Research on Intelligent Management and Assisted Decision Making Technology of Electric Power Research Enterprise Based on Knowledge Engineering

Currently, the internal management knowledge of electric power research enterprises is difficult to integrate and structured organization, resulting in greatly reduced decision-making efficiency. Knowledge Graph(KG), as an efficient knowledge organization, helps to connect the complex knowledge network. However, for a Q&A platform based solely on knowledge graphs, it can be challenging to interpret and analyze natural language input, especially when considering historical Q&A data. In this paper, we combine the KG with a Large Language Model(LLM), choose the ERNIE model fine-tuned by the text of electric power domain knowledge, and use the TransE method to integrate the KG into the model's structure to build a knowledge-based Q&A system that assists researchers in decision-making. The experimental results show that the ERNIE model combined with KG outperforms the comparison model in terms of precision, recall, and F1 value in entity and relation classification tasks, and the performance is further improved by increasing the number of attention heads.