Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Analyzing the effectiveness and applicability of co-training
Proceedings of the ninth international conference on Information and knowledge management
Learning the structure of Markov logic networks
ICML '05 Proceedings of the 22nd international conference on Machine learning
Machine Learning
Sound and efficient inference with probabilistic and deterministic dependencies
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Memory-efficient inference in relational domains
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
A fast linear-arithmetic solver for DPLL(T)
CAV'06 Proceedings of the 18th international conference on Computer Aided Verification
Just Add Weights: Markov Logic for the Semantic Web
Uncertainty Reasoning for the Semantic Web I
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This paper addresses the question of how statistical learning algorithms can be integrated into a larger AI system both from a practical engineering perspective and from the perspective of correct representation, learning, and reasoning. Our goal is to create an integrated intelligent system that can combine observed facts, hand-written rules, learned rules, and learned classifiers to perform joint learning and reasoning. Our solution, which has been implemented in the CALO system, integrates multiple learning components with a Markov Logic inference engine, so that the components can benefit from each other's predictions. We introduce two designs of the learning and reasoning layer in CALO: the MPE Architecture and the Marginal Probability Architecture. The architectures, interfaces, and algorithms employed in our two designs are described, followed by experimental evaluations of the performance of the two designs. We show that by integrating multiple learning components through Markov Logic, the performance of the system can be improved and that the Marginal Probability Architecture performs better than the MPE Architecture.