Using domain ontology for semantic web usage mining and next page prediction

Authors:
Nizar R. Mabroukeh;Christie I. Ezeife
Affiliations:
University of Windsor, Windsor, ON, Canada;University of Windsor, Windsor, ON, Canada
Venue:
Proceedings of the 18th ACM conference on Information and knowledge management
Year:
2009

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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.