The theory of joins in relational databases
ACM Transactions on Database Systems (TODS)
Machine Learning
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Managing uncertainty and vagueness in description logics for the Semantic Web
Web Semantics: Science, Services and Agents on the World Wide Web
Sound and efficient inference with probabilistic and deterministic dependencies
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
P-CLASSIC: a tractable probablistic description logic
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
A probabilistic ontological framework for the recognition of multilevel human activities
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
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Log-linear description logics are probabilistic logics combining several concepts and methods from the areas of knowledge representation and reasoning and statistical relational AI. We describe some of the implementation details of the log-linear reasoner ELOG. The reasoner employs database technology to dynamically transform inference problems to integer linear programs (ILP). In order to lower the size of the ILPs and reduce the complexity we employ a form of cutting plane inference during reasoning.