Raphael Bailly (UTC): Tensor factorization for multi-relational learning

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Date(s) - 19/12/2014
14 h 00 min - 15 h 00 min

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Title: Tensor factorization for multi-relational learning\n\nAbstract:\nLearning 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.\n\nTensor 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.\n\nWe 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.\n

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