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Semantic Interoperability in Legal Translation: Bridging the English–Uzbek Divide through Ontological Modeling

Year: 2025
Type: article
Abstract: This article explores the challenges and solutions associated with achieving semantic interoperability in the translation of legal terminology between English and Uzbek. Legal translation between these languages is often hindered by deep-rooted differences in legal systems, cultural frameworks, and conceptual structures. The study argues that a traditional word-for-word or even functional-equivalence approach is insufficient for accurately conveying legal meaning. Instead, it advocates an ontological modeling approach that maps legal terms onto shared conceptual structures to support more precise translation and interpretation. The research draws on a bilingual legal corpus encompassing constitutional, civil, and criminal law texts in English and Uzbek. Through a comparative lexico-semantic analysis, the study identifies mismatches, gaps, and partial overlaps in legal terminology and explores how these affect legal understanding across languages. The article introduces a prototype ontology that links core legal concepts from both systems and demonstrates how this model can improve semantic alignment and reduce ambiguity in translation tasks. Special attention is given to terms with no direct equivalents and to culturally contingent legal concepts that require conceptual mediation. The article contributes to the field of legal linguistics by offering a framework for machine-assisted legal translation and multilingual legal information retrieval. Ultimately, this research promotes a deeper integration of computational tools and linguistic theory in legal translation, supporting more accurate cross-linguistic communication in legal contexts. The findings are especially relevant for translators, legal drafters, and scholars involved in comparative law and multilingual legislation.
DOI: 10.14258/leglin(2025)3702
Language: English
Open Access status: diamond
OpenAlex ID: W4414833836
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