Finding equivalent keys in openstreetmap: semantic similarity computation based on extensional definitions

Abstract

Volunteered Geographic Information (VGI) projects, such as Open-StreetMap (OSM) enable the public to contribute to the collection of spatial data. In OSM, users may deviate from spatial feature annotation guidelines and create new tags (i.e. key=value pairs), even if recommended tags exist. This is problematic, as undocumented tags have no set meaning, and they potentially contribute to the dataset heterogeneity and thus reduce usability. This paper proposes an unsupervised approach to identify equivalent documented attribute keys to the used undocumented keys. Based on their extensional definitions through their values, co-occurring keys and geometries of the features they annotate, the semantic similarity of OSM keys is evaluated. The approach has been tested on the OSM dataset for the state of Victoria, Australia. Results have been evaluated against a set of manually detected equivalent keys and show that the method is plausible, but may fail if some assumptions about tag use are not enforced, e.g., semantically unique tags.