probabilistic inductive logic programming and Statistical Relational Learning are families of techniques that are exploited in Machine Learning applications to perform advanced tasks in several domains. Every day the ...
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probabilistic inductive logic programming and Statistical Relational Learning are families of techniques that are exploited in Machine Learning applications to perform advanced tasks in several domains. Every day the size and complexity of such problems increases and advanced, expressive and efficient tools are needed to successfully solve them. The literature proposes several algorithms to cope with these problems, each of them with its own quirks and perks. Among various solutions, logicprogramming with Annotated Disjunctions (LPAD) is one of the more attractive formalisms, thanks to the expressiveness and readability of its language. Unfortunately, its most advanced implementations are lacking efficient features and techniques that have been introduced for other formalisms, such as ProbLog. In this work, after introducing LPADs and an inference algorithm for computing the probability of a query, we investigate four different approximated algorithms, inspired by similar work done in ProbLog. In particular, we present each algorithm and we evaluate its performances on real and artificial datasets. The results show that our approaches have performances that are usually in line with ProbLog. The Monte Carlo algorithm, however, has performances that are better than the exact approach in terms of both the maximum size of the problems and the execution time, with a neglectable loss in the accuracy of the result.
Statistical Relational Learning and probabilistic inductive logic programming are two emerging fields that use representation languages able to combine logic and probability. In the field of logicprogramming, the dis...
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Statistical Relational Learning and probabilistic inductive logic programming are two emerging fields that use representation languages able to combine logic and probability. In the field of logicprogramming, the distribution semantics is one of the prominent approaches for representing uncertainty and underlies many languages such as ICL, PRISM, ProbLog and LPADs. Learning the parameters for such languages requires an Expectation Maximization algorithm since their equivalent Bayesian networks contain hidden variables. EMBLEM (EM over BDDs for probabilisticlogic programs Efficient Mining) is an EM algorithm for languages following the distribution semantics that computes expectations directly on the Binary Decision Diagrams that are built for inference. In this paper we present experiments comparing EMBLEM with LeProbLog, Alchemy, CEM, RIB and LFI-ProbLog on six real world datasets. 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.
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 high-perfo...
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ISBN:
(纸本)9783642212949;9783642212956
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 high-performing inference algorithms. The existing approaches take too long or fail for large problems. In this paper we adapt to LPAD the approaches for approximate inference that have been developed for ProbLog, namely k-best and Monte Carlo. k-Best finds a lower bound of the probability of a query by identifying the k most probable explanations while Monte Carlo estimates the probability by smartly sampling the space of programs. The two techniques have been implemented in the cplint suite and have been tested on real and artificial datasets representing graphs. The results show that both algorithms are able to solve larger problems often in less time than the exact algorithm.
We revisit an application developed originally using abductive inductivelogicprogramming (ILP) for modeling inhibition in metabolic networks. The example data was derived from studies of the effects of toxins on rat...
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We revisit an application developed originally using abductive inductivelogicprogramming (ILP) for modeling inhibition in metabolic networks. The example data was derived from studies of the effects of toxins on rats using Nuclear Magnetic Resonance (NMR) time-trace analysis of their biofluids together with background knowledge representing a subset of the Kyoto Encyclopedia of Genes and Genomes (KEGG). We now apply two probabilistic ILP (PILP) approaches-abductive Stochastic logic Programs (SLPs) and programming In Statistical modeling (PRISM) to the application. Both approaches support abductive learning and probability predictions. Abductive SLPs are a PILP framework that provides possible worlds semantics to SLPs through abduction. Instead of learning logic models from non-probabilistic examples as done in ILP, the PILP approach applied in this paper is based on a general technique for introducing probability labels within a standard scientific experimental setting involving control and treated data. Our results demonstrate that the PILP approach provides a way of learning probabilisticlogic models from probabilistic examples, and the PILP models learned from probabilistic examples lead to a significant decrease in error accompanied by improved insight from the learned results compared with the PILP models learned from non-probabilistic examples.
We revisit an application developed originally using abductive inductivelogicprogramming (ILP) for modeling inhibition in metabolic networks. The example data was derived from studies of the effects of toxins on rat...
详细信息
ISBN:
(纸本)3540784683
We revisit an application developed originally using abductive inductivelogicprogramming (ILP) for modeling inhibition in metabolic networks. The example data was derived from studies of the effects of toxins on rats using Nuclear Magnetic Resonance (NMR) time-trace analysis of their biofluids together with background knowledge representing a subset of the Kyoto Encyclopedia of Genes and Genomes (KEGG). We now apply two probabilistic ILP (PILP) approaches-abductive Stochastic logic Programs (SLPs) and programming In Statistical modeling (PRISM) to the application. Both approaches support abductive learning and probability predictions. Abductive SLPs are a PILP framework that provides possible worlds semantics to SLPs through abduction. Instead of learning logic models from non-probabilistic examples as done in ILP, the PILP approach applied in this paper is based on a general technique for introducing probability labels within a standard scientific experimental setting involving control and treated data. Our results demonstrate that the PILP approach provides a way of learning probabilisticlogic models from probabilistic examples, and the PILP models learned from probabilistic examples lead to a significant decrease in error accompanied by improved insight from the learned results compared with the PILP models learned from non-probabilistic examples.
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