Séminaire Donnees et APprentissage ArtificielRSS

Tensor factorization for multi-relational learning

Intervenant(s) : Raphael Bailly (Heudiasyc, UTC)
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.
R. Bailly is currently post-doc at Heudiasyc (since march 2014), Compiègne. He works with Antoine Bordes and Nicolas Usunier on multi-relational learning and word embeddings. He was previously in Barcelona for a post-doc with Xavier Carreras, whith whom he worked on spectral methods applied to unsupervised setting.
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benjamin.piwowarski (at) nulllip6.fr
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