Knowledge Graph–Enhanced NB-IoT Architecture for Intelligent Educational Data Management and System Innovation

This study proposes a Knowledge Graph–Enhanced NB-IoT Architecture to improve intelligent educational data management and support system innovation in learning environments. The purpose is to address limitations of traditional innovation and entrepreneurship education, which often lacks real-time contextual information and personalized guidance. The proposed approach integrates NB-IoT for distributed, real-time data acquisition with a domain-specific knowledge graph for semantic organization, correlation, and enrichment of learning resources. A multi-layer framework was designed to support data collection, knowledge representation, and intelligent recommendation processes. Experimental evaluation demonstrates that the architecture enhances the relevance of learning activities, improves resource organization, and delivers more accurate and personalized educational interventions. The results indicate that combining NB-IoT infrastructure with knowledge graph intelligence can significantly strengthen adaptive learning environments and better support the development of innovative competencies.

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Publication Details

Journal
OICC Press Journals
Published
2025-03-28
DOI
https://doi.org/10.57647/ijeee.2025.1603.10
Primary Topic
Advanced Graph Neural Networks
Type
article
Field-Weighted Citation Impact
0.00
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article

Knowledge Graph–Enhanced NB-IoT Architecture for Intelligent Educational Data Management and System Innovation

Li Jiangtao, Yang Xianxian
OICC Press Journals
Advanced Graph Neural Networks
article

Knowledge Graph–Enhanced NB-IoT Architecture for Intelligent Educational Data Management and System Innovation

Li Jiangtao, Yang Xianxian
article en

Abstract

This study proposes a Knowledge Graph–Enhanced NB-IoT Architecture to improve intelligent educational data management and support system innovation in learning environments. The purpose is to address limitations of traditional innovation and entrepreneurship education, which often lacks real-time contextual information and personalized guidance. The proposed approach integrates NB-IoT for distributed, real-time data acquisition with a domain-specific knowledge graph for semantic organization, correlation, and enrichment of learning resources. A multi-layer framework was designed to support data collection, knowledge representation, and intelligent recommendation processes. Experimental evaluation demonstrates that the architecture enhances the relevance of learning activities, improves resource organization, and delivers more accurate and personalized educational interventions. The results indicate that combining NB-IoT infrastructure with knowledge graph intelligence can significantly strengthen adaptive learning environments and better support the development of innovative competencies.

OICC Press Journals
Guangzhou Metro Group (China) (CN), Zhengzhou Railway Vocational & Technical College (CN)
Industry, innovation and infrastructure, Decent work and economic growth
Openalex Percentile: Top 15%
Advanced Graph Neural Networks
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Knowledge Graph–Enhanced NB-IoT Architecture for Intelligent Educational Data Management and System Innovation — Li Jiangtao, Yang Xianxian · OICC Press Journals (2025) | TGRS Research Map | TGRS