feat(vocabulary): Domain Vocabulary — identity vs representation; regulation aliases fix the KPI normalization
Before the next Journey: the LANGUAGE. With 5 knowledge objects but no vocabulary, the same reise gets named four different ways (ISO9001->MaschinenVO vs Quality Management->Product Safety vs ...). The spec answers ONE question: which terms are IDENTITIES and which are REPRESENTATIONS of the same meaning? - spec docs-src/architecture/domain-vocabulary-spec-v1.md (PROPOSAL): identity hierarchy (Requirement RQ / Capability MCAP [Registry 2C] / regulation-source-target / Journey Class MJRN [PROVISIONAL] / Journey instance / Playbook MPLB); canonical name + aliases; capability vocabulary = the Capability Registry (not rebuilt); reorder Vocabulary -> Transition #2 -> #3 -> Rule of Three. - knowledge/vocabulary/regulations.yaml: regulation/standard IDENTITIES (id + canonical + aliases). SOLVES the regulation-ID normalization the KPIs flagged: CRA == "Cyber Resilience Act" == "Regulation (EU) 2024/2847" all resolve to `cra`; ISO9001/QMS -> iso9001; etc. Shared artifact (@Legal-KG/@Execution please adopt). - knowledge/vocabulary/journey_classes.yaml (PROVISIONAL): clusters our transitions into classes (Information Security -> Product Cybersecurity; Quality Management -> Product Compliance/Safety). Finding: ISO9001->MaschinenVO is an INSTANCE of an existing class (like ISO9001->CRA, ISO13485->MDR), not a new kind -> avoids duplication. Journey Class is a new abstraction -> its own Rule of Three (no MJRN minting yet). - reference suite: both KPIs now read aliases from regulations.yaml instead of hard-coded maps; the "Regelwerk-ID-Normalisierung" line flips TODO -> PASS. KPI numbers unchanged (vocab is a superset). - Side effect = Requirements Intelligence: a Tender "Security Patch Procedure" resolves to MCAP-0017. 7 vocabulary tests (17 with domain programs), check-loc 0. Knowledge data + spec + reference harness = non-runtime -> no deploy (ADR-001). No new module, no runtime change, no minting (Freeze). Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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Benjamin Admin
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# Domain Vocabulary — Journey CLASSES (PROVISIONAL). A class clusters journey instances that are
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# "the same reise". So we do NOT write a new journey for every certification when many share a class.
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# PROVISIONAL: Journey Class is a NEW abstraction -> its OWN Rule of Three (>= 3 instances per class
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# before minting MJRN ids). Endpoints reference regulation vocabulary ids (see regulations.yaml).
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id: VOCAB-journey-classes-v1
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status: provisional
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classes:
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- id: infosec-to-product-cyber # provisional id, NOT a minted MJRN
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name: "Information Security → Product Cybersecurity"
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from_kind: information_security
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to_kind: product_cybersecurity
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instances:
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- {from: iso27001, to: cra} # ✅ modelled (TP-ISO27001-CRA-v1)
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- {from: tisax, to: cra} # ⏳ Rule-of-Three transition #3
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- {from: iec62443, to: cra} # ⏳
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- id: qm-to-product-compliance
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name: "Quality Management → Product Compliance/Safety"
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from_kind: quality_management
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to_kind: product_compliance_safety
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instances:
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- {from: iso9001, to: cra} # ✅ modelled (TP-ISO9001-CRA-v1)
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- {from: iso9001, to: maschinenvo} # ⏳ Rule-of-Three transition #2 — INSTANCE of this class, not a new kind
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- {from: iso13485, to: mdr} # same CLASS, different domain (medical) — proves the class generalises
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note: >
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Befund: ISO9001→MaschinenVO ist KEINE neue Journey-Art, sondern eine INSTANZ der Klasse
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„Quality Management → Product Compliance/Safety" (wie ISO9001→CRA, ISO13485→MDR). Das ist genau die
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Duplikation, die das Vokabular verhindert.
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# Domain Vocabulary — regulation/standard IDENTITIES (Requirement Sources + Targets).
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# Each has a stable id + a canonical name + every alias/spelling. SOLVES the regulation-ID
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# normalization that the Transition Coverage KPI + Knowledge Intake flagged (CRA vs "Cyber Resilience
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# Act"). Reasoning seeds this; @Legal-KG / @Execution please adopt as the SHARED vocabulary.
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# Not runtime, no minting — a shared knowledge artifact.
