# Entity-Alignment Interaction Model Based on Chinese RoBERTa
## 1 Introduction
In order to align the graphs using additional information, the most prevalent method is to initialize the embedding of each node with additional information and apply a variant GCN model to update the node embeddings by aggregating the embeddings of all neighbors [11]. However, different knowledge graphs are often highly heterogeneous, resulting in equivalent entities not always having similar neighbors. In this case, propagating different neighbors may introduce noise and thus impair performance. Although some studies have attempted to distinguish the effects of different neighbors, GCN-like models continue to integrate the additional information from all neighbor entities in order to represent a given entity. [PERSON] et al. [10] were aware of this problem and addressed it through a modeling process that thoroughly separates the additional information from the graph structure. Nevertheless, this approach neglects to consider the additional information pertaining to the neighbor entities. Furthermore, in a manner analogous to the aggregation of neighbors, all attributes are aggregated together in order to represent an entity, which also leads to noisy matching between entities. In order to address the aforementioned issues, we propose RoBERTa-INT, a RoBERTa-based interaction model. BERT is a pre-trained language model that encodes the context of  
Figure 1: An example of entity alignment. The nodes in the rectangle are the aligned entities, and the nodes in the ellipse represent the neighbors. all words in a sentence through a bidirectional Transformer architecture. RoBERTa is an optimized BERT model. By increasing the amount of training data, extending the training time, increasing the batch size, adopting a dynamic masking strategy, and removing the Next Sentence Prediction task, RoBERTa significantly improves the model's performance in various natural-language-processing tasks. We mimic the cognitive process of humans comparing two entities, typically starting with comparing the current entities and then checking for similar neighbors. Subsequently, based on the embedding of the name and attributes, each pair of neighbors or attributes is compared, and the relationship between them is thereafter referred to as an interaction, rather than simply being aggregated. In this way, RoBERTa-INT is able to effectively handle the noise-matching problem caused by aggregated neighbors or attributes, which improves the effect of entity alignment. The principal contributions of this study are as follows:  
1. This paper puts forward a novel entity-alignment model, RoBERTa-INT, which enhances the accuracy and robustness of entity alignment through the utilization of the additional information associated with entities. 2. Traditional entity-alignment models combine the graph-structure information, which often suffers from unreasonable aggregation of neighbor information, leading to the problem of noise matching. This paper presents a solution to the aforementioned problem through the introduction of an interaction model, which utilizes names, attributes, neighbors, and attentions of entities for interaction and captures the matching relationships between neighbors from a fine-grained and semantic perspective. 3. We evaluate the model in detail on the Chinese datasets MED-BBK-9K [12], and the results demonstrate that the efficacy of the method proposed in this paper is significantly better than that of baseline models.