Swift and Sure: Hardness-aware Contrastive Learning for Low-dimensional Knowledge Graph Embeddings

Instead of the traditional Knowledge Graph Embedding (KGE) represents entities and Negative Sampling, we design a new loss function based on relations of knowledge graphs (KGs) in the semantic vector space, query sampling that can balance two important training targets, and has shown great potential in automatic KG completion and Alignment and Uniformity. Furthermore, we analyze the hardnessaware knowledge-driven tasks [15, 16, 31, 33]. Given a query having an ability of recent low-dimensional hyperbolic models and entity and the relation of a triple, a typical KGE model learns propose a lightweight hardness-aware activation mechanism, which embedding vectors by predicting the missing entity from the can help the KGE models focus on hard instances and speed up whole entity set [30]. However, the existing KGE models have convergence. The experimental results show that in the limited limited practicality in real-world applications [19, 23]. To improve training time, HaLE can effectively improve the performance and the prediction accuracy, recent KGE models utilize complicated training speed of KGE models on five commonly-used datasets. The computational structures and high-dimensional vectors up to 500 or HaLE-trained models can obtain a high prediction accuracy after even 1,000 dimensions [7, 12, 22]. Training such high-dimensional training few minutes and are competitive compared to the state-ofthe-art models demands prohibitive training costs and storage space, yet models in both low-and high-dimensional conditions.