Download PDF
CC BY-NC-ND 4.0 · Yearb Med Inform 2021; 30(01): 185-190
DOI: 10.1055/s-0041-1726508
Section 6: Knowledge Representation and Management
Synopsis

Knowledge Representation and Management: Interest in New Solutions for Ontology Curation

Ferdinand Dhombres
1   Sorbonne Université, INSERM, Univ Sorbonne Paris Nord, LIMICS, Paris, France
2   Sorbonne Université, Service de Médecine Fœtale, DMU Origyne, AP-HP, Hôpital Armand Trousseau, Paris, France
,
Jean Charlet
1   Sorbonne Université, INSERM, Univ Sorbonne Paris Nord, LIMICS, Paris, France
3   AP-HP, DRCI, Paris, France
,
Section Editors for the IMIA Yearbook Section on Knowledge Representation and Management › Author Affiliations
› Further Information
Also available at
   Permissions and Reprints

Summary

Objective: To select, present and summarize some of the best papers in the field of Knowledge Representation and Management (KRM) published in 2020.

Methods: A comprehensive and standardized review of the medical informatics literature was performed to select the most interesting papers of KRM published in 2020, based on PubMed queries. This review was conducted according to the IMIA Yearbook guidelines.

Results: Four best papers were selected among 1,175 publications. In contrast with the papers selected last year, the four best papers of 2020 demonstrated a significant focus on methods and tools for ontology curation and design. The usual KRM application domains (bioinformatics, machine learning, and electronic health records) were also represented.

Conclusion: In 2020, ontology curation emerges as a significant topic of research interest. Bioinformatics, machine learning, and electronics health records remain significant research areas in the KRM community with various applications. Knowledge representations are key to advance machine learning by providing context and to develop novel bioinformatics metrics. As in 2019, representations serve a great variety of applications across many medical domains, with actionable results and now with growing adhesion to the open science initiative.

Keywords

Knowledge representation and management - ontologies - ontology design - ontology curation - IMIA


Publication History

Article published online:
03 September 2021

© 2021. 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 (01) 148-51
    Thieme ConnectPubMedSearch in Google Scholar
  • 2 Dhombres F, Charlet J. Section Editors for the IYSoKR, Management. 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 (01) 140-5
    Thieme ConnectPubMedSearch in Google Scholar
  • 3 Dhombres F, Charlet J. Section Editors for the IMIA Yearbook Section on Knowledge Representation and Management. Formal Medical Knowledge Representation Supports Deep Learning Algorithms, Bioinformatics Pipelines, Genomics Data Analysis, and Big Data Processes. Yearb Med Inform 2019; 28 (01) 152-5
    Thieme ConnectPubMedSearch in Google Scholar
  • 4 Dhombres F, Charlet J. Section Editors for the IYSoKR, Management. 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 (01) 163-8
    Thieme ConnectPubMedSearch in Google Scholar
  • 5 Zheng F, Shi J, Yang Y, Zheng WJ, Cui L. A transformation-based method for auditing the IS-A hierarchy of biomedical terminologies in the Unified Medical Language System. J Am Med Inform Assoc 2020; 27 (10) 1568-75
    CrossrefPubMedSearch in Google Scholar
  • 6 Slater LT, Gkoutos GV, Hoehndorf R. Towards semantic interoperability: finding and repairing hidden contradictions in biomedical ontologies. BMC Med Inform Decis Mak 2020; 20 (Suppl. 10) 311
    CrossrefPubMedSearch in Google Scholar
  • 7 Le DH. UFO: A tool for unifying biomedical ontology-based semantic similarity calculation, enrichment analysis and visualization. PLoS One 2020; 15 (07) e0235670
    CrossrefPubMedSearch in Google Scholar
  • 8 Robinson PN, Ravanmehr V, Jacobsen JOB, Danis D, Zhang XA, Carmody LC. et al. Interpretable Clinical Genomics with a Likelihood Ratio Paradigm. Am J Hum Genet 2020; 107 (03) 403-17
    CrossrefPubMedSearch in Google Scholar
  • 9 Hier DB, Brint SU. A Neuro-ontology for the neurological examination. BMC Med Inform Decis Mak 2020; 20 (01) 47
    CrossrefPubMedSearch in Google Scholar
  • 10 Hou L, Wu M, Kang HY, Zheng S, Shen L, Qian Q. et al. PMO: A knowledge representation model towards precision medicine. Math Biosci Eng 2020; 17 (04) 4098-114
    CrossrefPubMedSearch in Google Scholar
  • 11 Kulmanov M, Smaili FZ, Gao X, Hoehndorf R. Semantic similarity and machine learning with ontologies. Brief Bioinform 2020; bbaa199
    PubMedSearch in Google Scholar
  • 12 Sousa RT, Silva S, Pesquita C. Evolving knowledge graph similarity for supervised learning in complex biomedical domains. BMC Bioinformatics 2020; 21 (01) 6
    CrossrefPubMedSearch in Google Scholar
  • 13 Alag S. Unique insights from ClinicalTrials.gov by mining protein mutations and RSids in addition to applying the Human Phenotype Ontology. PLoS One 2020; 15 (05) e0233438
    CrossrefPubMedSearch in Google Scholar
  • 14 Blatti 3rd C, Emad A, Berry MJ, Gatzke L, Epstein M, Lanier D. et al. Knowledge-guided analysis of “omics” data using the KnowEnG cloud platform. PLoS Biol 2020; 18 (01) e3000583
    CrossrefPubMedSearch in Google Scholar
  • 15 Beck T, Shorter T, Brookes AJ. GWAS Central: a comprehensive resource for the discovery and comparison of genotype and phenotype data from genome-wide association studies. Nucleic Acids Res 2020; 48 D1 D933-D40
    PubMedSearch in Google Scholar
  • 16 Shefchek KA, Harris NL, Gargano M, Matentzoglu N, Unni D, Brush M. et al. The Monarch Initiative in 2019: an integrative data and analytic platform connecting phenotypes to genotypes across species. Nucleic Acids Res 2020; 48 D1 D704-D15
    CrossrefPubMedSearch in Google Scholar
  • 17 Jani A, Liyanage H, Okusi C, Sherlock J, Hoang U, Ferreira F. et al. Using an Ontology to Facilitate More Accurate Coding of Social Prescriptions Addressing Social Determinants of Health: Feasibility Study. J Med Internet Res 2020; 22 (12) e23721
    CrossrefPubMedSearch in Google Scholar
  • 18 Lamer A, Depas N, Doutreligne M, Parrot A, Verloop D, Defebvre MM. et al. Transforming French Electronic Health Records into the Observational Medical Outcome Partnership's Common Data Model: A Feasibility Study. Appl Clin Inform 2020; 11 (01) 13-22
    Thieme ConnectPubMedSearch in Google Scholar
  • 19 Colicchio TK, Dissanayake PI, Cimino JJ. Formal representation of patients’ care context data: the path to improving the electronic health record. J Am Med Inform Assoc 2020; 27 (11) 1648-57
    CrossrefPubMedSearch in Google Scholar