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Ontologies: Languages, Editors, Reasoners & Repositories

A directory of ontology resources — standards and languages, editors, reasoners and libraries, repositories of domain ontologies, enterprise semantic platforms and tools that generate ontologies automatically, such as NVIDIA Auto Ontology.

48 topics•7 categories•Updated November 2026

Languages & Standards

7 entries

W3C and community languages for modelling and querying knowledge.

Editors & Visualization

4 entries

Tools for building and viewing ontologies.

Reasoners, Libraries & Tooling

9 entries

Software for reasoning over and programming with ontologies.

Repositories & Domain Ontologies

7 entries

Reusable ontologies and where to find them.

Enterprise Semantic Platforms

3 entries

Commercial platforms with ontology and reasoning support.

More Ontology Repositories, Tools & Foundations

5 entries

Repositories, upper ontologies and tooling.

Automatic Ontology Generation & Agent Context Layers

8 entries

Tools and research that build ontologies and semantic layers from data and text with LLMs, led by NVIDIA Auto Ontology.

36NVIDIA Auto OntologyNVIDIA's open-source Python framework (Apache-2.0) that generates an enterprise ontology and semantic layer from database metadata, reconciles definitions across systems, grounds text-to-SQL agents in certified business meaning and returns provenance with every answer. It serves context through MCP, CLI and API, runs on Postgres with pgvector and NVIDIA NIM models, and ships a web UI with RBAC. Currently not accepting outside contributions; benchmark results are not published in the README. 37Auto Ontology Evaluation ToolkitNVIDIA's companion toolkit for text-to-SQL evaluation of Auto Ontology, including a script that seeds the Wide World Importers sample database. Use it to run benchmarks such as BIRD yourself. 38Auto Ontology MCP Server (community)Community project that exposes Auto Ontology's semantic layer and a personal knowledge base as an MCP server for Claude, ChatGPT and other MCP clients. Not an NVIDIA project; review it before use. 39End-to-End Ontology Learning with Large Language Models (OLLM)NeurIPS 2024 paper on building the taxonomic backbone of an ontology from scratch with an LLM, with metrics for comparing generated and reference ontologies. 40Ontology Generation using Large Language Models (Ontogenia)Study of prompting methods that draft OWL ontologies from user stories and competency questions, compared against novice ontology engineers. 41OntoGPTMonarch Initiative toolkit for LLM-based ontological extraction, including schema-guided extraction with LinkML. 42BIRD Text-to-SQL BenchmarkLarge benchmark of text-to-SQL over real databases, the kind of test Auto Ontology's evaluation repo points to. 43Spider 2.0Enterprise-scale text-to-SQL benchmark with real workflows and large schemas.

Related Pages

5 entries

Related directories on this site.

Frequently Asked Questions

What is an ontology?

In computing, an ontology is a formal, shared specification of the concepts in a domain and the relationships between them, usually expressed in a language such as OWL so machines can reason over it.

What is the difference between RDFS, OWL and SKOS?

RDFS gives basic classes and properties, OWL adds richer logic for reasoning, and SKOS models thesauri and taxonomies with looser semantics.

Which editor should I use?

Protégé is the long-standing open-source standard; WebProtégé and VocBench support collaborative editing in the browser.

What does SHACL do?

SHACL validates RDF graphs against shapes, checking that data conforms to expected structure, complementing OWL's inference.

How do ontologies relate to knowledge graphs?

Ontologies supply the schema and meaning for a knowledge graph; see the Knowledge Graphs and Semantic Graphs pages for platforms that store and query them.