Automatic Generation Method of Knowledge Graph for Complex Product Assembly Processes Based on Text Mining

Efficient preparation and assembly guidance for complex products relies heavily on semantic information in assembly process documents. This information encompasses various levels of elements and complex semantic relationships. However, there is currently a scarcity of effective modeling techniques to express these documents’ inherent assembly process knowledge. This study introduces a method for constructing an Assembly Process Knowledge Graph of Complex Products (APKG-CP) utilizing text mining techniques to tackle the challenges of high costs, low efficiency, and difficulty reusing process knowledge. Developing the assembly process knowledge graph involves categorizing entity and relationship classes from multiple levels. The Bert-BiLSTM-CRF model integrates BERT (bidirectional encoder representations from transformers), BiLSTM (bidirectional long short-term memory), and CRF (conditional random field) to extract knowledge entities and relationships in assembly process documents automatically. Furthermore, the knowledge fusion method automatically instantiates the assembly process knowledge graph. The proposed construction method is validated by constructing and visualizing an assembly process knowledge graph using data from an aerospace enterprise as an example. Integrating the knowledge graph with the assembly process preparation system demonstrates its effectiveness for process design.