MMM Data Model enables decentralized knowledge interoperability across disciplines

Mathilde Noual has introduced the MMM data model, a normative specification for knowledge interoperability in a decentralizable knowledge commons. The paper, published on arXiv, critiques current information systems built around rigid document-centric structures that hinder knowledge updating, sharing, and reuse. Formal approaches to knowledge representation often sacrifice human usability and scope for strict structure. MMM strikes a balance by combining a small set of normative constraints with the expressive freedom of free-text labels. This design enables interoperability across disciplines, applications, and deployments without requiring semantic convergence—a significant departure from systems that force a shared vocabulary.

MMM emerged from practical needs in interdisciplinary collaborative research and is positioned against the backdrop of AI reshaping document production without offering a unified portable alternative. The model includes a reference implementation and pilot deployment data that demonstrate early implementability and usability. For tech professionals, MMM could provide a lightweight yet robust framework for knowledge sharing in AI-driven workflows, especially in decentralized or federated environments. It addresses the tension between structure and flexibility, potentially enabling more effective human-AI collaboration and cross-domain knowledge commons. The full paper is available on arXiv with code and data links.