• Corpus ID: 383160

ProbLog: A Probabilistic Prolog and its Application in Link Discovery

@inproceedings{Raedt2007ProbLogAP,
  title={ProbLog: A Probabilistic Prolog and its Application in Link Discovery},
  author={Luc De Raedt and Angelika Kimmig and Hannu (TT) Toivonen},
  booktitle={IJCAI},
  year={2007}
}
We introduce ProbLog, a probabilistic extension of Prolog. A ProbLog program defines a distribution over logic programs by specifying for each clause the probability that it belongs to a randomly sampled program, and these probabilities are mutually independent. The semantics of ProbLog is then defined by the success probability of a query, which corresponds to the probability that the query succeeds in a randomly sampled program. The key contribution of this paper is the introduction of an… 
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This paper proposes 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 and shows that MCINTYRE is faster than the other Monte Carlo systems.
MCINTYRE: A Monte Carlo Algorithm for Probabilistic Logic Programming
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This paper proposes 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 and shows that MCINTYRE is faster than the other Monte Carlo algorithms.
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References

SHOWING 1-10 OF 38 REFERENCES
Stochastic Logic Programs
TLDR
Stochastic logic programs are introduced as a means of providing a structured deenition of such a probability distribution and it is shown that the probabilities can be computed directly for fail-free logic programs and by normalisation for arbitrary logic programs.
Parameter Learning of Logic Programs for Symbolic-Statistical Modeling
TLDR
A logical/mathematical framework for statistical parameter learning of parameterized logic programs, i.e. definite clause programs containing probabilistic facts with a parameterized distribution, and a new EM algorithm that can significantly outperform the Inside-Outside algorithm.
Probabilistic datalog: Implementing logical information retrieval for advanced applications
  • N. Fuhr
  • Computer Science
    J. Am. Soc. Inf. Sci.
  • 2000
TLDR
This work combines Datalog (function-free Horn clause predicate logic) with probability theory to allow for easy formulation of specific retrieval models for arbitrary applications, and classical probabilistic IR models can be implemented by specifying the appropriate rules.
Markov logic networks
TLDR
Experiments with a real-world database and knowledge base in a university domain illustrate the promise of this approach to combining first-order logic and probabilistic graphical models in a single representation.
Logic programming, abduction and probability
  • D. Poole
  • Computer Science
    New Generation Computing
  • 2009
TLDR
This paper proposes an “anytime” algorithm for estimating arbitrary conditional probabilities in probabilistic Horn abduction, an extension of pure Prolog that is useful for diagnosis and other evidential reasoning tasks.
Probabilistic Horn Abduction and Bayesian Networks
  • D. Poole
  • Computer Science
    Artif. Intell.
  • 1993
Probabilistic Logic Programs and their Semantics
TLDR
The aim of this paper is to generalize logic programs, for dealing with probabilistic knowledge, so that their clauses may be true or false with some probabilities and goals may succeed or fail with probabilities too.
On Computing Belief Change Operations using Quantified Boolean Formulas
TLDR
It is shown how an approach to belief revision and belief contraction can be axiomatized by means of quantified Boolean formulas, which furnishes an axiomatic specification of belief change with respect to belief change scenarios.
Graph-Based Algorithms for Boolean Function Manipulation
  • R. Bryant
  • Computer Science
    IEEE Transactions on Computers
  • 1986
TLDR
Experimental results from applying a new data structure for representing Boolean functions and an associated set of manipulation algorithms to problems in logic design verification demonstrate the practicality of this approach.
Logic Programming
  • K. Apt
  • Computer Science
    Handbook of Theoretical Computer Science, Volume B: Formal Models and Sematics
  • 1990
...
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