# Retrieval of Injection Molding Industrial Knowledge Graph Based on Transformer and BERT
## 1 Introduction
[10] designed a graph-theoretic knowledge model to enhance knowledge management and cited examples from the traditional Chinese medicine research field to illustrate the usability of this model. Their research results showed that the model could promote knowledge learning, sharing, and decision making among researchers. [PERSON] et al. [11] integrated medical text knowledge with physicians' clinical experiences to create a health knowledge graph, and designed an expandable mechanism for the graph that could add new diseases to the existing knowledge graph. The graph could assist clinical doctors with their diagnostic decision making. [PERSON] [12] constructed a knowledge graph that included all the healthcare facilities in Korea. The graph was interconnected with heterogeneous data sources such as administrative areas, allowing users to choose suitable healthcare facilities based on their requirements. [PERSON] et al. [13] established a Knowledge Graph of Construction Safety Standard (KGCCSS) to guide the development and revision of the Construction Safety Standards (CSS) for building safety. In addition, it also provided a search function. The construction of this KGCCSS promoted the analysis, search, and sharing of standard safety knowledge. [PERSON] et al. [14] introduced a construction method for a power system dispatch knowledge graph, which recorded the correlations among various power devices and the impacts of device failures. Through investigating the application of the knowledge graph in the practical field of power system dispatch, it could be proved that the knowledge graph could assist dispatchers with their knowledge management and emergency decision making. [PERSON] et al. [15] constructed a knowledge graph to express the interrelationships among enterprises, and based on this graph, they searched for related enterprises from the perspective of the target enterprise. They used Graph Neural Networks (GNN) to calculate the credit risk of the target enterprise, which served as the basis for investment decisions. The above literature shows that knowledge graphs have achieved excellent performances in various fields of knowledge management. Unlike traditional knowledge repositories, knowledge graphs can clearly express the interactive relationships between data and play an important role in knowledge retrieval, question answering, and knowledge recommendations. Knowledge graphs consist of multiple nodes, and in terms of knowledge analysis, they can observe the paths between two nodes and identify their potential relationships. If the paths between these nodes are understood as sequential data, applying machine learning methods will help to reason and analyze the knowledge graph. [PERSON] et al. [16] used a Transformer model to predict the future trend of influenza transmission based on the past 10 weeks of transmission data. The performance of the Transformer model was compared with traditional sequence models such as LSTM [17] and Seq2 Seq [18]. The results showed that the Transformer outperformed LSTM and Seq2 Seq, and this model could help public health experts to respond to future changes in an epidemic situation in a timely manner. [PERSON] [19] used Transformer-based models to input the amino acid sequence data of a target protein and generate molecular structures that could bind to this target protein, serving as the main basis for drug development. This approach significantly reduced the time and cost required for drug development. [PERSON] et al. [20] used Transformer models to predict the remaining lifespan of machines based on vibration signals and compared the performance of this approach with other models, such as CNN, LSTM, and RNN, in the task of machine lifespan prediction. The results showed that the Transformer achieved a better performance than the other models. [PERSON] et al. [21] trained a Transformer model to analyze the trends of factors such as temperature, light intensity, and carbon dioxide concentration, in order to predict future greenhouse temperatures.