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

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. © 2022 CIMS. All rights reserved.