As Ontologies Reach Maturity, Artificial Intelligence Starts Being Fully Efficient: Findings from the Section on Knowledge Representation and Management for the Yearbook 2018
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.
- 1 Dhombres F, Charlet J. Knowledge Representation and Management, It’s Time to Integrate!. Yearb Med Inform 2017; 26 (01) 148-51
- 2 Boudellioua I, Mahamad Razali RB, Kulmanov M, Hashish Y, Bajic VB, Goncalves-Serra E. , et al. Semantic prioritization of novel causative genomic variants. PLoS Comput Biol 2017; 13 (04) e1005500
- 3 Galeota E, Pelizzola M. Ontology-based annotations and semantic relations in large-scale (epi)genomics data. Brief Bioinform 2017; 18 (03) 403-12
- 4 Khan Y, Saleem M, Mehdi M, Hogan A, Mehmood Q, Rebholz-Schuhmann D. , et al. SAFE: SPARQL Federation over RDF Data Cubes with Access Control. J Biomed Semantics 2017; 8 (01) 5
- 5 Notaro M, Schubach M, Robinson PN, Valentini G. Prediction of Human Phenotype Ontology terms by means of hierarchical ensemble methods. BMC Bioinformatics 2017; 18 (01) 449
- 6 Petegrosso R, Park S, Hwang TH, Kuang R. Transfer learning across ontologies for phenome-genome association prediction. Bioinformatics 2017; 33 (04) 529-36
- 7 Alonso-Calvo R, Paraiso-Medina S, Perez-Rey D, Alonso-Oset E, van Stiphout R, Yu S. , et al. A semantic interoperability approach to support integration of gene expression and clinical data in breast cancer. Comput Biol Med 2017; 87: 179-86
- 8 Barton A, Ethier JF, Duvauferrier R, Burgun A. An ontological analysis of medical Bayesian indicators of performance. J Biomed Semantics 2017; 8 (01) 1
- 9 Cuzzola J, Jovanovic J, Bagheri E. RysannMD: A biomedical semantic annotator balancing speed and accuracy. J Biomed Inform 2017; 71: 91-109
- 10 Esteban-Gil A, Fernandez-Breis JT, Boeker M. Analysis and visualization of disease courses in a semantically-enabled cancer registry. J Biomed Semantics 2017; 8 (01) 46
- 11 Gipson DS, Kirkendall ES, Gumbs-Petty B, Quinn T, Steen A, Hicks A. , et al. Development of a Pediatric Adverse Events Terminology. . Pediatrics 2017 ; 139(1).
- 12 Kulmanov M, Hoehndorf R. Evaluating the effect of annotation size on measures of semantic similarity. J Biomed Semantics 2017; 8 (01) 7
- 13 Natale DA, Arighi CN, Blake JA, Bona J, Chen C, Chen SC. , et al. Protein Ontology (PRO): enhancing and scaling up the representation of protein entities. Nucleic Acids Res 2017; 45 (D1): D339-D46
- 14 Shi L, Li S, Yang X, Qi J, Pan G, Zhou B. Semantic Health Knowledge Graph: Semantic Integration of Heterogeneous Medical Knowledge and Services. Biomed Res Int 2017; 2017: 2858423
- 15 Vita R, Overton JA, Sette A, Peters B. Better living through ontologies at the Immune Epitope Database. . Database (Oxford) 2017 ;2017(1).
- 16 Zhang YF, Gou L, Zhou TS, Lin DN, Zheng J, Li Y. , et al. An ontology-based approach to patient follow-up assessment for continuous and personalized chronic disease management. J Biomed Inform 2017; 72: 45-59