###### Abstract
OTE [32] exploits the orthogonality relation variation, extending the rotation model from a two-dimensional complex domain to a higher-dimensional space. Although all these works display a good performance in knowledge graph completion, there are still two major challenges when modeling entities and relations of complex knowledge graphs. Challenge 1: Negative sampling, which is a very important perspective of KGE, is not sufficiently emphasized. The existing KGE models widely use the random sampling method, which generates negative samples by randomly replacing an entity in the positive sample with other entities in the knowledge graph. However, this method will most likely sample entities that do not belong to the same entity type or are semantically unrelated as replacements. For example, for a positive triple (China, Capital, Beijing), random sampling is used to select replacement tail entities to generate a negative triple. To illustrate this, suppose an entity, [PERSON], of the type \"person\", is selected from the entity set to form a negative triple (China, Capital, Yao Ming). Because \"Yao Ming\" has no semantic relationship with \"Beijing\", the quality of the generated negative triplet (Yao Ming, Capital, Beijing) is very low, which is not helpful for the training of link prediction model. Low-quality negative triplets are not beneficial for learning entity vectors and relation vectors, and prevent KGE from achieving the desired performance. Challenge 2: Figure 1 shows the vector distribution of triplets (China, Capital, Beijing and China, Located, Asia) in the embedding space. The embedding vector of each entity and relation in China, Capital, Beijing can represent the semantic relationship of this triplet well, while the embedding vector of each entity and relation in China, Located, Asia cannot represent the semantic relationship of this triplet well. Existing studies treat each embedding vector of entities and relations equally, ignoring the variability of their performance in different triplets. These studies are not flexible enough to accurately tackle the various mapping properties of triplets. The triplet mapping properties are the vector representation status of the triplet in the embedding space. Our intuition is that the mapping properties of each triplet are decided by the embedding vector of entities and relations. When triplet-mapping properties are ignored, it will lead to a fixed weight for the projection distance of entities and relations in each triplet. This would miss the important feature information in each triplet, thus reducing the representation power of the model and the accuracy of predicting candidate entities. In order to deal with the above problems, this paper propose a learning optimization framework for translation models (Op-Trans), aiming to improve the capturing capability of the model for triplet features to achieve an accurate knowledge base completion. The contributions of our work are summarized as follows:  
(1) We propose an effective negative sampling method to generate high-quality negative triplets, based on cluster-cache sampling (CCS). It is a general negative sampling method that can be applied to numerous KGE models. (2) We propose a weighting strategy based on triplet-mapping properties, associating each triplet with a weight that represents the degree of mapping. We will focus on the different contributions of each triplet to the optimization goal. (3) We combine the proposed optimization framework Op-Trans with underlying translation models, and we conduct experiments on four popular data sets, i.e., WN18 [33], FB15k [33], WN18 RR [34], and FB15K237 [35]. The experiments show that our method is effective and universal.