Entity relation joint extraction method for manufacturing industry knowledge data based on improved BERT algorithm.

The existing joint extraction methods for entity relationships in knowledge data only target specific fields or datasets, which may have insufficient coverage for large-scale and diverse manufacturing knowledge data. In order to achieve more accurate and efficient joint extraction of manufacturing knowledge data entity relationship, an improved method based on BERT algorithm is proposed in this paper. This method constructs a manufacturing specific knowledge graph, treating the extraction of quantitative knowledge as the extraction of manufacturing entity attributes, thereby achieving joint extraction of knowledge reasoning and data entity relationships. The improved BERT algorithm was used to establish a distribution feature set of entity relationships in manufacturing knowledge data, and a manufacturing specific knowledge graph was constructed, providing an effective way to analyze and extract entity relationships in manufacturing data, thereby helping decision-makers better understand knowledge in the manufacturing field and improving production efficiency and quality. The test results show that the knowledge graph generated by the method has good expression ability and strong logical reasoning ability, and the running time required by the application of the method is only 12 s, indicating that it can realize the joint clustering extraction of entity relationships in manufacturing knowledge data more efficiently and accurately. The innovation of this paper lies in applying BERT algorithm to joint extraction of entity relationship of manufacturing knowledge data, and improving the algorithm to improve the accuracy and efficiency of extraction, providing a new solution for the construction and application of manufacturing knowledge graph. [ABSTRACT FROM AUTHOR]