Probabilistic Ontologies in Datalog+/-

to induce a modular structure and allows users to explore the entire ontology in a sensi- ble manner so that we can find appropriate module.. Atomic decomposition, however,

Forward and backward reasoning is naturally supported by the ALP proof procedure, and the considered SCIFF language smoothly supports the integra- tion of rules, expressed in a

In this work, we show that Abductive Logic Programming (ALP) is also a suitable framework for representing Datalog ± ontologies, supporting query answering through an abductive

While an ordinary Datalog program specifies a unique extension of the database, soft rules specify a probability space over such extensions.. Intuitively, the probability of a

The semantics of a PPDL program is a probability distribution over the possible outcomes of the input database with respect to the program.. These outcomes are minimal solutions

We devise an enhanced resolution rewriting procedure for disjunctive datalog, and propose a second class of SHI ontologies that admits exponential datalog rewritings via reso-

We show that the query containment problem for monadic datalog on finite unranked labeled trees can be solved in 2-fold expo- nential time when (a) considering unordered trees using

A probabilistic (guarded) Datalog ± mapping has the form M = (S, T, pΣ st , pΣ t , M), where (i) S and T are the source and the target schemas or ontologies, respectively, (ii) pΣ st