Stable semantics for probabilistic deductive databases

Raymond Ng, V. S. Subrahmanian*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

63 Scopus citations


In this paper we study the semantics of non-monotonic negation in probabilistic deductive databases. Based on the stable semantics for classical logic programming, we examine three natural notions of stability: stable formula functions, stable families of probabilistic interpretations, and stable probabilistic models. We show that stable formula functions are minimal fixpoints of operators associated with probabilistic logic programs. We also prove that each member in a stable family of probabilistic interpretations is a probabilistic model of the program. Then we show that stable formula functions and stable families behave as duals of each other, tying together elegantly the fixpoint and model theories for probabilistic logic programs with negation. Furthermore, since a probabilistic logic program may not necessarily have a stable family of probabilistic interpretations, we provide a stable class semantics for such programs. Finally, we investigate the notion of stable probabilistic model. We show that this notion, though natural, is too weak in the probabilistic framework.

Original languageEnglish (US)
Pages (from-to)42-83
Number of pages42
JournalInformation and Computation
Issue number1
StatePublished - Apr 1994
Externally publishedYes

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics


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