Wikidata Ingestion Format

Purpose: Define how TriadicFrameworks ingests, queries, and structurally interprets Wikidata — the world's largest open knowledge graph (120M+ entities, CC0 license). Wikidata is Wikipedia's dimensional addressing layer — the machine‑readable substrate beneath every human‑readable article.

This file provides SPARQL templates, ingestion patterns, and RTT mappings so that students, researchers, and AIs can query Wikidata with structural awareness.

1 — What Is Wikidata?#

Wikidata is a free, collaborative, multilingual knowledge graph maintained by the Wikimedia Foundation. It stores structured data as entity–property–value triplets that can be queried via SPARQL.

Key Facts#

Why CC0 Matters#

Wikidata's CC0 license means no copyright restrictions on reuse. Unlike Wikipedia article text (CC BY‑SA 4.0, which requires attribution and share‑alike), Wikidata statements can be freely ingested, transformed, and republished without any license obligations. This makes Wikidata the ideal ingestion substrate for automated RTT analysis.

2 — RTT Structural Mapping#

2.1 — The Core Triplet#

Every Wikidata statement follows the pattern:

Subject (Q‑number) → Property (P‑number) → Value (Q‑number, string, or quantity)

In RTT terms:

Dimensional Address → Dimensional Operator → Structural Claim

Example:

Q283 (Water) → P274 (chemical formula) → "H₂O"

RTT reading:
  dimensional_address(Water)
    → dimensional_operator(chemical_formula)
      → structural_claim("H₂O")

2.2 — Full Mapping Table#

2.3 — Qualifiers as Regime Context#

Qualifiers are what make Wikidata structurally rich — they add context to claims, turning flat assertions into regime‑aware statements:

3 — SPARQL Query Templates#

All queries run at https://query.wikidata.org/

3.1 — Basic: Get All Properties for an Entity#

Use case: What does the knowledge graph say about a concept?

# All statements for a given entity
# Replace Q283 with any Q-number
SELECT ?property ?propertyLabel ?value ?valueLabel
WHERE {
  wd:Q283 ?claim ?statement .
  ?statement ?ps ?value .
  ?property wikibase:claim ?claim .
  ?property wikibase:statementProperty ?ps .
  SERVICE wikibase:label {
    bd:serviceParam wikibase:language "en" .
  }
}
ORDER BY ?propertyLabel

RTT reading: This returns the full regime declaration for a concept — every structural claim the knowledge graph makes about it.

3.2 — Cross‑Domain Bridging: Find All Domains a Concept Touches#

Use case: How many knowledge domains does this concept connect to?

# Count cross-domain P-number bridges for an entity
# Replace Q283 with any Q-number
SELECT ?property ?propertyLabel
       (COUNT(DISTINCT ?value) AS ?connections)
WHERE {
  wd:Q283 ?claim ?statement .
  ?statement ?ps ?value .
  ?property wikibase:claim ?claim .
  ?property wikibase:statementProperty ?ps .
  FILTER(ISIRI(?value))
  SERVICE wikibase:label {
    bd:serviceParam wikibase:language "en" .
  }
}
GROUP BY ?property ?propertyLabel
ORDER BY DESC(?connections)

RTT reading: Each P‑number that points to an entity in a different domain = a dimensional bridge. The count of cross‑domain bridges = the concept's structural connectivity score.

3.3 — Temporal Regime Analysis: Track Property Changes Over Time#

Use case: How has a concept's regime evolved?

# Temporal regime evolution for a property
# Example: Population of France over time
SELECT ?population ?pointInTime
WHERE {
  wd:Q142 p:P1082 ?statement .
  ?statement ps:P1082 ?population .
  ?statement pq:P585 ?pointInTime .
}
ORDER BY ?pointInTime

RTT reading: Each row = a temporal regime snapshot. The series reveals the regime's evolution curve — growth, stability, decline, or oscillation.

3.4 — Regime Hierarchy: Traverse the Class Tree#

Use case: Where does a concept sit in the regime hierarchy?

