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On Multi-Relational Link Prediction with Bilinear Models
We study bilinear embedding models for the task of multi-relational link
prediction and knowledge graph completion. Bilinear models belong to the most
basic models for this task, they are comparably efficient to train and use, and
they can provide good prediction performance. The main goal of this paper is to
explore the expressiveness of and the connections between various bilinear
models proposed in the literature. In particular, a substantial number of
models can be represented as bilinear models with certain additional
constraints enforced on the embeddings. We explore whether or not these
constraints lead to universal models, which can in principle represent every
set of relations, and whether or not there are subsumption relationships
between various models. We report results of an independent experimental study
that evaluates recent bilinear models in a common experimental setup. Finally,
we provide evidence that relation-level ensembles of multiple bilinear models
can achieve state-of-the art prediction performance
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