Drivers for the development of an Animal Health Surveillance Ontology (AHSO).
Classification
Standards
Syndromic surveillance
Terminology
Vocabulary
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
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
39-48Informations de copyright
Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.