CrowdGeoKG: Crowdsourced Geo-Knowledge Graph

Jiaoyan Chen, Shumin Deng, Huajun Chen

Abstract: Geo-Knowledge which is known as semantic enriched geographic information plays an important role in many intelligent applications like named-entity recognition and information retrieval. With the development of Internet, volunteers on web-based crowdsourcing platforms like OpenStreetMap (OSM) and Wikidata have contributed big geographic data which however have not been widely studied towards extracting and linking geo-knowledge. In this paper, we presented a crowdsourced geographic knowledge graph named CrowdGeoKG which extracted different kinds of geo-entities from OpenStreetMap and enriched them with human geography knowledge from Wikidata. We further exploited the part of CrowdGeoKG in China, studying the linkage between OpenStreetMap geo-entities and Wikidata geo-entities. CrowdGeoKG is stored in both RDF (Resource Description Framework) and JSON-LD formats, and shared for re-usage on an open knowledge graph community named OpenKG.