## 2 Related Work
In recent years, researchers began to map models to different complex vector spaces for modeling in order to better cope with the diversity of relationship attributes. TransH [23] uses a hyperplane to solve the above problems. TransR [24] uses a projection matrix to project the source vector to the entity space with specific relationships on the basis of TransH. RotatE [25] completes the rotation from head entities to tail entities based on the complex vector space, although the rotation ability is limited. CHoIE [26] models the entities and relationships with their type constraints in the complex vector space. A few effective models [27; 28; 29] even project entities and relationships into quaternion or octet spaces, which greatly increases computational complexity. HAKE [30] uses a polar coordinate system to solve the problem and to model semantic hierarchies. Because its rotation of the radial coordinate is limited, simlar to RotatE, its hierarchical modeling abilities are limited. HousE [31] uses household transformation to realize projections and rotations. Although this method has good interpretability, it has shortcomings when mining potential semantic knowledge. Compared with the above models, semantic matching models can be used to mine potential semantic knowledge. Semantic matching models calculate semantic similarity to complete the knowledge graph. RESCAL [32] uses low-dimensional vectors to represent entities and a matrix to represent relationships. The scoring function \(f_{r}(h,t)=h^{T}M_{r}t\) is used to evaluate semantic similarity. Distmult [33] simplifies the matrix into a diagonal matrix on the basis of RESCAL, reducing the number of parameters and maintaining good performances. ComplEx [34] enhances Distmult in a complex vector space, although it incurs a high cost. [PERSON] et al. [35] used relation prediction as an auxiliary training objective; however, the performance of this approach is limited when the number of relationships is small. With the development of neural networks, researchers have used neural networks to complete knowledge graphs. ConvE [36], ConvKB [37], and ConvR [38] all use convolutional neural networks to represent entities and relationships for information interaction. R-Men [39] uses a relational memory neural network to encode the internal dependencies of a triple. RGCN [40], SACN [41], and KBGAT [42] use graph neural networks to fuse the information of the surrounding entities. [PERSON] et al. [43] used graph-attenuated attention networks to complete the knowledge graph, although this was insufficient to deal with relationships. With the development of pretrained language models, many researchers have used such models to complete knowledge graphs. SimKGC [44] introduces contrastive learning and pretrained language models into the knowledge graph completion process. However, when the distribution difference between the pretrained language models and the target is large, the experimental effect is limited. Other knowledge graph completion models [45; 46; 47] obtain information from texts. Therefore, when there is no text that can accurately describe entities or relationships, the models cannot obtain sufficient reasoning basis, and the reasoning effect is poor. Although the above models have achieved fine results, they ignore the comprehensive utilization of entity information in the modeling process. In practice, a single model usually has limitations. [PERSON] et al. [48] demonstrated that a single model tends to produce large fluctuations in variance, bias, and accuracy during the training process, while the ensemble method can effectively reduce the probability of errors. [PERSON] [49] found that a single model has important deficiencies in the statistical, computational, and representational aspects. A single model is more prone to fall into a local optimal solution than an ensemble model. Motivated by the success of widely used ensemble models, we propose an ensemble model in which the base models perform different projection and rotation operations.