Temporal Bayesian Knowledge Bases - Reasoning about uncertainty with temporal constraints

Eugene Santos, Deqing Li, Eunice E. Santos, John Korah

Research output: Contribution to journalArticlepeer-review


Time is ubiquitous. Accounting for time and its interaction with change is crucial to modeling the dynamic world, especially in domains whose study of data is sensitive to time such as in medical diagnosis, financial investment, and natural language processing, to name a few. We present a framework that incorporates both uncertainty and time in its reasoning scheme. It is based on an existing knowledge representation called Bayesian Knowledge Bases. It provides a graphical representation of knowledge, time and uncertainty, and enables probabilistic and temporal inferencing. The reasoning scheme is probabilistically sound and the fusion of temporal fragments is well defined. We will discuss some properties of this framework and introduce algorithms to ensure groundedness during the construction of the model. The framework has been applied to both artificial and real world scenarios.

Original languageEnglish (US)
Pages (from-to)12905-12917
Number of pages13
JournalExpert Systems With Applications
Issue number17
StatePublished - Dec 1 2012
Externally publishedYes


  • Bayesian Knowledge-Base
  • Knowledge representation
  • Probabilistic reasoning
  • Temporal reasoning

ASJC Scopus subject areas

  • General Engineering
  • Computer Science Applications
  • Artificial Intelligence


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