Results 1  10
of
56
The SLGAD Procedure for Inference on Logic Programs with Annotated Disjunctions
"... Logic Programs with Annotated Disjunctions (LPADs) allow to express probabilistic information in logic programming. The semantics of an LPAD is given in terms of well founded models of the normal logic programs obtained by selecting one disjunct from each ground LPAD clause. The paper presents SLGAD ..."
Abstract
 Add to MetaCart
Logic Programs with Annotated Disjunctions (LPADs) allow to express probabilistic information in logic programming. The semantics of an LPAD is given in terms of well founded models of the normal logic programs obtained by selecting one disjunct from each ground LPAD clause. The paper presents
Adaptive MCMCBased Inference in Probabilistic Logic Programs
"... Probabilistic Logic Programming (PLP) languages enable programmers to specify systems that combine logical models with statistical knowledge. The inference problem, to determine the probability of query answers in PLP, is intractable in general, thereby motivating the need for approximate technique ..."
Abstract
 Add to MetaCart
Probabilistic Logic Programming (PLP) languages enable programmers to specify systems that combine logical models with statistical knowledge. The inference problem, to determine the probability of query answers in PLP, is intractable in general, thereby motivating the need for approximate
Inference in probabilistic logic programs using weighted CNF’s.
, 2011
"... Abstract Probabilistic logic programs are logic programs in which some of the facts are annotated with probabilities. Several classical probabilistic inference tasks (such as MAP and computing marginals) have not yet received a lot of attention for this formalism. The contribution of this paper is ..."
Abstract

Cited by 16 (10 self)
 Add to MetaCart
Abstract Probabilistic logic programs are logic programs in which some of the facts are annotated with probabilities. Several classical probabilistic inference tasks (such as MAP and computing marginals) have not yet received a lot of attention for this formalism. The contribution of this paper
Probabilistic datalog: Implementing logical information retrieval for advanced applications
 Journal of the American Society for Information Science
, 2000
"... Abstract In the logical approach to information retrieval (IR), retrieval is considered as uncertain inference. Whereas classical IR models are based on propositional logic, we combine Datalog (functionfree Horn clause predicate logic) with probability theory. Therefore, probabilistic weights may ..."
Abstract

Cited by 62 (9 self)
 Add to MetaCart
of recursive rules allows for more powerful inferences, and predicate logic gives the expressiveness required for multimedia retrieval. Furthermore, probabilistic Datalog can be used as a query language for integrated information retrieval and database systems.
Statistical Relational Learning and Probabilistic Inductive Logic Programming
"... Abstract. Logic Programs with Annotated Disjunctions (LPADs) are a promising language for Probabilistic Inductive Logic Programming. In order to develop efficient learning systems for LPADs, it is fundamental to have highperforming inference algorithms. The existing approaches take too long or fail ..."
Abstract
 Add to MetaCart
Abstract. Logic Programs with Annotated Disjunctions (LPADs) are a promising language for Probabilistic Inductive Logic Programming. In order to develop efficient learning systems for LPADs, it is fundamental to have highperforming inference algorithms. The existing approaches take too long
Model Checking with Probabilistic Tabled Logic Programming∗
"... We present a formulation of the problem of probabilistic model checking as one of query evaluation over probabilistic logic programs. To the best of our knowledge, our formulation is the first of its kind, and it covers a rich class of probabilistic models and probabilistic temporal logics. The infe ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
. The inference algorithms of existing probabilistic logicprogramming systems are well defined only for queries with a finite number of explanations. This restriction prohibits the encoding of probabilistic model checkers, where explanations correspond to executions of the system being model checked. To overcome
ColomoPalacios, “ODDIN: ontologydriven differential diagnosis based on logical inference and probabilistic refinements,” Expert Systems with Applications
, 2010
"... a b s t r a c t Medical differential diagnosis (ddx) is based on the estimation of multiple distinct parameters in order to determine the most probable diagnosis. Building an intelligent medical differential diagnosis system implies using a number of knowledge based technologies which avoid ambigui ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
ambiguity, such as ontologies rep resenting specific structured information, but also strategies such as computation of probabilities of var ious factors and logical inference, whose combination outperforms similar approaches. This paper presents ODDIN, an ontology driven medical diagnosis system which
On the proper treatment of quantifiers in probabilistic logic semantics
 IWCS
, 2015
"... As a format for describing the meaning of natural language sentences, probabilistic logic combines the expressivity of firstorder logic with the ability to handle graded information in a principled fashion. But practical probabilistic logic frameworks usually assume a finite domain in which each e ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
) inference problems in probabilistic logic in a way that takes the domain closure and closedworld assumptions into account. We evaluate our proposed technique on three RTE datasets, on a synthetic dataset with a focus on complex forms of quantification, on FraCas and on one more natural dataset. We show
IOS Press MCINTYRE: A Monte Carlo System for Probabilistic Logic Programming
"... Abstract. Probabilistic Logic Programming is receiving an increasing attention for its ability to model domains with complex and uncertain relations among entities. In this paper we concentrate on the problem of approximate inference in probabilistic logic programming languages based on the distribu ..."
Abstract
 Add to MetaCart
an approach for Monte Carlo inference that is based on a program transformation that translates a probabilistic program into a normal program to which the query can be posed. The current sample is stored in the internal database of the Yap Prolog engine. The resulting system, called MCINTYRE for Monte Carlo
Expectation Maximization over Binary Decision Diagrams for Probabilistic Logic Programs
"... Recently much work in Machine Learning has concentrated on using expressive representation languages that combine aspects of logic and probability. A whole field has emerged, called Statistical Relational Learning, rich of successful applications in a variety of domains. In this paper we present a M ..."
Abstract

Cited by 20 (13 self)
 Add to MetaCart
the computation up, expectations are computed directly on the Binary Decision Diagrams that are built for inference. The resulting system, called EMBLEM for “EM over Bdds for probabilistic Logic programs Efficient Mining”, has been applied to a number of datasets and showed good performances both in terms
Results 1  10
of
56