Ontology based web search results visualization using BSDSC

In a scenario where WWW has become more important every day, to have a clear and well organized web site has become one of the vital goals of enterprises and organizations on the Web. On enterprises' side goals are make more profits by reducing costs or increasing incomes. In the case of content-based web sites their goals is to spread its contents among web users. An automatic ontology learning method is developed for the search engine result analysis, which trains ontology with world knowledge of hundreds of different subjects in a multilevel taxonomy. This ontology is then mined to find important classification rules, and these rules are used to perform an extensive analysis of the content of the largest general-purpose Internet search engines in use today. Instead of representing collections as a set of terms, which commonly occurs in collection selection, they are represented as a set of subjects, leading to a more robust representation of information and a decrease of synonymy. Parallelizing the web search results incorporated with Bookshelf Data Structure owe to the simplistic applicability of the aforementioned strategy, and the use of simple data structure. The method was also used to analyze the content of the most popular search engines in use today, including Google and Yahoo. In conclusion, this research paper focus on the ontology based semantic web search results visualization using BSDSC(Book Shelf Data Structure Code) based on BookShelf Data Structure. Finally, the search results are presented in visual mode, which allows a user to navigate between extracted schemas.