Semantic-Rich Markov Models for Web Prefetching

Domain knowledge for Web applications is currently being made available as domain ontology with the advent of the semantic Web, in which semantics govern relationships among objects of interest (e. g., commercial items to be purchased in an e-commerce Web site). Our earlier work proposed to integrate semantic information into all phases of the Web usage mining process, for an intelligent semantics-aware Web usage mining framework. There are ways to integrate semantic information into Markov models used in the third phase for next page request prediction. Semantic information is combined with the transition probability matrix of a Markov model. This way, it provides a low order Markov model with intelligent accurate predictions and less complexity than higher order models, also solving the problem of contradicting prediction. This paper proposes to use semantic information to prune states in selective Markov models SMM, semantic information can lead to context-aware higher order Markov models with about 16% less space complexity.