OntoCast – ontology-assisted KG generation
Hey HN, I'm excited to announce a new release of OntoCast — an open-source framework for extracting semantic triples and building knowledge graphs (KG) from unstructured documents (PDF, JSON, Markdown, and more). Before extracting facts, OntoCast automatically selects or creates a relevant ontology and iteratively refines it, leading to much more accurate and context-aware fact extraction. This is especially valuable for cross-domain or complex documents where a static ontology falls short. - Agentic workflow: Uses LLMs (OpenAI/Ollama) to drive the extraction and ontology refinement…
What it does
In the maker’s words, at launch
Hey HN, I'm excited to announce a new release of OntoCast — an open-source framework for extracting semantic triples and building knowledge graphs (KG) from unstructured documents (PDF, JSON, Markdown, and more). Before extracting facts, OntoCast automatically selects or creates a relevant ontology and iteratively refines it, leading to much more accurate and context-aware fact extraction. This is especially valuable for cross-domain or complex documents where a static ontology falls short. - Agentic workflow: Uses LLMs (OpenAI/Ollama) to drive the extraction and ontology refinement process. - MCP-compatible API server: Easy to integrate into your stack. - Flexible storage: Works with Jena Fuseki and Neo4j for knowledge graph storage. - Open source: Apache licensed. Uses cases include extracting structured knowledge from scientific papers, financial reports, or clinical trial documents — even when they span multiple domains. Repo: https://github.com/growgraph/ontocast Docs: https://growgraph.github.io/ontocast Would love feedback, questions, or suggestions!
Does the same job
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