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

2019

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Objective: To select, present, and summarize the best papers published in 2018 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 published in 2018 in KRM, based on PubMed and ISI Web Of Knowledge queries. Results: Four best papers were selected among the 962 publications retrieved following the Yearbook review process. The research areas in 2018 were mainly related to the ontology-based data integration for phenotype-genotype association mining, the design of ontologies and their application, and the semantic annotation of clinical texts. Conclusion: In the KRM selection for 2018, research on semantic representations demonstrated their added value for enhanced deep learning approaches in text mining and for designing novel bioinformatics pipelines based on graph databases. In addition, the ontology structure can enrich the analyses of whole genome expression data. Finally, semantic representations demonstrated promising results to process phenotypic big data.

Section: Resultssupporting

confidence: 55%

Section: Paper Selection Methodsmentioning

confidence: 99%

Formal Medical Knowledge Representation Supports Deep Learning Algorithms, Bioinformatics Pipelines, Genomics Data Analysis, and Big Data Processes

2019

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“…We conducted the selection of KRM papers based on the set of queries established in the 2019 edition of the IMIA Yearbook of Medical Informatics 1 . As compared with the previous editions of the IMIA Yearbook in 2017 and 2018 2 3 , both PubMed/MELDINE and Web of Knowledge were used to search for KRM articles published in 2019. We followed a generic method to select the best papers, commonly used in all sections of the Yearbook since 2013 [].…”

Section: Paper Selection Methodsmentioning

confidence: 99%

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

2020

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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.

“…We conducted the selection of KRM papers, based on the set of queries optimized during the last editions of the International Medical Informatics (IMIA) Yearbook [ 1 2 3 4 ]. In comparison with the latest editions of the IMIA Yearbook [ 3 , 4 ], where both PubMed/MELDINE and Web of Science™ were used to search for KRM articles, we did not expand the search to the Web of Science (WoS) database this year.…”

Section: Paper Selection Methodsmentioning

confidence: 99%

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

2021

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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.