Learning probabilistic Datalog rules for information classification and transformation

International Conference on Information and Knowledge Management, Proceedings

Volumn , Issue , 2001, Pages 387-394

  Nottelmann, Henrik ^a   Fuhr, Norbert ^a  

^a TU DORTMUND UNIVERSITY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); LEARNING ALGORITHMS; LEARNING SYSTEMS; PROBABILITY;

INFORMATION TRANSFORMATION;

INFORMATION RETRIEVAL;

EID: 0035747551     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/502649.502651     Document Type: Conference Paper

References (19)

1. Extraction of logical rules from training data using backpropagation networks
2. Towards data abstraction in networked information retrieval systems
3. Probabilistic Datalog: Implementing logical information retrieval for advanced applications
4. A comparison of two learning algorithms for text categorization
5. Modular stratification and magic sets for Datalog programs with negation
6. Term weighting approaches in automatic text retrieval
7. Theory of the learnable
8. A non-classical logic for information retrieval
9. On the learning of rule uncertainties and their integration into probabilistic knowledge bases
10. An evaluation of statistical approaches to text categorization