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Alexander P. Cox [3]Alexander Cox [2]
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Alexander Cox
Australian National University
  1. The Space Object Ontology.Alexander Cox, Christopher Nebelecky, Ronald Rudnicki, William Tagliaferri, John L. Crassidis & Barry Smith - 2016 - In 19th International Conference on Information Fusion (FUSION 2016). IEEE.
    Achieving space domain awareness requires the identification, characterization, and tracking of space objects. Storing and leveraging associated space object data for purposes such as hostile threat assessment, object identification, and collision prediction and avoidance present further challenges. Space objects are characterized according to a variety of parameters including their identifiers, design specifications, components, subsystems, capabilities, vulnerabilities, origins, missions, orbital elements, patterns of life, processes, operational statuses, and associated persons, organizations, or nations. The Space Object Ontology provides a consensus-based realist framework (...)
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  2. The Neurological Disease Ontology.Mark Jensen, Alexander P. Cox, Naveed Chaudhry, Marcus Ng, Donat Sule, William Duncan, Patrick Ray, Bianca Weinstock-Guttman, Barry Smith, Alan Ruttenberg, Kinga Szigeti & Alexander D. Diehl - 2013 - Journal of Biomedical Semantics 4 (42):42.
    We are developing the Neurological Disease Ontology (ND) to provide a framework to enable representation of aspects of neurological diseases that are relevant to their treatment and study. ND is a representational tool that addresses the need for unambiguous annotation, storage, and retrieval of data associated with the treatment and study of neurological diseases. ND is being developed in compliance with the Open Biomedical Ontology Foundry principles and builds upon the paradigm established by the Ontology for General Medical Science (OGMS) (...)
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  3.  98
    Ontologies for the Study of Neurological Disease.Alexander P. Cox, Mark Jensen, William Duncan, Bianca Weinstock-Guttman, Kinga Szigeti, Alan Ruttenberg, Barry Smith & Alexander D. Diehl - 2012 - In Towards an Ontology of Mental Functioning (ICBO Workshop), Third International Conference on Biomedical Ontology. Graz:
    We have begun work on two separate but related ontologies for the study of neurological diseases. The first, the Neurological Disease Ontology (ND), is intended to provide a set of controlled, logically connected classes to describe the range of neurological diseases and their associated signs and symptoms, assessments, diagnoses, and interventions that are encountered in the course of clinical practice. ND is built as an extension of the Ontology for General Medical Sciences — a high-level candidate OBO Foundry ontology that (...)
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  4.  56
    Representing Disease Courses: An Application of the Neurological Disease Ontology to Multiple Sclerosis Typology.Mark Jensen, Alexander P. Cox, Barry Smith & Alexander Diehl - 2013 - In Proceedings of the Fourth International Conference on Biomedical Ontology (ICBO), CEUR, vol. 1060.
    The Neurological Disease Ontology (ND) is being developed to provide a comprehensive framework for the representation of neurological diseases (Diehl et al., 2013). ND utilizes the model established by the Ontology for General Medical Science (OGMS) for the representation of entities in medicine and disease (Scheuermann et al., 2009). The goal of ND is to include information for each disease concerning its molecular, genetic, and environmental origins, the processes involved in its etiology and realization, as well as its clinical presentation (...)
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  5. Conceptual Space Modeling for Space Event Characterization.Jeremy R. Chapman, David Kasmier, David Limbaugh, Stephen R. Gagnon, John L. Crassidis, James Llinas, Barry Smith & Alexander Cox - 2020 - IEEE 23rd International Conference on Information Fusion (FUSION).
    This paper provides a method for characterizing space events using the framework of conceptual spaces. We focus specifically on estimating and ranking the likelihood of collisions between space objects. The objective is to design an approach for anticipatory decision support for space operators who can take preventive actions on the basis of assessments of relative risk. To make this possible our approach draws on the fusion of both hard and soft data within a single decision support framework. Contextual data is (...)
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