@unrdf/knowledge-engine

Rule Engine, Inference, and Pattern Matching (Optional Extension)

Apply business rules and inference to RDF data. Includes reasoning engine and pattern matching.

Installation

pnpm add @unrdf/knowledge-engine

📚 Examples

See these examples that demonstrate @unrdf/knowledge-engine:

• knowledge-engine-example.mjs - Complete AI semantic analysis demo (1 hour)
• ai-semantic-example.mjs - Embedding-based semantic search
• comprehensive-feature-test.mjs - Knowledge engine integration

Want AI-powered queries? Start with knowledge-engine-example.mjs.

Quick Start

import { inferPatterns, createRuleSet } from '@unrdf/knowledge-engine'

// Define rules
const rules = createRuleSet([
  {
    name: 'infer-person',
    pattern: '?x rdf:type foaf:Person',
    then: '?x rdfs:label ?name'
  }
])

// Apply inference
const inferred = await inferPatterns(store, rules)

Features

• ✅ Forward chaining inference
• ✅ Rule definition and execution
• ✅ Pattern matching on RDF data
• ✅ Built-in ontology reasoning (RDF, RDFS, OWL)
• ✅ Custom rule sets
• ✅ Performance optimization

Use Cases

• Business rules: Apply domain rules to RDF
• Data enrichment: Infer new facts from existing data
• Ontology reasoning: RDFS and OWL inference
• Data quality: Detect and fix inconsistencies
• Knowledge extraction: Mine patterns from data

Documentation

• API Reference - Complete API documentation
• User Guide - Rule definition and usage
• Examples - Code examples
• Contributing - How to contribute

Status

Optional Extension - Not required for core UNRDF functionality. Use only if you need rule-based inference.

Depends On

• @unrdf/core - RDF substrate
• @unrdf/streaming - Change subscriptions

VOC Usage

• VOC-3: ML Agent (pattern matching)

License

MIT