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Recent Advances in Markov Logic Networks

Romany F. Mansour, Samar Hosni
2017 Indian Journal of Science and Technology  
Objectives: To identify recent progress and areas of application for one technique in soft computing, specifically. This technique is known as Markov Logic Networks.  ...  One promising technique that has come to the forefront of soft computing research in recent years is the heavily probabilistic-reasoning-based Markov Logic Network (MLNs).  ...  Figure 2 . 2 Overview of Hidden Markov Models (HMMs). Figure 3 . 3 Lifting Structural Motifs from Similar Markov Logic Networks (MLNs). Romany F. Mansour and Samar Hosni  ... 
doi:10.17485/ijst/2017/v10i19/113384 fatcat:t2prmajkfrcljaiieiufcnr4nq

Learning Compact Markov Logic Networks with Decision Trees [chapter]

Hassan Khosravi, Oliver Schulte, Jianfeng Hu, Tianxiang Gao
2012 Lecture Notes in Computer Science  
Bayes net learning techniques can be applied to learn a set of probabilistic Horn clauses. Inference can be carried out by converting the Horn clauses to Markov Logic Network clauses (MLNs).  ...  In this paper we show that using decision trees to represent conditional probabilities in the Bayes net is an effective remedy for reducing the number of model parameters.  ...  Markov Logic Networks (MLNs) are a prominent generative relational model [2] . An MLN is represented as a set of weighted clauses in first-order logic.  ... 
doi:10.1007/978-3-642-31951-8_5 fatcat:2l6qxpl4nfeplbjmdvyxohyi7i

Learning Markov Logic Networks via Functional Gradient Boosting

Tushar Khot, Sriraam Natarajan, Kristian Kersting, Jude Shavlik
2011 2011 IEEE 11th International Conference on Data Mining  
Recent years have seen a surge of interest in Statistical Relational Learning (SRL) models that combine logic with probabilities. One prominent example is Markov Logic Networks (MLNs).  ...  We use two kinds of representations for the gradients: clausebased and tree-based.  ...  Views and conclusions contained in this document are those of the authors and do not necessarily represent the official opinion or policies, either expressed or implied of the US government or of AFRL.  ... 
doi:10.1109/icdm.2011.87 dblp:conf/icdm/KhotNKS11 fatcat:cxmmbe5wtvcnbdrtg2z75a5lai

Learning graphical models for relational data via lattice search

Oliver Schulte, Hassan Khosravi
2012 Machine Learning  
, pp. 129-173, 2007), and Markov Logic Networks (MLNs) (Domingos and Richardson in Introduction to statistical relational learning, 2007).  ...  Keywords Statistical-relational learning · Graphical models · Markov logic networks · Bayes nets Introduction Many databases store data in relational format, with different types of entities and information  ...  We are grateful to the audiences for helpful questions and comments, especially Lise Getoor.  ... 
doi:10.1007/s10994-012-5289-4 fatcat:gf34zcfaozbcljq42zduqiunhq

Learning compact Markov logic networks with decision trees

Hassan Khosravi, Oliver Schulte, Jianfeng Hu, Tianxiang Gao
2012 Machine Learning  
Markov Logic Networks (MLNs) are a prominent model class that generalizes both first-order logic and undirected graphical models (Markov networks).  ...  The directed clauses are learned using Bayes net methods. The moralization approach takes advantage of the high-quality inference algorithms for MLNs and their ability to handle cyclic dependencies.  ...  Acknowledgments The anonymous referees for the ILP conference and the Machine Learning Journal provided helpful comments and pointers to related literature, especially on learning 21 probability estimation  ... 
doi:10.1007/s10994-012-5307-6 fatcat:j5xq4oxrevhplb5tqagfrzb4pi

Fast Parameter Learning for Markov Logic Networks Using Bayes Nets [chapter]

Hassan Khosravi
2013 Lecture Notes in Computer Science  
Markov Logic Networks (MLNs) are a prominent statistical relational model that have been proposed as a unifying framework for statistical relational learning.  ...  This conversion is exact for converting propositional Markov networks to propositional Bayes nets however, it fails to perform well for the relational case.  ...  Run Times Table 2 shows the time taken in seconds for learning the parameters for Markov Logic Networks using the structures generated by MBN and MBN-DT.  ... 
doi:10.1007/978-3-642-38812-5_8 fatcat:tjbgq3kagndtdk34au24ilz6ke

Lifted graphical models: a survey

Angelika Kimmig, Lilyana Mihalkova, Lise Getoor
2014 Machine Learning  
We also review work in learning lifted graphical models from data.  ...  There is a growing need for statistical relational models (whether they go by that name or another), as we are inundated with data which is a mix of structured and unstructured, with entities and relations  ...  Acknowledgements We would like to thank Galileo Namata and Theodoros Rekatsinas for their comments on earlier versions of this paper. A.  ... 
doi:10.1007/s10994-014-5443-2 fatcat:isfmfjtsmfgezk37jze7rh6xuq

Assisted living technologies for older adults

Parisa Rashidi
2012 Proceedings of the 2nd ACM SIGHIT symposium on International health informatics - IHI '12  
Hidden Semi-Markov Model y 1 y 2 y 3 y 4 x 1 x 2 x 3 x 4 Arbitrary Duration Distribution 51 Markov Network First Order Logic Markov Logic NetworkMarkov logic networks  ...  [Helaoui 2011]  Easily including background knowledge of activities + non-deterministic approach  First order logic + Markov network Markov Logic Network 52  Dynamic Bayesian Network (DBN)  ... 
doi:10.1145/2110363.2110478 dblp:conf/ihi/Rashidi12 fatcat:vavobpvbqzfslm4343duxh7yfe

