Aevum Encyclopedia — Content Architecture

01 • Information Architecture

Content Hierarchy & Navigation Structure

A multi-layered tree structure ensuring intuitive discoverability, scalable categorization, and consistent user journeys across all disciplines.

🌐 Root / Encyclopedia 2.4M nodes

🔬 Natural Sciences 680K

🧪Chemistry92K

🧬Biology & Life Sciences215K

⚛️Physics & Astronomy148K

📜 Humanities & Social Sciences 820K

🏛️History & Archaeology310K

⚖️Law, Politics & Society185K

🎨Arts, Literature & Philosophy245K

💻 Technology & Engineering 540K

🤖Computer Science & AI190K

⚙️Engineering & Applied Sciences165K

🌍 Geography, Environment & Earth Sciences 360K

02 • Taxonomy & Categorization

Controlled Vocabulary & Faceted Classification

A hybrid taxonomy combining hierarchical categories, flat facets, and semantic tags to enable precision filtering and cross-domain discovery.

🗂️

Primary Taxonomy

Rigid hierarchical structure aligned with UNESCO subject classification, extended for modern disciplines and emerging fields.

🏷️

Faceted Dimensions

Dynamic filters: Era, Region, Discipline, Complexity Level, Media Type, Verification Status, and Contributor Tier.

🔗

Semantic Tagging

AI-generated ontology tags mapped to Wikidata concepts, enabling cross-lingual and cross-domain knowledge alignment.

Sample Facet Mapping

Facet	Type	Cardinality	Example Values
Discipline	Hierarchical	Single	Physics → Quantum Mechanics
Era / Time Period	Chronological	Multiple	Classical Antiquity, 19th Century
Geographic Scope	Geo-Spatial	Multiple	Global, East Asia, Sub-Saharan Africa
Content Maturity	Workflow	Single	Starter, Featured, Comprehensive, Stub
Media Enrichment	Multimedia	Multiple	Infographic, 3D Model, Audio Clip

03 • Content Types & Templates

Structured Article Models

Seven distinct content archetypes, each with predefined sections, validation rules, and rendering components.

📖 Standard Article

Core encyclopedia entry with abstract, body sections, references, see-also, and categories. Supports rich media and interactive widgets.

// schema: article.standard
{
  "type": "standard",
  "sections": [
    "abstract", "introduction",
    "body[1..n]", "references",
    "further_reading", "categories"
  ],
  "min_refs": 3,
  "requires_review": true
}
                    

🧪 Scientific Paper / Monograph

Structured for academic rigor: abstract, methodology, results, discussion, citations (DOI-linked), and peer-review metadata.

// schema: article.scientific
{
  "type": "scientific",
  "sections": [
    "abstract", "methodology",
    "results", "discussion",
    "doi_citations", "peer_review_log"
  ],
  "verification_level": "tier_1",
  "data_tables": true
}
                    

📍 Geo-Entity / Place

Location-centric template: coordinates, demographics, climate, history, economy, infrastructure, and map integration.

// schema: article.geo_entity
{
  "type": "geo",
  "geojson": { "lat": 40.7128, "lng": -74.0060 },
  "modules": [
    "demographics", "climate",
    "economy", "history_timeline"
  ],
  "map_embed": true
}
                    

👤 Biography / Person

Life timeline, achievements, publications, relationships, legacy, and verified archival sources.

// schema: article.biography
{
  "type": "biography",
  "timeline": true,
  "relations": "graph_linked",
  "works": "citable_list",
  "legacy_score": 0.87
}
                    

04 • Metadata Schema

Structured Attributes & SEO Architecture

A dual-layer metadata system: core attributes for platform functionality, and extended schema.org markup for search engines and AI agents.

// core_metadata.json
{
  "id": "ae:qmc:784920",
  "title": "Quantum Computing",
  "slug": "quantum-computing",
  "status": "published",
  "maturity": "featured",
  "created_at": "2022-03-14T08:30:00Z",
  "updated_at": "2025-11-02T14:22:00Z",
  "word_count": 4820,
  "read_time_min": 19,
  "primary_taxonomy": "technology.computer_science",
  "facets": {
    "era": "contemporary",
    "complexity": "intermediate",
    "media_types": ["diagram", "video"]
  },
  "contributors": {
    "authors": ["user:8821"],
    "reviewers": ["expert:phys_04"],
    "edit_count": 47
  },
  "verification": {
    "fact_check_status": "verified",
    "source_quality_score": 0.94,
    "bias_risk": "low"
  }
}
                    

