On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Machine Learning - Special issue on learning with probabilistic representations
Robust Learning with Missing Data
Machine Learning
Machine Learning
An empirical comparison of supervised learning algorithms
ICML '06 Proceedings of the 23rd international conference on Machine learning
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Learning Reliable Classifiers From Small or Incomplete Data Sets: The Naive Credal Classifier 2
The Journal of Machine Learning Research
Learning with Kernels in Description Logics
ILP '08 Proceedings of the 18th international conference on Inductive Logic Programming
Just Add Weights: Markov Logic for the Semantic Web
Uncertainty Reasoning for the Semantic Web I
Towards Machine Learning on the Semantic Web
Uncertainty Reasoning for the Semantic Web I
ReduCE: A Reduced Coulomb Energy Network Method for Approximate Classification
ESWC 2009 Heraklion Proceedings of the 6th European Semantic Web Conference on The Semantic Web: Research and Applications
Exploiting Data Missingness in Bayesian Network Modeling
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
Concept learning in description logics using refinement operators
Machine Learning
Query answering and ontology population: an inductive approach
ESWC'08 Proceedings of the 5th European semantic web conference on The semantic web: research and applications
Adding data mining support to SPARQL via statistical relational learning methods
ESWC'08 Proceedings of the 5th European semantic web conference on The semantic web: research and applications
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
Relational kernel machines for learning from graph-structured RDF data
ESWC'11 Proceedings of the 8th extended semantic web conference on The semantic web: research and applications - Volume Part I
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Knowledge available through Semantic Web standards can be missing, generally because of the adoption of the Open World Assumption. We present a Statistical Relational Learning system for learning terminological naïve Bayesian classifiers, which estimate the probability that an individual belongs to a target concept given its membership to a set of Description Logic concepts. During the learning process, we consistently handle the lack of knowledge that may be introduced by the adoption of the Open World Assumption, depending on the varying nature of the missing knowledge itself.