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Regularization in probabilistic inductive logic programming 1

32nd International Conference on inductive logic programming (ILP)

作者： Gentili, Elisabetta Bizzarri, Alice Azzolini, Damiano Zese, Riccardo Riguzzi, Fabrizio Univ Ferrara Dept Engn Ferrara Italy Univ Ferrara Dept Environm & Prevent Sci Ferrara Italy Univ Ferrara Dept Chem Pharmaceut & Agr Sci Ferrara Italy Univ Ferrara Dept Math & Comp Sci Ferrara Italy

ISBN: (数字)9783031492990

ISBN: (纸本)9783031492983;9783031492990

probabilistic logic programming combines uncertainty and logic-based languages. Liftable probabilistic logic Programs have been recently proposed to perform inference in a lifted way. LIFTCOVER is an algorithm used to perform parameter and structure learning of liftable probabilistic logic programs. In particular, it performs parameter learning via Expectation Maximization and LBFGS. In this paper, we present an updated version of LIFTCOVER, called LIFTCOVER+, in which regularization was added to improve the quality of the solutions and LBFGS was replaced by gradient descent. We tested LIFTCOVER+ on the same 12 datasets on which LIFTCOVER was tested and compared the performances in terms of AUC-ROC, AUC-PR, and execution times. Results show that in most cases Expectation Maximization with regularization improves the quality of the solutions.

关键词： probabilistic inductive logic programming Regularization Statistical Relational Artificial Intelligence

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Pruning strategies for the efficient traversal of the search space in PILP environments

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KNOWLEDGE AND INFORMATION SYSTEMS 2021年第12期63卷 3183-3215页

作者： Corte-Real, Joana Dutra, Ines Rocha, Ricardo Univ Porto Fac Sci Dept Comp Sci Rua Campo Alegre 1021 P-4169007 Porto Portugal CRACS Porto Portugal INESC TEC Porto Portugal CINTESIS Porto Portugal

probabilistic inductive logic programming (PILP) is a statistical relational learning technique which extends inductive logic programming by considering probabilistic data. The ability to use probabilities to represent uncertainty comes at the cost of an exponential evaluation time when composing theories to model the given problem. For this reason, PILP systems rely on various pruning strategies in order to reduce the search space. However, to the best of the authors' knowledge, there has been no systematic analysis of the different pruning strategies, how they impact the search space and how they interact with one another. This work presents a unified representation for PILP pruning strategies which enables end-users to understand how these strategies work both individually and combined and to make an informed decision on which pruning strategies to select so as to best achieve their goals. The performance of pruning strategies is evaluated both time and quality-wise in two state-of-the-art PILP systems with datasets from three different domains. Besides analysing the performance of the pruning strategies, we also illustrate the utility of PILP in one of the application domains, which is a real-world application.

关键词： Statistical relational learning probabilistic inductive logic programming Search space evaluation Implementation Performance

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Towards a logic programming Tool for Cancer Data Analysis

Towards a Logic Programming Tool for Cancer Data Analysis

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Fundamenta Informaticae

作者： Tarzariol, Alice Zanazzo, Eugenia Dovier, Agostino Policriti, Alberto Dipartimento di Scienze Matematiche Informatiche e Fisiche Università Degli Studi di Udine Italy

The main goal of this work is to propose a tool-chain capable of analyzing a data collection of temporally qualified (genetic) mutation profiles, i.e., a collection of DNA-sequences (genes) that present variations with respect to their 'healthy' versions. We implemented a system consisting of a front-end, a reasoning core, and a post-processor: the first transforms the input data retrieved from medical databases into a set of logical facts, while the last displays the computation results as graphs. Concerning the reasoning core, we employed the Answer Set programming paradigm, which is capable of deducing complex information from data. However, since the system is modular, this component can be replaced by any logic programming tool for different kinds of data analysis. Indeed, we tested the use of a probabilistic inductive logic programming core. © 2020 - IOS Press and the authors. All rights reserved.

