title: Structuring an event ontology for disease outbreak detection.
abstract: BACKGROUND
  This paper describes the design of an event ontology being developed for application in the machine understanding of infectious disease-related events reported in natural language text. This event ontology is designed to support timely detection of disease outbreaks and rapid judgment of their alerting status by 1) bridging a gap between layman's language used in disease outbreak reports and public health experts' deep knowledge, and 2) making multi-lingual information available.
  This event ontology integrates a model of experts' knowledge for disease surveillance, and at the same time sets of linguistic expressions which denote disease-related events, and formal definitions of events. In this ontology, rather general event classes, which are suitable for application to language-oriented tasks such as recognition of event expressions, are placed on the upper-level, and more specific events of the experts' interest are in the lower level. Each class is related to other classes which represent participants of events, and linked with multi-lingual synonym sets and axioms.
  
  
  CONCLUSIONS
  We consider that the design of the event ontology and the methodology introduced in this paper are applicable to other domains which require integration of natural language information and machine support for experts to assess them. The first version of the ontology, with about 40 concepts, will be available in March 2008.
authors.affiliation: National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo, 101-8430, Japan. zoeai@nii.ac.jp
authors.forename: Ai
authors.initials: A
authors.lastname: Kawazoe
authors.forename: Hutchatai
authors.initials: H
authors.lastname: Chanlekha
authors.forename: Mika
authors.initials: M
authors.lastname: Shigematsu
authors.forename: Nigel
authors.initials: N
authors.lastname: Collier
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D004196:Disease Outbreaks / Q000517:prevention & control*; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D011159:Population Surveillance / Q000379:methods*; D018875:Vocabulary, Controlled