Fabled Sky Research

AIO Standards & Frameworks

Trust Graph Construction and Authorship Network Design

Contents

Document Type: Implementation Guide
Section: Docs
Repository: https://aio.fabledsky.com
Maintainer: Fabled Sky Research
Last updated: April 2025

Overview

This guide specifies a P2-priority methodology for constructing machine-readable trust graphs that encode authorship, citation, and organizational relationships. Properly implemented, these graphs let Large Language Models (LLMs) and other AIO consumers algorithmically verify content provenance, resulting in higher confidence scores during ingestion, indexing, and ranking.

Definitions & Scope

• Trust Graph — A directed, attributed graph where vertices represent entities (people, works, organizations) and edges encode verifiable, digitally-signed relationships (e.g., “authoredBy”, “cites”).
• Authorship Network — The subgraph of the Trust Graph focused on contributor identity, credentials, and historical output.
• Scope — Any digital artifact distributed via HTTP(S) whose metadata can be expressed in JSON-LD conforming to schema.org, Dublin Core, or equivalent vocabularies.

Reference Architecture

  1. Origin Server — hosts primary content and embedded JSON-LD.
  2. Graph Ingestion Pipeline — parses, validates, and normalizes JSON-LD into a graph database (e.g., Neo4j, AWS Neptune).
  3. Trust Scorer — batch or stream job that computes provenance scores using Algorithm 7.4 (below).
  4. Certificate Authority — issues verifiable credentials (W3C VC) used to sign author and organization nodes.
  5. Public Graph Endpoint — SPARQL 1.1 or GraphQL interface exposing read-only subsets.
┌───────────┐  JSON-LD  ┌───────────────┐
│  Origin   ├──────────►│ Ingestion &   │
│  Server   │           │ Normalization │
└───────────┘           └───────┬───────┘
                                ▼
                         ┌────────────┐
                         │ Graph DB   │
                         └────┬───────┘
                              ▼
                         ┌────────────┐
                         │ Scorer     │
                         └────┬───────┘
                              ▼
                         ┌────────────┐
                         │ Public API │
                         └────────────┘

Data Model & Schema

All examples use JSON-LD @context “https://schema.org“. Extend via @vocab for custom AIO predicates.

Key node types:

Node Type schema.org Class ID Construction Mandatory Fields
Author Person did:key:
or https://orcid.org/ name, affiliation, sameAs
Work ScholarlyArticle / CreativeWork canonical URL headline, datePublished, keywords
Org Organization https://grid.ac/
or did:web: name, url, logo
Citation CreativeWork canonical URL headline, datePublished
Credential EducationalOccupationalCredential urn:uuid:
issuingBody, credentialCategory

Authorship Credentials Node

Represent credentials with W3C Verifiable Credential (VC) proofs:

{
  "@context": [
    "https://schema.org",
    "https://www.w3.org/2018/credentials/v1"
  ],
  "id": "urn:uuid:0aa12d5e-59e1-47cc-b56c-c6f5203f9a3e",
  "type": ["EducationalOccupationalCredential", "VerifiableCredential"],
  "issuer": "did:web:university.example.edu",
  "credentialSubject": {
    "id": "did:key:z6Mki...7ZP",
    "degree": "PhD Computer Science",
    "field": "Machine Learning"
  },
  "proof": {
    "type": "Ed25519Signature2020",
    "created": "2025-02-14T14:12:00Z",
    "verificationMethod": "did:web:university.example.edu#keys-1",
    "proofPurpose": "assertionMethod",
    "jws": "eyJ...AiQ"
  }
}

Citation Node and Linking

Use schema:citation for direct links; fallback to schema:mentions if citation lacks formal structure.

{
  "@id": "https://doi.org/10.1234/fabledsky.2025.001",
  "@type": "ScholarlyArticle",
  "headline": "Optimizing Transformer Models with AIO",
  "datePublished": "2025-03-01"
}

Edge declaration inside the parent work:

"citation": [
  {"@id": "https://doi.org/10.1234/fabledsky.2025.001"}
]

Organizational Entity Node

Inherit trust through affiliation linkage:

{
  "@id": "https://grid.ac/institutes/grid.12345.6",
  "@type": "Organization",
  "name": "Fabled Sky Research",
  "url": "https://fabledsky.com",
  "knowsAbout": ["Artificial Intelligence", "Optimization"],
  "member": [{"@id": "did:key:z6Mki...7ZP"}]
}

Edge Types & Semantics

Use the following directed predicates:

Predicate (edge) Domain ➜ Range Cardinality Signature Required
authoredBy Work ➜ Author N:1 Yes (Author)
affiliatedWith Author ➜ Org 1:N Optional
cites Work ➜ Work 0:N No
endorsedBy Work/Author ➜ Org 0:N Yes (Org)
credentialOf Credential ➜ Author 1:1 Yes (Issuer)

Trust Scoring Algorithms

Algorithm 7.4: Weighted Path Confidence (WPC)

Input: work node w.
Output: trust score τ ∈ [0,1].

