一种基于大模型的装备知识图谱本体自动化构建方法OA

With the continual increase of complexity of equipment maintenance support tasks,construction of high-quality domain ontologies has become a crucial prerequisite between knowledge sharing and intelli-gent reasoning.Aimed at the problems that the traditional ontology construction methods are dependent on manual modeling,are low in efficiency and difficult to meet the needs of large-scale corpora,meanwhile,ex-isting large model-based ontology construction methods are unstable in generation,poor in semantic consis-tency,and lack of domain adaptation,this paper propose an automated equipment knowledge graph ontolo-gy construction method based on large models.This method is met with a challenge in the equipment main-tenance domain,i.e.highly specialized terminology,complex relationship semantics,and sparse cross-docu-ment associations.The paper employs a three-stage process of block parallel extraction(Map)-local merge and redundancy removal(Reduce)-global context refinement(Refine).Through a context-aware relationship completion mechanism and a domain-adaptive configuration module,the automatic transforma-tion is completed from the unstructured text to a highly connected ontology.The experiments show that the OLMM-Refine method can be used to effectively improve the completeness and logical consistency of relationship extraction in ensuring coverage of entities and attributes,reducing the proportion of isolated nodes and forming a knowledge graph with stronger connectivity and more reasonable semantic structure.Compared to the existing large language model-driven ontology construction frameworks,the OLMM-Re-fine is superior at performance in terms of completeness,consistency and accuracy,and the efficiency is within an acceptable range.The results indicate that the OLMM-Refine can take account of the generality and the domain adaptability,providing a new approach and pathway for the construction and application of knowledge graphs in equipment maintenance support.