Ontology-driven knowledge graph extraction from text
TrustGraph now supports automatic knowledge graph construction guided by OWL ontologies. You provide an ontology (OWL/Turtle format or build one in the Workbench editor), point it at your documents, and it extracts entities and relationships that conform to your schema. The problem this solves: generic GraphRAG approaches extract whatever relationships an LLM thinks are relevant, which often misses domain-specific semantics. If you're working in healthcare, finance, or intelligence analysis, you likely already have ontologies (or can adapt standards like SOSA, FIBO, etc.) that define…
In plain words
TrustGraph is an AI tool that automatically builds knowledge graphs from text using OWL ontologies as a guide. Users provide an ontology in OWL or Turtle format, or create one in the built-in editor, and point it at their documents. The system extracts entities and relationships that conform to the defined schema rather than relying on generic language model interpretation. This approach suits professionals in healthcare, finance, and intelligence analysis who need domain-specific semantics captured accurately in their knowledge graphs.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
TrustGraph now supports automatic knowledge graph construction guided by OWL ontologies. You provide an ontology (OWL/Turtle format or build one in the Workbench editor), point it at your documents, and it extracts entities and relationships that conform to your schema. The problem this solves: generic GraphRAG approaches extract whatever relationships an LLM thinks are relevant, which often misses domain-specific semantics. If you're working in healthcare, finance, or intelligence analysis, you likely already have ontologies (or can adapt standards like SOSA, FIBO, etc.) that define what matters. TrustGraph uses these to constrain extraction, so the resulting graph reflects your domain model rather than the LLM's interpretation. How it works: The ontology defines classes and properties. During extraction, the LLM is prompted to identify instances of those classes and relationships matching those properties. The output is validated against the schema before being written to the graph store. Built on Apache Pulsar for scalability, supports multiple graph backends (Memgraph, FalkorDB, others), and runs locally or in cloud. Apache 2.0 licensed. Repo: https://github.com/trustgraph-ai/trustgraph Ontology RAG docs: https://docs.trustgraph.ai/guides/ontology-rag/ Happy to answer questions about the extraction approach or architecture.
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