# KGNER: Improving Chinese Named Entity Recognition by BERT Infused with the Knowledge Graph
## 2 Related Work
### Knowledge Graph  
A knowledge graph can be understood as a semantic network whose contents reveal specific relationships between entities. It is often used to provide a structured and visual description of things that exist in the real world and their interrelationships. Today's knowledge graphs can be used to refer to various large-scale visual knowledge bases in general. The triad form is generally used as a generalized representation of knowledge graphs, i.e., G(E, R, S), where \(E=\{e_{1},e_{2},...,e_{n}\}\) denotes the set of entities in the knowledge base, which contains \(|E|\) different entities; \(R=\{r_{1},r_{2},...,r_{n}\}\) is the set of relations in the knowledge base, containing \(|R|\) different relations; \(S\subseteq E\times R\times E\) represents the set of triples in the knowledge base. Usually, the relationship between entities is defined as a semantic predicate, so the representation also occurs in the form of a Subject, Predicate, Object (SPO) triad. The basic form of the triad mainly includes (Entity1, Relationship, Entity2) and (Concept, Attribute, Attribute Value), etc. The entity is the most basic knowledge element in the whole knowledge graph, and different relationships exist between different entities. Knowledge graphs contribute to various applications, from search engines to question-answering systems. In recent years, a number of large-scale, open-domain knowledge graphs have been made available to the public, such as CN-DBpedia [34], HowNet [35], and also domain-specific knowledge graphs, such as MedicalKG [12], have been developed for medical care. Researchers are also actively exploring how to integrate knowledge graphs into NLP tasks. TransE [36] and other KG embedding approaches have been described for encoding the entities and relations into numerical representations. Some scholars attempt to learn entity/relation embeddings together with their semantic information [37; 38], while some strategies focus on graph structure encoding [39; 40; 36; 41]. Then, certain NLP jobs will profit from the use of these graph embeddings. In order to classify the text, [PERSON] et al. [42] suggested a knowledge-graph-augmented neural network in which the context vector (via LSTM, which encapsulates the complete input text) is combined with the entity and relation vectors that are obtained from the database. Knowledge attention is stated as a function of the entity that is to be typed. The limitation lies in the application of a recurrent architecture, resulting in time inefficiency and highly computational waste. [PERSON] et al. [43] proposed a novel attention mechanism that leverages information from knowledge bases (KB) and jointly takes text and KB into consideration. [PERSON] et al. [11] proposed a new word representation for named-entity recognition by encoding entity information (from an external knowledge base) through a new gated recurrent unit (GRU) and by modeling the relation context between entities through a new attention function. To use knowledge graph embeddings (KGE), we observed that the previously proposed approaches mainly used pre-trained static embeddings acquired from considerable sources such as Wikidata. KGE in these models is radically dependent on the assumption that the tail entity is a linear transformation of the head entity as well as the relationship, making them non-contextualized in nature. Therefore, the incorporation of knowledge in pre-trained models to obtain performance gains in NLP tasks has received considerable attention in recent years. ERNIE [15; 44] combined pre-trained entity embedding in knowledge graphs with corresponding entity mentions in a text to enhance the textual representation. To solve the HIF problem, entity vectors and text representations are fused by nonlinear transformations at locations where entity inputs are available to fuse lexical, syntactic, and knowledge information. K-Adapter [45] is capable of injecting multiple knowledge by independently training different adapters for diverse pre-training tasks.