Research – Paper 164

Global RDF Vector Space Embeddings

Michael Cochez, Petar Ristoski, Simone Paolo Ponzetto and Heiko Paulheim

Research

clock_eventOctober 25, 2017, 11:10.
house Lehár 1-3
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Abstract

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.

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