# A Novel Embedding Model for Knowledge Graph Entity Alignment Based on Graph Neural Networks
## 2 Related Work  
**Entity Alignment based on Translation. ** This methodology presented in this study is chiefly rooted in TransE [14] and certain of its adaptations, with the core idea of representing the relationship between two entities as the transformation between their embedded representations in order to ensure that entities with analogous structures in different KGs are in close proximity in the embedding space, achieving the goal of preserving entity structural information. MtransE [15] is the first work to introduce embeddings in a multilingual setting. This work models entities and relationships using TransE, embedding each entity and relationship in different embedding spaces in each knowledge graph, and based on pre-aligned entities, the transformation between the two vector spaces is evaluated. The model includes a knowledge module for encoding and an alignment module for learning. This work proposes three learning strategies, including linear transformations, translation vectors, and distance-based axis alignment, with linear transformations having the best performance. JAPE [16] utilizes embeddings of relations and properties to optimize the embedding effect of the knowledge graph. Specifically, the method jointly embeds two distinct knowledge graphs into a shared vector space and improves the effect by embedding property information. In addition, customized data preprocessing techniques are used to facilitate the sharing of the same or similar embeddings among aligned entities in the seed alignment, allowing the model to achieve cross-lingual entity alignment. IPTransE [17] adopts semi-supervised learning and a margin-based loss function and uses bootstrapping techniques to add newly aligned entities to the seed entities, thereby expanding the number of available resources while ensuring quality. This model improves upon the underlying TransE method with PTransE, which captures indirectly connected entities by observing the paths between entities and constructing transformations between entities based on the path information composed of relation predicates connecting multiple entities. This model relies on seed entities and divides the transformation between the embedding spaces of both knowledge graphs into three strategies: translation, linear transformation, and parameter sharing, with parameter sharing being the most effective. **Entity alignment based on GNNs. ** The main approach for entity alignment using graph attention networks (GATs) and graph convolutional networks (GCNs) involves aggregating the neighborhood features of each entity to obtain neighborhood similarities between the corresponding aligned entities. GCN_align [6] was the first GNN-based EA study, which achieved alignment through the margin-based loss function. This study treated property triplets as relation triplets, learned entity embeddings from structural information, and used two GCNs to embed entities from two knowledge graphs into a unified space with a shared weight matrix. RDGCN [7], also a margin-based loss function, integrated relation information through an attentional interaction mechanism and extended GCNs with relation information and a high-speed gating mechanism to capture neighborhood structural information, similar to HGCN [11]. SEA [18] achieved alignment by utilizing cyclic consistency constraints and aligning unaligned entities. **Entity alignment based on heterogeneous GNNs. ** Recently, numerous academic studies have attempted to apply graph neural networks (GNNs) for modeling heterogeneous graphs. Among them, RGCNs [19] and RGATs [20] describe heterogeneous graphs by utilizing weight matrices for each relationship. HAN [21] has innovatively proposed a hierarchical attention mechanism, learning weights of nodes and meta-paths from both the node level and semantic level. Meanwhile, HetGNN [22] employs various recurrent neural networks (RNNs) to integrate multimodal features to deal with various types of nodes.