Séminaire (Organisé par l’Equipe de recherche DI)

Raphaël BAILLY

Post-doctorant Heudiasyc


Tensor factorization for multi-relational learning


Jeudi 27 novembre 2014 à 14h en salle A108


Résumé :

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 two benchmarks.



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FR SHIC 3272

Collegium UTC/CNRS