Markov logic networks

By Matthew Richardson, Pedro M. Domingos. 2006

3,163 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Probabilistic & Bayesian Methods

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Citations in the corpus

The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence (2020, background): “Domingo's Markov Logic Networks seeks to combine symbol-manipulation with the strengths of machine learning (Richardson & Domingos, 2006).”
Neural Logic Machines (2019, background): “Being a powerful way of reasoning over discrete symbols, it is successfully applied to various language-related problems, and has been integrated into modern learning frameworks (Kerst-ing et al., 2000; Richardson & Domingos, 2006; Kimmig et al., 2012).”
Combining Retrieval, Statistics, and Inference to Answer Elementary Science Questions (2016, background): “Praline uses Markov Logic Networks (MLNs) (Richardson and Domingos 2006) to align lexical elements of questions and background science knowledge, and to control inference.”
Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View (2019, background): “Markov networks [18], [70] and Bayesian networks [19], [71] are the”
DeepProbLog: Neural Probabilistic Logic Programming (2018, neutral): “This is reminiscent to the difference between ProbLog and Markov Logic [39] or PSL [40].”
Logic meets Probability: Towards Explainable AI Systems for Uncertain Worlds (2017, background): “For example, in Markov logic networks [Richardson and Domingos, 2006], the weighted formula: is indicative of a fairly involved Markov network obtained by grounding the formula wrt a ﬁnite set of constants D and assigning a potential proportional to 1.2 to the edges of the network.”
Logic Tensor Networks for Semantic Image Interpretation (2017, background): “es are combined with knowledge gathered from datasets, web resources or annotators, about object labels, properties such as shape, colour and size, and affordances, using Markov Logic Networks (MLNs) [25] to predict facts in unseen images. Due to the speciﬁc knowledge-base schema adopted, the effect”
Practical Recommendations for Gradient-Based Training of Deep Architectures (2012, methodology): “, 2011), Markov Logic Networks (Richardson and Domingos, 2006) and relational learning (Getoor and Taskar, 2006).”
Structural-RNN: Deep Learning on Spatio-Temporal Graphs (2015, methodology): “This has been the incentive for many efforts in computer vision and machine learning, such as Logic Networks [46], Graphical Models [28], and Structured SVMs [26].”
Injecting Logical Background Knowledge into Embeddings for Relation Extraction (2015, methodology): “Specifically, approaches based on Markov Logic Networks (MLNs) (Richardson and Domingos, 2006)”
Ordinal Common-sense Inference (2016, positive): “Garrette et al. (2011) and Beltagy et al. (2016) treated textual entailment as probabilistic logical inference in Markov Logic Networks (Richardson and Domingos, 2006).”
Question Answering via Integer Programming over Semi-Structured Knowledge (2016, methodology): “We compare against their best performing system, namely Praline, which uses Markov Logic Networks [Richardson and Domin-gos, 2006] to (a) align lexical elements of the question with probabilistic ﬁrst-order science rules and (b) to control inference.”
Harnessing Deep Neural Networks with Logic Rules (2016, background): “A related line of research, such as Markov logic networks (Richardson and Domingos, 2006), derives probabilistic graphical models (rather than neural networks) from the rule set.”
Church: a language for generative models (2008, background): “…unlike several widely used languages grounded in propositional logic (e.g. BUGS [9]) and ﬁrst-order logic (e.g. the logic programming approaches of [13, 19], BLOG [12], and Markov logic [18]), generative processes in Church are ﬁrst-class objects that can be arbitrarily composed and abstracted.”
A Review of Relational Machine Learning for Knowledge Graphs (2015, methodology): “A common approach is to use Markov logic [126], which is a template language based on logical formulae: Given a set of formulae F 1⁄4 fFigLi1⁄41, we create an edge between nodes in the dependency graph if the corresponding facts occur in at least one grounded formula.”