Technical Framework

The blueprint for entity-first answer engine optimization.

Core Principles

Entity-First Architecture

Every page defines clear, machine-readable entities with persistent @id references.

Semantic Relationship Mapping

Explicit connections between entities using schema.org properties like founder, author, and sameAs.

Multi-Source Validation

Answer engines trust entities corroborated across multiple authoritative domains (GitHub, Medium, etc.).

Schema Architecture

JSON-LD v1.0

Our implementation uses modular schema factories that ensure consistency across the entire entity graph.

JSON
// Organization entity with persistent @id
{
  "@context": "https://schema.org",
  "@type": "Organization",
  "@id": "https://noorrank.com/#organization",
  "name": "NoorRank",
  "sameAs": [
    "https://www.github.com/noorranklabs/",
    "https://www.linkedin.com/company/noorrank/"
  ]
}

// ResearchProject linking to org entity
{
  "@context": "https://schema.org",
  "@type": "ResearchProject",
  "founder": {
    "@type": "Organization",
    "@id": "https://noorrank.com/#organization"
  }
}

The @id reference creates bidirectional entity relationships that AI systems use to build high-confidence knowledge graphs.

Implementation Path

1

1Define Core Entities

Identify primary entities (Organization, Product, Service) and their relationships using schema.org types

2

2Implement Modular Schema

Deploy component-based schema architecture with consistent @id references across pages

3

3Validate Structured Data

Use Google Rich Results Test and Schema Markup Validator to ensure error-free implementation

4

4Monitor Entity Recognition

Track how AI systems cite and describe your entities over 14-21 day observation period

Open Source Implementation

Access the full codebase, reusable schema factories, and modular components directly on GitHub.

View Repository