# Bridging Domain Knowledge and Process Discovery Using Large Language Models

arXiv: https://arxiv.org/abs/2408.17316 · HTML: https://mlsanity.com/paper/2408.17316/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 16 references that are in the map.
- First posted on arXiv 30 Aug 2024.

## Builds on (most cited first, 5 of 5)

- [A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://mlsanity.com/paper/2311.05232/) (2023)
- [Large Language Models can accomplish Business Process Management Tasks](https://mlsanity.com/paper/2307.09923/) (2023)
- [Automated Discovery of Process Models from Event Logs: Review and Benchmark](https://mlsanity.com/paper/1705.02288/) (2017)
- [Process Modeling With Large Language Models](https://mlsanity.com/paper/2403.07541/) (2024)
- [Large Language Models for Business Process Management: Opportunities and Challenges](https://mlsanity.com/paper/2304.04309/) (2023)

## Built on by (most cited first, 4 of 4)

- [On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks](https://mlsanity.com/paper/2504.21074/) (2025)
- [LLMs that Understand Processes: Instruction-tuning for Semantics-Aware Process Mining](https://mlsanity.com/paper/2508.16270/) (2025)
- [Evaluating the Process Modeling Abilities of Large Language Models -- Preliminary Foundations and Results](https://mlsanity.com/paper/2503.13520/) (2025)
- [Assessing the Business Process Modeling Competences of Large Language Models](https://mlsanity.com/paper/2601.21787/) (2026)

## Abstract (arXiv)

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.