# Link Prediction Based on Data Augmentation and Metric Learning Knowledge Graph Embedding
## 2 Related Work  
### Data Augmentation for Link Prediction Task  
Data augmentation is a useful method for expanding dataset size and enhancing data diversity. Recently, techniques employing generative adversarial networks to generate textual descriptions in knowledge graphs have emerged. OnToZSL [12] developed an ontology encoder to learn relationship representations by encoding intricate logical information between textual descriptions in knowledge graphs. However, this approach solely focuses on generating textual descriptions, overlooking the potential benefits of incorporating an entity's structural information to enrich knowledge graph data samples. WRAN [13] leveraged adversarial networks to learn entity embeddings with invariant relationships from an entity's textual features and structural encodings. With the advancement of natural language corpora, various completion methods have utilized pre-trained models to tackle issues. KG-BERT [14], for instance, is a BERT-based knowledge graph representation learning method that achieves more accurate and expressive entity and relationship representations by utilizing context-aware word vectors. Despite these advancements, the challenge of insufficient knowledge graph samples remains inadequately addressed.  
Negative sampling was put forward by [15]. It was originally used to speed up the training of the Skip Gram model. Now, it has been widely used in CV (Computer Vision), recommendation systems, NLP (Natural Language processing), and other fields. In the field of knowledge graph, [PERSON]. [16] demonstrated that negative sampling is equally crucial with positive sampling and experimentally negated the conventional intuition in graph representation learning that \"sampling adjacent nodes as positive node pairs and sampling distant nodes as negative node pairs\".  
In the knowledge map embedding task, negative sampling is regarded as an important module that affects model performance. Take the TransE model [9] as an illustration; its objective function is defined as the gap between positive and negative samples. GANs (Generative Adversarial Networks) have also been introduced for generating negative samples. After a detailed analysis of static negative sampling methods, KBGAN [17] found that the majority of negative samples are weak negative examples rather than strong negative examples. During training, the scoring of these negative examples rapidly decreases, leading to the problem of gradient vanishing. By incorporating GANs [18], one can generate well-crafted negative sample triplets and leverage new distributions in the training process. IGAN [19] designed a generator and performed advanced negative sampling based on softmax, using the boundary loss training of positive and negative examples in the discriminator to obtain the final embedded model. A knowledge-guided attention mechanism was introduced by [20], and they devised a semantic sampler along with comparative learning to acquire diverse representations for negative samples across various relationships. NSCaching [21] designed two modules: head entity cache and tail entity cache. During the process of training, to replace true triplets, entities are selected from the head entity cache and tail entity cache. In addition, the two cache modules are also updated as the model is trained.  
In addition, there are some semantic related negative sampling models, such as joint relationship context negative sampling, entity similarity-based negative sampling, etc. As the performance of models encounters limitations in link prediction, researchers are exploring solutions to improve embedding quality from multiple perspectives beyond model design. Starting from the underlying logic of embedded models, negative sampling is undoubtedly a research perspective with great potential.