## 2 Related Work  
The current knowledge graph embedding methods can be roughly divided into two types based on whether they fuse multi-source information. The knowledge graph embedding method without fusing multi-source information mainly relies on the inherent information in triplets and graph structure for knowledge representation learning. Classic knowledge embedding models are based on a single triplet, such as TransE [8] and its variants. TransE regards the relation as the translation distance between entities in vector space, and proposes the assumption that the tail entity in a triple minus the head entity is approximate to the vector representation of the relation. This simple but intuitive method has obtained effective results in the experiments for single relationships, but there are still limitations for the representation of 1-N and N-N relationships. The TransH [9] proposes that an entity should have different representations under different relationships. The initialization vector representation of the entity is projected onto the unit vector to obtain a new entity representation, but it still maps the entity and relationship to the same semantic space, which to some extent limits the representation ability. TransD [10] projects the head entity and tail entity into different vector spaces, respectively, and constructs the projection matrix dynamically through the projection vector, which greatly saves the amounts of parameters and calculations. TransR [11] believes that entities in different relationships have different semantic spaces, so entities and relationships are embedded in two different spaces; that is, entity space and multiple relationship-specific entity spaces, and the entities are projected into a shared hyperplane through the relationship projection matrix in the TransR model. [PERSON] [12] and [PERSON] [13] optimized the model by changing the evaluation function of TransE, improving its ability to represent complex relationships. The knowledge representation learning method based on graph structure information, such as DeepWalk [14], mainly draws on the idea of Word2 vec training word vectors and applies this idea to the data of graph structure information. The Word2 vec [15] algorithm utilizes skip-gram and CBOW methods to train word vectors based on the co-occurrence relationship of words in the corpus, while DeepWalk uses random walk for node sampling to generate a text sequence, and then uses the skip-gram model to convert the sequence into embedding. Compared with the random walk strategy of the graph in DeepWalk, Node2 vec [16] weighs the results of graph embedding by adjusting the probability of random walk. Node2 vec believes that the word sequence generated by simple random walk cannot explain the relationship between nodes accurately, it optimizes the process of random walk, and designs a search strategy to control the search through parameter settings. Node2 vec adjusts the parameters to generate the relationship between the nodes in the sequence generated by random walk, which effectively balances the homogeneity and isomorphism of embedding. GCN [17] (Graph Convolutional Network) ingeniously designs a method to extract features from graph data, introduces the concept of convolution into heterogeneous graph structures, and aggregates neighbor information according to the Laplacian matrix. RGCN [18] (Relational Graph Convolutional Network) proposes a method for multi-relationship aggregation in heterogeneous graphs, applying the method of GCN to model relationship data in multi-relationship graphs with relationship weights, especially for link prediction and entity classification tasks. GraphSAGE [19] proposes a general inductive framework that learns the topological structure of neighborhoods of each node and the distribution of node characteristics in the neighborhoods. It trains a multi-level aggregate function to generate node embedding according to the local neighborhoods' sampling and aggregation characteristics of the node.