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Evidence Graph Architecture

Vision

Infrastructure for pragmatic epistemology in investigative journalism. Combines: - i-docs navigation principles: Navigation over narration, reader agency - PROMPT framework: 6-dimensional epistemological scoring - Boundary objects theory: Multiple perspectives on same evidence

Philosophical Foundation

The Core Argument

We didn’t fall from Truth to Post-Truth; we evolved to complex epistemology without building infrastructure. This system IS that infrastructure.

Design Principles

  1. Coordination without consensus: Different audiences navigate same evidence differently

  2. Measurable epistemology: PROMPT scores make evidence quality explicit

  3. Progressive enhancement: Works without JavaScript, enhanced with it

  4. Open by default: Open source, EU data sovereignty, NUJ network adoption

Data Model

Core Entities

Claims
%Claim{
  id: UUID,
  text: String,
  investigation_id: UUID,
  claim_type: :primary | :supporting | :counter,
  confidence_level: 0.0..1.0,
  prompt_scores: %PromptScores{
    provenance: 0..100,      # Source credibility
    replicability: 0..100,   # Can others verify?
    objective: 0..100,       # Operational definitions
    methodology: 0..100,     # Research quality
    publication: 0..100,     # Peer review, venue
    transparency: 0..100     # Open data/methods
  },
  created_by: String,
  created_at: DateTime,
  updated_at: DateTime,
  metadata: Map
}
Evidence
%Evidence{
  id: UUID,
  title: String,
  evidence_type: :document | :dataset | :interview | :media | :other,
  source_url: String,
  local_path: String,
  ipfs_hash: String,        # Provenance via IPFS
  zotero_key: String,       # Two-way sync
  dublin_core: Map,         # Standardized metadata
  schema_org: Map,          # Semantic web compatibility
  extraction_date: DateTime,
  prompt_scores: %PromptScores{},
  tags: [String],
  created_at: DateTime,
  metadata: Map
}
Relationships (Edges)
%Relationship{
  _from: "claims/:uuid",
  _to: "evidence/:uuid",
  relationship_type: :supports | :contradicts | :contextualizes,
  weight: -1.0..1.0,        # -1 = strong contradiction, +1 = strong support
  confidence: 0.0..1.0,     # How certain is this relationship?
  created_by: String,
  reasoning: String,        # Why this relationship exists
  created_at: DateTime,
  metadata: Map
}
Navigation Paths
%NavigationPath{
  id: UUID,
  investigation_id: UUID,
  audience_type: :activist | :policymaker | :researcher | :skeptic | :affected_person | :journalist,
  name: String,
  description: String,
  entry_points: [UUID],     # Starting claims/evidence
  path_nodes: [%PathNode{
    entity_id: UUID,
    entity_type: :claim | :evidence,
    order: Integer,
    context: String,        # Why show this here?
    emphasis: Map           # What to highlight
  }],
  created_at: DateTime
}

Investigations (Container)

%Investigation{
  id: UUID,
  title: String,
  description: String,
  status: :draft | :review | :published | :archived,
  lead_journalist: String,
  collaborators: [String],
  publication_date: DateTime,
  tags: [String],
  created_at: DateTime,
  updated_at: DateTime
}

Database Architecture

Phase 1-2: ArangoDB (Multi-Model)

Why ArangoDB: - Native graph + document database - Strong Elixir integration via arangox - AQL (query language) supports complex traversals - Production-ready, €45/month managed hosting - JSON storage preserves Schema.org/Dublin Core

Collections: - investigations (document) - claims (document) - evidence (document) - navigation_paths (document) - relationships (edge collection)

Indexes:

// Full-text search on claims/evidence
db.claims.ensureIndex({ type: "fulltext", fields: ["text"] })
db.evidence.ensureIndex({ type: "fulltext", fields: ["title", "metadata"] })

// PROMPT score queries
db.claims.ensureIndex({ type: "skiplist", fields: ["prompt_scores.provenance"] })
db.evidence.ensureIndex({ type: "skiplist", fields: ["prompt_scores.methodology"] })

// Investigation queries
db.claims.ensureIndex({ type: "hash", fields: ["investigation_id"] })
db.evidence.ensureIndex({ type: "hash", fields: ["investigation_id"] })

// Zotero sync
db.evidence.ensureIndex({ type: "hash", fields: ["zotero_key"] })

Phase 3+: Optional Virtuoso (RDF/SPARQL)

Add only if semantic web integration becomes critical: - Academic repository linking - Cross-investigation semantic queries - Linked Open Data publishing

Migration path: JSON-LD stored from day 1 enables easy export to RDF.

