Relational Dependency Networks
The Journal of Machine Learning Research
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Probabilistic databases: diamonds in the dirt
Communications of the ACM - Barbara Liskov: ACM's A.M. Turing Award Winner
Learning systems of concepts with an infinite relational model
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Learning probabilistic relational models
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Discriminative probabilistic models for relational data
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Latent topics in graph-structured data
Proceedings of the 21st ACM international conference on Information and knowledge management
| Hi-index | 0.00 |
Probabilistic graphical model representations of relational data provide a number of desired features, such as inference of missing values, detection of errors, visualization of data, and probabilistic answers to relational queries. However, adoption has been slow due to the high level of expertise expected both in probability and in the domain from the user. Instead of requiring a domain expert to specify the probabilistic dependencies of the data, we present an approach that uses the relational DB schema to automatically construct a Bayesian graphical model for a database. This resulting model contains customized distributions for the attributes, latent variables that cluster the records, and factors that reflect and represent the foreign key links, whilst allowing efficient inference. Experiments demonstrate the accuracy of the model and scalability of inference on synthetic and real-world data.