OntoCast
OntoCast: Agentic LLM-Powered Knowledge Graph Builder
OntoCast is an agentic ontology-assisted framework for semantic triple extraction and knowledge graph construction. Powered by LLMs and advanced NLP, it transforms unstructured documents into structured knowledge graphs with ontology-guided semantic consistency.
🤖 Perfect for LLM-Enhanced Knowledge Graph Workflows
OntoCast bridges the gap between unstructured documents and structured knowledge graphs, enabling:
- Agentic Optimization: Automate knowledge graph construction with LLM-powered agents
- RAG Systems: Build retrieval-augmented generation pipelines with structured knowledge
- Semantic Consistency: Ontology-guided extraction ensures consistent entity and relation modeling
- Multi-Format Support: Process text, JSON, PDF, and Markdown documents seamlessly
Transform unstructured documents into knowledge graphs using LLM-powered semantic extraction and agentic optimization techniques. Ideal for building RAG systems, enhancing LLM workflows, and constructing domain-specific knowledge graphs.
Key Features
- Agentic LLM Integration: Leverage large language models for intelligent semantic extraction and knowledge graph construction
- Ontology-Guided Extraction: Ensure semantic consistency with ontology-driven entity and relation extraction
- Entity Disambiguation: Resolve entity references across document chunks for accurate knowledge graph nodes
- Multi-Format Support: Process text, JSON, PDF, and Markdown documents seamlessly
- Semantic Chunking: Intelligent document segmentation for optimal LLM processing
- RDF/Turtle Output: Standard semantic web formats for triple store integration (Neo4j, Fuseki)
- API Endpoints: Easy integration with LLM workflows, RAG systems, and agentic applications
- Agentic Optimization: Automated knowledge graph construction with minimal manual intervention
Use Cases
- LLM-Enhanced Knowledge Graphs: Build structured knowledge graphs from unstructured documents using LLM-powered extraction
- RAG Systems: Create retrieval-augmented generation pipelines with structured knowledge bases
- Agentic Optimization: Automate knowledge graph construction for agentic workflows and decision-making
- Semantic Search: Enable semantic search and retrieval over structured knowledge graphs
- Ontology Management: Automate ontology population and maintenance from unstructured sources
- Data Integration: Integrate diverse unstructured data sources into unified knowledge graphs
- Domain-Specific KGs: Construct specialized knowledge graphs for finance, healthcare, research, and more
Installation
pip install ontocast
Requires Python 3.11 or higher. Available on PyPI.
Resources
- Full documentation and API reference
- GitHub repository - Source code, issues, and contributions
- PyPI package - Install via pip
Related Open Source Projects
OntoCast is part of GrowGraph's open source ecosystem. Also check out:
- GraFlo - Universal data-to-graph transformer for structured data (CSV, SQL, JSON, XML)