Graph

System Graph Index

The Graph System is the structural relationship layer of GEO.or.id. It represents how entities, concepts, datasets, evidence, and system modules connect across multiple dimensions of meaning, dependency, and causality.

Unlike taxonomy or hierarchy systems, the graph layer is non-linear. It models the ecosystem as an interconnected network of semantic and operational relationships.

1. Graph System Role

The Graph System functions as the global relationship fabric:

Node (Entity / Concept / System) → Edge (Relationship) → Weighted Graph → Inference Layer → System Interpretation

It enables cross-system reasoning and dependency tracking across GEO.or.id.

2. Core Graph Domains

• Entity Graph Layer
• Ontology Relationship Graph
• Relationship Signals Graph
• Retrieval Dependency Graph

3. Evidence Relationship Graph

• Evidence Graph System
• Cross Source Correlation Graph
• Evidence Fusion Graph

4. System Dependency Graph

The system architecture dependency structure:

Query → Retrieval → Evidence → Reasoning → Trust → Output
            ↓
        Signals Layer
            ↓
        Observatory Feedback

Each layer feeds back into system optimization loops.

5. Cross-System Graph Mapping

• Query System
• Retrieval System
• Reasoning System
• Trust System
• Observatory System

6. Graph Construction Model

Node Definition
→ Relationship Extraction
→ Edge Weighting
→ Multi-Hop Expansion
→ Signal Enrichment
→ Graph Normalization

Graph construction is validated through: Multi Model Comparison Experiments

7. Graph Use Cases

• Entity relationship discovery
• Cross-source evidence validation
• Retrieval path optimization
• Reasoning chain construction
• Trust propagation analysis

8. Graph Intelligence Layer

Graph data is used to enhance:

• Ontology consistency (Ontology System)
• Evidence reliability (Evidence System)
• Retrieval accuracy (Retrieval System)
• Reasoning depth (Reasoning System)

9. Structural Constraints

• All nodes must be resolvable entities or concepts
• Edges must represent verifiable relationships
• Graph drift must be measurable via experiments
• No orphan nodes without contextual linkage
• Multi-hop relationships must remain traceable

10. System Principle

• Reality in the system is relational, not isolated
• Meaning emerges from connections, not nodes
• Graph structure is dynamic, not static
• All system behavior can be mapped as a graph
• Graph integrity determines system intelligence quality