Exploring the Generalization of Knowledge Graph Embedding Knowledge graph embedding aims to represent structured entities and relations as continuous and dense lowdimensional vectors. With more and more embedding models being proposed, it has been widely used in many tasks such as semantic search, knowledge graph completion and intelligent question and answer. Most knowledge graph embedding models focus on how to get information about different entities and relations. However, the generalization of knowledge graph embedding or the link prediction ability is not wellstudied empirically and theoretically. The study of generalization ability is conducive to further improving the performance of the model. In this paper, we propose two measures to quantify the generalization ability of knowledge graph embedding and use them to analyze the performance of translationbased models. Extensive experimental results show that our measures can well evaluate the generalization ability of a knowledge graph embedding model.

Knowledge Graph Embedding by Bias Vectors Knowledge graph completion can predict the possible relation between entities. Previous work such as TransE, TransR, TransPES and GTrans embed knowledge graph into vector space and treat relations between entities as translations. In most cases, the more complex the algorithm is, the better the result will be, but it is difficult to apply to largescale knowledge graphs. Therefore, we propose TransB, an efficient model, in this paper. We avoid the complex matrix or vector multiplication operation. Meanwhile, we make the representation of entities not too simple, which can satisfy the operation in the case of nononetoone relation. We use link prediction to evaluate the performance of our model in the experiment. The experimental results show that our model is valid and has low time complexity.