Article Details

Abstract

Addressing the issue that the abundant but scattered case resources accumulated in network protocol troubleshooting cannot be effectively organized and utilized, this study, supported by knowledge graph and Deep Learning technologies, innovatively proposes a method to construct a network protocol troubleshooting knowledge graph with powerful capabilities such as self-learning, easy expansion, and comprehensive perspectives. First, an ontology-based network protocol troubleshooting knowledge base architecture is designed according to the case data pattern. Then, a knowledge extraction method and a model that integrate domain features are proposed. Experiments demonstrate that the proposed model outperforms the baseline model in all metrics, achieving the best performance with accuracy, recall, and F1 score of 82.3%, 81.6%, and 81.9%, respectively. Finally, based on the experimental results, a network protocol troubleshooting knowledge graph is implemented.

Keywords

network protocol; troubleshooting case; Knowledge Graph; knowledge extraction

References

1. [1] Martín A，León C，Guerrero J I，et al. Applying Integrated Expert System in Network Management [C]//Proceedings of the Ninth International Conference on Enterprise Information Systems-AIDSS，2007：101-106.
2. [2] Singhal A. Introducing the Knowledge Graph: Things, not Strings [EB/OL].[2026-03-11].https://blog.google/products-and-platforms/products/search/introducing-knowledge-graph-things-not/.
3. [3] Yun W，Zhang X，Li Z D，et al. Knowledge Modeling: A Survey of Processes and Techniques [J].International Journal of Intelligent Systems，2021，36（4）：1686-1720.
4. [4] Wei W，Cheng J J，Cheng W，et al. TSOKG: A Methodology for Constructing a Telecommunications Service Operation Knowledge Graph [C]//2024 International Conference on Ubiquitous Computing and Communications (IUCC)，2024：512-517.
5. [5] Li Z T，Zhao Y L，Li Y J，et al. Fault Localization Based on Knowledge Graph in Software-defined Optical Networks [J].Journal of Lightwave Technology，2021，39（13）：4236-4246.
6. [6] Borst W N. Construction of Engineering Ontologies for Knowledge Sharing and Reuse [D].Enschede: University of Twente，1997.
7. [7] Bamigboye O，Padayachee I，Subramaniam P R. A Systematic Review of Ontology Development Methodologies for Knowledge Representation System for Different Domains [J].Issues in Information Systems，2025，26（4）：476-496.
8. [8] Noy N F，Mcguinness D L. Ontology Development 101: A Guide to Creating Your First Ontology [J/OL].Stanford University: Knowledge Systems Laboratory，2001.https://protege.stanford.edu/publications/ontology_development/ontology101-noy-mcguinness.html.
9. [9] Choi S，Jung Y. Knowledge Graph Construction: Extraction, Learning, and Evaluation [J/OL].Applied Sciences，2025，15（7）：3727.https://doi.org/10.3390/app15073727.
10. [10] Devlin J，Chang M W，Lee K，et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding [PP/OL].arXiv:1810.04805 [cs.CL].https://arxiv.org/abs/1810.04805.
11. [11] Vaswani A，Shazeer N，Parmar N，et al. Attention is All You Need [PP/OL].arXiv:1706.03762 [cs.CL].https://arxiv.org/abs/1706.03762.
12. [12] Lecun Y，Bottou L，Bengio Y，et al. Gradient-based Learning Applied to Document Recognition [J].Proceedings of the IEEE，1998，86（11）：2278-2324.
13. [13] Goodfellow I，Bengio Y，Courville A. Deep Learning [M].Cambridge：MIT Press，2016.
14. [14] Chaudhari S，Mithal V，Polatkan G，et al. An Attentive Survey of Attention Models [J/OL].ACM Transactions on Intelligent Systems and Technology (TIST)，2021，12（5）：1-32.https://doi.org/10.1145/3465055.
15. [15] Hochreiter S，Schmidhuber J. Long Short-Term Memory [J].Neural Computation，1997，9（8）：1735-1780.
16. [16] Sundermeyer M，Schlüter R，Ney H. LSTM Neural Networks for Language Modeling [C]//Proceedings of the Thirteenth Annual Conference of the International Speech Communication Association，2012：194-197.
17. [17] Lafferty J，Mccallum A，Pereira F C N，et al. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data [C]//Proceedings of the Eighteenth International Conference on Machine Learning，2001：282-289.
18. [18] Lample G，Ballesteros M，Subramanian S，et al. Neural Architectures for Named Entity Recognition [C]//Proceedings of NAACL-HLT，2016：260-270.
19. [19] Dai Z J，Wang X T，Ni P，et al. Named Entity Recognition Using BERT BiLSTM CRF for Chinese Electronic health records [C]//2019 12th International Congress on Image and Signal Processing, Biomedical Engineering and Informatics (CISP-BMEI)，2019：1-5.
20. [20] Mikolov T，Chen K，Corrado G，et al. Efficient Estimation of Word Representations in Vector Space [PP/OL].arXiv:1301.3781 [cs.CL].https://arxiv.org/abs/1301.3781.
21. [21] Wei Z，Su J，Wang Y，et al. A Novel Cascade Binary Tagging Framework for Relational Triple Extraction [C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics，2020：1476-1488.
22. [22] Wei H R，Sun Y F，Li Y K. DeepSeek-OCR2: Visual Causal Flow [PP/OL].arXiv:2601.20552 [cs.CV].https://arxiv.org/abs/2601.20552.
23. [23] Morwal S，Jahan N，Chopra D. Named Entity Recognition Using Hidden Markov Model (HMM) [J].International Journal on Natural Language Computing (IJNLC)，2012，1（2）：21-29.
24. [24] Drools Community. Drools: A Business Rules Management System [EB/OL].[2026-03-10].https://www.drools.org/: Drools Project.

About the authors

Fu Yingjun (1974-), male, Han ethnicity, from Xingning, Guangdong, senior engineer, master's student, research interests include mobile communication systems, network routing and switching control and computer information processing.