Lifted Graphical Models: A Survey [article]

Lilyana Mihalkova, Lise Getoor
2011 arXiv   pre-print
We also review work in learning lifted graphical models from data.  ...  It is our belief that the need for statistical relational models (whether it goes by that name or another) will grow in the coming decades, as we are inundated with data which is a mix of structured and  ...  Acknowledgement We would like to thank Galileo Namata and Theodoros Rekatsinas for their comments on earlier versions of this paper. L.  ... 
arXiv:1107.4966v2 fatcat:tkmesnkqjveqnedmos2eeyzy2e

Gradient-based boosting for statistical relational learning: the Markov logic network and missing data cases

Tushar Khot, Sriraam Natarajan, Kristian Kersting, Jude Shavlik
2015 Machine Learning  
We extend our algorithm for MLN structure learning to handle missing data by using an EM-based approach and show this algorithm can also be used to learn Relational Dependency Networks and relational policies  ...  One prominent and highly expressive SRL model is Markov Logic Networks (MLNs), but the expressivity comes at the cost of learning complexity.  ...  Markov logic networks A popular SRL representation is Markov Logic Networks (MLNs) (Domingos and Lowd 2009 ).  ... 
doi:10.1007/s10994-015-5481-4 fatcat:jgqyhgaj7bdrtfmrkp42c6jaya

Learning the Structure of Probabilistic Logic Programs [chapter]

Elena Bellodi, Fabrizio Riguzzi
2012 Lecture Notes in Computer Science  
SLIPCASE has been tested on three real world datasets and compared with SEM-CP-logic and Learning using Structural Motifs, an algorithm for Markov Logic Networks.  ...  In this paper we present the algorithm SLIPCASE for "Structure LearnIng of ProbabilistiC logic progrAmS with Em over bdds".  ...  In [18] the algorithm Learning Markov Logic Networks using Structural Motifs (LSM) is presented: it is based on the observation that relational data frequently contain recurring patterns of densely connected  ... 
doi:10.1007/978-3-642-31951-8_10 fatcat:vnrcxz4pibh4fjffwlepskgl4q

Global Expectation-Violation as Fitness Function in Evolutionary Composition [chapter]

Tim Murray Browne, Charles Fox
2009 Lecture Notes in Computer Science  
Using local musical expectation violation as a measure of tension, we show how global tension structure may be extracted from a source composition and used in a fitness function.  ...  Previous approaches to Common Practice Period style automated composition -such as Markov models and Context-Free Grammars (CFGs) -do not well characterise global, context-sensitive structure of musical  ...  Bharucha's network uses a hierarchical structure of keys, chords and tones, with tones linked to chords they occur in and chords linked to keys they occur in (see fig. 1 ).  ... 
doi:10.1007/978-3-642-01129-0_60 fatcat:ezd7b5fcxbfebpludr7mymztdi

Relational Restricted Boltzmann Machines: A Probabilistic Logic Learning Approach [chapter]

Navdeep Kaur, Gautam Kunapuli, Tushar Khot, Kristian Kersting, William Cohen, Sriraam Natarajan
2018 Lecture Notes in Computer Science  
Specifically, we use lifted random walks to generate features for predicates that are then used to construct the observed features in the RBM in a manner similar to Markov Logic Networks.  ...  This allows one to exploit the feature hierarchies and the non-linearity inherent in RBMs over the rich representations used in statistical relational learning (SRL).  ...  Structure Learning Approaches: Many structure learning approaches for Probabilistic Logical Models (PLMs), including MLNs, use graph representations.  ... 
doi:10.1007/978-3-319-78090-0_7 fatcat:27ssh7u3r5bw7fxmb6ml7l5oti

Comparison of Gene Co-expression Networks and Bayesian Networks [chapter]

Saurabh Nagrecha, Pawan J. Lingras, Nitesh V. Chawla
2013 Lecture Notes in Computer Science  
Dynamic network models provide room for handling noisy or missing prelearned data.  ...  Inferring genetic networks is of great importance in unlocking gene behaviour, which in turn provides solutions for drug testing, disease resistance, and many other applications.  ...  Bayesian Networks The Bayesian networks can be formed using two approaches-score based structure learning algorithms like hill-climbing and Tabu search, and using constraint based structure learning algorithms  ... 
doi:10.1007/978-3-642-36546-1_52 fatcat:gtfmpl2cqvfebo23dfann7i3ey

Learning directed relational models with recursive dependencies

Oliver Schulte, Hassan Khosravi, Tong Man
2012 Machine Learning  
Emprirical evaluation compares our approach to learning recursive dependencies with undirected models (Markov Logic Networks).  ...  An issue for modelling recursive dependencies with Bayes nets are redundant edges that increase the complexity of learning.  ...  LSM Learning Structural Motifs [32] uses random walks to identify densely connected objects in data, and groups them and their associated relations into a motif.  ... 
doi:10.1007/s10994-012-5308-5 fatcat:hjo6n4dnlbh55oomtzgyeumqzq
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