Bridging Domain Knowledge and Process Discovery Using Large Language Models
This work addresses the gap in process discovery for business analysis by incorporating domain knowledge, though it is incremental in applying LLMs to a specific domain.
The paper tackled the problem of automated process discovery overlooking domain knowledge by leveraging Large Language Models (LLMs) to integrate such knowledge into model construction, demonstrating practical benefits in a case study with the UWV employee insurance agency.
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.