# Improving Entity Linking by Introducing Knowledge Graph Structure Information
## 5 Experiments
### Main Results  
The following methods are selected as baselines.  
1. AIDA [48] combines the previous methods into a comprehensive framework that contains three measures: the prior probability of an entity being mentioned, the similarity between the context of mention and the candidate entity, and the consistency among candidate entities for all mentions. It constructs a weighted graph whose nodes are mentions and candidate entities and calculates a dense subgraph to obtain an approximately optimal mention-entity mapping.
2. GLOW is a global entity disambiguation system proposed by [49], which formulates the entity disambiguation task as an optimization problem with local and global variants.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Dataset** & **Number Mentions** & **Number Docs** & **Mentions per Doc** & **Gold Recall** \\ \hline AIDA-train & 18,448 & 946 & 19.5 & - \\ AIDA-A & 4791 & 216 & 22.1 & 97.3 \\ AIDA-B & 4485 & 231 & 19.4 & 98.3 \\ \hline MSNBC & 656 & 20 & 32.8 & 98.5 \\ AQUAINT & 727 & 50 & 14.5 & 94.2 \\ ACE2004 & 257 & 36 & 7.1 & 90.6 \\ \hline CWEB & 11,154 & 320 & 34.8 & 91.1 \\ WIKI & 6821 & 320 & 21.3 & 92.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics of experiment datasets. Gold recall is the probability that the candidate sets of mentions contain the ground truth entities.  
RI [50] combines statistical methods to perform richer relational analysis on the text. It proposes a modular formulation that includes the entity-relation inference problem. It also proves that the recognition of relations in the text is not only helpful for candidate entities, but also the subsequent ranking stage.
4. PBoH [51] uses a graphical model to perform global entity disambiguation. It simultaneously disambiguates mentions in a document by using the co-occurrence probability between entities in the document and the local context information of the mentions. It uses LBP to perform approximate inference.
5. Deep-ED [6] introduces an attention mechanism into the local model, and the context words of mentions are hard pruned. Its global model is a fully-connected pairwise conditional random field. Because the problem is NP-hard, it uses LBP to iteratively propagate entity scores to reduce complexity.
6. Ment-Norm [7] models the latent relations between mentions and adds them to the global model in the form of features. There are two options for normalization, where it is normalization over mentions.
7. DCA-SL [9] regards entity linking as a sequence decision task and uses the previous decision as dynamic contexts to improve the later decisions. It explores supervised learning strategies for learning the DCA model.
8. DCA-RL [9] involves the use of reinforcement-learning strategies to learn the DCA model.  
Table 3 shows micro F1 scores on AIDA-B and five out-domain test sets. Compared with Deep-ED [6], our method achieves a substantial improvement on both the in-domain dataset AIDA-B and the average result on five out-domain datasets. Moreover, KGEL's F1 score is still 0.4% higher than Ment-Norm on the AIDA-B dataset, and for the average result on the five out-domain datasets, KGEL also has an improvement of 0.2% F1 on Ment-Norm. It should be noted that although the DCA-SL model has good results on the datasets AIDA-B and MSNBC, it has poor results on the dataset CWE, so its average result on the out-domain datasets is not good. The same is true for DCA-RL. This indicates that our method has better generalization. Therefore, overall, our method achieves very competitive results on the AIDA-B dataset. Moreover, KGEL achieves higher F1 scores than previous methods on the ACE2004 dataset as well as on the average of out-domain datasets. This fully demonstrates the effectiveness of our method, i.e., the importance of knowledge graph structure for entity linking.