## 2 Related Work
GAT [20] (Graph Attention Network) utilizes the attention mechanism method to calculate and assign different attention weight values to each node in the graph based on the characteristics of neighboring nodes. Each operation of GAT needs to traverse all nodes on the graph, which breaks away from the constraint of the Laplacian matrix, and therefore solves the limitation that GCN cannot handle dynamic graphs. HRAN [21] (Heterogeneous Relation Attention Network) integrates the attention mechanism and proposes an end-to-end heterogeneous relational attention network framework. It utilizes the attention mechanism to learn the importance of each relation path, selectively hierarchically aggregates the information characteristics of neighbors, and fuses each type of specific semantic information through the relationship path. RGHAT [22] (Relational Graph Neural Network with Hierarchical Attention) uses a hierarchical attention approach that explicitly utilizes the local neighborhood information of each entity, introducing a relational attention layer and an entity attention layer, distinguishing the importance of different relations and the importance of different adjacent entities under the same relation in a hierarchical structure. The hierarchical attention mechanism provides a fine-grained learning process for the model, which increases its interpretability. The key task of knowledge representation based on multi-source information is data level fusion, the essence of which is to expand and fuse rich additional information related to the knowledge base, such as entity type information, text description information, logic rules, graph structure information, and other information. Integrating multi-source information of entities can effectively explore semantic correlations between entities, and more accurate semantic representations can improve the distinguishability of triplets. Simultaneously, it can also enhance the ability of the model to solve zero-shot problems. Many large-scale knowledge graphs contain concise descriptions of entities, which contain numerous pieces of implicit information about the entities, and fully utilizing the text statements describing entities can better complete knowledge embedding. DKRL fuses the description text information of entities in the knowledge representation learning. It considers two models of CBOW and CNN as the embedding method of text description information to obtain the text-based entity embedding representation [7], then fuses the text information into the entity embedding through TransE training. [PERSON] et al. [23], by utilizing the abstract and attribute information of entities in the knowledge graph, and learning the representation of entities through the relationship triplets, attribute triplets, and abstracts, then completed further entity alignment tasks. [PERSON] et al. [24] proposed TADW (Text-Associated DeepWalk), which proved that the DeepWalk method is actually equivalent to the matrix decomposition method, extending the matrix decomposition model to support embedding of text feature information. The CANE [25] model utilizes the rich external attributes of nodes and the mutual attention mechanism to model complex network relations; it fuses the structural and textual information of nodes and ultimately produces dynamic contextual vectors, allowing entities to have different representations when interacting with different neighborhoods. Several studies fused entity attributes and type information to obtain more accurate embedding representations, and demonstrated the effectiveness of this method through experiments. SSE [26] adds constraints to the objective function, so that entities belonging to the same type have similar positions in the vector space to ensure semantic smoothness. TransT [27] proposes a method of entity embedding that integrates structured information and describes entity types, capturing the prior distribution of entities and relationships that utilize the type-based semantic similarity of related entities and relationships.