关键词： inductive logic programming (ILP) Computation theory Computer circuits Data handling DNA sequences Genetic programming Information analysis Medical computing Answer set programming Complex information Data collection Front end Input datas Medical database Post processor probabilistic inductive logic programming

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Lifted discriminative learning of probabilistic logic programs

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MACHINE LEARNING 2019年第7期108卷 1111-1135页

作者： Fadja, Arnaud Nguembang Riguzzi, Fabrizio Univ Ferrara Dipartimento Ingn Via Saragat 1 I-44122 Ferrara Italy Univ Ferrara Dipartimento Matemat & Informat Via Saragat 1 I-44122 Ferrara Italy

probabilistic logic programming (PLP) provides a powerful tool for reasoning with uncertain relational models. However, learning probabilistic logic programs is expensive due to the high cost of inference. Among the proposals to overcome this problem, one of the most promising is lifted inference. In this paper we consider PLP models that are amenable to lifted inference and present an algorithm for performing parameter and structure learning of these models from positive and negative examples. We discuss parameter learning with EM and LBFGS and structure learning with LIFTCOVER, an algorithm similar to SLIPCOVER. The results of the comparison of LIFTCOVER with SLIPCOVER on 12 datasets show that it can achieve solutions of similar or better quality in a fraction of the time.

关键词： Statistical relational learning probabilistic inductive logic programming probabilistic logic programming Lifted inference Expectation maximization

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Learning failure-free PRISM programs

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2015年 67卷 73-110页

作者： Alsanie, Waleed Cussens, James King Abdulaziz City Sci & Technol Commun & Informat Technol Res Inst Natl Ctr Computat Technol & Appl Math Riyadh Saudi Arabia Univ York Dept Comp Sci York YO10 5DD N Yorkshire England

PRISM is a probabilistic logic programming formalism which allows defining a probability distribution over possible worlds. This paper investigates learning a class of generative PRISM programs known as failure-free. The aim is to learn recursive PRISM programs which can be used to model stochastic processes. These programs generalise dynamic Bayesian networks by defining a halting distribution over the generative process. Dynamic Bayesian networks model infinite stochastic processes. Sampling from infinite process can only be done by specifying the length of sequences that the process generates. In this case, only observations of a fixed length of sequences can be obtained. On the other hand, the recursive PRISM programs considered in this paper are self-terminating upon some halting conditions. Thus, they generate observations of different lengths of sequences. The direction taken by this paper is to combine ideas from inductive logic programming and learning Bayesian networks to learn PRISM programs. It builds upon the inductive logic programming approach of learning from entailment. (C) 2015 Elsevier Inc. All rights reserved.

关键词： probabilistic inductive logic programming probabilistic logic PRISM Structure learning

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Structure learning of probabilistic logic programs by searching the clause space

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THEORY AND PRACTICE OF logic programming 2015年第2期15卷 169-212页

作者： Bellodi, Elena Riguzzi, Fabrizio Univ Ferrara Dipartimento Ingn I-44122 Ferrara Italy Univ Ferrara Dipartimento Matemat & Informat I-44122 Ferrara Italy

Learning probabilistic logic programming languages is receiving an increasing attention, and systems are available for learning the parameters (PRISM, LeProbLog, LFI-ProbLog and EMBLEM) or both structure and parameters (SEM-CP-logic and SLIPCASE) of these languages. In this paper we present the algorithm SLIPCOVER for "Structure LearnIng of probabilistic logic programs by searChing OVER the clause space." It performs a beam search in the space of probabilistic clauses and a greedy search in the space of theories using the log likelihood of the data as the guiding heuristics. To estimate the log likelihood, SLIPCOVER performs Expectation Maximization with EMBLEM. The algorithm has been tested on five real world datasets and compared with SLIPCASE, SEM-CP-logic, Aleph and two algorithms for learning Markov logic Networks (Learning using Structural Motifs (LSM) and ALEPH++ExactL1). SLIPCOVER achieves higher areas under the precision-recall and receiver operating characteristic curves in most cases.

关键词： probabilistic inductive logic programming statistical relational learning structure learning distribution semantics logic programs with annotated disjunction CP-logic

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SkILL - a Stochastic inductive logic Learner 14

SkILL - a Stochastic Inductive Logic Learner

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IEEE 14th International Conference on Machine Learning and Applications ICMLA

作者： Corte-Real, Joana Mantadelis, Theofrastos Dutra, Ines Rocha, Ricardo Burnside, Elizabeth Univ Porto CRACS Rua Campo Alegre P-4169007 Oporto Portugal Univ Porto INESC TEC P-4169007 Oporto Portugal Univ Wisconsin Dept Radiol Madison WI 53706 USA

ISBN: (纸本)9781509002870

probabilistic inductive logic programming (PILP) is a relatively unexplored area of Statistical Relational Learning which extends classic inductive logic programming (ILP). Within this scope, we introduce SkILL, a Stochastic inductive logic Learner, which takes probabilistic annotated data and produces First Order logic (FOL) theories. Data in several domains such as medicine and bioinformatics have an inherent degree of uncertainty, and because SkILL can handle this type of data, the models produced for these areas are closer to reality. SkILL can then use probabilistic data to extract non-trivial knowledge from databases, and also address efficiency issues by introducing an efficient search strategy for finding hypotheses in PILP environments. SkILL's capabilities are demonstrated using a real world medical dataset in the breast cancer domain.

