Design and Use of Semantic Resources: Findings from the Section on Knowledge Representation and Management of the 2020 International Medical Informatics Association Yearbook

Ferdinand Dhombres; Jean Charlet; Section Editors for the IMIA Yearbook Section on Knowledge Representation and Management

doi:10.1055/s-0040-1702010

Yearbook of Medical Informatics, Table of Contents

CC BY-NC-ND 4.0 · Yearb Med Inform 2020; 29(01): 163-168
DOI: 10.1055/s-0040-1702010

Section 6: Knowledge Representation and Management

Synopsis

Georg Thieme Verlag KG Stuttgart

Design and Use of Semantic Resources: Findings from the Section on Knowledge Representation and Management of the 2020 International Medical Informatics Association Yearbook

Authors

Ferdinand Dhombres

¹Sorbonne Université, Université Paris Nord, INSERM, UMR_S 1142, LIMICS, Paris, France

²Médecine Sorbonne Université, Service de Médecine Fœtale, Hôpital Armand Trousseau, Paris, France
Jean Charlet

¹Sorbonne Université, Université Paris Nord, INSERM, UMR_S 1142, LIMICS, Paris, France

³AP-HP, DRCI, Paris, France

Section Editors for the IMIA Yearbook Section on Knowledge Representation and Management

Abstract

Summary

Objective: To select, present, and summarize the best papers in the field of Knowledge Representation and Management (KRM) published in 2019.

Methods: A comprehensive and standardized review of the biomedical informatics literature was performed to select the most interesting papers of KRM published in 2019, based on PubMed and ISI Web Of Knowledge queries.

Results: Four best papers were selected among 1,189 publications retrieved, following the usual International Medical Informatics Association Yearbook reviewing process. In 2019, research areas covered by pre-selected papers were represented by the design of semantic resources (methods, visualization, curation) and the application of semantic representations for the integration/enrichment of biomedical data. Besides new ontologies and sound methodological guidance to rethink knowledge bases design, we observed large scale applications, promising results for phenotypes characterization, semantic-aware machine learning solutions for biomedical data analysis, and semantic provenance information representations for scientific reproducibility evaluation.

Conclusion: In the KRM selection for 2019, research on knowledge representation demonstrated significant contributions both in the design and in the application of semantic resources. Semantic representations serve a great variety of applications across many medical domains, with actionable results.

Keywords

Knowledge management - knowledge representation - vocabulary, controlled - information storage and retrieval - semantics

Full Text

References

References
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