Théo Trouillon, Johannes Welbl, Sebastian Riedel, Eric Gaussier, Guillaume Bouchard
ICML, New York City, USA, June 19-24, 2016.
In statistical relational learning, the link prediction
problem is key to automatically understand
the structure of large knowledge bases. As in previous
studies, we propose to solve this problem
through latent factorization. However, here we
make use of complex valued embeddings. The
composition of complex embeddings can handle
a large variety of binary relations, among them
symmetric and antisymmetric relations. Compared
to state-of-the-art models such as Neural
Tensor Network and Holographic Embeddings,
our approach based on complex embeddings is
arguably simpler, as it only uses the Hermitian
dot product, the complex counterpart of the standard
dot product between real vectors. Our approach
is scalable to large datasets as it remains
linear in both space and time, while consistently
outperforming alternative approaches on standard link prediction benchmarks.
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