Structured knowledge representation is of great significance for constructing a knowledge graph of coal mine equipment safety. However, traditional methods encounter substantial difficulties when handling the complex semantics and domain-specific terms in technical texts. To tackle this challenge, we propose a knowledge extraction framework that integrates large language models (LLMs) with prompt engineering to achieve the efficient joint extraction of information. This framework strengthens the traditional triple structure by introducing symmetric entity-type information encompassing the head entity type and the tail entity type. Furthermore, it enables simultaneous entity recognition and relation extraction within a unified model. Experimental results demonstrate that the proposed knowledge extraction framework significantly outperforms the traditional step-by-step approach of first extracting entities and then relations. To meet the requirements of actual industrial production, we verified the impacts of different prompt strategies, as well as small lightweight models and large complex models, on the extraction task. Through multiple sets of comparative experiments, we found that the Chain-of-Thought (CoT) prompting strategy can effectively improve performance across different models, and the choice of model architecture has a significant impact on task performance. Our research provides an accurate and scalable solution for knowledge graph construction in the coal mine equipment safety domain, and its symmetry-aware design exhibits great potential for cross-domain knowledge transfer.
Zhang et al. (2025) studied this question.