Machine Translation using Semantic Web Technologies: A Survey
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Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 26 Nov 2017.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 0; 2025: 3.
4 papers.
- 2021Neural Machine Translation for Low-Resource Languages: A Surveycited by 109
- 2025The Box is in the Pen: Evaluating Commonsense Reasoning in Neural Machine Translationcited by 5
- 2025Mind the Language Gap in Digital Humanities: LLM-Aided Translation of SKOS Thesauricited by 0
- 2025CEFR-Annotated WordNet: LLM-Based Proficiency-Guided Semantic Database for Language Learningcited by 0
- 2025CEFR-Annotated WordNet: LLM-Based Proficiency-Guided Semantic Database for Language Learning21 Oct 2025
- 2025Mind the Language Gap in Digital Humanities: LLM-Aided Translation of SKOS Thesauri22 Jul 2025
- 2025The Box is in the Pen: Evaluating Commonsense Reasoning in Neural Machine Translation5 Mar 2025
- 2021Neural Machine Translation for Low-Resource Languages: A Survey29 Jun 2021