中国标准化Issue(2)：36-42,7.DOI:10.3969/j.issn.1002-5944.2026.02.001
面向标准数字化的语义知识库自动构建技术研究
Research on Automatic Construction Technology of Semantic Knowledge Base for Standards Digitalization
摘要
Abstract
To address the core challenges of fragmented standard documents,lack of semantic correlation,and poor machine readability,and in response to the strategic requirements for the digital transformation of standardization outlined in the National Standardization Development Outline,this paper proposes a semi-automatic construction method for a standard semantic knowledge base that integrates domain ontology and deep learning technologies.First,through systematic domain analysis and formal modeling,a quintuple conceptual model centered on"standardized object-stylistic norm-indicator item-indicator value-qualifier class"is constructed,providing a unified framework for machine-readable knowledge expression.Second,a two-phase technical architecture is designed and implemented:In the first phase,a joint extraction model based on domain-adaptive pre-training and rule guidance is developed,enabling the accurate identification and structuring of key knowledge triples from unstructured standard texts;In the second phase,graph neural networks are introduced for knowledge representation learning,automatically mining and complementing potential deep semantic correlations through link prediction tasks,thereby optimizing the structural integrity and semantic richness of the knowledge graph.Finally,an empirical study is conducted with safety and environmental standards in the agricultural and food domain as the dataset.Experimental results show that the proposed method achieves an F1-score of 89.7％in the knowledge element extraction task and can effectively construct a well-structured knowledge network rich in semantic associations.The core contributions of this research are:systematically proposing for the first time a large-scale automated computational method for semantic correlation oriented to standard content;establishing a universal specification for standard knowledge representation;significantly enhancing the interoperability and reuse value of digitalization outcomes across different standard domains;and laying a high-quality,structured data foundation for downstream advanced applications such as intelligent Q&A and compliance review.关键词
标准数字化/语义知识库/知识图谱/本体/BERT/图神经网络Key words
standards digitalization/semantic knowledge base/knowledge graph/ontology/BERT/graph neural networks引用本文复制引用
甘克勤,牛月琪,梁朔,高亮..面向标准数字化的语义知识库自动构建技术研究[J].中国标准化,2026,(2):36-42,7.基金项目
本文受中国标准化研究院基本科研业务费项目"基于标准语义知识的智能问答关键技术应用研究"(项目编号:252024Y-11459)资助. （项目编号:252024Y-11459）
