Learning Markov logic network structure via hypergraph lifting

Kok, Stanley; Domingos, Pedro

doi:10.1145/1553374.1553440

Cited by 68 publications

(75 citation statements)

References 12 publications

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“…It is unclear whether it is feasible to alter LSM to efficiently learn clauses with constants since such constants may need to be considered individually which dramatically increases the search space. This problem also holds for other existing MLN structure learners [13,21,1,14].…”

Section: Resultsmentioning

confidence: 96%

“…Algorithm 3 gives the pseudocode for mode-guided relational pathfinding, M odeGuidedF indP aths, on the constructed hypergraph. It is an extension of a variant of relational pathfinding presented in [14]. 1 Starting from each true ground atom r(c 1 , ..., c r ) ∈ Δy t , it recursively adds to the path ground atoms or hyperedges that satisfy the mode declarations.…”

Section: Online Max-margin Structure Learning With Mode-guided Relatimentioning

confidence: 99%

“…2 In MLNs, a conjunction of positive literals with weight w is equivalent to a clause of negative literals with weight −w. Previous work [14,15] Finally, for each new clause c, OSL computes the difference in the number of true groundings of c in the ground-truth possible world (x t , y t ) and the predicted possible world (x t , y P t ), Δn c = n c (x t , y t ) − n c (x t , y P t ). Then, only clauses whose difference in number of true groundings is greater than or equal to a predefined threshold minCountDiff will be added to the existing MLN.…”

Section: Modec(t Oken(c V V)) Modec(n Ext(v V)) Modec(inf Ield(c mentioning

confidence: 99%

“…logical clauses) of an MLN [13,21,1,14,15] are batch algorithms that are effectively designed for training data with relatively few mega-examples [20]. A mega-example is a large set of connected facts, and mega-examples are disconnected and independent from each other.…”

Section: Introductionmentioning

confidence: 99%

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Online Structure Learning for Markov Logic Networks

Huynh

Mooney

2011

Machine Learning and Knowledge Discovery in Databases

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Abstract. Most existing learning methods for Markov Logic Networks (MLNs) use batch training, which becomes computationally expensive and eventually infeasible for large datasets with thousands of training examples which may not even all fit in main memory. To address this issue, previous work has used online learning to train MLNs. However, they all assume that the model's structure (set of logical clauses) is given, and only learn the model's parameters. However, the input structure is usually incomplete, so it should also be updated. In this work, we present OSL-the first algorithm that performs both online structure and parameter learning for MLNs. Experimental results on two realworld datasets for natural-language field segmentation show that OSL outperforms systems that cannot revise structure.

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Section: Resultsmentioning

confidence: 96%

Section: Online Max-margin Structure Learning With Mode-guided Relatimentioning

confidence: 99%

Section: Modec(t Oken(c V V)) Modec(n Ext(v V)) Modec(inf Ield(c mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Online Structure Learning for Markov Logic Networks

Huynh

Mooney

2011

Machine Learning and Knowledge Discovery in Databases

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“…Finally, BUSL considers clauses to put into the MLN one-by-one, using WPLL measure for choosing clauses and L-BFGS algorithm for setting parameters. The most recent proposed algorithms are Iterated Local Search (ILS) [1] and Learning via Hyper-graph Lifting (LHL) [9]. ILS is based on the iterated local search meta-heuristic that explores the space of structures through a biased sampling of the set of local optima.…”

Section: Generative Learning Of Mlnsmentioning

confidence: 99%

Discriminative Markov Logic Network Structure Learning Based on Propositionalization and χ 2-Test

Dinh

Exbrayat

Vrain

2010

Advanced Data Mining and Applications

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Abstract. In this paper we present a bottom-up discriminative algorithm to automatically learn Markov Logic Network structures. Our approach relies on a new propositionalization method that transforms the learning dataset into an approximative representation in the form of a boolean table. Using this table, the algorithm constructs a set of candidate clauses according to a χ 2 independence test. To compute and choose clauses, we successively use two different optimization criteria, namely log-likelihood (LL) and conditional log-likelihood (CLL), in order to combine the efficiency of LL optimization algorithms together with the accuracy of CLL ones. First experiments show that our approach outperforms existing discriminative MLN structure learning algorithms.

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Modeling Success, Failure, and Intent of Multi-Agent Activities Under Severe Noise

Sadilek

Kautz

2012

Mobile Context Awareness

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Learning Markov logic network structure via hypergraph lifting

Cited by 68 publications

References 12 publications

Online Structure Learning for Markov Logic Networks

Online Structure Learning for Markov Logic Networks

Discriminative Markov Logic Network Structure Learning Based on Propositionalization and χ 2-Test

Modeling Success, Failure, and Intent of Multi-Agent Activities Under Severe Noise

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