Journal of Intelligent Communication

Article

Knowledge Graph Construction, Management, and Application in Wireless Networks

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https://doi.org/10.54963/jic.v5i1.1868
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Lin, W., Liu, Y., Song, Y. and Li, Y. 2026. Knowledge Graph Construction, Management, and Application in Wireless Networks. Journal of Intelligent Communication. 5, 1 (Jun. 2026), 47–60. DOI:https://doi.org/10.54963/jic.v5i1.1868.
Volume 5 Issue 1: June 2026
Copyright (c) 2026 Wenhao Lin, Yinguo Liu, Yingjie Song, Ying Li
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Authors

  • Wenhao Lin

    College of Computer Science and Technology, Qingdao University, Qingdao 266071, China
  • Yinguo Liu

    College of Computer Science and Technology, Qingdao University, Qingdao 266071, China
  • Yingjie Song

    College of Computer Science and Technology, Qingdao University, Qingdao 266071, China
  • Ying Li

    College of Computer Science and Technology, Qingdao University, Qingdao 266071, China

Received: 10 November 2025; Revised: 17 December 2025; Accepted: 26 December 2025; Published: 6 January 2026

Wireless networks generate large volumes of heterogeneous data from network elements, user equipment, and management systems, posing significant challenges for effective network monitoring, fault management, and resource optimization. Traditional rule-based or data-driven approaches often lack unified knowledge representation and reasoning capability, limiting their scalability and interpretability. To address these challenges, this paper proposes a knowledge-graph-based framework for wireless network knowledge construction, management, and application. The proposed framework integrates multi-source network data through ontology-driven modeling and rule-based semantic mapping, enabling structured representation of network entities, events, and their relationships. An event-driven incremental update mechanism is introduced to efficiently maintain the knowledge graph in dynamic network environments without full reconstruction. Furthermore, a lightweight reasoning mechanism is employed to infer implicit network states and support intelligent network management decisions. The framework is designed to balance expressiveness and computational efficiency, making it suitable for large-scale wireless networks. To quantitatively evaluate the effectiveness of the proposed approach, extensive experiments are conducted under different network scales. The experimental results demonstrate that the proposed framework consistently outperforms traditional rule-based methods in terms of fault localization accuracy and resource utilization efficiency, while exhibiting lower query latency and better scalability as the network size increases. The results indicate that the proposed knowledge-graph-based framework provides an effective and scalable solution for intelligent wireless network management, with potential applicability to fault detection, resource optimization, and network security analysis.

Keywords:

