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Knowledge Graph - Json To Triples Converter

Data & APIs

Convert JSON documents into RDF triples or graph-ready subject–predicate–object statements for knowledge graphs and semantic databases.

Install

openclaw skills install json-to-triples-converter

JSON to Triples Converter

Transform JSON documents into RDF triples and graph-ready structures.

This skill converts hierarchical JSON data into subject–predicate–object triples, enabling the data to be ingested into knowledge graphs, semantic web systems, and graph databases. The generated triples can be exported in multiple formats including RDF Turtle, N-Triples, JSON-LD, and Graph JSON.

Quick Start

Use When

  • Converting JSON datasets to RDF triples
  • Transforming JSON APIs into knowledge graphs
  • Creating semantic web representations from JSON
  • Building linked data from structured JSON
  • Converting JSON to JSON-LD for semantic markup
  • Preparing JSON for graph database ingestion
  • Extracting semantic relationships from JSON

Inputs

  • JSON documents or data structures
  • Entity type specifications
  • Property-to-predicate mappings
  • Namespace/URI definitions
  • Output format preferences
  • Custom mapping rules
  • Vocabulary specifications (schema.org, FOAF, etc.)

Outputs

  • RDF Turtle triples
  • N-Triples format
  • JSON-LD documents
  • Graph JSON (nodes/edges)
  • Property graph structures
  • Semantic metadata
  • URI mappings

Example

Input JSON:

{
  "person": {
    "name": "Alice",
    "age": 30,
    "email": "alice@example.com",
    "company": {
      "name": "Acme Corp",
      "industry": "Technology"
    }
  }
}

Generated RDF Triples (Turtle):

@prefix ex: <http://example.org/> .
@prefix foaf: <http://xmlns.com/foaf/0.1/> .
@prefix schema: <http://schema.org/> .

ex:person_alice a foaf:Person ;
  foaf:name "Alice" ;
  foaf:age 30 ;
  foaf:mbox "alice@example.com" ;
  schema:worksFor ex:company_acme .

ex:company_acme a schema:Organization ;
  foaf:name "Acme Corp" ;
  schema:industry "Technology" .

JSON-to-Triples Conversion Strategy

1. JSON Structure Analysis

Analyze the JSON document to understand:

  • Root entities (objects)
  • Entity properties (keys)
  • Nested relationships (nested objects)
  • Arrays and collections
  • Data types and literals

2. Entity Identification

Detect entities that represent real-world objects:

  • Person - Individual entities
  • Organization - Companies, groups
  • Product - Items, goods
  • Location - Places, regions
  • Event - Occurrences, activities
  • Custom - Domain-specific entities

3. Subject Generation

Create unique URIs for each entity:

Person "Alice" → ex:person_alice
Company "Acme" → ex:company_acme
Location "New York" → ex:location_newyork

4. Predicate Mapping

Map JSON keys to RDF predicates:

"name" → foaf:name
"age" → foaf:age
"worksFor" → schema:worksFor
"email" → foaf:mbox

5. Object Generation

Generate appropriate object values:

Literal values: "Alice", 30, true
References: ex:company_acme (URI reference)
Typed literals: "2024-04-09"^^xsd:date

6. Relationship Extraction

Convert nested objects to relationships:

{
  "employee": {
    "name": "Alice",
    "manager": {
      "name": "Bob"
    }
  }
}

Becomes:

ex:employee_alice schema:manager ex:person_bob .

7. Deduplication

Ensure shared entities are represented once:

  • Same entity referenced multiple times → Single URI
  • Identical values → Deduplicated
  • Entity linking across documents

Conversion Patterns

Simple Property Mapping

{"name": "Alice", "age": 30}
↓
ex:subject foaf:name "Alice" .
ex:subject foaf:age 30 .

Nested Object Pattern

{"person": {"name": "Alice", "company": {"name": "Acme"}}}
↓
ex:person_alice foaf:name "Alice" .
ex:person_alice ex:worksAt ex:company_acme .
ex:company_acme foaf:name "Acme" .

Array/Collection Pattern

{"tags": ["python", "graph", "rdf"]}
↓
ex:subject ex:tag "python" .
ex:subject ex:tag "graph" .
ex:subject ex:tag "rdf" .