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id: VOCAB-regulations-v1
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regulations:
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- {id: cra, canonical: "Cyber Resilience Act", aliases: [CRA, "Cyber Resilience Act", "Regulation (EU) 2024/2847"]}
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- {id: maschinenvo, canonical: "Maschinenverordnung", aliases: [MaschinenVO, Maschinenverordnung, "Machinery Regulation", "Regulation (EU) 2023/1230"]}
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- {id: iso9001, canonical: "ISO 9001", aliases: [ISO9001, "ISO 9001", "ISO/IEC 9001", QMS, "Quality Management System"]}
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- {id: iso27001, canonical: "ISO/IEC 27001", aliases: [ISO27001, "ISO 27001", "ISO/IEC 27001", ISMS, "Information Security Management System"]}
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- {id: tisax, canonical: "TISAX", aliases: [TISAX, "Trusted Information Security Assessment Exchange"]}
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- {id: iec62443, canonical: "IEC 62443", aliases: [IEC62443, "IEC 62443", "ISO/IEC 62443"]}
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- {id: nis2, canonical: "NIS2", aliases: [NIS2, "NIS 2", "Directive (EU) 2022/2555"]}
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- {id: dataact, canonical: "Data Act", aliases: [DataAct, "Data Act", "Regulation (EU) 2023/2854"]}
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- {id: iso13485, canonical: "ISO 13485", aliases: [ISO13485, "ISO 13485"]}
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- {id: mdr, canonical: "MDR", aliases: [MDR, "Medical Device Regulation", "Regulation (EU) 2017/745"]}
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- {id: iec62304, canonical: "IEC 62304", aliases: [IEC62304, "IEC 62304"]}
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- {id: iso14001, canonical: "ISO 14001", aliases: [ISO14001, "ISO 14001"]}
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- {id: iatf16949, canonical: "IATF 16949", aliases: [IATF16949, "IATF 16949", IATF]}
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@@ -141,6 +141,25 @@ def completeness_section() -> None:
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])
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def _regulation_aliases(base_dir):
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"""Build a normalized alias -> canonical-id map from the Domain Vocabulary (regulations.yaml)."""
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import os
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import yaml
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path = os.path.join(base_dir, "..", "knowledge", "vocabulary", "regulations.yaml")
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amap = {}
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with open(path, encoding="utf-8") as h:
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for r in (yaml.safe_load(h) or {}).get("regulations", []):
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for name in [r["canonical"]] + list(r.get("aliases", [])):
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amap["".join(c for c in str(name).lower() if c.isalnum())] = r["id"]
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return amap
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def _canon_reg(s, amap):
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"""Canonicalize a regulation string via the vocabulary (replaces the old hard-coded alias maps)."""
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return amap.get("".join(c for c in str(s).lower() if c.isalnum()),
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"".join(c for c in str(s).lower() if c.isalnum()))
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def domain_programs_section(base_dir) -> None:
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"""Domain Knowledge Program v1 — per-domain maturity KPI DERIVED from the corpus (computed-not-stored)."""
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import os
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@@ -159,11 +178,10 @@ def domain_programs_section(base_dir) -> None:
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for f in sorted(os.listdir(pdir)) if f.endswith(".yaml")]
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progs = sorted((p for p in _all if "backlog_rank" in p), key=lambda p: p["backlog_rank"]) # domain programs only
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_ALIAS = {"cyber resilience act": "cra", "maschinenverordnung": "maschinenvo", "iatf": "iatf16949"}
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_amap = _regulation_aliases(base_dir) # Domain Vocabulary (regulations.yaml)
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def _canon(r):
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k = str(r).strip().lower()
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return _ALIAS.get(k, k)
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return _canon_reg(r, _amap)
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def _hits(reg_lists, src):
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cs = {_canon(s) for s in src}
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@@ -199,7 +217,7 @@ def domain_programs_section(base_dir) -> None:
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coverage_table([
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("Domain Knowledge Program (7-Stufen-Produktionsstraße)", "PASS", "%d Domänen im Backlog, Industrial Automation #1" % len(progs)),
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("Reifegrad-KPI (computed-not-stored)", "PASS", "aus echtem Korpus abgeleitet (TP/PB/RTS je Domäne)"),
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("Regelwerk-ID-Normalisierung", "TODO", "Alias CRA/MaschinenVO im KPI — kanonische IDs ausstehend"),
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("Regelwerk-ID-Normalisierung (Domain Vocabulary)", "PASS", "Aliase aus `vocabulary/regulations.yaml`, nicht mehr hartkodiert"),
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])
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@@ -224,12 +242,10 @@ def transition_coverage_section(base_dir) -> None:
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for it in (to if isinstance(to, list) else [to]) if isinstance(it, dict)]
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pats.append((frm, [t for t in tos if t], str(d.get("status", "draft"))))
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_ALIAS = {"isoiec27001": "iso27001", "isoiec62443": "iec62443",
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"cyberresilienceact": "cra", "maschinenverordnung": "maschinenvo"}
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_amap = _regulation_aliases(base_dir) # Domain Vocabulary (regulations.yaml)
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def _c(s):
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k = "".join(ch for ch in str(s).lower() if ch.isalnum())
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return _ALIAS.get(k, k)
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return _canon_reg(s, _amap)
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_RANK = {"draft": 1, "reviewed": 2, "validated": 3, "proven": 4}
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_ICON = {0: "⚪ nicht begonnen", 1: "🟡 Draft", 2: "✅ reviewed", 3: "✅ validated", 4: "✅ Gold"}
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@@ -385,7 +385,7 @@ _Industry-Einstieg + ETO-Hypothese: jede Domäne kennt ihre typischen Sources +
