Dynamic temporal interpretation contexts for temporal abstraction

  • Yuval Shahar


Temporal abstraction is the task of abstracting higher‐level concepts from time‐stamped data in a context‐sensitive manner. We have developed and implemented a formal knowledge‐based framework for decomposing and solving that task that supports acquisition, maintenance, reuse, and sharing of temporal‐abstraction knowledge. We present the logical model underlying the representation and runtime formation of interpretation contexts. Interpretation contexts are relevant for abstraction of time‐oriented data and are induced by input data, concluded abstractions, external events, goals of the temporal‐abstraction process, and certain combinations of interpretation contexts. Knowledge about interpretation contexts is represented as a context ontology and as a dynamic induction relation over interpretation contexts and other proposition types. Induced interpretation contexts are either basic, composite, generalized, or nonconvex. We provide two examples of applying our model using an implemented system; one in the domain of clinical medicine (monitoring of diabetes patients) and one in the domain of traffic engineering (evaluation of traffic‐control actions). We discuss several distinct advantages to the explicit separation of interpretation‐context propositions from the propositions inducing them and from the abstractions created within them.


Parameter Interval Abstract Parameter Temporal Abstraction Context Ontology Interpretation Context 
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  1. [1]
    J.F. Allen, Towards a general theory of action and time, Artificial Intelligence 23(2) (1984) 123–;154.zbMATHGoogle Scholar
  2. [2]
    B.G. Buchanan and E.H. Shortliffe, eds., Rule Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project(Addison-Wesley, Reading, MA, 1984).Google Scholar
  3. [3]
    S. Buvac, Qunatifactional Logic of Context, in: Proc. Thirteenth National Conf. Artificial Intelligence (AAAI Press, Menlo Park, 1996) pp. 600–;606.Google Scholar
  4. [4]
    S. Buvac, V. Buvac and I.A. Mason, Metamathematics of contexts, Fundamenta Informaticae 23(3) (1995).Google Scholar
  5. [5]
    S. Buvac and I.A. Mason, Propositional logic of context, in: Proc. Eleventh National Conf. Artificial Intelligence(1993).Google Scholar
  6. [6]
    H. Eriksson, Y. Shahar, S.W. Tu, A.R. Puerta and M.A. Musen, Task modeling with reusable problem-solving methods, Artificial Intelligence 79(2) (1995) 293–;326.Google Scholar
  7. [7]
    L.M. Fagan, VM: Representing time dependent relations in a medical setting, Ph.D. dissertation, Department of Computer Science, Stanford University, Stanford, CA (1980).Google Scholar
  8. [8]
    L.M. Fagan, J.C. Kunz, E.A. Feigenbaum and J.J. Osborn, Extensions to the rule-based formalism for a monitoring task, in: Rule Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project, eds. B.G. Buchanan and E.H. Shortliffe (Addison-Wesley, Reading, MA, 1984) pp. 397–;423.Google Scholar
  9. [9]
    R.V. Guha, Contexts: A formalization and some applications, Ph.D. dissertation, Department of Computer Science, Report No. STAN-CS-91-1399, Stanford University, Stanford, CA (1991).Google Scholar
  10. [10]
    I.J. Haimowitz and I.S. Kohane, Automated trend detection with alternate temporal hypotheses, in: Proc. Thirteenth Internat. Joint Conf. Artificial Intelligence(Morgan Kaufmann, San Mateo, 1993) pp. 146–;151.Google Scholar
  11. [11]
    M.G. Kahn, Combining physiologic models and symbolic methods to interpret time-varying patient data, Methods of Information in Medicine 30(3) (1991) 167–;178.Google Scholar
  12. [12]
    I.S. Kohane, Temporal reasoning in medical expert systems, Technical Report 389, Laboratory of Computer Science, Massachusetts Institute of Technology, Cambridge, MA (1987).Google Scholar
  13. [13]
    R.A. Kowalski and M.J. Sergot, A logic-based calculus of events, New Generation Computing 4 (1986) 67–;95.CrossRefGoogle Scholar
  14. [14]
