文章基本信息

标题：On Inductive Abilities of Latent Factor Models for Relational Learning
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作者：Théo Trouillon ; Eric Gaussier ; Christopher R. Dance 等
期刊名称：Journal of Artificial Intelligence Research
印刷版ISSN：1076-9757
出版年度：2019
卷号：64
页码：21-53
DOI：10.1613/jair.1.11305
出版社：American Association of Artificial
摘要：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.