Ranking Tagged Resources Using Social Semantic Relevance

The WWW today is overwhelmed with information on almost every topic. Therefore, relevance ranking of web pages to a user’s expectations is a challenge, rather than retrieving a collection of thousands of web pages selected by keyword matching. This paper presents an approach to rank tagged web pages retrieved from a Social Bookmarking Site for a learner who needs web resources containing content on a given topic. Besides the popularity of the web page in the community, the relevance of a web page for ranking is computed based on the semantic distance between tags and a given topic using domain ontology. An experimental study has been conducted to evaluate the ranks generated by the proposed approach. The test collection was created using a questionnaire which was designed to judge the crawled web pages for their graded relevance on a topic.