Bridging Domain Knowledge and Process Discovery Using Large Language Models

TL;DR

This paper introduces a novel approach that uses Large Language Models to incorporate domain knowledge into process discovery, improving the alignment and robustness of process models.

Contribution

It presents a framework leveraging LLMs to integrate natural language domain knowledge into process discovery, which is a significant advancement over traditional methods.

Findings

Successful case study with UWV employee insurance agency

Enhanced process model alignment with domain knowledge

Demonstrated practical benefits of LLM-guided discovery

Abstract

Discovering good process models is essential for different process analysis tasks such as conformance checking and process improvements. Automated process discovery methods often overlook valuable domain knowledge. This knowledge, including insights from domain experts and detailed process documentation, remains largely untapped during process discovery. This paper leverages Large Language Models (LLMs) to integrate such knowledge directly into process discovery. We use rules derived from LLMs to guide model construction, ensuring alignment with both domain knowledge and actual process executions. By integrating LLMs, we create a bridge between process knowledge expressed in natural language and the discovery of robust process models, advancing process discovery methodologies significantly. To showcase the usability of our framework, we conducted a case study with the UWV employee insurance agency, demonstrating its practical benefits and effectiveness.