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Resource Name
OntoNeuroLOG
RRID:SCR_008957 RRID Copied      
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OntoNeuroLOG (RRID:SCR_008957)
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Resource Information

URL: http://neurolog.i3s.unice.fr/public_namespace/ontology

Proper Citation: OntoNeuroLOG (RRID:SCR_008957)

Description: An ontology for neuroimaging or medical imaging studies based on DOLCE (Descriptive Ontology for Linguistic and Cognitive Engineering), as the foundational ontology. Detailed description from web: Our aim is the design of a common semantic model providing a unified view on all data and tools to be shared between NeuroLOG partners. For this purpose, we built a multi-layered and multi-components formal ontology. We chose a design framework that structures the ontology at different levels of abstraction while respecting common conceptualization choices. At the highest level is a top-level ontology that includes abstract concepts and relationships valid across domains. We adopted DOLCE (Descriptive Ontology for Linguistic and Cognitive Engineering), as the foundational ontology. We then added Core ontologies, which provide generic, basic and minimal concepts and relations in a specific domain. By minimal we mean that core ontologies should include only the most reusable and widely applicable categories. These kinds of ontologies are essential for sharing intended meaning between different domains. We adopted I& DA (Information and Discourse Acts), a core ontology initially built for classifying documents as a function of their content.We use it to model medical images, which we consider as types of documents. Participant Roles is the core ontology we use to describe the modes of image participation in data processing. I& DA and Participant Roles are built according to DOLCE ontological commitments. On the basis of these two layers, we constructed our Domain ontology dedicated to conceptualizing a specific domain, in this case neuroimaging. Obviously, large domains such as neuroimaging can be divided into sub-domains for the sake of modularization.

Synonyms: Onto Neuro Log, OntoNeuro LOG

Resource Type: ontology, controlled vocabulary, data or information resource

Defining Citation: PMID:18440282

Keywords: neuroimaging, medical imaging, dicom term

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NeuroLOG

has parent organization

University of Nice Sophia Antipolis; Nice; France

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