TY  - JOUR
AU  - Rebai, Olfa
AU  - Charni, Maram
AU  - Aribi, Hiba Ben
AU  - Temessek, Malek Ben
AU  - Fattouch, Sami
AU  - Raboudi, Faten
PY  - 2025
DA  - 2025/12/07
TI  - AI-driven Data Management of Traditional Tunisian Nutritional Dishes: A Cultural Heritage Conservation
JO  - Next-Generation Computing Systems and Technologies
T2  - Next-Generation Computing Systems and Technologies
JF  - Next-Generation Computing Systems and Technologies
VL  - 1
IS  - 2
SP  - 54
EP  - 61
DO  - 10.62762/NGCST.2025.714702
UR  - https://www.icck.org/article/abs/NGCST.2025.714702
KW  - cultural heritage preservation
KW  - nutritional data systems
KW  - natural language processing
KW  - interactive platform
KW  - food heritage digitization
AB  - The potential loss of traditional Tunisian dishes threatens the sustainability of valuable cultural and nutritional traditions. To help preserve this rich heritage, a conversational AI system has been developed that employs advanced language processing and machine learning techniques to bring Tunisia’s culinary traditions to life in a digital space. Multilingual transformer models have been adapted to understand Tunisian dialects and combined with a detailed Food Heritage Knowledge Graph, allowing personalized, interactive access to authentic recipes and the stories behind them. A hybrid dialogue system operated by a chatbot has been implemented to ensure smooth, meaningful conversations that respect cultural sensitivities and build user trust and engagement.Despite challenges such as dialect diversity and limited data, it is demonstrated that modern AI can effectively capture and share complex cultural knowledge. Plans are underway to expand dialect support through federated learning and to improve contextual understanding with smarter memory models. Overall, this project highlights how technology and tradition can be connected through AI, supporting cultural preservation, promoting gastronomic tourism, and encouraging healthier eating habits in Tunisia.
SN  - 3070-3328
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Rebai2025AIdriven,
  author = {Olfa Rebai and Maram Charni and Hiba Ben Aribi and Malek Ben Temessek and Sami Fattouch and Faten Raboudi},
  title = {AI-driven Data Management of Traditional Tunisian Nutritional Dishes: A Cultural Heritage Conservation},
  journal = {Next-Generation Computing Systems and Technologies},
  year = {2025},
  volume = {1},
  number = {2},
  pages = {54-61},
  doi = {10.62762/NGCST.2025.714702},
  url = {https://www.icck.org/article/abs/NGCST.2025.714702},
  abstract = {The potential loss of traditional Tunisian dishes threatens the sustainability of valuable cultural and nutritional traditions. To help preserve this rich heritage, a conversational AI system has been developed that employs advanced language processing and machine learning techniques to bring Tunisia’s culinary traditions to life in a digital space. Multilingual transformer models have been adapted to understand Tunisian dialects and combined with a detailed Food Heritage Knowledge Graph, allowing personalized, interactive access to authentic recipes and the stories behind them. A hybrid dialogue system operated by a chatbot has been implemented to ensure smooth, meaningful conversations that respect cultural sensitivities and build user trust and engagement.Despite challenges such as dialect diversity and limited data, it is demonstrated that modern AI can effectively capture and share complex cultural knowledge. Plans are underway to expand dialect support through federated learning and to improve contextual understanding with smarter memory models. Overall, this project highlights how technology and tradition can be connected through AI, supporting cultural preservation, promoting gastronomic tourism, and encouraging healthier eating habits in Tunisia.},
  keywords = {cultural heritage preservation, nutritional data systems, natural language processing, interactive platform, food heritage digitization},
  issn = {3070-3328},
  publisher = {Institute of Central Computation and Knowledge}
}