• Corpus ID: 15382316

Towards digesting the alphabet-soup of statistical relational learning

@inproceedings{Raedt2008TowardsDT,
  title={Towards digesting the alphabet-soup of statistical relational learning},
  author={Luc De Raedt and Bart Demoen and Daan Fierens and Bernd Gutmann and Gerda Janssens and Angelika Kimmig and Niels Landwehr and Theofrastos Mantadelis and Wannes Meert and Ricardo Rocha and V{\'i}tor Santos Costa and Ingo Thon and Joost Vennekens},
  year={2008}
}
This paper reports on our work towards the development of a probabilistic logic programming environment intended as a target language in which other probabilistic languages can be compiled, thereby contributing to the digestion of the “alphabet soup”. 
On the implementation of the probabilistic logic programming language ProbLog
TLDR
Algorithms that allow the efficient execution of queries in ProbLog are introduced, their implementation on top of the YAP-Prolog system is discussed, and their performance in the context of large networks of biological entities is evaluated.
Learning the Structure of Probabilistic Logic Programs
TLDR
The algorithm SLIPCASE performs a beam search in the space of the language of Logic Programs with Annotated Disjunctions using the log likelihood of the data as the guiding heuristics and achieves higher areas under the precision-recall and ROC curves and is more scalable.
EM over Binary Decision Diagrams for Probabilistic Logic Programs
TLDR
A technique for parameter learning targeted to a family of formalisms where uncertainty is represented using Logic Programming techniques the so-called Probabilistic Logic Programs such as ICL, PRISM, ProbLog and LPAD is presented.
Reasoning on Logic Programs with Annotated Disjunctions
TLDR
Four different approximated algorithms are investigated, inspired by similar work done in ProbLog, and each has performances that are usually in line with ProbLog.
Experimentation of an expectation maximization algorithm for probabilistic logic programs
TLDR
The results show that EMBLEM is able to solve problems on which the other systems fail and it often achieves significantly higher areas under the Precision Recall and the ROC curves in a similar time.
Applying the information bottleneck to statistical relational learning
TLDR
This paper presents the algorithm Relational Information Bottleneck (RIB) that learns the parameters of SRL languages reducible to Bayesian Networks and presents the specialization of RIB to a language belonging to the family of languages based on the distribution semantics, Logic Programs with Annotated Disjunction (LPADs).
Structure learning of probabilistic logic programs by searching the clause space
TLDR
The algorithm SLIPCOVER performs a beam search in the space of probabilistic clauses and a greedy search inThe space of theories using the log likelihood of the data as the guiding heuristics and achieves higher areas under the precision-recall and receiver operating characteristic curves in most cases.
Extending ProbLog with Continuous Distributions
TLDR
This paper extends ProbLog with abilities to specify continuous distributions and shows how ProbLog's exact inference mechanism can be modified to cope with such distributions.
Expectation maximization over binary decision diagrams for probabilistic logic programs
TLDR
A Machine Learning technique targeted to Probabilistic Logic Programs, a family of formalisms where uncertainty is represented using Logic Programming tools, and an Expectation Maximization EM algorithm is adopted, showing good performances both in terms of speed and memory usage.
Chapter 10 Probabilistic Inductive Querying Using ProbLog
TLDR
It is shown how probabilistic reasoning and inductive querying can be combined within ProbLog, a recent probabilism extension of Prolog, and how it can be applied to the mining of large biological networks.
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 19 REFERENCES
On the Efficient Execution of ProbLog Programs
TLDR
Algorithms that allow the efficient execution of queries are introduced, their implementation on top of the YAP-Prolog system is discussed, and their performance in the context of large networks of biological entities is evaluated.
Expressivity Analysis for PL-Languages
TLDR
This work proposes a framework for analyzing the expressivity of probabilistic logical languages and suggests a number of approaches that could be used to improve the quality of these languages.
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.
A Statistical Learning Method for Logic Programs with Distribution Semantics
TLDR
The distribution semantics is a straightforward generalization of the traditional least model semantics and can capture semantics of diverse information processing systems ranging from Bayesian networks to Hidden Markov models to Boltzmann machines in a single framework with mathematical rigor.
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.
Model-Theoretic Expressivity Analysis
  • M. Jaeger
  • Biology
    Probabilistic Inductive Logic Programming
  • 2008
TLDR
In the preceding chapter the problem of comparing languages was considered from a behavioral perspective, but this chapter develops an alternative, model-theoretic approach that addresses the problem from a theoretical perspective.
Relational Bayesian Networks
TLDR
A new method is developed to represent probabilistic relations on multiple random events by using a powerful way of specifying conditional probability distributions in these networks, which provides for constraints on equalities of events and allows to define complex, nested combination functions.
ProbLog: A Probabilistic Prolog and its Application in Link Discovery
TLDR
The key contribution of this paper is the introduction of an effective solver for computing success probabilities, which essentially combines SLD-resolution with methods for computing the probability of Boolean formulae.
Integrating by Separating : Combining Probability and Logic with ICL , PRISM and SLPs
TLDR
The close relationship that obtains between the ICL, PRISM and SLP frameworks is described and ‘Lazy’ sampling, based on SLD-resolution, is discussed.
CLP(BN): Constraint Logic Programming for Probabilistic Knowledge
TLDR
The CLP(BN) language represents the joint probability distribution over missing values in a database or logic program by using constraints to represent Skolem functions.
...
1
2
...