On Multi-Relational Link Prediction With Bilinear Models

Authors

  • Yanjie Wang University of Mannheim
  • Rainer Gemulla University of Mannheim
  • Hui Li The University of Hong Kong

Keywords:

Relational Learning, Embedding Learning, Knowledge Graph

Abstract

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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Published

2018-04-29

How to Cite

Wang, Y., Gemulla, R., & Li, H. (2018). On Multi-Relational Link Prediction With Bilinear Models. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11738