Markov Logic
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Markov Logic
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Author : Pedro Domingos
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2009-05-08
Markov Logic written by Pedro Domingos and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-05-08 with Computers categories.
Most subfields of computer science have an interface layer via which applications communicate with the infrastructure, and this is key to their success (e.g., the Internet in networking, the relational model in databases, etc.). So far this interface layer has been missing in AI. First-order logic and probabilistic graphical models each have some of the necessary features, but a viable interface layer requires combining both. Markov logic is a powerful new language that accomplishes this by attaching weights to first-order formulas and treating them as templates for features of Markov random fields. Most statistical models in wide use are special cases of Markov logic, and first-order logic is its infinite-weight limit. Inference algorithms for Markov logic combine ideas from satisfiability, Markov chain Monte Carlo, belief propagation, and resolution. Learning algorithms make use of conditional likelihood, convex optimization, and inductive logic programming. Markov logic has been successfully applied to problems in information extraction and integration, natural language processing, robot mapping, social networks, computational biology, and others, and is the basis of the open-source Alchemy system.
Practical Markov Logic Containing First Order Quantifiers With Application To Identity Uncertainty
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Author :
language : en
Publisher:
Release Date : 2005
Practical Markov Logic Containing First Order Quantifiers With Application To Identity Uncertainty written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with categories.
Markov logic is a highly expressive language recently introduced to specify the connectivity of a Markov network using first-order logic. While Markov logic is capable of constructing arbitrary first-order formulae over the data, the complexity of these formulae is often limited in practice because of the size and connectivity of the resulting network. In this paper, we present approximate inference and training methods that incrementally instantiate portions of the network as needed to enable first-order existential and universal quantifiers in Markov logic networks. When applied to the problem of object identification, this approach results in a conditional probabilistic model that can reason about objects, combining the expressively of recently introduced BLOG models with the predictive power of conditional training. We validate our algorithms on the tasks of citation matching and author disambiguation.
Markov Logic
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Author : Pedro Dechter
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Markov Logic written by Pedro Dechter and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Computers categories.
Most subfields of computer science have an interface layer via which applications communicate with the infrastructure, and this is key to their success (e.g., the Internet in networking, the relational model in databases, etc.). So far this interface layer has been missing in AI. First-order logic and probabilistic graphical models each have some of the necessary features, but a viable interface layer requires combining both. Markov logic is a powerful new language that accomplishes this by attaching weights to first-order formulas and treating them as templates for features of Markov random fields. Most statistical models in wide use are special cases of Markov logic, and first-order logic is its infinite-weight limit. Inference algorithms for Markov logic combine ideas from satisfiability, Markov chain Monte Carlo, belief propagation, and resolution. Learning algorithms make use of conditional likelihood, convex optimization, and inductive logic programming. Markov logic has been successfully applied to problems in information extraction and integration, natural language processing, robot mapping, social networks, computational biology, and others, and is the basis of the open-source Alchemy system. Table of Contents: Introduction / Markov Logic / Inference / Learning / Extensions / Applications / Conclusion
Efficient Maximum A Posteriori Inference In Markov Logic And Application In Description Logics
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Author : Jan Nößner
language : en
Publisher:
Release Date : 2014
Efficient Maximum A Posteriori Inference In Markov Logic And Application In Description Logics written by Jan Nößner and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.
Statistical Relational Artificial Intelligence
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Author : Luc De Kang
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Statistical Relational Artificial Intelligence written by Luc De Kang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Computers categories.
An intelligent agent interacting with the real world will encounter individual people, courses, test results, drugs prescriptions, chairs, boxes, etc., and needs to reason about properties of these individuals and relations among them as well as cope with uncertainty. Uncertainty has been studied in probability theory and graphical models, and relations have been studied in logic, in particular in the predicate calculus and its extensions. This book examines the foundations of combining logic and probability into what are called relational probabilistic models. It introduces representations, inference, and learning techniques for probability, logic, and their combinations. The book focuses on two representations in detail: Markov logic networks, a relational extension of undirected graphical models and weighted first-order predicate calculus formula, and Problog, a probabilistic extension of logic programs that can also be viewed as a Turing-complete relational extension of Bayesian networks.
