# Semantic Web for Machine Translation: Challenges and Directions

## TL;DR

This paper reviews how Semantic Web technologies can address challenges in machine translation, highlighting current opportunities and the early stage of integrating these technologies for improved translation quality.

## Contribution

It provides a systematic review of Semantic Web approaches in machine translation, identifying challenges, opportunities, and the infancy of combined technology.

## Key findings

- Semantic Web can improve translation quality
- Challenges include lexical and syntactic ambiguity
- Integration of Semantic Web in MT is still emerging

## 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 is an extended abstract of our systematic review on machine translation approaches that rely on Semantic Web technologies for improving the translation of texts. Overall, we present the challenges and opportunities in the use of Semantic Web technologies in Machine Translation. Moreover, our research 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.