ProbLog: A Probabilistic Prolog and its Application in Link Discovery

By Luc De Raedt, Angelika Kimmig, Hannu (TT) Toivonen. 2007

269 citations recorded by the source corpus.

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Citations in the corpus

Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoning (2019, methodology): “In a related approach, called DeepProbLog, end-to-end learning and reasoning have been studied [28] where outputs of neural networks are used as "neural predicates" for ProbLog [10].”
Statistical Relational Learning of Object Affordances for Robotic Manipulation (2011, methodology): “We outline initial results of using SRL, in particular ProbLog [8], in a manipulation scenario with an iCub robot.”
Gradient-based boosting for statistical relational learning: The relational dependency network case (2011, background): “These models range from directed models (Getoor et al. 2001; Kersting and De Raedt 2007; Fierens et al. 2005; Jaeger 1997; Getoor and Grant 2006) to undirected models (Domingos and Lowd 2009; Koller et al. 2002) and sampling-based approaches (Sato and Kameya 2001; De Raedt et al. 2007; Poole 1993).”
Probabilistic logic programs: Unifying program trace and possible world semantics (2017, background): “, each mention of a random variable within a possible world or program trace refers to the same value, as in ProbLog [20] and ICL [16], or memoize no random variable, i.”
Diachronic Embedding for Temporal Knowledge Graph Completion (2019, background): “StaRAI: Statistical relational AI (StaRAI) [50, 31] approaches are mainly based on soft (hanf-crafted or learned) rules [51, 11, 29, 26] where the probability of a world is typically proportional to the number of rules that are satisfied/violated in that world and the confidence for each rule.”
Uncertainty Aware AI ML: Why and How (2018, background): “However, MLNs are computationally inefficient and perhaps other probabilistic logic reasoning systems such as probabilistic soft logic (Bach et al. 2017) or ProbLog (De Raedt, Kimmig, and Toivonen 2007) should be considered first.”
Computing LPMLN using ASP and MLN solvers* (2017, methodology): “1 Computing ProbLog ProbLog (De Raedt et al. 2007) can be viewed as a special case of the LP language (Lee and Wang 2016), in which soft rules are atomic facts only. The precise relation between the semantics of the two languages is stated by Lee and Wang (2016). System problog2 implements a native ”
Probabilistic Logical Inference on the Web (2016, background): “This problem is difficult (#P complexity) but converting the DNF into a language from which the computation of the probability is polynomial (knowledge compilation [1]) yields algorithm able to handle problems of significant size [2,16].”