PDF herunterladen
CC BY-NC-ND 4.0 · Yearb Med Inform 2023; 32(01): 225-229
DOI: 10.1055/s-0043-1768747
Section 9: Knowledge Representation and Management
Synopsis

Knowledge Representation and Management 2022: Findings in Ontology Development and Applications

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 › Institutsangaben
› Weitere Informationen
Auch verfügbar auf
   Lizenzen und Reprints

Summary

Objectives: To select, present, and summarize the best papers in 2022 for the Knowledge Representation and Management (KRM) section of the International Medical Informatics Association (IMIA) Yearbook.

Methods: We conducted PubMed queries and followed the IMIA Yearbook guidelines for performing biomedical informatics literature review to select the best papers in KRM published in 2022.

Results: We retrieved 1,847 publications from PubMed. We nominated 15 candidate best papers, and two of them were finally selected as the best papers in the KRM section. The topics covered by the candidate papers include ontology and knowledge graph creation, ontology applications, ontology quality assurance, ontology mapping standard, and conceptual model.

Conclusions: In the KRM best paper selection for 2022, the candidate best papers encompassed a broad range of topics, with ontology and knowledge graph creation remaining a considerable research focus.

Keywords

Knowledge representation and management - ontology - knowledge graph - International Medical Informatics Association


