Global RDF Vector Space Embeddings
Cochez, M., Ristoski, P., Ponzetto, S. P., & Paulheim, H. (2017). Global RDF Vector Space Embeddings. In C. d'Amato, M. Fernandez, V. Tamma, F. Lecue, P. Cudré-Mauroux, J. Sequeda, . . . , & J. Heflin (Eds.), ISWC 2017 - The Semantic Web : 16th International Semantic Web Conference, Proceedings, Part I (pp. 190-207). Lecture Notes in Computer Science, 10587. Cham: Springer. doi:10.1007/978-3-319-68288-4_12
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Lecture Notes in Computer Science;10587Date
2017Discipline
TietotekniikkaCopyright
© Springer International Publishing AG 2017. This is a final draft version of an article whose final and definitive form has been published by Springer. Published in this repository with the kind permission of the publisher.
Vector space embeddings have been shown to perform well when using
RDF data in data mining and machine learning tasks. Existing approaches, such as
RDF2Vec, use local information, i.e., they rely on local sequences generated for
nodes in the RDF graph. For word embeddings, global techniques, such as GloVe,
have been proposed as an alternative. In this paper, we show how the idea of global
embeddings can be transferred to RDF embeddings, and show that the results are
competitive with traditional local techniques like RDF2Vec.