Stable Semantics for Probabilistic Deductive Databases

Stable Semantics of Temporal Deductive Databases

We de ne a preferential semantics based on stable generated models for a very general class of temporal deductive databases. We allow two kinds of temporal information to be represented and queried: timepoint and timestamp formulas, and show how each of them can be translated into the other. Because of their generality, our formalism and our semantics can serve as a basis for comparing and exte...

Processing Deductive Databases under the Disjunctive Stable Model Semantics

Cyclic covers are shown to characterise disjunctive stable models of unstratified deductive databases, and to facilitate top-down query processing, query compilation and view updating under the disjunctive stable model semantics. Such processing is shown to be more complex than comparable processing of stratified databases.

Probabilistic Deductive Databases

Disjunctive Deductive Databases : Semantics , Updates , and Architecture ?

The basic assumption in relational and deductive databases is that there are no gaps in our knowledge. That is, the database cannot store data that contain null values or data that is indeenite. In practical situations knowledge is not precise, and there are gaps in our knowledge. These gaps may be due to null values in the data, may arise when we combine several databases that lead to inconsis...

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...