Relational Logistic Regression: The Directed Analog of Markov Logic Networks

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DeepProbLog: Neural Probabilistic Logic Programming (2018, methodology): “[51] treat symbolic rules expressed in first-order logic as a template for constructing a neural network, while Kazemi and Poole [52] compose a relational neural network by adding hidden layers to relational logistic regression [53].”
Structure learning for relational logistic regression: an ensemble approach (2018, methodology): “We consider the more recent, well-understood directed model of Relational Logistic Regression (RLR) (Kazemi et al. 2014a; Kazemi et al. 2014b; Fatemi, Kazemi, and Poole 2016).”
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.”
Relational Representation Learning for Dynamic (Knowledge) Graphs: A Survey (2019, unknown): “ models. Examples include Markov logic networks (Richardson and Domingos (2006)), Problog (De Raedt et al. (2007)), probabilistic soft logic (Kimmig et al. (2012)) and relational logistic regression (Kazemi et al. (2014)). These models typically use soft rules such as: <w: Friends(x;y) ^Friends(y”