# Generating Natural Language Descriptions from OWL Ontologies: the NaturalOWL System

arXiv: https://arxiv.org/abs/1405.6164 · HTML: https://mlsanity.com/paper/1405.6164/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2014.

## Built on by (most cited first, 5 of 5)

- [Triples-to-Text Generation with Reinforcement Learning Based Graph-augmented Neural Networks](https://mlsanity.com/paper/2111.10545/) (2021)
- [Ontology Revision based on Pre-trained Language Models](https://mlsanity.com/paper/2310.18378/) (2023)
- [A Review of Multilingualism in and for Ontologies](https://mlsanity.com/paper/2210.02807/) (2022)
- [An Embedding-based Approach to Inconsistency-tolerant Reasoning with Inconsistent Ontologies](https://mlsanity.com/paper/2304.01664/) (2023)
- [A System for Automatic English Text Expansion](https://mlsanity.com/paper/2405.18350/) (2024)

## Abstract (arXiv)

We present NaturalOWL, a natural language generation system that produces texts describing individuals or classes of OWL ontologies. Unlike simpler OWL verbalizers, which typically express a single axiom at a time in controlled, often not entirely fluent natural language primarily for the benefit of domain experts, we aim to generate fluent and coherent multi-sentence texts for end-users. With a system like NaturalOWL, one can publish information in OWL on the Web, along with automatically produced corresponding texts in multiple languages, making the information accessible not only to computer programs and domain experts, but also end-users. We discuss the processing stages of NaturalOWL, the optional domain-dependent linguistic resources that the system can use at each stage, and why they are useful. We also present trials showing that when the domain-dependent llinguistic resources are available, NaturalOWL produces significantly better texts compared to a simpler verbalizer, and that the resources can be created with relatively light effort.