Markov Logic: An Interface Layer for Artificial Intelligence

By Pedro M. Domingos, Daniel Lowd. Markov Logic, 2009

481 citations recorded by the source corpus.

Subfields: Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/b6eb2448e5aefc47586f8149b917a6801fcda1d3

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In Computer Networks and Communications

Citations in the corpus

Unsupervised Ontology Induction from Text (2010, methodology): “In this paper, we use Markov logic (Domingos and Lowd, 2009), which is the leading unify-”
Tuffy: Scaling up Statistical Inference in Markov Logic Networks using an RDBMS (2011, background): “Finding the most likely world is trivial in these models; in contrast, it is highly non-trivial in MLN s (in fact, it is NP -hard [6]).”
Learning to Refine an Automatically Extracted Knowledge Base Using Markov Logic (2012, unknown): “For more background on Markov logic, see Domingos and Lowd [4].”
Convex Adversarial Collective Classification (2013, methodology): “This model can also be easily expressed as a Markov logic network (MLN) (Domingos & Lowd, 2009) in which formulas relate class labels to other attributes and the labels of linked objects.”
Grounded Unsupervised Semantic Parsing (2013, methodology): “USP defines a probabilistic model over the dependency tree and semantic parse using Markov logic (Domingos and Lowd, 2009), and recursively clusters and composes synonymous dependency treelets using a hard EM-like procedure.”
Incremental Knowledge Base Construction Using DeepDive (2015, unknown): “Our language is based on Markov Logic [16,35], and our current language inherits Markov Logic’s formal semantics.”
DeepDive: Declarative Knowledge Base Construction (2016, methodology): “In addition to specifying sets of classiﬁers, DeepDive inherits Markov Logic’s ability to specify rich correlations between entities via weighted rules.”
Human-Guided Learning for Probabilistic Logic Models (2018, background): “that several formalisms such as Markov Logic Networks (MLNs) (Domingos and Lowd, 2009) and Relational Dependency Networks (RDNs) (Neville and Jensen, 2007) and tasks such as relational imitation learning (Natarajan et al.”
News events prediction using Markov logic networks (2018, methodology): “To answer this question, we used the Naive Bayes (NB) algorithm as a propositional learner [54] in which the domain first requires to be propositionalised.”
Machine Reading: A "Killer App" for Statistical Relational AI (2010, methodology): “This novel form of relational clustering is governed by a joint probability distribution P ( T, L ) deﬁned in Markov logic (Domingos and Lowd 2009), where T are input dependency trees, and L semantic parses.”
Structure learning for relational logistic regression: an ensemble approach (2018, methodology): “, they scale well with increase in population sizes unlike other methods such as Markov Logic Networks [4].”
Transfer Learning via Relational Type Matching (2015, background): “One of the most popular PLMs is Markov logic networks (MLNs) [9].”
Machine Reading at the University of Washington (2010, background): “All these can be compactly encoded in Markov logic (Domingos and Lowd, 2009), which provides a unifying framework for knowledge representation and joint inference.”
A user-guided approach to program analysis (2015, background): “For the precise semantics of weights, the reader is referred to [10].”
Neural-Symbolic Learning and Reasoning: A Survey and Interpretation (2017, background): “Markov logic is a probabilistic extension of FOL (Richardson & Domingos, 2006; Domingos & Lowd, 2009).”