How to identify appropriate key-value pairs for querying OSM

Abstract

This paper presents a study on how natural language words that designate types of spatial entities (metropolis, city, creek, etc.) can automatically be translated to the entity classification used in OpenStreetMap (OSM) that assigns key-value tags to entities. The problem of identifying key-value pairs for querying OSM occurs in geographic information retrieval based on natural language text and is difficult for three reasons: Conceptualisation of entities in natural language text and in OSM often differs. Even classification of a single entity type is subject to variations throughout the OSM database. Language is rich and offers many words to communicate nuances of a single entity type. The contribution of this paper is to analyse the contribution of semantic word similarity using Word-Net to identify a mapping from natural language to OSM tags. We present a strategy to identify key-value pairs for natural language words using WordNet and analyse its effectiveness.