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Knowledge Graph - Csv Graph Loader Generator

Data & APIs

Generate graph database loaders and triple mappings from CSV datasets. Converts tabular CSV data into graph-ready nodes, edges, and triples for graph databases or knowledge graphs.

Install

openclaw skills install csv-graph-loader-generator

CSV Graph Loader Generator

Convert CSV datasets into graph-ready structures for knowledge graph construction.

This skill transforms tabular CSV data into graph database ingestion formats such as nodes, edges, or triples. It generates mappings and loader scripts for graph systems like Neo4j, RDF triple stores, property graphs, and knowledge graphs.

Quick Start

Use When

  • Converting CSV datasets to graph structures
  • Preparing data for graph database import
  • Generating Neo4j LOAD CSV scripts
  • Creating RDF triple mappings
  • Building property graphs from tables
  • Constructing knowledge graphs from structured data
  • Automating CSV → graph ETL workflows

Inputs

  • CSV file or data
  • Column definitions and data types
  • Entity type specifications
  • Relationship definitions
  • Target graph format
  • Custom mapping rules

Outputs

  • Node definitions (labels, properties)
  • Relationship mappings (types, directions)
  • Neo4j Cypher import scripts
  • RDF triple definitions
  • Property graph JSON
  • Graph schema definitions
  • Mapping configuration files

Example

Input CSV:

person_id,name,company_name,company_industry,job_title
1,Alice Johnson,Acme Corp,Technology,Software Engineer
2,Bob Smith,Acme Corp,Technology,Product Manager
3,Carol Davis,TechStart Inc,Technology,CTO

Generated Output (Nodes & Edges):

{
  "nodes": [
    {"id": "p1", "type": "Person", "properties": {"name": "Alice Johnson", "job": "Software Engineer"}},
    {"id": "p2", "type": "Person", "properties": {"name": "Bob Smith", "job": "Product Manager"}},
    {"id": "c1", "type": "Company", "properties": {"name": "Acme Corp", "industry": "Technology"}},
    {"id": "c2", "type": "Company", "properties": {"name": "TechStart Inc", "industry": "Technology"}}
  ],
  "edges": [
    {"source": "p1", "target": "c1", "type": "WORKS_AT"},
    {"source": "p2", "target": "c1", "type": "WORKS_AT"},
    {"source": "p3", "target": "c2", "type": "WORKS_AT"}
  ]
}

CSV-to-Graph Transformation Strategy

1. Entity Detection

Automatically identify entity columns:

  • ID columns - Unique identifiers (person_id, company_id)
  • Name columns - Entity names (name, title, company_name)
  • Category columns - Entity types (type, category, industry)

2. Entity Classification

Create node types from detected entities:

person_id → Person node
company_name → Company node
department → Department node

3. Relationship Inference

Map column associations to relationships:

person_id + company_name → WORKS_AT
employee_id + manager_id → REPORTS_TO
product_id + category_id → BELONGS_TO

4. Property Assignment

Remaining columns become node properties:

name → Person.name
salary → Person.salary
industry → Company.industry

5. Output Generation

Create loader scripts for target system:

Neo4j: LOAD CSV WITH HEADERS...
RDF: :Alice rdf:type :Person
JSON: {"nodes": [...], "edges": [...]}

Supported Entity Patterns

ID-Based Entities

pattern: column_name contains "id" or "identifier"
example: person_id, user_id, company_id

Name-Based Entities

pattern: column_name contains "name" or "title"
example: person_name, company_name, job_title

Category-Based Entities

pattern: column_name contains "type" or "category"
example: person_type, company_category, product_category

Implicit Entities

pattern: column values represent entities
example: department column with values like "Sales", "Engineering"

Output Formats

Neo4j Cypher Script

LOAD CSV WITH HEADERS FROM 'file:///employees.csv' AS row
MERGE (p:Person {id: row.person_id})
SET p.name = row.name, p.job_title = row.job_title
MERGE (c:Company {name: row.company_name})
SET c.industry = row.company_industry
MERGE (p)-[:WORKS_AT]->(c)

RDF Triples (Turtle)

@prefix ex: <http://example.org/> .

ex:person1 a ex:Person ;
  ex:name "Alice Johnson" ;
  ex:jobTitle "Software Engineer" ;
  ex:worksAt ex:acme_corp .

ex:acme_corp a ex:Company ;
  ex:name "Acme Corp" ;
  ex:industry "Technology" .

Property Graph JSON

{
  "nodes": [
    {"id": "p1", "type": "Person", "properties": {"name": "Alice", "job": "Engineer"}},
    {"id": "c1", "type": "Company", "properties": {"name": "Acme", "industry": "Tech"}}
  ],
  "edges": [
    {"source": "p1", "target": "c1", "type": "WORKS_AT"}
  ]
}

CSV to Node/Edge Tables

# nodes.csv
id,type,name,job_title
p1,Person,Alice,Software Engineer
p2,Person,Bob,Product Manager
c1,Company,Acme Corp,

# edges.csv
source,target,type
p1,c1,WORKS_AT
p2,c1,WORKS_AT

Mapping Strategies

Automatic Detection

pros: No manual configuration needed
cons: May infer wrong relationships
use: Quick prototyping, simple datasets

Semi-Automated with Hints

pros: Balance between automation and control
cons: Requires some input
use: Most common production use case

Explicit Mapping

pros: Full control, exact desired output
cons: Requires complete configuration
use: Complex schemas, strict requirements

Data Type Inference

The loader automatically infers:

  • String - Text columns
  • Integer - Numeric whole numbers
  • Float - Decimal numbers
  • Boolean - True/false values
  • DateTime - Date and time formats
  • Reference - Columns pointing to other entities

Duplicate Handling

Merge Strategy

Identical entities across rows are merged
example: Two rows with "Acme Corp" → one Company node

Deduplication

Removes duplicate edges from same source/target
example: Multiple WORKS_AT edges → single edge

ID-Based Deduplication

Uses unique identifiers to prevent duplicates
example: person_id as stable key for Person nodes

Execution Steps

  1. Parse CSV – Read file and validate structure
  2. Analyze Schema – Detect data types and patterns
  3. Detect Entities – Identify entity columns
  4. Infer Relationships – Map column associations
  5. Create Schema – Define node types and relationship types
  6. Generate Mappings – Create column-to-property mappings
  7. Validate Data – Check for data quality issues
  8. Generate Loaders – Output scripts for target system

Recommended Libraries

  • CSV Processing: pandas, csv, polars
  • Graph Generation: neo4j, rdflib, networkx
  • Data Validation: pydantic, jsonschema
  • RDF/OWL: rdflib, owlready2, sparql-client
  • JSON Transformation: jsonschema, jinja2

Best Practices

✓ Use stable, meaningful identifiers
✓ Normalize entity names to prevent duplicates
✓ Define explicit entity types rather than guessing
✓ Validate data before loading to graph database
✓ Document custom mapping rules
✓ Test with sample data first
✓ Monitor for duplicate nodes or relationships
✓ Keep mapping configurations version-controlled
✓ Handle missing values explicitly
✓ Implement referential integrity checks

Integration with Downstream Skills

The generated graph data feeds into:

  • Graph Query Optimization – Optimize generated Cypher queries
  • Schema Validation – Validate against graph schema
  • Graph Constraint Generator – Define constraints for loaded data
  • Knowledge Graph Construction – Build KGs from CSV sources
  • ETL Pipeline Generator – Orchestrate full CSV → Graph workflows

References

See loader-patterns.md for detailed CSV loader patterns and example-loaders.md for complete domain-specific examples.

Version: 1.0.0

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