Implicitly preserving semantics during incremental knowledge base acquisition under uncertainty

Eugene Santos, Eugene S. Santos, Solomon Eyal Shimony

Research output: Contribution to journalArticlepeer-review

26 Scopus citations


New knowledge is incrementally introduced to an existing knowledge base in a typical knowledge-engineering cycle. Unfortunately, at most given stages, the knowledge base is incomplete but must still satisfy sufficient consistency conditions in order to provide sound semantics. Maintaining semantics for uncertainty is of primary concern. We examine Bayesian knowledge bases (BKBs), which are a generalization of Bayesian networks. BKBs provide a highly flexible and intuitive representation following a basic "if-then" structure in conjunction with probability theory. We present new theoretical and algorithmic results concerning BKBs and how they can naturally and implicitly preserve semantics as new knowledge is added. In particular, equivalence of rule weights and conditional probabilities is achieved through stability of inferencing in BKBs. Furthermore, efficient algorithms are developed to guarantee stability of BKBs during construction. Finally, we examine and prove formal conditions that hold during the incremental construction of BKBs.

Original languageEnglish
Pages (from-to)71-94
Number of pages24
JournalInternational Journal of Approximate Reasoning
Issue number1
StatePublished - 1 Apr 2003


  • Bayesian knowledge bases
  • Knowledge acquisition
  • Knowledge engineering
  • Probabilistic semantics
  • Uncertainty

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Theoretical Computer Science
  • Applied Mathematics


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