基于有序神经元LSTM的业务流程结构自动发现初探
Automatic Business Process Structure Discovery using Ordered Neurons LSTM: A Preliminary Study
摘要：
从文本流程文档中自动发现流程对于减少组织中业务流程管理（BPM）实施的时间和成本是非常必要的。然而，现有的自动过程发现方法主要集中在识别活动的文档。推导活动之间的结构关系，这在整个过程发现范围内是重要的，仍然是一个挑战。事实上，业务流程具有潜在的语义层次结构，它定义了不同层次的细节以反映复杂的业务逻辑。神经机器学习领域的最新研究成果表明，通过语言建模和结构学习相结合的方法，可以得到有意义的语言结构。受这些发现的启发，我们建议通过构建一个神经网络来检索文本业务流程文档中存在的潜在层次结构，该网络利用一种新的递归架构，有序神经元LSTM（ON-LSTM），具有流程级语言模型目标。我们测试了我们的实际机器人过程自动化（RPA）项目的过程描述文档（PDD）的数据集所提出的方法。初步实验显示了有希望的结果。
英文摘要：
Automatic process discovery from textual process documentations is highly desirable to reduce time and cost of Business Process Management (BPM) implementation in organizations. However, existing automatic process discovery approaches mainly focus on identifying activities out of the documentations. Deriving the structural relationships between activities, which is important in the whole process discovery scope, is still a challenge. In fact, a business process has latent semantic hierarchical structure which defines different levels of detail to reflect the complex business logic. Recent findings in neural machine learning area show that the meaningful linguistic structure can be induced by joint language modeling and structure learning. Inspired by these findings, we propose to retrieve the latent hierarchical structure present in the textual business process documents by building a neural network that leverages a novel recurrent architecture, Ordered Neurons LSTM (ON-LSTM), with process-level language model objective. We tested the proposed approach on data set of Process Description Documents (PDD) from our practical Robotic Process Automation (RPA) projects. Preliminary experiments showed promising results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
