Drivers for the development of an Animal Health Surveillance Ontology (AHSO).


Journal

Preventive veterinary medicine
ISSN: 1873-1716
Titre abrégé: Prev Vet Med
Pays: Netherlands
ID NLM: 8217463

Informations de publication

Date de publication:
01 May 2019
Historique:
received: 05 03 2018
revised: 07 01 2019
accepted: 05 03 2019
entrez: 3 4 2019
pubmed: 3 4 2019
medline: 17 4 2019
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 30935504
pii: S0167-5877(18)30170-3
doi: 10.1016/j.prevetmed.2019.03.002
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

39-48

Informations de copyright

Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.

Auteurs

Fernanda C Dórea (FC)

Department of Disease Control and Epidemiology, National Veterinary Institute, Sweden. Electronic address: fernanda.dorea@sva.se.

Flavie Vial (F)

Epi-Connect, Skogås, Sweden.

Karl Hammar (K)

Department of Computer Science and Informatics, Jönköping University, Sweden; Department of Computer and Information Science, Linköping University, Sweden.

Ann Lindberg (A)

Department of Disease Control and Epidemiology, National Veterinary Institute, Sweden.

Patrick Lambrix (P)

Department of Computer and Information Science, Linköping University, Sweden; Swedish e-Science Centre, Linköping University, Sweden.

Eva Blomqvist (E)

Department of Computer and Information Science, Linköping University, Sweden.

Crawford W Revie (CW)

Atlantic Veterinary College, University of Prince Edward Island, Canada.

Articles similaires

Robotic Surgical Procedures Animals Humans Telemedicine Models, Animal

Odour generalisation and detection dog training.

Lyn Caldicott, Thomas W Pike, Helen E Zulch et al.
1.00
Animals Odorants Dogs Generalization, Psychological Smell
Animals TOR Serine-Threonine Kinases Colorectal Neoplasms Colitis Mice
Animals Tail Swine Behavior, Animal Animal Husbandry

Classifications MeSH