A latent factor model for highly multi-relational data

Part of Advances in Neural Information Processing Systems 25 (NIPS 2012)

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Rodolphe Jenatton, Nicolas Roux, Antoine Bordes, Guillaume R. Obozinski


Many data such as social networks, movie preferences or knowledge bases are multi-relational, in that they describe multiple relationships between entities. While there is a large body of work focused on modeling these data, few considered modeling these multiple types of relationships jointly. 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 the various orders of interaction of the data, but 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.