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Drivers for the development of an Animal Health Surveillance Ontology (AHSO)
Department of Disease Control and Epidemiology, National Veterinary Institute, Sweden.
Epi-Connect, Skogås, Sweden.
Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Department of Computer and Information Science, Linköping University, Sweden.ORCID iD: 0000-0001-8767-4136
Department of Disease Control and Epidemiology, National Veterinary Institute, Sweden.
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2019 (English)In: Preventive Veterinary Medicine, ISSN 0167-5877, E-ISSN 1873-1716, Vol. 166, p. 39-48Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Elsevier, 2019. Vol. 166, p. 39-48
Keywords [en]
syndromic surveillance, classification, vocabulary, terminology standards
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:hj:diva-43339DOI: 10.1016/j.prevetmed.2019.03.002ISI: 000465055100006PubMedID: 30935504Scopus ID: 2-s2.0-85062891881Local ID: HOA JTH 2019;JTHDatateknikISOAI: oai:DiVA.org:hj-43339DiVA, id: diva2:1296025
Available from: 2019-03-13 Created: 2019-03-13 Last updated: 2019-08-19Bibliographically approved

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Hammar, Karl

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