# Improving Entity Linking by Introducing Knowledge Graph Structure Information
## 1 Introduction  
The named entity linking (NEL) task refers to correctly linking entity mentions in text to entities in a structured knowledge base (such as Wikipedia, Freebase [1], or YAGO [2]), which can solve the ambiguity of mentions in natural language processing. In Figure 1, for example, a mention of \"[PERSON]\" may correspond to entity entries in the knowledge base (KB) such as \"[PERSON]\", \"[PERSON]\", \"[PERSON] (footballer)\", \"[PERSON]\", etc. The entity linking (EL) involves linking the mention \"[PERSON]\" to the correct entity \"[PERSON]\" in the KB. Entity linking is also the basis of many other natural language processing tasks, such as knowledge base question and answer [3], information retrieval [4], and content analysis [5]. Given a document, the named entity mentions are recognized in advance by a named entity recognition (NER) method. Generally speaking, a typical entity linking system consists of two steps: (1) candidate entity generation, in which a model retrieves a set of candidate entities, which contains the entities that the mention may refer to; and (2) candidate entity ranking, in which a model ranks the entities in the candidate set and selects the entity that the mention is most likely to link to. Recently, some methods such as techniques based on a named dictionary and techniques based on surface form expansion have achieved high candidate recalls, and thus most work focuses on methods for downstream candidate entity ranking, as described in this paper. In early work, prior distribution and local contexts played important roles in disambiguating different candidate entities. However, in many cases, local features alone cannot provide sufficient information for disambiguation. Therefore, many global models have emerged to solve the task of entity linking. For example, [PERSON] and [PERSON] [6] combine local and global information. First, the word-entity co-occurrence counts are used to train the entity embeddings, then the local scores between contexts of mentions and the entity embeddings are calculated in the local model, and the scores between candidate entities of all mentions in the document are calculated in the global model. On the basis of [6], [PERSON] and [PERSON] [7] model the latent relations between mentions. Based on [7], [PERSON] et al. [8] inject fine-grained semantic information into entity embeddings. In addition, [PERSON] et al. [9] propose the dynamic context augmentation method, which uses the entity embedding in [6]. However, the above methods still have some shortcomings. They essentially calculate the similarity between entity embeddings when obtaining global scores, which only consider the semantic proximity between entities. While there are real relations between some entity mentions in a document, these relations are contained in some knowledge graphs, and comprise the so-called knowledge graph structural information. As shown in Figure 1, there is an association relation of \"colleague\" between entity \"[PERSON]\" and entity \"Xiandong Jing\" in the knowledge base. In addition, although there are also some works [10; 11; 12; 13] that involve knowledge graphs, this is because their target knowledge base is a knowledge graph, and our method is different from them essentially. For example, [PERSON] et al. [12] use bi-directional long short-term memory (Bi-LSTM) to encode graph triplets. [PERSON] et al. [13] develop a context-aware attentive neural network approach on Wikidata. Instead, on the basis of Wikipedia, we introduce the structural information of other knowledge graphs to complement the semantic information of Wikipedia, which is somewhat similar to the fusion of information from different knowledge bases. To address the limitations of existing methods, we propose an entity linking model that introduces knowledge graph structural information (KGEL). First, under the premise that the target knowledge base is Wikipedia, we obtain the entities and triples in the knowledge graph Wikidata corresponding to the candidate entities.