Ambiguity Detection and Elimination in Automated Executable Process Modeling

2026-04-12

AI Summary

This work addresses the challenge of behavioral inconsistency in automatically generated BPMN models due to semantic ambiguity in natural language process descriptions. It proposes the first closed-loop diagnosis and repair framework that operates without requiring ground-truth BPMN annotations. By analyzing the distribution of key performance indicators (KPIs) across multiple model generations, the approach identifies behavioral variations and employs model-based diagnostic techniques to pinpoint gateway logic discrepancies. These discrepancies are traced back to specific source text fragments, which are then refined through an evidence-driven textual revision process. Evaluated on clinical guidelines for diabetic kidney disease management, the method significantly reduces behavioral variability in regenerated models and enhances the semantic stability of executable process models, establishing an end-to-end mapping from behavioral inconsistency to targeted textual correction.

Technology Category

• Natural Language Processing: Generation
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Models for Web evolution
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Automated generation of executable Business Process Model and Notation (BPMN) models from natural-language specifications is increasingly enabled by large language models. However, ambiguous or underspecified text can yield structurally valid models with different simulated behavior. Our goal is not to prove that one generated BPMN model is semantically correct, but to detect when a natural-language specification fails to support a stable executable interpretation under repeated generation and simulation. We present a diagnosis-driven framework that detects behavioral inconsistency from the empirical distribution of key performance indicators (KPIs), localizes divergence to gateway logic using model-based diagnosis, maps that logic back to verbatim narrative segments, and repairs the source text through evidence-based refinement. Experiments on diabetic nephropathy health-guidance policies show that the method reduces variability in regenerated model behavior. The result is a closed-loop approach for validating and repairing executable process specifications in the absence of ground-truth BPMN models.

Problem

• ambiguity detection
• executable process modeling
• BPMN
• natural-language specifications
• behavioral inconsistency

Innovation

• ambiguity detection
• executable BPMN generation
• model-based diagnosis
• behavioral consistency
• natural-language specification repair