基于ON-LSTM的业务过程模型深度自动生成  

Automatic business process model deep generation based on ordered neurons long short term memory

作　　者：朱锐[1,2] 吕昌龙 李彤 何亚辉 刘航[1] 张存明 陈晔婷 ZHU Rui;LYU Changlong;LI Tong;HE Yahui;LIU Hang;ZHANG Cunming;CHEN Yeting(School of Software,Yunnan University,Kunming 650091,China;Key Laboratory in Software Engineering of Yunnan Province,Yunnan University,Kunming 650091,China;School of Big Data,Yunnan Agricultural University,Kunming 650201,China;School of Economics and Management,Yunnan Normal University,Kunming 650091,China)

机构地区：[1]云南大学软件学院,云南昆明650091 [2]云南省软件工程重点实验室,云南昆明650091 [3]云南农业大学大数据学院,云南昆明650201 [4]云南师范大学经济与管理学院,云南昆明650091

出　　处：《计算机集成制造系统》2022年第10期3225-3237,共13页Computer Integrated Manufacturing Systems

基　　金：国家自然科学基金资助项目(62002310);云南省重大科技专项计划资助项目(202002AD080002);云南省自然科学基金基础研究面上资助项目(202101AT070004);云南省软件工程重点实验室开放基金资助项目(2020SE404);云南哲学社会科学青年资助项目(QN2020024)。

摘　　要：为打破现有过程挖掘算法在日志缺失时无法使用带来的局限性,基于现有的深度学习、自然语言处理技术基础,提出一种新颖的从过程文本描述中深度自动生成业务过程模型的方法。对现有命名实体方法进行改进,通过BERT,BiLSTM,CRF构建活动实体识别模型,提出面向业务过程的活动实体识别方法;将语言模型从句子级别扩展到文档级别,提出一种通过递归体系结构有序神经网络(ON-LSTM)无监督地发现过程描述文档中所蕴含的活动实体间潜在的层次结构;通过活动实体的层次深度原则,将层次结构树转化为业务过程模型。通过对人工采集与标注的150个真实的SAP产品用户指南文本作为训练数据进行实验,并在ON-LSTM基础上采用K折交叉验证思想对数据进行多次分组实验,验证了所提方法的有效性。To break the limitations brought by existing process mining algorithms that could not be used when logs were missing, a novel method for deep automatic generation of business process models from process text descriptions was proposed based on the existing deep learning and natural language processing technology base. The existing named entity method was improved, and the activity entity recognition model was constructed by Bidirectional Encoder Representation from Transformers(BERT), Bi-directional Long Short Term Memory(BiLSTM), Conditional Random Fields(CRF), and the business process-oriented activity entity recognition method was proposed. The language model was extended from sentence level to document level, and a recursive architecture Ordered Neurons LSTM(ON-LSTM) was proposed to unsupervised discover the activity embedded in the process description document The hierarchical tree was finally transformed into a business process model by using the principle of hierarchical depth of active entities. Experiments were conducted on 150 real System Applications and Products(SAP) product user guide texts collected and labeled manually as training data, and several group experiments were conducted on the basis of ON-LSTM using K-fold cross-validation idea, which verified the effectiveness of the proposed method.

关 键 词：深度学习 业务过程发现 活动实体 层次结构 有序神经长短期记忆网络 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]