OntoGeneration – A Cutting-Edge NLG System for Ontology-Driven Text

In today’s data-rich world, vast volumes of structured semantic knowledge (ontologies, knowledge graphs, taxonomies) remain under-utilised because human authorship is time-consuming and expensive.
OntoGeneration enables seamless transformation of those formal knowledge structures into fluent prose, making knowledge accessible to non-technical audiences and supporting downstream applications in publishing, reporting and analytics.
By bridging the gap between semantic data and natural language, OntoGeneration supports tasks such as documentation generation, automatic summarisation of domain models, and generation of narrative descriptions from knowledge graphs.

Ontology-aware input processing: The system ingests ontologies (classes, properties, axioms, instances) and computes meaningful textual content via semantic interpretation.
Content selection and planning: It decides which parts of the ontology are relevant to express, how to structure the narrative, and how to aggregate and reference concepts for clarity.
Lexicalisation and realisation: OntoGeneration chooses appropriate lexical items (labels, synonyms) and generates grammatically correct, fluent sentences in a target natural language.
Customisable style and output formats: Users can tailor output to different document types (e.g., technical description, executive summary) and control tone, level of detail, and vocabulary.

Technical documentation: Automatically generate descriptions of ontology classes and properties for knowledge-base publications.
Knowledge-graph reporting: Produce narrative summaries of knowledge-graph changes, new instance additions, or domain model evolution.
Semantic-web publishing: Convert OWL/RDF ontological definitions into human-friendly documentation for domain experts, stakeholders or end-users.
Domain-specific summarisation: Use in healthcare, engineering, cultural heritage, or enterprise-metadata management to generate textual glossaries, data-dictionaries and explainers from semantic data.

Scalability: Unlike manual authoring, OntoGeneration can operate on large ontologies with hundreds or thousands of entities, enabling high-volume text production.
Consistency: It ensures consistent terminology, phrasing and style across all generated texts, which is essential for large-scale knowledge-management systems.
Efficiency: By automating repetitive writing tasks, teams can focus on analytical work, model design and higher-level semantic tasks rather than text generation.
Accessibility: It lowers the barrier for non-technical stakeholders to understand complex semantic models, fostering cross-disciplinary collaboration and knowledge sharing.

Input preparation: Provide the target ontology (in OWL, RDF-Schema or other supported format).
Configuration: Specify text-generation parameters such as target language, style profile, and output document structure.
Generation: Run the OntoGeneration engine to produce textual output from the ontology, including class descriptions, property explanations, instance narratives, and aggregated summaries.
Post-processing: Review and refine generated text, optionally integrate into content management systems or publishing workflows.
Feedback loop: Adjust generation rules, lexical mappings or style templates and re-generate to improve quality and alignment with organisational standards.

Developed specifically for ontology-to-text tasks, rather than generic template-based systems — giving it an edge in semantic depth and precision.
Supports multilingual generation and style control, enabling output in multiple languages or domain-specific tone variants.
Can be integrated into semantic-web stacks, enterprise knowledge-systems and AI-driven content pipelines — making it a flexible asset for organisations tackling knowledge-management, documentation automation or AI-driven content generation.

Explore sample outputs: sample ontology descriptions and generated text to evaluate style, fluency and coverage.
Review supported input formats and configuration options: know which ontology languages (OWL, RDF, SKOS, etc.) and which output languages or templates are supported.
Experiment with a small ontology of your own domain: run the engine, inspect output, refine lexical mappings and style templates.
Scale to full domain: once the configuration is validated, apply OntoGeneration to your full knowledge base or ontology collection for automated documentation generation.