Knowledge graphs play an important role in the field of knowledge management by providing a simple and clear way of expressing complex data relationships. Injection molding is a highly knowledge-intensive technology, and in our previous research, we have used knowledge graphs to manage and express relevant knowledge, gradually establishing an injection molding industrial knowledge graph. However, the current way of retrieving knowledge graphs is still mainly through programming, which results in many difficulties for users without programming backgrounds when it comes to searching a graph. This study will utilize the previously established injection molding industrial knowledge graph and employ a BERT (Bidirectional Encoder Representations from Transformers) fine-tuning model to analyze the semantics of user questions. A knowledge graph will be retrieved through a search engine built on the Transformer Encoder, which can reason based on the structure of the graph to find relevant knowledge that satisfies a user’s questions. The experimental results show that both the BERT fine-tuned model and the search engine achieve an excellent performance. This approach can help engineers who do not have a knowledge graph background to retrieve information from the graph by inputting natural language queries, thereby improving the usability of the graph.

Injection molding is a technique with a high knowledge content. However, most of the injection molding knowledge is stored in books, and it is difficult for personnel to clarify the influence of the different factors. This study applies the concept of a knowledge graph by using three types of nodes and edges to express the complex injection molding knowledge in the related literature, and also combines SBERT and search engine building to retrieve the graph. The search engine can follow different search logics, according to the types of nodes, then find the knowledge related to the node, classify it according to the search path, and visualize the search results to the user. Users can clarify the relationship between various process factors and product qualities in a different way. We also use multiple tests to show the actual search results and verify the performance of the search engine. The results show that the search engine can quickly and correctly find the relevant knowledge in the graph, and maintain its performance when the graph is expanded. At the same time, users can clarify the impact of various process factors on the product quality, according to the search results.

Injection molding, the most common method used to process plastics, is a technique with a high knowledge content; however, relevant knowledge has not been systematically organized, and as a result, there have been many bottlenecks in talent cultivation. Moreover, most of the knowledge stored in books and online articles remains in the form of unstructured data, while some even remains unwritten, resulting in many difficulties in the construction of knowledge bases. Therefore, how to extract knowledge from unstructured data and engineers’ statements is a common goal of many enterprises. This study introduced the concept of a Knowledge Graph, a triplet extraction model based on bidirectional encoder representations from transformers (BERT) which was used to extract injection molding knowledge entities from text data, as well as the relationships between such entities, which were then stored in the form of knowledge graphs after entity alignment and classification with sentence-bidirectional encoder representations from transformers. In a test, the triplet extraction model achieved an F1 score of 0.899, while the entity alignment model and the entity classification model achieved accuracies of 0.92 and 0.93, respectively. Finally, a web platform was built to integrate the functions to allow engineers to expand the knowledge graphs by inputting learning statements.