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We present a corpus of 2,380 natural language queries paired with machine readable formulae that can be executed against world wide geographic data of the OpenStreetMap (OSM) database. We use the corpus to learn an accurate semantic parser that builds the basis of a natural language interface to OSM. Furthermore, we use response-based learning on parser feedback to adapt a statistical machine translation system for multilingual database access to OSM. Our framework allows to map fuzzy natural language expressions such as “nearby”, “north of”, or “in walking distance” to spatial polygons on an interactive map. Furthermore, it combines syntactic complexity and compositionality with a reasonable lexical variability of queries, making it an interesting new publicly available dataset for research on semantic parsing.

A Corpus and Semantic Parser for Multilingual Natural Language Querying of OpenStreetMap