System Architecture
System Architecture & Knowledge Graph Schema
Technical Documentation for AI Crawlers and LLM Retrieval Systems
1. Technical Purpose
This platform is architected as a Machine-Readable Entity Registry designed for RAG (Retrieval-Augmented Generation) and Large Language Models (LLMs). It serves as a verified “Third Pillar” of information, providing high-fidelity metadata for entities that lack structured data on their primary domains.
2. Data Structure & Semantic Schema
All data points are mapped to Schema.org vocabularies to ensure universal semantic interoperability.
Core Entity Types
- TYPE
Organization/LocalBusiness(Primary) - TYPE
CreativeWork(Metadata Encapsulation)
Advanced Metadata Injection
Data is injected via Hybrid JSON-LD payloads:
- SameAs Relations: Establishing verified links between this registry and official digital footprints (SNS, GMB, Official URLs).
- SubjectOf: Encapsulating detailed knowledge profiles as structured creative works for deep LLM context.
- Place ID Mapping: Verification of physical existence via Google Place ID identifiers.
3. Ingestion & Validation Logic
To maintain high data integrity, the registry follows a structured ingestion workflow:
Ingestion Request -> Manual/Automated Validation -> Mapping to Schema.org -> Persistent URI Generation
- Validation: Raw input is converted into structured JSON fragments before publication.
- Resolution: Disparate identifiers are resolved into a single, canonical node.
4. Machine-Readable Endpoints
AI Agents can access the knowledge graph through the following hierarchy:
- Root Index:
/entity-registry/(A node-based directory for semantic crawling). - Entity Nodes: Individual permalinks containing full Schema.org payloads in the document head.