Knowledge extraction and alignment for mine ventilation: A knowledge graph construction framework based on large language models

Mine ventilation corpora contain fragmented entities, attributes, and rule-based knowledge, marked by heterogeneous expressions, implicit structures, and ambiguous terminology. These characteristics hinder systematic modeling and intelligent utilization. To address these challenges, we propose an automated extraction and semantic alignment method based on large language models (LLMs), aiming to construct a high-quality knowledge graph (KG) tailored for mine ventilation. We design an ontology-driven extraction framework for three textual sources — regulations, books, and websites — using prompt engineering and few-shot strategies to extract information, domain knowledge, entity attributes, and rule-based relations in a unified way. For entity alignment, we develop a dual-filtering mechanism that integrates semantic similarity and structural adjacency, and leverage large language models (LLMs) for verification, enabling high-confidence alignment of entities and predicates. We propose a rule-path modeling strategy using the structure “subject entity (with condition) - predicate - object entity (with condition), ” integrating multi-source triples into conditional rule chains. These are mapped into a graph database to support structured knowledge representation. Under few-shot conditions, the extraction accuracy of entity attributes and rule-based relations reached 94% and 99%, respectively. The final alignment of entities and predicates, manually verified, achieved 100% precision. The resulting knowledge graph (KG) comprises 58, 358 entities and 63, 630 edges, demonstrating strong semantic consistency and structural integrity. It provides structured knowledge support for risk warning, question answering (QA), and intelligent decision-making in mine ventilation systems as part of intelligent mining applications.

Authors

• Jinyang Dong
• Junqiao Li
• Yucheng Li Amazon AGI
• Wei Zhang
• Zhitao Zhang
• Chenyang Guo
• Yu Dang
• Mei Chen The University of Kentucky
• Jing Du

Keywords

• Artificial intelligence
• Mine ventilation
• Large language models
• Intelligent mining systems
• Entity alignment
• Knowledge graph construction