{"ID":2840607,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.13369","arxiv_id":"2511.13369","title":"Unifying points of interest taxonomies: mapping OpenStreetMap tags to the Foursquare category system","abstract":"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.","short_abstract":"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 presen...","url_abs":"https://arxiv.org/abs/2511.13369","url_pdf":"https://arxiv.org/pdf/2511.13369v1","authors":"[\"Lilou Soulas\",\"Lorenzo Lucchini\",\"Maurizio Napolitano\",\"Sebastiano Bontorin\",\"Simone Centellegher\",\"Bruno Lepri\",\"Riccardo Gallotti\",\"Eleonora Andreotti\"]","published":"2025-11-17T13:43:25Z","proceeding":"cs.SI","tasks":"[\"cs.SI\",\"physics.soc-ph\"]","methods":"[\"Large Language Model\"]","has_code":false}