January 27th

14:30 , R2014 Digiteo Shannon (660) (see location):

Raphaël Bailly

Title : Tensor factorization for multi-relational learning

Abstract :

Learning relational data has been of a growing interest in fields as
diverse as modeling social networks, semantic web, or bioinformatics.
To some extent, a network can be seen as multi-relational data, where
a particular relation represents a particular type of link between
entities. It can be modeled as a three-way tensor.

Tensor factorization have shown to be a very efficient way to learn
such data. It can be done either in a 3-way factorization style
(trigram, e.g. RESCAL) or by sum of 2-way factorization (bigram, e.g
TransE). Those methods usually achieve state-of-the-art accuracy on
benchmarks. Though, all those learning methods suffer from
regularization processes which are not always adequate.

We show that both 2-way and 3-way factorization of a relational tensor
can be formulated as a simple matrix factorization problem. This
class of problems can naturally be relaxed in a convex way. We show
that this new method outperforms RESCAL on several benchmarks.


Contact: cyril.furtlehner@inria.fr