# Entity-Alignment Interaction Model Based on Chinese RoBERTa
## 2 Related Works
### Methods Based on Graph-Structure Information  
In the area of entity alignment using graph-structure information, researchers have worked on mining the relationships, paths, and subgraph structures among entities in the knowledge graph for accurate entity alignment.  
**TransE-based methods.** [PERSON] et al. [13] proposed the TransE model, which models entities by interpreting triples as translations of entities over low-dimensional embeddings and maps entities into low-dimensional vector spaces using graph-structure information. Although TransE is capable of achieving good results, it is unable to handle complex relationships, such as those involving one-to-many, many-to-one, and many-to-many cases. [PERSON] et al. [4] proposed the MTransE model, an extension of TransE, which maps the representation of entities in different knowledge graphs to a unified embedding space by modeling the translation of relationships across knowledge graphs. [PERSON] et al. [5] proposed the IPTransE model, an iterative path-based transformational embedding approach that aims to optimize the entity-alignment task by exploiting the path information between entities in the knowledge graph.  
**GCN-based methods.** [PERSON] et al. [14] proposed the GCN-Align model, which represents the knowledge graph as a graph structure and uses GCN to learn the representation of entities, where the node representations are continuously updated by aggregating the information on neighbor nodes. However, GCN-Align also suffers from the disadvantages of high requirements with regard to the quality of graph structure and high computational complexity.  
**GNN-based methods.** [PERSON] et al. (2018) proposed the MuGNN model, which uses GNN to encode structural features from different perspectives to learn alignment-oriented embeddings. This approach can explicitly complete the knowledge graph and also prune exclusive entities, in turn utilizing the heterogeneous structural information from the knowledge graph to effectively improve the quality of the alignment. [PERSON] et al. (2018) proposed the AliNet model, which aggregates the information associated with multi-hop neighbors of entities at each layer through a gating mechanism and uses an attention mechanism to reduce noise. However, the model relies on high-quality graph-structure data and requires a certain number of high-quality pre-aligned entities as training data.  
**Other methods.** [PERSON] et al. (2018) proposed the RALG model, constructed a heterogeneous linear graph that clearly captures the correlation between relationships and designed a new aggregation method in the form of triples to enhance the correlation between entities and their correspondences. [PERSON] et al. (2018) proposed a framework called ASGEA, which connects entities in different knowledge graphs through anchor links, extends along these paths to construct alignment subgraphs containing all potential alignment paths, and designs interpretable path-based GNNs to improve alignment performance and interpretability. ASGEA performs well in entity alignment and cross-modal entity-alignment tasks. However, it also suffers from drawbacks such as high computational complexity and poor results in cases of sparse anchor links.