计算机科学与探索2026，Vol.20Issue(1)：169-181,13.DOI:10.3778/j.issn.1673-9418.2503074
智能家居信息安全知识图谱构建研究
Research on Construction of Smart Home Information Security Knowledge Graph
摘要
Abstract
With the advancement of smart home technology and growing demands for information security,the complexity of security-related design considerations has significantly increased.Although standard documents contain extensive design requirements for smart home information security,their unstructured nature hinders effective text segmentation to optimize knowledge extraction performance for large language models.This paper proposes a knowledge extraction methodology integrating large language models(LLMs)with a title-based segmentation strategy and prompt engineering.The implemen-tation involves three phases:(1)Data layer construction through format conversion to Markdown,domain-specific term extraction via TF-IDF for text filtering,and semi-structured text segmentation leveraging Markdown features;(2)Concept layer development by establishing dual-domain ontologies connecting scenarios-devices-components with hazards-accidents-control measures through expert knowledge integration;(3)Instance layer creation employing LLMs with a"domain-template-requirement"prompt engineering framework guided by title-based segmentation.Experimental results demonstrate that domain-specific text filtering reduces processing volume by approximately 60％,significantly lowering API(application programming interface)costs.The optimal F1-score of 83.1％validates the methodology's effectiveness,with ablation experi-ments confirming the semantic preservation capability of the title-based segmentation and prompt engineering frame-work.Comparative analysis reveals LLMs' superior performance over traditional deep learning model UIE(universal information extraction)in handling long-text contexts with complex structures,indicating substantial compatibility improvements.The constructed knowledge graph establishes connections between dispersed hazards and accidents through scenario-device-component relationships while extracting relevant countermeasures from standards.It provides visualized query capabilities and accident causation analysis,assisting smart home designers in identifying and evaluating potential risks to prevent information security incidents.关键词
智能家居/知识图谱/大语言模型/知识抽取/信息安全Key words
smart home/knowledge graph/large language model/knowledge extraction/information security分类
信息技术与安全科学引用本文复制引用
杨跃翔,郑怀城,刘学文,涂新雨..智能家居信息安全知识图谱构建研究[J].计算机科学与探索,2026,20(1):169-181,13.基金项目
国家重点研发计划(2022YFF0607100) （2022YFF0607100）
中央基本科研业务经费项目(552023Y-10371).This work was supported by the National Key Research and Development Program of China(2022YFF0607100),and the Central Basic Business Research Funding Project of China(552023Y-10371). （552023Y-10371）
