27,951 research outputs found
Answer Sets for Consistent Query Answering in Inconsistent Databases
A relational database is inconsistent if it does not satisfy a given set of
integrity constraints. Nevertheless, it is likely that most of the data in it
is consistent with the constraints. In this paper we apply logic programming
based on answer sets to the problem of retrieving consistent information from a
possibly inconsistent database. Since consistent information persists from the
original database to every of its minimal repairs, the approach is based on a
specification of database repairs using disjunctive logic programs with
exceptions, whose answer set semantics can be represented and computed by
systems that implement stable model semantics. These programs allow us to
declare persistence by defaults and repairing changes by exceptions. We
concentrate mainly on logic programs for binary integrity constraints, among
which we find most of the integrity constraints found in practice.Comment: 34 page
Lifted Variable Elimination for Probabilistic Logic Programming
Lifted inference has been proposed for various probabilistic logical
frameworks in order to compute the probability of queries in a time that
depends on the size of the domains of the random variables rather than the
number of instances. Even if various authors have underlined its importance for
probabilistic logic programming (PLP), lifted inference has been applied up to
now only to relational languages outside of logic programming. In this paper we
adapt Generalized Counting First Order Variable Elimination (GC-FOVE) to the
problem of computing the probability of queries to probabilistic logic programs
under the distribution semantics. In particular, we extend the Prolog Factor
Language (PFL) to include two new types of factors that are needed for
representing ProbLog programs. These factors take into account the existing
causal independence relationships among random variables and are managed by the
extension to variable elimination proposed by Zhang and Poole for dealing with
convergent variables and heterogeneous factors. Two new operators are added to
GC-FOVE for treating heterogeneous factors. The resulting algorithm, called
LP for Lifted Probabilistic Logic Programming, has been implemented by
modifying the PFL implementation of GC-FOVE and tested on three benchmarks for
lifted inference. A comparison with PITA and ProbLog2 shows the potential of
the approach.Comment: To appear in Theory and Practice of Logic Programming (TPLP). arXiv
admin note: text overlap with arXiv:1402.0565 by other author
Logical Foundations of Object-Oriented and Frame-Based Languages
We propose a novel logic, called Frame Logic (abbr., F-logic), that accounts in a clean, declarative fashion for most of the structural aspects of object-oriented and frame-based languages. These features include object identity, complex objects, inheritance, polymorphic types, methods, encapsulation, and others. In a sense, F-logic stands in the same relationship to the object-oriented paradigm as classical predicate calculus stands to relational programming. The syntax of F-logic is higher-order, which, among other things, allows the user to explore data and schema using the same declarative language. F-logic has a model-theoretic semantics and a sound and complete resolution-based proof procedure. This paper also discusses various aspects of programming in declarative object-oriented languages based on F-logic
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