS.2024.337476912(42992-43026)Online publication date: 2024
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Gao JPeng PLu FClaramunt CQiu PXu Y(2024)Mining tourist preferences and decision support via tourism-oriented knowledge graphInformation Processing and Management: an International Journal10.1016/j.ipm.2023.10352361:1Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.ipm.2023.103523
Li JBikakis A(2023)Towards a Semantics-Based Recommendation System for Cultural Heritage CollectionsApplied Sciences10.3390/app1315890713:15(8907)Online publication date: 2-Aug-2023
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Cursi SMartinelli LParaciani NCalcerano FGigliarelli E(2022)Linking external knowledge to heritage BIMAutomation in Construction10.1016/j.autcon.2022.104444141(104444)Online publication date: Sep-2022
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