Using Domain Ontology for Semantic Web Usage Mining and Next Page Prediction
Document Type
Conference Paper
Publication Date
2009
Publication Title
Proceedings of the 18th ACM Conference on Information and Knowledge Management
First Page
1677
Last Page
1680
Abstract
This paper proposes the integration of semantic information drawn from a web application's domain knowledge into all phases of the web usage mining process (preprocessing, pattern discovery, and recommendation/prediction). The goal is to have an intelligent semantics-aware web usage mining framework. This is accomplished by using semantic information in the sequential pattern mining algorithm to prune the search space and partially relieve the algorithm from support counting. In addition, semantic information is used in the prediction phase with low order Markov models, for less space complexity and accurate prediction, that will help ambiguous predictions problem. Experimental results show that semantics-aware sequential pattern mining algorithms can perform 4 times faster than regular non-semantics-aware algorithms with only 26% of the memory requirement.
DOI
10.1145/1645953.1646202
Recommended Citation
Mabroukeh, N. R. and Ezeife, C. I.. (2009). Using Domain Ontology for Semantic Web Usage Mining and Next Page Prediction. Proceedings of the 18th ACM Conference on Information and Knowledge Management, 1677-1680.
https://scholar.uwindsor.ca/computersciencepub/38