# Improving Entity Linking by Introducing Knowledge Graph Structure Information
## 2 Background and Related Work
### Entity Linking  
As it is an important task in natural language processing, there is a lot of work in the field of entity linking. Most of the early work comprises methods based on manually designed features and rule-based methods, which are not enough to capture the potential dependence and interaction in the data. With the rapid development of deep learning, a large number of deep-learning-based methods have appeared in the field of entity linking, and they have achieved better results than previous methods. Topics related to the work of this article are as follows. **Local model. ** The local model uses the local text context information around the entity mention to independently resolve the ambiguity of each entity mention. [PERSON] et al. [14] were early adopters of deep learning for entity linking. They learned distributed representations of entities to measure similarity, avoiding manually designed features, so that words and entities could be in the joint semantic space, and then candidate entities could be sorted based on vector similarity. Subsequently, [PERSON] et al. [15] used neural networks to encode mentions, contexts of mentions, and entities. Among them, contexts of mentions are encoded by convolutional neural networks (CNN), which are combined with representations of the mention titles to obtain the final mention representations. The entity representations are obtained from the entity titles and entity categories. Finally, the similarities between the mention representations and the entity representations are calculated to obtain local scores. Based on [15], [PERSON] et al. [16] used CNN and stacked denoising auto-encoders to encode different granular information of mentions and entities to enhance the representation. In addition, [PERSON] et al. [17] cascaded the output of two long short-term memory (LSTM) [18] networks. The two LSTM networks independently encode the left and right context of the entity mention, including the entity mention itself. [PERSON] et al. [19] expressed entity mention as a combination of LSTM hidden states contained in the span of entity mention. [PERSON] et al. [20] used a variant of LSTM-GRU [21]. [PERSON] and [PERSON] [6] introduced an attention mechanism in the local model. They assumed that a context word was important if it was strongly related to at least one candidate entity, and the context words were hard pruned. The local model in this paper is based on [PERSON] and [PERSON] [6]. **Global model. ** The global model links all the mentions in a document at the same time and considers that the target entities of all the mentions are consistent on the subject. The previous global methods usually executed RandomWalk [22] or PageRank [23] algorithms on the graph containing candidate entities. Another solution is to maximize the conditional random field [24], but the problem is NP-hard. [PERSON] and [PERSON] [6] used loopy belief propagation (LBP) [25] to iteratively propagate entity scores to reduce complexity. Based on [6], [PERSON] and [PERSON] [7] modeled the latent relations between mentions and added them to the global model in the form of features, achieving better results. Some recent studies have defined the global entity linking problem as a sequential decision task, where the linking of the new entity is based on the already linked entity. [PERSON] et al. [26] used LSTM to maintain long-term memory for previous decisions; [PERSON] et al. [9] proposed a dynamic context integration method that uses previous decisions as dynamic context to improve subsequent decisions; [PERSON] et al. [27] calculated the confidence scores based on the previous decisions. In addition, graph neural networks (GNNs) can also be used for the global model of entity linking. [PERSON] et al. [28] proposed a dynamic graph convolutional network model, in which the graph structure is dynamically calculated and changed during training, and fusion of knowledge through dynamically linked nodes can effectively obtain the theme consistency in the document. [PERSON] et al.