Association rules over interval data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
On the Discovery of Interesting Patterns in Association Rules
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Selecting the right objective measure for association analysis
Information Systems - Knowledge discovery and data mining (KDD 2002)
Mining Non-Redundant Association Rules
Data Mining and Knowledge Discovery
The Description Logic Handbook
The Description Logic Handbook
Frequent Subtree Mining - An Overview
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
A tool for data cube construction from structurally heterogeneous XML documents
Journal of the American Society for Information Science and Technology
SPARQLeR: Extended Sparql for Semantic Association Discovery
ESWC '07 Proceedings of the 4th European conference on The Semantic Web: Research and Applications
Updating generalized association rules with evolving taxonomies
Applied Intelligence
Metric-based stochastic conceptual clustering for ontologies
Information Systems
A relational data harmonization approach to XML
Journal of Information Science
Efficient retrieval of ontology fragments using an interval labeling scheme
Information Sciences: an International Journal
Semantic clustering of XML documents
ACM Transactions on Information Systems (TOIS)
Web Semantics: Science, Services and Agents on the World Wide Web
A document clustering algorithm for discovering and describing topics
Pattern Recognition Letters
Kernel methods for mining instance data in ontologies
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Adding data mining support to SPARQL via statistical relational learning methods
ESWC'08 Proceedings of the 5th European semantic web conference on The semantic web: research and applications
Analysis of the Effectiveness of the Genetic Algorithms based on Extraction of Association Rules
Fundamenta Informaticae - Intelligent Data Analysis in Granular Computing
Knowledge-Based Interactive Postmining of Association Rules Using Ontologies
IEEE Transactions on Knowledge and Data Engineering
Text clustering using frequent itemsets
Knowledge-Based Systems
Approximate weighted frequent pattern mining with/without noisy environments
Knowledge-Based Systems
Mining association rules from semantic web data
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
Mining the semantic web: a logic-based methodology
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
Mining Frequent Generalized Patterns for Web Personalization in the Presence of Taxonomies
International Journal of Data Warehousing and Mining
Empower service directories with knowledge
Knowledge-Based Systems
Hybrid genetic algorithm and association rules for mining workflow best practices
Expert Systems with Applications: An International Journal
A unified approach to matching semantic data on the Web
Knowledge-Based Systems
A lattice-based approach for mining most generalization association rules
Knowledge-Based Systems
Discovering interesting information with advances in web technology
ACM SIGKDD Explorations Newsletter
AMIE: association rule mining under incomplete evidence in ontological knowledge bases
Proceedings of the 22nd international conference on World Wide Web
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The amount of ontologies and semantic annotations available on the Web is constantly growing. This new type of complex and heterogeneous graph-structured data raises new challenges for the data mining community. In this paper, we present a novel method for mining association rules from semantic instance data repositories expressed in RDF/(S) and OWL. We take advantage of the schema-level (i.e. Tbox) knowledge encoded in the ontology to derive appropriate transactions which will later feed traditional association rules algorithms. This process is guided by the analyst requirements, expressed in the form of query patterns. Initial experiments performed on semantic data of a biomedical application show the usefulness and efficiency of the approach.