A Method for Constructing a Digital Transformation Driving Mechanism Based on Semantic Understanding of Large Models

TL;DR

This paper presents a novel method combining large language models, knowledge graphs, and reinforcement learning to enhance digital transformation mechanisms, significantly improving decision-making speed and accuracy in manufacturing.

Contribution

It introduces an integrated approach using LLMs, GNNs, and reinforcement learning to construct dynamic enterprise knowledge graphs for digital transformation.

Findings

Reduced equipment failure response time from 7.8 to 3.7 hours

Achieved 94.3% F1 score in entity recognition

Decreased decision error costs by 45.3%

Abstract

In the process of digital transformation, enterprises are faced with problems such as insufficient semantic understanding of unstructured data and lack of intelligent decision-making basis in driving mechanisms. This study proposes a method that combines a large language model (LLM) and a knowledge graph. First, a fine-tuned BERT (Bidirectional Encoder Representations from Transformers) model is used to perform entity recognition and relationship extraction on multi-source heterogeneous texts, and GPT-4 is used to generate semantically enhanced vector representations; secondly, a two-layer graph neural network (GNN) architecture is designed to fuse the semantic vectors output by LLM with business metadata to construct a dynamic and scalable enterprise knowledge graph; then reinforcement learning is introduced to optimize decision path generation, and the reward function is used to drive the mechanism iteration. In the case of the manufacturing industry, this mechanism reduced the response time for equipment failure scenarios from 7.8 hours to 3.7 hours, the F1 value reached 94.3%, and the compensation for decision errors in the annual digital transformation cost decreased by 45.3%. This method significantly enhances the intelligence level and execution efficiency of the digital transformation driving mechanism by integrating large model semantic understanding with structured knowledge.