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Ontology-Driven Enhancements in Statistical Machine Translation: Methods and Applications

Daniel Rojas Plata No´e Alejandro Castro S´anchez
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Ontology-Driven Enhancements in Statistical Machine Translation: Methods and Applications

by Daniel Rojas Plata, No´e Alejandro Castro S´anchez
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 64
Year of Publication: 2025
Authors: Daniel Rojas Plata, No´e Alejandro Castro S´anchez
10.5120/ijca2025924438

Daniel Rojas Plata, No´e Alejandro Castro S´anchez . Ontology-Driven Enhancements in Statistical Machine Translation: Methods and Applications. International Journal of Computer Applications. 186, 64 ( Jan 2025), 7-13. DOI=10.5120/ijca2025924438

@article{ 10.5120/ijca2025924438,
author = { Daniel Rojas Plata, No´e Alejandro Castro S´anchez },
title = { Ontology-Driven Enhancements in Statistical Machine Translation: Methods and Applications },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2025 },
volume = { 186 },
number = { 64 },
month = { Jan },
year = { 2025 },
issn = { 0975-8887 },
pages = { 7-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number64/ontology-driven-enhancements-in-statistical-machine-translation-methods-and-applications/ },
doi = { 10.5120/ijca2025924438 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-01-31T17:28:36+05:30
%A Daniel Rojas Plata
%A No´e Alejandro Castro S´anchez
%T Ontology-Driven Enhancements in Statistical Machine Translation: Methods and Applications
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 64
%P 7-13
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper analyzes the role of ontologies in improving translation systems. Statistical-based technologies were chosen as the analysis model, as they do not rely on grammar-based models or any other linguistic implementation. Since this architecture is based solely on probabilistic inferences, implementations like ontologies can help reduce ambiguity and thus improve the semantic and lexical aspects, which remain persistent issues. Specifically, this study reviews these problems and outlines guidelines for ontology development.

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Index Terms

Computer Science
Information Sciences
Ontologies
Machine Translation

Keywords

Statistical Translation Semantic Analysis Natural Language Processing Knowledge Representation

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