A Machine Learning Framework for Harvesting and Harmonizing Cultural and Touristic Data
保存先:
| 出版年: | Information vol. 16, no. 12 (2025), p. 1038-1083 |
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| 第一著者: | |
| その他の著者: | , , , , |
| 出版事項: |
MDPI AG
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| 主題: | |
| オンライン･アクセス: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| タグ: |
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MARC
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| 003 | UK-CbPIL | ||
| 022 | |a 2078-2489 | ||
| 024 | 7 | |a 10.3390/info16121038 |2 doi | |
| 035 | |a 3286306681 | ||
| 045 | 2 | |b d20251201 |b d20251231 | |
| 084 | |a 231474 |2 nlm | ||
| 100 | 1 | |a Deligiannis Kimon | |
| 245 | 1 | |a A Machine Learning Framework for Harvesting and Harmonizing Cultural and Touristic Data | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Machine learning | ||
| 653 | |a Culture | ||
| 653 | |a Sentiment analysis | ||
| 653 | |a Data mining | ||
| 653 | |a Multilingual systems | ||
| 653 | |a Small & medium sized enterprises-SME | ||
| 653 | |a Social networks | ||
| 653 | |a User generated content | ||
| 653 | |a Homogenization | ||
| 653 | |a Data collection | ||
| 653 | |a Natural language processing | ||
| 653 | |a Multilingualism | ||
| 653 | |a Tourism | ||
| 653 | |a Cultural heritage | ||
| 700 | 1 | |a Tryfonopoulos Christos | |
| 700 | 1 | |a Raftopoulou Paraskevi | |
| 700 | 1 | |a Vassilakis Costas | |
| 700 | 1 | |a Kaffes Vassilis | |
| 700 | 1 | |a Skiadopoulos Spiros | |
| 773 | 0 | |t Information |g vol. 16, no. 12 (2025), p. 1038-1083 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3286306681/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3286306681/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3286306681/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |