Automatic Business Process Structure Discovery using Ordered Neurons LSTM: A Preliminary Study

TL;DR

This paper explores using Ordered Neurons LSTM to automatically discover hierarchical structures in textual business process documents, aiming to improve process modeling efficiency.

Contribution

It introduces a novel neural network approach leveraging ON-LSTM for extracting hierarchical process structures from textual data.

Findings

Preliminary experiments show promising results.

The approach effectively captures latent hierarchical structures.

Potential for improved process discovery in BPM.

Abstract

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.