scholarly journals Uncertainty Management in Logic Programming: Simple and Effective Top-Down Query Answering

2005 ◽  
pp. 753-760 ◽  
Author(s):  
Umberto Straccia
Keyword(s):  
Logic Programming ◽  
Uncertainty Management ◽  
Query Answering ◽  
Top Down

GLOBAL PROPAGATION IN BAYESIAN NETWORKS VS SEMIJOIN PROGRAMS IN RELATIONAL DATABASES

2005 ◽  
Vol 13 (05) ◽  
pp. 539-560
Author(s):  
DAN WU ◽  
MICHAEL WONG
Keyword(s):  
Bayesian Networks ◽  
Relational Databases ◽  
Probabilistic Reasoning ◽  
Uncertainty Management ◽  
Query Answering ◽  
Knowledge Representations

Bayesian networks have been well established as an effective framework for uncertainty management using probability. Various methods for probabilistic reasoning in Bayesian networks have been developed and matured. Recently, research has shown that there exists an intriguing relationship between Bayesian networks and relational databases. Adding to that intriguing relationship, in this paper, we reveal that the global propagation method for probabilistic reasoning in Bayesian networks has a close tie with the well known semijoin programs for query answering in relational databases. This linkage between these two apparently different but closely related knowledge representations suggests that well developed techniques for query answering in relational databases could be applied to probabilistic reasoning in Bayesian networks for large and complex domains.

scholarly journals Proving Semantic Properties as First-Order Satisfiability (Extended Abstract)

2020 ◽  
Author(s):  
Salvador Lucas
Keyword(s):  
Logic Programming ◽  
Programming Languages ◽  
Answer Set Programming ◽  
Deductive Databases ◽  
Query Answering ◽  
Logical Theory ◽  
Data Bases ◽  
First Order ◽  
Computational Systems ◽  
Semantic Properties

The semantics of computational systems (e.g., relational and knowledge data bases, query-answering systems, programming languages, etc.) can often be expressed as (the specification of) a logical theory Th. Queries, goals, and claims about the behavior or features of the system can be expressed as formulas φ which should be checked with respect to the intended model of Th, which is often huge or even incomputable. In this paper we show how to prove such semantic properties φ of Th by just finding a model A of Th∪{φ}∪Zφ, where Zφ is an appropriate (possibly empty) theory depending on φ only. Applications to relational and deductive databases, rewriting-based systems, logic programming, and answer set programming are discussed.

AN IMPROVEMENT TO TOP-DOWN CLAUSE SPECIALIZATION

1998 ◽  
Vol 07 (01) ◽  
pp. 71-102
Author(s):  
PO-CHI CHEN ◽  
SUH-YIN LEE
Keyword(s):  
Machine Learning ◽  
Logic Programming ◽  
Inductive Logic Programming ◽  
Inductive Logic ◽  
Hill Climbing ◽  
Theory Revision ◽  
Time Increase ◽  
Top Down ◽  
Relational Domains ◽  
Remarkable Progress

One remarkable progress of recent research in machine learning is inductive logic programming (ILP). In most ILP system, clause specialization is one of the most important tasks. Usually, the clause specialization is performed by adding a literal at a time using hill-climbing heuristics. However, the single-literal addition can be caught by local pits when more than one literal needs to be added at a time increase the accuracy. Several techniques have been proposed for this problem but are restricted to relational domains. In this paper, we propose a technique called structure subtraction to construct a set of candidates for adding literals, single-literal or multiple-literals. This technique can be employed in any ILP system using top-down specilization and is not restricted to relational domains. A theory revision system is described to illustrate the use of structural subtraction.

scholarly journals Top-down and Bottom-up Evaluation Procedurally Integrated

2018 ◽  
Vol 18 (3-4) ◽  
pp. 706-721
Author(s):  
DAVID S. WARREN
Keyword(s):  
Program Evaluation ◽  
Data Structure ◽  
Logic Programming ◽  
Logic Program ◽  
Programming System ◽  
Top Down ◽  
Recursive Procedure ◽  
Bottom Up ◽  
Prolog Programs ◽  
Logical Rules

AbstractThis paper describes how the Logic Programming System XSB combines top-down and bottom-up computation through the mechanisms of variant tabling and subsumptive tabling with abstraction, respectively.It is well known that top-down evaluation of logical rules in Prolog has a procedural interpretation as recursive procedure invocation (Kowalski 1986). Tabling adds the intuition of short-circuiting redundant computations (Warren 1992). This paper shows how to introduce into tabled logic program evaluation a bottom-up component, whose procedural intuition is the initialization of a data structure, in which a relation is initially computed and filled, on first demand, and then used throughout the remainder of a larger computation for efficient lookup. This allows many Prolog programs to be expressed fully declaratively, programs which formerly required procedural features, such as assert, to be made efficient.

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