Unifying points of interest taxonomies: mapping OpenStreetMap tags to the Foursquare category system

TL;DR

This paper presents a framework for aligning OpenStreetMap POI tags with Foursquare categories, enabling better integration of urban datasets and services through a benchmark, embedding evaluation, and LLM-based refinement.

Contribution

It introduces a scalable, reproducible methodology for unifying heterogeneous POI taxonomies using a benchmark, embedding models, and language models, facilitating urban data interoperability.

Findings

Embedding models effectively align OSM tags with FS categories.

LLM-based refinement improves alignment robustness.

The framework supports scalable updates as OSM evolves.

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.