On the implementation of the probabilistic logic programming language ProbLog

Kimmig, Angelika; Demoen, Bart; Raedt, Luc De; Costa, Vítor Santos; Rocha, Ricardo

doi:10.1017/s1471068410000566

Cited by 110 publications

(147 citation statements)

References 29 publications

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“…As regards the sum-of-products problem, algorithms based on BDDs were able to solve problems with hundred of thousand of variables (see e.g. the works on inference on probabilistic logic programs (De Raedt et al, 2007;Riguzzi, 2007Riguzzi, , 2009Riguzzi and Swift, 2010;Kimmig et al, 2011;Riguzzi and Swift, 2013)). Also methods for weighted model counting (Sang et al, 2005;Chavira and Darwiche, 2008) can be used to solve the sum-of-products problem.…”

Section: Computational Complexitymentioning

confidence: 99%

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Probabilistic Description Logics under the distribution semantics

Riguzzi

Bellodi

Lamma

et al. 2015

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Representing uncertain information is crucial for modeling real world domains. In this paper we present a technique for the integration of probabilistic information in Description Logics (DLs) that is based on the distribution semantics for probabilistic logic programs. In the resulting approach, that we called DISPONTE, the axioms of a probabilistic knowledge base (KB) can be annotated with a real number between 0 and 1. A probabilistic knowledge base then defines a probability distribution over regular KBs called worlds and the probability of a given query can be obtained from the joint distribution of the worlds and the query by marginalization. We present the algorithm BUNDLE for computing the probability of queries from DISPONTE knowledge bases. The algorithm exploits an underlying DL reasoner, such as Pellet, that is able to return explanations for queries. The explanations are encoded in a Binary Decision Diagram from which the probability of the query is computed. The experimentation of BUNDLE on probabilistic knowledge bases shows that it can handle knowledge bases of realistic size.

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Section: Computational Complexitymentioning

confidence: 99%

“…The distribution semantics was applied successfully in many domains (De Raedt et al, 2007;Sato and Kameya, 2001;Bellodi and Riguzzi, 2012) and various inference and learning algorithms are available for it (Kimmig et al, 2011;Riguzzi, 2009;Bellodi and Riguzzi, 2013).…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Description Logics under the distribution semantics

Riguzzi

Bellodi

Lamma

et al. 2015

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“…For example, there now exist logical versions of Markov networks, called Markov logic networks [40], and of Bayesian networks, called Bayesian logic programs [41]. Influential is also Poole's independent choice logic [42,43], in addition to ProbLog [44] and CP-Logic [19]. These probabilistic logics offer a very natural and flexible choice for modelling complex domains involving uncertainty.…”

Section: Probabilistic Verificationmentioning

confidence: 99%

A Hybrid Approach to the Verification of Computer Interpretable Guidelines

Anselma

Bottrighi

Giordano

et al. 2015

Foundations of Biomedical Knowledge Representation

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Abstract. Computer Interpretable Guidelines (CIGs) are assuming a major role in the medical area, in order to enhance the quality of medical assistance by providing physicians with evidence-based recommendations. However, the complexity of CIGs (which may contain hundreds of related clinical activities) demands for a verification process, aimed at assuring that a CIG satisfies several different types of properties (e.g., verification of the CIG correctness with respect to several criteria). Verification is a demanding task, which may be enhanced through the adoption of advanced Artificial Intelligence techniques. In this paper, we propose a general and hybrid approach to address such a task, suggesting that, given the heterogeneous character of the knowledge in CIGs, different forms of verification should be supported, through the adoption of proper (and different) methodologies.

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“…For experimental results obtained using the various methods in the context of this network as well as for further implementation details, we refer to [25].…”

Section: Monte Carlomentioning

confidence: 99%

Probabilistic Inductive Querying Using ProbLog

Raedt

Kimmig

Gutmann

et al. 2010

Inductive Databases and Constraint-Based Data Mining

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We study how probabilistic reasoning and inductive querying can be combined within ProbLog, a recent probabilistic extension of Prolog. ProbLog can be regarded as a database system that supports both probabilistic and inductive reasoning through a variety of querying mechanisms. After a short introduction to ProbLog, we provide a survey of the different types of inductive queries that ProbLog supports, and show how it can be applied to the mining of large biological networks. IntroductionIn recent years, both probabilistic and inductive databases have received considerable attention in the literature. Probabilistic databases [1] allow one to represent and reason about uncertain data, while inductive databases [2] aim at tight integration of data mining primitives in database query languages. Despite the current interest in these types of databases, there have, to the best of the authors' knowledge, been no attempts to integrate these two trends of research. This chapter wants to contribute to a better understanding of the issues involved by providing a survey of the developments around ProbLog [3] 1 , an extension of Prolog, which supports both inductive and probabilistic querying. ProbLog has been motivated by the need to develop intelligent tools for supporting life scientists analyzing large biological networks. The analysis of such networks typically involves uncertain data, requiring probabilistic representations and inference, as well as the need to find patterns in data, and hence, supporting data mining. ProbLog can be conveniently regarded as a probabilistic database supporting several types of inductive and probabilistic queries. This paper provides an overview of the different types of queries that ProbLog supports.A ProbLog program defines a probability distribution over logic programs (or databases) by specifying for each fact (or tuple) the probability that it belongs to a randomly sampled program (or database), where probabilities are mutually independent. The semantics of ProbLog is then defined by the success probability of a query, which corresponds to the probability that the query succeeds in a randomly sampled program (or database). ProbLog is closely related to other probabilistic logics and probabilistic databases that have been developed over the past two decades to face the general need of combining deductive abilities with reasoning about uncertainty, see e.g. [4,5,6,7,8]. The semantics of ProbLog is studied in Section 10.2. In Section 10.10, we discuss related work in statistical relational learning.We now give a first overview of the types of queries ProbLog supports. Throughout the chapter, we use the graph in Figure 1(a) for illustration, inspired on the application in biological networks discussed in Section 10.9. It contains several nodes (representing entities) as well as edges (representing relationships). Furthermore, the edges are probabilistic, that is, they are present only with the probability indicated. Probabilistic Inference What is the probability that a query...

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On the implementation of the probabilistic logic programming language ProbLog

Cited by 110 publications

References 29 publications

Probabilistic Description Logics under the distribution semantics

Probabilistic Description Logics under the distribution semantics

A Hybrid Approach to the Verification of Computer Interpretable Guidelines

Probabilistic Inductive Querying Using ProbLog

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