On a theory of probabilistic deductive databases

On a theory of probabilistic deductive databases

We propose a framework for modeling uncertainty where both belief and doubt can be given independent, first-class status. We adopt probability theory as the mathematical formalism for manipulating uncertainty. An agent can express the uncertainty in her knowledge about a piece of information in the form of a confidence level, consisting of a pair of intervals of probability, one for each of her...

Probabilistic Deductive Databases

Deductive Databases - Theory Meets Practice

Deductive Databases are coming of age with the emergence of efficient and easy to use systems that support queries, reasoning, and application development on databases through declarative logic-based languages. Building on solid theoretical foundations, the field has benefited in the recent years form dramatic advances in the enabling technology. This progress is demonstrated by the completion ...

Non-Monotonic Negation in Probabilistic Deductive Databases

In this paper we study tlw uses and the se­ mantics of non-monotonic negation in prob­ abilistic deductive databases. Based on the stahl<semantics for classical logic program­ ming, we in I roduce the notion of stable for­ mula functions. We show that. stable for­ mula functions are minimal fixpoints of op­ erators associated with probabilistic dednc1 ive databases with negation. Furthermore, s...

On Modal Deductive Databases

We present a query language called MDatalog, which is an extension of Datalog for multimodal deductive databases. We define modal relational algebras and give the seminaive evaluation algorithm and the magic-set transformation for MDatalog queries. Results of this paper are proved for the multimodal logics of belief KDI4s5, KDI45, KD4s5s, KD45(m), which are extensions of the monomodal logic KD4...