###### Abstract  
Knowledge graphs are a popular research field in artificial intelligence, and store large amounts of real-world data. Since data are enriched over time, the knowledge graph is often incomplete. Therefore, knowledge graph completion is particularly important as it predicts missing links based on existing facts. Currently, the family of translation models delivers a better performance in knowledge graph completion. However, most of these models randomly generate negative triplets during the training process, resulting in the low quality of negative triplets. In addition, such models ignore the important characteristics of triplet-mapping properties during model learning. Therefore, we propose an optimization framework based on the translation models (Op-Trans). It enhances the knowledge-graph completion effect from both negative sampling and triplet-mapping properties. First, we propose a clustering cache to generate negative triplets, which generate negative triplets based on entity similarity. This sampling method can directly use the cache to track the negative triplets with large scores. In addition, we focus on the different contributions of the triplets to the optimization goal. We calculate the distinct weight for each triplet according to its mapping properties. In this way, the scoring function deals with each triplet depending on its own weight. The experimental results show that Op-Trans can help the state-of-the-art baselines to obtain a better performance in a link prediction task. knowledge graph completion; negative sampling triplet-mapping properties; link prediction 2023202330/19/052817105/202330/19/052817105/2023202330/19/052817105/2023202330/19/052817105/2023202330/19/052817105/202320230/19/052817105  
need an inference engine to predict the missing links. Therefore, knowledge graph completion, known as link prediction, which automatically predicts missing links based on the given links, has recently become a significant topic of interest. However, triplets in knowledge graphs are hard to manipulate, and their symbols and logic are difficult for digital machines to learn. Thus, one fundamental problem is how to find a good representation for the entities and relations in the knowledge graph. In recent years, knowledge graph embedding (KGE) [13] has been introduced into knowledge graphs and has shown a good performance. It maps entities and relations into a low-dimensional vector space while capturing the structural and semantic information of the knowledge graph [14]. KGE learns the representation vectors of entities and relations based on constructed knowledge graphs, so that these vectors can reflect information about the structure of nodes and edges. At present, scholars have proposed some classical KGE models in the field of knowledge graph completion, such as the structured embedding (SE) [15] model, semantic matching energy (SME) [16] model, neural tensor network (NTN) [17] model, and matrix factorization (RESCAL) [18] model. However, these KGE models have a high computational complexity and cannot learn the semantic information of entities and relations effectively. They are difficult to apply to some practical application scenarios. Therefore, researchers began to explore new KGE models. After practical tests, translation models have gradually become the mainstream in KGE. The TransE [19] model first proposed to use relations vectors as the geometric distance between entities [20], which is a typical representative of translation models. To represent entities and relations more effectively, TransE has been continuously improved and has established models of the Trans (Translation Embedding Methods) family. These include TransH [21], TransR [22], TransD [23], TransM [24], TranSparse [25], STransE [26], TransA [27], TransAt [28], PTransE [29], etc. Recently, RotatE [30] has changed the translation process of the head and tail entity vectors in the plane into the rotation translation of the vectors in space. HAKE [31] maps the entities into spatial polar coordinates.