Learning the structure of Markov logic networks

By Stanley Kok, Pedro M. Domingos. International Conference on Machine Learning, 2005

354 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/4ea2a70809d812a0d33effaee6d4fc64c4dcf4b3

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In Artificial Intelligence

In Management Science and Operations Research

Citations in the corpus

Statistical Relational Artificial Intelligence: Logic, Probability, and Computation (2016, background): “e earliest work in this direction was due to Kok and Domingos [2005] who searched”
GMNN: Graph Markov Neural Networks (2019, methodology): “The pseudolikelihood approach is widely used for learning Markov networks (Kok & Domingos, 2005; Richardson & Domingos, 2006).”
Structure learning for relational logistic regression: an ensemble approach (2018, background): “edu) [19] with the discriminative weight learning preconditioner scaled conjugate gradient (PSCG) [23].”
Hinge-Loss Markov Random Fields and Probabilistic Soft Logic (2015, background): “Initially, Kok and Domingos (2005) learned rules by generating candidates and performing a beam search to identify rules that improved a weighted pseudolikelihood objective.”
Learning relational dependency networks in hybrid domains (2015, background): “First, to measure the quality of the probability estimates, we report the weighted pseudologlikelihood (WPLL) (Kok and Domingos 2005).”
Entity Resolution with Markov Logic (2006, methodology): “While it was originally developed for the special case of hidden Markov models, and used the Viterbi algorithm to find the MAP state, Singla and Domingos [41] generalized it to MLNs by replacing Viterbi with MaxWalkSAT.”
Inductive biases for deep learning of higher-level cognition (2020, background): “through time with dynamic Bayes nets, or in undirected graphical models such as Markov Networks (Kok and Domingos, 2005) which allow one to “instantiate” general “patterns” into multiple factors of the factor graph.”
Mapping and Revising Markov Logic Networks for Transfer Learning (2007, methodology): “A complete MLN transfer system was developed by augmenting the Alchemy MLN software (Kok et al. 2005).”
Learning Systems of Concepts with an Infinite Relational Model (2006, background): “Methods for learning logical theories (Muggleton & De Raedt 1994; Kok & Domingos 2005) consider relational systems at the more complex end of the spectrum, and it may be worth thinking about hybrid approaches where the clusters discovered by the IRM serve as primitives in more complex 1 Figure 6…”
StatSnowball: a statistical approach to extracting entity relationships (2009, background): “Pattern selection in StatSnowball is the problem of structure learning in Markov logic networks [12] or the problem of feature induction in Markov random fields [19, 16].”
Structure learning in random fields for heart motion abnormality detection (2008, neutral): “[18] (G ,U ,N) Inductive logic program Markov net [25] (G ,U ,Y) L1MB Gaussian [36] (G ,U ,Y) L1MB Binary [22] (G ,U ,Y) L1RF + LBP Binary [15] (G ,U ,Y) MER + jtree Bnry, Thin [27] (G ,U ,N) Exhaustive search -”
The relationship between Precision-Recall and ROC curves (2006, methodology): “…Precision-Recall points will be Often, the area under the curve is used as a simple metric to de(cid:12)ne how an algorithm performs over the whole space (Bradley, 1997; Davis et al., 2005; Goad-rich et al., 2004; Kok & Domingos, 2005; Macskassy & Provost, 2005; Singla & Domingos, 2005).”
Formalizing Visualization Design Knowledge as Constraints: Actionable and Extensible Models in Draco (2018, methodology): “To learn from noisy data (common in the visualization domain!), we could combine inductive logic programming with statistical models such as Markov logic networks [11, 32].”