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Front Neuroinform. 2014 Jun 20;8:62. doi: 10.3389/fninf.2014.00062. eCollection 2014.

Interdisciplinary perspectives on the development, integration, and application of cognitive ontologies.

Author information

1
European Molecular Biology Laboratory - European Bioinformatics Institute, Wellcome Trust Genome Campus Hinxton, UK ; Department of Philosophy and Swiss Center for Affective Sciences, University of Geneva Switzerland ; Evolutionary Bioinformatics, Swiss Institute for Bioinformatics Lausanne, Switzerland.
2
Department of Psychology/Neuroscience Institute, Georgia State University GA, USA.
3
Department of Philosophy and National Center for Ontological Research, University at Buffalo NY, USA.
4
Imaging Research Center, University of Texas at Austin TX, USA ; Department of Psychology, University of Texas at Austin TX, USA ; Department of Neuroscience, University of Texas at Austin TX, USA.
5
European Molecular Biology Laboratory - European Bioinformatics Institute, Wellcome Trust Genome Campus Hinxton, UK.
6
Neuroinformatics Framework Project, University of California San Diego, CA, USA.
7
Department of Psychology/Neuroscience Institute, Georgia State University GA, USA ; Mind Research Network Albuquerque, NM, USA.

Abstract

We discuss recent progress in the development of cognitive ontologies and summarize three challenges in the coordinated development and application of these resources. Challenge 1 is to adopt a standardized definition for cognitive processes. We describe three possibilities and recommend one that is consistent with the standard view in cognitive and biomedical sciences. Challenge 2 is harmonization. Gaps and conflicts in representation must be resolved so that these resources can be combined for mark-up and interpretation of multi-modal data. Finally, Challenge 3 is to test the utility of these resources for large-scale annotation of data, search and query, and knowledge discovery and integration. As term definitions are tested and revised, harmonization should enable coordinated updates across ontologies. However, the true test of these definitions will be in their community-wide adoption which will test whether they support valid inferences about psychological and neuroscientific data.

KEYWORDS:

annotation; big data; brain science; cognition; integration; mental functioning; neuroscience; ontology

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