API Design

GraphQL Schema (Absinthe)

type Investigation {
  id: ID!
  title: String!
  description: String
  status: InvestigationStatus!
  claims(limit: Int, offset: Int): [Claim!]!
  evidence(limit: Int, offset: Int): [Evidence!]!
  navigationPaths: [NavigationPath!]!
  createdAt: DateTime!
  updatedAt: DateTime!
}

type Claim {
  id: ID!
  text: String!
  claimType: ClaimType!
  confidenceLevel: Float!
  promptScores: PromptScores!
  supportingEvidence: [Evidence!]!
  contradictingEvidence: [Evidence!]!
  relationships: [Relationship!]!
  createdAt: DateTime!
}

type Evidence {
  id: ID!
  title: String!
  evidenceType: EvidenceType!
  sourceUrl: String
  ipfsHash: String
  zoteroKey: String
  promptScores: PromptScores!
  supportsClaims: [Claim!]!
  contradictsClaims: [Claim!]!
  metadata: JSON
  tags: [String!]!
}

type PromptScores {
  provenance: Int!        # 0-100
  replicability: Int!
  objective: Int!
  methodology: Int!
  publication: Int!
  transparency: Int!
  overall: Float!         # Calculated average
}

type Relationship {
  id: ID!
  from: Node!
  to: Node!
  relationshipType: RelationType!
  weight: Float!          # -1.0 to 1.0
  confidence: Float!      # 0.0 to 1.0
  reasoning: String
}

type NavigationPath {
  id: ID!
  audienceType: AudienceType!
  name: String!
  description: String
  pathNodes: [PathNode!]!
}

type PathNode {
  entity: Node!
  order: Int!
  context: String
  emphasis: JSON
}

union Node = Claim | Evidence

enum ClaimType { PRIMARY, SUPPORTING, COUNTER }
enum EvidenceType { DOCUMENT, DATASET, INTERVIEW, MEDIA, OTHER }
enum RelationType { SUPPORTS, CONTRADICTS, CONTEXTUALIZES }
enum AudienceType { ACTIVIST, POLICYMAKER, RESEARCHER, SKEPTIC, AFFECTED_PERSON, JOURNALIST }
enum InvestigationStatus { DRAFT, REVIEW, PUBLISHED, ARCHIVED }

# Queries
type Query {
  investigation(id: ID!): Investigation
  investigations(limit: Int, offset: Int, status: InvestigationStatus): [Investigation!]!

  claim(id: ID!): Claim
  searchClaims(query: String!, investigationId: ID): [Claim!]!

  evidence(id: ID!): Evidence
  evidenceByZoteroKey(zoteroKey: String!): Evidence
  searchEvidence(query: String!, investigationId: ID): [Evidence!]!

  # Graph traversal
  evidenceChain(claimId: ID!, maxDepth: Int): EvidenceGraph!
  findPath(fromId: ID!, toId: ID!, maxDepth: Int): [PathStep!]!

  # Navigation
  navigationPath(id: ID!): NavigationPath
  navigationPathsFor(investigationId: ID!, audienceType: AudienceType): [NavigationPath!]!
}

type EvidenceGraph {
  rootClaim: Claim!
  nodes: [Node!]!
  edges: [Relationship!]!
  depth: Int!
}

type PathStep {
  node: Node!
  relationship: Relationship
  depth: Int!
}

# Mutations
type Mutation {
  # Investigations
  createInvestigation(input: CreateInvestigationInput!): Investigation!
  updateInvestigation(id: ID!, input: UpdateInvestigationInput!): Investigation!

  # Claims
  createClaim(input: CreateClaimInput!): Claim!
  updateClaim(id: ID!, input: UpdateClaimInput!): Claim!
  updatePromptScores(claimId: ID!, scores: PromptScoresInput!): Claim!

  # Evidence
  createEvidence(input: CreateEvidenceInput!): Evidence!
  updateEvidence(id: ID!, input: UpdateEvidenceInput!): Evidence!
  importFromZotero(zoteroKey: String!, investigationId: ID!): Evidence!

  # Relationships
  createRelationship(input: CreateRelationshipInput!): Relationship!
  updateRelationshipWeight(id: ID!, weight: Float!, confidence: Float!): Relationship!
  deleteRelationship(id: ID!): Boolean!

