A latent factor model for highly multi-relational data

Rodolphe Jenatton 1, 2, 3 Nicolas Le Roux 1, 2 Antoine Bordes 4 Guillaume Obozinski 1, 2
2 SIERRA - Statistical Machine Learning and Parsimony
DI-ENS - Département d'informatique de l'École normale supérieure, ENS Paris - École normale supérieure - Paris, Inria Paris-Rocquencourt, CNRS - Centre National de la Recherche Scientifique : UMR8548
Abstract : Many data such as social networks, movie preferences or knowledge bases are multi-relational, in that they describe multiple relations between entities. While there is a large body of work focused on modeling these data, modeling these multiple types of relations jointly remains challenging. Further, existing approaches tend to breakdown when the number of these types grows. In this paper, we propose a method for modeling large multi relational datasets, with possibly thousands of relations. Our model is based on a bilinear structure, which captures various orders of interaction of the data, and also shares sparse latent factors across different relations. We illustrate the performance of our approach on standard tensor-factorization datasets where we attain, or outperform, state-of-the-art results. Finally, a NLP application demonstrates our scalability and the ability of our model to learn efficient and semantically meaningful verb representations.
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Rodolphe Jenatton, Nicolas Le Roux, Antoine Bordes, Guillaume Obozinski. A latent factor model for highly multi-relational data. Advances in Neural Information Processing Systems 25 (NIPS 2012), Dec 2012, Lake Tahoe, Nevada, United States. pp.3176-3184. ⟨hal-00776335⟩

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