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2007
Springer

Extending Markov Logic to Model Probability Distributions in Relational Domains

11 years 5 months ago
Extending Markov Logic to Model Probability Distributions in Relational Domains
Abstract. Markov logic, as a highly expressive representation formalism that essentially combines the semantics of probabilistic graphical models with the full power of first-order logic, is one of the most intriguing representations in the field of probabilistic logical modelling. However, as we will show, models in Markov logic often fail to generalize because the parameters they contain are highly domain-specific. We take the perspective of generative stochastic processes in order to describe probability distributions in relational domains and illustrate the problem in this context by means of simple examples. We propose an extension of the language that involves the specification of a priori independent attributes and that furthermore introduces a dynamic parameter adjustment whenever a model in Markov logic is instantiated for a certain domain (set of objects). Our extension removes the corresponding restrictions on processes for which models can be learned using standard meth...
Dominik Jain, Bernhard Kirchlechner, Michael Beetz
Added 08 Jun 2010
Updated 08 Jun 2010
Type Conference
Year 2007
Where KI
Authors Dominik Jain, Bernhard Kirchlechner, Michael Beetz
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