FB2BPMN: An End-to-End Pipeline for Translating Unstructured User Feedback into BPMN Using LLM and Fuzzy Matching (Informatica, 2026)
Translating unstructured user feedback into Business Process Model and Notation (BPMN) is challenging due to informal language, contextual ambiguity, and the lack of explicit structural cues. We present FB2BPMN, an end-to-end pipeline that combines natural language processing (NLP), large language models (LLMs), and fuzzy string matching to automatically generate BPMN elements from raw feedback. The pipeline comprises four stages: sentence structuring, fact extraction, role-activity mapping, and fuzzy-based semantic alignment. We evaluate FB2BPMN on 125 annotated feedback instances sampled from academic journal management systems. Using expert-authored BPMN as reference, FB2BPMN attains precision 0.97, recall 0.88, and F1 0.91 on element identification and accuracy 0.85 on process flow construction, outperforming a rule-based baseline. Results indicate strong structural and semantic correspondence, showing that FB2BPMN effectively bridges informal feedback and formal process representations.