Ontology-Based Multilingual Terminological Resource for Biomedical Abbreviations

Biomedical abbreviations play a central role in scientific and clinical communication, allowing textual economy and easing expert interaction. However, their widespread use can generate ambiguity, especially in multilingual and interdisciplinary contexts, where a single abbreviation may designate different concepts, while multiple abbreviations may refer to the same concept. Although biomedical abbreviation ambiguity has been significantly investigated, the conceptual organisation of abbreviation usage across biomedical knowledge resources remains underexplored. This study investigates patterns of intra- and interlingual denominative variation, as well as mono- and polyreferential ambiguity involving abbreviated and full forms. A comparative analysis of conceptual representation, preferred labels, indexed variants, and abbreviation coverage across authoritative biomedical knowledge resources was conducted, being supported by a multilingual comparable corpus of specialised texts that focused on neurological, neurodegenerative, gastrointestinal, psychiatric, and metabolic disorders linked to gut–brain axis dysfunction. Data were processed through automated extraction techniques and conceptual alignment procedures to compare usage found in corpora with structured knowledge representations. The results revealed systematic variations in how abbreviations are used across languages and recurring differences between textual evidence and ontological representations, reflecting the challenges of integrating biomedical knowledge from resources built according to different perspectives and purposes. In response to these challenges, the study introduced an ontology, designed for accurately representing the conceptual and linguistic dimensions of biomedical abbreviation ambiguity, together with a multilingual dictionary integrating corpus evidence and terminological data. Overall, this work supports the use of ontology-driven and corpus-informed approaches to improve semantic interoperability and structured biomedical knowledge representation across languages.