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In this paper, we present an automatic alignment strategy for the medical domain. Standard medical thesauri as the 11th Revision of the International Classification of Diseases (ICD-11) and a vocabulary of radiological terms (RadLex) are aligned with a multimodal medical data set (MedPix 2.0). The alignment process was conducted over their ontological representation and using Natural Language Processing techniques and Large Language Models to annotate both corpora. The obtained automatic pipeline ensures the terminology alignment between the terms in MedPix 2.0 and the standard terminology in ICD-11 and RadLex. Data and developed pipeline are available on https://github.com/CHILab1/MedOntoAlignment .

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An Automatic Alignment Strategy for Medical Ontologies: the MedPix 2.0 Case Study

  • 会议论文

  • 发表日期：

    2026年

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  • Luigi Colucci Cante,
  • Mariangela Graziano,
  • Irene Siragusa,
  • Beniamino Di Martino,
  • Roberto Pirrone

摘要

In this paper, we present an automatic alignment strategy for the medical domain. Standard medical thesauri as the 11th Revision of the International Classification of Diseases (ICD-11) and a vocabulary of radiological terms (RadLex) are aligned with a multimodal medical data set (MedPix 2.0). The alignment process was conducted over their ontological representation and using Natural Language Processing techniques and Large Language Models to annotate both corpora. The obtained automatic pipeline ensures the terminology alignment between the terms in MedPix 2.0 and the standard terminology in ICD-11 and RadLex. Data and developed pipeline are available on https://github.com/CHILab1/MedOntoAlignment .