ONION: A Multi-Layered Framework for Participatory ER Design

2025-07-11

AI Summary

Entity-relationship (ER) modeling suffers from pronounced designer bias, insufficient stakeholder engagement, and low process transparency. Method: This paper introduces ONION—a five-stage, multi-layer participatory ER modeling framework (Observe → Nurture → Integrate → Optimize → Normalize) that incrementally abstracts unstructured stakeholder inputs into structured ER diagrams. Grounded in design justice and participatory AI principles, ONION establishes a collaborative modeling paradigm involving diverse stakeholders across all stages. Iterative validation was conducted via participatory workshops and a layered abstraction architecture. Contribution/Results: Applied in a Ukrainian socio-technical system, ONION significantly reduced design bias while enhancing model inclusivity, semantic richness, and depth of collaborative understanding. It provides a scalable, methodologically rigorous foundation for equitable and transparent data modeling.

Technology Category

• Knowledge Representation and Reasoning: Ontologies
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

We present ONION, a multi-layered framework for participatory Entity-Relationship (ER) modeling that integrates insights from design justice, participatory AI, and conceptual modeling. ONION introduces a five-stage methodology: Observe, Nurture, Integrate, Optimize, Normalize. It supports progressive abstraction from unstructured stakeholder input to structured ER diagrams. Our approach aims to reduce designer bias, promote inclusive participation, and increase transparency through the modeling process. We evaluate ONION through real-world workshops focused on sociotechnical systems in Ukraine, highlighting how diverse stakeholder engagement leads to richer data models and deeper mutual understanding. Early results demonstrate ONION's potential to host diversity in early-stage data modeling. We conclude with lessons learned, limitations and challenges involved in scaling and refining the framework for broader adoption.

Problem

• Reduces designer bias in ER modeling process
• Promotes inclusive stakeholder participation in data modeling
• Enhances transparency from input to structured ER diagrams

Innovation

• Multi-layered framework for participatory ER modeling
• Five-stage methodology: Observe, Nurture, Integrate, Optimize, Normalize
• Reduces designer bias, promotes inclusivity, increases transparency