Publikationsverlauf

Artikel online veröffentlicht:
26. Dezember 2023

© 2023. IMIA and Thieme. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Dhombres F, Charlet J. Knowledge Representation and Management, It's Time to Integrate! Yearb Med Inform 2017;26(1):148-51. doi: 10.15265/IY-2017-030.
    PubMed
  • 2 Dhombres F, Charlet J. As Ontologies Reach Maturity, Artificial Intelligence Starts Being Fully Efficient: Findings from the Section on Knowledge Representation and Management for the Yearbook 2018. Yearb Med Inform 2018;27(1):140-5. doi: 10.1055/s-0038-1667078.
    PubMed
  • 3 Dhombres F, Charlet J. Formal Medical Knowledge Representation Supports Deep Learning Algorithms, Bioinformatics Pipelines, Genomics Data Analysis, and Big Data Processes. Yearb Med Inform 2019;28(1):152-5. doi: 10.1055/s-0039-1677933.
    PubMed
  • 4 Dhombres F, Charlet J. Design and Use of Semantic Resources: Findings from the Section on Knowledge Representation and Management of the 2020 International Medical Informatics Association Yearbook. Yearb Med Inform 2020;29(1):163-8. doi: 10.1055/s-0040-1702010.
    PubMed
  • 5 Dhombres F, Charlet J. Knowledge Representation and Management: Interest in New Solutions for Ontology Curation. Yearb Med Inform 2021;30(01):185-90. doi: 10.1055/s-0041-1726508.
    PubMed
  • 6 Cui L, Dhombres F, Charlet J. Knowledge Representation and Management: Notable Contributions in 2021. Yearb Med Inform 2022;31(01):236-40. doi: 10.1055/s-0042-1742523.
    PubMed
  • 7 Lamy JB, Séroussi B, Griffon N, Kerdelhué G, Jaulent MC, Bouaud J. Toward a formalization of the process to select IMIA Yearbook best papers. Methods Inf Med 2015;54(02):135-44. doi: 10.3414/ME14-01-0031.
    PubMed
  • 8 Kaliyaperumal R, Wilkinson MD, Moreno PA, Benis N, Cornet R, dos Santos Vieira B, et al. Semantic modelling of common data elements for rare disease registries, and a prototype workflow for their deployment over registry data. J Biomed Semantics 2022;13(1):9. doi: 10.1186/s13326-022-00264-6.
    PubMed
  • 9 Matentzoglu N, Goutte-Gattat D, Tan SZ, Balhoff JP, Carbon S, Caron AR, et al. Ontology Development Kit: a toolkit for building, maintaining and standardizing biomedical ontologies. Database 2022:baac087. doi: 10.1093/database/baac087.
    PubMed
  • 10 Azzi S, Michalowski W, Iglewski M. Developing a pneumonia diagnosis ontology from multiple knowledge sources. Health Informatics J 2022;28(2). doi: 10.1177/14604582221083850.
    PubMed
  • 11 Cardoso J, Castro LJ, Ekaputra FJ, Jacquemot MC, Suchánek M, Miksa T, et al. DCSO: towards an ontology for machine-actionable data management plans. J Biomed Semantics 2022;13(1):21. doi: 10.1186/s13326-022-00274-4.
    PubMed
  • 12 Gillespie TH, Tripathy SJ, Sy MF, Martone ME, Hill SL. The Neuron Phenotype Ontology: a FAIR approach to proposing and classifying neuronal types. Neuroinformatics 2022;20(3):793-809. doi: 10.1007/s12021-022-09566-7.
    PubMed
  • 13 Fisher ME, Segerdell E, Matentzoglu N, Nenni MJ, Fortriede JD, Chu S, et al. The Xenopus phenotype ontology: bridging model organism phenotype data to human health and development. BMC Bioinformatics 2022;23(1):99. doi: 10.1186/s12859-022-04636-8.
    PubMed
  • 14 González-Eras A, Dos Santos R, Aguilar J, Lopez A. Ontological engineering for the definition of a COVID-19 pandemic ontology. Inform Med Unlocked 2022;28:100816. doi: 10.1016/j.imu.2021.100816.
    PubMed
  • 15 Lokala U, Lamy F, Daniulaityte R, Gaur M, Gyrard A, Thirunarayan K, et al. Drug abuse ontology to harness web-based data for substance use epidemiology research: ontology development study. JMIR Public Health Surveill 2022;8(12):e24938. doi: 10.2196/24938.
    PubMed
  • 16 Morse RB, Bretzin AC, Canelón SP, D'Alonzo BA, Schneider AL, Boland MR. Design and Evaluation of a Postpartum Depression Ontology. Appl Clin Inform 2022;13(01):287-300. doi: 10.1055/s-0042-1743240.
    PubMed
  • 17 Wood EC, Glen AK, Kvarfordt LG, Womack F, Acevedo L, Yoon TS, et al. RTX-KG2: a system for building a semantically standardized knowledge graph for translational biomedicine. BMC Bioinformatics 2022;23(1):400. doi: 10.1186/s12859-022-04932-3.
    PubMed
  • 18 Rosenau L, Majeed RW, Ingenerf J, Kiel A, Kroll B, Köhler T, et al. Generation of a Fast Healthcare Interoperability Resources (FHIR)-based ontology for federated feasibility queries in the context of COVID-19: feasibility study. JMIR Med Inform 2022;10(4):e35789. doi: 10.2196/35789.
    PubMed
  • 19 Yan S, Luo L, Lai PT, Veltri D, Oler AJ, Xirasagar S, et al. PhenoRerank: A re-ranking model for phenotypic concept recognition pre-trained on human phenotype ontology. J Biomed Inform 2022;129:104059. doi: 10.1016/j.jbi.2022.104059.
    PubMed
  • 20 Burse R, McArdle G, Bertolotto M. Targeting stopwords for quality assurance of SNOMED-CT. Int J Med Inform 2022;167:104870. doi: 10.1016/j.ijmedinf.2022.104870.
    PubMed
  • 21 Matentzoglu N, Balhoff JP, Bello SM, Bizon C, Brush M, Callahan TJ, et al. A simple standard for sharing ontological mappings (SSSOM). Database 2022:baac035. doi: 10.1093/database/baac035.
    PubMed
  • 22 Bernasconi A, Guizzardi G, Pastor O, Storey VC. Semantic interoperability: ontological unpacking of a viral conceptual model. BMC bioinformatics 2022;23(Suppl 11):491. doi: 10.1186/s12859-022-05022-0.
    PubMed