On Inductive Abilities of Latent Factor Models for Relational Learning
This work addresses the interpretability and improvement of latent factor models in knowledge graphs, which is an incremental contribution to the field.
The authors investigated the inductive abilities of latent factor models for relational learning by creating simple tasks to assess their strengths and weaknesses on atomic properties and inter-relational inference, proposing new research directions based on experimental results.
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.