The uncertain reasoner's companion: a mathematical perspective
The uncertain reasoner's companion: a mathematical perspective
Three companions for data mining in first order logic
Relational Data Mining
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Conditionals in nonmonotonic reasoning and belief revision: considering conditionals as agents
Conditionals in nonmonotonic reasoning and belief revision: considering conditionals as agents
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
An approach to learning relational probabilistic FO-PCL knowledge bases
SUM'12 Proceedings of the 6th international conference on Scalable Uncertainty Management
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By using the principle of maximum entropy incomplete probabilistic knowledge can be completed to a full joint distribution. This inductive knowledge representation method can be reversed to extract probabilistic rules from an empirical probability distribution. Based on this idea propositional learning approach has been developed. Recently, an extension to a relational language has been presented, where, however, a central aspect, finding and resolving algebraic equations needed for the solution, has been treated as a black box. Here, we investigate both problems in more detail. We explain how equations for relational knowledge bases can be resolved, and give a comprehensive example of computing a relational knowledge base from a probability distribution. Furthermore, we describe how propositional mechanisms for finding equations can be refined to focus on more interesting equations and to reduce the number of candidates.