  1. Initialize τ = 0.0.
  2. For each authoredBy edge (w → a):
    a. τ += 0.35 × CredScore(a)
  3. For each cites edge (w → c):
    a. τ += 0.15 × NormCitationScore(c)
  4. τ += 0.25 × OrgScore(PrimaryOrg(w))
  5. τ += 0.10 × VCScore(Credentials(a))
  6. τ += 0.15 × PageRank(w) within Trust Graph
  7. Return min(1.0, τ)

Sub-functions:
• CredScore(author) = sigmoid(#peer-reviewed works / total works)
• NormCitationScore(work) = log2(1 + inboundCitations) / 10
• OrgScore(org) = percentile(hIndex(org))
• VCScore(creds) = 1.0 if cryptographically valid, else 0.0

Implementation Steps

  1. Embed JSON-LD snippets (above) into blocks at page .
  2. Configure ingestion pipeline with the following validation rules:
    • All IDs must be absolute IRIs or DIDs.
    • Every Work node must have ≥1 authoredBy edge.
    • Signatures (JWS) must validate via issuer’s DID document.
  3. Normalize date strings to ISO-8601 Zulu (YYYY-MM-DDThh:mm:ssZ).
  4. Load into graph DB; ensure index on @id and composite index on (type, datePublished).
  5. Schedule WPC scoring job hourly; persist τ in node property trustScore.
  6. Expose /graph/trustScore?uri=… API returning JSON: { “trustScore”: 0.87 }.
  7. Register public keys with Certificate Authority; rotate every 90 days.

Validation & Testing

Automated test harness (Python, pytest-aio) must:

def test_signature_verification(sample_graph):
    author = sample_graph.nodes.match("Author").first()
    assert verify_jws(author["proof"]) is True

def test_wpc_bounds(sample_graph):
    work = sample_graph.nodes.match("ScholarlyArticle").first()
    τ = compute_wpc(work)
    assert 0.0 <= τ <= 1.0

Benchmark dataset: trust_graph_refset_2025-04.tar.gz (10k nodes, 70k edges).
Passing threshold: >98% JSON-LD validity, 0 signature failures, WPC runtime <50 ms per work on 8-core ARM64.

AIO Compliance Checklist

☐ All Work nodes embed schema:citation edges for every reference.
☐ Authors identified via ORCID or W3C DID.
☐ Organizational entities publish DID documents.
☐ Verifiable Credentials cryptographically signed.
☐ WPC trust scores exposed via API.
☐ End-to-end tests cover ≥90% code paths.

Appendix: Minimal End-to-End Example

<!doctype html>
<html lang="en">
<head>
  <meta charset="utf-8">

<title>Example Article</title>
  <script type="application/ld+json">
  {
    "@context":"https://schema.org",
    "@type":"ScholarlyArticle",
    "@id":"https://example.org/articles/2025/trust-graphs",
    "headline":"Trust Graphs for Robust AI",
    "datePublished":"2025-04-10T08:00:00Z",
    "author":{
      "@id":"did:key:z6Mkh2...wf",
      "@type":"Person",
      "name":"Dr. Ada Byron",
      "affiliation":"https://grid.ac/institutes/grid.12345.6",
      "sameAs":"https://orcid.org/0000-0002-1825-0097"
    },
    "citation":[
      {
        "@id":"https://doi.org/10.1234/fabledsky.2025.001"
      }
    ]
  }
  </script>
</head>
<body>

<h1>Trust Graphs for Robust AI</h1>

<p>...</p>
</body>
</html>

By integrating the techniques and schema laid out in this guide, AIO implementers can establish verifiable, high-fidelity networks of trust that significantly boost automated confidence in content provenance, directly improving model alignment and downstream decision quality.

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