# Link Prediction Based on Data Augmentation and Metric Learning Knowledge Graph Embedding  
[PERSON]  
1 Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China; [EMAIL_ADDRESS] (S.H. ); [EMAIL_ADDRESS] (M.H.) 2 Beijing Key Laboratory of Trusted Computing, Beijing 100124, China  
3 National Engineering Laboratory for Critical Technologies of Information Security Classified Protection, Beijing 100124, China  
4 Chinese Academy of Cyberspace Studies, Beijing 100048, China  
5 Faculty of Science, Beijing University of Technology, Beijing 100124, China; [EMAIL_ADDRESS]  
6  
[PERSON]  
1 Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China; [EMAIL_ADDRESS] (S.H. ); [EMAIL_ADDRESS] (M.H.) 2 Beijing Key Laboratory of Trusted Computing, Beijing 100124, China  
3 National Engineering Laboratory for Critical Technologies of Information Security Classified Protection, Beijing 100124, China  
4 Chinese Academy of Cyberspace Studies, Beijing 100048, China  
5 Faculty of Science, Beijing University of Technology, Beijing 100124, China; [EMAIL_ADDRESS]  
7  
[PERSON]  
4 Chinese Academy of Cyberspace Studies, Beijing 100048, China  
between the two is that the former uses distance as the scoring function, and the latter uses the similarity measure as the evaluation function. TDMs are widely used in link prediction tasks within the realm of KGE models due to their simplicity and efficiency. However, it is important to note that TDMs have limitations when it comes to expressiveness, as observed in [6]. Empirically, they tend to underperform when compared to other models, particularly in modeling intricate relation patterns. Compared with other methods, SMM has excellent performance in both result performance and theoretical guarantee. Nonetheless, the substantial number of parameters in SMMs presents obstacles when it comes to deploying them in extensive knowledge graphs within industrial contexts. In summary, the challenge lies in achieving robust modeling capabilities for intricate relation patterns simultaneously while maintaining performance in KGE learning. Meanwhile, because the knowledge graph only contains positive samples, in order to assure the model's generalization capacity, negative examples must be generated throughout the training process using a tailored technique as a data augmentation strategy to increase the model's training effect. Currently, the widely adopted method of negative sampling randomly selects negative samples by substituting either the head or tail entity in the triplet. However, as training progresses, an abundance of zero-loss negative samples emerges, leading to gradient disappearance due to the challenge of adjusting these negatives effectively for each positive sample. Furthermore, negative sampling should be considered to avoid generating erroneous negative triples. For example, for \((\)_Beijing_, _located in_, _China_\()\), generating pseudo-negative examples by randomly replacing the head entity with \((\)_Shanghai_, _located in_, _China_\()\) will not only affect the model training effect, but may also cause test set leakage. As a result, generating high-quality negative samples is also a pressing issue. To tackle the limitations of existing TDMs in obtaining semantic information and the challenges posed by SMMs with an excess of parameters, we introduce a novel KGE model. This approach dynamically represents entities and comprises two modules: Relation Fusion and Metric Learning (ML). The ML module optimizes learning efficiency by extracting shared parameters from the complete KG, inspired by prior work [7; 8]. This minimizes model parameters while enhancing semantic information content. Additionally, we propose a technique involving Relation Cutting and the development of a Mapping Matrix, influenced by [9; 10]. The core aim of this strategy is to amplify the differentiation between head and tail entities while maintaining the coherence of identical triplets.