# Class hierarchy (upward traversal) for an entity
# Replace Q283 with any Q-number
SELECT ?class ?classLabel ?depth
WHERE {
  wd:Q283 wdt:P31/wdt:P279* ?class .
  {
    SELECT ?class (COUNT(?mid) AS ?depth)
    WHERE {
      wd:Q283 wdt:P31/wdt:P279* ?mid .
      ?mid wdt:P279* ?class .
    }
    GROUP BY ?class
  }
  SERVICE wikibase:label {
    bd:serviceParam wikibase:language "en" .
  }
}
ORDER BY ?depth

RTT reading: This traces the concept's position in the regime hierarchy — from specific instance up through increasingly general regime classifications. Depth = regime granularity. Breadth at each level = regime diversity.

3.5 — Cross‑Language Regime Comparison: Sitelink Coverage#

Use case: How many languages declare this concept?

# Count Wikipedia sitelinks (language versions) for an entity
SELECT ?sitelink
WHERE {
  ?sitelink schema:about wd:Q283 .
  ?sitelink schema:isPartOf/wikibase:wikiGroup "wikipedia" .
}

RTT reading: Each sitelink = a regime declaration in a different cultural context. High sitelink count = universally recognized concept. Low count = culturally specific or specialized regime. Comparing article lengths across sitelinks reveals cultural regime variance — same concept, different structural emphasis.

3.6 — Validation Corridor: Find Featured Articles by Domain#

Use case: What are the structurally validated reference points in a domain?

# Find items with Featured Article sitelinks in English Wikipedia
# Filter by domain using instance-of (P31) or subclass-of (P279)
SELECT ?item ?itemLabel ?article
WHERE {
  ?article schema:about ?item .
  ?article schema:isPartOf <https://en.wikipedia.org/> .
  ?article wikibase:badge wd:Q17437796 .
  ?item wdt:P31/wdt:P279* wd:Q11344 .
  SERVICE wikibase:label {
    bd:serviceParam wikibase:language "en" .
  }
}
LIMIT 50

RTT reading: Featured Articles = validation corridor gold standard. These are the concepts whose regime declarations have been community‑verified as structurally complete. They serve as reference templates for the domain.

3.7 — Regime Collision: Find Disambiguation Entities#

Use case: Which concepts have competing regime claims on the same term?

# Find disambiguation items — concepts where multiple regimes
# claim the same term
SELECT ?item ?itemLabel ?article
WHERE {
  ?item wdt:P31 wd:Q4167410 .
  ?article schema:about ?item .
  ?article schema:isPartOf <https://en.wikipedia.org/> .
  SERVICE wikibase:label {
    bd:serviceParam wikibase:language "en" .
  }
}
LIMIT 100

RTT reading: Every disambiguation entity = a regime collision point — two or more structural regimes claiming the same term. The list of disambiguated meanings reveals the competing regime declarations.

4 — Ingestion Patterns#

4.1 — Single Entity Deep Profile#

Purpose: Build a complete RTT structural profile for one concept.

Steps:

1. Resolve the Q‑number — search https://www.wikidata.org/w/api.php?action=wbsearchentities&search=TERM&language=en&format=json
2. Pull all statements — use Query 3.1
3. Count dimensional bridges — use Query 3.2
4. Trace the regime hierarchy — use Query 3.4
5. Check sitelink coverage — use Query 3.5
6. Check for Featured Article badge — use Query 3.6

Output format:

{
  "entity": "Q283",
  "label": "Water",
  "regime_declaration": "chemical compound, binary compound, oxide",
  "dimensional_bridges": 47,
  "regime_hierarchy_depth": 8,
  "sitelink_count": 298,
  "featured_article": true,
  "top_properties": [
    "P274 (chemical formula): H₂O",
    "P31 (instance of): chemical compound",
    "P361 (part of): hydrosphere",
    "P2054 (density): 997 kg/m³",
    "P2101 (melting point): 0°C"
  ],
  "cross_domain_connections": [
    "Chemistry (compound properties)",
    "Physics (thermodynamic constants)",
    "Biology (biological role)",
    "Earth Sciences (hydrosphere)",
    "Engineering (industrial solvent)",
    "Medicine (essential nutrient)"
  ]
}

4.2 — Domain Sweep#

Purpose: Map all Wikidata entities within a knowledge domain.

