# A Survey of Information Extraction Based on Deep Learning
## 2 Entity Relationship Extraction Based on Deep Learning
### Supervised Learning--Pipeline Extraction
#### 2.1.5 Entity Relational Extraction Based on Knowledge Embedding  
Most current methods of remote supervision relationship extraction focus on noise reduction processing of noisy data, while ignoring the mining and utilization of external knowledge information. Deep learning-based relationship extraction efforts are mainly dependent on word or character embeddings, regardless of the knowledge information from entities, which may lead to semantic ambiguities in the extracted relationships. [PERSON] et al. proposed a knowledge-enhanced relationship extraction (KEE) model using TransE and word2 vec models to learn word entity information, generate knowledge-guided word embeddings, and use lexical features to enhance word semantic understanding [41]. [PERSON] experimentally found that adding lexical knowledge embeddings (LKE) or non-lexical knowledge embeddings to relationship extraction with knowledge embeddings (REKE) improved the level of relationship extraction technology [42]. A network-based attention model (NAM) for chemical-disease relationship (CDR) extraction was proposed by [PERSON] et al. [43]. [PERSON] et al. proposed a new cross-sentence n-element relationship extraction method based on self-attention, which leverages the multi-head attention and knowledge representation learned from the knowledge atlas [44]. [PERSON] et al. proposed an adaptive thresholding and localized context pooling (ATLOP) model for document-level relationship extraction designed to address multi-label and multi-entity problems through adaptive thresholds and local context pooling [45]. [PERSON] et al. proposed a model called DeNERT-KG designed to extract subjects and objects via NER, and extract the relationship between them using a knowledge graph. If the proposed model has multiple entities in a sentence, each entity can be identified as a single entity and multiple relationships can be extracted; however, there is a limitation in that equivalent entities cannot be extracted into the same relationship. To overcome this limitation, techniques such as remote monitoring have been used to extract unlabeled relationships [46]. KeMRE is a medical relationship extraction method that was proposed by [PERSON] et al. KeMRE, which predicts a relationship between TCM instructions using Chinese character sequences and medical knowledge [47]. The model is divided into four modules. First, it includes a BERT-CNN-LSTM-based text modeling framework, in which a pre-trained BERT model is used to help the model capture the semantic information of the input text better, a text CNN network for local context modeling, and a bi-LSTM network for global context modeling. A representation of each entity is obtained through careful aggregation of the role representations in each entity. Second, entities were used to model the CNN-LSTM framework to better understand the relationships between entities, using entity CNN networks to capture local associations between entities and using entity bi-LSTM networks to capture global associations between entities. Third, in the knowledge-modeling framework, to obtain medical knowledge of the drugs, the instructions for each drug were randomly selected, and the medical relationship was annotated according to the instructions. With the help of medical knowledge, knowledge embeddings were further constructed between any two entities to represent the potential relationships between them. Fourth, the relationship between any two entities is predicted based on the representations and knowledge embeddings throughout the multi-layer perceptron network (MLP).