    M.M. Kuilboer, Y. Shahar, D.M. Wilson and M.A. Musen, Knowledge reuse: Temporal-abstraction mechanisms for the assessment of children’s growth, in: Proc. Seventeenth Ann. Symp. Computer Applications in Medicine, Washington, DC (1993) pp. 449–;453.Google Scholar
  15. [15]
    P. Ladkin, Time representation: A taxonomy of interval relations, in: Proc. Sixth National Conf. Artificial Intelligence, Philadelphia, PA (1986) pp. 360–;366.Google Scholar
  16. [16]
    J. McCarthy, Notes on formalizing context, in: Proc. Thirteenth Internat. Joint Conf. Artificial Intelligence(Morgan Kaufmann, San Mateo, 1993) pp. 555–;560.Google Scholar
  17. [17]
    J. McCarthy and P. Hayes, Some philosophical problems from the standpoint of artificial intelligence, in: Machine Intelligence(Edinburgh University Press, Edinburgh, UK, 1969).Google Scholar
  18. [18]
    M. Molina and Y. Shahar, Problem-solving method reuse and assembly: From clinical monitoring to traffic control, in: Proc. Tenth Banff Knowledge Acquisition for Knowledge-Based Systems Workshop, Banff, Alberta, Canada, Vol. 1 (1996) pp. 19-1–;19-20.Google Scholar
  19. [19]
    M.A. Musen, S.W. Tu, A.K. Das and Y. Shahar, EON: A component-based approach to automation of protocol-directed therapy, Journal of the American Medical Association 3(6) (1996) 367–;388.Google Scholar
  20. [20]
    T.A. Russ, Using hindsight in medical decision making, in: Proc. Thirteenth Ann. Symp. Computer Applications in Medical Care, ed. L.C. Kingsland (IEEE Computing Society Press, Washington, 1989) pp. 38–;44.Google Scholar
  21. [21]
    Y. Shahar, A knowledge-based method for temporal abstraction of clinical data, Ph.D. dissertation, Program in Medical Information Sciences, Knowledge Systems Laboratory Report No. KSL-94-64, Department of Computer Science Report No. STAN-CS-TR-94-1529, Stanford University, Stanford, CA (1994).Google Scholar
  22. [22]
    Y. Shahar, A framework for knowledge-based temporal abstraction, Artificial Intelligence 90(1–;2) (1997) 79–;133.zbMATHGoogle Scholar
  23. [23]
    Y. Shahar and M. Molina, Knowledge-based spatiotemporal abstraction, in: Notes of the AAAI-96 Workshop on Spatial and Temporal Reasoning, Portland, OR (1996) pp. 21–;29.Google Scholar
  24. [24]
    Y. Shahar and M.A. Musen, RéSUMé: A temporal-abstraction system for patient monitoring, Computers and Biomedical Research 26(3) (1993) 255–;273. Reprinted in: Yearbook of Medical Informatics 1994, eds. J.H. van Bemmel, T. McRay and F.K. Schattauer (The International Medical Informatics Association, Stuttgart, 1994) pp. 443–;461.CrossRefGoogle Scholar
  25. [25]
    Y. Shahar and M.A. Musen, Knowledge-based temporal abstraction in clinical domains, Artificial Intelligence in Medicine 8(3) (1996) 267–;298.Google Scholar
  26. [26]
    Y. Shahar and G. Purcell, The context-sensitive pattern-matching task, in: Working Notes of the Workshop on Modelling Context in Knowledge Representation and Reasoning, Internat. Joint Conf. Artificial Intelligence, Montreal, Québec, Canada (1995) pp. 133–;143.Google Scholar
  27. [27]
    Y. Shahar, S.W. Tu and M.A. Musen, Knowledge acquisition for temporal-abstraction mechanisms, Knowledge Acquisition 4(2) (1992) 217–;236.Google Scholar
  28. [28]
    Y. Shoham, Temporal logics in AI: Semantical and ontological considerations, Artificial Intelligence 33(1) (1987) 89–;104.zbMATHMathSciNetGoogle Scholar
  29. [29]
    A. Stein, M.A. Musen and Y. Shahar, A knowledge-acquisition tool for temporal abstraction, in: Proc. 1996 AMIA Ann. Fall Symp.(formerly Symp. Computer Applications in Medical Care), Hanley and Belfus, PH (1996) pp. 204–;208.Google Scholar
  30. [30]
    S.W. Tu, H. Eriksson, J.G. Gennari, Y. Shahar and M.A. Musen, Ontology-based configuration of problem-solving methods and generation of knowledge-acquisition tools: Application of PROTÉGÉII to protocol-based decision support, Artificial Intelligence in Medicine 7(3) (1995) 257–;289.CrossRefGoogle Scholar

Copyright information

© Kluwer Academic Publishers 1998

Authors and Affiliations

  • Yuval Shahar
    • 1
  1. 1.Section on Medical Informatics, Medical School Office Building (MSOB) x215Stanford UniversityStanfordUSA

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