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Bayesian Markov Logic Networks - Bayesian Inference for Statistical Relational Learning
No full textOne of the most important foundational challenge of Statistical relational learning is the development of a uniform framework in which learning and logical reasoning are seamlessly integrated. State of the art approaches propose to modify well known machine learning methods based on parameter optimization (e.g., neural networks and graphical models) in order to take into account structural knowledge expressed by logical constraints. In this paper, we follow an alternative direction, considering the Bayesian approach to machine learning. In particular, given a partial knowledge in hybrid domains (i.e., domains that contains relational structure and continuous features) as a set of axioms and a stochastic (in)dependence hypothesis F encoded in a first order language , we propose to model it by a probability distribution function (PDF) (∣,F) over the -interpretations . The stochastic (in)dependence F is represented as a Bayesian Markov Logic Network w.r.t. a parametric undirected graph, interpreted as the PDF. We propose to approximate (∣,F) by variational inference and show that such approximation is possible if and only if F satisfies a property called orthogonality. This property can be achieved also by extending , and adjusting and F
Ontologický přístup k integraci geografických dat
No full textA key point in modern automated data processing is metadata semantics representation. Employing Semantic Web existing features - ontologies - is a promising option. Ontologies open a novel approach to knowledge representation. The paper presents a GIS (Geographic Information System) domain application illustrating ontological approach to data integration and data processing automation in the specific system. This VirGIS system is an integration system that works with spatio-temporal data. We start our study with developing the data representation based on common Semantic Web techniques and build a VirGIS ontology
Relační model dat s uspořádáním
No full textThe paper proposes to extend the classical relational data model by the notion of preference realized through a partial ordering on the set of relation instances. The extension involves not only data representation but also data manipulation
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