// schema.org JSON-LD injection
{
  "@context": "https://schema.org",
  "@type": "Article",
  "headline": "Quantum Computing",
  "description": "Comprehensive overview of quantum computing principles...",
  "author": { "@type": "Organization", "name": "Aevum Encyclopedia"  },
  "datePublished": "2022-03-14",
  "dateModified": "2025-11-02",
  "mainEntity": {
    "@type": "Thing",
    "name": "Quantum Computing",
    "url": "https://aevum.edu/quantum-computing",
    "sameAs": "http://www.wikidata.org/entity/Q41780"
  },
  "educationalLevel": "undergraduate",
  "inLanguage": "en"
}
                    

// ai_retrieval_metadata.json
{
  "embeddings": {
    "model": "aevum-embed-v3",
    "dimensions": 1536,
    "vector_id": "vec:qc:99281"
  },
  "semantic_tags": [
    "qubit", "superposition", "entanglement",
    "quantum_gate", "error_correction"
  ],
  "related_concepts": [
    { "id": "ae:phy:1120", "relation": "foundational_to" },
    { "id": "ae:cs:8840", "relation": "application_of" }
  ],
  "qa_pairs": 24,
  "llm_context_window_optimized": true,
  "citation_density": 0.082
}
                    

05 • Knowledge Graph & Relations

Interconnected Knowledge Network

Every article is a node in a directed, weighted graph. Relationships are explicitly typed, enabling AI reasoning, pathfinding, and dynamic cross-referencing.

🔬

Physics

Discipline

⚛️

Quantum Mechanics

Subfield

🖥️

Quantum Computing

Core Node

💻

Computer Science

Discipline

🔐

Cryptography

Application

🧠

AI / ML

Related Field

👤

Richard Feynman

Person

🏢

IBM Q

Organization

📄

Shor's Algorithm

Concept

Relation Types & Cardinality

Relation Type	Direction	Cardinality	Usage Example
subfield_of	Child → Parent	1:1	Quantum Computing → Physics
application_of	Node → Node	N:N	Shor's Algorithm → Cryptography
authored_by	Work → Person	N:1	Paper → Richard Feynman
contradicts	Claim → Claim	N:N	Theory A → Theory B
temporal_precedes	Event → Event	1:N	Renaissance → Industrial Revolution
part_of	Component → Whole	N:1	Mitochondria → Eukaryotic Cell

06 • Search & Discovery Architecture

Hybrid Retrieval System

Combines lexical matching, semantic embeddings, graph traversal, and facet filtering for precision recall at scale.

🔍 Query Input

Text, voice, image, or natural language

→

🧠 Intent Parsing

NLP classifies: fact, concept, person, geo, comparison

→

🔎 Hybrid Search

BM25 + Vector Embeddings + Graph Hops

→

⚖️ Ranking & Fusion

Relevance, authority, freshness, recency, bias check

→

📤 Result Assembly

Snippets, cards, graph preview, facets applied

07 • Editorial Workflow & Governance

Content Lifecycle Management

A structured, role-based pipeline ensuring quality, neutrality, and academic rigor from creation to publication.

📝 Draft → Review

Contributors submit drafts. AI performs initial syntax, citation format, and plagiarism checks before human triage.

🔍 Peer Expert Review

Domain-specific editors verify claims, source quality, and neutrality. Minimum 2 approvals required for Tier-1 topics.

🚀 Publish → Monitor

Live publication with version control. Continuous monitoring for drift, new research updates, and community flags.

Content Maturity Levels

Level	Criteria	Review Depth	AI Assistance
Stub	< 300 words, minimal refs	Automated format check	Outline suggestions, ref expansion
Start	300–1000 words, 3+ sources	Community peer check	Fact-check prompts, tone analysis
C-Class	1k–3k words, well-structured	Assigned editor review	Graph relation suggestions
Featured	3k+ words, comprehensive, media	Expert panel + double review	Full AI audit, bias scan, citation validation
Comprehensive	Definitive reference, 10k+ words	Academic board approval	Continuous update alerts, version diff

08 • Multilingual & Localization Matrix

Global Content Synchronization

Translation pipelines, localization rules, and coverage tracking across 142 languages with semantic alignment to prevent drift.

Localization Pipeline

Machine translation init → Community editor refinement → Native speaker approval → Semantic cross-lingual alignment → Publish.

// translation_metadata.json
{
  "source_lang": "en",
  "target_lang": "zh",
  "mt_model": "aevum-nllb-3.2",
  "human_reviewed": true,
  "semantic_drift_score": 0.03,
  "cultural_adaptation": "minimal"
}
                    

Coverage Snapshot

🇺🇸

English

100%

🇪🇸

Spanish

94%

🇫🇷

French

91%

🇩🇪

German

89%

🇯🇵

Japanese

82%

🇨🇳

Chinese

78%

🇧🇷

Portuguese

76%

🇮🇳

Hindi

64%

🇷🇺

Russian

71%

🇦🇷

Arabic

69%

🇰🇷

Korean

62%

🌐

+131 more

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