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
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Web Usage Mining as a Tool for Personalization: A Survey
User Modeling and User-Adapted Interaction
SEWeP: using site semantics and a taxonomy to enhance the Web personalization process
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
SPIN: mining maximal frequent subgraphs from graph databases
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Advanced Methods for Knowledge Discovery from Complex Data (Advanced Information and Knowledge Processing)
Toward Recommendation Based on Ontology-Powered Web-Usage Mining
IEEE Internet Computing
Sequential Patterns for Maintaining Ontologies over Time
OTM '08 Proceedings of the OTM 2008 Confederated International Conferences, CoopIS, DOA, GADA, IS, and ODBASE 2008. Part II on On the Move to Meaningful Internet Systems
Ontology-based data mining approach implemented for sport marketing
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
Ontologies are used to represent data and share knowledge of a specific domain, and in recent years they tend to be used in many applications such as database integration, peer-to-peer systems, e-commerce, semantic web services, bioinformatics, or social networks. Feeding ontological domain knowledge into those applications has proven to increase flexibility and inter-operability and interpretability of data and knowledge. As more data is gathered/generated by those applications, it becomes important to analyze and transform it to meaningful information. One possibility is to use data mining techniques to extract patterns from those large amounts of data. One challenging general problem in mining ontological data is taking into account not only domain concepts, properties and instances, but also hierarchical structures of those concepts and properties. In this paper, the authors research the specific problem of extracting ontology-based sequential patterns.