• 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”. 
lirias.kuleuven.be
46 Citations
Highly Influential Citations
4
Background Citations
31
Methods Citations
8

46 Citations

Citation Type

Citation Type

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.
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References

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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.
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TLDR
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