Of course I know RDF2vec ;-)
However, Heiko Paulheim's RDF2vec website has grown nicely and has become a rather valuable resource for this #knowledgegraph embedding method, including implementations, models and services, variations and extensions, as well as more than 150 references in scientific papers!
RDF2vec website: http://www.rdf2vec.org/
original paper: https://madoc.bib.uni-mannheim.de/41307/1/Ristoski_RDF2Vec.pdf
Petar Ristoski's PhD thesis (with RDF2vec): https://ub-madoc.bib.uni-mannheim.de/43730/1/Ristoski_PhDThesis_final.pdf
#kge #deeplearning #embeddings
![Screenshot of the RDF2vec website.
About RDF2vec:
RDF2vec is a tool for creating vector representations of RDF graphs. In essence, RDF2vec creates a numeric vector for each node in an RDF graph.
RDF2vec was developed by Petar Ristoski as a key contribution of his PhD thesis Exploiting Semantic Web Knowledge Graphs in Data Mining [Ristoski, 2019], which he defended in January 2018 at the Data and Web Science Group at the University of Mannheim, supervised by Heiko Paulheim. In 2019, he was awarded the SWSA Distinguished Dissertation Award for this outstanding contribution to the field.](https://cdn.masto.host/sigmoidsocial/media_attachments/files/110/984/109/937/269/890/original/e6bad321ebbe5226.png)