Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
ISWC '02 Proceedings of the First International Semantic Web Conference on The Semantic Web
Web Usage Mining as a Tool for Personalization: A Survey
User Modeling and User-Adapted Interaction
Frequent pattern mining: current status and future directions
Data Mining and Knowledge Discovery
Taxonomy-superimposed graph mining
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Toward Recommendation Based on Ontology-Powered Web-Usage Mining
IEEE Internet Computing
Integration des connaissances ontologiques dans la fouille de motifs sequentiels avec application a la personnalisation web
Ontology-based filtering mechanisms for web usage patterns retrieval
EC-Web'05 Proceedings of the 6th international conference on E-Commerce and Web Technologies
Introducing semantics in web personalization: the role of ontologies
EWMF'05/KDO'05 Proceedings of the 2005 joint international conference on Semantics, Web and Mining
Ontology-Enhanced association mining
EWMF'05/KDO'05 Proceedings of the 2005 joint international conference on Semantics, Web and Mining
Semantic Formalization of Cross-Site User Browsing Behavior
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
| Hi-index | 0.00 |
Semantic Web (SW) is a new trend in the evolution of the current Web aimed at extending its basic functionalities by providing computer-readable semantic meta-data about the Web content. The meta-data is typically organized into a domain ontology where key concepts and relations from the domain appear. The benefits of such a representation are manifold: a more topical information seeking process, better content adaptation and higher interoperability even on the current, still largely syntactical, Web, to name only a few. As the SW is, arguably, the future of the Web, it is only too natural that Web mining, i.e., the application of data mining techniques to web-related data, tackles the processing semantically annotated data. In this context, we study the detecting of typical navigation scenarios on an ontology-powered Web portal, i.e., an instance of usage mining on the SW. In the present paper, we tackle the fundamental aspects of the underlying mining problem and clarify the impact a fully-fledged ontology has on the data and pattern languages. Indeed, current ontology-aware mining approaches tend to limit their scope to the core conceptual hierarchy (taxonomy) of an ontology whereas in a realistic settings there will be a lot more knowledge in the ontology, in particular, on semantic relations between domain concepts, the way they instantiate into links between content objects, etc. We show that reflecting domain relations in the navigation patterns results in a new pattern structure that combines elements from sequential, generalized and graph pattern mining and therefore requires a dedicated mining strategy. After characterizing the underlying pattern space, we describe a dedicated level-wise mining method and present some empirical evidence of its viability.