Resumen
Knowledge graph embedding learning aims to represent the entities and relationships of real-world knowledge as low-dimensional dense vectors. Existing knowledge representation learning methods mostly aggregate only the internal information of triplets and graph structure information. Recent research has proved that multi-source information of entities is conducive to more accurate knowledge embedding tasks. In this paper, we propose a model based on an attention mechanism and integrating the type information of entities, named KTAT. This model is based on the graph attention mechanism, to distribute corresponding attention mechanisms according to different weights between nodes. We introduce a type-specific hyperplane, which enables entities to have different embedding representations according to their type in the current triplet. Simultaneously, we also used textual description information of entities to improve the performance of the model. We conducted link prediction experiments on the FB15k and FB15k-237 datasets. The experimental results show that our model outperforms previous advanced methods compared to some baseline models, and demonstrate that combining type information can effectively improve the performance of link prediction.