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A Machine Learning Framework for Harvesting and Harmonizing Cultural and Touristic Data

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Publicado en:Information vol. 16, no. 12 (2025), p. 1038-1083
Autor principal: Deligiannis Kimon
Otros Autores: Tryfonopoulos Christos, Raftopoulou Paraskevi, Vassilakis Costas, Kaffes Vassilis, Skiadopoulos Spiros
Publicado:
MDPI AG
Materias:
Machine learning
Culture
Sentiment analysis
Data mining
Multilingual systems
Small & medium sized enterprises-SME
Social networks
User generated content
Homogenization
Data collection
Natural language processing
Multilingualism
Tourism
Cultural heritage
Acceso en línea:Citation/Abstract
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Resumen:Cultural and touristic information is increasingly available through a multitude of heterogeneous sources, including official repositories, community platforms, and open data initiatives. While prominent landmarks are typically covered across sources, less-known attractions are also documented with varying degrees of detail, resulting in fragmented, overlapping, or complementary content. To enable integrated access to this wealth of information, harvesting and consolidation mechanisms are required to collect, reconcile, and unify distributed content referring to the same entities. This paper presents a machine learning-driven framework for harvesting, homogenizing, and augmenting cultural and touristic data across multilingual sources. Our approach addresses entity resolution, duplication detection, and content harmonization, laying the foundation for enriched, unified representations of attractions and points of interest. The framework is designed to support scalable integration pipelines and can be deployed in applications aimed at tourism promotion, digital heritage, and smart travel services.
ISSN:2078-2489
DOI:10.3390/info16121038
Fuente:Advanced Technologies & Aerospace Database