Automated extraction and evaluation of business process entities and relations via pre-trained models

Wan Xiang; Li Shan; Liu Zesan; Zhang Bin; Tong Shuaichen; Gou Yi; Zhao Shuman

doi:10.11959/j.issn.1000-0801.2026031

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Automated extraction and evaluation of business process entities and relations via pre-trained models

Research and Development | 更新时间：2026-04-08

- Automated extraction and evaluation of business process entities and relations via pre-trained models
- Telecommunications Science Vol. 42, Issue 3, Pages: 169-179(2026)
- 作者机构：
  
  1.四川中电启明星信息技术有限公司，四川成都 610213
  2.国网信息通信产业集团有限公司，北京 100052
  3.国网辽宁省电力有限公司电力科学研究院，辽宁沈阳 110001
- 作者简介：
- 基金信息：
  
  State Grid Corporation of China's Scientific and Technological Innovation(5700-202319836A-4-2-KJ)
- DOI：10.11959/j.issn.1000-0801.2026031
  CLC： TP311.52
- Received：03 June 2025，
  
  Revised：2025-07-11，
  
  Accepted：25 September 2025，
  
  Published：20 March 2026
- 稿件说明：
移动端阅览
万向,李杉,刘泽三等.基于预训练大模型的业务流程实体与关系的自动提取和评估[J].电信科学,2026,42(03):169-179.

Wan Xiang,Li Shan,Liu Zesan,et al.Automated extraction and evaluation of business process entities and relations via pre-trained models[J].Telecommunications Science,2026,42(03):169-179.
万向,李杉,刘泽三等.基于预训练大模型的业务流程实体与关系的自动提取和评估[J].电信科学,2026,42(03):169-179. DOI： 10.11959/j.issn.1000-0801.2026031.

Wan Xiang,Li Shan,Liu Zesan,et al.Automated extraction and evaluation of business process entities and relations via pre-trained models[J].Telecommunications Science,2026,42(03):169-179. DOI： 10.11959/j.issn.1000-0801.2026031.

摘要

在数字化转型背景下，精准建模电力行业业务流程对实现智能运营与标准化管理具有重要意义。为此，提出了一种面向电力工作流程标准化的领域自适应语言模型ElecBPM-LoRA。该模型通过引入低秩微调（low-rank adaptation，LoRA）技术与结构化提示词机制，能够将自然语言准确转化为结构化语句，并自动生成符合BPMN 2.0规范的业务流程图。实验表明，ElecBPM-LoRA在电力行业流程要素的识别任务中的表现均优于目前主流大语言模型，且在流程结构建模的完整性与语义一致性方面表现出显著优势，为关键基础设施领域的流程自动化与标准化建模提供了可靠支撑。

Abstract

In the context of digital transformation

accurate modeling of power industry business processes is considered crucial for achieving intelligent operation and standardized management. To this end

a domain-adaptive language model named ElecBPM-LoRA was proposed for power workflow standardization. By incorporating low-rank adaptation (LoRA) and a structured prompting mechanism

the model was enabled to accurately transform natural language into structured statements and automatically generate business process diagrams compliant with the BPMN 2.0 specification. Experimental results demonstrate that ElecBPM-LoRA outperforms mainstream large language models in identifying power industry process elements

and exhibits significant advantages in the completeness of process structure modeling and semantic consistency. The proposed approach provides reliable support for process automation and standardized modeling in the field of critical infrastructure.

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references

Grohs M , Abb L , Elsayed N , et al . Large language models can accomplish business process management tasks [C ] // Business Process Management Workshops . Cham : Springer , 2024 : 453 - 465 .

Sola D , Meilicke C , Van D A H , et al . A rule-based recommendation approach for business process modeling [C ] // Advanced Information Systems Engineering . Cham : Springer , 2021 : 328 - 343 .

Beheshti S M R , Benatallah B , Sakr S , et al . Process analytics: concepts and techniques for querying and analyzing process data [M ] . Cham : Springer , 2016 .

Dumas M , La Rosa M , Mendling J , et al . Fundamentals of business process management [M ] . Berlin, Heidelberg : Springer , 2013 .

Beheshti A , Ghodratnama S , Elahi M , et al . Social data analytics [M ] . Boca Raton : CRC Press , 2022 .

Delgado A , Calegari D , García F , et al . Model-driven management of BPMN-based business process families [J ] . Software and Systems Modeling , 2022 , 21 ( 6 ): 2517 - 2553 .

Schunselaar D M M , Slaats T , Maggi F M , et al . Mining hybrid business process models: a quest for better precision [C ] // Business Information Systems . Cham : Springer , 2018 : 190 - 205 .

Manning C , Surdeanu M , Bauer J , et al . The Stanford CoreNLP natural language processing toolkit [C ] // Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations . Stroudsburg, PA, USA : ACL , 2014 : 55 - 60 .

Padró L , Stanilovsky E . FreeLing 3.0: towards wider multilinguality [C ] // Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC’12) . Kerrville : Association for Computational Linguistics , 2012 : 2473 - 2479 .

Bird S , Klein E , Loper E . Natural language processing with python: analyzing text with the natural language toolkit [M ] . Sebastopol, CA : O'Reilly Media , 2009 .

Bommarito II M J , Katz D M , Detterman E M . LexNLP: Natural language processing and information extraction for legal and regulatory texts [PP ] . V1. arXiv ( 2018-06-10 )[ 2025-04-03 ] . arXiv: 1806.03688 .

Luo R Q , Sun L A , Xia Y C , et al . BioGPT: generative pre-trained transformer for biomedical text generation and mining [J ] . Briefings in Bioinformatics , 2022 , 23 ( 6 ): bbac409 .

Chalkidis I , Fergadiotis M , Malakasiotis P , et al . LEGAL-BERT: the muppets straight out of law school [PP ] . V1. arXiv ( 2020-10-06 )[ 2025-04-03 ] . arXiv: 2010.02559 .

Wang N , Yang H Y , Wang C D . FinGPT: instruction tuning benchmark for open-source large language models in financial datasets [PP ] . V2. arXiv ( 2023-11-11 )[ 2025-04-03 ] . arXiv: 2310.04793 .

DeepSeek-AI , Guo D Y , Yang D J , et al . DeepSeek-R1: incentivizing reasoning capability in LLMs via reinforcement learning [PP ] . V2. arXiv ( 2026-01-04 )[ 2025-04-03 ] . arXiv: 2501.12948 .

Pfister R , Jud H . Understanding and benchmarking artificial intelligence: OpenAI’s O3 is not AGI [PP ] . V1. arXiv ( 2025-01-13 )[ 2025-04-03 ] . arXiv: 2501.07458 .

Guo D , Wu F M , Zhu F D , et al . Seed 1 .5-VL technical report[PP ] . V1. arXiv ( 2025-05-11 )[ 2025-05-23 ] . arXiv: 2505.07062 .

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