WorldKG: A World-Scale Geographic Knowledge Graph
This provides a more usable geographic data source for real-world applications, though it is incremental as it builds on existing OpenStreetMap data.
The paper tackles the problem of heterogeneous and incomplete geographic entity representation in OpenStreetMap by introducing WorldKG, a comprehensive semantic geographic knowledge graph, resulting in a large-scale dataset with high precision.
OpenStreetMap is a rich source of openly available geographic information. However, the representation of geographic entities, e.g., buildings, mountains, and cities, within OpenStreetMap is highly heterogeneous, diverse, and incomplete. As a result, this rich data source is hardly usable for real-world applications. This paper presents WorldKG -- a new geographic knowledge graph aiming to provide a comprehensive semantic representation of geographic entities in OpenStreetMap. We describe the WorldKG knowledge graph, including its ontology that builds the semantic dataset backbone, the extraction procedure of the ontology and geographic entities from OpenStreetMap, and the methods to enhance entity annotation. We perform statistical and qualitative dataset assessment, demonstrating the large scale and high precision of the semantic geographic information in WorldKG.