Abstract
Preservation and development of intangible cultural heritage are one of the burning problems in rich cultural countries. They are significant characteristics to express the substantial identity of a nation. At present, leading artificial intelligence (AI) into the cultural development strategy is a promising approach to introduce remarkable features to the world community, especially in tourism. However, detecting and discovering the festival knowledge at a local place in a country is not only an exciting problem for travelers but also a challenge for researchers and managers. We, therefore, propose a festival information preservation framework that applied AI techniques to seek and exploit the festival knowledge in Vietnam. We provide a Vietnamese festival lightweight ontology. We investigate and implement a practical application for identifying and mining the Vietnamese festival knowledge in Mekong Delta for the first work. Our initial approach concentrates on providing a Web application that classifies the snapshot and automatically answers questions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Afsahhosseini F, Al-Mulla Y (2020) Machine learning in tourism. In: ICMLMI’20
Alotaibi E (2020) Application of machine learning in the hotel industry: a critical review. J AAUTH
Barile S, Saviano M (2015) From the management of cultural heritage to the governance of the cultural heritage system. Springer, Cham, pp 71–103
Bulchand-Gidumal J (2020) Impact of artificial intelligence in travel, tourism, and hospitality. Springer, Cham, pp 1–20
Chang M, Yuan Y, Yue Q, Mincheol H (2020) A CNN image classification analysis for clean-coast detector as tourism service distribution. SST 18:15–26
Chen Y, Li H, Hua Y, Qi G (2020) Formal query building with query structure prediction for complex question answering over knowledge base. In: IJCAI-20, pp 3751–3758
Chollet F (2017) Deep learning with Python. Manning Publications Co., USA
Colace F, De Santo M, Greco L, Chianese A, Moscato V, Picariello A (2013) CHIS: cultural heritage information system. IJKSR 4:18–26
Daramola O, Adigun M, Ayo C (2009) Building an ontology-based framework for tourism recommendation services. In: ICA’09, pp 135–147
Devlin J, Chang MW, Lee K, Toutanova K (2019) BERT: pre-training of deep bidirectional transformers for language understanding. In: ACL’19, pp 4171–4186
Do PQ (2007) Traditional festivals in Viet Nam. World Publisher, p 253
Do TN, Pham TP, Pham NK, Huu Hoa N, Tabia K, Benferhat S (2020) Stacking of SVMs for classifying intangible cultural heritage images, pp 186–196
Heaton J (2021) Applications of deep neural networks
Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) MobileNets: efficient CNNs for mobile vision applications
Jankovic R (2019) Machine learning models for cultural heritage image classification: comparison based on attribute selection. information (Switzerland) 11
Jin R, Dou Y, Wang Y, Niu X (2017) Confusion graph: detecting confusion communities in large scale image classification. In: IJCAI-17, pp 1980–1986
Kim D, Kang Y, Park Y, Kim N, Lee J (2019) Understanding tourists’ urban images with geotagged photos using convolutional neural networks. In: SIR 28
Laddha S (2018) Indian tourism information retrieval system: an onto-semantic approach. Procedia Comput Sci 132C:1363–1374
Lan Z, Chen M, Goodman S, Gimpel K, Sharma P, Soricut R (2020) ALBERT: a Lite BERT for self-supervised learning of language representations. In: NCLR-20
Lu D, Weng Q (2007) A survey of image classification methods and techniques for improving classification performance. Int J Remote Sens 28(5):823–870
Ma TT, Benferhat S, Bouraoui Z, Tabia K, Do TN, Nguyen H (2018) An ontology-based modelling of Vietnamese traditional dances. In: SEKE
Ma TT, Benferhat S, Bouraoui Z, Tabia K, Do TN, Pham NK (2019) An automatic extraction tool for ethnic Vietnamese Thai dances concepts. In: ICMLA’19
Nguyen DQ, Nguyen AT (2020) PhoBERT: pre-trained language models for Vietnamese
Prantner K, Ding Y, Luger M, Yan Z, Herzog C (2007) Tourism ontology and semantic management system: state-of-the-arts analysis. IADIS’07
Sribunthankul P, Sureephong P, Tabia K, Ma TT (2019) Developing the evaluation system of the Thai dance training tool. ECTI DAMT-NCON’19, pp 163–167
Tanasijevic I, Pavlovic-Lazetic G (2020) HerCulB: content-based information extraction and retrieval for cultural heritage of the Balkans. The Electronic Library
Virmani C, Sinha S, Khatri SK (2017) Unified ontology for data integration for tourism sector. In: ICTUS’17. pp 152–156
Wang P, Wu Q, Shen C, Dick A, van den Hengel A (2017) Explicit knowledge-based reasoning for visual question answering. In: IJCAI-17, pp 1290–1296
Wilcock G (2018) Using a deep learning dialogue research toolkit in a multilingual multidomain practical application. In: IJCAI-18, pp 5880–5882
Xia X, Xu C, Nan B (2017) Inception-v3 for flower classification. In: ICIVC’17, pp 783–787
Xue W, Wang W (2020) One-shot image classification by learning to restore prototypes. AAAI’20, vol 34, pp 6558–6565
Zhang H, Wu H, Sun W, Zheng B (2018) DeepTravel: a neural network based travel time estimation model with auxiliary supervision. In: IJCAI-18, pp 3655–3661
Zhang L, Sun Z (2019) The application of artificial intelligence technology in the tourism industry of Jinan. J Phys: Conf Ser 1302:032005
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Chau, NK., Ma, TT., Bouraoui, Z., Do, TN. (2022). A Vietnamese Festival Preservation Application. In: Ullah, A., Anwar, S., Rocha, Á., Gill, S. (eds) Proceedings of International Conference on Information Technology and Applications. Lecture Notes in Networks and Systems, vol 350. Springer, Singapore. https://doi.org/10.1007/978-981-16-7618-5_39
Download citation
DOI: https://doi.org/10.1007/978-981-16-7618-5_39
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-7617-8
Online ISBN: 978-981-16-7618-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)