# Entity-Alignment Interaction Model Based on Chinese RoBERTa
## 2 Related Works
### Methods Based on Additional Information  
In addition to graph-structure information, many existing approaches facilitate entity-alignment performance by employing additional information from the graph, including entity names, descriptions, images, attributes, etc.  
**Entity-name-based methods.** [PERSON] et al. (2018) utilized word-embedding and sentence-embedding models trained on an external corpus to convert entity names and attributes into vector representations, which are blocked by locally sensitive hashing and clustering algorithms to identify semantically similar entity records. On the test datasets, this approach proved to be effective in exploiting the semantic information of entity names. [PERSON] et al. (2018) proposed an effective end-to-end entity alignment framework, EASY, which can eliminate labor-intensive preprocessing by fully discovering the name information provided by the entities themselves and jointly fuse the features captured by entity names to improve alignment results. [PERSON] et al. (2018) proposed the MultiKE model, a framework for unifying multiple views of entities, which embeds entities using various combination strategies based on the views of entity names, relationships, and attributes. In addition, several cross-graph inference methods were designed to enhance the alignment between two graphs. [PERSON] et al. (2018) proposed the UEA model, an unsupervised framework for performing entity alignment in the open world, which generates structural representations and integrates them with additional information to provide a more comprehensive view of the alignment to enhance alignment performance.  
**Image-based methods.** [PERSON] et al. (2018) investigated the use of visual semantic representations to align entities in heterogeneous knowledge graphs. The proposed new method, EVA, combines visual knowledge with other auxiliary information to create an overall representation of entities, providing strong signals for cross-graph entity alignment.  
**Entity-attribute-based methods.** [PERSON] et al. (2018) believed that attribute triplets can also provide key alignment signals, but this concept has not been well explored. They proposed to use attribute value encoders and divide KG into subgraphs to effectively model various types of attribute triplets, achieving remarkable results on the baselines of cross-lingual and monolingual datasets. [PERSON] et al. (2018) proposed a new method for directly learning entity-pair embeddings for entity alignment. The pairwise connected graphs of two graphs are first generated, and then the entity-pair embeddings of the pairwise connected graphs are learned for predicting the equivalence of entities. In this process, similar features are generated through the attributes of the entity pairs and the similar features are propagated using GNN to obtain the final embeddings of the entity pairs. Different graphs may havemany different attribute types, and even the same attributes may have different data structures and value granularities. To solve this problem, [PERSON] et al. [26] proposed the COTSAE model, which combines structural and attribute information associated with entities by co-training the two embedding learning components separately, and proposed a joint-attention approach to collaboratively learn attribute type and value attention in the model.