On the relative expressiveness of description logics and predicate logics
Artificial Intelligence
Combining Horn rules and description logics in CARIN
Artificial Intelligence
{\cal A}{\cal L}-log: Integrating Datalog and Description Logics
Journal of Intelligent Information Systems
Foundations of Inductive Logic Programming
Foundations of Inductive Logic Programming
Relational Data Mining
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
Discovery of frequent DATALOG patterns
Data Mining and Knowledge Discovery
Mining Multiple-Level Association Rules in Large Databases
IEEE Transactions on Knowledge and Data Engineering
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
ILP '00 Proceedings of the 10th International Conference on Inductive Logic Programming
Inducing Multi-Level Association Rules from Multiple Relations
Machine Learning
The Description Logic Handbook
The Description Logic Handbook
Building rules on top of ontologies for the semantic web with inductive logic programming
Theory and Practice of Logic Programming
Statistical Relational Learning with Formal Ontologies
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Faster association rules for multiple relations
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Query Answering for OWL-DL with rules
Web Semantics: Science, Services and Agents on the World Wide Web
Web Semantics: Science, Services and Agents on the World Wide Web
Learnability of description logic programs
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
Theory and Practice of Logic Programming
Inductive logic programming in databases: From datalog to $\mathcal{dl}+log}^{\neg\vee}$
Theory and Practice of Logic Programming
Combining safe rules and ontologies by interfacing of reasoners
PPSWR'06 Proceedings of the 4th international conference on Principles and Practice of Semantic Web Reasoning
Integrating datalog with OWL: exploring the AL-log approach
ICLP'06 Proceedings of the 22nd international conference on Logic Programming
Semantic and computational advantages of the safe integration of ontologies and rules
PPSWR'05 Proceedings of the Third international conference on Principles and Practice of Semantic Web Reasoning
Semantic knowledge discovery from heterogeneous data sources
EKAW'12 Proceedings of the 18th international conference on Knowledge Engineering and Knowledge Management
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Onto-Relational Learning is an extension of Relational Learning aimed at accounting for ontologies in a clear, well-founded and elegant manner. The system -QuIn supports a variant of the frequent pattern discovery task by following the Onto-Relational Learning approach. It takes taxonomic ontologies into account during the discovery process and produces descriptions of a given relational database at multiple granularity levels. The functionalities of the system are illustrated by means of examples taken from a Semantic Web Mining case study concerning the analysis of relational data extracted from the on-line CIA World Fact Book.