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Machine Translation Using Semantic Web Technologies: A Survey

24 Pages Posted: 12 Sep 2018 First Look: Accepted

See all articles by Diego Moussallem

Diego Moussallem

University of Leipzig - Agile Knowledge Engineering and Semantic Web (AKSW)

Matthias Wauer

University of Leipzig - Agile Knowledge Engineering and Semantic Web (AKSW)

Axel-Cyrille Ngonga Ngomo

University of Paderborn - Data Science Group

Abstract

A large number of machine translation approaches have recently been developed to facilitate the fluid migration of content across languages. However, the literature suggests that many obstacles must still be dealt with to achieve better automatic translations. One of these obstacles is lexical and syntactic ambiguity. A promising way of overcoming this problem is using Semantic Web technologies. This article presents the results of a systematic review of machine translation approaches that rely on Semantic Web technologies for translating texts. Overall, our survey suggests that while Semantic Web technologies can enhance the quality of machine translation outputs for various problems, the combination of both is still in its infancy.

Keywords: machine translation, semantic web, ontology, linked data, multilinguality, knowledge graphs

Suggested Citation

Moussallem, Diego and Wauer, Matthias and Ngomo, Axel-Cyrille Ngonga, Machine Translation Using Semantic Web Technologies: A Survey (September 12, 2018). Journal of Web Semantics First Look . Available at SSRN: https://ssrn.com/abstract=3248493 or http://dx.doi.org/10.2139/ssrn.3248493

Diego Moussallem (Contact Author)

University of Leipzig - Agile Knowledge Engineering and Semantic Web (AKSW) ( email )

Augustusplatz 10/11
Leipzig, 04109
Germany

Matthias Wauer

University of Leipzig - Agile Knowledge Engineering and Semantic Web (AKSW) ( email )

Augustusplatz 10/11
Leipzig, 04109
Germany

Axel-Cyrille Ngonga Ngomo

University of Paderborn - Data Science Group ( email )

Warburger Str. 100
Paderborn, D-33098
Germany

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