%0 Conference Paper %1 dsouza2021worldkg %A Dsouza, Alishiba %A Tempelmeier, Nicolas %A Yu, Ran %A Gottschalk, Simon %A Demidova, Elena %B Proc. of the 30th ACM International Conference on Information and Knowledge Management (ACM CIKM 2021) %D 2021 %I ACM %K dsouza gottschalk myown tempelmeier worldkg yu %P 4475-4484 %R 10.1145/3459637.3482023 %T WorldKG: A World-Scale Geographic Knowledge Graph %X 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.

@inproceedings{dsouza2021worldkg, abstract = {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.}, added-at = {2021-08-14T23:05:33.000+0200}, author = {Dsouza, Alishiba and Tempelmeier, Nicolas and Yu, Ran and Gottschalk, Simon and Demidova, Elena}, biburl = {https://www.bibsonomy.org/bibtex/2b70dd470a6f1fa5ead7225bbe8a29f64/demidova}, booktitle = {Proc. of the 30th ACM International Conference on Information and Knowledge Management (ACM CIKM 2021)}, doi = {10.1145/3459637.3482023}, interhash = {cd7365d181262ed488561f98d15628ba}, intrahash = {b70dd470a6f1fa5ead7225bbe8a29f64}, keywords = {dsouza gottschalk myown tempelmeier worldkg yu}, pages = {4475-4484}, publisher = {ACM}, timestamp = {2022-02-27T13:49:49.000+0100}, title = {WorldKG: A World-Scale Geographic Knowledge Graph}, year = 2021 }