Skip to content

Seed

Seed data management system for initializing Knowledge Graphs from verified sources.

🎯 Overview

The Seed Module provides a system for initializing Knowledge Graphs with verified, structured data from trusted sources. It enables bootstrapping knowledge graphs with reference data, taxonomies, and verified entities.

What is Seed Data?

Seed data is verified, structured data used to bootstrap or enhance knowledge graphs. Examples include:

  • Taxonomies: Hierarchical classifications (product categories, organizational structures)
  • Reference Data: Immutable reference information (countries, codes, standards)
  • Verified Entities: Pre-validated entities from authoritative sources
  • Foundation Graphs: Initial graph structures to build upon

Why Use the Seed Module?

  • Bootstrap KGs: Start with verified data instead of empty graphs
  • Quality Assurance: Use trusted, validated data sources
  • Faster Development: Skip initial data extraction for known entities
  • Data Integration: Merge seed data with extracted data
  • Versioning: Manage different versions of seed data

How It Works

  1. Load Seed Data: Load from CSV, JSON, databases, or APIs
  2. Validate: Validate data quality and schema compliance
  3. Transform: Convert to knowledge graph format
  4. Merge: Integrate with extracted data using configurable strategies
  5. Version: Track versions of seed data sources

  • Multi-Source Loading

    Load seed data from CSV, JSON, Databases, and APIs

  • Foundation Graph

    Create reliable foundation graphs to bootstrap your KG

  • Data Integration

    Merge seed data with extracted data using configurable strategies

  • Validation

    Validate seed data quality and schema compliance

  • Versioning

    Manage versions of seed data sources

  • Export

    Export seed data to standard formats

When to Use

  • Bootstrapping: When starting a new KG and you have existing structured data (taxonomies, user lists, product catalogs).
  • Reference Data: To load immutable reference data (countries, codes, constants).
  • Testing: To load consistent test datasets for development.

⚙️ Algorithms Used

Data Loading

Purpose: Load seed data from various formats efficiently.

How it works:

  • Format Detection: Auto-detection of CSV delimiters, JSON structure
  • Streaming: Row-by-row processing for large files
  • Normalization: Type conversion and encoding handling

Integration & Merging

Purpose: Merge seed data with extracted data using configurable strategies.

How it works:

  • Seed-First Strategy: Seed data overrides extracted data (Trust Seed)
  • Extracted-First Strategy: Extracted data overrides seed (Trust Extraction)
  • Smart Merge: Property-level merging with conflict resolution
  • ID Matching: Entity resolution between seed and extracted entities

Validation

Purpose: Validate seed data quality and schema compliance.

How it works:

  • Schema Validation: Template-based structure checking
  • Constraint Checking: Required field and type validation
  • Consistency Check: Reference integrity (relationships point to existing entities)

Main Classes

SeedDataManager

Coordinator for all seed data operations.

Methods:

Method Description
register_source(name, format, location) Add data source
create_foundation_graph() Build KG from sources
validate_quality(seed_data) Check data quality
integrate_with_extracted(seed, extracted) Merge graphs
export_seed_data(path, format) Export data

Example:

from semantica.seed import SeedDataManager

manager = SeedDataManager()
manager.register_source("countries", "csv", "data/countries.csv")
foundation_kg = manager.create_foundation_graph()

SeedDataSource

Data class defining a source.

Attributes: - name: Source identifier - type: csv, json, api, sql - path: File path or connection string - config: Parsing options

Configuration

Environment Variables

export SEED_DATA_DIR=./data/seed
export SEED_MERGE_STRATEGY=seed_first

YAML Configuration

seed:
  sources:
    - name: "employees"
      type: "csv"
      path: "./data/employees.csv"

  merge:
    strategy: "seed_first"

  validation:
    strict: true

Integration Examples

Bootstrapping a KG

from semantica.seed import SeedDataManager
from semantica.ingest import Ingestor
from semantica.kg import KnowledgeGraph

# 1. Load Foundation (Seed)
seed_manager = SeedDataManager()
seed_manager.register_source("taxonomy", "json", "taxonomy.json")
foundation_kg = seed_manager.create_foundation_graph()

# 2. Ingest New Data
ingestor = Ingestor()
new_data = ingestor.ingest("news_articles.pdf")

# 3. Merge
final_kg = seed_manager.integrate_seed_extracted(
    seed_graph=foundation_kg,
    extracted_data=new_data,
    strategy="seed_first"  # Keep taxonomy strict
)

Best Practices

  1. Trust Seed Data: Usually, seed data is verified. Use seed_first merge strategy.
  2. Version Control: Keep seed data files in version control (git).
  3. Validate Schema: Ensure seed data matches your target ontology.
  4. Clean IDs: Use consistent ID schemes in seed data to facilitate merging.

Cookbook

Interactive tutorials that use seed data:

  • Financial Data Integration MCP: Merging financial data with seed data integration
  • Topics: Finance, data fusion, MCP integration, seed data
  • Difficulty: Intermediate

  • Use Cases: Integrating structured seed data with extracted data

  • Energy Market Analysis: Analyzing trends with seed data integration

  • Topics: Energy, time series, temporal analysis, seed data
  • Difficulty: Intermediate
  • Use Cases: Bootstrapping knowledge graphs with verified data

See Also