## 1 Introduction  
Knowledge graphs organize data into a graph structure from a semantic perspective, to establishing a comprehensive knowledge base that powers intelligent search services. An increasing number of large-scale knowledge graphs have been published on the internet recently, such as Freebase, DBpedia, and YAGO, and the deep integration of knowledge mapping technology and various industries has become an important trend. As the focus of knowledge graphs has grown in specific fields, domain knowledge graphs have also flourished in finance, health care, education, and other fields. A knowledge graph is a knowledge carrier of graph structure, where nodes represent entities and edges represent the interrelationships between nodes; by this means, it stores real things, concepts, and their relationships in the objective world. The principal assignment of embedding representation learning is to map the semantic information contained in the real-world things and transform it into low-dimensional dense vectors that are suitable for computational operations. Some studies, as well as expressing the information contained in the knowledge graph itself, have incorporated multi-source information to enrich the embedded representation of entities and relationships, usually including various forms of external information such as entity attribute information, text description, image information, etc. Integrating multi-source heterogeneous information adequately is of great significance for achieving knowledge embedding. It can simultaneously enrich the embedding information of entitiesand relationships, and improve the sparsity problem of data, thus enhancing the representation ability of rare nodes and enhancing the separability of knowledge representation. The entities in the knowledge graph always have multiple types, and each entity has different types in different roles. For example, the entity \"Apple\" may belong to the different types such as \"Fruit\" and \"Company\", and then the role played by \"Apple\" in different scenarios may also exist differently. Consequently, the vector representations of the entity \"Apple\" in the triplet (apple, established in, 1976) and triplet (apple, be rich in, vitamins) should be different. Therefore, distinguishing the different type representations of entities contributes to making knowledge embedding more accurate. Several recent studies have incorporated entity type information into embedded representations. [PERSON] et al. [1] integrated prior knowledge in the form of type constraints, treating entity types as hard constraints. The TKRL [2] (Type-embodied Knowledge Representation Learning) model integrates the hierarchical type information of entities and embeds types hierarchically to enrich entity information. KR-EAR [3] distinguishes existing knowledge graph relationships into attributes and relationships; it can learn representations of entities, attributes, and relationships to complete the knowledge completion task by separating relationship triplets and attribute triplets for modeling. [PERSON] et al. [4] utilized triplet information and entity attributes simultaneously for knowledge representation learning, encoding attribute information through convolutional methods, and fusing triplet information and attribute information to entity representations. [PERSON] et al. [5] proposed a joint attribute preserving vector model for cross-language entity alignment, which embeds the vectors obtained from the two knowledge representations into a unified vector space, and further refines them by using the attribute correlation in knowledge embedding. Combining text description information for embed representation learning could enrich the information expression by entity vectors.