Knowledge Operationalization from iNformal To EXecutable Models
An interactive workshop bridging the gap between informal domain knowledge and structured, operational conceptual models in model-driven engineering
An interactive workshop bridging the gap between informal domain knowledge and structured, operational conceptual models in model-driven engineering
Despite decades of research in model-driven engineering, a persistent challenge remains: how to systematically capture informal domain knowledge and structure it into conceptual models that can be effectively operationalized.
In practice, crucial knowledge about systems often exists in informal forms—natural language requirements, user stories, expert narratives, sketches, spreadsheets, and tacit understanding held by stakeholders.
Without a comprehensive modelling vision that integrates conceptual and technical models, this fragmentation hinders harmonization, limits information fusion, and undermines the consistency and operational value of models.
Methods, languages, and tools for transforming informal, unstructured and semi-formal knowledge into structured conceptual models, and operationalizing these within model-driven engineering ecosystems.
Researchers and practitioners working on extraction, structuration, and operationalization of knowledge from informal artifacts
Manual, semi-automatic, and fully automatic approaches to conceptual model derivation and transformation
Shared evaluation frameworks, benchmarks, and best practices for assessing model quality and operational readiness
Coherent, evolvable, and trustworthy model-driven systems through systematic knowledge capture and connection to operational models
Model-driven engineering, requirements engineering, knowledge representation, and AI/NLP-assisted modelling
Domain engineers facing day-to-day challenges of formalizing informal knowledge in industrial contexts
Developers of platforms, frameworks, or AI-assisted environments for conceptual modelling
Interested in how modelling skills evolve with increasing automation and AI support
Submit your research papers, experience reports, vision papers, or tool demonstrations
Authors notified of acceptance decisions
Final papers due for publication proceedings
Workshop held during MODELS 2026 conference
Note: page limits include references.
All submitted papers must adhere to the ACM Conference format.
For LaTeX users: Use the provided acmart.cls and ACM-Reference-Format.bst without modification. Enable the conference format in the preamble of your document:
\documentclass[sigconf,review]{acmart}
Use the ACM reference format for the bibliography:
\bibliographystyle{ACM-Reference-Format}
The review option adds line numbers, to reference specific lines for comments.
Submit your paper via EasyChair
Review Process: Each submission will be evaluated by a minimum of three program committee members to ensure comprehensive and rigorous assessment.
Questions? Contact the organizers at david.manrique@list.lu
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Luxembourg Institute of Science and Technology (LIST), Luxembourg
Luxembourg Institute of Science and Technology (LIST), Luxembourg