Knowledge Graph Wireless Networks Construction Management Application

References

  1. Li, H.; Ota, K.; Dong, M. Learning IoV in 6G: Intelligent Edge Computing for Internet of Vehicles in 6G Wireless Communications. IEEE Wireless Commun. 2023, 30, 96–101. DOI: https://doi.org/10.1109/MWC.017.2200089
  2. Sangaiah, A.K.; Rezaei, S.; Javadpour, A.; et al. Automatic Fault Detection and Diagnosis in Cellular Networks and Beyond 5G: Intelligent Network Management. Algorithms 2022, 15, 432. DOI: https://doi.org/10.3390/a15110432
  3. Noor-A-Rahim, M.; Liu, Z.; Lee, H.; et al. A Survey on Resource Allocation in Vehicular Networks. IEEE Trans. Intell. Transport. Syst. 2022, 23, 701–721. DOI: https://doi.org/10.1109/TITS.2020.3019322
  4. Lv, Z.; Cheng, Z.; Li, J.; et al. TreeCN: Time Series Prediction With the Tree Convolutional Network for Traffic Prediction. IEEE Trans. Intell. Transport. Syst. 2024, 25, 3751–3766. DOI: https://doi.org/10.1109/TITS.2023.3325817
  5. Sheng, Z.; Lv, Z.; Li, J.; et al. Deep Spatial-Temporal Travel Time Prediction Model Based on Trajectory Feature. Comput. Electr. Eng. 2023, 110, 108868.
  6. Alotaibi, E.; Bin Sulaiman, R.; Almaiah, M.; et al. Assessment of Cybersecurity Threats and Defense Mechanisms in Wireless Sensor Networks. J. Cyber Secur. Risk Audit. 2025, 2025, 47–59. DOI: https://doi.org/10.63180/jcsra.thestap.2025.1.5
  7. Xu, Z.; Lv, Z.; Chu, B.; et al. Progress and Prospects of Future Urban Health Status Prediction. Eng. Appl. Artif. Intell. 2024, 129, 107573.DOI: https://doi.org/10.1016/j.engappai.2023.107573
  8. Peng, C.; Xia, F.; Naseriparsa, M.; et al. Knowledge Graphs: Opportunities and Challenges. Artif. Intell. Rev. 2023, 56, 13071–13102. DOI: https://doi.org/10.1007/s10462-023-10465-9
  9. Ji, L.; Yan, D.; Cheng, Z.; et al. Improving Unified Named Entity Recognition by Incorporating Mention Relevance. Neural Comput Appl 2023, 35, 22223–22234. DOI: https://doi.org/10.1007/s00521-023-08820-6
  10. Li, J.; Lv, Z.; Ma, Z.; et al. Optimization of Spatial-Temporal Graph: A Taxi Demand Forecasting Model Based on Spatial-Temporal Tree. Information Fusion 2024, 104, 102178. DOI: https://doi.org/10.1016/j.inffus.2023.102178
  11. Seo, S.; Cheon, H.; Kim, H.; et al. Structural Quality Metrics to Evaluate Knowledge Graphs. arXiv preprint 2022, arXiv: 2211.10011. DOI: https://doi.org/10.48550/ARXIV.2211.10011
  12. Shirvani-Mahdavi, N.; Akrami, F.; Saeef, M.S.; et al. Comprehensive Analysis of Freebase and Dataset Creation for Robust Evaluation of Knowledge Graph Link Prediction Models. In The Semantic Web-ISWC 2023; Payne, T.R., Presutti, V., Qi, G., et al., Eds.; Springer Nature Switzerland: Cham, Switzerland, 2023; 14266, pp. 113–133. DOI: https://doi.org/10.1007/978-3-031-47243-5_7
  13. Chang, E.; Mostafa, J. The Use of SNOMED CT, 2013-2020: A Literature Review. J. Am. Med. Inf. Assoc. 2021, 28, 2017–2026. DOI: https://doi.org/10.1093/jamia/ocab084
  14. Zheng, J.; Wu, X.; Tan, L.; et al. Intelligent Financial Risk Warning for Enterprises Through Knowledge Graph-Based Deep Learning. J. Circuits Syst. Comput. 2024, 33, 2450262. DOI: https://doi.org/10.1142/S0218126624502621
  15. Cai, C.; Jiang, Z.; Wu, H.; et al. Research on Knowledge Graph-Driven Equipment Fault Diagnosis Method for Intelligent Manufacturing. Int. J. Adv. Manuf. Technol. 2024, 130, 4649–4662. DOI: https://doi.org/10.1007/s00170-024-12998-x
  16. Lv, Z.; Wang, X.; Cheng, Z.; et al. ST-TDCN: A Two-Channel Tree-Structure Spatial–Temporal Convolutional Network Model for Traffic Velocity Prediction. Expert Syst. Appl. 2024, 257, 125053. DOI: https://doi.org/10.1016/j.eswa.2024.125053
  17. Xu, Z.; Li, J.; Lv, Z.; et al. A Classification Method for Urban Functional Regions Based on the Transfer Rate of Empty Cars. IET Intell. Transp. Syst. 2022, 16, 133–147. DOI: https://doi.org/10.1049/itr2.12134
  18. Lv, Z.; Li, J.; Dong, C.; et al. DeepPTP: A Deep Pedestrian Trajectory Prediction Model for Traffic Intersection. KSII TIIS 2021, 15. DOI: https://doi.org/10.3837/tiis.2021.07.002
  19. Yan, H.; Lv, Z.; Li, J.; et al. Shared Mobility Demand Prediction via a Fast Spatiotemporal Tensor Autoregression. Eng. Appl. Artif. Intell. 2025, 158, 111467. DOI: https://doi.org/10.1016/j.engappai.2025.111467
  20. Ma, Z.; Lv, Z.; Xu, Z.; et al. Spatio-Temporal Traffic Flow Forecasting Based on Second-Order Continuous Graph Neural Network. Comput. J. 2025, 68, 1300–1314. DOI: https://doi.org/10.1093/comjnl/bxaf038
  21. Yan, H.; Li, J.; Xu, Z.; Lv, Z. Meta Analysis of Traffic Safety Prediction. In Proceedings of the 2024 Twelfth International Conference on Advanced Cloud and Big Data (CBD), Brisbane, Australia, 28 November 2024; pp 216–221. DOI: https://doi.org/10.1109/CBD65573.2024.00047
  22. Ma, Y.; Liu, Z.; Yang, N.; et al. Research on Knowledge Graph Construction and Semantic Representation of Low Earth Orbit Satellite Spectrum Sensing Data. Electronics 2024, 13, 672. DOI: https://doi.org/10.3390/electronics13040672
  23. Dogan, M.; Gjertsen, T.; James, N.; et al. An Integrated Home Monitoring System with a Scalable IoT Architecture Using UDP and TCP Connections. J. Intell. Commun. 2025, 4, 1–12. DOI: https://doi.org/10.54963/jic.v4i2.1274
  24. Bratanic, T. Graph Algorithms for Data Science: With Examples in Neo4j. Simon and Schuster: London, UK, 2024.
  25. Leon, M. From Lexicons to Transformers: An AI View of Sentiment Analysis. J. Intell. Commun. 2025, 4, 13–25. DOI: https://doi.org/10.54963/jic.v4i2.1434
  26. Kalita, C.S.; Barooah, M. Dynamic QLearningBased Handover in VANETs: An Approach for LiFi Based Handover Techniques. J. Intell. Commun. 2025, 4, 26–39. DOI: https://doi.org/10.54963/jic.v4i2.1496
  27. Li, H.; Li, J.; Lv, Z.; et al. MFAGCN: Multi-Feature Based Attention Graph Convolutional Network for Traffic Prediction. In Wireless Algorithms, Systems, and Applications; Liu, Z., Wu, F., Das, S.K., Eds.; Springer International Publishing: Cham, Switzerland, 2021; 12937, pp. 227–239. DOI: https://doi.org/10.1007/978-3-030-85928-2_18
  28. Xu, Z.; Lv, Z.; Li, J. Fast-TrafficNet: A Hybrid Model for Efficient Prediction of Nonlinear Traffic Flow with Sparse Data. Chaos, Solitons Fract. 2025, 201, 117230. DOI: https://doi.org/10.1016/j.chaos.2025.117230
  29. Tong, J.; Guo, W.; Shao, J.; et al. WirelessAgent: Large Language Model Agents for Intelligent Wireless Networks. arXiv preprint 2025, arXiv: 2505.01074. DOI: https://doi.org/10.48550/arXiv.2505.01074
  30. Guo, Z.; Xia, L.; Yu, Y.; et al. LightRAG: Simple and Fast Retrieval-Augmented Generation. In Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2025, Suzhou, China, 4 November 2025; pp. 10746–10761. DOI: https://doi.org/10.18653/v1/2025.findings-emnlp.568