An Approach to Web Prefetching Agent Based on Web Ontology with Hidden Markov Model
With the rapid growth of web services on the Internet, users are experiencing access delays more often than ever. Recent studies showed that web prefetching could alleviate the WWW latency to a larger extent than the traditional caching. Web prefetching is one of the most popular strategies in web mining research domain, which are proposed for reducing the perceived access delay, improving the service quality of web site and mining the user requirement information in advance. In this paper, we introduce the features of the web site model named web ontology, and build a web prefetching agent-WebAGENT based on the web ontology and the hidden Markov model. With the agent, we analyze the user access path and how to mine the latent information requirement concepts, then we could make semantic-based prefetching decisions. Experimental results show that the web prefetching scheme of the WebAGENT has better predictive mining effect and prefetching precision.
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