An Inductive Logic Programming Approach To Statistical Relational Learning
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Author : Kristian Kersting
language : en
Publisher: IOS Press
Release Date : 2006
An Inductive Logic Programming Approach To Statistical Relational Learning written by Kristian Kersting and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with Computers categories.
Talks about Logic Programming, Uncertainty Reasoning and Machine Learning. This book includes definitions that circumscribe the area formed by extending Inductive Logic Programming to cases annotated with probability values. It investigates the approach of Learning from proofs and the issue of upgrading Fisher Kernels to Relational Fisher Kernels.
Probabilistic Inductive Logic Programming
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Author : Luc De Raedt
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-03-14
Probabilistic Inductive Logic Programming written by Luc De Raedt and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-03-14 with Computers categories.
The question, how to combine probability and logic with learning, is getting an increased attention in several disciplines such as knowledge representation, reasoning about uncertainty, data mining, and machine learning simulateously. This results in the newly emerging subfield known under the names of statistical relational learning and probabilistic inductive logic programming. This book provides an introduction to the field with an emphasis on the methods based on logic programming principles. It is concerned with formalisms and systems, implementations and applications, as well as with the theory of probabilistic inductive logic programming. The 13 chapters of this state-of-the-art survey start with an introduction to probabilistic inductive logic programming; moreover the book presents a detailed overview of the most important probabilistic logic learning formalisms and systems such as relational sequence learning techniques, using kernels with logical representations, Markov logic, the PRISM system, CLP(BN), Bayesian logic programs, and the independent choice logic. The third part provides a detailed account of some show-case applications of probabilistic inductive logic programming. The final part touches upon some theoretical investigations and includes chapters on behavioural comparison of probabilistic logic programming representations and a model-theoretic expressivity analysis.
Ecai 2008
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Author : European Coordinating Committee for Artificial Intelligence
language : en
Publisher: IOS Press
Release Date : 2008
Ecai 2008 written by European Coordinating Committee for Artificial Intelligence and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Computers categories.
Includes subconference "Prestigious Applications of Intelligent Systems (PAIS 2008)."
Introduction To Statistical Relational Learning
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Author : Lise Getoor
language : en
Publisher: MIT Press
Release Date : 2019-09-22
Introduction To Statistical Relational Learning written by Lise Getoor and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-22 with Computers categories.
Advanced statistical modeling and knowledge representation techniques for a newly emerging area of machine learning and probabilistic reasoning; includes introductory material, tutorials for different proposed approaches, and applications. Handling inherent uncertainty and exploiting compositional structure are fundamental to understanding and designing large-scale systems. Statistical relational learning builds on ideas from probability theory and statistics to address uncertainty while incorporating tools from logic, databases and programming languages to represent structure. In Introduction to Statistical Relational Learning, leading researchers in this emerging area of machine learning describe current formalisms, models, and algorithms that enable effective and robust reasoning about richly structured systems and data. The early chapters provide tutorials for material used in later chapters, offering introductions to representation, inference and learning in graphical models, and logic. The book then describes object-oriented approaches, including probabilistic relational models, relational Markov networks, and probabilistic entity-relationship models as well as logic-based formalisms including Bayesian logic programs, Markov logic, and stochastic logic programs. Later chapters discuss such topics as probabilistic models with unknown objects, relational dependency networks, reinforcement learning in relational domains, and information extraction. By presenting a variety of approaches, the book highlights commonalities and clarifies important differences among proposed approaches and, along the way, identifies important representational and algorithmic issues. Numerous applications are provided throughout.
Logical And Relational Learning
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Author : Luc De Raedt
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-09-12
Logical And Relational Learning written by Luc De Raedt and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-09-12 with Computers categories.
This first textbook on multi-relational data mining and inductive logic programming provides a complete overview of the field. It is self-contained and easily accessible for graduate students and practitioners of data mining and machine learning.