OsmT: Bridging OpenStreetMap Queries and Natural Language with Open-source Tag-aware Language Models

2025-12-04

AI Summary

This work addresses the high cost, low transparency, and deployment difficulty of large-parameter closed-source models in natural language-to-OverpassQL (NL2OverpassQL) translation. We propose a lightweight, open-source solution centered on a Tag Retrieval Augmentation (TRA) mechanism that explicitly models the hierarchical and relational structure of OpenStreetMap (OSM) tags. To enhance semantic alignment, we introduce a reverse task—OverpassQL-to-text generation—enabling bidirectional semantic grounding. Our approach leverages open pre-trained language models and integrates context-aware tag retrieval with dual generative learning: NL2OverpassQL and OverpassQL2Text. Evaluated on a public benchmark, our method significantly outperforms strong baselines despite using substantially fewer parameters. Results demonstrate superior efficiency, interpretability, and deployment feasibility for complex geospatial semantic parsing.

Technology Category

• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Planning, Routing, and Scheduling: Planning with Language Models
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Large language models for search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Bridging natural language and structured query languages is a long-standing challenge in the database community. While recent advances in language models have shown promise in this direction, existing solutions often rely on large-scale closed-source models that suffer from high inference costs, limited transparency, and lack of adaptability for lightweight deployment. In this paper, we present OsmT, an open-source tag-aware language model specifically designed to bridge natural language and Overpass Query Language (OverpassQL), a structured query language for accessing large-scale OpenStreetMap (OSM) data. To enhance the accuracy and structural validity of generated queries, we introduce a Tag Retrieval Augmentation (TRA) mechanism that incorporates contextually relevant tag knowledge into the generation process. This mechanism is designed to capture the hierarchical and relational dependencies present in the OSM database, addressing the topological complexity inherent in geospatial query formulation. In addition, we define a reverse task, OverpassQL-to-Text, which translates structured queries into natural language explanations to support query interpretation and improve user accessibility. We evaluate OsmT on a public benchmark against strong baselines and observe consistent improvements in both query generation and interpretation. Despite using significantly fewer parameters, our model achieves competitive accuracy, demonstrating the effectiveness of open-source pre-trained language models in bridging natural language and structured query languages within schema-rich geospatial environments.

Problem

• Bridging natural language and Overpass Query Language for OpenStreetMap
• Enhancing query accuracy with tag retrieval augmentation mechanism
• Translating structured queries to natural language for user accessibility

Innovation

• Open-source tag-aware language model for OSM queries
• Tag Retrieval Augmentation mechanism for query accuracy
• Reverse task OverpassQL-to-Text for query interpretation