A Approach to Clinical Proteomics Data Quality Control and Import

Naubourg, Pierre; Savonnet, Marinette; Leclercq, Éric; Yétongnon, Kokou

doi:10.1007/978-3-642-23208-4_15

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…Finally, application ontologies correspond to a more specific domain than the previous class. Furthermore, while domain ontologies capture domain knowl- edge with a formal and consensual vocabulary [19], application ontologies describe components of a domain application and provide semantics to map their integration [20,21].…”

Section: Ontologymentioning

confidence: 99%

Construction of a base ontology to represent accident expertise knowledge

Sounchio¹,

Kamsu-Foguem²,

Geneste³

2023

Cogn Tech Work

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Expertise is an activity carried out by experts that contributes to societal progress, as it helps to elucidate unknown situations. For example, accident expertise eases accident understanding, by describing how it happened, and by identifying its causes and consequences. As a result, the design of accident expertise in a convenient human-machine structure will enable the querying, reasoning, and reuse of accident knowledge in other tools, such as safety and decision-making systems. However, existing representations of accident knowledge, such as documents, relational databases, or accident ontologies, do not fulfill accident expertise expectations. Moreover, these representations are unlikely to provide the appropriate use of accident expertise knowledge. This study presents a base ontology for accident expertise knowledge representation designed with a model-driven methodology and implemented with semantic web technologies. The study obtained satisfactory results from the evaluation and application of extension and reuse of this ontology with aircraft accident expertise taken from the French bureau of Enquiries and Analysis (BEA) for civil aviation safety.

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Section: Ontologymentioning

confidence: 99%

Construction of a base ontology to represent accident expertise knowledge

Sounchio¹,

Kamsu-Foguem²,

Geneste³

2023

Cogn Tech Work

View full text Add to dashboard Cite

show abstract

Section: Ontologymentioning

confidence: 99%

Construction of a base ontology to represent accident expertise knowledge

SOUNCHIO

Foguem

Geneste

2022

Preprint

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Expertise is an activity carried out by experts that contributes to societal progress, as it helps to elucidate unknown situations. Forexample, accident expertise eases accident understanding, by describing how it happened, and by identifying its causes and conse-quences. As a result, the design of accident expertise in a convenient human-machine structure will enable the querying, reasoning,and reuse of accident knowledge in other tools, such as safety and decision-making systems. However, existing representations ofaccident knowledge, such as documents, relational databases, or accident ontologies, do not fulfill accident expertise expectations.Moreover, these representations are unlikely to provide the appropriate use of accident expertise knowledge. This study presents a base ontology for accident expertise knowledge representation designed with a model-driven methodologyand implemented with semantic web technologies. The study obtained satisfactory results from the evaluation and application ofextension and reuse of this ontology with aircraft accident expertise taken from the French bureau of Enquiries and Analysis (BEA)for civil aviation safety.

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eClims: An Extensible and Dynamic Integration Framework for Biomedical Information Systems

Savonnet

Leclercq

Naubourg³

2016

IEEE J. Biomed. Health Inform.

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Biomedical information systems (BIS) require consideration of three types of variability: data variability induced by new high throughput technologies, schema or model variability induced by large scale studies or new fields of research, and knowledge variability resulting from new discoveries. Beyond data heterogeneity, managing variabilities in the context of BIS requires extensible and dynamic integration process. In this paper, we focus on data and schema variabilities and we propose an integration framework based on ontologies, master data, and semantic annotations. The framework addresses issues related to: 1) collaborative work through a dynamic integration process; 2) variability among studies using an annotation mechanism; and 3) quality control over data and semantic annotations. Our approach relies on two levels of knowledge: BIS-related knowledge is modeled using an application ontology coupled with UML models that allow controlling data completeness and consistency, and domain knowledge is described by a domain ontology, which ensures data coherence. A system build with the eClims framework has been implemented and evaluated in the context of a proteomic platform.

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A Approach to Clinical Proteomics Data Quality Control and Import

Cited by 3 publications

References 19 publications

Construction of a base ontology to represent accident expertise knowledge

Construction of a base ontology to represent accident expertise knowledge

Construction of a base ontology to represent accident expertise knowledge

eClims: An Extensible and Dynamic Integration Framework for Biomedical Information Systems

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