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CC BY 4.0 · Yearb Med Inform 2024; 33(01): 223-226
DOI: 10.1055/s-0044-1800748
Section 9: Knowledge Representation and Management
Synopsis

Knowledge Representation and Management: 2023 Highlights and the Rise of Knowledge Graph Embeddings

Autoren

  • Jean Charlet

    1   Sorbonne Université, INSERM, Univ Sorbonne Paris Nord, LIMICS, Paris, France
    2   AP-HP, DRCI, Paris, France

  • Licong Cui

    3   McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
    • Section Editors for the IMIA Yearbook Section on Knowledge Representation and Management
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Summary

Objectives: We aim to identify, select, and summarize the best papers published in 2023 for the Knowledge Representation and Management (KRM) section of the International Medical Informatics Association (IMIA) Yearbook.

Methods: We performed PubMed queries and adhered to the IMIA Yearbook guidelines for conducting biomedical informatics literature review to select the best papers in KRM published in 2023.

Results: Our search yielded a total of 1,666 publications from PubMed. From these, we identified 15 papers as potential candidates for the best papers, and three of them were finally selected as the best papers in the KRM section. The candidate best papers covered three main topics: knowledge graph, knowledge interoperability, and ontology. Notably, two of the three selected best papers explored the potential of knowledge graph embeddings for predicting intensive care unit readmissions and measuring disease distances, respectively.

Conclusions: The selection process for the best papers in the KRM section for 2023 showcased a wide spectrum of topics, with knowledge graph embeddings emerging as a promising area for supporting machine learning applications in biomedicine.

Keywords:

Knowledge Representation and Management - Knowledge Graph - Ontology - International Medical Informatics Association


Publikationsverlauf

Artikel online veröffentlicht:
08. April 2025

© 2024. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

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