智能家居信息安全知识图谱构建研究OA

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.