Communications of the ACM
Principles of database and knowledge-base systems, Vol. I
Principles of database and knowledge-base systems, Vol. I
Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
On the learning of rule uncertainties and their integration into probabilistic knowledge bases
Journal of Intelligent Information Systems - Special issue on methodologies for intelligent systems
Modular stratification and magic sets for Datalog programs with negation
Journal of the ACM (JACM)
Towards data abstraction in networked information retrieval systems
Information Processing and Management: an International Journal
Probabilistic Datalog: implementing logical information retrieval for advanced applications
Journal of the American Society for Information Science
An Evaluation of Statistical Approaches to Text Categorization
Information Retrieval
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
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
A descriptive approach to classification
ICTIR'11 Proceedings of the Third international conference on Advances in information retrieval theory
sPLMap: a probabilistic approach to schema matching
ECIR'05 Proceedings of the 27th European conference on Advances in Information Retrieval Research
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Probabilistic Datalog is a combination of classical Datalog (i.e., function-free Horn clause predicate logic) with probability theory. Therefore, probabilistic weights may be attached to both facts and rules. But it is often impossible to assign exact rule weights or even to construct the rules themselves. Instead of specifying them manually, learning algorithms can be used to learn both rules and weights. In practice, these algorithms are very slow because they need a large example set and have to test a high number of rules. We apply a number of extensions to these algorithms in order to improve efficiency. Several applications demonstrate the power of learning probabilistic Datalog rules, showing that learning rules is suitable for low dimensional problems (e.g., schema mapping) but inappropriate for higher dimensions like e.g. in text classification.