# Improving Entity Linking by Introducing Knowledge Graph Structure Information
## 5 Experiments
### Other Ways of Using KG Structure  
In addition to using knowledge graph embedding methods such as TransE on triples, we also try to use triples directly. We consider two entities to be related if there is a relation between them, i.e., two entities that can form a triple are related. Therefore, for entity \(e_{1}\), we obtain the entity set \(E_{r}\) related to it from the triples. For example, in Table 1, the related entity set of entity Q1 is {Q523, Q136407, Q323}. To incorporate information about its related entities in the representation of entity \(e_{1}\), we perform the following operations:  
\[\mathbf{e}_{r}=\frac{1}{a}\sum_{i=1}^{a}\mathbf{e}_{i} \tag{20}\]  
\[\mathbf{e}=\alpha\mathbf{e}_{1}+(1-\alpha)\mathbf{e}_{r} \tag{21}\]  
where \(\mathbf{e}_{i}\in E_{r}\) is the entity associated with entity \(e_{1}\), \(a\) is the size of the entity set \(E_{r}\), \(\mathbf{e}_{r}\) is the average embedding of entities associated with entity \(e_{1}\), \(\mathbf{e}_{1}\) is the original embedding of entity \(e_{1}\), \(\mathbf{e}\) is the embedding of entity \(e_{1}\) after fusing information, and \(\alpha\) is a hyperparameter. This operation is equivalent to using 1-hop information of the knowledge graph.  
In order to determine the optimal value of \(\alpha\), we performed a lot of experiments for different \(\alpha\); that is, directly replacing the original entity embedding with the entity embedding after fusion, and the model structure is consistent with [PERSON] and [PERSON] [7]. The experimental results are shown in Figure 4.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Model** & **AIDA-B** & **MSNBC** & **AQUAINT** & **ACE2004** & **CWEB** & **WIKI** & **Avg** \\ \hline _KGEL_ & 93.47 & 94.26 & 88.11 & 90.54 & 77.21 & 78.40 & 85.7 \\ - _KGEmbs_ & 93.07 & 93.9 & 88.3 & 89.9 & 77.5 & 78.0 & 85.5 \\ - _WikiEmbs_ & 87.16 & 92.12 & 81.54 & 87.73 & 72.84 & 68.96 & 80.64 \\ \hline - _local_ & & & & & & & \\ - _WikiEmbs_ & 84.86 & 91.05 & 79.16 & 86.92 & 70 & 64.46 & 78.32 \\ \hline \end{tabular}
\end{table}
Table 4: F1 scores of the ablation experiments.  
From the figure, it is clear that the best results are obtained when \(\alpha=0.9\). In addition, we also tried some other variants:  
1. Ment-Norm is the model of [PERSON] and [PERSON] [7] and also our basic model.
2. _KGEL_ is our main model; that is, the entity and relation embeddings obtained by the knowledge graph embedding method are used in the global model of entity linking.
3. _Related-Fixed_ refers to the method of using related entities mentioned in this section, in which the parameter \(\alpha\) is fixed at 0.9.
4. _Related-Vari_ means that the parameter \(\alpha\) is variable; that is, it changes during training.
5. Based on _Related-Vari_, _Related-Vari-diff_ makes the \(\alpha\) in the global model and the local model different.
6. _Related-nn_ indicates the use of a neural network to fuse \(\mathbf{e}_{1}\) and \(\mathbf{e}_{r}\).  
From the Table 5, it can be seen that the parameter \(\alpha\) fixed to 0.9 is the optimal result when using related entities. The result of _Related-Fixed_ is slightly better than that of _Ment-Norm_, indicating that the knowledge graph structure is beneficial for the effect of entity linking. However, the result of _Related-Fixed_ is worse than that of _KGEL_, which shows that how the knowledge graph structure is used is also very important. Obviously, it is better for us to use the entity embedding obtained by the knowledge graph embedding for the characteristics of the global model considering the correlations between entities.