GeoVectors - Knowledge Graph

The GeoVectors corpus is a comprehensive large-scale linked open corpus of OpenStreetMap (https://www.openstreetmap.org/) entity embeddings that provides latent representations of over 980 million entities. The GeoVectors capture the semantic and geographic dimensions of OpenStreetMap entities and m...

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Main Authors: Tempelmeier, Nicolas, Gottschalk, Simon, Demidova, Elena
Format: Dataset
Language:unknown
Published: 2020
Subjects:
Online Access:https://zenodo.org/record/4339524
https://doi.org/10.5281/zenodo.4339524
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spelling ftzenodo:oai:zenodo.org:4339524 2023-05-15T14:00:37+02:00 GeoVectors - Knowledge Graph Tempelmeier, Nicolas Gottschalk, Simon Demidova, Elena 2020-12-17 https://zenodo.org/record/4339524 https://doi.org/10.5281/zenodo.4339524 unknown doi:10.5281/zenodo.4339523 https://zenodo.org/record/4339524 https://doi.org/10.5281/zenodo.4339524 oai:zenodo.org:4339524 info:eu-repo/semantics/openAccess https://opendatacommons.org/licenses/odbl/1-0/ info:eu-repo/semantics/other dataset 2020 ftzenodo https://doi.org/10.5281/zenodo.433952410.5281/zenodo.4339523 2023-03-11T03:42:21Z The GeoVectors corpus is a comprehensive large-scale linked open corpus of OpenStreetMap (https://www.openstreetmap.org/) entity embeddings that provides latent representations of over 980 million entities. The GeoVectors capture the semantic and geographic dimensions of OpenStreetMap entities and make them directly accessible to machine learning applications. The "-tags" datasets provide embeddings that capture the semantic dimension of OpenStreetMap entities. The "-location" datasets provide the geographic dimension. This repository contains the GeoVectors Knowledge graph that models metadata of the embeddings and links to well-established sources such as Wikidata and DBpedia. The GeoVectors corpus is partitioned into regional subsets. The GeoVectors knowledge graph can be used to identify the subset that contains a particular linked entity. GeoVectors consists of the following subsets: Africa Africa. Tags: 10.5281/zenodo.4320881. Location: 10.5281/zenodo.4322486. Antarctica Antarctica. Tags: 10.5281/zenodo.4320869. Location: 10.5281/zenodo.4322563. Asia Asia. Tags: 10.5281/zenodo.4320895. Location: 10.5281/zenodo.4322859. Japan. Tags: 10.5281/zenodo.4320895. Location: 10.5281/zenodo.4323105. Indonesia. Tags: 10.5281/zenodo.4320895. Location: 10.5281/zenodo.4323074. Australia-Oceania Australia-Oceania. Tags: 10.5281/zenodo.4320963. Location: 10.5281/zenodo.4322931. Central-America Central-America. Tags: 10.5281/zenodo.4321010. Location: 10.5281/zenodo.4322935. Europe Europe-East. Tags: 10.5281/zenodo.4321012. Location: 10.5281/zenodo.4322948. Europe-West. Tags: 10.5281/zenodo.4321099. Location: 10.5281/zenodo.4322972. France. Tags: 10.5281/zenodo.4321153. Location: 10.5281/zenodo.4322994. Germany (Nodes, Relations). Tags: 10.5281/zenodo.4321406. Location: 10.5281/zenodo.4323008. Germany (Ways). Tags: 10.5281/zenodo.4321420. Location: 10.5281/zenodo.4323008. Great-Britain. Tags: 10.5281/zenodo.4321175. Location: 10.5281/zenodo.4323020. Italy. Tags: 10.5281/zenodo.4321206. Location: 10.5281/zenodo.4323088. ... Dataset Antarc* Antarctica Zenodo
institution Open Polar
collection Zenodo
op_collection_id ftzenodo
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description The GeoVectors corpus is a comprehensive large-scale linked open corpus of OpenStreetMap (https://www.openstreetmap.org/) entity embeddings that provides latent representations of over 980 million entities. The GeoVectors capture the semantic and geographic dimensions of OpenStreetMap entities and make them directly accessible to machine learning applications. The "-tags" datasets provide embeddings that capture the semantic dimension of OpenStreetMap entities. The "-location" datasets provide the geographic dimension. This repository contains the GeoVectors Knowledge graph that models metadata of the embeddings and links to well-established sources such as Wikidata and DBpedia. The GeoVectors corpus is partitioned into regional subsets. The GeoVectors knowledge graph can be used to identify the subset that contains a particular linked entity. GeoVectors consists of the following subsets: Africa Africa. Tags: 10.5281/zenodo.4320881. Location: 10.5281/zenodo.4322486. Antarctica Antarctica. Tags: 10.5281/zenodo.4320869. Location: 10.5281/zenodo.4322563. Asia Asia. Tags: 10.5281/zenodo.4320895. Location: 10.5281/zenodo.4322859. Japan. Tags: 10.5281/zenodo.4320895. Location: 10.5281/zenodo.4323105. Indonesia. Tags: 10.5281/zenodo.4320895. Location: 10.5281/zenodo.4323074. Australia-Oceania Australia-Oceania. Tags: 10.5281/zenodo.4320963. Location: 10.5281/zenodo.4322931. Central-America Central-America. Tags: 10.5281/zenodo.4321010. Location: 10.5281/zenodo.4322935. Europe Europe-East. Tags: 10.5281/zenodo.4321012. Location: 10.5281/zenodo.4322948. Europe-West. Tags: 10.5281/zenodo.4321099. Location: 10.5281/zenodo.4322972. France. Tags: 10.5281/zenodo.4321153. Location: 10.5281/zenodo.4322994. Germany (Nodes, Relations). Tags: 10.5281/zenodo.4321406. Location: 10.5281/zenodo.4323008. Germany (Ways). Tags: 10.5281/zenodo.4321420. Location: 10.5281/zenodo.4323008. Great-Britain. Tags: 10.5281/zenodo.4321175. Location: 10.5281/zenodo.4323020. Italy. Tags: 10.5281/zenodo.4321206. Location: 10.5281/zenodo.4323088. ...
format Dataset
author Tempelmeier, Nicolas
Gottschalk, Simon
Demidova, Elena
spellingShingle Tempelmeier, Nicolas
Gottschalk, Simon
Demidova, Elena
GeoVectors - Knowledge Graph
author_facet Tempelmeier, Nicolas
Gottschalk, Simon
Demidova, Elena
author_sort Tempelmeier, Nicolas
title GeoVectors - Knowledge Graph
title_short GeoVectors - Knowledge Graph
title_full GeoVectors - Knowledge Graph
title_fullStr GeoVectors - Knowledge Graph
title_full_unstemmed GeoVectors - Knowledge Graph
title_sort geovectors - knowledge graph
publishDate 2020
url https://zenodo.org/record/4339524
https://doi.org/10.5281/zenodo.4339524
genre Antarc*
Antarctica
genre_facet Antarc*
Antarctica
op_relation doi:10.5281/zenodo.4339523
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op_rights info:eu-repo/semantics/openAccess
https://opendatacommons.org/licenses/odbl/1-0/
op_doi https://doi.org/10.5281/zenodo.433952410.5281/zenodo.4339523
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