English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

OpenStreetMap (OSM) is currently the richest publicly available informationsource on geographic entities (e.g., buildings and roads) worldwide. However,using OSM entities in machine learning models and other applications ischallenging due to the large scale of OSM, the extreme heterogeneity of entityannotations, and a lack of a well-defined ontology to describe entity semanticsand properties. This paper presents GeoVectors - a unique, comprehensiveworld-scale linked open corpus of OSM entity embeddings covering the entire OSMdataset and providing latent representations of over 980 million geographicentities in 180 countries. The GeoVectors corpus captures semantic andgeographic dimensions of OSM entities and makes these entities directlyaccessible to machine learning algorithms and semantic applications. We createa semantic description of the GeoVectors corpus, including identity links tothe Wikidata and DBpedia knowledge graphs to supply context information.Furthermore, we provide a SPARQL endpoint - a semantic interface that offersdirect access to the semantic and latent representations of geographic entitiesin OSM.

GeoVectors: A Linked Open Corpus of OpenStreetMap Embeddings on World Scale