KGvec2go is a semantic resource consisting of RDF2Vec knowledge graph embeddings trained currently on 4 different
The embeddings can be downloaded or consumed by the provided Web API in a lightweight fashion.
You can also explore the data set by trying
out our Web UI.
Feel free to contact us in case of questions of problems.
Below you can find publications where you can read more about RDF2Vec, word embeddings, and KGvec2go.
Ristoski, Petar; Petar Ristoski
Rosati, Jessica; Jessica Rosati
Di Noia, Tommaso; Tomaso Di Noia
De Leonec, Renato; Renato De Leonec
Paulheim, Heiko. Heiko Paulheimhttps://orcid.org/0000-0003-4386-8195http://www.heikopaulheim.de/
RDF2Vec: RDF Graph Embeddings and Their Applications. Semantic Web (10#4). IOS Press. Amsterdam. 2019.
- Mikolov, Thomas; Sutskever, Ilya; Chen, Kai; Corrado, Gregory; Dean, Jeffrey. Distributed Representations of Words and Phrases and their Compositionality. NIPS. 2013.
Portisch, Jan; Jan Portischhttps://orcid.org/0000-0001-5420-0663http://www.jan-portisch.eu/Hladik, Michael; Michael Hladikhttps://orcid.org/0000-0002-2204-3138Paulheim, Heiko. Heiko Paulheimhttps://orcid.org/0000-0003-4386-8195http://www.heikopaulheim.de/KGvec2go - Knowledge Graph Embeddings as a Service. LREC 2020.
If you want to find more resources concerning RDF2Vec, visit http://www.rdf2vec.org
Do you want to train RDF2Vec embeddings yourself? Have a look at jRDF2Vec
The implementations are publicly available on GitHub. Note that we reimplemented the original RDF2Vec approach
for a higher performance on our data sets. Feel free to contact us in case of questions of problems.