A framework for knowledge-based temporal abstraction

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Abstract

A new domain-independent knowledge-based inference structure is presented, specific to the task of abstracting higher-level concepts from time-stamped data. The framework includes a model of time, parameters, events, and contexts. A formal specification of a domain's temporal abstraction knowledge supports acquisition, maintenance, reuse, and sharing of that knowledge. The knowledge-based temporal abstraction method decomposes the temporal abstraction task into five subtasks. These subtasks are solved by five domain-independent temporal abstraction mechanisms. The temporal abstraction mechanisms depend on four domain-specific knowledge types: structural, classification (functional), temporal semantic (logical), and temporal dynamic (probabilistic) knowledge. Domain values for all knowledge types are specified when a temporal abstraction system is developed. The knowledge-based temporal abstraction method has been implemented in the RÉSUMÉ system, and has been evaluated in several clinical domains (protocol-based care, monitoring of children's growth, and therapy of diabetes) and in an engineering domain (monitoring of traffic control), with encouraging results.

Original languageEnglish
Pages (from-to)79-133
Number of pages55
JournalArtificial Intelligence
Volume90
Issue number1-2
DOIs
StatePublished - 1 Jan 1997
Externally publishedYes

Keywords

  • Knowledge acquisition
  • Knowledge representation
  • Temporal abstraction
  • Temporal reasoning

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