A framework for knowledge-based temporal abstraction

  • Shahar Y
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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.

Author-supplied keywords

  • 1
  • and
  • data into higher-level concepts
  • data over time
  • endeavor require the collection
  • introduction
  • knowledge acquisition
  • knowledge representation
  • level
  • many domains of human
  • meaningful for that domain
  • much work had been
  • of substantial numbers of
  • temporal abstraction
  • temporal reasoning
  • temporal-abstraction and the knowledge
  • the abstraction of those

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  • Yuval Shahar

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