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On Inductive Abilities of Latent Factor Models for Relational Learning

Abstract : Latent factor models are increasingly popular for modeling multi-relational knowledge graphs. By their vectorial nature, it is not only hard to interpret why this class of models works so well, but also to understand where they fail and how they might be improved. We conduct an experimental survey of state-of-the-art models, not towards a purely comparative end, but as a means to get insight about their inductive abilities. To assess the strengths and weaknesses of each model, we create simple tasks that exhibit first, atomic properties of binary relations, and then, common inter-relational inference through synthetic genealogies. Based on these experimental results, we propose new research directions to improve on existing models.
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Contributor : Eric Gaussier Connect in order to contact the contributor
Submitted on : Thursday, September 23, 2021 - 9:39:10 AM
Last modification on : Wednesday, July 6, 2022 - 4:20:55 AM

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Théo Trouillon, Éric Gaussier, Christopher Dance, Guillaume Bouchard. On Inductive Abilities of Latent Factor Models for Relational Learning. Journal of Artificial Intelligence Research, 2019, 64, pp.21-53. ⟨10.1613/jair.1.11305⟩. ⟨hal-03352271⟩



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