CC BY-NC-ND 4.0 · Yearb Med Inform 2018; 27(01): 140-145
DOI: 10.1055/s-0038-1667078
Section 6: Knowledge Representation and Management
Georg Thieme Verlag KG Stuttgart

As Ontologies Reach Maturity, Artificial Intelligence Starts Being Fully Efficient: Findings from the Section on Knowledge Representation and Management for the Yearbook 2018

Ferdinand Dhombres
1  Sorbonne Université, Université Paris 13, Sorbonne Paris Cité, INSERM, UMR_S 1142, LIMICS, Paris, France
2  Sorbonne Université Médecine, Service de Médecine Foetale, AP-HP/HUEP, Hôpital Armand Trousseau, Paris, France
Jean Charlet
1  Sorbonne Université, Université Paris 13, Sorbonne Paris Cité, INSERM, UMR_S 1142, 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

Publication History

Publication Date:
29 August 2018 (online)


Objectives: To select, present, and summarize the best papers published in 2017 in the field of Knowledge Representation and Management (KRM).

Methods: A comprehensive and standardized review of the medical informatics literature was performed to select the most interesting papers of KRM published in 2017, based on a PubMed query.

Results: In direct line with the research on data integration presented in the KRM section of the 2017 edition of the International Medical Informatics Association (IMIA) Yearbook, the five best papers for 2018 demonstrate even further the added-value of ontology-based integration approaches for phenotype-genotype association mining. Additionally, among the 15 preselected papers, two aspects of KRM are in the spotlight: the design of knowledge bases and new challenges in using ontologies.

Conclusions: Ontologies are demonstrating their maturity to integrate medical data and begin to support clinical practices. New challenges have emerged: the query on distributed semantically annotated datasets, the efficiency of semantic annotation processes, the semantic representation of large textual datasets, the control of biases associated with semantic annotations, and the computation of Bayesian indicators on data annotated with ontologies.