Theoretical Foundations & Lineage

1. The Mathematical Substrate: Deterministic Semantics

Traditional Semantic Web technologies (RDF/OWL) operate under the Open World Assumption, where the absence of a statement does not imply its negation. This is powerful for knowledge discovery across federated graphs, but it introduces inherent interpretive ambiguity at the data-exchange boundary—the same triple can carry different meanings depending on context.

The Model Executable Business System (MEBS) theory, developed by Robert Vane, demonstrates mathematically that semantic ambiguity can be eliminated through disjoint atomic taxonomies organized as partition lattices with unique semantic paths. In information-theoretic terms, this achieves zero Shannon entropy (H = 0): every term has exactly one interpretation within its taxonomy, leaving no probabilistic uncertainty about meaning.

SDC's design is aligned with this zero-entropy principle. By enforcing strict XML Schema (XSD 1.1) constraints, CUID2 identity, and mandatory semantic bindings at every data element, SDC ensures that each data packet carries a deterministic, self-describing interpretation. SDC leverages RDF and OWL for semantic linking and interoperability—the Open World Assumption remains valuable in the graph layer—while enforcing closed-world constraints at the data-packet level where unambiguous interpretation is required.

Key Alignment Points

• Unique semantic paths — Every SDC component is identified by a globally unique CUID2 and bound to explicit ontology predicates, preventing synonymy and homonymy at the structural level.
• Closed-world data packets — Within an SDC schema, every required element must be present and validated against XSD constraints. The data packet is self-contained; its meaning does not depend on external inference.
• Deterministic validation — XSD 1.1 assertions combined with SHACL shape constraints provide machine-verifiable proof that data conforms to its schema, enabling fail-closed behavior in high-assurance environments.

2. The Architectural Paradigm: Two-Level Modeling

Conventional software systems encode domain knowledge directly into application code and database schemas. When business requirements change, the software must change with them—creating a tight coupling between domain evolution and system maintenance that becomes increasingly expensive over time.

The two-level modeling paradigm, pioneered by Thomas Beale and the openEHR community, addresses this by strictly separating two concerns:

• Reference Model (Level 1) — A stable, small set of software-level types that define how data is structured, identified, and transported. This layer changes infrequently and is implemented once in the platform.
• Archetypes / Domain Models (Level 2) — Constraint-based definitions of real-world concepts (clinical observations, business transactions, sensor readings) expressed as restrictions over the Reference Model. Domain experts can create and modify these without changing the underlying software.

SDC adopts this paradigm directly. The SDC4 Reference Model provides 15+ data types (XdString, XdBoolean, XdQuantity, XdTemporal, etc.), structural containers (Cluster, XdAdapter), and infrastructure elements (spatio-temporal metadata, access control tags, provenance). Domain-specific schemas are composed as constraint definitions over these types, enabling the data to describe itself independently of any particular application.

Sovereign Content

SDC extends Beale's architectural insight with mandatory sovereign identity: every data element carries its own CUID2 identifier, its own semantic bindings, and its own spatio-temporal context. Data exists independently of the application that created it. When a system is decommissioned, the data remains fully interpretable because the meaning travels with the content, not with the software.