The MMM Data Model -- A Normative Specification for Knowledge Interoperability in a Decentralisable Knowledge Commons
arXiv:2607.00032v1 Announce Type: new Abstract: Many information systems are built around documents: self-contained units optimised for print production and linear reading. While effective for large-scale dissemination, the document-centric organisation constrains how knowledge can be structured, updated, shared, and reused. Formal approaches address some of these limitations but struggle to achieve widespread contribution and adoption due to their prioritisation of formal structure over other system properties such as human usability and scope. AI systems are reshaping document production, bu
The proliferation of AI systems is reshaping document production and creating an urgent need for more interoperable and granular knowledge representation beyond traditional document-centric models.
This data model addresses a fundamental constraint in how knowledge is structured and shared, directly impacting the efficiency and scalability of AI systems and data commons.
Knowledge will transition from being primarily document-centric to being structured in a more granular, interoperable, and machine-readable format, accelerating knowledge reuse and integration.
- · AI developers
- · Knowledge management platforms
- · Open-source communities
- · Data commons initiatives
- · Legacy document-centric information systems
- · Silos of unstructured data
- · Publishers resistant to granular content models
Improved efficiency and accuracy of knowledge retrieval and synthesis by advanced AI systems.
Accelerated development of sophisticated AI agents that can reason over and combine disparate knowledge sources.
Emergence of new knowledge economies facilitated by highly interoperable and permissioned data exchange protocols.
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Read at arXiv cs.AI