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Concepts

Here we introduce the main concepts of OntoCast, a framework for transforming data into semantic triples.

Ontology Management

OntoCast manages ontologies with automatic versioning and timestamp tracking:

  • Semantic Versioning: Automatic version increments (MAJOR/MINOR/PATCH) based on change analysis
  • Hash-Based Lineage: Git-style versioning with parent hashes for tracking ontology evolution
  • Multiple Versions: Versions stored as separate named graphs in Fuseki triple stores
  • Timestamp Tracking: updated_at field tracks when ontology was last modified
  • Smart Analysis: Analyzes ontology changes (classes, properties, instances) to determine appropriate version bump:
  • MAJOR: Substantial breaking changes (deletions of classes/properties)
  • MINOR: New features (new classes/properties) or any deletions
  • PATCH: Updates to existing structures (instances, descriptions, small changes)
  • Property Syncing: Version and timestamp are synced to the RDF graph as owl:versionInfo and dcterms:modified
  • Versioned IRIs: Each version gets a unique IRI with hash fragment for storage organization

GraphUpdate System

OntoCast uses a token-efficient GraphUpdate system for incremental graph modifications:

  • Structured Operations: LLM outputs GraphUpdate objects containing TripleOp operations (insert/delete) instead of full TTL graphs
  • Token Efficiency: Only changes are generated, dramatically reducing LLM token usage compared to full graph regeneration
  • SPARQL Generation: Operations are automatically converted to executable SPARQL queries
  • Incremental Updates: Graph updates are applied incrementally, allowing for precise modifications
  • Operation Types: Supports both insert and delete operations with explicit prefix declarations
  • Custom Queries: Also supports GenericSparqlQuery for complex custom SPARQL operations

How GraphUpdate Saves Tokens

Instead of generating the entire graph in Turtle format (which can be thousands of tokens), the LLM now outputs only the changes:

  • Before: Full TTL graph with all triples (e.g., 5000 tokens)
  • After: Structured operations with only changes (e.g., 200 tokens)
  • Savings: Typically 80-95% reduction in output tokens

Budget Tracking

OntoCast provides comprehensive budget tracking for LLM usage and triple generation:

  • LLM Statistics: Tracks API calls, characters sent/received for cost monitoring
  • Triple Metrics: Tracks ontology and facts triples generated per operation
  • Operation Counts: Tracks number of update operations for both ontology and facts
  • Summary Reports: Budget summaries logged at end of processing with format: 
    LLM: X calls, Y sent, Z received | Triples: A ontology, B facts
  • Integrated Tracking: Budget tracker integrated into AgentState for clean dependency injection
  • Automatic Updates: Budget tracker automatically updated when LLM calls are made or triples are generated

Key Components

  • Ontology: RDF graph with properties (id, title, description, version, timestamp, hash, parent_hashes)
  • AgentState: Central state management with budget tracking and GraphUpdate operations
  • ToolBox: Collection of tools for processing and caching
  • Triple Stores: Support for filesystem, Fuseki, and Neo4j storage
  • GraphUpdate: Structured representation of graph modifications as SPARQL operations
  • BudgetTracker: Lightweight tracker for LLM usage and triple generation statistics