Machine Learning - special issue on inductive logic programming
Enhanced hypertext categorization using hyperlinks
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Relational learning with statistical predicate invention: better models for hypertext
Machine Learning - Special issue on inducive logic programming
Using web structure for classifying and describing web pages
Proceedings of the 11th international conference on World Wide Web
Algorithmic Program DeBugging
An introduction to inductive logic programming
Relational Data Mining
Inducing classification and regression trees in first order logic
Relational Data Mining
How to upgrade propositional learners to first order logic: case study
Relational Data Mining
Propositionalization approaches to relational data mining
Relational Data Mining
Discovery of frequent DATALOG patterns
Data Mining and Knowledge Discovery
Data Mining and Knowledge Discovery
Learning Probabilistic Models of Relational Structure
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Linkage and Autocorrelation Cause Feature Selection Bias in Relational Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Relational Reinforcement Learning
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Propositionalisation and Aggregates
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Exploiting Structural Information for Text Classification on the WWW
IDA '99 Proceedings of the Third International Symposium on Advances in Intelligent Data Analysis
Towards Combining Inductive Logic Programming with Bayesian Networks
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
Maximum Entropy Modeling with Clausal Constraints
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Relational Markov models and their application to adaptive web navigation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Statistical Relational Learning for Document Mining
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Aggregation-based feature invention and relational concept classes
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Relational learning via propositional algorithms: an information extraction case study
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Top-down induction of first-order logical decision trees
Artificial Intelligence
Discriminative probabilistic models for relational data
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Statistical Relational Learning for Document Mining
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
CiteSeer-API: towards seamless resource location and interlinking for digital libraries
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Prediction and ranking algorithms for event-based network data
ACM SIGKDD Explorations Newsletter
The case for anomalous link discovery
ACM SIGKDD Explorations Newsletter
Learning Contextual Dependency Network Models for Link-Based Classification
IEEE Transactions on Knowledge and Data Engineering
ACM Transactions on Database Systems (TODS)
Integrating Naïve Bayes and FOIL
The Journal of Machine Learning Research
Relational Dependency Networks
The Journal of Machine Learning Research
First-Order Probabilistic Languages: Into the Unknown
Inductive Logic Programming
Discriminative Structure Learning of Markov Logic Networks
ILP '08 Proceedings of the 18th international conference on Inductive Logic Programming
kFOIL: learning simple relational kernels
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
nFOIL: integrating Naïve Bayes and FOIL
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Structure learning for statistical relational models
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 4
Change of representation for statistical relational learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
View learning for statistical relational learning: with an application to mammography
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Document classification utilising ontologies and relations between documents
Proceedings of the Eighth Workshop on Mining and Learning with Graphs
Mining citation information from CiteSeer data
Scientometrics
An approach to mining picture objects based on textual cues
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
Link prediction in citation networks
Journal of the American Society for Information Science and Technology
Scalable analysis for large social networks: the data-aware mean-field approach
SocInfo'12 Proceedings of the 4th international conference on Social Informatics
Transforming graph data for statistical relational learning
Journal of Artificial Intelligence Research
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A major obstacle to fully integrated deployment of manydata mining algorithms is the assumption that data sitsin a single table, even though most real-world databaseshave complex relational structures. We propose an integratedapproach to statistical modeling from relationaldatabases. We structure the search space based on "refinementgraphs", which are widely used in inductive logic programmingfor learning logic descriptions. The use of statisticsallows us to extend the search space to include richerset of features, including many which are not boolean.Search and model selection are integrated into a single process,allowing information criteria native to the statisticalmodel, for example logistic regression, to make feature selectiondecisions in a step-wise manner. We present experimentalresults for the task of predicting where scientific paperswill be published based on relational data taken fromCiteSeer. Our approach results in classification accuraciessuperior to those achieved when using classical "flat" features.The resulting classifier can be used to recommendwhere to publish articles.