Machine Translation using Semantic Web Technologies: A Survey

TL;DR

This survey reviews how Semantic Web technologies are used to improve machine translation, highlighting current progress and challenges in integrating these approaches for better multilingual content translation.

Contribution

It provides a systematic review of existing machine translation methods that leverage Semantic Web technologies, emphasizing their potential and current limitations.

Findings

Semantic Web technologies can enhance translation quality.

The integration of Semantic Web and machine translation is still developing.

Challenges remain in fully realizing the benefits of Semantic Web in translation.

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.