Bridging Domain Knowledge and Process Discovery Using Large Language Models

arXiv (Cornell University)

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.

Authors 4

1. Ali Norouzifar Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH University , Aachen , Germany

2. Humam Kourani Aachen

   RWTH Aachen University · Fraunhofer Institute for Applied Information Technology

   Affiliation as printed

   Fraunhofer FIT , Sankt Augustin , Germany

   RWTH University , Aachen , Germany

3. Marcus Dees

   Uitvoeringsinstituut Werknemersverzekeringen

   Affiliation as printed

   UWV Employee Insurance Agency , Amsterdam , Netherlands

4. Wil M. P. van der Aalst Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH University , Aachen , Germany