Semantic bookmark system for dynamic modeling of users browsing preferences

Abstract

Web search engines are successful at finding relevant resources; however, the search engine results page (SERP) list contains so many results that the intended ones are difficult to identify, therefore, requires manual user inspection and selection. A user may equally likely visit an already visited web page, which requires them to repeat the same search process. To deal with this issue, several re-visitation approaches are used, which include history, bookmarking and URL auto-completion. Among these, bookmarking is the most effective and user-friendly approach. Today, bookmarking is available in almost all the frequently used web browsers. However, they provide static and unstructured bookmarks. This makes it difficult for the end-user to easily manage, organize and maintain the hierarchical structure of bookmarks especially when their number exceeds a certain limit. Besides their hierarchical structure, the available bookmarking systems provide keyword-based searching with no exploitation of the semantics of the visited resources making it difficult to re-visit a required resource without reference to its context. We exploit Semantic Web technologies to devise a more effective, accurate and precise bookmarking service so that the cognitive efforts of the users could be reduced to a significant level. The proposed solution uses an extension that uses ontology to generate semantic bookmarks by tracking the user browsing activities, resulting into a more user-friendly re-visitation experience.