Intent Recognition Enhanced by RAG and Knowledge Graphs for Regulation Execution and O&M Support in Power Engineering Secondary Systems
DOI:
https://doi.org/10.4108/ew.14131Keywords:
secondary systems in power engineering, intent recognition, knowledge graph, retrieval-augmented generation, dispatching support, operation and maintenance supportAbstract
INTRODUCTION: Secondary systems in power engineering involve complex regulatory documents, operational procedures, dispatching requirements, and maintenance knowledge. Natural-language queries with implicit domain semantics create difficulties for conventional intent recognition in regulation matching and operation-support scenarios.
OBJECTIVES: This study aims to enhance regulatory semantic understanding and intent-recognition accuracy, thereby supporting dispatching, maintenance, regulation execution, and operation and maintenance of power engineering secondary systems.
METHODS: A knowledge-enhanced intent-recognition method combining Retrieval-Augmented Generation (RAG) and Knowledge Graphs (KGs) is proposed. A seven-label regulatory corpus is constructed, and structured semantic triples together with retrieved regulatory contexts are used as composite inputs.
RESULTS: Experimental results show that the proposed method outperforms traditional techniques in terms of Accuracy and Macro-F1 score, improving the recognition of regulation-related, operation-guidance, maintenance-related, and procedure-support intents.
CONCLUSION: The proposed approach strengthens the semantic alignment between technical queries and regulatory knowledge, providing an effective foundation for dispatching support, maintenance guidance, regulation execution, and intelligent operation and maintenance in the power industry.
Downloads
References
[1] Shah H, Chakravorty J, Chothani N. Protection challenges on integration of renewable energy sources to power system network: A review[J]. Journal of applied research and technology, 2023, 21(2): 212-226.
[2] Rubio S, Bogarra S, Nunes M, et al. Smart grid protection, automation and control: Challenges and opportunities[J]. Applied Sciences, 2025, 15(6): 3186.
[3] Zhong B, He W, Huang Z, et al. A building regulation question answering system: A deep learning methodology[J]. Advanced Engineering Informatics, 2020, 46: 101195.
[4] Gao M, Li M, Ji T, et al. Key technologies of intelligent question-answering system for power system rules and regulations based on improved BERTserini algorithm[J]. Processes, 2023, 12(1): 58.
[5] WU Y, DAI T, ZHENG Z, et al. Active Discovering New Slots for Task-Oriented Conversation[J]. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2024, 32: 1882-1893.
[6] Song Y, Liu X, Zhou Z. A Comprehensive Review of Text Classification Algorithms[J]. Journal of Electronics and Information Science, 2024, 9(2).
[7] Li D, Wang S, Zhao B, et al. A novel model based on a transformer for intent detection and slot filling[J]. Urban Informatics, 2024, 3(1): 24.
[8] Dash T, Chitlangia S, Ahuja A, et al. A review of some techniques for inclusion of domain-knowledge into deep neural networks[J]. Scientific Reports, 2022, 12(1): 1040.
[9] Klesel M, Wittmann H F. Retrieval-Augmented Generation (RAG)[J]. Business & Information Systems Engineering, 2025, 67: 643-659.
[10] Peng C, Xia F, Naseriparsa M, et al. Knowledge graphs: Opportunities and challenges[J]. Artificial Intelligence Review, 2023, 56: 13071-13102.
[11] Amugongo, L. M., Mascheroni, P., Brooks, S., Doering, S., & Seidel, J. (2025). Retrieval augmented generation for large language models in healthcare: A systematic review. PLOS Digital Health, 4(1), e0000877.
[12] Palanivinayagam, A., El-Bayeh, C. Z., & Damaševičius, R. (2023). Twenty years of machine-learning-based text classification: A systematic review. Algorithms, 16(5), 236.
[13] Zhao J, Wen Y, Li Q, et al. Deep Learning Approaches for Multimodal Intent Recognition: A Survey[J]. arXiv preprint arXiv:2507.22934, 2025.
[14] Ran Z, Chen Y, Jiang Q, et al. Intent Recognition in Dialogue Systems[C]//Proceedings of the 4th Asia-Pacific Artificial Intelligence and Big Data Forum. 2024: 165-173.
[15] Lewis P, Perez E, Piktus A, et al. Retrieval-augmented generation for knowledge-intensive nlp tasks[J]. Advances in neural information processing systems, 2020, 33: 9459-9474.
[16] Yu W. Retrieval-augmented generation across heterogeneous knowledge[C]//Proceedings of the 2022 conference of the North American chapter of the association for computational linguistics: human language technologies: student research workshop. 2022: 52-58.
[17] Gupta S, Ranjan R, Singh S N. A comprehensive survey of retrieval-augmented generation (rag): Evolution, current landscape and future directions[J]. arXiv preprint arXiv:2410.12837, 2024.
[18] Hogan A, Blomqvist E, Cochez M, et al. Knowledge graphs[J]. ACM Computing Surveys (Csur), 2021, 54(4): 1-37.
[19] Guo, J., Zhao, M., Zhang, W., Xu, T., Xu, L., Yu, J., Yu, M., & Yu, R. (2024). Hierarchy-Aware Quaternion Embedding for Knowledge Graph Completion. In 2024 International Joint Conference on Neural Networks (IJCNN).
[20] Wu L, Chen Y, Shen K, et al. Graph neural networks for natural language processing: A survey[J]. Foundations and Trends® in Machine Learning, 2023, 16(2): 119-328.
[21] Yasunaga M, Bosselut A, Ren H, et al. Deep bidirectional language-knowledge graph pretraining[J]. Advances in Neural Information Processing Systems, 2022, 35: 37309-37323.
[22] Wang, R., & Wang, Y. (2023). Knowledge Graph Reasoning with Bidirectional Relation-Guided Graph Attention Network. In Proceedings of the 2023 IEEE International Conference on Data Mining (ICDM). IEEE.
[23] Su H, Li H, Li D. Knowledge reasoning with multiple relational paths[J]. Connection Science, 2023, 35(1): 2161480.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Qiangchao Xu, Kairu Chen, Zhaolun Deng, Jinhua Wu, Dan Lin, Zhaolin Shi
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 4.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.
