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  • 1.
    Dórea, Fernanda C.
    et al.
    Department of Disease Control and Epidemiology, National Veterinary Institute, Sweden.
    Vial, Flavie
    Epi-Connect, Skogås, Sweden.
    Hammar, Karl
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, Faculty of Science & Engineering. Department of Computer Science and Informatics, Jönköping University, Sweden.
    Lindberg, Ann
    Department of Disease Control and Epidemiology, National Veterinary Institute, Sweden.
    Lambrix, Patrick
    Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, Faculty of Science & Engineering.
    Blomqvist, Eva
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, Faculty of Science & Engineering.
    Revie, Crawford W.
    Atlantic Veterinary College, University of Prince Edward Island, Canada.
    Drivers for the development of an Animal Health Surveillance Ontology (AHSO)2019In: Preventive Veterinary Medicine, ISSN 0167-5877, E-ISSN 1873-1716, Vol. 166, no 1, p. 39-48Article in journal (Refereed)
    Abstract [en]

    Comprehensive reviews of syndromic surveillance in animal health have highlighted the hindrances to integration and interoperability among systems when data emerge from different sources. Discussions with syndromic surveillance experts in the fields of animal and public health, as well as computer scientists from the field of information management, have led to the conclusion that a major component of any solution will involve the adoption of ontologies. Here we describe the advantages of such an approach, and the steps taken to set up the Animal Health Surveillance Ontological (AHSO) framework. The AHSO framework is modelled in OWL, the W3C standard Semantic Web language for representing rich and complex knowledge. We illustrate how the framework can incorporate knowledge directly from domain experts or from data-driven sources, as well as by integrating existing mature ontological components from related disciplines. The development and extent of AHSO will be community driven and the final products in the framework will be open-access.

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