  # Navigation Paths
  createNavigationPath(input: CreateNavigationPathInput!): NavigationPath!
  updateNavigationPath(id: ID!, input: UpdateNavigationPathInput!): NavigationPath!
}

# Input types (truncated for brevity)
input CreateClaimInput {
  investigationId: ID!
  text: String!
  claimType: ClaimType!
  confidenceLevel: Float
}

input PromptScoresInput {
  provenance: Int!
  replicability: Int!
  objective: Int!
  methodology: Int!
  publication: Int!
  transparency: Int!
}

input CreateRelationshipInput {
  fromId: ID!
  toId: ID!
  relationshipType: RelationType!
  weight: Float!
  confidence: Float!
  reasoning: String
}

REST Endpoints (Phoenix)

For Zotero integration and simple operations:

POST   /api/v1/evidence/import          # Zotero → Evidence
GET    /api/v1/evidence/:id/export      # Evidence → Zotero JSON
POST   /api/v1/investigations/:id/export # Export investigation
GET    /api/v1/health                   # System health

Technology Stack

Backend

  • Elixir 1.16+ - Functional, concurrent, fault-tolerant

  • Phoenix 1.7+ - Web framework

  • Absinthe 1.7+ - GraphQL implementation

  • ArangoDB 3.11+ - Multi-model database

  • arangox - Elixir ArangoDB driver

Frontend

  • Phoenix LiveView - Server-rendered, real-time UI (progressive enhancement)

  • D3.js - Graph visualization

  • Alpine.js (minimal) - Progressive JavaScript enhancement

  • Tailwind CSS - Styling

Integration

  • Julia 1.10+ - Statistical analysis, PROMPT scoring algorithms

  • IPFS - Evidence provenance and archival

  • Zotero API - Two-way sync

Infrastructure

  • Podman/Docker - Containerization

  • Hetzner Cloud - EU hosting (data sovereignty)

  • GitHub Actions - CI/CD

Key Algorithms

PROMPT Overall Score Calculation

defmodule EvidenceGraph.Scoring do
  @weights %{
    provenance: 0.20,
    replicability: 0.15,
    objective: 0.15,
    methodology: 0.20,
    publication: 0.15,
    transparency: 0.15
  }

  def calculate_overall(scores) do
    Enum.reduce(@weights, 0.0, fn {dimension, weight}, acc ->
      acc + (Map.get(scores, dimension, 0) * weight)
    end)
  end
end

Relationship Weight Propagation

For evidence chains: claim₁ → evidence₁ → claim₂ → evidence₂

defmodule EvidenceGraph.Traversal do
  def propagated_weight(path) do
    path
    |> Enum.map(& &1.weight)
    |> Enum.reduce(1.0, &(&1 * &2))  # Multiplicative decay
  end

  def confidence_adjusted_weight(relationship) do
    relationship.weight * relationship.confidence
  end
end

Navigation Path Scoring

Different audiences prioritize different PROMPT dimensions:

@audience_weights %{
  researcher: %{methodology: 0.35, replicability: 0.30, transparency: 0.20},
  policymaker: %{provenance: 0.30, publication: 0.25, objective: 0.25},
  skeptic: %{transparency: 0.35, replicability: 0.30, methodology: 0.20},
  activist: %{provenance: 0.30, objective: 0.25, publication: 0.20},
  affected_person: %{objective: 0.35, provenance: 0.30, transparency: 0.20},
  journalist: %{provenance: 0.25, transparency: 0.25, replicability: 0.20}
}

Security & Privacy

Authentication

  • JWT tokens for API access

  • Role-based access: admin, journalist, reviewer, reader

  • Optional DIDs (Decentralized Identifiers) for blockchain integration

Data Protection

  • EU GDPR compliant

  • Interview subjects can be anonymized

  • Evidence can be marked as sensitive (restricted access)

  • Audit logs for all mutations

IPFS Integration

defmodule EvidenceGraph.IPFS do
  def store_evidence(file_path) do
    {:ok, hash} = Kubo.add(file_path)
    # Store hash in Evidence record
    # Original file + hash = tamper-proof provenance
  end

  def verify_evidence(evidence) do
    current_hash = Kubo.add(evidence.local_path)
    current_hash == evidence.ipfs_hash
  end
end

