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
Published inLecture Notes in Computer Science;10587
© 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.