llm-sysml-alignment

name: llm-sysml-alignment description: "LLM-assisted semantic alignment methodology for SysML v2 model integration in collaborative MBSE. Use when working with cross-organizational system model integration, SysML v2 semantic alignment, or LLM-based MBSE workflows. Keywords: SysML, MBSE, LLM, semantic alignment, model integration, SysML v2."

LLM-SysML Alignment

LLM-assisted methodology for semantic alignment and integration of SysML v2 models in collaborative Model-Based Systems Engineering (MBSE).

Problem Statement

Cross-organizational collaboration in MBSE faces challenges in achieving semantic alignment across independently developed system models. Different organizations use different naming conventions, model structures, and domain-specific terminology, making integration difficult.

Solution Approach

Structured, prompt-driven approach leveraging:

• SysML v2 constructs: alias, import, metadata extensions
• LLM capabilities: semantic matching, syntax verification, traceability
• Iterative process: model extraction → semantic matching → verification

Core Methodology

Step 1: Model Extraction

Extract semantic information from SysML v2 models:

# Key elements to extract:
- Element names and aliases
- Relationships and dependencies
- Domain-specific terminology
- Metadata and annotations

Step 2: Semantic Matching

Use LLM for semantic alignment:

Prompt structure:
1. Identify equivalent elements across models
2. Detect semantic similarities despite naming differences
3. Generate alignment mappings
4. Create traceability links

Step 3: Verification and Integration

Verify alignment consistency:

Verification checks:
- Syntax correctness (SysML v2 compliant)
- Semantic consistency (equivalent meanings)
- Traceability (alignment rationale documented)
- Completeness (all elements covered)

SysML v2 Constructs Used

Alias

alias ModelA.Part as ModelB.Component;

Import

import ModelA::*;
import ModelB::*;

Metadata Extensions

metadata alignmentSource = "ModelA";
metadata alignmentConfidence = 0.95;

Workflow Example

Scenario: Two companies developing subsystem models for a larger system.

Model A (Company 1):
- EngineSubsystem
- FuelSystem
- PowerControl

Model B (Company 2):
- PropulsionModule
- FuelManagement
- EnergyRegulator

Alignment Process:
1. LLM identifies semantic equivalents
2. Creates alias mappings
3. Generates import statements
4. Adds metadata for traceability

LLM Prompt Patterns

Semantic Extraction Prompt

Extract semantic information from SysML v2 model [MODEL]:
1. Identify core concepts and their domain
2. List element relationships
3. Document naming conventions used
4. Extract domain-specific terminology

Alignment Matching Prompt

Match elements between Model A and Model B:
1. Identify equivalent elements by semantics (not names)
2. Generate alias mappings
3. Document alignment rationale
4. Flag ambiguous matches for human review

Verification Prompt

Verify alignment correctness:
1. Check SysML v2 syntax compliance
2. Verify semantic equivalence
3. Ensure traceability completeness
4. Identify missing alignments

Best Practices

1. Iterative refinement: LLM alignment may need multiple iterations
2. Human verification: Flag ambiguous matches for review
3. Metadata traceability: Always document alignment rationale
4. Soft alignment: Use aliases instead of renaming
5. Domain context: Provide domain-specific context to LLM

Key Findings (from Research)

• LLMs effectively assist in semantic alignment across engineering models
• SysML v2 provides robust framework for model integration
• Structured prompts improve alignment accuracy
• Traceability essential for maintaining alignment over time
• Soft alignment (aliases) preferred over hard renaming

Applications

• Cross-company system integration
• Legacy model modernization
• Domain-specific model translation
• Multi-team collaborative MBSE
• System of systems integration

Related Skills

• arxiv-search: Search for latest MBSE papers
• kg-research-workflow: Import papers to knowledge graph
• skill-creator: Create new skills from research

Source Paper

LLM-Assisted Semantic Alignment and Integration in Collaborative Model-Based Systems Engineering

• arxiv ID: 2508.16181
• Authors: Li, Zirui et al.
• Published: 2026

Notes

• Requires SysML v2 knowledge
• LLM prompts should be domain-specific
• Alignment confidence varies by domain complexity
• Human review essential for safety-critical systems