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- Volume 31, Issue 2, 2025
Terminology. International Journal of Theoretical and Applied Issues in Specialized Communication - Volume 31, Issue 2, 2025
Volume 31, Issue 2, 2025
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Representing terminological data in the Semantic Web
pp.: 171–207 (37)show More to view fulltext, buy and share links for: show Less to hide fulltext, buy and share links for:AbstractThis paper describes an approach to represent terminologies in the machine-readable format of the Semantic Web, which improves the interoperability between terminological resources and opens up new possibilities yet to be discovered. The study’s motivation stems from the realization that the existing formalisms, such as SKOS or OntoLex-lemon, might not adequately capture the information within authoritative terminological resources. Therefore, we identified model requirements by formulating a set of Competency Questions derived from the analysis of terminological resources across various fields and domains, in line with the ontology development methodologies adopted in this work. During this analysis, we faced different representation challenges such as the various sources of term descriptions and the quality indicators related to terms. Consequently, we propose Termlex, a proposal based on the OntoLex-lemon model that combines the conceptual structure of the SKOS model with the lexical information as modelled in OntoLex-lemon. In Termlex, we define new classes and properties to cover the specific needs of terminological resources coming from a variety of approaches. The paper concludes with the instantiation of the Termlex model through three different use cases that follow different modelling approaches as a validation attempt.
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ONTODIC
Author(s): Amparo Alcinapp.: 208–237 (30)show More to view fulltext, buy and share links for: show Less to hide fulltext, buy and share links for:AbstractAfter analyzing other linguistic ontologies and lexical models with a variety of perspectives, objectives and results, it was found that they were unsuitable for building dictionaries. To help overcome these issues, a model of linguistic knowledge representation was designed. The ONTODIC model presents two major differences with respect to other ontologies. On the one hand, the aim of ONTODIC has been to model language from the premises of linguistics. This means studying the linguistic elements in their natural contexts, that is, the texts. On the other hand, the design of ONTODIC is based on the principles of description logic.
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Imposing order on a creative chaos
Author(s): Ylva Byrman and Andreas Nordpp.: 238–266 (29)show More to view fulltext, buy and share links for: show Less to hide fulltext, buy and share links for:AbstractIt is uncontested that terminologists leading multi-professional terminology projects need a thorough knowledge of the principles and standards of terminology work. However, the softer skills involved in the work have gained less attention. In this paper, we apply interaction analysis to a multi-professional expert meeting led by a terminologist and highlight the communicative and interpersonal work he carries out. Our results show how the terminologist allows for a certain “creative chaos” and makes the team “feel good”, while still keeping epistemic quality under permanent scrutiny and never losing focus on the task at hand. He does this through flexible agenda management — sticking to his meeting agenda, but not rigidly, and allowing for and taking note of useful contributions, even if they come in the wrong phase. He also upholds a sufficient degree of conceptual rigour, by holding back premature decisions but not unnecessarily correcting the other’s erroneous use of meta-terminology.
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A corpus-based cognitive linguistic analysis of taste terms
Author(s): Ting Zhang, Hicham Lahlou and Yasir Azampp.: 267–310 (44)show More to view fulltext, buy and share links for: show Less to hide fulltext, buy and share links for:AbstractFrom a cognitive linguistic perspective, this article delves into the polysemy between the English term sour and its Chinese counterpart suan. The research aims to achieve two key objectives: (1) To explore the similarities and differences in the polysemy of sour in English and suan in Chinese; (2) To identify the cognitive mechanisms that motivate the semantic expansion of sour in English and suan in Chinese. To this end, 《汉语大词典》 (the Great Chinese Dictionary), The Oxford English Dictionary (OED), the British National Corpus (BNC), and the Centre for Chinese Linguistics (CCL) Chinese-English Parallel Corpus were used. The dictionaries are utilized to explore the polysemy of sour and suan, while the BNC and CCL Chinese-English Parallel Corpus are employed to investigate the cognitive mechanisms underlying the semantic extensions of the selected terms. Theoretically, this article draws upon the conceptual metaphor and metonymy theory proposed by Lakoff and Johnson. The findings reveal significant semantic overlap between sour in English and suan in Chinese, yet notable distinctions remain. This study has implications for vocabulary teaching as well as cross-linguistic and cross-cultural communication.
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Pruning translation of logical and accidental polysemy in traditional Chinese medicine terminology
Author(s): Yuehui Houpp.: 311–334 (24)show More to view fulltext, buy and share links for: show Less to hide fulltext, buy and share links for:AbstractExcessive and arbitrary polysemy within traditional Chinese medicine (TCM) terminology presents a notable challenge to both the intralingual standardization of TCM terms and the interlingual development of a TCM knowledge system. This article categorizes polysemy in TCM based on the origins and relationships of the various senses of a polysemous term: Inherent polysemy, where terms retain their ordinary senses; logical polysemy, which includes both ordinary and technical senses; and accidental polysemy, characterized by exclusively technical senses. For addressing logical and accidental polysemy, this article proposes “pruning translation,” a methodology in terminology translation that refines and aligns a term closely with its original form by reducing multiple senses to its most essential meanings for enhanced clarity and precision. Three approaches, namely “centralization” for an underspecification account, “aggregation” for an overspecification account, and “literal translation” for literalism, are employed to demonstrate the application of pruning translation. This is exemplified through an analysis of five polysemous TCM terms: mào (冒), qīng (清), mài (脉), guǐ tāi (鬼胎), and xià xiè (下泄). The rationale for pruning translation stems from two key aspects: Firstly, the generation of polysemy, highlighting the need to eliminate context-dependent, unrecognized, or superficial senses for accurate cross-lingual translation; secondly, the representation of polysemy, supported by psycholinguistic evidence indicating that multiple senses in one language can often be effectively represented by a single lexical form in another, facilitating the consolidation of senses into a unified translation. This proposed methodology of pruning translation represents an innovative approach in the translation of polysemous TCM terminology, contributing to the field of terminology translation.
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Review of Faber & L’Homme (2022): Theoretical Perspectives on Terminology: Explaining terms, concepts and specialized knowledge
Author(s): Xiaofang Wu and Runze Liupp.: 335–348 (14)show More to view fulltext, buy and share links for: show Less to hide fulltext, buy and share links for:This article reviews Theoretical Perspectives on Terminology: Explaining terms, concepts and specialized knowledge978 90 272 1106 4
Volumes & issues
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Volume 32 (2026)
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Volume 31 (2025)
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Volume 30 (2024)
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Volume 29 (2023)
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Volume 28 (2022)
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Volume 27 (2021)
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Volume 26 (2020)
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Volume 25 (2019)
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Volume 24 (2018)
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Volume 23 (2017)
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Volume 22 (2016)
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Volume 21 (2015)
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Volume 20 (2014)
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Volume 19 (2013)
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Volume 18 (2012)
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Volume 17 (2011)
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Volume 16 (2010)
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Volume 15 (2009)
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Volume 14 (2008)
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Volume 13 (2007)
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Volume 12 (2006)
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Volume 11 (2005)
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Volume 10 (2004)
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Volume 9 (2003)
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Volume 8 (2002)
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Volume 7 (2001)
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Volume 6 (2000)
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Volume 5 (1998)
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Volume 4 (1997)
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Volume 3 (1996)
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Volume 2 (1995)
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Volume 1 (1994)
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Methods of automatic term recognition: A review
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