Constructing a knowledge graph-driven intelligent data-enabled design system for mold using deep semantic understanding and intelligent decision support.

ABSTRACT: To address the inefficiency and high error rates in traditional methods for handling complex design processes in modern mold design, this study proposes a Knowledge Graph-driven Intelligent Data-enabled Design System for molds. Initially, deep semantic understanding techniques are employed to intelligently parse a large volume of mold design documents and data. Using Bidirectional Encoder Representations from Transformers (BERT) and Random Forest (RF) algorithms, key information and knowledge points are accurately extracted from the design documents, laying a solid foundation for constructing the knowledge graph. The study collects a significant number of representative mold design documents, followed by detailed data preprocessing and cleaning. Subsequently, the BERT model is utilized for

SUBMITTER: Deng J 

PROVIDER: S-EPMC11873260 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature