  This paper tackles the problem of endogenous link prediction for Knowledge
Base completion. Knowledge Bases can be represented as directed graphs whose
nodes correspond to entities and edges to relationships. Previous attempts
either consist of powerful systems with high capacity to model complex
connectivity patterns, which unfortunately usually end up overfitting on rare
relationships, or in approaches that trade capacity for simplicity in order to
fairly model all relationships, frequent or not. In this paper, we propose
Tatec a happy medium obtained by complementing a high-capacity model with a
simpler one, both pre-trained separately and then combined. We present several
variants of this model with different kinds of regularization and combination
strategies and show that this approach outperforms existing methods on
different types of relationships by achieving state-of-the-art results on four
benchmarks of the literature.