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|---|---|---|
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| Domain Knowledge Program (7-Stufen-Produktionsstraße) | **PASS** | 5 Domänen im Backlog, Industrial Automation #1 |
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| Reifegrad-KPI (computed-not-stored) | **PASS** | aus echtem Korpus abgeleitet (TP/PB/RTS je Domäne) |
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| Regelwerk-ID-Normalisierung | **TODO** | Alias CRA/MaschinenVO im KPI — kanonische IDs ausstehend |
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| Regelwerk-ID-Normalisierung (Domain Vocabulary) | **PASS** | Aliase aus `vocabulary/regulations.yaml`, nicht mehr hartkodiert |
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## Transition Coverage — die Transition ist die Wissenseinheit (Operational Knowledge)
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@@ -424,5 +424,5 @@ _Der Kunde kauft nicht „EMV-Domain", sondern „wir haben ISO 9001 — helfen
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## Suite-Status (Roll-up)
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- Coverage-Zellen gesamt: **50**
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- PASS: **38** · PARTIAL: 3 · UNSUPPORTED: 1 · TODO: 7 · N/A: 1 · NEEDS_FACTS: 0
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- PASS: **39** · PARTIAL: 3 · UNSUPPORTED: 1 · TODO: 6 · N/A: 1 · NEEDS_FACTS: 0
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- Fortschritt = PASS-Anteil steigt, wenn Epics RS-001…004 landen (objektiver Maßstab, kein LOC).
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"""Characterization tests for the Domain Vocabulary (data, not code).
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Pins the IDENTITY-vs-REPRESENTATION contract: regulations have a stable id + canonical name + aliases
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(so CRA and "Cyber Resilience Act" resolve to the SAME identity — the normalization that the KPIs
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flagged). Journey classes cluster transition instances so we do not duplicate the same reise; they
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are PROVISIONAL (no MJRN minting) and reference regulation ids that exist in the vocabulary.
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"""
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from __future__ import annotations
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import os
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import yaml
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_VOCAB = os.path.join(os.path.dirname(__file__), "..", "knowledge", "vocabulary")
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def _regs():
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with open(os.path.join(_VOCAB, "regulations.yaml"), encoding="utf-8") as h:
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return yaml.safe_load(h)["regulations"]
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def _classes():
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with open(os.path.join(_VOCAB, "journey_classes.yaml"), encoding="utf-8") as h:
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return yaml.safe_load(h)
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def _norm(s):
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return "".join(c for c in str(s).lower() if c.isalnum())
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def _alias_map():
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amap = {}
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for r in _regs():
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for name in [r["canonical"]] + list(r.get("aliases", [])):
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amap[_norm(name)] = r["id"]
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return amap
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def test_every_regulation_has_id_canonical_aliases():
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for r in _regs():
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assert r["id"] and r["canonical"] and r["aliases"]
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assert r["id"] == r["id"].lower() # ids are lowercase stable keys
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def test_cra_spellings_resolve_to_one_identity():
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amap = _alias_map()
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# the exact normalization the KPIs needed: CRA == Cyber Resilience Act
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assert amap[_norm("CRA")] == "cra" and amap[_norm("Cyber Resilience Act")] == "cra"
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assert amap[_norm("Regulation (EU) 2024/2847")] == "cra"
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def test_iso_and_management_system_aliases_resolve():
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amap = _alias_map()
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assert amap[_norm("ISO9001")] == "iso9001" and amap[_norm("QMS")] == "iso9001"
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assert amap[_norm("ISO/IEC 27001")] == "iso27001" and amap[_norm("ISMS")] == "iso27001"
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assert amap[_norm("Maschinenverordnung")] == "maschinenvo" and amap[_norm("MaschinenVO")] == "maschinenvo"
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def test_aliases_are_unambiguous():
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# no normalized alias maps to two different regulation identities
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seen = {}
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for r in _regs():
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for name in [r["canonical"]] + list(r.get("aliases", [])):
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k = _norm(name)
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assert seen.get(k, r["id"]) == r["id"], "ambiguous alias %r" % name
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seen[k] = r["id"]
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def test_journey_classes_are_provisional():
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assert _classes()["status"] == "provisional" # new abstraction -> own Rule of Three
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def test_iso9001_maschinenvo_is_an_instance_not_a_new_kind():
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classes = _classes()["classes"]
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qm = [c for c in classes if c["id"] == "qm-to-product-compliance"][0]
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pairs = {(i["from"], i["to"]) for i in qm["instances"]}
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assert ("iso9001", "maschinenvo") in pairs # same CLASS as iso9001->cra, iso13485->mdr
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assert ("iso13485", "mdr") in pairs # class generalises across domains
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def test_class_endpoints_reference_known_regulations():
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reg_ids = {r["id"] for r in _regs()}
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for c in _classes()["classes"]:
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for inst in c["instances"]:
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assert inst["from"] in reg_ids and inst["to"] in reg_ids # vocabulary is internally consistent
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