Lifted discriminative learning of probabilistic logic programs

Fadja, Arnaud Nguembang; Riguzzi, Fabrizio

doi:10.1007/s10994-018-5750-0

Cited by 10 publications

(5 citation statements)

References 55 publications

(68 reference statements)

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“…Results show that PASCAL is able to achieve better or comparable results both in terms of quality of the learnt models (measured by the area under the Precision-Recall and the Receiver Operating Characteristic curves) and learning time with respect to all systems. SLIPCOVER, LIFTCOVER and LEMUR, in turn, were shown to be comparable with state-of-art ILP systems (Bellodi and Riguzzi 2015;Nguembang Fadja and Riguzzi 2018).…”

Section: Introductionmentioning

confidence: 83%

“…Datasets Datasets are specific for the learning from entailment setting as they were used in Nguembang Fadja and Riguzzi (2018) to compare LIFTCOVER with SLIPCOVER: they are composed of a set of mega-interpretations, each possibly containing more than one example (i.e., fact for a target predicate). However, those mega-interpretations contain in practice a single fact for the target predicate, so it is possible to classify each megainterpretation as positive or negative depending on the target predicate example.…”

Section: Methodsmentioning

confidence: 99%

“…TILDE was executed with default values for its settings. SLIPCOVER and LIFTCOVER settings can be found in Table 2 of Nguembang Fadja and Riguzzi (2018) for all datasets except Bongard. We applied SLIPCOVER and LIFTCOVER on Bongard by setting NB = 100, NI = 20, NInt = 4, NS = NA = 1, NV = 4, WMin = 0, NIS = 50 (see Section 7 of Nguembang Fadja and Riguzzi 2018).…”

Section: Methodsmentioning

confidence: 99%

“…Tables 4, 5 and 6 show the AUC-PR, AUC-ROC and learning time in seconds respectively, averaged over the folds, for PASCAL, SLIPCOVER, LIFTCOVER and TILDE. SLIPCOVER and LIFTCOVER results are taken from Tables 3, 4 and 5 of Nguembang Fadja and Riguzzi (2018).…”

Section: Methodsmentioning

confidence: 99%

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Probabilistic inductive constraint logic

et al. 2020

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Probabilistic logical models deal effectively with uncertain relations and entities typical of many real world domains. In the field of probabilistic logic programming usually the aim is to learn these kinds of models to predict specific atoms or predicates of the domain, called target atoms/predicates. However, it might also be useful to learn classifiers for interpretations as a whole: to this end, we consider the models produced by the inductive constraint logic system, represented by sets of integrity constraints, and we propose a probabilistic version of them. Each integrity constraint is annotated with a probability, and the resulting probabilistic logical constraint model assigns a probability of being positive to interpretations. To learn both the structure and the parameters of such probabilistic models we propose the system PASCAL for “probabilistic inductive constraint logic”. Parameter learning can be performed using gradient descent or L-BFGS. PASCAL has been tested on 11 datasets and compared with a few statistical relational systems and a system that builds relational decision trees (TILDE): we demonstrate that this system achieves better or comparable results in terms of area under the precision–recall and receiver operating characteristic curves, in a comparable execution time.

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Section: Introductionmentioning

confidence: 83%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Probabilistic inductive constraint logic

et al. 2020

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“…Beam searching with clause refinement was developed for structure learning for probabilistic logic programs (Bellodi and Riguzzi 2015;Nguembang Fadja and Riguzzi 2019). We use this approach because it requires fewer declarative biases than approaches based only on templates.…”

Section: Related Workmentioning

confidence: 99%

Differentiable Inductive Logic Programming for Structured Examples

Shindo

Nishino

Yamamoto

2021

AAAI

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The differentiable implementation of logic yields a seamless combination of symbolic reasoning and deep neural networks. Recent research, which has developed a differentiable framework to learn logic programs from examples, can even acquire reasonable solutions from noisy datasets. However, this framework severely limits expressions for solutions, e.g., no function symbols are allowed, and the shapes of clauses are fixed. As a result, the framework cannot deal with structured examples. Therefore we propose a new framework to learn logic programs from noisy and structured examples, including the following contributions. First, we propose an adaptive clause search method by looking through structured space, which is defined by the generality of the clauses, to yield an efficient search space for differentiable solvers. Second, we propose for ground atoms an enumeration algorithm, which determines a necessary and sufficient set of ground atoms to perform differentiable inference functions. Finally, we propose a new method to compose logic programs softly, enabling the system to deal with complex programs consisting of several clauses. Our experiments show that our new framework can learn logic programs from noisy and structured examples, such as sequences or trees. Our framework can be scaled to deal with complex programs that consist of several clauses with function symbols.

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Expectation Maximization in Deep Probabilistic Logic Programming

Fadja

Riguzzi

Lamma

2018

AI*IA 2018 – Advances in Artificial Intelligence

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Probabilistic Logic Programming (PLP) combines logic and probability for representing and reasoning over domains with uncertainty. Hierarchical probability Logic Programming (HPLP) is a recent language of PLP whose clauses are hierarchically organized forming a deep neural network or arithmetic circuit. Inference in HPLP is done by circuit evaluation and learning is therefore cheaper than any generic PLP language. We present in this paper an Expectation Maximization algorithm, called Expectation Maximization Parameter learning for HIerarchical Probabilistic Logic programs (EMPHIL), for learning HPLP parameters. The algorithm converts an arithmetic circuit into a Bayesian network and performs the belief propagation algorithm over the corresponding factor graph.

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Lifted discriminative learning of probabilistic logic programs

Cited by 10 publications

References 55 publications

Probabilistic inductive constraint logic

Probabilistic inductive constraint logic

Differentiable Inductive Logic Programming for Structured Examples

Expectation Maximization in Deep Probabilistic Logic Programming

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