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    <titleInfo>
        <title>A Holistic Natural Language Generation Framework for the Semantic Web</title>
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    <name type="personal">
        <namePart type="given">Axel-Cyrille</namePart>
        <namePart type="family">Ngonga Ngomo</namePart>
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        <namePart type="given">Diego</namePart>
        <namePart type="family">Moussallem</namePart>
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    <name type="personal">
        <namePart type="given">Lorenz</namePart>
        <namePart type="family">Bühmann</namePart>
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        <dateIssued>2019-sep</dateIssued>
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            <title>Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)</title>
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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.</abstract>
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    <identifier type="doi">10.26615/978-954-452-056-4_095</identifier>
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        <url>https://aclanthology.org/R19-1095</url>
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    <part>
        <date>2019-sep</date>
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            <start>819</start>
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%0 Conference Proceedings
%T A Holistic Natural Language Generation Framework for the Semantic Web
%A Ngonga Ngomo, Axel-Cyrille
%A Moussallem, Diego
%A Bühmann, Lorenz
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
%D 2019
%8 sep
%I INCOMA Ltd.
%C Varna, Bulgaria
%F ngonga-ngomo-etal-2019-holistic
%X 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.
%R 10.26615/978-954-452-056-4_095
%U https://aclanthology.org/R19-1095
%U https://doi.org/10.26615/978-954-452-056-4_095
%P 819-828

Markdown (Informal)

[A Holistic Natural Language Generation Framework for the Semantic Web](https://aclanthology.org/R19-1095) (Ngonga Ngomo et al., RANLP 2019)

• A Holistic Natural Language Generation Framework for the Semantic Web (Ngonga Ngomo et al., RANLP 2019)

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..