Performance Considerations

Query Optimization

Problem: Deep evidence chains (10+ hops) can be slow

Solution: Pre-computed materialized paths

// ArangoDB: Store paths up to depth 3
FOR claim IN claims
  LET paths = (
    FOR v, e, p IN 1..3 OUTBOUND claim relationships
      RETURN p
  )
  UPDATE claim WITH { materialized_paths: paths } IN claims

Caching Strategy

  • GraphQL: DataLoader for N+1 query prevention

  • LiveView: ETS cache for PROMPT score calculations

  • ArangoDB: Query result cache (built-in)

Benchmarks (Target)

  • Single investigation load: < 500ms

  • Evidence chain (depth 5): < 1s

  • Full-text search: < 200ms

  • GraphQL mutation: < 100ms

Testing Strategy

Unit Tests (ExUnit)

  • Data model validations

  • PROMPT score calculations

  • Relationship weight propagation

Integration Tests

  • GraphQL query/mutation flows

  • Zotero import/export

  • ArangoDB CRUD operations

E2E Tests (Wallaby)

  • Complete investigation workflow

  • Navigation path creation

  • Graph visualization interaction

User Testing (Phase 1 Goal)

  • 25 participants from NUJ network

  • One complete investigation (7 claims, 30 evidence items)

  • 3 navigation paths tested

  • Qualitative feedback on PROMPT UI

Migration & Deployment

Development

# Local ArangoDB
podman run -p 8529:8529 -e ARANGO_ROOT_PASSWORD=dev arangodb/arangodb:3.11

# Elixir/Phoenix
mix phx.new evidence_graph --database postgres  # For user auth only
mix deps.get
mix ecto.setup
iex -S mix phx.server

Production (Hetzner)

  • ArangoDB Cloud: €45/month managed instance

  • Phoenix: Debian 12, systemd service

  • Nginx reverse proxy, Let’s Encrypt SSL

  • Automated backups via ArangoDB Cloud

Data Migration Path

Phase 1 → Phase 2: Schema evolution within ArangoDB Phase 2 → Phase 3: If adding Virtuoso:

# Export to JSON-LD
evidence |> Jason.encode!() |> JSONLD.expand()

# Import to Virtuoso
Virtuoso.import_turtle(jsonld_to_turtle(data))

Open Questions

  1. PROMPT scoring UI: Sliders vs. dropdowns vs. questionnaire?

  2. Real-time collaboration: Conflict resolution for concurrent edits?

  3. Evidence versioning: Full history or snapshots?

  4. Mobile experience: Responsive web or native app (future)?

References

Last Updated: 2026-03-13 Status: Phase 1 complete (v1.0.0). Phase 2 in progress: migrating to Lithoglyph as primary evidence store.

Lithoglyph Migration (Phase 2→3)

Current Architecture (Phase 1)

Browser → nginx → Phoenix (LiveView + REST + GraphQL)
                      ↓                    ↓
                 PostgreSQL            ArangoDB 3.11+
                (user auth)     (evidence, claims, entities,
                                 relationships, navigation)

Target Architecture (Phase 2, in progress)

Browser → nginx → Phoenix (LiveView + REST + GraphQL)
                      ↓           ↓              ↓
                 PostgreSQL   Lithoglyph      ArangoDB
                (user auth)  (evidence,    (relationships
                              claims,       edge collection
                              entities)     only)

Final Architecture (Phase 3)

Browser → nginx → Phoenix (LiveView + REST + GraphQL)
                      ↓              ↓
                 PostgreSQL      Lithoglyph
                (user auth)   (all domain data:
                               evidence, claims,
                               entities, relationships)

Why Lithoglyph

ArangoDB was the right choice for Phase 1 (quick to prototype, multi-model). Lithoglyph is the right choice long-term because:

  1. Provenance is mandatory — every mutation is a story event with actor + rationale

  2. PROMPT scores as first-class types — BoundedNat 0 100 verified at compile time

  3. GQL-DT dependent types — type-safe queries with proof obligations

  4. Audit-grade WAL — every write is journaled, reversible

  5. No data duplication — Docudactyl → Lithoglyph → bofig queries directly (no import step)

Migration Strategy

Each collection migrates independently: 1. Evidence (first) — highest value, most data, Lithoglyph already stores it 2. Entities — NER-resolved entities with aliases and merge history 3. Claims — with PROMPT scores as GQL-DT types 4. Relationships (last) — requires Factor GQL graph traversal support