Steps:

1. Identify the domain's root class (e.g., Physics → Q413 "physics")
2. Query all instances and subclasses:

   SELECT ?item ?itemLabel
   WHERE {
     ?item wdt:P31/wdt:P279* wd:Q413 .
     SERVICE wikibase:label {
       bd:serviceParam wikibase:language "en" .
     }
   }
   LIMIT 1000

3. For each entity, count cross‑domain P‑number bridges
4. Rank by structural connectivity
5. The top‑ranked entities = the domain's most structurally connected concepts

4.3 — Temporal Regime Tracking#

Purpose: Monitor how a concept's regime evolves over time.

Steps:

1. Select a property with temporal qualifiers (P580/P582/P585)
2. Use Query 3.3 to extract the time series
3. Plot the series: stable plateaus = crystallized regime; sharp transitions = regime shifts
4. Cross‑reference with Wikipedia revision history for the same period — do article edits correlate with Wikidata property changes?

4.4 — Cross‑Language Regime Divergence#

Purpose: Compare how the same concept is structurally declared across cultures.

Steps:

1. Use Query 3.5 to get all sitelinks
2. For the top 5 largest language editions, compare:
   • Article length (word count)
   • Section structure (headings)
   • Lead paragraph (regime declaration)
   • Categories assigned
3. Divergences = cultural regime variance — same dimensional address, different structural emphasis

5 — Property Families for RTT Analysis#

Not all 12,000+ P‑numbers are equally useful for RTT analysis. These property families provide the highest structural signal:

5.1 — Classification Properties (Regime Identity)#

5.2 — Relationship Properties (Dimensional Bridges)#

5.3 — Temporal Properties (Regime Dynamics)#

5.4 — Quantitative Properties (Regime Metrics)#

6 — Rate Limits and Ethical Use#

Wikidata Query Service Limits#

Ethical Guidelines#

1. Respect rate limits — do not flood the endpoint with parallel queries
2. Set a User‑Agent header — identify yourself: User-Agent: TriadicFrameworks/1.0 (https://www.triadicframeworks.org/; contact@triadicframeworks.org)
3. Cache results — don't re‑query the same data repeatedly
4. Use database dumps for bulk analysis — the query endpoint is for interactive and moderate‑scale use
5. Contribute back — if you discover missing data or errors, edit Wikidata directly (it's open for anyone to edit)

7 — Relationship to Other Module Files#

8 — Student Exercises#

Exercise 1 — Entity Profile (15 minutes)#

1. Pick any concept you know well
2. Find its Wikidata Q‑number at https://www.wikidata.org/
3. List its top 10 properties (P‑numbers)
4. Classify each property into one of the 4 property families from Section 5
5. Write a 1‑sentence regime declaration based on the P31 (instance of) value

Exercise 2 — Dimensional Bridging (30 minutes)#

1. Pick a concept with high cross‑domain connectivity (try: Energy Q11379, Information Q11028, Evolution Q1063, or Network Q1900326)
2. Run Query 3.2 to count its dimensional bridges
3. List the top 5 P‑numbers by connection count
4. For each, identify which knowledge domain the bridge connects to
5. Draw a simple diagram: your concept in the center, domains around the edges, P‑numbers as labeled connections

Exercise 3 — Cross‑Language Regime Variance (30 minutes)#

1. Pick a concept you expect to have cultural variance (try: Democracy Q7174, Marriage Q8445, or Freedom Q124490)
2. Run Query 3.5 to count its sitelinks
3. Open the Wikipedia article in English + 2 other languages (use Google Translate if needed)
4. Compare: article length, section headings, lead paragraph framing
5. Write a 2‑sentence summary: "The English Wikipedia declares [concept] as [X]. The [other language] Wikipedia declares it as [Y]. The structural difference reveals [Z]."

Exercise 4 — Temporal Regime Tracking (45 minutes)#

1. Pick a concept with temporal data (try: any country's population, a city's mayor, or a company's CEO)
2. Run Query 3.3 to extract the time series
3. Identify: stable plateaus, sharp transitions, gradual trends
4. Cross‑reference with the Wikipedia article's revision history for the same period
5. Answer: "Do Wikidata property changes and Wikipedia article edits correlate? If so, which leads — the data change or the narrative change?"

This file is part of the Wikipedia Awareness Module in the TriadicFrameworks canon.