semantica/docs/concepts.md

Core Concepts

Understand the fundamental concepts behind Semantica.

!!! note "Conceptual Foundation" This guide covers the theoretical foundations of Semantica. For hands-on examples, see the Quickstart Guide or Examples.

What is a Knowledge Graph?

A knowledge graph is a structured representation of information where:

!!! tip "Visual Understanding" The diagram below shows how entities (nodes) are connected by relationships (edges) to form a knowledge graph. This structure enables powerful querying and reasoning capabilities.

• Entities are the nodes (people, places, concepts, etc.)
• Relationships are the edges connecting entities
• Properties describe attributes of entities

graph LR
    A[Apple Inc.<br/>Organization] -->|founded_by| B[Steve Jobs<br/>Person]
    A -->|located_in| C[Cupertino<br/>Location]
    C -->|in_state| D[California<br/>Location]
    
    style A fill:#e3f2fd
    style B fill:#fff3e0
    style C fill:#f3e5f5
    style D fill:#f3e5f5

Semantic Layer

A semantic layer provides:

• Structured meaning from unstructured data
• Contextual relationships between concepts
• Queryable knowledge for AI systems
• Quality-assured data with conflict resolution

Key Components

1. Data Ingestion

Import data from various sources:

• Documents (PDF, DOCX, HTML)
• Databases
• APIs and web content
• Structured data (JSON, CSV)

2. Entity Extraction

Identify and extract:

• Named Entities: People, organizations, locations
• Concepts: Ideas, topics, themes
• Events: Actions, occurrences
• Relations: Connections between entities

3. Relationship Extraction

Discover relationships:

• Explicit: Directly stated in text
• Implicit: Inferred from context
• Temporal: Time-based relationships
• Causal: Cause-and-effect connections

4. Knowledge Graph Construction

Build structured graphs:

• Node creation: Entities as nodes
• Edge creation: Relationships as edges
• Property assignment: Attributes and metadata
• Graph validation: Quality checks

5. Conflict Resolution

Handle conflicting information:

• Multiple sources: Same entity, different facts
• Resolution strategies: Voting, credibility, recency
• Quality assurance: Validation and verification

6. Embedding Generation

Create vector representations:

• Text embeddings: Semantic text vectors
• Graph embeddings: Node and edge vectors
• Multimodal: Text, image, audio embeddings

Workflow

Typical Semantica workflow:

flowchart TD
    A[Data Source] --> B[Ingestion]
    B --> C[Parsing]
    C --> D[Extraction<br/>Entities & Relationships]
    D --> E[Normalization]
    E --> F[Conflict Resolution]
    F --> G[Knowledge Graph]
    G --> H[Embeddings]
    H --> I[Export]
    
    style A fill:#e3f2fd
    style G fill:#c8e6c9
    style I fill:#fff9c4

Use Cases

GraphRAG

Enhance RAG systems with knowledge graphs:

• Context expansion: Follow relationships
• Multi-hop reasoning: Traverse graph paths
• Structured queries: Query graph directly

AI Agents

Provide agents with:

• Persistent memory: Knowledge graph as memory
• Context understanding: Semantic relationships
• Action validation: Check against knowledge

Data Integration

Unify data from multiple sources:

• Schema mapping: Automatic schema discovery
• Entity resolution: Match entities across sources
• Conflict resolution: Handle contradictions

Best Practices

!!! note "Best Practices" Following these practices will help you build high-quality knowledge graphs and avoid common pitfalls.

1. Start Small

Begin with a single document or small dataset to understand the workflow.

2. Iterate

Build knowledge graphs incrementally, refining as you learn.

3. Validate

Always validate extracted entities and relationships.

4. Resolve Conflicts

Use appropriate conflict resolution strategies for your use case.

5. Export Regularly

Export your knowledge graphs for backup and analysis.

Next Steps

• Quick Start - Build your first knowledge graph
• Examples - See real-world applications
• API Reference - Explore the full API