# A Holistic Natural Language Generation Framework for the Semantic Web

arXiv: https://arxiv.org/abs/1911.01248 · HTML: https://mlsanity.com/paper/1911.01248/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2019.

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

- [EvoLearner: Learning Description Logics with Evolutionary Algorithms](https://mlsanity.com/paper/2111.04879/) (2021)
- [DRILL-- Deep Reinforcement Learning for Refinement Operators in ALC](https://mlsanity.com/paper/2106.15373/) (2021)
- [Detection, Explanation and Filtering of Cyber Attacks Combining Symbolic and Sub-Symbolic Methods](https://mlsanity.com/paper/2212.13991/) (2022)
- [Q-NL Verifier: Leveraging Synthetic Data for Robust Knowledge Graph Question Answering](https://mlsanity.com/paper/2503.01385/) (2025)
- [Improving the Question Answering Quality using Answer Candidate Filtering based on Natural-Language Features](https://mlsanity.com/paper/2112.05452/) (2021)

## Abstract (arXiv)

With the ever-growing generation of data for the Semantic Web comes an increasing demand for this data to be made available to non-semantic Web experts. One way of achieving this goal is to translate the languages of the Semantic Web into natural language. We present LD2NL, a framework for verbalizing the three key languages of the Semantic Web, i.e., RDF, OWL, and SPARQL. Our framework is based on a bottom-up approach to verbalization. We evaluated LD2NL in an open survey with 86 persons. Our results suggest that our framework can generate verbalizations that are close to natural languages and that can be easily understood by non-experts. Therewith, it enables non-domain experts to interpret Semantic Web data with more than 91% of the accuracy of domain experts.