mlsanityData to 7 Oct 2026

A Holistic Natural Language Generation Framework for the Semantic Web

arXiv 1911.01248 ↗First posted 4 Nov 2019cs.CL cs.DBcited by 5

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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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Nov 2019.

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Citing papers per year: 2021: 3; 2022: 1; 2023: 0; 2024: 0; 2025: 1.

5 papers.

  1. 2021EvoLearner: Learning Description Logics with Evolutionary Algorithmscited by 17
  2. 2021DRILL-- Deep Reinforcement Learning for Refinement Operators in ALCcited by 3
  3. 2022Detection, Explanation and Filtering of Cyber Attacks Combining Symbolic and Sub-Symbolic Methodscited by 3
  4. 2025Q-NL Verifier: Leveraging Synthetic Data for Robust Knowledge Graph Question Answeringcited by 2
  5. 2021Improving the Question Answering Quality using Answer Candidate Filtering based on Natural-Language Featurescited by 0