Unifying points of interest taxonomies: mapping OpenStreetMap tags to the Foursquare category system
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909908540063744 |
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| author | Soulas, Lilou Lucchini, Lorenzo Napolitano, Maurizio Bontorin, Sebastiano Centellegher, Simone Lepri, Bruno Gallotti, Riccardo Andreotti, Eleonora |
| author_facet | Soulas, Lilou Lucchini, Lorenzo Napolitano, Maurizio Bontorin, Sebastiano Centellegher, Simone Lepri, Bruno Gallotti, Riccardo Andreotti, Eleonora |
| contents | The heterogeneity of Point of Interest (POI) taxonomies is a persistent challenge for the integration of urban datasets and the development of location-based services. OpenStreetMap (OSM) adopts a flexible, community-driven tagging system, while Foursquare (FS) relies on a curated hierarchical structure. Here we present an openly available benchmark and mapping framework that aligns OSM tags with the FS taxonomy. This resource integrates the richness of community-driven OSM data with the hierarchical structure of FS, enabling reproducible and interoperable urban analytics. The dataset is complemented by an evaluation of embedding and LLM-based alignment strategies and a pipeline that supports scalable updates as OSM evolves. Together, these elements provide both a robust reference resource and a practical tool for the community. Our approach is structured around three components: the construction of a manually curated benchmark as a gold standard, the evaluation of pretrained text embedding models for semantic alignment between OSM tags and FS categories, and an LLM-based refinement stage that enhances robustness and adaptability. The proposed methodology provides a scalable and reproducible solution for taxonomy unification, with direct applications to urban analytics, mobility studies, and smart city services. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_13369 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unifying points of interest taxonomies: mapping OpenStreetMap tags to the Foursquare category system Soulas, Lilou Lucchini, Lorenzo Napolitano, Maurizio Bontorin, Sebastiano Centellegher, Simone Lepri, Bruno Gallotti, Riccardo Andreotti, Eleonora Social and Information Networks Physics and Society The heterogeneity of Point of Interest (POI) taxonomies is a persistent challenge for the integration of urban datasets and the development of location-based services. OpenStreetMap (OSM) adopts a flexible, community-driven tagging system, while Foursquare (FS) relies on a curated hierarchical structure. Here we present an openly available benchmark and mapping framework that aligns OSM tags with the FS taxonomy. This resource integrates the richness of community-driven OSM data with the hierarchical structure of FS, enabling reproducible and interoperable urban analytics. The dataset is complemented by an evaluation of embedding and LLM-based alignment strategies and a pipeline that supports scalable updates as OSM evolves. Together, these elements provide both a robust reference resource and a practical tool for the community. Our approach is structured around three components: the construction of a manually curated benchmark as a gold standard, the evaluation of pretrained text embedding models for semantic alignment between OSM tags and FS categories, and an LLM-based refinement stage that enhances robustness and adaptability. The proposed methodology provides a scalable and reproducible solution for taxonomy unification, with direct applications to urban analytics, mobility studies, and smart city services. |
| title | Unifying points of interest taxonomies: mapping OpenStreetMap tags to the Foursquare category system |
| topic | Social and Information Networks Physics and Society |
| url | https://arxiv.org/abs/2511.13369 |