# Entity-Alignment Interaction Model Based on Chinese RoBERTa
## 3 Methodology
### The Model Framework
#### 3.2.2 RoBERTa-Based Interaction Model  
Based on the Basic RoBERTa Unit, we constructed an interaction model consisting of Name Interaction, Neighbor Interaction, Attention Interaction and Attribute Interaction. The main components are as follows:  
**Name Interaction. ** Given two entities \(e\) and \(e^{\prime}\) in the graphs \(G\) and \(G^{\prime}\), the name vector representations \(T(e)\) and \(T(e^{\prime})\) of entities \(e\) and \(e^{\prime}\) are computed using the RoBERTa unit  
Figure 2: The framework of RoBERTa-INT. described above, and then the cosine similarity of \(T(e)\) and \(T(e^{\prime})\) is computed, and the result of the computation is the name interaction. **Neighbor Interaction. ** In this section, we established the interaction between neighbors \(N(e)\) and \(N(e^{\prime})\). The idea was to compare the names of each neighbor pair, rather than learning the global representation of entities \(e\) and \(e^{\prime}\) by aggregating the names of all neighbors as in existing work. We applied the basic RoBERTa unit to obtain \(\{T(e_{i})\}_{i=1}^{|N(e)|}\) and \(\big{\{}T\big{(}e^{\prime}_{i}\big{)}\big{\}}_{i=1}^{|N(e^{\prime})|}\), and, for the neighbor entities of entities \(e\) and \(e^{\prime}\), to compute the similarity matrix between the two embedding sets based on their names, and then to apply a dual aggregation function to extract similarity features from the matrix. We denote by \(B\) the interaction between the neighbors of entities \(e\) and \(e^{\prime}\), and each element \(b_{ij}\) represents the interaction, i.e., the cosine similarity between \(T(e_{i})\) and \(T\big{(}e^{\prime}_{j}\big{)}\):  
\[b_{ij}=\frac{T(e_{i})\cdot T\Big{(}e^{\prime}_{j}\Big{)}}{\|T(e_{i})\|\cdot\|T \Big{(}e^{\prime}_{j}\Big{)}\|} \tag{3}\]  
where \(T(e_{i})\) and \(T\Big{(}e^{\prime}_{j}\Big{)}\) are obtained by Formula (1) for the \(i\)-th neighbor of \(e\) and the j-th neighbor of \(e^{\prime}\), respectively. We applied the dual aggregation function to extract the similarity features of rows and columns of \(B\). Neighbors are unordered and independent of each other. Therefore, we used the RBF kernel-aggregation function [28] to extract the cumulative similarity features. The RBF kernel-aggregation function calculates the similarities between the input feature vectors and uses these similarities as weights to perform weighted summation on the input feature vectors to obtain an aggregated feature vector. This method can effectively capture the complex relationship between the input features. Before extracting the similarity cumulative features, we performed a maximum-pooling operation on each row \(B_{i}=\big{\{}b_{i0},...,b_{ij},...,b_{in}\big{\}}\) to obtain the maximum similarity \(b_{i}^{max}\), i.e., we select the most likely aligned sample for the i-th neighbor of entity \(e\) from the neighbors of entity \(e^{\prime}\). The reason for this is that due to the heterogeneous nature of the knowledge graph, the neighbor entities of two aligned entities are not identical, and we need to care only about the similarity between the neighbor entity of entity \(e\) and the one that is most similar to the one in the neighbors of entity \(e^{\prime}\). Next, the maximum similarity \(b_{i}^{max}\) is converted into a row-based feature vector \(X^{r}(B_{i})\), where the k-th element \(X_{k}\big{(}b_{i}^{max}\big{)}\) is converted by the k-th RBF kernel into the mean \(\mu_{k}\) and variance \(\sigma_{k}\).