On beyond Gruber: “Ontologies” in today’s biomedical information systems and the limits of OWL

Rector, Alan L.; Schulz, Stefan; Rodrigues, Jean Marie; Chute, Christopher G.; Solbrig, Harold R.

doi:10.1016/j.yjbinx.2019.100002

Cited by 41 publications

(26 citation statements)

References 33 publications

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“…After a second review of this set of papers, including full text reviews, a selection of 15 candidate best papers was established [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. Five reviewers reviewed these pre-selected papers to best four best papers [4][5][6][7]. In direct line with the research presented last year [1], the four best papers published in 2019 demonstrated even further the added-value of ontology-based data integration approaches and that the development of ontology methods is an active area of bioinformatics research.…”

Section: Resultsmentioning

confidence: 99%

“…Section editors achieved a first selection of 148 papers based on titles and abstracts. After a second review of this set of papers, including full text reviews, a selection of 15 candidate best papers was established 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 . Five reviewers reviewed these pre-selected papers to best four best papers 4 5 6 7 .…”

Section: Resultsmentioning

confidence: 99%

“…This work provides six options to represent time in ontologies, with a supportive description of extensibility, maintainability, Terminological Box/Assertional Box (T-Box/A-Box) complexity, and adequateness for a use case. In the same methodological vein, the article of Rector et al, [6] recalls the experiences of commonly constructed ontologies and the development of ontological reasoning. These works are described from an historical perspective, and the paper invites us to rethink about knowledge modeling while describing possible directions for the future development of semantic resources.…”

Section: Best Papers Selection For 2019mentioning

confidence: 99%

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

Dhombres

Charlet

2020

Yearb Med Inform

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Best Papers Selection For 2019mentioning

confidence: 99%

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

Dhombres

Charlet

2020

Yearb Med Inform

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“…An ontology is a specification of a conceptualization [ 17 , 18 ]. It is a description of the concepts and relationships that can exist for an agent or a community of agents.…”

Section: Introductionmentioning

confidence: 99%

A Health eLearning Ontology and Procedural Reasoning Approach for Developing Personalized Courses to Teach Patients about Their Medical Condition and Treatment

Michalowski

Wilk

Michalowski

et al. 2021

IJERPH

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We propose a methodological framework to support the development of personalized courses that improve patients’ understanding of their condition and prescribed treatment. Inspired by Intelligent Tutoring Systems (ITSs), the framework uses an eLearning ontology to express domain and learner models and to create a course. We combine the ontology with a procedural reasoning approach and precompiled plans to operationalize a design across disease conditions. The resulting courses generated by the framework are personalized across four patient axes—condition and treatment, comprehension level, learning style based on the VARK (Visual, Aural, Read/write, Kinesthetic) presentation model, and the level of understanding of specific course content according to Bloom’s taxonomy. Customizing educational materials along these learning axes stimulates and sustains patients’ attention when learning about their conditions or treatment options. Our proposed framework creates a personalized course that prepares patients for their meetings with specialists and educates them about their prescribed treatment. We posit that the improvement in patients’ understanding of prescribed care will result in better outcomes and we validate that the constructs of our framework are appropriate for representing content and deriving personalized courses for two use cases: anticoagulation treatment of an atrial fibrillation patient and lower back pain management to treat a lumbar degenerative disc condition. We conduct a mostly qualitative study supported by a quantitative questionnaire to investigate the acceptability of the framework among the target patient population and medical practitioners.

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“…The sources of information on the Web can be heterogeneous which adds more complexity in the design of decision-making systems. Languages such as OWL or C-OWL have devised a path towards the standardization of these logical systems [19,20], but now, they are presenting certain drawbacks because some ontologies could change or coexist with some others that are, apparently contradictory or introduce ambiguity [21], this happens in both cases with unstructured information, and texts. Regarding the automatic generation of ontologies from a text, several proposals have been presented [22][23][24][25][26][27], they have tried to solve the difficulties described above, but with partial success.…”

Section: Introductionmentioning

confidence: 99%

An Abstract System for Converting and Recovering Texts like Structured Information

Verdesoto¹,

Herrera²,

Ortiz

2021

Preprint

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This paper introduces an abstract system for converting texts into structured information. The proposed architecture incorporates several strategies based on scientific models of how the brain records and recovers memories, and approaches that convert texts into structured data. The applications of this proposal are vast because, in general, the information that can be expressed like a text way, such as reports, emails, web contents, etc., is considered unstructured and, hence, the repositories based on a SQL do not capable to deal efficiently with this kind of data. The model in which was based on this proposal divides a sentence into clusters of words which in turn are transformed into members of a taxonomy of algebraic structures. The algebraic structures must comply properties of Abelian groups. Methodologically, an incremental prototyping approach has been applied to develop a satisfactory architecture that can be adapted to any language. A special case is studied, this deals with the Spanish language. The developed abstract system is a framework that permits to implements applications that convert unstructured textual information to structured information, this can be useful in contexts such as Natural Language Generation, Data Mining, dynamically generation of theories, among others.

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On beyond Gruber: “Ontologies” in today’s biomedical information systems and the limits of OWL

Cited by 41 publications

References 33 publications

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

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

A Health eLearning Ontology and Procedural Reasoning Approach for Developing Personalized Courses to Teach Patients about Their Medical Condition and Treatment

An Abstract System for Converting and Recovering Texts like Structured Information

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