Fast discovery of association rules
Advances in knowledge discovery and data mining
{\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
Description logic programs: combining logic programs with description logic
WWW '03 Proceedings of the 12th international conference on World Wide Web
Inducing Multi-Level Association Rules from Multiple Relations
Machine Learning
On Reducing Redundancy in Mining Relational Association Rules from the Semantic Web
RR '08 Proceedings of the 2nd International Conference on Web Reasoning and Rule Systems
Theory and Practice of Logic Programming
Frequent pattern discovery from OWL DLP knowledge bases
EKAW'06 Proceedings of the 15th international conference on Managing Knowledge in a World of Networks
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This paper follows the research direction that has received a growing interest recently, namely application of knowledge discovery methods to complex data representations. Among others, there have been methods proposed for learning in expressive, hybrid languages, combining relational component with terminological (description logics) component. In this paper we present a novel approach to frequent pattern discovery over the knowledge base represented in such a language, the combination of the basic subset of description logics with DL-safe rules, that can be seen as a subset of Semantic Web Rule Language. Frequent patterns in our approach are represented as conjunctive DL-safe queries over the hybrid knowledge base. We present also an illustrative example of our method based on the financial dataset.