# Leveraging Multi-Modal Information for Cross-Lingual Entity Matching across Knowledge Graphs
## 3 Method
### Entity Representation Learning in Different Views
#### 3.3.3 Description View  
The description view contains rich textual semantic information, which is difficult to obtain from other views. To extract such semantic information from the description view, we encode the entity text descriptions through two steps. Firstly, the embedded representation of words in the entity text descriptions is obtained by using a pre-training model. After pre-training, the description of each entity will be converted into a vector sequence, which will be input to the description encoder, and then the encoder will output the embedded representation of the entity text description. The details are as follows:  
For the pre-training language model, we also use BERT which is proposed by [PERSON] et al. [33]. As for the encoder, we adopt gated recurrent unit (GRU) [34] since it can model the sequential data well, while it is computationally more efficient than LSTM [35] or other models. GRU is a kind of recurrent neural network (RNN), which is often used to encode natural language text. It consists of two types of gating units, namely the reset gate and the update gate, which are used to track the sequence status without using a separate storage unit. In this paper, two stacked GRU layers are used to model entity descriptions in different knowledge graphs. The input is the embeddings of an entity and its descriptions obtained by BERT. We follow the standard method where the tokens are surrounded by [CLS] and [SEP] on the left and right, respectively. For example, if the input entity and description are _\"[PERSON]\"_ and _\"President [PERSON] has insisted on a full-scale convention.\"_, the input to our encoder would be: [CLS]+ _\"[PERSON]\"_ +[SEP]+ _\"[PERSON] [PERSON]  
Figure 2: The example of the entity attribute graph for the entity “[PERSON]”.  
has insisted on a full-scale convention.\"_ + [SEP]. The output of the second GRU layer can be obtained as follows:  
\[\mathbf{t}_{e}=ReLU(\mathbf{W}_{o}[\mathbf{s}_{1};\mathbf{s}_{2};\dots;\mathbf{ s}_{l}]+\mathbf{b}_{o}) \tag{10}\]  
where \(\mathbf{W}_{o}\) denotes the mapping matrix, \(\mathbf{b}_{o}\) is the bias, and \(l\) is the length of the entity and description sequence. We also choose ReLU as the activation function. Then, each \(\mathbf{t}_{e}\) is normalized to \(||\mathbf{t}_{e}||_{2}^{2}=1\). Finally, we can maximize the similarity (i.e., the dot product) between the embeddings of descriptions for matched entities and minimize the dot product between the embeddings of descriptions for irrelevant entities. This objective can be achieved by minimizing the following loss function:  
\[Loss_{text}=-\sum_{(\epsilon_{1},\epsilon_{2})\in S}\{log(ReLU(\mathbf{t}_{ \epsilon_{1}}^{T}\mathbf{t}_{\epsilon_{2}}))+\sum_{i=1}^{k}E_{i^{\prime}\sim \cup(i^{\prime}\in\mathbb{R}_{2})}[log(ReLU(-\mathbf{t}_{\epsilon_{1}}^{T} \mathbf{t}_{\epsilon^{\prime}}))]\} \tag{11}\]  
where \(S\) means the known equivalent entity set, \(k\) is the number of negative entity examples, and \(U\) is the distribution of the entities in the entity set \(E_{2}\).