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Machine Translation using Semantic Web Technologies: A Survey

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arxiv 1711.09476 v3 pith:7A7CDC7U submitted 2017-11-26 cs.CL

classification cs.CL
keywords machinesemantictechnologiestranslationapproachesobstaclesstillsuggests
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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  1. Mind the Language Gap in Digital Humanities: LLM-Aided Translation of SKOS Thesauri

    cs.CL 2025-07 conditional novelty 6.0 of 10

    WOKIE combines external translation services with LLM-based refinement to translate SKOS thesauri, improving translation quality and boosting ontology matching F1 scores.

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