# Semantic Coherence Score **Meta-Universe Specification** **Document ID:** MU-V2-ECO-006 **Title:** Semantic Coherence Score **Document Class:** Informative **Version:** 2.0 (Draft) **Status:** Working Draft **Normative References:** None **Informative References:** [Validation](../02-architecture/Validation.md), [Certification](../06-ecosystem/Certification.md), [Traceability](../02-architecture/Traceability.md), [Event](../04-core-concepts/Event.md), [Provenance-Graph](../02-architecture/Provenance-Graph.md) **Copyright:** © Orkestron.AI **License:** Apache-2.0 --- # 1. Purpose This document describes the **Semantic Coherence Score** — an automated, transparent measure of *how well a conforming Meta-Model uses the semantic capabilities available to it*. Where [Validation](../02-architecture/Validation.md) answers a binary question (does the model conform?), the Semantic Coherence Score answers a graded one (how rich, traceable and trustworthy is its content?). The score enables **graded certification**: two models can both pass validation yet differ greatly in how completely they record Events, document concepts or carry provenance. The score makes that difference visible and comparable. --- # 2. What the Score Measures — and What It Does Not The Semantic Coherence Score measures **content quality**, not architecture. - It does not re-check conformance; a model is scored only after it passes the pass/fail levels of [Validation](../02-architecture/Validation.md). - It does not judge whether the *modeled domain* is correct; it judges how coherently the model expresses what it claims. - A high score is not a substitute for certification, and a low score does not imply non-conformance — a minimal but valid model can score low and still be perfectly usable. In short: validation asks *is it well-formed?*; the score asks *is it well-developed?* --- # 3. Contributing Dimensions The score is composed from independent dimensions, each measurable from the model and its history: - **Event completeness** — the share of significant state changes recorded as immutable [Event](../04-core-concepts/Event.md)s rather than implied by current state alone. - **Relationship consistency** — the proportion of Relationships that are well-typed, bidirectionally resolvable and free of dangling endpoints. - **Projection coverage** — whether the Objects that are meant to be shared have appropriate Projections defined for their intended contexts. - **Provenance presence** — the share of significant facts carrying provenance and participating in the [Provenance Graph](../02-architecture/Provenance-Graph.md). - **Semantic Mapping quality** — for federated models, how completely and cleanly concepts map to the standards and partners they declare interoperability with. - **Documentation completeness** — the share of public concepts with Display Names, descriptions and documentation (including localized coverage). - **Data trust** — the strength of the Trust Vector attached to the model's sources and assertions. - **Freshness** — how recently the model and its high-impact facts were reviewed or updated relative to their expected cadence. --- # 4. The Scoring Model The score is a transparent **weighted sum** over the dimensions, normalized to a **0–100** scale: ```text Score = round( Σ ( weight_d × dimension_d ) ) dimension_d ∈ [0, 1] d ``` A reference weighting (publishers and communities can adjust and publish their own, since the model is transparent): | Dimension | Weight | |-----------|-------:| | Event completeness | 20 | | Provenance presence | 18 | | Relationship consistency | 15 | | Projection coverage | 12 | | Semantic Mapping quality | 12 | | Documentation completeness | 10 | | Data trust | 8 | | Freshness | 5 | | **Total** | **100** | Reported bands give the number an interpretation: | Band | Range | Reading | |------|-------|---------| | Exemplary | 90–100 | Rich, fully traceable, federation-ready content. | | Strong | 75–89 | Well-developed; minor gaps. | | Adequate | 60–74 | Usable; noticeable gaps in history or provenance. | | Developing | 40–59 | Valid but thin; significant content gaps. | | Minimal | 0–39 | Conformant skeleton; little semantic richness. | A coherence report publishes the total, the band, and the per-dimension sub-scores, so the number is never opaque: a reader can always see *which* dimension lowered it. --- # 5. How It Complements Validation and MMQS The score sits between binary validation and a future graded-quality standard: - [**Validation**](../02-architecture/Validation.md) is the **gate** — V0–V5, pass or fail. A model must pass before it is scored. - The **Semantic Coherence Score** is the **gradient** — a comparable number over the same evidence, surfaced through [Certification](../06-ecosystem/Certification.md) so that certification can report *graded* quality rather than a single pass/fail. - The **Meta-Model Quality Standard (MMQS)** is the **future standard** that would make this gradient normative across the ecosystem (see Future Directions). Because every dimension is computed from artifacts that validation already produces — Events, Relationships, Projections, provenance, mappings, documentation — the score reuses existing evidence and reproduces the same way a [Validation Report](../02-architecture/Validation.md) does. --- # 6. Worked Sample Scoring Consider an *Employee* Meta-Model that has passed Validation. Measured dimensions (each on 0–1) and the reference weights: ```text Event completeness 0.90 × 20 = 18.0 Provenance presence 0.80 × 18 = 14.4 Relationship consistency 1.00 × 15 = 15.0 Projection coverage 0.75 × 12 = 9.0 Semantic Mapping quality 0.60 × 12 = 7.2 Documentation completeness 0.70 × 10 = 7.0 Data trust 0.85 × 8 = 6.8 Freshness 0.40 × 5 = 2.0 ------ Total 79.4 → 79 (Strong) ``` The published report shows the total (79, "Strong") *and* the breakdown, which makes the improvement path obvious: Freshness (2.0/5) and Semantic Mapping quality (7.2/12) are the weakest contributors, so reviewing stale facts and completing the federation mappings would raise the score the fastest — without changing the model's architecture at all. --- # 7. Architectural Position The Semantic Coherence Score preserves the ecosystem's invariants: it reads published evidence without modifying the scored model, it does not centralize authority (any party can recompute it from the same evidence), and it never overrides conformance. It is a lens on quality, layered on top of validation — never a replacement for it. --- # 8. Future Directions The Semantic Coherence Score is the concrete seed of a future **Meta-Model Quality Standard (MMQS)**. MMQS could standardize the dimension definitions and their measurement, publish reference weight profiles per domain, define how graded scores surface in [Certification](../06-ecosystem/Certification.md) and the Registry, and integrate with a **Semantic Validation Framework (SVF)** so that the same test runs produce both the pass/fail verdict and the coherence gradient. It could also let the [Trust Vector](../02-architecture/Traceability.md) and the [Provenance Graph](../02-architecture/Provenance-Graph.md) feed freshness and provenance scoring automatically. --- # Final Statement The Semantic Coherence Score turns "is this model good?" from an opinion into a transparent, reproducible number. By measuring how completely a model records its history, grounds its facts and documents its meaning — and by always showing the breakdown behind the total — it lets the ecosystem reward semantic richness without ever compromising the binary clarity of conformance.