关键词： probabilistic inductive logic programming Statistical relational learning

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Web Stream Reasoning Using probabilistic Answer Set programming

Web Stream Reasoning Using Probabilistic Answer Set Programm...

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8th International Conference on Web Reasoning and Rule Systems (RR)

作者： Nickles, Matthias Mileo, Alessandra Natl Univ Ireland INSIGHT Ctr Data Analyt Galway Ireland

ISBN: (纸本)9783319111131;9783319111124

We propose a framework for reasoning about dynamic Web data, based on probabilistic Answer Set programming (ASP). Our approach, which is prototypically implemented, allows for the annotation of first-order formulas as well as ASP rules and facts with probabilities, and for learning of such weights from examples (parameter estimation). Knowledge as well as examples can be provided incrementally in the form of RDF data streams. Optionally, stream data can be configured to decay over time. With its hybrid combination of various contemporary AI techniques, our framework aims at prevalent challenges in relation to data streams and Linked Data, such as inconsistencies, noisy data, and probabilistic processing rules.

关键词： Web Reasoning Uncertainty Stream Reasoning Answer Set programming RDF probabilistic inductive logic programming Machine Learning

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Expectation maximization over binary decision diagrams for probabilistic logic programs

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INTELLIGENT DATA ANALYSIS 2013年第2期17卷 343-363页

作者： Bellodi, Elena Riguzzi, Fabrizio Univ Ferrara ENDIF Dipartimento Ingn I-44122 Ferrara Italy

Recently much work in Machine Learning has concentrated on using expressive representation languages that combine aspects of logic and probability. A whole field has emerged, called Statistical Relational Learning, rich of successful applications in a variety of domains. In this paper we present a Machine Learning technique targeted to probabilistic logic Programs, a family of formalisms where uncertainty is represented using logic programming tools. Among various proposals for probabilistic logic programming, the one based on the distribution semantics is gaining popularity and is the basis for languages such as ICL, PRISM, ProbLog and logic Programs with Annotated Disjunctions. This paper proposes a technique for learning parameters of these languages. Since their equivalent Bayesian networks contain hidden variables, an Expectation Maximization (EM) algorithm is adopted. In order to speed the computation up, expectations are computed directly on the Binary Decision Diagrams that are built for inference. The resulting system, called EMBLEM for "EM over Bdds for probabilistic logic programs Efficient Mining", has been applied to a number of datasets and showed good performances both in terms of speed and memory usage. In particular its speed allows the execution of a high number of restarts, resulting in good quality of the solutions.

关键词： Statistical relational learning probabilistic inductive logic programming probabilistic logic programs logic programs with annotated disjunctions expectation maximization binary decision diagrams

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Applying the information bottleneck to statistical relational learning

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MACHINE LEARNING 2012年第1期86卷 89-114页

作者： Riguzzi, Fabrizio Di Mauro, Nicola Univ Ferrara Dipartimento Ingn I-44100 Ferrara Italy Univ Bari Aldo Moro Dipartimento Informat Bari Italy

In this paper we propose to apply the Information Bottleneck (IB) approach to the sub-class of Statistical Relational Learning (SRL) languages that are reducible to Bayesian networks. When the resulting networks involve hidden variables, learning these languages requires the use of techniques for learning from incomplete data such as the Expectation Maximization (EM) algorithm. Recently, the IB approach was shown to be able to avoid some of the local maxima in which EM can get trapped when learning with hidden variables. Here we present the algorithm Relational Information Bottleneck (RIB) that learns the parameters of SRL languages reducible to Bayesian Networks. In particular, we present the specialization of RIB to a language belonging to the family of languages based on the distribution semantics, logic Programs with Annotated Disjunction (LPADs). This language is prototypical for such a family and its equivalent Bayesian networks contain hidden variables. RIB is evaluated on the IMDB, Cora and artificial datasets and compared with LeProbLog, EM, Alchemy and PRISM. The experimental results show that RIB has good performances especially when some logical atoms are unobserved. Moreover, it is particularly suitable when learning from interpretations that share the same Herbrand base.

关键词： probabilistic inductive logic programming Statistical relational learning inductive logic programming Knowledge based model construction Distribution semantics

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