Knowledge Graphs can contain millions of entities. Entities (like persons, animals or films) have identifiers. Use the button below to get some examples out of available entities.
Embeddings can be computed through analysis of properties and relationships of entities. Embeddings are vectors with the desired property that close embeddings correspond to a semantic similarity of related entities. Use the form below to view an example.
Embeddings computed on Web Data Commons data with the DeCaL embedding model. You can access the underlying data dump here, which currently contains about 9 billion IRIs and is continuously updated.
Embeddings computed on Wikidata truthy dumps with the DeCaL embedding model. You can access the underlying data dump here.
Embeddings computed on DBpedia Snapshot 2022-12 with the DeCaL embedding model. You can access the underlying data dump here.
The Wikidata and DBpedia embeddings are also queryable through our public SPARQL endpoint.
Old data related to the article Universal Knowledge Graph Embeddings can be found on this page.
And now? We offer embeddings as a service with a convenient API.
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