Knowledge Graph Architecture¶

⚠️ Status: Under Development - Basic structure implemented, full functionality pending.

Current Implementation¶

The omnivore-core::graph module provides foundational data structures for graph representation:

Data Structures¶

// Basic node representation
pub struct Node {
    pub id: String,
    pub node_type: NodeType,
    pub properties: HashMap<String, Value>,
}

// Basic edge representation  
pub struct Edge {
    pub source: String,
    pub target: String,
    pub edge_type: EdgeType,
    pub properties: HashMap<String, Value>,
}

Implemented Components¶

Graph Structure (omnivore-core/src/graph/mod.rs)
Basic graph container using petgraph
Node and edge data structures
Property storage using HashMaps
Schema Module (omnivore-core/src/graph/schema.rs)
Basic type definitions for nodes and edges
Entity and relationship type enums
Builder Module (omnivore-core/src/graph/builder.rs)
Basic structure for graph construction
Methods for adding nodes and edges

Not Yet Implemented¶

Graph Database Integration¶

The graph_db module exists but contains only stub implementations: - No actual database connections - No persistence layer - No query execution

Advanced Features¶

Entity recognition and extraction
Relationship inference
Graph algorithms (PageRank, community detection)
Graph visualization exports
SPARQL or Cypher query support

Planned Architecture¶

┌─────────────────────────────────────┐
│         Crawled Content             │
└────────────┬────────────────────────┘
             │
             ▼
┌─────────────────────────────────────┐
│      Content Extraction             │
│   (Metadata, Text, Structure)       │
└────────────┬────────────────────────┘
             │
             ▼
┌─────────────────────────────────────┐
│      Entity Recognition             │ ◀── Not Implemented
│    (NER, Entity Linking)            │
└────────────┬────────────────────────┘
             │
             ▼
┌─────────────────────────────────────┐
│    Relationship Extraction          │ ◀── Not Implemented
│   (Patterns, Co-occurrence)         │
└────────────┬────────────────────────┘
             │
             ▼
┌─────────────────────────────────────┐
│       Graph Construction            │ ◀── Basic Structure Only
│    (Nodes, Edges, Properties)       │
└────────────┬────────────────────────┘
             │
             ▼
┌─────────────────────────────────────┐
│      Graph Persistence              │ ◀── Not Implemented
│   (Neo4j, ArangoDB, or Custom)      │
└─────────────────────────────────────┘

Usage (Current State)¶

Currently, the graph module can only be used programmatically for basic graph operations:

use omnivore_core::graph::{Graph, Node, Edge};

// Create a graph
let mut graph = Graph::new();

// Add nodes (basic implementation)
let node = Node {
    id: "page1".to_string(),
    node_type: NodeType::WebPage,
    properties: HashMap::new(),
};
graph.add_node(node);

// Add edges (basic implementation)
let edge = Edge {
    source: "page1".to_string(),
    target: "page2".to_string(),
    edge_type: EdgeType::LinksTo,
    properties: HashMap::new(),
};
graph.add_edge(edge);

Limitations¶

No Persistence: Graphs exist only in memory
No Entity Extraction: Must manually define nodes
No Relationship Inference: Must manually define edges
No Query Language: Only programmatic access
No Visualization: No export to graph formats
No Graph Algorithms: Basic structure only

Future Development¶

Phase 1: Storage Layer¶

Implement graph database adapter interface
Add Neo4j or ArangoDB integration
Create persistence layer

Phase 2: Entity Extraction¶

Integrate NLP libraries for NER
Add entity linking capabilities
Implement entity resolution

Phase 3: Relationship Extraction¶

Pattern-based extraction
Co-occurrence analysis
Link prediction

Phase 4: Query and Analysis¶

Graph query language support
Graph algorithms (centrality, clustering)
Export to standard formats (GraphML, GEXF)

Alternative Approaches¶

For immediate graph needs, consider:

Export to External Tools
Export crawled data as JSON
Import into Neo4j or other graph databases
Use external tools for visualization
Custom Processing
Use the parser to extract structured data
Process with external NLP tools
Build graphs using dedicated graph libraries
Wait for Updates
Monitor the GitHub repository
Contribute to development
Use current crawler features until graph support matures