A Holistic Natural Language Generation Framework for the Semantic Web

Axel-Cyrille Ngonga Ngomo, Diego Moussallem, Lorenz Bühmann

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Abstract
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 that allows 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.
Anthology ID:
R19-1095
Volume:
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
Month:
September
Year:
2019
Address:
Varna, Bulgaria
Editors:
Ruslan Mitkov, Galia Angelova
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
819–828
Language:
URL:
https://aclanthology.org/R19-1095
DOI:
10.26615/978-954-452-056-4_095
Bibkey:
Cite (ACL):
Axel-Cyrille Ngonga Ngomo, Diego Moussallem, and Lorenz Bühmann. 2019. A Holistic Natural Language Generation Framework for the Semantic Web. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 819–828, Varna, Bulgaria. INCOMA Ltd..
Cite (Informal):
A Holistic Natural Language Generation Framework for the Semantic Web (Ngonga Ngomo et al., RANLP 2019)
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PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/R19-1095.pdf
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