text: 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.
  @cite_14: Large-scale relational learning becomes crucial for handling the huge amounts of structured data generated daily in many application domains ranging from computational biology or information retrieval, to natural language processing. In this paper, we present a new neural network architecture designed to embed multi-relational graphs into a flexible continuous vector space in which the original data is kept and enhanced. The network is trained to encode the semantics of these graphs in order to assign high probabilities to plausible components. We empirically show that it reaches competitive performance in link prediction on standard datasets from the literature.
  @cite_23: Relational learning is becoming increasingly important in many areas of application. Here, we present a novel approach to relational learning based on the factorization of a three-way tensor. We show that unlike other tensor approaches, our method is able to perform collective learning via the latent components of the model and provide an efficient algorithm to compute the factorization. We substantiate our theoretical considerations regarding the collective learning capabilities of our model by the means of experiments on both a new dataset and a dataset commonly used in entity resolution. Furthermore, we show on common benchmark datasets that our approach achieves better or on-par results, if compared to current state-of-the-art relational learning solutions, while it is significantly faster to compute.
summary: While 2-way models were shown to have very good performances on some KB datasets, they have limited expressiveness and they can fail dramatically on harder datasets. In contrast, 3-way models perform some form of low-rank tensor factorization, and in that respect can have extremely high expressiveness depending on the rank constraints. In the context of link prediction for multi-relational data, @cite_23 follows natural modeling assumptions. Similarly to , learns one low-dimensional embedding for each entity. However, relationships are represented as a bilinear operator in the embedding space, i.e. each relationship corresponds to a matrix. The training objective of is the Frobenius norm between the original data tensor and its low-rank reconstruction, whereas uses the margin ranking criterion of . Another related 3-way model is @cite_14 . The parameterization of is a constrained version of , and also uses a ranking criterion as training objective.