Blog content based recommendation framework using WordNet and multiple Ontologies

Social networking portals like Twitter, Facebook, LinkedIn etc. are getting popular day by day among users´ community and many more such portals are getting users attentions to cater their specific needs. Users write blogs on these social networking websites on a variety of topics as per their and other user´s interests. By means of social networking blogs, a large amount of interesting information is scattered on the Web which could be structured in a meaningful way for better services. The objective of this paper is to focus on categorization of blog content into ten demanding themes like Technology, Entertainment, News, Business, Health, Sports, Tourism, Widgets, Vehicles, and Products for effective retrieval of information from categorized blog content. Further, a user can also search by feeding specific query to retrieve information from blog. In this paper, we are proposing a WordNet and multiple Ontologies based blog content theme expansion approach and a concept combination based ranking algorithm for blog content based recommendation framework that considers original themes of blog content as an input and recommends conceptually related expanded themes of blog content. The distinctive point of this research is to use concept combination approach based on rough sets to categorize retrieved results for demanding themes as well as for user specific preferences. This kind of blog content categorization approach would be very effective to retrieve meaningful and conceptually related blog information written by a large number of users using different vocabularies. We have experimented with the contents of top blogs related to each theme and got very good results.