Learning Bayesian networks with local structure
Learning in graphical models
Well-founded and stable semantics of logic programs with aggregates
Theory and Practice of Logic Programming
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Parameter Learning in Probabilistic Databases: A Least Squares Approach
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Learning Ground CP-Logic Theories by Leveraging Bayesian Network Learning Techniques
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
Learning directed probabilistic logical models: ordering-search versus structure-search
Annals of Mathematics and Artificial Intelligence
Probabilistic inductive logic programming
Probabilistic inductive logic programming
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Learning directed relational models with recursive dependencies
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
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A significant part of current research on (inductive) logic programming deals with probabilistic logical models. Over the last decade many logics or languages for representing such models have been introduced. There is currently a great need for insight into the relationships between all these languages. One kind of languages are those that extend probabilistic models with elements of logic, such as the language of Logical Bayesian Networks (LBNs). Some other languages follow the converse strategy of extending logic programs with a probabilistic semantics, often in a way similar to that of Sato's distribution semantics. In this paper we study the relationship between the language of LBNs and languages based on the distribution semantics. Concretely, we define a mapping from LBNs to theories in the Independent Choice Logic (ICL). We also show how this mapping can be used to learn ICL theories from data.