# A Domain-Oriented Entity Alignment Approach Based on Filtering Multi-Type Graph Neural Networks
## 2 Related Work
Since then, various improved models, such as TransH [22], TransG [23], PTransE [24], and PTransR [25] have been proposed, which embed entities and relationships into different vector spaces, and can express more complex semantic relationships. Although this kind of method performs well in cross-language translation alignment problems, for domain knowledge graphs, the available structural information is frequently limited, which ultimately leads to sparsity in the vector representation, and which poses a challenge for the methods that heavily rely on this information. The other is the Graph Neural Network (GNN), which learns the representation of the target entity by recursively aggregating the representation of adjacent entities. Graphembedding methods mostly focus on the structural information and attribute information of the knowledge graphs. MuGNN [26] employs GNN to embed the structural information of the knowledge graph into multiple channels, then assigns weights to the relationships between the entities through the attention mechanism, and finally calculates the similarity of the embedding features to achieve alignment. HGCN [27] is an entity alignment method for jointly learning entity and relationship representations, and iteratively learns the embedding representations of entities and relationships. In addition, other methods, such as R-GCN [28] and FuAlign [29], embed the structural information of the entity and infer alignment relationships. In addition to using the structural information of entities for embedding, many scholars use the attribute information of entities to construct the feature vectors of entities. The literature [30] transforms the alignment task into a two-classification problem, then connects the attribute values of the entities to form a sequence of attribute values and, finally, uses the BERT classifier to make the final label prediction. However, it is difficult to completely learn the characteristics of entities by only using the structural information or attribute information of the knowledge base, so some methods which comprehensively utilize both structural and attribute information have been proposed. GCN-Align [31] utilizes GCNs to capture the structural and attribute information of entities and to generate high-quality embedding vectors. This method calculates the similarity of the structural feature vectors and attribute feature vectors of entities, and integrates these vectors through a weighted summation, which ultimately serves as the criterion for entity similarity assessment. AttrGNN [32] utilizes GNN to co-encode the structural and attribute information of entities, and assigns weights to different attributes of the entity using an attention mechanism. The representation learning methods have stronger robustness and better generalization performance. In summary, compared to statistical and classifier methods, the entity alignment methods based on representation learning have the characteristics of needing less manual annotation, higher execution efficiency, and better knowledge expression ability. For the entity alignment task, learning the structural and attribute information of the entity at the same time is conducive to enhancing the feature representation of the embedded entity and improving the effect of entity alignment. However, in the process of attribute embedding, the traditional representation learning method only utilizes a single attribute or assigns all attributes with the same weight, and ignores important attribute information, which is non-trivial for entity alignment problems and is sensitive to irrelevant attribute information.