Type Inference Pattern

{"age": 30, "created": "2024-04-09"}
↓
ex:subject foaf:age "30"^^xsd:integer .
ex:subject schema:dateCreated "2024-04-09"^^xsd:date .

Output Formats

RDF Turtle Format

@prefix ex: <http://example.org/> .
@prefix foaf: <http://xmlns.com/foaf/0.1/> .

ex:subject foaf:name "Alice" .
ex:subject foaf:age 30 .
ex:subject foaf:workplaceHomepage ex:company .

N-Triples Format

<http://example.org/subject> <http://xmlns.com/foaf/0.1/name> "Alice" .
<http://example.org/subject> <http://xmlns.com/foaf/0.1/age> "30"^^<http://www.w3.org/2001/XMLSchema#integer> .

JSON-LD Format

{
  "@context": {
    "@vocab": "http://schema.org/",
    "foaf": "http://xmlns.com/foaf/0.1/"
  },
  "@type": "Person",
  "@id": "http://example.org/subject",
  "foaf:name": "Alice",
  "foaf:age": 30
}

Graph JSON Format

{
  "nodes": [
    {"id": "person_alice", "type": "Person", "properties": {"name": "Alice", "age": 30}},
    {"id": "company_acme", "type": "Organization", "properties": {"name": "Acme"}}
  ],
  "edges": [
    {"source": "person_alice", "target": "company_acme", "type": "worksFor"}
  ]
}

Namespace Management

Standard Vocabularies

foaf:     http://xmlns.com/foaf/0.1/
schema:   http://schema.org/
rdf:      http://www.w3.org/1999/02/22-rdf-syntax-ns#
rdfs:     http://www.w3.org/2000/01/rdf-schema#
owl:      http://www.w3.org/2002/07/owl#
xsd:      http://www.w3.org/2001/XMLSchema#
dbo:      http://dbpedia.org/ontology/

Custom Namespaces

namespaces:
  ex: http://example.org/
  myapp: http://myapp.example.org/
  custom: http://custom.vocabulary.org/

Data Type Inference

Type Detection

String values     → xsd:string
Numbers (integer) → xsd:integer
Numbers (float)   → xsd:decimal
Booleans          → xsd:boolean
ISO dates         → xsd:date / xsd:dateTime
URIs              → xsd:anyURI

Literal Language Tags

{"title": {"en": "Alice", "fr": "Aline"}}
↓
ex:subject rdfs:label "Alice"@en .
ex:subject rdfs:label "Aline"@fr .

Execution Steps

  1. Parse JSON – Load and validate JSON structure
  2. Infer Schema – Detect entities and relationships
  3. Generate URIs – Create unique identifiers for entities
  4. Build Triples – Generate subject-predicate-object statements
  5. Manage Namespaces – Apply namespace prefixes
  6. Deduplicate – Remove duplicate entities
  7. Validate Output – Check triple validity
  8. Format Output – Generate desired output format

Recommended Libraries

  • RDF Processing: rdflib, oxrdflib, pyld
  • JSON Processing: json, jsonld, python-jsonld
  • Validation: rdflib.plugins.sparql, owlready2
  • URI Management: URIRef, Namespace
  • Schema.org: schema, schema-org
  • Data Type Handling: xsd, dateutil

Best Practices

✓ Use consistent namespace URIs
✓ Generate meaningful entity identifiers
✓ Normalize entity names to prevent duplicates
✓ Use standard vocabularies (schema.org, FOAF)
✓ Include language tags for multilingual content
✓ Type literal values appropriately
✓ Document custom vocabulary mappings
✓ Validate triples before output
✓ Handle nested structures recursively
✓ Manage URIs consistently

Integration with Downstream Skills

The generated triples feed into:

  • Graph Constraint Generator – Define constraints on triple data
  • Graph Schema Validation – Validate against RDF schemas
  • Graph Query Optimization – Optimize SPARQL queries
  • Knowledge Graph Construction – Build KGs from triples
  • ETL Pipeline Generator – Orchestrate conversion workflows

References

See conversion-patterns.md for detailed JSON-to-triples conversion patterns and example-conversions.md for complete real-world examples.

Version: 1.0.0

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