Mirrored Translation Embedding Model Based on Mirrored Space

Knowledge graphs have been widely used in many artificial intelligence (AI) tasks. However, the existing knowledge map is usually incomplete and needs to be supplemented or linked for prediction knowledge graphs. In this paper, the problem of missing links in the knowledge map is predicted by embedding entities and relationships into the knowledge map. A mirror space translation method is introduced to learn the symmetric/antisymmetric patterns. In the new space model, relations are still modelled as translations, while entities are modelled as points with mirrors. In this space, the translation-based models gain the ability to model symmetry/anti-symmetry relations. Finally, the proposed model MTransE applies the concept of mirrored space to TransE. By the experiments on four well-known datasets, it is verified that the proposed mode can attain better performance than other baseline models.