An LLM-based Method for Automatic Construction of Equipment Failure Knowledge Graphs

doi:10.3969/j.issn.1006-2475.2024.11.008

Abstract

Abstract: Fault operation and maintenance is an important research topic in the field of industrial production. The research of fault prediction， fault diagnosis， question-answering systems based on the fault knowledge graph have been greatly developed and applied. However， a high-quality fault operation and maintenance knowledge graph is the foundation for these methods. Considering that traditional knowledge graph construction techniques require data preprocessing， entity recognition， relationship extraction and entity alignment of raw data， to improve the efficiency of knowledge graphs， this paper focuses on using large language models for unsupervised knowledge extraction from fault operation and maintenance data to achieve automatic construction of large-scale fault operation and maintenance knowledge graphs. This method mainly includes two parts： 1） Two zero-shot Prompts oriented towards the construction of fault operation and maintenance knowledge graphs are proposed. These Prompts can guide large language models to generate conceptual layers and extract elemental knowledge for the fault operation and maintenance knowledge graph represented and output in RDF syntax; 2） A method based on large language models for constructing knowledge graphs is proposed. This method can guide large language models to extract knowledge from fault operation and maintenance data through zero-shot Prompts and complete the construction of large-scale fault operation and maintenance knowledge graphs iteratively. Experimental results show that the proposed method is scientific and effective.

Key words: , prompt learning, knowledge extraction, knowledge graph construction, fault operation and maintenance, large language model

CLC Number:

TP391

ZHANG Kun1, ZHANG Yongwei1, WU Yongcheng1, ZHANG Xiaowen2, ZHAI Shichen2. An LLM-based Method for Automatic Construction of Equipment Failure Knowledge Graphs[J]. Computer and Modernization, 2024, 0(11): 46-53.

References

［1］刘峤，李杨，段宏，等. 知识图谱构建技术综述［J］. 计算机研究与发展， 2016，53（3）：582-600.
［2］高云君，葛丛丛，郭宇翔，等. 面向关系型数据与知识图谱的数据集成技术综述［J］. 软件学报， 2023，34（5）：2365-2391.
［3］ KAADOUD I C， ROUGIER N P， ALEXANDRE F. Knowledge extraction from the learning of sequences in a long short term memory（LSTM）architecture［J］. Knowledge-Based Systems， 2022，235. DOI: 10.1016/j.knosy.2021.107657.
［4］ CALLAN D， FOSTER J. How interesting and coherent are the stories generated by a large-scale neural language model? Comparing human and automatic evaluations of machine-generated text［J］. Expert Systems： The International Transactions on Electrical Energy Systems， 2023，40（6）. DOI: 10.1111/exsy.13292.
［5］程华龙，周志宇，余志斌，等. 基于知识图谱的航空制造故障知识库构建［J］. 中国机械， 2023（36）：2-8.
［6］韦伟，郑杨，袁嘉梁，等. 轨道交通设备故障知识图谱构建与应用研究［J］. 中国新技术新产品， 2023（17）：53-55.
［7］任浩，屈剑锋，柴毅，等. 深度学习在故障诊断领域中的研究现状与挑战［J］. 控制与决策， 2017，32（8）：1345-1358.
［8］ IOVINE A， FANG A J， FETAHU B， et al. CycleNER： An unsupervised training approach for named entity recognition［C］// Proceedings of the ACM Web Conference 2022. ACM， 2022：2916-2924.
［9］ VEENA G， KANJIRANGAT V， GUPTA D. AGRONER： An unsupervised agriculture named entity recognition using weighted distributional semantic model［J］. Expert Systems with Applications， 2023，229. DOI: 10.1016/j.eswa.2023.
120440.
［10］ LI Z R， HU C M， GUO X H， et al. An unsupervised multiple-task and multiple-teacher model for cross-lingual named entity recognition［C］// Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics. ACL， 2022：170-179.
［11］ SIMON É， GUIGUE V， PIWOWARSKI B. Unsupervised information extraction： Regularizing discriminative approaches with relation distribution losses［C］// Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. ACL， 2019：1378-1387.
［12］ TRAN T T， LE P， ANANIADOU S. Revisiting unsupervised relation extraction［C］// Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. ACL， 2020：7498-7505.
［13］ WANG Q， ZHOU K， QIAO Q， et al. Improving unsupervised relation extraction by augmenting diverse sentence pairs［C］// Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. ACL， 2023： 12136-12147.
［14］ CONNEAU A， LAMPLE G， RANZATO M A， et al. Word translation without parallel data［C］// The 6th International Conference on Learning Representations. ICLR， 2018. DOI: 10.48550/arXiv.1710.04087.
［15］ LIU X， HONG H Y， WANG X H， et al. SelfKG： Self-supervised entity alignment in knowledge graphs［C］// Proceedings of the ACM Web Conference 2022. ACM， 2022：860-870.
［16］周慧，徐名海，许晓东. 基于Attention-BIGRU-CRF的中文分词模型［J］. 计算机与现代化， 2022（8）：7-12.
［17］张军，邱龙龙. 一种基于BERT和池化操作的文本分类模型［J］. 计算机与现代化， 2022（6）：1-7.
［18］ FERREIRA F G D C， GANDOMI A H， CARDOSO R T N. Artificial intelligence applied to stock market trading： A review［J］. IEEE Access， 2021，9：30898-30917.
［19］ MIKOLOV T， SUTSKEVER I， CHEN K， et al. Distributed representations of words and phrases and their compositionality［C］// Proceedings of the 26th International Conference on Neural Information Processing Systems. ACM， 2013，2：3111-3119.
［20］ KIM B， KIM J， YE J C. Task-agnostic vision transformer for distributed learning of image processing［J］. IEEE Transactions on Image Processing， 2023，32：203-218.
［21］ DEVLIN J， CHANG M W， LEE K， et al. BERT： Pre-training of deep bidirectional transformers for language understanding［C］// Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics： Human Language Technologies. ACL，2019：4171-4186
［22］ RADFORD A， WU J， CHILD R， et al. Language models are unsupervised multitask learners［J］. arXiv preprint arXiv:1810.04805， 2018.
［23］ RAFFEL C， SHAZEER N， ROBERTS A， et al. Exploring the limits of transfer learning with a unified text-to-text Transformer［J］. The Journal of Machine Learning Research， 2020，21：5485-5551.
［24］ BROWN T B， MANN B， RYDER N， et al. Language models are few-shot learners［C］// Proceedings of the 34th International Conference on Neural Information Processing Systems. ACM， 2020：1877-1901.
［25］阿里云计算有限公司.通义千问［EB/OL］. （2023-04-07）［2024-03-12］. https：//tongyi.aliyun.com/qianwen/.
［26］科大讯飞股份有限公司.星火认知大模型［EB/OL］. （2023-05-06）［2024-03-12］. https：//xinghuo.xfyun.cn/.
［27］北京智谱华章科技有限公司. 智谱AI［EB/OL］. （2023-10-27）［2024-03-16］. https：//github.com/THUDM/ChatGLM3.
［28］ HAN X， GAO T Y， YAO Y， et al. OpenNRE： An open and extensible toolkit for neural relation extraction［C］// Proceedings of the 2019 EMNLP and the 9th IJCNLP（System Demonstrations）. ACL， 2019：169-174.
［29］ CHE W X， FENG Y L， QIN L B， et al. N-LTP： An open-source neural language technology platform for Chinese［C］// Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing： System Demonstrations. ACL， 2021：42-49.
［30］苏剑林. Bert4Keras［EB/OL］. （2022-03-22）［2024-05-05］. https：//github.com/bojone/bert4keras.

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